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A

A - Variable in class weka.core.matrix.Matrix
Array for internal storage of elements.
abortExperiment() - Method in class weka.experiment.RemoteExperiment
Set the abort flag
ABS - Static variable in interface weka.core.mathematicalexpression.sym
 
ABS - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
absDev(int, Instances) - Static method in class weka.classifiers.trees.m5.Rule
Returns the absolute deviation value of the supplied attribute index.
AbstractAssociator - Class in weka.associations
Abstract scheme for learning associations.
AbstractAssociator() - Constructor for class weka.associations.AbstractAssociator
 
AbstractClusterer - Class in weka.clusterers
Abstract clusterer.
AbstractClusterer() - Constructor for class weka.clusterers.AbstractClusterer
 
AbstractDataSink - Class in weka.gui.beans
Abstract class for objects that store instances to some destination.
AbstractDataSink() - Constructor for class weka.gui.beans.AbstractDataSink
 
AbstractDataSinkBeanInfo - Class in weka.gui.beans
Bean info class for the AbstractDataSink
AbstractDataSinkBeanInfo() - Constructor for class weka.gui.beans.AbstractDataSinkBeanInfo
 
AbstractDataSource - Class in weka.gui.beans
Abstract class for objects that can provide instances from some source
AbstractDataSource() - Constructor for class weka.gui.beans.AbstractDataSource
Creates a new AbstractDataSource instance.
AbstractDataSourceBeanInfo - Class in weka.gui.beans
Bean info class for AbstractDataSource.
AbstractDataSourceBeanInfo() - Constructor for class weka.gui.beans.AbstractDataSourceBeanInfo
 
AbstractDensityBasedClusterer - Class in weka.clusterers
Abstract clustering model that produces (for each test instance) an estimate of the membership in each cluster (ie.
AbstractDensityBasedClusterer() - Constructor for class weka.clusterers.AbstractDensityBasedClusterer
 
AbstractEvaluator - Class in weka.gui.beans
Abstract class for objects that can provide some kind of evaluation for classifier, clusterers etc.
AbstractEvaluator() - Constructor for class weka.gui.beans.AbstractEvaluator
Constructor
AbstractFileLoader - Class in weka.core.converters
Abstract superclass for all file loaders.
AbstractFileLoader() - Constructor for class weka.core.converters.AbstractFileLoader
 
AbstractFileSaver - Class in weka.core.converters
Abstract class for Savers that save to a file Valid options are: -i input arff file
The input filw in arff format.
AbstractFileSaver() - Constructor for class weka.core.converters.AbstractFileSaver
 
AbstractLoader - Class in weka.core.converters
Abstract class gives default implementation of setSource methods.
AbstractLoader() - Constructor for class weka.core.converters.AbstractLoader
 
AbstractSaver - Class in weka.core.converters
Abstract class for Saver
AbstractSaver() - Constructor for class weka.core.converters.AbstractSaver
 
AbstractStringDistanceFunction - Class in weka.core
Represents the abstract ancestor for string-based distance functions, like EditDistance.
AbstractStringDistanceFunction() - Constructor for class weka.core.AbstractStringDistanceFunction
Constructor that doesn't set the data
AbstractStringDistanceFunction(Instances) - Constructor for class weka.core.AbstractStringDistanceFunction
Constructor that sets the data
AbstractTestSetProducer - Class in weka.gui.beans
Abstract class for TestSetProducers that contains default implementations of add/remove listener methods and defualt visual representation.
AbstractTestSetProducer() - Constructor for class weka.gui.beans.AbstractTestSetProducer
Creates a new AbstractTestSetProducer instance.
AbstractTestSetProducerBeanInfo - Class in weka.gui.beans
BeanInfo class for AbstractTestSetProducer
AbstractTestSetProducerBeanInfo() - Constructor for class weka.gui.beans.AbstractTestSetProducerBeanInfo
 
AbstractTimeSeries - Class in weka.filters.unsupervised.attribute
An abstract instance filter that assumes instances form time-series data and performs some merging of attribute values in the current instance with attribute attribute values of some previous (or future) instance.
AbstractTimeSeries() - Constructor for class weka.filters.unsupervised.attribute.AbstractTimeSeries
 
AbstractTrainAndTestSetProducer - Class in weka.gui.beans
Abstract base class for TrainAndTestSetProducers that contains default implementations of add/remove listener methods and defualt visual representation.
AbstractTrainAndTestSetProducer() - Constructor for class weka.gui.beans.AbstractTrainAndTestSetProducer
Creates a new AbstractTrainAndTestSetProducer instance.
AbstractTrainAndTestSetProducerBeanInfo - Class in weka.gui.beans
Bean info class for AbstractTrainAndTestSetProducers
AbstractTrainAndTestSetProducerBeanInfo() - Constructor for class weka.gui.beans.AbstractTrainAndTestSetProducerBeanInfo
 
AbstractTrainingSetProducer - Class in weka.gui.beans
Abstract class for TrainingSetProducers that contains default implementations of add/remove listener methods and default visual representation
AbstractTrainingSetProducer() - Constructor for class weka.gui.beans.AbstractTrainingSetProducer
Creates a new AbstractTrainingSetProducer instance.
AbstractTrainingSetProducerBeanInfo - Class in weka.gui.beans
BeanInfo class for AbstractTrainingSetProducer
AbstractTrainingSetProducerBeanInfo() - Constructor for class weka.gui.beans.AbstractTrainingSetProducerBeanInfo
 
accept(File) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERFileFilter
Whether the given file is accepted by this filter.
accept(File) - Method in class weka.gui.ExtensionFileFilter
Returns true if the supplied file should be accepted (i.e.: if it has the required extension or is a directory).
accept(File, String) - Method in class weka.gui.ExtensionFileFilter
Returns true if the file in the given directory with the given name should be accepted.
ACCEPT - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
States that the user has accepted the tree.
acceptClassifier(BatchClassifierEvent) - Method in interface weka.gui.beans.BatchClassifierListener
Accept a BatchClassifierEvent
acceptClassifier(BatchClassifierEvent) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator
Accept a classifier to be evaluated
acceptClassifier(IncrementalClassifierEvent) - Method in class weka.gui.beans.IncrementalClassifierEvaluator
Accepts and processes a classifier encapsulated in an incremental classifier event
acceptClassifier(IncrementalClassifierEvent) - Method in interface weka.gui.beans.IncrementalClassifierListener
Accept the event
acceptClassifier(IncrementalClassifierEvent) - Method in class weka.gui.beans.PredictionAppender
Accept and process an incremental classifier event
acceptClassifier(BatchClassifierEvent) - Method in class weka.gui.beans.PredictionAppender
Accept and process a batch classifier event
acceptClassifier(IncrementalClassifierEvent) - Method in class weka.gui.beans.SerializedModelSaver
Accept and save an incrementally trained classifier.
acceptClassifier(BatchClassifierEvent) - Method in class weka.gui.beans.SerializedModelSaver
Accept and save a batch trained classifier.
acceptClusterer(BatchClustererEvent) - Method in interface weka.gui.beans.BatchClustererListener
Accept a BatchClustererEvent
acceptClusterer(BatchClustererEvent) - Method in class weka.gui.beans.ClustererPerformanceEvaluator
Accept a clusterer to be evaluated
acceptClusterer(BatchClustererEvent) - Method in class weka.gui.beans.PredictionAppender
Accept and process a batch clusterer event
acceptClusterer(BatchClustererEvent) - Method in class weka.gui.beans.SerializedModelSaver
Accept and save a batch trained clusterer.
acceptDataPoint(ChartEvent) - Method in interface weka.gui.beans.ChartListener
 
acceptDataPoint(ChartEvent) - Method in class weka.gui.beans.StripChart
Accept a data point (encapsulated in a chart event) to plot
acceptDataPoint(double[]) - Method in class weka.gui.beans.StripChart
Accept a data point to plot
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.AbstractDataSink
Accept a data set
acceptDataSet(ThresholdDataEvent) - Method in class weka.gui.beans.AbstractDataSink
Accept a threshold data set
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
Subclass must implement
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.Associator
 
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.ClassAssigner
 
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.ClassValuePicker
 
acceptDataSet(ThresholdDataEvent) - Method in class weka.gui.beans.CostBenefitAnalysis
Accept a threshold data event and set up the visualization.
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.CrossValidationFoldMaker
Accept a data set
acceptDataSet(DataSetEvent) - Method in interface weka.gui.beans.DataSourceListener
 
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.DataVisualizer
Accept a data set
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.Filter
Accept a data set
acceptDataSet(ThresholdDataEvent) - Method in class weka.gui.beans.ModelPerformanceChart
Display a threshold curve.
acceptDataSet(VisualizableErrorEvent) - Method in class weka.gui.beans.ModelPerformanceChart
Display a scheme error plot.
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.Saver
Method reacts to a dataset event and starts the writing process in batch mode
acceptDataSet(ThresholdDataEvent) - Method in class weka.gui.beans.Saver
Method reacts to a threshold data event ans starts the writing process in batch mode.
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.TestSetMaker
Accepts and processes a data set event
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.TextViewer
Accept a data set for displaying as text
acceptDataSet(ThresholdDataEvent) - Method in interface weka.gui.beans.ThresholdDataListener
 
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.TrainingSetMaker
Accept a data set
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.TrainTestSplitMaker
Accept a data set
acceptDataSet(VisualizableErrorEvent) - Method in interface weka.gui.beans.VisualizableErrorListener
 
acceptGraph(GraphEvent) - Method in interface weka.gui.beans.GraphListener
Describe acceptGraph method here.
acceptGraph(GraphEvent) - Method in class weka.gui.beans.GraphViewer
Accept a graph
acceptInstance(InstanceEvent) - Method in class weka.gui.beans.AbstractDataSink
Accept an instance
acceptInstance(InstanceEvent) - Method in class weka.gui.beans.ClassAssigner
 
acceptInstance(InstanceEvent) - Method in class weka.gui.beans.Classifier
Accepts an instance for incremental processing.
acceptInstance(InstanceEvent) - Method in class weka.gui.beans.Filter
Accept an instance for processing by StreamableFilters only
acceptInstance(InstanceEvent) - Method in interface weka.gui.beans.InstanceListener
Accept and process an instance event
acceptInstance(InstanceEvent) - Method in class weka.gui.beans.InstanceStreamToBatchMaker
Accept an instance to add to the batch.
acceptInstance(InstanceEvent) - Method in class weka.gui.beans.Saver
Methods reacts to instance events and saves instances incrementally.
acceptInstance(InstanceEvent) - Method in class weka.gui.beans.StripChart
 
acceptResult(ResultProducer, Object[], Object[]) - Method in class weka.experiment.AveragingResultProducer
Accepts results from a ResultProducer.
acceptResult(ResultProducer, Object[], Object[]) - Method in class weka.experiment.CSVResultListener
Just prints out each result as it is received.
acceptResult(ResultProducer, Object[], Object[]) - Method in class weka.experiment.DatabaseResultListener
Submit the result to the appropriate table of the database
acceptResult(ResultProducer, Object[], Object[]) - Method in class weka.experiment.DatabaseResultProducer
Accepts results from a ResultProducer.
acceptResult(ResultProducer, Object[], Object[]) - Method in class weka.experiment.InstancesResultListener
Collects each instance and adjusts the header information.
acceptResult(ResultProducer, Object[], Object[]) - Method in class weka.experiment.LearningRateResultProducer
Accepts results from a ResultProducer.
acceptResult(ResultProducer, Object[], Object[]) - Method in interface weka.experiment.ResultListener
Accepts results from a ResultProducer.
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.AbstractDataSink
Accept a test set
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.ClassAssigner
 
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.Classifier
Accepts a test set for a batch trained classifier
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.Clusterer
Accepts a test set for a batch trained clusterer
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.CrossValidationFoldMaker
Accept a test set
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.DataVisualizer
Accept a test set
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.Filter
Accept a test set
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.Saver
Method reacts to a test set event and starts the writing process in batch mode
acceptTestSet(TestSetEvent) - Method in interface weka.gui.beans.TestSetListener
Accept and process a test set event
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.TextViewer
Accept a test set for displaying as text
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.TrainTestSplitMaker
Accept a test set
acceptText(TextEvent) - Method in interface weka.gui.beans.TextListener
Accept and process a text event
acceptText(TextEvent) - Method in class weka.gui.beans.TextViewer
Accept some text
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.AbstractDataSink
Accept a training set
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.Associator
Accept a training set
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.ClassAssigner
 
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.Classifier
Accepts a training set and builds batch classifier
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.Clusterer
Accepts a training set and builds batch clusterer
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.CrossValidationFoldMaker
Accept a training set
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.DataVisualizer
Accept a training set
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.Filter
Accept a training set
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.Saver
Method reacts to a training set event and starts the writing process in batch mode
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.TextViewer
Accept a training set for displaying as text
acceptTrainingSet(TrainingSetEvent) - Method in interface weka.gui.beans.TrainingSetListener
Accept and process a training set
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.TrainTestSplitMaker
Accept a training set
accu - Variable in class weka.classifiers.rules.JRip.Antd
The accurate data for this antecedent in the growing data
accuracy() - Method in class weka.associations.RuleItem
Gets the expected predictive accuracy of a rule
ACCURACY - Static variable in class weka.classifiers.meta.ThresholdSelector
accuracy
accuRate - Variable in class weka.classifiers.rules.JRip.Antd
The accurate rate of this antecedent test on the growing data
actEntropy - Variable in class weka.classifiers.lazy.kstar.KStarWrapper
used/reused to hold the actual entropy
action_obj - Variable in class weka.core.mathematicalexpression.Parser
Instance of action encapsulation class.
action_obj - Variable in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
Instance of action encapsulation class.
action_table() - Method in class weka.core.mathematicalexpression.Parser
Access to parse-action table.
action_table() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
Access to parse-action table.
actionPerformed(ActionEvent) - Method in class weka.gui.arffviewer.ArffPanel
invoked when an action occurs
actionPerformed(ActionEvent) - Method in class weka.gui.arffviewer.ArffViewerMainPanel
invoked when an action occurs
actionPerformed(ActionEvent) - Method in class weka.gui.experiment.AlgorithmListPanel
Handle actions when buttons get pressed.
actionPerformed(ActionEvent) - Method in class weka.gui.experiment.DatasetListPanel
Handle actions when buttons get pressed.
actionPerformed(ActionEvent) - Method in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Handles the various button clicking type activities.
actionPerformed(ActionEvent) - Method in class weka.gui.experiment.HostListPanel
Handle actions when text is entered into the host field or the delete button is pressed.
actionPerformed(ActionEvent) - Method in class weka.gui.experiment.RunPanel
Controls starting and stopping the experiment.
actionPerformed(ActionEvent) - Method in class weka.gui.SimpleCLIPanel
Only gets called when return is pressed in the input area, which starts the command running.
actionPerformed(ActionEvent) - Method in class weka.gui.streams.InstanceLoader
 
actionPerformed(ActionEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
Performs the action associated with the ActionEvent.
actual() - Method in class weka.classifiers.evaluation.NominalPrediction
Gets the actual class value.
actual() - Method in class weka.classifiers.evaluation.NumericPrediction
Gets the actual class value.
actual() - Method in interface weka.classifiers.evaluation.Prediction
Gets the actual class value.
actualNumBags() - Method in class weka.classifiers.trees.j48.Distribution
Returns number of non-empty bags of distribution.
actualNumClasses() - Method in class weka.classifiers.trees.j48.Distribution
Returns number of classes actually occuring in distribution.
actualNumClasses(int) - Method in class weka.classifiers.trees.j48.Distribution
Returns number of classes actually occuring in given bag.
acuityTipText() - Method in class weka.clusterers.Cobweb
Returns the tip text for this property
AdaBoostM1 - Class in weka.classifiers.meta
Class for boosting a nominal class classifier using the Adaboost M1 method.
AdaBoostM1() - Constructor for class weka.classifiers.meta.AdaBoostM1
Constructor.
add(Object) - Method in class weka.associations.tertius.SimpleLinkedList
 
add(int, GridSearch.Performance) - Method in class weka.classifiers.meta.GridSearch.PerformanceCache
adds the performance to the cache
add(int, Instance) - Method in class weka.classifiers.trees.j48.Distribution
Adds given instance to given bag.
add(int, double[]) - Method in class weka.classifiers.trees.j48.Distribution
Adds counts to given bag.
add(double, Object) - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueue
Adds a new Object to the queue
add(double, Object, String) - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueue
Adds a new Object to the queue
add(AlgVector) - Method in class weka.core.AlgVector
Returns the sum of this vector with another.
add(Instance) - Method in class weka.core.Instances
Adds one instance to the end of the set.
add(Matrix) - Method in class weka.core.Matrix
Deprecated.
Returns the sum of this matrix with another.
add(String) - Method in class weka.core.Stopwords
adds the given word to the stopword list (is automatically converted to lower case and trimmed)
add(TechnicalInformation) - Method in class weka.core.TechnicalInformation
adds the given information to the list of additional technical informations
add(TechnicalInformation.Type) - Method in class weka.core.TechnicalInformation
Adds an empty technical information with the given type to the list of additional informations and returns the instance.
add(PrintStream) - Method in class weka.core.Tee
adds the given PrintStream to the list of streams, with NO timestamp and NO prefix.
add(PrintStream, boolean) - Method in class weka.core.Tee
adds the given PrintStream to the list of streams, with NO prefix.
add(PrintStream, boolean, String) - Method in class weka.core.Tee
adds the given PrintStream to the list of streams.
add(String) - Method in class weka.core.Trie
Ensures that this collection contains the specified element.
add(String) - Method in class weka.core.Trie.TrieNode
adds the given string to its children (creates children if necessary)
add(Character) - Method in class weka.core.Trie.TrieNode
adds the given charater to its children
add(String, Method) - Method in class weka.core.xml.MethodHandler
adds the specified method for the property with the given displayname to its internal list.
add(Class, Method) - Method in class weka.core.xml.MethodHandler
adds the specified method for the given class to its internal list.
add(double, double) - Method in class weka.experiment.PairedStats
Add an observed pair of values.
add(double[], double[]) - Method in class weka.experiment.PairedStats
Adds an array of observed pair of values.
add(Instance) - Method in class weka.experiment.PairedTTester.Dataset
Adds the given instance to the dataset
add(Instance) - Method in class weka.experiment.PairedTTester.DatasetSpecifiers
Add an instance to the list of specifiers (if necessary)
add(Instance) - Method in class weka.experiment.PairedTTester.Resultset
Adds an instance to this resultset
add(double) - Method in class weka.experiment.Stats
Adds a value to the observed values
add(double, double) - Method in class weka.experiment.Stats
Adds a value that has been seen n times to the observed values
Add - Class in weka.filters.unsupervised.attribute
An instance filter that adds a new attribute to the dataset.
Add() - Constructor for class weka.filters.unsupervised.attribute.Add
 
add(int, Object) - Method in class weka.gui.CheckBoxList.CheckBoxListModel
Inserts the specified element at the specified position in this list.
add(String) - Method in class weka.gui.HierarchyPropertyParser
Add the given item of property to the tree
ADD_CHILDREN - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
 
addActionListener(ActionListener) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Register a listener to be notified when plotting completes
addActionListener(ActionListener) - Method in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Add a listener interested in kowing about editor status changes
addActionListener(ActionListener) - Method in class weka.gui.visualize.ClassPanel
Add an action listener that will be notified if the user changes the colour of a label
addActionListener(ActionListener) - Method in class weka.gui.visualize.VisualizePanel
Add a listener for this visualize panel
addAll(SimpleLinkedList) - Method in class weka.associations.tertius.SimpleLinkedList
 
addAll(Collection) - Method in class weka.core.neighboursearch.covertrees.Stack
Adds all the given elements in the stack.
addAll(Collection<? extends String>) - Method in class weka.core.Trie
Adds all of the elements in the specified collection to this collection
addAllBeansToContainer(JComponent) - Static method in class weka.gui.beans.BeanInstance
Adds all beans to the supplied component
addAllowed(Class, String) - Method in class weka.core.xml.PropertyHandler
adds the given property (display name) to the list of allowed properties for the specified class.
addAndUpdate(Rule) - Method in class weka.classifiers.rules.RuleStats
Add a rule to the ruleset and update the stats
addArc(String, String) - Method in class weka.classifiers.bayes.net.EditableBayesNet
Add arc between two nodes Distributions are updated by duplication for every value of the parent node.
addArc(int, int) - Method in class weka.classifiers.bayes.net.EditableBayesNet
Add arc between two nodes Distributions are updated by duplication for every value of the parent node.
addArc(String, FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet
Add arc between parent node and each of the nodes in a given list.
addArcMakesSense(BayesNet, Instances, int, int) - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
AddArcMakesSense checks whether adding the arc from iAttributeTail to iAttributeHead does not already exists and does not introduce a cycle
addAttribute(Element, Attribute) - Method in class weka.core.xml.XMLInstances
adds the attribute to the XML structure
addAttributePanelListener(AttributePanelListener) - Method in class weka.gui.visualize.AttributePanel
Add a listener to the list of things listening to this panel
addBag(Instances, Instances, Instances, int, double, double) - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
adds a new bag out of the given data and adds it to the output
addBatchClassifierListener(BatchClassifierListener) - Method in class weka.gui.beans.Classifier
Add a batch classifier listener
addBatchClustererListener(BatchClustererListener) - Method in class weka.gui.beans.Clusterer
Add a batch clusterer listener
addBean(JComponent) - Method in class weka.gui.beans.BeanInstance
Adds this bean to the global list of beans and to the supplied container.
addBeanConnectionRelation(MetaBean, String) - Method in class weka.gui.beans.xml.XMLBeans
adds the given connection-relation for the specified MetaBean (or null in case of regular connections)
addBeanInstances(Vector) - Method in class weka.gui.beans.xml.XMLBeans
traverses over all BeanInstances (or MetaBeans) and stores them in a vector (recurses into MetaBeans, since the sub-BeanInstances are not visible)
addBefore(Object) - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListIterator
 
addCache(Class, String, Vector) - Static method in class weka.core.ClassDiscovery
adds the list of classnames to the cache.
addCancelListener(ActionListener) - Method in class weka.gui.GenericObjectEditor.GOEPanel
This is used to hook an action listener to the cancel button.
addCapabilities(String, Capabilities) - Method in class weka.gui.PropertySheetPanel.CapabilitiesHelpDialog
generates a string from the capapbilities, suitable to add to the help text.
addCapabilitiesFilterListener(Explorer.CapabilitiesFilterChangeListener) - Method in class weka.gui.explorer.Explorer
adds the listener to the list of objects that listen for changes of the CapabilitiesFilter
addChangeListener(ChangeListener) - Method in class weka.gui.arffviewer.ArffPanel
Adds a ChangeListener to the panel
addChangeListener(ChangeListener) - Method in class weka.gui.arffviewer.ArffTable
Adds a ChangeListener to the panel
addChartListener(ChartListener) - Method in class weka.gui.beans.IncrementalClassifierEvaluator
Add a chart listener
addCheckBoxActionListener(ActionListener) - Method in class weka.gui.experiment.DistributeExperimentPanel
Enable objects to listen for changes to the check box
addChild(Splitter, ADTree) - Method in class weka.classifiers.trees.adtree.PredictionNode
Adds a child to this node.
addChild(LADTree.Splitter) - Method in class weka.classifiers.trees.LADTree.PredictionNode
 
addChild(Edge) - Method in class weka.gui.treevisualizer.Node
Set the value of children.
addChildClique(MarginCalculator.JunctionTreeNode) - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode
 
addChildFrame(Container) - Method in class weka.gui.GUIChooser
adds the given child frame to the list of frames.
addChildFrame(Container) - Method in class weka.gui.Main
adds the given child frame to the list of frames.
addChildrenToTree(GenericObjectEditor.GOETreeNode, HierarchyPropertyParser) - Method in class weka.gui.GenericObjectEditor
Recursively builds a JTree from an object heirarchy.
AddClassification - Class in weka.filters.supervised.attribute
A filter for adding the classification, the class distribution and an error flag to a dataset with a classifier.
AddClassification() - Constructor for class weka.filters.supervised.attribute.AddClassification
 
AddCluster - Class in weka.filters.unsupervised.attribute
A filter that adds a new nominal attribute representing the cluster assigned to each instance by the specified clustering algorithm.
AddCluster() - Constructor for class weka.filters.unsupervised.attribute.AddCluster
 
addConnectionListener(ConnectionListener) - Method in class weka.gui.sql.ConnectionPanel
adds the given listener to the list of listeners.
addConnectionListener(ConnectionListener) - Method in class weka.gui.sql.SqlViewer
adds the given listener to the list of listeners.
addCons(int[]) - Method in class weka.associations.PriorEstimation
generates a class association rule out of a given premise.
addCVParameter(String) - Method in class weka.classifiers.meta.CVParameterSelection
Adds a scheme parameter to the list of parameters to be set by cross-validation
addDataFormatListener(DataFormatListener) - Method in class weka.gui.beans.ClassAssigner
 
addDataFormatListener(DataFormatListener) - Method in class weka.gui.beans.ClassValuePicker
 
addDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.AbstractDataSource
Add a listener
addDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.ClassAssigner
 
addDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.ClassValuePicker
 
addDataSourceListener(DataSourceListener) - Method in interface weka.gui.beans.DataSource
Add a data source listener
addDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.DataVisualizer
Add a listener
addDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.Filter
Add a data source listener
addDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.InstanceStreamToBatchMaker
 
addDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.Loader
Add a listener
addDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.PredictionAppender
Add a datasource listener
addDistinct(double, int) - Method in class weka.core.AttributeStats
Updates the counters for one more observed distinct value.
addElement(Literal) - Method in class weka.associations.tertius.LiteralSet
Add a Literal to this set.
addElement(Object) - Method in class weka.core.FastVector
Adds an element to this vector.
addElement(int, int, double) - Method in class weka.core.Matrix
Deprecated.
Add a value to an element.
addElement(double) - Method in class weka.core.matrix.DoubleVector
Adds an element into the vector
addElement(Element, String, String, boolean) - Method in class weka.core.xml.XMLSerialization
appends a new node to the parent with the given parameters (a non-array)
addElement(Element, String, String, boolean, int) - Method in class weka.core.xml.XMLSerialization
appends a new node to the parent with the given parameters
addElement(Element, String, String, boolean, int, boolean) - Method in class weka.core.xml.XMLSerialization
appends a new node to the parent with the given parameters
addElement(Object) - Method in class weka.gui.CheckBoxList.CheckBoxListModel
Adds the specified component to the end of this list.
addErrs(double, double, float) - Static method in class weka.classifiers.trees.j48.Stats
Computes estimated extra error for given total number of instances and error using normal approximation to binomial distribution (and continuity correction).
AddExpression - Class in weka.filters.unsupervised.attribute
An instance filter that creates a new attribute by applying a mathematical expression to existing attributes.
AddExpression() - Constructor for class weka.filters.unsupervised.attribute.AddExpression
 
addFile(String) - Static method in class weka.core.ClassloaderUtil
Add file to CLASSPATH
addFile(File) - Static method in class weka.core.ClassloaderUtil
Add file to CLASSPATH
addFirst(Object) - Method in class weka.associations.tertius.SimpleLinkedList
 
addGraphListener(GraphListener) - Method in class weka.gui.beans.Associator
Add a graph listener
addGraphListener(GraphListener) - Method in class weka.gui.beans.Classifier
Add a graph listener
addGraphListener(GraphListener) - Method in class weka.gui.beans.Clusterer
Add a graph listener
addHeader(String, String) - Method in class weka.experiment.ResultMatrix
adds the key-value pair to the header
addHistory(String) - Method in class weka.gui.sql.ConnectionPanel
adds the given string to the history (removes duplicates).
addHistory(String) - Method in class weka.gui.sql.QueryPanel
adds the given string to the history (removes duplicates).
addHistoryChangedListener(HistoryChangedListener) - Method in class weka.gui.sql.ConnectionPanel
adds the given listener to the list of listeners.
addHistoryChangedListener(HistoryChangedListener) - Method in class weka.gui.sql.QueryPanel
adds the given listener to the list of listeners.
addHistoryChangedListener(HistoryChangedListener) - Method in class weka.gui.sql.SqlViewer
adds the given listener to the list of listeners.
AddID - Class in weka.filters.unsupervised.attribute
An instance filter that adds an ID attribute to the dataset.
AddID() - Constructor for class weka.filters.unsupervised.attribute.AddID
 
addIgnored(String) - Method in class weka.core.xml.PropertyHandler
adds the given display name of a property to the ignore list.
addIgnored(Class, String) - Method in class weka.core.xml.PropertyHandler
adds the given class with the display name of a property to the ignore list.
addIncrementalClassifierListener(IncrementalClassifierListener) - Method in class weka.gui.beans.Classifier
Add an incremental classifier listener
ADDING - Static variable in class weka.gui.beans.KnowledgeFlowApp
 
addInstance(Instance) - Method in class weka.clusterers.Cobweb
Deprecated.
updateClusterer(Instance) should be used instead
addInstance(BallNode, Instance) - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
Adds an instance to the ball tree.
addInstance(BallNode, Instance) - Method in class weka.core.neighboursearch.balltrees.BottomUpConstructor
Adds an instance to the ball tree.
addInstance(BallNode, Instance) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Adds an instance to the tree.
addInstance(BallNode, Instance) - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor
Adds an instance to the ball tree.
addInstance(Element, Instance) - Method in class weka.core.xml.XMLInstances
adds the instance to the XML structure
addInstanceInfo(Instance) - Method in class weka.core.neighboursearch.BallTree
Adds the given instance's info.
addInstanceInfo(Instance) - Method in class weka.core.neighboursearch.CoverTree
Adds the given instance info.
addInstanceInfo(Instance) - Method in class weka.core.neighboursearch.KDTree
Adds one instance to KDTree loosly.
addInstanceInfo(Instance) - Method in class weka.core.neighboursearch.LinearNNSearch
Adds the given instance info.
addInstanceInfo(Instance) - Method in class weka.core.neighboursearch.NearestNeighbourSearch
Adds information from the given instance without modifying the datastructure a lot.
addInstanceListener(InstanceListener) - Method in class weka.gui.beans.AbstractDataSource
Add an instance listener
addInstanceListener(InstanceListener) - Method in class weka.gui.beans.ClassAssigner
 
addInstanceListener(InstanceListener) - Method in interface weka.gui.beans.DataSource
Add an instance listener
addInstanceListener(InstanceListener) - Method in class weka.gui.beans.Filter
Add an instance listener
addInstanceListener(InstanceListener) - Method in class weka.gui.beans.InstanceStreamToBatchMaker
 
addInstanceListener(InstanceListener) - Method in class weka.gui.beans.Loader
Add an instance listener
addInstanceListener(InstanceListener) - Method in class weka.gui.beans.PredictionAppender
Add an instance listener
addInstanceListener(InstanceListener) - Method in class weka.gui.streams.InstanceJoiner
 
addInstanceListener(InstanceListener) - Method in class weka.gui.streams.InstanceLoader
 
addInstanceListener(InstanceListener) - Method in interface weka.gui.streams.InstanceProducer
 
addInstanceNumberAttribute() - Method in class weka.gui.visualize.PlotData2D
Adds an instance number attribute to the plottable instances,
addInstances(Instances, Instances) - Method in class weka.classifiers.meta.Decorate
Add new instances to the given set of instances.
AddInstanceToBestCluster(Instance) - Method in class weka.clusterers.CLOPE
Add instance to best cluster
addInstanceToTree(Instance, KDTreeNode) - Method in class weka.core.neighboursearch.KDTree
Recursively adds an instance to the tree starting from the supplied KDTreeNode.
addInstWithUnknown(Instances, int) - Method in class weka.classifiers.trees.j48.Distribution
Adds all instances with unknown values for given attribute, weighted according to frequency of instances in each bag.
addItem(FPGrowth.BinaryItem) - Method in class weka.associations.FPGrowth.FrequentBinaryItemSet
Add an item to this item set.
addItemSet(Collection<FPGrowth.BinaryItem>, Map<FPGrowth.BinaryItem, FPGrowth.FPTreeRoot.Header>, int) - Method in class weka.associations.FPGrowth.FPTreeNode
Insert an item set into the tree at this node.
addItemSet(FPGrowth.FrequentBinaryItemSet) - Method in class weka.associations.FPGrowth.FrequentItemSets
Add an item set.
additional() - Method in class weka.core.TechnicalInformation
returns an enumeration of all the additional technical informations (if there are any)
AdditionalMeasureProducer - Interface in weka.core
Interface to something that can produce measures other than those calculated by evaluation modules.
AdditiveRegression - Class in weka.classifiers.meta
Meta classifier that enhances the performance of a regression base classifier.
AdditiveRegression() - Constructor for class weka.classifiers.meta.AdditiveRegression
Default constructor specifying DecisionStump as the classifier
AdditiveRegression(Classifier) - Constructor for class weka.classifiers.meta.AdditiveRegression
Constructor which takes base classifier as argument.
addLayoutCompleteEventListener(LayoutCompleteEventListener) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
Method to add a LayoutCompleteEventListener
addLayoutCompleteEventListener(LayoutCompleteEventListener) - Method in interface weka.gui.graphvisualizer.LayoutEngine
This method adds a LayoutCompleteEventListener to the LayoutEngine.
addLiteral(Literal) - Method in class weka.associations.tertius.Predicate
 
addMethods(MethodHandler, Method, Method[]) - Method in class weka.core.xml.XMLSerializationMethodHandler
adds all methods that are like template to the method list
addMethods() - Method in class weka.core.xml.XMLSerializationMethodHandler
automatically adds all fitting methods to the custom read/write lists, it excludes only the generic ones.
addMissing(Instances, int, boolean) - Method in class weka.clusterers.CheckClusterer
Add missing values to a dataset.
addMissing(Instances, int, boolean, boolean) - Method in class weka.core.CheckScheme
Add missing values to a dataset.
addMissing(Instances, int, boolean, boolean, int) - Method in class weka.estimators.CheckEstimator
Add missing values to a dataset.
addMouseListener(MouseListener) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Adds a mouse listener.
addMouseListenerToHeader(JTable) - Method in class weka.gui.SortedTableModel
Adds a mouselistener to the header: left-click on the header sorts in ascending manner, using shift-left-click in descending manner.
addNode(String, int) - Method in class weka.classifiers.bayes.net.EditableBayesNet
Add new node to the network, initializing instances, parentsets, distributions.
addNode(String, int, int, int) - Method in class weka.classifiers.bayes.net.EditableBayesNet
Add node to network at a given position, initializing instances, parentsets, distributions.
addNodeValue(int, String) - Method in class weka.classifiers.bayes.net.EditableBayesNet
Add node value to a node.
AddNoise - Class in weka.filters.unsupervised.attribute
An instance filter that changes a percentage of a given attributes values.
AddNoise() - Constructor for class weka.filters.unsupervised.attribute.AddNoise
 
addNoise(Instances, int, int, int, boolean) - Method in class weka.filters.unsupervised.attribute.AddNoise
add noise to the dataset a given percentage of the instances are changed in the way, that a set of instances are randomly selected using seed.
addNumericTrainClass(double, double) - Method in class weka.classifiers.Evaluation
Adds a numeric (non-missing) training class value and weight to the buffer of stored values.
addObject(String, Object) - Method in class weka.gui.ResultHistoryPanel
Adds an object to the results list
addOkListener(ActionListener) - Method in class weka.gui.GenericObjectEditor.GOEPanel
This is used to hook an action listener to the ok button.
addParent(int, Instances) - Method in class weka.classifiers.bayes.net.ParentSet
Add parent to parent set and update internals (specifically the cardinality of the parent set)
addParent(int, int, Instances) - Method in class weka.classifiers.bayes.net.ParentSet
Add parent to parent set at specific location and update internals (specifically the cardinality of the parent set)
addPlot(PlotData2D) - Method in class weka.gui.visualize.Plot2D
Add a plot to the list of plots to display
addPlot(PlotData2D) - Method in class weka.gui.visualize.VisualizePanel
Set a new plot to the visualize panel
addPlot(PlotData2D) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
Adds a plot.
addPrediction(NominalPrediction) - Method in class weka.classifiers.evaluation.ConfusionMatrix
Includes a prediction in the confusion matrix.
addPredictions(FastVector) - Method in class weka.classifiers.evaluation.ConfusionMatrix
Includes a whole bunch of predictions in the confusion matrix.
addPropertyChangeListener(String, PropertyChangeListener) - Method in class weka.gui.beans.AbstractDataSource
Add a property change listener to this bean
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.AssociatorCustomizer
Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.BeanVisual
Add a listener for property change events
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.ClassAssignerCustomizer
Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.ClassifierCustomizer
Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.ClassValuePickerCustomizer
Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.ClustererCustomizer
Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.CrossValidationFoldMakerCustomizer
Add a property change listener
addPropertyChangeListener(String, PropertyChangeListener) - Method in class weka.gui.beans.DataVisualizer
Add a property change listener to this bean
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.FilterCustomizer
Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.IncrementalClassifierEvaluatorCustomizer
Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.LoaderCustomizer
Add a property change listener
addPropertyChangeListener(String, PropertyChangeListener) - Method in class weka.gui.beans.ModelPerformanceChart
Add a property change listener to this bean
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.PredictionAppenderCustomizer
Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.SaverCustomizer
Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.SerializedModelSaverCustomizer
Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.StripChartCustomizer
Add a property change listener
addPropertyChangeListener(String, PropertyChangeListener) - Method in class weka.gui.beans.TextViewer
Add a property change listener to this bean
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.TrainTestSplitMakerCustomizer
Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.CostMatrixEditor
Adds an object to the list of those that wish to be informed when the cost matrix changes.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.experiment.SetupModePanel
Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.experiment.SetupPanel
Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.experiment.SimpleSetupPanel
Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.explorer.PreprocessPanel
Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.GenericArrayEditor
Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.GenericObjectEditor
Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.PropertySheetPanel
Adds a PropertyChangeListener.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.SetInstancesPanel
Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.SimpleDateFormatEditor
Adds an object to the list of those that wish to be informed when the date format changes.
addPropertyChangeListenersSubFlow(PropertyChangeListener) - Method in class weka.gui.beans.MetaBean
 
addPSFontReplacement(String, String) - Static method in class weka.gui.visualize.PostscriptGraphics
adds the PS font name to replace and its replacement in the replacement hashtable
addQueryExecuteListener(QueryExecuteListener) - Method in class weka.gui.sql.QueryPanel
adds the given listener to the list of listeners.
addQueryExecuteListener(QueryExecuteListener) - Method in class weka.gui.sql.SqlViewer
adds the given listener to the list of listeners.
addRandomInstances(Instances, int, Random) - Method in class weka.classifiers.meta.RotationForest
Adds random instances to the dataset.
addRange(int, Instances, int, int) - Method in class weka.classifiers.trees.j48.Distribution
Adds all instances in given range to given bag.
addReference(Instance) - Method in class weka.classifiers.trees.adtree.ReferenceInstances
Adds one instance reference to the end of the set.
addRelation(Instances) - Method in class weka.core.Attribute
Adds a relation to a relation-valued attribute.
addRemoteExperimentListener(RemoteExperimentListener) - Method in class weka.experiment.RemoteExperiment
Add an object to the list of those interested in recieving update information from the RemoteExperiment
addRemoteExperimentListener(RemoteExperimentListener) - Method in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
Add an object to the list of those interested in recieving update information from the RemoteExperiment
addRemoteHost(String) - Method in class weka.experiment.RemoteExperiment
Add a host name to the list of remote hosts
addRenderingHints(Map) - Method in class weka.gui.visualize.PostscriptGraphics
 
addRepaintNotify(Component) - Method in class weka.gui.visualize.ClassPanel
Adds a component that will need to be repainted if the user changes the colour of a label.
addRepaintNotify(Component) - Method in class weka.gui.visualize.LegendPanel
Adds a component that will need to be repainted if the user changes the colour of a label.
addResult(String, StringBuffer) - Method in class weka.gui.ResultHistoryPanel
Adds a new result to the result list.
addResultChangedListener(ResultChangedListener) - Method in class weka.gui.sql.ResultPanel
adds the given listener to the list of listeners
addResultChangedListener(ResultChangedListener) - Method in class weka.gui.sql.SqlViewer
adds the given listener to the list of listeners.
addStartupListener(StartUpListener) - Static method in class weka.gui.beans.KnowledgeFlowApp
Add a listener to be notified when startup is complete
addStartupListener(StartUpListener) - Static method in class weka.gui.Main
Add a listener to be notified when startup is complete.
addStringValue(String) - Method in class weka.core.Attribute
Adds a string value to the list of valid strings for attributes of type STRING and returns the index of the string.
addStringValue(Attribute, int) - Method in class weka.core.Attribute
Adds a string value to the list of valid strings for attributes of type STRING and returns the index of the string.
addTableModelListener(TableModelListener) - Method in class weka.gui.arffviewer.ArffSortedTableModel
adds a listener to the list that is notified each time a change to data model occurs
addTableModelListener(TableModelListener) - Method in class weka.gui.arffviewer.ArffTableModel
adds a listener to the list that is notified each time a change to data model occurs
addTableModelListener(TableModelListener) - Method in class weka.gui.sql.ResultSetTableModel
adds a listener to the list that is notified each time a change to data model occurs.
addTestSetListener(TestSetListener) - Method in class weka.gui.beans.AbstractTestSetProducer
Add a listener for test sets
addTestSetListener(TestSetListener) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
Add a test set listener
addTestSetListener(TestSetListener) - Method in class weka.gui.beans.ClassAssigner
 
addTestSetListener(TestSetListener) - Method in class weka.gui.beans.Filter
Add a test set listener
addTestSetListener(TestSetListener) - Method in class weka.gui.beans.PredictionAppender
Add a test set listener
addTestSetListener(TestSetListener) - Method in interface weka.gui.beans.TestSetProducer
Add a listener for test set events
addTextListener(TextListener) - Method in class weka.gui.beans.Associator
Add a text listener
addTextListener(TextListener) - Method in class weka.gui.beans.Classifier
Add a text listener
addTextListener(TextListener) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator
Add a text listener
addTextListener(TextListener) - Method in class weka.gui.beans.Clusterer
Add a text listener
addTextListener(TextListener) - Method in class weka.gui.beans.ClustererPerformanceEvaluator
Add a text listener
addTextListener(TextListener) - Method in class weka.gui.beans.IncrementalClassifierEvaluator
Add a text listener
addTextListener(TextListener) - Method in class weka.gui.beans.TextViewer
Add a text listener
addThresholdDataListener(ThresholdDataListener) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator
Add a threshold data listener
addToBlacklist(String) - Static method in class weka.datagenerators.DataGenerator
adds the given option, e.g., for "-V" use "V", to the blacklist of options that are not to be output via the makeOptionString method
addToList(Object[], double) - Method in class weka.attributeSelection.BestFirst.LinkedList2
adds an element (Link) to the list.
addToList(Object[], double) - Method in class weka.attributeSelection.LFSMethods.LinkedList2
adds an element (Link) to the list.
addTrainingInstance(Instance) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Adds a training instance to the visualization dataset.
addTrainingInstanceFromMouseLocation(int, int, int, double) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Adds a training instance to our dataset, based on the coordinates of the mouse on the panel.
addTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
Add a training set listener
addTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.AbstractTrainingSetProducer
Add a training set listener
addTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.ClassAssigner
 
addTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.Filter
Add a training set listener
addTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.PredictionAppender
Add a training set listener
addTrainingSetListener(TrainingSetListener) - Method in interface weka.gui.beans.TrainingSetProducer
Add a training set listener
addUndoPoint() - Method in interface weka.core.Undoable
adds an undo point to the undo history
addUndoPoint() - Method in class weka.gui.arffviewer.ArffPanel
adds the current state of the instances to the undolist
addUndoPoint() - Method in class weka.gui.arffviewer.ArffSortedTableModel
adds an undo point to the undo history
addUndoPoint() - Method in class weka.gui.arffviewer.ArffTableModel
adds an undo point to the undo history, if the undo support is enabled
addUndoPoint() - Method in class weka.gui.explorer.PreprocessPanel
Backs up the current state of the dataset, so the changes can be undone.
addURL(URL) - Static method in class weka.core.ClassloaderUtil
Add URL to CLASSPATH
addValue(double, double) - Method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes
Add a new data value to the current estimator.
addValue(double, double, double) - Method in interface weka.estimators.ConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double, double) - Method in class weka.estimators.DDConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double) - Method in class weka.estimators.DiscreteEstimator
Add a new data value to the current estimator.
addValue(double, double, double) - Method in class weka.estimators.DKConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double, double) - Method in class weka.estimators.DNConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double) - Method in class weka.estimators.Estimator
Add a new data value to the current estimator.
addValue(double, double) - Method in interface weka.estimators.IncrementalEstimator
Add one value to the current estimator.
addValue(double, double, double) - Method in class weka.estimators.KDConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double) - Method in class weka.estimators.KernelEstimator
Add a new data value to the current estimator.
addValue(double, double, double) - Method in class weka.estimators.KKConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double) - Method in class weka.estimators.MahalanobisEstimator
Add a new data value to the current estimator.
addValue(double, double, double) - Method in class weka.estimators.NDConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double, double) - Method in class weka.estimators.NNConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double) - Method in class weka.estimators.NormalEstimator
Add a new data value to the current estimator.
addValue(double, double) - Method in class weka.estimators.PoissonEstimator
Add a new data value to the current estimator.
addValues(Instances, int) - Method in class weka.estimators.Estimator
Initialize the estimator with a new dataset.
addValues(Instances, int, double, double, double) - Method in class weka.estimators.Estimator
Initialize the estimator with all values of one attribute of a dataset.
addValues(Instances, int, int, int) - Method in class weka.estimators.Estimator
Initialize the estimator using only the instance of one class.
addValues(Instances, int, int, int, double, double) - Method in class weka.estimators.Estimator
Initialize the estimator using only the instance of one class.
AddValues - Class in weka.filters.unsupervised.attribute
Adds the labels from the given list to an attribute if they are missing.
AddValues() - Constructor for class weka.filters.unsupervised.attribute.AddValues
 
addVariable(String, String) - Method in class weka.core.Environment
Add a variable to the internal map.
addVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.AbstractDataSource
Add a vetoable change listener to this bean
addVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.CostBenefitAnalysis
 
addVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.DataVisualizer
Add a vetoable change listener to this bean
addVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.GraphViewer
Add a vetoable change listener to this bean
addVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.ModelPerformanceChart
Add a vetoable change listener to this bean
addVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.TextViewer
Add a vetoable change listener to this bean
addVisualizableErrorListener(VisualizableErrorListener) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator
Add a visualizable error listener
addWeights(Instance, double[]) - Method in class weka.classifiers.trees.j48.Distribution
Adds given instance to all bags weighting it according to given weights.
adjustCenter(double) - Method in class weka.gui.treevisualizer.Node
Will increase or decrease the postion of center.
adjustSize(SERObject) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel
Adjusts the size of this panel in respect of the shown content
adjustWeightsTipText() - Method in class weka.filters.supervised.instance.SpreadSubsample
Returns the tip text for this property
ADNode - Class in weka.classifiers.bayes.net
The ADNode class implements the ADTree datastructure which increases the speed with which sub-contingency tables can be constructed from a data set in an Instances object.
ADNode() - Constructor for class weka.classifiers.bayes.net.ADNode
Creates new ADNode
ADTree - Class in weka.classifiers.trees
Class for generating an alternating decision tree.
ADTree() - Constructor for class weka.classifiers.trees.ADTree
 
advanceCounters() - Method in class weka.experiment.Experiment
Increments iteration counters appropriately.
advanceCounters() - Method in class weka.experiment.RemoteExperiment
overides the one in Experiment
afterAddInstance(KDTreeNode) - Method in class weka.core.neighboursearch.KDTree
Corrects the start and end indices of a KDTreeNode after an instance is added to the tree.
Agrawal - Class in weka.datagenerators.classifiers.classification
Generates a people database and is based on the paper by Agrawal et al.:
R.
Agrawal() - Constructor for class weka.datagenerators.classifiers.classification.Agrawal
initializes the generator with default values
Agrawal.ClassFunction - Interface in weka.datagenerators.classifiers.classification
the interface for the class functions
AIC - Static variable in interface weka.classifiers.bayes.net.search.local.Scoreable
 
ALGORITHM_HAAR - Static variable in class weka.filters.unsupervised.attribute.Wavelet
the type of algorithm: Haar wavelet
ALGORITHM_PLS1 - Static variable in class weka.filters.supervised.attribute.PLSFilter
the type of algorithm: PLS1
ALGORITHM_SIMPLS - Static variable in class weka.filters.supervised.attribute.PLSFilter
the type of algorithm: SIMPLS
AlgorithmListPanel - Class in weka.gui.experiment
This panel controls setting a list of algorithms for an experiment to iterate over.
AlgorithmListPanel(Experiment) - Constructor for class weka.gui.experiment.AlgorithmListPanel
Creates the algorithm list panel with the given experiment.
AlgorithmListPanel() - Constructor for class weka.gui.experiment.AlgorithmListPanel
Create the algorithm list panel initially disabled.
AlgorithmListPanel.ObjectCellRenderer - Class in weka.gui.experiment
Class required to show the Classifiers nicely in the list
algorithmTipText() - Method in class weka.filters.supervised.attribute.PLSFilter
Returns the tip text for this property
algorithmTipText() - Method in class weka.filters.unsupervised.attribute.Wavelet
Returns the tip text for this property
ALGORITHMTYPE_ARITHMETIC - Static variable in class weka.classifiers.mi.MILR
collective MI assumption, arithmetic mean for posteriors
ALGORITHMTYPE_DEFAULT - Static variable in class weka.classifiers.mi.MILR
standard MI assumption
ALGORITHMTYPE_GEOMETRIC - Static variable in class weka.classifiers.mi.MILR
collective MI assumption, geometric mean for posteriors
algorithmTypeTipText() - Method in class weka.classifiers.mi.MILR
Returns the tip text for this property
AlgVector - Class in weka.core
Class for performing operations on an algebraic vector of floating-point values.
AlgVector(int) - Constructor for class weka.core.AlgVector
Constructs a vector and initializes it with default values.
AlgVector(double[]) - Constructor for class weka.core.AlgVector
Constructs a vector using a given array.
AlgVector(Instances, Random) - Constructor for class weka.core.AlgVector
Constructs a vector using a given data format.
AlgVector(Instance) - Constructor for class weka.core.AlgVector
Constructs a vector using an instance.
alignBottom(FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet
align set of nodes with the bottom most node in the list
alignLeft(FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet
align set of nodes with the left most node in the list
alignRight(FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet
align set of nodes with the right most node in the list
alignTop(FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet
align set of nodes with the top most node in the list
ALL - Static variable in class weka.core.Debug
the log level All
AllFilter - Class in weka.filters
A simple instance filter that passes all instances directly through.
AllFilter() - Constructor for class weka.filters.AllFilter
 
AllJavadoc - Class in weka.core
Applies all known Javadoc-derived classes to a source file.
AllJavadoc() - Constructor for class weka.core.AllJavadoc
 
allocateInputs() - Method in class weka.classifiers.functions.neural.NeuralConnection
This will allocate more space for input connection information if the arrays for this have been filled up.
allocateInputs() - Method in class weka.classifiers.functions.neural.NeuralNode
This will allocate more space for input connection information if the arrays for this have been filled up.
allocateOutputs() - Method in class weka.classifiers.functions.neural.NeuralConnection
Allocates more space for output connection information if the arrays have been filled up.
allowed() - Method in class weka.core.xml.PropertyHandler
returns an enumeration of the classnames for which only certain properties (display names) are allowed
allowUnclassifiedInstancesTipText() - Method in class weka.classifiers.trees.RandomTree
Returns the tip text for this property
AlphabeticTokenizer - Class in weka.core.tokenizers
Alphabetic string tokenizer, tokens are to be formed only from contiguous alphabetic sequences.
AlphabeticTokenizer() - Constructor for class weka.core.tokenizers.AlphabeticTokenizer
 
alphaTipText() - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator
 
alphaTipText() - Method in class weka.classifiers.functions.Winnow
Returns the tip text for this property
amplitudeTipText() - Method in class weka.datagenerators.classifiers.regression.Expression
Returns the tip text for this property
amplitudeTipText() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Returns the tip text for this property
AnalysisPanel() - Constructor for class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
and(Capabilities) - Method in class weka.core.Capabilities
performs an AND conjunction with the capabilities of the given Capabilities object and updates itself
AND - Static variable in interface weka.core.mathematicalexpression.sym
 
AND - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
Antd(Attribute) - Constructor for class weka.classifiers.rules.JRip.Antd
Constructor
AODE - Class in weka.classifiers.bayes
AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less detrimental) independence assumptions than naive Bayes.
AODE() - Constructor for class weka.classifiers.bayes.AODE
 
AODEsr - Class in weka.classifiers.bayes
AODEsr augments AODE with Subsumption Resolution.AODEsr detects specializations between two attribute values at classification time and deletes the generalization attribute value.
For more information, see:
Fei Zheng, Geoffrey I.
AODEsr() - Constructor for class weka.classifiers.bayes.AODEsr
 
appearanceDesign() - Method in class weka.gui.beans.AttributeSummarizer
 
appearanceDesign() - Method in class weka.gui.beans.CostBenefitAnalysis
 
appearanceDesign() - Method in class weka.gui.beans.DataVisualizer
 
appearanceDesign() - Method in class weka.gui.beans.GraphViewer
 
appearanceDesign() - Method in class weka.gui.beans.Loader
 
appearanceDesign() - Method in class weka.gui.beans.ModelPerformanceChart
 
appearanceDesign() - Method in class weka.gui.beans.ScatterPlotMatrix
 
appearanceDesign() - Method in class weka.gui.beans.TextViewer
 
appearanceFinal() - Method in class weka.gui.beans.AttributeSummarizer
 
appearanceFinal() - Method in class weka.gui.beans.CostBenefitAnalysis
 
appearanceFinal() - Method in class weka.gui.beans.DataVisualizer
 
appearanceFinal() - Method in class weka.gui.beans.GraphViewer
 
appearanceFinal() - Method in class weka.gui.beans.Loader
 
appearanceFinal() - Method in class weka.gui.beans.ModelPerformanceChart
 
appearanceFinal() - Method in class weka.gui.beans.ScatterPlotMatrix
 
appearanceFinal() - Method in class weka.gui.beans.TextViewer
 
append(String) - Method in class weka.core.logging.FileLogger
Appends the given string to the log file (without new line!).
append(MiddleOutConstructor.MyIdxList, MiddleOutConstructor.MyIdxList) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList
Appends one list at the end of the other.
append(String, String) - Method in class weka.gui.sql.InfoPanel
adds the given message to the end of the list (with the associated icon at the beginning)
append(Object) - Method in class weka.gui.sql.InfoPanel
adds the given message to the end of the list
appendElements(FastVector) - Method in class weka.core.FastVector
Appends all elements of the supplied vector to this vector.
appendPredictedProbabilitiesTipText() - Method in class weka.gui.beans.PredictionAppender
Return a tip text suitable for displaying in a GUI
applyClassifier(PMMLModel, Instances) - Static method in class weka.core.pmml.PMMLFactory
 
applyCostMatrix(Instances, Random) - Method in class weka.classifiers.CostMatrix
Applies the cost matrix to a set of instances.
applyFilter(Filter) - Method in class weka.gui.explorer.PreprocessPanel
Passes the dataset through the filter that has been configured for use.
applyMinMaxRescaleCast(double) - Method in class weka.core.pmml.TargetMetaInfo
Apply min and max, rescaleFactor, rescaleConstant and castInteger - in that order (where defined).
applyMissingAndOutlierTreatments(double[]) - Method in class weka.core.pmml.MiningSchema
Apply both missing and outlier treatments to an incoming instance.
applyMissingValuesTreatment(double[]) - Method in class weka.core.pmml.MiningSchema
Apply the missing value treatments (if any) to an incoming instance.
applyMissingValueTreatment(double) - Method in class weka.core.pmml.MiningFieldMetaInfo
Apply the missing value treatment method for this field.
applyOutlierTreatment(double) - Method in class weka.core.pmml.MiningFieldMetaInfo
Apply the outlier treatment method for this field.
applyOutlierTreatment(double[]) - Method in class weka.core.pmml.MiningSchema
Apply the outlier treatment methods (if any) to an incoming instance.
APPROVE_OPTION - Static variable in class weka.gui.experiment.OutputFormatDialog
Signifies an OK property selection.
APPROVE_OPTION - Static variable in class weka.gui.ListSelectorDialog
Signifies an OK property selection
APPROVE_OPTION - Static variable in class weka.gui.PropertySelectorDialog
Signifies an OK property selection
APPROVE_OPTION - Static variable in class weka.gui.ViewerDialog
Signifies an OK property selection
Apriori - Class in weka.associations
Class implementing an Apriori-type algorithm.
Apriori() - Constructor for class weka.associations.Apriori
Constructor that allows to sets default values for the minimum confidence and the maximum number of rules the minimum confidence.
aprioriGen(FastVector) - Static method in class weka.associations.gsp.Sequence
Generates all possible candidate k-Sequences and prunes the ones that contain an infrequent (k-1)-Sequence.
AprioriItemSet - Class in weka.associations
Class for storing a set of items.
AprioriItemSet(int) - Constructor for class weka.associations.AprioriItemSet
Constructor
areaUnderROC(int) - Method in class weka.classifiers.Evaluation
Returns the area under ROC for those predictions that have been collected in the evaluateClassifier(Classifier, Instances) method.
ARFF_ATTRIBUTE - Static variable in class weka.core.Attribute
The keyword used to denote the start of an arff attribute declaration
ARFF_ATTRIBUTE_DATE - Static variable in class weka.core.Attribute
The keyword used to denote a date attribute
ARFF_ATTRIBUTE_INTEGER - Static variable in class weka.core.Attribute
A keyword used to denote a numeric attribute
ARFF_ATTRIBUTE_NUMERIC - Static variable in class weka.core.Attribute
A keyword used to denote a numeric attribute
ARFF_ATTRIBUTE_REAL - Static variable in class weka.core.Attribute
A keyword used to denote a numeric attribute
ARFF_ATTRIBUTE_RELATIONAL - Static variable in class weka.core.Attribute
The keyword used to denote a relation-valued attribute
ARFF_ATTRIBUTE_STRING - Static variable in class weka.core.Attribute
The keyword used to denote a string attribute
ARFF_DATA - Static variable in class weka.core.Instances
The keyword used to denote the start of the arff data section
ARFF_END_SUBRELATION - Static variable in class weka.core.Attribute
The keyword used to denote the end of the declaration of a subrelation
ARFF_RELATION - Static variable in class weka.core.Instances
The keyword used to denote the start of an arff header
ArffLoader - Class in weka.core.converters
Reads a source that is in arff (attribute relation file format) format.
ArffLoader() - Constructor for class weka.core.converters.ArffLoader
 
ArffLoader.ArffReader - Class in weka.core.converters
Reads data from an ARFF file, either in incremental or batch mode.
ArffPanel - Class in weka.gui.arffviewer
A Panel representing an ARFF-Table and the associated filename.
ArffPanel() - Constructor for class weka.gui.arffviewer.ArffPanel
initializes the panel with no data
ArffPanel(String) - Constructor for class weka.gui.arffviewer.ArffPanel
initializes the panel and loads the specified file
ArffPanel(Instances) - Constructor for class weka.gui.arffviewer.ArffPanel
initializes the panel with the given data
ArffReader(Reader) - Constructor for class weka.core.converters.ArffLoader.ArffReader
Reads the data completely from the reader.
ArffReader(Reader, int) - Constructor for class weka.core.converters.ArffLoader.ArffReader
Reads only the header and reserves the specified space for instances.
ArffReader(Reader, Instances, int) - Constructor for class weka.core.converters.ArffLoader.ArffReader
Reads the data without header according to the specified template.
ArffReader(Reader, Instances, int, int) - Constructor for class weka.core.converters.ArffLoader.ArffReader
Initializes the reader without reading the header according to the specified template.
ArffSaver - Class in weka.core.converters
Writes to a destination in arff text format.
ArffSaver() - Constructor for class weka.core.converters.ArffSaver
Constructor
ArffSortedTableModel - Class in weka.gui.arffviewer
A sorter for the ARFF-Viewer - necessary because of the custom CellRenderer.
ArffSortedTableModel(String) - Constructor for class weka.gui.arffviewer.ArffSortedTableModel
initializes the sorter w/o a model, but loads the given file and creates from that a model
ArffSortedTableModel(Instances) - Constructor for class weka.gui.arffviewer.ArffSortedTableModel
initializes the sorter w/o a model, but uses the given data to create a model from that
ArffSortedTableModel(TableModel) - Constructor for class weka.gui.arffviewer.ArffSortedTableModel
initializes the sorter with the given model
ArffTable - Class in weka.gui.arffviewer
A specialized JTable for the Arff-Viewer.
ArffTable() - Constructor for class weka.gui.arffviewer.ArffTable
initializes with no model
ArffTable(TableModel) - Constructor for class weka.gui.arffviewer.ArffTable
initializes with the given model
ArffTable.RelationalCellEditor - Class in weka.gui.arffviewer
a special Editor for editing the relation attribute.
ArffTableCellRenderer - Class in weka.gui.arffviewer
Handles the background colors for missing values differently than the DefaultTableCellRenderer.
ArffTableCellRenderer() - Constructor for class weka.gui.arffviewer.ArffTableCellRenderer
initializes the Renderer with a standard color
ArffTableCellRenderer(Color, Color) - Constructor for class weka.gui.arffviewer.ArffTableCellRenderer
initializes the Renderer with the given colors
ArffTableCellRenderer(Color, Color, Color, Color) - Constructor for class weka.gui.arffviewer.ArffTableCellRenderer
initializes the Renderer with the given colors
ArffTableModel - Class in weka.gui.arffviewer
The model for the Arff-Viewer.
ArffTableModel(String) - Constructor for class weka.gui.arffviewer.ArffTableModel
initializes the object and loads the given file
ArffTableModel(Instances) - Constructor for class weka.gui.arffviewer.ArffTableModel
initializes the model with the given data
ArffViewer - Class in weka.gui.arffviewer
A little tool for viewing ARFF files.
ArffViewer() - Constructor for class weka.gui.arffviewer.ArffViewer
initializes the object
ArffViewerMainPanel - Class in weka.gui.arffviewer
The main panel of the ArffViewer.
ArffViewerMainPanel(Container) - Constructor for class weka.gui.arffviewer.ArffViewerMainPanel
initializes the object
arrayLeftDivide(Matrix) - Method in class weka.core.matrix.Matrix
Element-by-element left division, C = A.\B
arrayLeftDivideEquals(Matrix) - Method in class weka.core.matrix.Matrix
Element-by-element left division in place, A = A.\B
arrayRightDivide(Matrix) - Method in class weka.core.matrix.Matrix
Element-by-element right division, C = A./B
arrayRightDivideEquals(Matrix) - Method in class weka.core.matrix.Matrix
Element-by-element right division in place, A = A./B
arrayTimes(Matrix) - Method in class weka.core.matrix.Matrix
Element-by-element multiplication, C = A.*B
arrayTimesEquals(Matrix) - Method in class weka.core.matrix.Matrix
Element-by-element multiplication in place, A = A.*B
arrayToList(String[]) - Static method in class weka.core.CheckScheme
turns the array into a comma-separated list
arrayToList(String[]) - Static method in class weka.core.TestInstances
turns the array into a comma-separated list
arrayToString(Object) - Static method in class weka.core.Utils
Returns the given Array in a string representation.
arrayToString(Object[]) - Static method in class weka.experiment.DatabaseUtils
Converts an array of objects to a string by inserting a space between each element.
artificialSizeTipText() - Method in class weka.classifiers.meta.Decorate
Returns the tip text for this property
ASEvaluation - Class in weka.attributeSelection
Abstract attribute selection evaluation class
ASEvaluation() - Constructor for class weka.attributeSelection.ASEvaluation
 
ASSearch - Class in weka.attributeSelection
Abstract attribute selection search class.
ASSearch() - Constructor for class weka.attributeSelection.ASSearch
 
assign(Capabilities) - Method in class weka.core.Capabilities
retrieves the data from the given Capabilities object
assign(TestInstances) - Method in class weka.core.TestInstances
updates itself with all the settings from the given TestInstances object
assign(Tester) - Method in class weka.experiment.PairedTTester
retrieves all the settings from the given Tester
assign(ResultMatrix) - Method in class weka.experiment.ResultMatrix
acquires the data from the given matrix
assign(Tester) - Method in interface weka.experiment.Tester
retrieves all the settings from the given Tester
assignIDs(int) - Method in class weka.associations.FPGrowth.FPTreeNode
 
assignIDs(int) - Method in class weka.classifiers.trees.ft.FTtree
Assigns unique IDs to all nodes in the tree
assignIDs(int) - Method in class weka.classifiers.trees.j48.ClassifierTree
Assigns a uniqe id to every node in the tree.
assignIDs(int) - Method in class weka.classifiers.trees.lmt.LMTNode
Assigns unique IDs to all nodes in the tree
assignIDs(int) - Method in class weka.classifiers.trees.m5.RuleNode
Assigns a unique identifier to each node in the tree
assignLeafModelNumbers(int) - Method in class weka.classifiers.trees.ft.FTtree
Assigns numbers to the logistic regression models at the leaves of the tree
assignLeafModelNumbers(int) - Method in class weka.classifiers.trees.lmt.LMTNode
Assigns numbers to the logistic regression models at the leaves of the tree
assignLevels(int[], int, int, int) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
This method assigns a vertical level to each node.
assignSubToCenters(KDTreeNode, Instances, int[], int[]) - Method in class weka.core.neighboursearch.KDTree
Assigns instances of this node to center.
assignToCenters(KDTree, Instances, int[][], int[], int[], int) - Method in class weka.clusterers.XMeans
Assigns instances to centers.
assignToCenters(KDTree, Instances, int[][], int[], int) - Method in class weka.clusterers.XMeans
Assign instances to centers using KDtree.
assignToCenters(Instances, int[][], int[], int[]) - Method in class weka.clusterers.XMeans
Assign instances to centers.
associatedConnections(Vector) - Static method in class weka.gui.beans.BeanConnection
Returns a vector of BeanConnections associated with the supplied vector of BeanInstances, i.e.
AssociationRule(Collection<FPGrowth.BinaryItem>, Collection<FPGrowth.BinaryItem>, FPGrowth.AssociationRule.METRIC_TYPE, int, int, int, int) - Constructor for class weka.associations.FPGrowth.AssociationRule
Construct a new association rule.
AssociationsPanel - Class in weka.gui.explorer
This panel allows the user to select, configure, and run a scheme that learns associations.
AssociationsPanel() - Constructor for class weka.gui.explorer.AssociationsPanel
Creates the associator panel
Associator - Interface in weka.associations
 
Associator - Class in weka.gui.beans
Bean that wraps around weka.associations
Associator() - Constructor for class weka.gui.beans.Associator
Creates a new Associator instance.
AssociatorBeanInfo - Class in weka.gui.beans
BeanInfo class for the Associator wrapper bean
AssociatorBeanInfo() - Constructor for class weka.gui.beans.AssociatorBeanInfo
 
AssociatorCustomizer - Class in weka.gui.beans
GUI customizer for the associator wrapper bean
AssociatorCustomizer() - Constructor for class weka.gui.beans.AssociatorCustomizer
 
AssociatorEvaluation - Class in weka.associations
Class for evaluating Associaters.
AssociatorEvaluation() - Constructor for class weka.associations.AssociatorEvaluation
default constructor
associatorTipText() - Method in class weka.associations.SingleAssociatorEnhancer
Returns the tip text for this property
aSubsumesB(RuleItem, RuleItem) - Static method in class weka.associations.CaRuleGeneration
Methods that decides whether or not rule a subsumes rule b.
aSubsumesB(RuleItem, RuleItem) - Static method in class weka.associations.RuleGeneration
Methods that decides whether or not rule a subsumes rule b.
att - Variable in class weka.classifiers.rules.JRip.Antd
The attribute of the antecedent
ATT_ARRAY - Static variable in class weka.core.xml.XMLSerialization
the tag whether array or not (yes/no)
ATT_ARRAY_DEFAULT - Static variable in class weka.core.xml.XMLSerialization
default value for attribute ATT_ARRAY
ATT_CLASS - Static variable in class weka.core.xml.XMLInstances
the class attribute
ATT_CLASS - Static variable in class weka.core.xml.XMLSerialization
the tag for the class
ATT_FORMAT - Static variable in class weka.core.xml.XMLInstances
the format attribute (for date attributes)
ATT_INDEX - Static variable in class weka.core.xml.XMLInstances
the index attribute
ATT_MISSING - Static variable in class weka.core.xml.XMLInstances
the missing attribute
ATT_NAME - Static variable in class weka.core.xml.XMLDocument
the "name" attribute.
ATT_NAME - Static variable in class weka.core.xml.XMLOptions
the name attribute.
ATT_NAME - Static variable in class weka.core.xml.XMLSerialization
the tag for the name
ATT_NULL - Static variable in class weka.core.xml.XMLSerialization
the tag whether null or not (yes/no)
ATT_NULL_DEFAULT - Static variable in class weka.core.xml.XMLSerialization
default value for attribute ATT_NULL
ATT_PRIMITIVE - Static variable in class weka.core.xml.XMLSerialization
the tag whether primitive or not (yes/no)
ATT_PRIMITIVE_DEFAULT - Static variable in class weka.core.xml.XMLSerialization
default value for attribute ATT_PRIMITIVE
ATT_TYPE - Static variable in class weka.core.xml.XMLInstances
the type attribute
ATT_TYPE - Static variable in class weka.core.xml.XMLOptions
the type attribute.
ATT_VALUE - Static variable in class weka.core.xml.XMLOptions
the value attribute.
ATT_VERSION - Static variable in class weka.core.xml.XMLDocument
the "version" attribute.
ATT_VERSION - Static variable in class weka.core.xml.XMLInstances
the version attribute
ATT_VERSION - Static variable in class weka.core.xml.XMLSerialization
the version attribute
ATT_WEIGHT - Static variable in class weka.core.xml.XMLInstances
the weight attribute
attIndex() - Method in class weka.classifiers.trees.j48.BinC45Split
Returns index of attribute for which split was generated.
attIndex() - Method in class weka.classifiers.trees.j48.C45Split
Returns index of attribute for which split was generated.
attIndex() - Method in class weka.classifiers.trees.j48.NBTreeSplit
Returns index of attribute for which split was generated.
attIndex - Variable in class weka.classifiers.trees.LADTree.Splitter
 
attList_IrrTipText() - Method in class weka.datagenerators.classifiers.classification.RDG1
Returns the tip text for this property
attribute() - Method in class weka.classifiers.trees.j48.GraftSplit
 
Attribute - Class in weka.core
Class for handling an attribute.
Attribute(String) - Constructor for class weka.core.Attribute
Constructor for a numeric attribute.
Attribute(String, ProtectedProperties) - Constructor for class weka.core.Attribute
Constructor for a numeric attribute, where metadata is supplied.
Attribute(String, String) - Constructor for class weka.core.Attribute
Constructor for a date attribute.
Attribute(String, String, ProtectedProperties) - Constructor for class weka.core.Attribute
Constructor for a date attribute, where metadata is supplied.
Attribute(String, FastVector) - Constructor for class weka.core.Attribute
Constructor for nominal attributes and string attributes.
Attribute(String, FastVector, ProtectedProperties) - Constructor for class weka.core.Attribute
Constructor for nominal attributes and string attributes, where metadata is supplied.
Attribute(String, Instances) - Constructor for class weka.core.Attribute
Constructor for relation-valued attributes.
Attribute(String, Instances, ProtectedProperties) - Constructor for class weka.core.Attribute
Constructor for relation-valued attributes.
Attribute(String, int) - Constructor for class weka.core.Attribute
Constructor for a numeric attribute with a particular index.
Attribute(String, String, int) - Constructor for class weka.core.Attribute
Constructor for date attributes with a particular index.
Attribute(String, FastVector, int) - Constructor for class weka.core.Attribute
Constructor for nominal attributes and string attributes with a particular index.
Attribute(String, Instances, int) - Constructor for class weka.core.Attribute
Constructor for a relation-valued attribute with a particular index.
attribute(int) - Method in class weka.core.Instance
Returns the attribute with the given index.
attribute(int) - Method in class weka.core.Instances
Returns an attribute.
attribute(String) - Method in class weka.core.Instances
Returns an attribute given its name.
ATTRIBUTE - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
attributeAsClass() - Method in class weka.gui.arffviewer.ArffPanel
sets the current attribute as class attribute, i.e.
attributeAsClass() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
sets the current selected Attribute as class attribute, i.e.
attributeAsClassAt(int) - Method in class weka.gui.arffviewer.ArffSortedTableModel
sets the attribute at the given col index as the new class attribute
attributeAsClassAt(int) - Method in class weka.gui.arffviewer.ArffTableModel
sets the attribute at the given col index as the new class attribute, i.e.
attributeCaseFix(String) - Method in class weka.experiment.DatabaseUtils
returns key column headings in their original case.
AttributeEvaluator - Interface in weka.attributeSelection
Interface for classes that evaluate attributes individually.
attributeEvaluatorTipText() - Method in class weka.attributeSelection.FilteredAttributeEval
Returns the tip text for this property
attributeEvaluatorTipText() - Method in class weka.attributeSelection.RaceSearch
Returns the tip text for this property
attributeEvaluatorTipText() - Method in class weka.attributeSelection.RankSearch
Returns the tip text for this property
AttributeExpression - Class in weka.core
A general purpose class for parsing mathematical expressions involving attribute values.
AttributeExpression() - Constructor for class weka.core.AttributeExpression
 
attributeIndexesTipText() - Method in class weka.filters.unsupervised.attribute.NominalToString
Returns the tip text for this property
attributeIndexTipText() - Method in class weka.filters.unsupervised.attribute.Add
Returns the tip text for this property.
attributeIndexTipText() - Method in class weka.filters.unsupervised.attribute.AddNoise
Returns the tip text for this property
attributeIndexTipText() - Method in class weka.filters.unsupervised.attribute.AddValues
Returns the tip text for this property
attributeIndexTipText() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
 
attributeIndexTipText() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
 
attributeIndexTipText() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
 
attributeIndexTipText() - Method in class weka.filters.unsupervised.attribute.SwapValues
 
attributeIndexTipText() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Returns the tip text for this property
attributeIndexTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.core.NormalizableDistance
Returns the tip text for this property.
attributeIndicesTipText() - Method in class weka.filters.supervised.attribute.Discretize
Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.Copy
Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.Discretize
Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.FirstOrder
Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.NominalToBinary
Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.NumericToNominal
Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.NumericTransform
Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.RELAGGS
Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.Remove
Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.Reorder
Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Returns the tip text for this property.
attributeList(BitSet) - Method in class weka.attributeSelection.BestFirst
converts a BitSet into a list of attribute indexes
attributeList(BitSet) - Method in class weka.attributeSelection.GreedyStepwise
converts a BitSet into a list of attribute indexes
attributeList(BitSet) - Method in class weka.attributeSelection.LinearForwardSelection
converts a BitSet into a list of attribute indexes
attributeList(BitSet) - Method in class weka.attributeSelection.ScatterSearchV1
converts a BitSet into a list of attribute indexes
attributeList(BitSet) - Method in class weka.attributeSelection.SubsetSizeForwardSelection
converts a BitSet into a list of attribute indexes
attributeList(BitSet) - Method in class weka.classifiers.rules.DTNB.BackwardsWithDelete
converts a BitSet into a list of attribute indexes
AttributeListPanel - Class in weka.gui
Creates a panel that displays the attributes contained in a set of instances, letting the user select a single attribute for inspection.
AttributeListPanel() - Constructor for class weka.gui.AttributeListPanel
Creates the attribute selection panel with no initial instances.
AttributeLocator - Class in weka.core
This class locates and records the indices of a certain type of attributes, recursively in case of Relational attributes.
AttributeLocator(Instances, int) - Constructor for class weka.core.AttributeLocator
Initializes the AttributeLocator with the given data for the specified type of attribute.
AttributeLocator(Instances, int, int, int) - Constructor for class weka.core.AttributeLocator
Initializes the AttributeLocator with the given data for the specified type of attribute.
AttributeLocator(Instances, int, int[]) - Constructor for class weka.core.AttributeLocator
initializes the AttributeLocator with the given data for the specified type of attribute.
attributeNamePrefixTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Returns the tip text for this property.
attributeNames() - Method in class weka.classifiers.functions.SMO
Returns the attribute names.
attributeNames() - Method in class weka.classifiers.mi.MISMO
Returns the attribute names.
attributeNameTipText() - Method in class weka.filters.unsupervised.attribute.Add
Returns the tip text for this property.
attributeNameTipText() - Method in class weka.filters.unsupervised.attribute.AddID
Returns the tip text for this property
AttributePanel - Class in weka.gui.visualize
This panel displays one dimensional views of the attributes in a dataset.
AttributePanel() - Constructor for class weka.gui.visualize.AttributePanel
 
AttributePanel(Color) - Constructor for class weka.gui.visualize.AttributePanel
This constructs an attributePanel.
AttributePanel.AttributeSpacing - Class in weka.gui.visualize
inner inner class used for plotting the points into a bar for a particular attribute.
AttributePanelEvent - Class in weka.gui.visualize
Class encapsulating a change in the AttributePanel's selected x and y attributes.
AttributePanelEvent(boolean, boolean, int) - Constructor for class weka.gui.visualize.AttributePanelEvent
Constructor
AttributePanelListener - Interface in weka.gui.visualize
Interface for classes that want to listen for Attribute selection changes in the attribute panel
attributeRangeTipText() - Method in class weka.filters.unsupervised.attribute.StringToNominal
 
AttributeSelectedClassifier - Class in weka.classifiers.meta
Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
AttributeSelectedClassifier() - Constructor for class weka.classifiers.meta.AttributeSelectedClassifier
Default constructor.
AttributeSelection - Class in weka.attributeSelection
Attribute selection class.
AttributeSelection() - Constructor for class weka.attributeSelection.AttributeSelection
constructor.
AttributeSelection - Class in weka.filters.supervised.attribute
A supervised attribute filter that can be used to select attributes.
AttributeSelection() - Constructor for class weka.filters.supervised.attribute.AttributeSelection
Constructor
attributeSelectionChange(AttributePanelEvent) - Method in interface weka.gui.visualize.AttributePanelListener
Called when the user clicks on an attribute bar
attributeSelectionMethodTipText() - Method in class weka.classifiers.functions.LinearRegression
Returns the tip text for this property
AttributeSelectionPanel - Class in weka.gui
Creates a panel that displays the attributes contained in a set of instances, letting the user toggle whether each attribute is selected or not (eg: so that unselected attributes can be removed before classification).
AttributeSelectionPanel() - Constructor for class weka.gui.AttributeSelectionPanel
Creates the attribute selection panel with no initial instances.
AttributeSelectionPanel(boolean, boolean, boolean, boolean) - Constructor for class weka.gui.AttributeSelectionPanel
Creates the attribute selection panel with no initial instances.
AttributeSelectionPanel - Class in weka.gui.explorer
This panel allows the user to select and configure an attribute evaluator and a search method, set the attribute of the current dataset to be used as the class, and perform attribute selection using one of two selection modes (select using all the training data or perform a n-fold cross validation---on each trial selecting features using n-1 folds of the data).
AttributeSelectionPanel() - Constructor for class weka.gui.explorer.AttributeSelectionPanel
Creates the classifier panel
AttributeSetEvaluator - Class in weka.attributeSelection
Abstract attribute set evaluator.
AttributeSetEvaluator() - Constructor for class weka.attributeSelection.AttributeSetEvaluator
 
AttributeSpacing(Attribute, int) - Constructor for class weka.gui.visualize.AttributePanel.AttributeSpacing
This constructs the bar with the specified attribute and sets its index to be used for selecting by the mouse.
attributeSparse(int) - Method in class weka.core.Instance
Returns the attribute with the given index.
attributeSparse(int) - Method in class weka.core.SparseInstance
Returns the attribute associated with the internal index.
attributesPermutation(int, int, Random) - Method in class weka.classifiers.meta.RotationForest
generates a permutation of the attributes.
AttributeStats - Class in weka.core
A Utility class that contains summary information on an the values that appear in a dataset for a particular attribute.
AttributeStats() - Constructor for class weka.core.AttributeStats
 
attributeStats(int) - Method in class weka.core.Instances
Calculates summary statistics on the values that appear in this set of instances for a specified attribute.
attributesToString() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Make a string from the attribues list.
attributeString(Instances) - Method in class weka.classifiers.trees.adtree.Splitter
Gets the string describing the attributes the split depends on.
attributeString(Instances) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit
Gets the string describing the attributes the split depends on.
attributeString(Instances) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit
Gets the string describing the attributes the split depends on.
attributeString() - Method in class weka.classifiers.trees.LADTree.Splitter
 
attributeString() - Method in class weka.classifiers.trees.LADTree.TwoWayNominalSplit
 
attributeString() - Method in class weka.classifiers.trees.LADTree.TwoWayNumericSplit
 
AttributeSummarizer - Class in weka.gui.beans
Bean that encapsulates displays bar graph summaries for attributes in a data set.
AttributeSummarizer() - Constructor for class weka.gui.beans.AttributeSummarizer
Creates a new AttributeSummarizer instance.
AttributeSummarizerBeanInfo - Class in weka.gui.beans
Bean info class for the attribute summarizer bean
AttributeSummarizerBeanInfo() - Constructor for class weka.gui.beans.AttributeSummarizerBeanInfo
 
AttributeSummaryPanel - Class in weka.gui
This panel displays summary statistics about an attribute: name, type number/% of missing/unique values, number of distinct values.
AttributeSummaryPanel() - Constructor for class weka.gui.AttributeSummaryPanel
Creates the instances panel with no initial instances.
attributeToDoubleArray(int) - Method in class weka.core.Instances
Gets the value of all instances in this dataset for a particular attribute.
AttributeTransformer - Interface in weka.attributeSelection
Abstract attribute transformer.
attributeTypeTipText() - Method in class weka.filters.unsupervised.attribute.Add
Returns the tip text for this property
attributeTypeTipText() - Method in class weka.filters.unsupervised.attribute.RemoveType
Returns the tip text for this property
attributeTypeToString(int) - Static method in class weka.core.CheckScheme
returns a string representation of the attribute type
AttributeValueLiteral - Class in weka.associations.tertius
 
AttributeValueLiteral(Predicate, String, int, int, int) - Constructor for class weka.associations.tertius.AttributeValueLiteral
 
attributeValuesString(Instance, Range) - Static method in class weka.classifiers.Evaluation
Builds a string listing the attribute values in a specified range of indices, separated by commas and enclosed in brackets.
AttributeVisualizationPanel - Class in weka.gui
Creates a panel that shows a visualization of an attribute in a dataset.
AttributeVisualizationPanel() - Constructor for class weka.gui.AttributeVisualizationPanel
Constructor - If used then the class will not show the class selection combo box.
AttributeVisualizationPanel(boolean) - Constructor for class weka.gui.AttributeVisualizationPanel
Constructor.
attrIndexRangeTipText() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Returns the tip text for this property
attrSplit(int, Instances) - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo
Finds the best splitting point for an attribute in the instances
attrSplit(int, Instances) - Method in interface weka.classifiers.trees.m5.SplitEvaluate
Finds the best splitting point for an attribute in the instances
attrSplit(int, Instances) - Method in class weka.classifiers.trees.m5.YongSplitInfo
Finds the best splitting point for an attribute in the instances
attsToEliminatePerIterationTipText() - Method in class weka.attributeSelection.SVMAttributeEval
Returns a tip text for this property suitable for display in the GUI
autoBuildTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
autoKeyGenerationTipText() - Method in class weka.core.converters.DatabaseSaver
Returns the tip text for this property.
AVAILABLE - Static variable in class weka.experiment.RemoteExperiment
status of the remote host: available
AVAILABLE - Static variable in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
 
availableHost(int) - Method in class weka.experiment.RemoteExperiment
Pushes a host back onto the queue of available hosts and attempts to launch a waiting experiment (if any).
availableHost(int) - Method in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
Push a host back onto the list of available hosts and launch a waiting Task (if any).
AVERAGE_RULE - Static variable in class weka.classifiers.meta.Vote
combination rule: Average of Probabilities
AveragingResultProducer - Class in weka.experiment
Takes the results from a ResultProducer and submits the average to the result listener.
AveragingResultProducer() - Constructor for class weka.experiment.AveragingResultProducer
 
avgCost() - Method in class weka.classifiers.Evaluation
Gets the average cost, that is, total cost of misclassifications (incorrect plus unclassified) over the total number of instances.
avgProb - Variable in class weka.classifiers.lazy.kstar.KStarWrapper
used/reused to hold the average transformation probability

B

B_ENTROPY - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
 
B_SPHERE - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
Blend setting modes
backfitData(Instances) - Method in class weka.classifiers.trees.RandomTree
Backfits the given data into the tree.
backfitData(Instances, double[]) - Method in class weka.classifiers.trees.RandomTree
Recursively backfits data into the tree.
backfitHoldOutSet() - Method in class weka.classifiers.trees.REPTree.Tree
Backfits data from holdout set.
BackgroundDesktopPane(String) - Constructor for class weka.gui.Main.BackgroundDesktopPane
intializes the desktop pane.
backQuoteChars(String) - Static method in class weka.core.Utils
Converts carriage returns and new lines in a string into \r and \n.
backward(PaceMatrix, IntVector, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Backward ordering of columns in terms of response explanation.
BackwardsWithDelete() - Constructor for class weka.classifiers.rules.DTNB.BackwardsWithDelete
 
Bagging - Class in weka.classifiers.meta
Class for bagging a classifier to reduce variance.
Bagging() - Constructor for class weka.classifiers.meta.Bagging
Constructor.
bagSizePercentTipText() - Method in class weka.classifiers.meta.Bagging
Returns the tip text for this property
bagSizePercentTipText() - Method in class weka.classifiers.meta.MetaCost
Returns the tip text for this property
balanceClassTipText() - Method in class weka.datagenerators.classifiers.classification.Agrawal
Returns the tip text for this property
balancedTipText() - Method in class weka.classifiers.functions.Winnow
Returns the tip text for this property
BallNode - Class in weka.core.neighboursearch.balltrees
Class representing a node of a BallTree.
BallNode(int) - Constructor for class weka.core.neighboursearch.balltrees.BallNode
Constructor.
BallNode(int, int, int) - Constructor for class weka.core.neighboursearch.balltrees.BallNode
Creates a new instance of BallNode.
BallNode(int, int, int, Instance, double) - Constructor for class weka.core.neighboursearch.balltrees.BallNode
Creates a new instance of BallNode.
BallSplitter - Class in weka.core.neighboursearch.balltrees
Abstract class for splitting a ball tree's BallNode.
BallSplitter() - Constructor for class weka.core.neighboursearch.balltrees.BallSplitter
default constructor.
BallSplitter(int[], Instances, EuclideanDistance) - Constructor for class weka.core.neighboursearch.balltrees.BallSplitter
Creates a new instance of BallSplitter.
ballSplitterTipText() - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor
Returns the tip text for this property.
BallTree - Class in weka.core.neighboursearch
Class implementing the BallTree/Metric Tree algorithm for nearest neighbour search.
The connection to dataset is only a reference.
BallTree() - Constructor for class weka.core.neighboursearch.BallTree
Creates a new instance of BallTree.
BallTree(Instances) - Constructor for class weka.core.neighboursearch.BallTree
Creates a new instance of BallTree.
BallTreeConstructor - Class in weka.core.neighboursearch.balltrees
Abstract class for constructing a BallTree .
BallTreeConstructor() - Constructor for class weka.core.neighboursearch.balltrees.BallTreeConstructor
Creates a new instance of BallTreeConstructor.
ballTreeConstructorTipText() - Method in class weka.core.neighboursearch.BallTree
Returns the tip text for this property.
baseTipText() - Method in class weka.core.neighboursearch.CoverTree
Returns the tip text for this property.
BATCH - Static variable in interface weka.core.converters.Loader
 
BATCH - Static variable in interface weka.core.converters.Saver
 
BATCH_FINISHED - Static variable in class weka.gui.beans.IncrementalClassifierEvent
 
BATCH_FINISHED - Static variable in class weka.gui.beans.InstanceEvent
 
BATCH_FINISHED - Static variable in class weka.gui.streams.InstanceEvent
Specifies that the batch of instances is finished
batch_insert(Integer, int, int, Stack<CoverTree.DistanceNode>, Stack<CoverTree.DistanceNode>) - Method in class weka.core.neighboursearch.CoverTree
Creates a cover tree recursively using batch insert method.
batch_nearest_neighbor(int, CoverTree.CoverTreeNode, CoverTree.CoverTreeNode, Stack<NearestNeighbourSearch.NeighborList>) - Method in class weka.core.neighboursearch.CoverTree
Performs k-NN search for a batch of queries provided in the form of a cover tree.
BatchClassifierEvent - Class in weka.gui.beans
Class encapsulating a built classifier and a batch of instances to test on.
BatchClassifierEvent(Object, Classifier, DataSetEvent, DataSetEvent, int, int) - Constructor for class weka.gui.beans.BatchClassifierEvent
Creates a new BatchClassifierEvent instance.
BatchClassifierEvent(Object, Classifier, DataSetEvent, DataSetEvent, int, int, int, int) - Constructor for class weka.gui.beans.BatchClassifierEvent
Creates a new BatchClassifierEvent instance.
BatchClassifierListener - Interface in weka.gui.beans
Interface to something that can process a BatchClassifierEvent
BatchClustererEvent - Class in weka.gui.beans
Class encapsulating a built clusterer and a batch of instances to test on.
BatchClustererEvent(Object, Clusterer, DataSetEvent, int, int, int) - Constructor for class weka.gui.beans.BatchClustererEvent
Creates a new BatchClustererEvent instance.
BatchClustererListener - Interface in weka.gui.beans
Interface to something that can process a BatchClustererEvent
BatchConverter - Interface in weka.core.converters
Marker interface for a loader/saver that can retrieve instances in batch mode
batchFilterFile(Filter, String[]) - Static method in class weka.filters.Filter
Method for testing filters ability to process multiple batches.
batchFinished() - Method in class weka.filters.Filter
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.MultiFilter
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.SimpleBatchFilter
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.SimpleStreamFilter
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.supervised.attribute.AttributeSelection
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.supervised.attribute.ClassOrder
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.supervised.attribute.Discretize
Signifies that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.supervised.attribute.NominalToBinary
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.supervised.instance.Resample
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.supervised.instance.SMOTE
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.supervised.instance.SpreadSubsample
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Signifies that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.AddCluster
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.AddID
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.AddNoise
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.Center
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.ClusterMembership
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.Discretize
Signifies that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.MathExpression
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.NominalToString
Signifies that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.Normalize
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.RandomProjection
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.RemoveType
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.RemoveUseless
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.Standardize
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.StringToNominal
Signifies that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.instance.Randomize
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.instance.RemoveFolds
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Signifies that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.instance.RemovePercentage
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.instance.RemoveRange
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.instance.Resample
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.instance.ReservoirSample
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.gui.streams.InstanceJoiner
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.gui.streams.InstanceSavePanel
 
batchFinished() - Method in class weka.gui.streams.InstanceTable
 
batchFinished() - Method in class weka.gui.streams.InstanceViewer
 
BAYES - Static variable in interface weka.classifiers.bayes.net.search.local.Scoreable
score types
BayesianLogisticRegression - Class in weka.classifiers.bayes
Implements Bayesian Logistic Regression for both Gaussian and Laplace Priors.

For more information, see

Alexander Genkin, David D.
BayesianLogisticRegression() - Constructor for class weka.classifiers.bayes.BayesianLogisticRegression
 
BayesNet - Class in weka.classifiers.bayes
Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier.
BayesNet() - Constructor for class weka.classifiers.bayes.BayesNet
 
BayesNet - Static variable in interface weka.core.Drawable
 
BayesNet - Class in weka.datagenerators.classifiers.classification
Generates random instances based on a Bayes network.
BayesNet() - Constructor for class weka.datagenerators.classifiers.classification.BayesNet
initializes the generator
BayesNetEstimator - Class in weka.classifiers.bayes.net.estimate
BayesNetEstimator is the base class for estimating the conditional probability tables of a Bayes network once the structure has been learned.
BayesNetEstimator() - Constructor for class weka.classifiers.bayes.net.estimate.BayesNetEstimator
 
BayesNetGenerator - Class in weka.classifiers.bayes.net
Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier.
BayesNetGenerator() - Constructor for class weka.classifiers.bayes.net.BayesNetGenerator
Constructor for BayesNetGenerator.
BDeu - Static variable in interface weka.classifiers.bayes.net.search.local.Scoreable
 
BEAN_EXECUTING - Static variable in class weka.gui.beans.BeanInstance
 
BEAN_PROPERTIES - Static variable in class weka.gui.beans.KnowledgeFlowApp
Contains the editor properties
BeanCommon - Interface in weka.gui.beans
Interface specifying routines that all weka beans should implement in order to allow the bean environment to exercise some control over the bean and also to allow the bean to exercise some control over connections.
BeanConnection - Class in weka.gui.beans
Class for encapsulating a connection between two beans.
BeanConnection(BeanInstance, BeanInstance, EventSetDescriptor) - Constructor for class weka.gui.beans.BeanConnection
Creates a new BeanConnection instance.
BeanInstance - Class in weka.gui.beans
Class that manages a set of beans.
BeanInstance(JComponent, Object, int, int) - Constructor for class weka.gui.beans.BeanInstance
Creates a new BeanInstance instance.
BeanInstance(JComponent, String, int, int) - Constructor for class weka.gui.beans.BeanInstance
Creates a new BeanInstance instance given the fully qualified name of the bean
BeanLayout() - Constructor for class weka.gui.beans.KnowledgeFlowApp.BeanLayout
 
BeanVisual - Class in weka.gui.beans
BeanVisual encapsulates icons and label for a given bean.
BeanVisual(String, String, String) - Constructor for class weka.gui.beans.BeanVisual
Constructor
bestCnt - Variable in class weka.classifiers.lazy.LBR
 
BestFirst - Class in weka.attributeSelection
BestFirst:

Searches the space of attribute subsets by greedy hillclimbing augmented with a backtracking facility.
BestFirst() - Constructor for class weka.attributeSelection.BestFirst
Constructor
BestFirst.Link2 - Class in weka.attributeSelection
Class for a node in a linked list.
BestFirst.LinkedList2 - Class in weka.attributeSelection
Class for handling a linked list.
Beta - Variable in class weka.classifiers.bayes.blr.Prior
 
betaTipText() - Method in class weka.classifiers.functions.Winnow
Returns the tip text for this property
BetaVector - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Array for storing coefficients of Bayesian regression model.
BFTree - Class in weka.classifiers.trees
Class for building a best-first decision tree classifier.
BFTree() - Constructor for class weka.classifiers.trees.BFTree
 
bias() - Method in class weka.classifiers.functions.SMO
Returns the bias of each binary SMO.
bias() - Method in class weka.classifiers.mi.MISMO
Returns the bias of each binary SMO.
biasTipText() - Method in class weka.classifiers.functions.LibLINEAR
Returns the tip text for this property
biasTipText() - Method in class weka.classifiers.misc.VFI
Returns the tip text for this property
biasToUniformClassTipText() - Method in class weka.filters.supervised.instance.Resample
Returns the tip text for this property.
BIBTEX_ENDTAG - Static variable in class weka.core.TechnicalInformationHandlerJavadoc
the end comment tag for inserting the generated BibTex
BIBTEX_STARTTAG - Static variable in class weka.core.TechnicalInformationHandlerJavadoc
the start comment tag for inserting the generated BibTex
BIFFileTipText() - Method in class weka.classifiers.bayes.BayesNet
 
BIFFormatException - Exception in weka.gui.graphvisualizer
This is the Exception thrown by BIFParser, if there was an error in parsing the XMLBIF string or input stream.
BIFFormatException(String) - Constructor for exception weka.gui.graphvisualizer.BIFFormatException
 
BIFParser - Class in weka.gui.graphvisualizer
This class parses an inputstream or a string in XMLBIF ver.
BIFParser(String, FastVector, FastVector) - Constructor for class weka.gui.graphvisualizer.BIFParser
Constructor (if our input is a String)
BIFParser(InputStream, FastVector, FastVector) - Constructor for class weka.gui.graphvisualizer.BIFParser
Constructor (if our input is an InputStream)
BIFReader - Class in weka.classifiers.bayes.net
Builds a description of a Bayes Net classifier stored in XML BIF 0.3 format.

For more details on XML BIF see:

Fabio Cozman, Marek Druzdzel, Daniel Garcia (1998).
BIFReader() - Constructor for class weka.classifiers.bayes.net.BIFReader
the default constructor
big - Static variable in class weka.core.Statistics
 
bigF(double, double) - Static method in class weka.classifiers.bayes.BayesianLogisticRegression
This is a convient function that defines and upper bound (Delta>0) for values of r(i) reachable by updates in the trust region.
biginv - Static variable in class weka.core.Statistics
 
binarizeNumericAttributesTipText() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
Returns the tip text for this property
binarizeNumericAttributesTipText() - Method in class weka.attributeSelection.InfoGainAttributeEval
Returns the tip text for this property
BINARY - Static variable in class weka.gui.beans.SerializedModelSaver
 
binaryAttributesNominalTipText() - Method in class weka.filters.supervised.attribute.NominalToBinary
Returns the tip text for this property
binaryAttributesNominalTipText() - Method in class weka.filters.unsupervised.attribute.NominalToBinary
Returns the tip text for this property
BinaryItem(Attribute, int) - Constructor for class weka.associations.FPGrowth.BinaryItem
 
BinaryMISMO() - Constructor for class weka.classifiers.mi.MISMO.BinaryMISMO
 
BinarySMO() - Constructor for class weka.classifiers.functions.SMO.BinarySMO
 
BinarySparseInstance - Class in weka.core
Class for storing a binary-data-only instance as a sparse vector.
BinarySparseInstance(Instance) - Constructor for class weka.core.BinarySparseInstance
Constructor that generates a sparse instance from the given instance.
BinarySparseInstance(SparseInstance) - Constructor for class weka.core.BinarySparseInstance
Constructor that copies the info from the given instance.
BinarySparseInstance(double, double[]) - Constructor for class weka.core.BinarySparseInstance
Constructor that generates a sparse instance from the given parameters.
BinarySparseInstance(double, int[], int) - Constructor for class weka.core.BinarySparseInstance
Constructor that inititalizes instance variable with given values.
BinarySparseInstance(int) - Constructor for class weka.core.BinarySparseInstance
Constructor of an instance that sets weight to one, all values to 1, and the reference to the dataset to null.
binarySplitsTipText() - Method in class weka.classifiers.rules.PART
Returns the tip text for this property
binarySplitsTipText() - Method in class weka.classifiers.trees.J48
Returns the tip text for this property
binarySplitsTipText() - Method in class weka.classifiers.trees.J48graft
Returns the tip text for this property
binaryToKOML(String, String) - Static method in class weka.core.xml.SerialUIDChanger
converts a binary file into a KOML XML file
BinC45ModelSelection - Class in weka.classifiers.trees.j48
Class for selecting a C4.5-like binary (!) split for a given dataset.
BinC45ModelSelection(int, Instances) - Constructor for class weka.classifiers.trees.j48.BinC45ModelSelection
Initializes the split selection method with the given parameters.
BinC45Split - Class in weka.classifiers.trees.j48
Class implementing a binary C4.5-like split on an attribute.
BinC45Split(int, int, double) - Constructor for class weka.classifiers.trees.j48.BinC45Split
Initializes the split model.
binomialDistribution(double, double, double) - Static method in class weka.associations.RuleGeneration
calculates the probability using a binomial distribution.
binomialStandardError(double, int) - Static method in class weka.core.Statistics
Computes standard error for observed values of a binomial random variable.
binomP(double, double, double) - Method in class weka.classifiers.lazy.LBR
Significance test binomp:
binSplitTipText() - Method in class weka.classifiers.trees.FT
Returns the tip text for this property
binsTipText() - Method in class weka.filters.unsupervised.attribute.Discretize
Returns the tip text for this property
binsTipText() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Returns the tip text for this property
binValueTipText() - Method in class weka.clusterers.XMeans
Returns the tip text for this property.
biprob(double, double, double) - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG
Significance test
BIRCHCluster - Class in weka.datagenerators.clusterers
Cluster data generator designed for the BIRCH System

Dataset is generated with instances in K clusters.
Instances are 2-d data points.
Each cluster is characterized by the number of data points in itits radius and its center.
BIRCHCluster() - Constructor for class weka.datagenerators.clusterers.BIRCHCluster
initializes the generator with default values
blocker(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron
A function used to stop the code that called buildclassifier from continuing on before the user has finished the decision tree.
BMAEstimator - Class in weka.classifiers.bayes.net.estimate
BMAEstimator estimates conditional probability tables of a Bayes network using Bayes Model Averaging (BMA).
BMAEstimator() - Constructor for class weka.classifiers.bayes.net.estimate.BMAEstimator
 
BMPWriter - Class in weka.gui.visualize
This class takes any JComponent and outputs it to a BMP-file.
BMPWriter() - Constructor for class weka.gui.visualize.BMPWriter
initializes the object
BMPWriter(JComponent) - Constructor for class weka.gui.visualize.BMPWriter
initializes the object with the given Component
BMPWriter(JComponent, File) - Constructor for class weka.gui.visualize.BMPWriter
initializes the object with the given Component and filename
Body - Class in weka.associations.tertius
Class representing the body of a rule.
Body() - Constructor for class weka.associations.tertius.Body
Constructor without storing the counter-instances.
Body(Instances) - Constructor for class weka.associations.tertius.Body
Constructor storing the counter-instances.
bodyContains(Literal) - Method in class weka.associations.tertius.Rule
Test if the body of the rule contains a literal.
BOOL - Static variable in class weka.experiment.DatabaseUtils
Type mapping for BOOL used for reading experiment results.
BOOLEAN - Static variable in interface weka.core.mathematicalexpression.sym
 
BOOLEAN - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
booleanColsTipText() - Method in class weka.datagenerators.ClusterGenerator
Returns the tip text for this property
booleanToString(boolean) - Method in class weka.core.xml.XMLSerialization
returns either VAL_YES or VAL_NO depending on the value of b
boost(Instances) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
performs a boosting iteration, returning a new model for the committee
boost() - Method in class weka.classifiers.trees.ADTree
Performs a single boosting iteration, using two-class optimized method.
BottomUpConstructor - Class in weka.core.neighboursearch.balltrees
The class that constructs a ball tree bottom up.
BottomUpConstructor() - Constructor for class weka.core.neighboursearch.balltrees.BottomUpConstructor
Creates a new instance of BottomUpConstructor.
BottomUpConstructor.TempNode - Class in weka.core.neighboursearch.balltrees
Temp class to represent either a leaf node or an internal node.
BoundaryPanel - Class in weka.gui.boundaryvisualizer
BoundaryPanel.
BoundaryPanel(int, int) - Constructor for class weka.gui.boundaryvisualizer.BoundaryPanel
Creates a new BoundaryPanel instance.
BoundaryPanel.PlotThread - Class in weka.gui.boundaryvisualizer
 
BoundaryPanelDistributed - Class in weka.gui.boundaryvisualizer
This class extends BoundaryPanel with code for distributing the processing necessary to create a visualization among a list of remote machines.
BoundaryPanelDistributed(int, int) - Constructor for class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
Creates a new BoundaryPanelDistributed instance.
BoundaryVisualizer - Class in weka.gui.boundaryvisualizer
BoundaryVisualizer.
BoundaryVisualizer() - Constructor for class weka.gui.boundaryvisualizer.BoundaryVisualizer
Creates a new BoundaryVisualizer instance.
branchInstanceGoesDown(Instance) - Method in class weka.classifiers.trees.adtree.Splitter
Gets the index of the branch that an instance applies to.
branchInstanceGoesDown(Instance) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit
Gets the index of the branch that an instance applies to.
branchInstanceGoesDown(Instance) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit
Gets the index of the branch that an instance applies to.
branchInstanceGoesDown(Instance) - Method in class weka.classifiers.trees.LADTree.Splitter
 
branchInstanceGoesDown(Instance) - Method in class weka.classifiers.trees.LADTree.TwoWayNominalSplit
 
branchInstanceGoesDown(Instance) - Method in class weka.classifiers.trees.LADTree.TwoWayNumericSplit
 
breakUp(String) - Method in class weka.core.PropertyPath.Path
breaks up the given path and returns it as vector
BrowserHelper - Class in weka.gui
A little helper class for browser related stuff.
BrowserHelper() - Constructor for class weka.gui.BrowserHelper
 
brute_nearest(int, CoverTree.CoverTreeNode, Stack<CoverTree.d_node>, CoverTree.MyHeap, Stack<NearestNeighbourSearch.NeighborList>) - Method in class weka.core.neighboursearch.CoverTree
Does a brute force NN search on the nodes in the given zero set.
bubbleSubsetSort(List<ScatterSearchV1.Subset>) - Method in class weka.attributeSelection.ScatterSearchV1
Sort a List of subsets according to their merits
bufferInput(Instance) - Method in class weka.filters.Filter
Adds the supplied input instance to the inputformat dataset for later processing.
build(String, String) - Method in class weka.gui.HierarchyPropertyParser
Build a tree from the given property with the given delimitor
buildAssociations(Instances) - Method in class weka.associations.Apriori
Method that generates all large itemsets with a minimum support, and from these all association rules with a minimum confidence.
buildAssociations(Instances) - Method in interface weka.associations.Associator
Generates an associator.
buildAssociations(Instances) - Method in class weka.associations.FilteredAssociator
Build the associator on the filtered data.
buildAssociations(Instances) - Method in class weka.associations.FPGrowth
Method that generates all large item sets with a minimum support, and from these all association rules with a minimum metric (i.e.
buildAssociations(Instances) - Method in class weka.associations.GeneralizedSequentialPatterns
Extracts all sequential patterns out of a given sequential data set and prints out the results.
buildAssociations(Instances) - Method in class weka.associations.PredictiveApriori
Method that generates all large itemsets with a minimum support, and from these all association rules.
buildAssociations(Instances) - Method in class weka.associations.Tertius
Method that launches the search to find the rules with the highest confirmation.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.AODE
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.AODEsr
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
(1) Set the data to the class attribute m_Instances. (2)Call the method initialize() to initialize the values.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.BayesNet
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.ComplementNaiveBayes
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.DMNBtext
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.HNB
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.NaiveBayes
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.NaiveBayesMultinomial
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.NaiveBayesSimple
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.WAODE
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.Classifier
Generates a classifier.
buildClassifier(Instances) - Method in class weka.classifiers.functions.GaussianProcesses
Method for building the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.functions.IsotonicRegression
Builds an isotonic regression model given the supplied training data.
buildClassifier(Instances) - Method in class weka.classifiers.functions.LeastMedSq
Build lms regression
buildClassifier(Instances) - Method in class weka.classifiers.functions.LibLINEAR
builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.functions.LibSVM
builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.functions.LinearRegression
Builds a regression model for the given data.
buildClassifier(Instances) - Method in class weka.classifiers.functions.Logistic
Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.functions.MultilayerPerceptron
Call this function to build and train a neural network for the training data provided.
buildClassifier(Instances) - Method in class weka.classifiers.functions.PaceRegression
Builds a pace regression model for the given data.
buildClassifier(Instances) - Method in class weka.classifiers.functions.PLSClassifier
builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.functions.RBFNetwork
Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.functions.SimpleLinearRegression
Builds a simple linear regression model given the supplied training data.
buildClassifier(Instances) - Method in class weka.classifiers.functions.SimpleLogistic
Builds the logistic regression using LogitBoost.
buildClassifier(Instances, int, int, boolean, int, int) - Method in class weka.classifiers.functions.SMO.BinarySMO
Method for building the binary classifier.
buildClassifier(Instances) - Method in class weka.classifiers.functions.SMO
Method for building the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.functions.SMOreg
Method for building the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.functions.SPegasos
Method for building the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.functions.supportVector.RegOptimizer
learn SVM parameters from data.
buildClassifier(Instances) - Method in class weka.classifiers.functions.supportVector.RegSMO
learn SVM parameters from data using Smola's SMO algorithm.
buildClassifier(Instances) - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
learn SVM parameters from data using Keerthi's SMO algorithm.
buildClassifier(Instances) - Method in class weka.classifiers.functions.VotedPerceptron
Builds the ensemble of perceptrons.
buildClassifier(Instances) - Method in class weka.classifiers.functions.Winnow
Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.IteratedSingleClassifierEnhancer
Stump method for building the classifiers.
buildClassifier(Instances) - Method in class weka.classifiers.lazy.IB1
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.lazy.IBk
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.lazy.KStar
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.lazy.LBR
For lazy learning, building classifier is only to prepare their inputs until classification time.
buildClassifier(Instances) - Method in class weka.classifiers.lazy.LWL
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.AdaBoostM1
Boosting method.
buildClassifier(Instances) - Method in class weka.classifiers.meta.AdditiveRegression
Build the classifier on the supplied data
buildClassifier(Instances) - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Build the classifier on the dimensionally reduced data.
buildClassifier(Instances) - Method in class weka.classifiers.meta.Bagging
Bagging method.
buildClassifier(Instances) - Method in class weka.classifiers.meta.ClassificationViaClustering
builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.meta.ClassificationViaRegression
Builds the classifiers.
buildClassifier(Instances) - Method in class weka.classifiers.meta.CostSensitiveClassifier
Builds the model of the base learner.
buildClassifier(Instances) - Method in class weka.classifiers.meta.CVParameterSelection
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.Dagging
Bagging method.
buildClassifier(Instances) - Method in class weka.classifiers.meta.Decorate
Build Decorate classifier
buildClassifier(Instances) - Method in class weka.classifiers.meta.END
Builds the committee of randomizable classifiers.
buildClassifier(Instances) - Method in class weka.classifiers.meta.FilteredClassifier
Build the classifier on the filtered data.
buildClassifier(Instances) - Method in class weka.classifiers.meta.GridSearch
builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.meta.LogitBoost
Builds the boosted classifier
buildClassifier(Instances) - Method in class weka.classifiers.meta.MetaCost
Builds the model of the base learner.
buildClassifier(Instances) - Method in class weka.classifiers.meta.MultiBoostAB
Method for building this classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.MultiClassClassifier
Builds the classifiers.
buildClassifier(Instances) - Method in class weka.classifiers.meta.MultiScheme
Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
buildClassifier(Instances) - Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
Builds tree recursively.
buildClassifier(Instances) - Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
Builds tree recursively.
buildClassifier(Instances) - Method in class weka.classifiers.meta.nestedDichotomies.ND
Builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.OrdinalClassClassifier
Builds the classifiers.
buildClassifier(Instances) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.RandomCommittee
Builds the committee of randomizable classifiers.
buildClassifier(Instances) - Method in class weka.classifiers.meta.RandomSubSpace
builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.RegressionByDiscretization
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.RotationForest
builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.Stacking
Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
buildClassifier(Instances) - Method in class weka.classifiers.meta.ThresholdSelector
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.Vote
Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
buildClassifier(Instances) - Method in class weka.classifiers.mi.CitationKNN
Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.mi.MDD
Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.mi.MIBoost
Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.mi.MIDD
Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.mi.MIEMDD
Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.mi.MILR
Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.mi.MINND
As normal Nearest Neighbour algorithm does, it's lazy and simply records the exemplar information (i.e.
buildClassifier(Instances) - Method in class weka.classifiers.mi.MIOptimalBall
Builds the classifier
buildClassifier(Instances, int, int, boolean, int, int) - Method in class weka.classifiers.mi.MISMO.BinaryMISMO
Method for building the binary classifier.
buildClassifier(Instances) - Method in class weka.classifiers.mi.MISMO
Method for building the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.mi.MISVM
Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.mi.MIWrapper
Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.mi.SimpleMI
Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.misc.HyperPipes
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.misc.SerializedClassifier
loads only the serialized classifier
buildClassifier(Instances) - Method in class weka.classifiers.misc.VFI
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.pmml.consumer.PMMLClassifier
Throw an exception - PMML models are pre-built.
buildClassifier(Instances) - Method in class weka.classifiers.rules.ConjunctiveRule
Builds a single rule learner with REP dealing with nominal classes or numeric classes.
buildClassifier(Instances) - Method in class weka.classifiers.rules.DecisionTable
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.rules.DTNB
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.rules.JRip
Builds Ripper in the order of class frequencies.
buildClassifier(Instances) - Method in class weka.classifiers.rules.NNge
Generates a classifier.
buildClassifier(Instances) - Method in class weka.classifiers.rules.OneR
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.rules.PART
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.rules.part.MakeDecList
Builds dec list.
buildClassifier(Instances) - Method in class weka.classifiers.rules.Prism
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.rules.Ridor
Builds a ripple-down manner rule learner.
buildClassifier(Instances) - Method in class weka.classifiers.rules.ZeroR
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.ADTree
Builds a classifier for a set of instances.
buildClassifier(Instances) - Method in class weka.classifiers.trees.BFTree
Method for building a BestFirst decision tree classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.DecisionStump
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.FT
Builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.ft.FTInnerNode
Method for building a Functional Inner tree (only called for the root node).
buildClassifier(Instances) - Method in class weka.classifiers.trees.ft.FTLeavesNode
Method for building a Functional Leaves tree (only called for the root node).
buildClassifier(Instances) - Method in class weka.classifiers.trees.ft.FTNode
Method for building a Functional tree (only called for the root node).
buildClassifier(Instances) - Method in class weka.classifiers.trees.ft.FTtree
Method for building a Functional Tree (only called for the root node).
buildClassifier(Instances) - Method in class weka.classifiers.trees.Id3
Builds Id3 decision tree classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.BinC45Split
Creates a C4.5-type split on the given data.
buildClassifier(Instances) - Method in class weka.classifiers.trees.J48
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTree
Method for building a pruneable classifier tree.
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG
Method for building a pruneable classifier tree.
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.C45Split
Creates a C4.5-type split on the given data.
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Builds the classifier split model for the given set of instances.
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.ClassifierTree
Method for building a classifier tree.
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.GraftSplit
builds m_graftdistro using the passed data
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.NBTreeClassifierTree
Method for building a naive bayes classifier tree
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.NBTreeNoSplit
Build the no-split node
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.NBTreeSplit
Creates a NBTree-type split on the given data.
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.NoSplit
Creates a "no-split"-split for a given set of instances.
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.PruneableClassifierTree
Method for building a pruneable classifier tree.
buildClassifier(Instances) - Method in class weka.classifiers.trees.J48graft
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.LADTree
Builds a classifier for a set of instances.
buildClassifier(Instances) - Method in class weka.classifiers.trees.LMT
Builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.lmt.LMTNode
Method for building a logistic model tree (only called for the root node).
buildClassifier(Instances) - Method in class weka.classifiers.trees.lmt.LogisticBase
Builds the logistic regression model usiing LogitBoost.
buildClassifier(Instances, double[][], double[][]) - Method in class weka.classifiers.trees.lmt.ResidualSplit
Builds the split.
buildClassifier(Instances) - Method in class weka.classifiers.trees.lmt.ResidualSplit
Method not in use
buildClassifier(Instances) - Method in class weka.classifiers.trees.m5.M5Base
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.m5.PreConstructedLinearModel
Builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.m5.Rule
Generates a single rule or m5 model tree.
buildClassifier(Instances) - Method in class weka.classifiers.trees.m5.RuleNode
Build this node (find an attribute and split point)
buildClassifier(Instances) - Method in class weka.classifiers.trees.NBTree
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.RandomForest
Builds a classifier for a set of instances.
buildClassifier(Instances) - Method in class weka.classifiers.trees.RandomTree
Builds classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.REPTree
Builds classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.SimpleCart
Build the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.UserClassifier
Call this function to build a decision tree for the training data provided.
buildClassifierForNode(ND.NDTree, Instances) - Method in class weka.classifiers.meta.nestedDichotomies.ND
Builds the classifier for one node.
buildClassifierUsingResampling(Instances) - Method in class weka.classifiers.meta.AdaBoostM1
Boosting method.
buildClassifierWithWeights(Instances) - Method in class weka.classifiers.meta.AdaBoostM1
Boosting method.
buildClusterer(Instances) - Method in class weka.clusterers.AbstractClusterer
Generates a clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.CLOPE
Generate Clustering via CLOPE
buildClusterer(Instances) - Method in interface weka.clusterers.Clusterer
Generates a clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.Cobweb
Builds the clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.DBScan
Generate Clustering via DBScan
buildClusterer(Instances) - Method in class weka.clusterers.EM
Generates a clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.FarthestFirst
Generates a clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.FilteredClusterer
Build the clusterer on the filtered data.
buildClusterer(Instances) - Method in class weka.clusterers.HierarchicalClusterer
 
buildClusterer(Instances) - Method in class weka.clusterers.MakeDensityBasedClusterer
Builds a clusterer for a set of instances.
buildClusterer(Instances) - Method in class weka.clusterers.OPTICS
Generate Clustering via OPTICS
buildClusterer(Instances) - Method in class weka.clusterers.sIB
Generates a clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.SimpleKMeans
Generates a clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.XMeans
Generates the X-Means clusterer.
buildCNN() - Method in class weka.classifiers.mi.CitationKNN
generates all the variables associated to the citation classifier
buildCoverTree(Instances) - Method in class weka.core.neighboursearch.CoverTree
Builds the tree on the given set of instances.
buildDecList(Instances, boolean) - Method in class weka.classifiers.rules.part.C45PruneableDecList
Builds the partial tree without hold out set.
buildDecList(Instances, boolean) - Method in class weka.classifiers.rules.part.ClassifierDecList
Builds the partial tree without hold out set.
buildDecList(Instances, Instances, boolean) - Method in class weka.classifiers.rules.part.PruneableDecList
Builds the partial tree with hold out set
buildDistribution(double, double) - Method in class weka.associations.PriorEstimation
updates the distribution of the confidence values.
buildEstimator(Estimator, String[], boolean) - Static method in class weka.estimators.Estimator
Build an estimator using the options.
buildEstimator(Estimator, Instances, int, int, int, boolean) - Static method in class weka.estimators.Estimator
 
buildEvaluator(Instances) - Method in class weka.attributeSelection.ASEvaluation
Generates a attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.CfsSubsetEval
Generates a attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.ChiSquaredAttributeEval
Initializes a chi-squared attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.ClassifierSubsetEval
Generates a attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.ConsistencySubsetEval
Generates a attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.CostSensitiveASEvaluation
Generates a attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.FilteredAttributeEval
Initializes a filtered attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.FilteredSubsetEval
Initializes a filtered attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.GainRatioAttributeEval
Initializes a gain ratio attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.InfoGainAttributeEval
Initializes an information gain attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.LatentSemanticAnalysis
Initializes the singular values/vectors and performs the analysis
buildEvaluator(Instances) - Method in class weka.attributeSelection.OneRAttributeEval
Initializes a OneRAttribute attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.PrincipalComponents
Initializes principal components and performs the analysis
buildEvaluator(Instances) - Method in class weka.attributeSelection.ReliefFAttributeEval
Initializes a ReliefF attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.SVMAttributeEval
Initializes the evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Initializes a symmetrical uncertainty attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.WrapperSubsetEval
Generates a attribute evaluator.
buildEvaluator(Instances) - Method in class weka.classifiers.rules.DTNB.EvalWithDelete
 
buildFPTree(ArrayList<FPGrowth.BinaryItem>, Instances, int) - Method in class weka.associations.FPGrowth
Construct the frequent pattern tree by inserting each transaction in the data into the tree.
buildGenerator(Instances) - Method in interface weka.gui.boundaryvisualizer.DataGenerator
Build the data generator
buildGenerator(Instances) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator
Initialize the generator using the supplied instances
buildKDTree(Instances) - Method in class weka.core.neighboursearch.KDTree
Builds the KDTree on the supplied set of instances/points.
buildKernel(Instances) - Method in class weka.classifiers.functions.supportVector.CachedKernel
builds the kernel with the given data.
buildKernel(Instances) - Method in class weka.classifiers.functions.supportVector.Kernel
builds the kernel with the given data
buildKernel(Instances) - Method in class weka.classifiers.functions.supportVector.Puk
builds the kernel with the given data.
buildKernel(Instances) - Method in class weka.classifiers.functions.supportVector.RBFKernel
builds the kernel with the given data.
buildKernel(Instances) - Method in class weka.classifiers.functions.supportVector.StringKernel
builds the kernel with the given data.
buildKernel(Instances) - Method in class weka.classifiers.mi.supportVector.MIRBFKernel
builds the kernel with the given data.
buildLeavesMiddleOut(BallNode) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Applies the middle out build procedure to the leaves of the tree.
buildLogisticModelsTipText() - Method in class weka.classifiers.functions.SMO
Returns the tip text for this property
buildLogisticModelsTipText() - Method in class weka.classifiers.mi.MISMO
Returns the tip text for this property
buildRegressionTreeTipText() - Method in class weka.classifiers.trees.m5.M5Base
Returns the tip text for this property
buildRule(Instances) - Method in class weka.classifiers.rules.part.ClassifierDecList
Method for building a pruned partial tree.
buildRule(Instances, Instances) - Method in class weka.classifiers.rules.part.PruneableDecList
Method for building a pruned partial tree.
buildStructure() - Method in class weka.classifiers.bayes.BayesNet
buildStructure determines the network structure/graph of the network.
buildStructure(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.fixed.FromFile
 
buildStructure(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.fixed.NaiveBayes
 
buildStructure(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.global.TAN
buildStructure determines the network structure/graph of the network using the maximimum weight spanning tree algorithm of Chow and Liu
buildStructure(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
buildStructure determines the network structure/graph of the network with the K2 algorithm, restricted by its initial structure (which can be an empty graph, or a Naive Bayes graph.
buildStructure(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.local.TAN
buildStructure determines the network structure/graph of the network using the maximimum weight spanning tree algorithm of Chow and Liu
buildStructure(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
buildStructure determines the network structure/graph of the network.
buildTree(Instances, SimpleLinearRegression[][], double, double) - Method in class weka.classifiers.trees.ft.FTInnerNode
Method for building the tree structure.
buildTree(Instances, SimpleLinearRegression[][], double, double) - Method in class weka.classifiers.trees.ft.FTLeavesNode
Method for building the tree structure.
buildTree(Instances, SimpleLinearRegression[][], double, double) - Method in class weka.classifiers.trees.ft.FTNode
Method for building the tree structure.
buildTree(Instances, SimpleLinearRegression[][], double, double) - Method in class weka.classifiers.trees.ft.FTtree
Abstract method for building the tree structure.
buildTree(Instances, boolean) - Method in class weka.classifiers.trees.j48.ClassifierTree
Builds the tree structure.
buildTree(Instances, Instances, boolean) - Method in class weka.classifiers.trees.j48.ClassifierTree
Builds the tree structure with hold out set
buildTree(Instances, SimpleLinearRegression[][], double, double) - Method in class weka.classifiers.trees.lmt.LMTNode
Method for building the tree structure.
buildTree(Instances, double[], Instances, double, boolean, int[], Random, int, boolean) - Method in class weka.classifiers.trees.RandomTree
Recursively generates a tree.
buildTree(int[][][], double[][][], Instances, double, double[], Instances, double, double, int, int) - Method in class weka.classifiers.trees.REPTree.Tree
Recursively generates a tree.
buildTree() - Method in class weka.core.neighboursearch.BallTree
Builds the BallTree on the supplied set of instances/points (supplied with setInstances(Instances) method and referenced by the m_Instances field).
buildTree() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
Builds the ball tree.
buildTree() - Method in class weka.core.neighboursearch.balltrees.BottomUpConstructor
Builds the ball tree bottom up.
buildTree() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Builds a ball tree middle out.
buildTree() - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor
Builds the ball tree top down.
buildTreeMiddleOut(int, int) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Builds a ball tree middle out from the portion of the master index array given by supplied start and end index.
BuiltInArithmetic - Class in weka.core.pmml
Built-in function for +, -, *, /.
BuiltInArithmetic(BuiltInArithmetic.Operator) - Constructor for class weka.core.pmml.BuiltInArithmetic
Construct a new Arithmetic built-in pmml function.
builtInFunctions - Static variable in class weka.datagenerators.classifiers.classification.Agrawal
built in functions are based on the paper (page 924), which turn out to be functions pred20 thru pred29 in the public c code
BuiltInMath - Class in weka.core.pmml
Built-in function for min, max, sum, avg, log10, ln, sqrt, abs, exp, pow, threshold, floor, ceil and round.
BuiltInMath(BuiltInMath.MathFunc) - Constructor for class weka.core.pmml.BuiltInMath
Construct a new built-in pmml Math function.
BuiltInString - Class in weka.core.pmml
Built-in function for uppercase, substring and trimblanks.
BVDecompose - Class in weka.classifiers
Class for performing a Bias-Variance decomposition on any classifier using the method specified in:

Ron Kohavi, David H.
BVDecompose() - Constructor for class weka.classifiers.BVDecompose
 
BVDecomposeSegCVSub - Class in weka.classifiers
This class performs Bias-Variance decomposion on any classifier using the sub-sampled cross-validation procedure as specified in (1).
The Kohavi and Wolpert definition of bias and variance is specified in (2).
The Webb definition of bias and variance is specified in (3).

Geoffrey I.
BVDecomposeSegCVSub() - Constructor for class weka.classifiers.BVDecomposeSegCVSub
 
BYTE - Static variable in class weka.experiment.DatabaseUtils
Type mapping for BYTE used for reading experiment results.

C

C45Loader - Class in weka.core.converters
Reads a file that is C45 format.
C45Loader() - Constructor for class weka.core.converters.C45Loader
 
C45ModelSelection - Class in weka.classifiers.trees.j48
Class for selecting a C4.5-type split for a given dataset.
C45ModelSelection(int, Instances) - Constructor for class weka.classifiers.trees.j48.C45ModelSelection
Initializes the split selection method with the given parameters.
C45PruneableClassifierTree - Class in weka.classifiers.trees.j48
Class for handling a tree structure that can be pruned using C4.5 procedures.
C45PruneableClassifierTree(ModelSelection, boolean, float, boolean, boolean) - Constructor for class weka.classifiers.trees.j48.C45PruneableClassifierTree
Constructor for pruneable tree structure.
C45PruneableClassifierTreeG - Class in weka.classifiers.trees.j48
Class for handling a tree structure that can be pruned using C4.5 procedures and have nodes grafted on.
C45PruneableClassifierTreeG(ModelSelection, boolean, float, boolean, boolean, boolean) - Constructor for class weka.classifiers.trees.j48.C45PruneableClassifierTreeG
Constructor for pruneable tree structure.
C45PruneableClassifierTreeG(ModelSelection, Instances, ClassifierSplitModel, boolean, float, boolean, boolean, boolean, boolean) - Constructor for class weka.classifiers.trees.j48.C45PruneableClassifierTreeG
Constructor for pruneable tree structure.
C45PruneableDecList - Class in weka.classifiers.rules.part
Class for handling a partial tree structure pruned using C4.5's pruning heuristic.
C45PruneableDecList(ModelSelection, double, int) - Constructor for class weka.classifiers.rules.part.C45PruneableDecList
Constructor for pruneable tree structure.
C45Saver - Class in weka.core.converters
Writes to a destination that is in the format used by the C4.5 algorithm.
Therefore it outputs a names and a data file.
C45Saver() - Constructor for class weka.core.converters.C45Saver
Constructor
C45Split - Class in weka.classifiers.trees.j48
Class implementing a C4.5-type split on an attribute.
C45Split(int, int, double) - Constructor for class weka.classifiers.trees.j48.C45Split
Initializes the split model.
CachedKernel - Class in weka.classifiers.functions.supportVector
Base class for RBFKernel and PolyKernel that implements a simple LRU.
CachedKernel() - Constructor for class weka.classifiers.functions.supportVector.CachedKernel
default constructor - does nothing.
CachedKernel(Instances, int) - Constructor for class weka.classifiers.functions.supportVector.CachedKernel
Initializes the kernel cache.
cacheKeyNameTipText() - Method in class weka.experiment.DatabaseResultListener
Returns the tip text for this property
cacheSizeTipText() - Method in class weka.classifiers.functions.LibSVM
Returns the tip text for this property
cacheSizeTipText() - Method in class weka.classifiers.functions.supportVector.CachedKernel
Returns the tip text for this property
cacheSizeTipText() - Method in class weka.classifiers.functions.supportVector.StringKernel
Returns the tip text for this property
CacheTable(int, float) - Constructor for class weka.classifiers.lazy.kstar.KStarCache.CacheTable
Constructs a new hashtable with a default capacity and load factor.
CacheTable() - Constructor for class weka.classifiers.lazy.kstar.KStarCache.CacheTable
Constructs a new hashtable with a default capacity and load factor.
calcCentroidPivot(int[], Instances) - Static method in class weka.core.neighboursearch.balltrees.BallNode
Calculates the centroid pivot of a node.
calcCentroidPivot(int, int, int[], Instances) - Static method in class weka.core.neighboursearch.balltrees.BallNode
Calculates the centroid pivot of a node.
calcColBC(int, int[][]) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
See Sugiyama et al.
calcColumnWidth(int) - Method in class weka.gui.JTableHelper
calcs the optimal column width of the given column
calcColumnWidth(JTable, int) - Static method in class weka.gui.JTableHelper
Calculates the optimal width for the column of the given table.
calcFreqSequencesTotal() - Method in class weka.associations.GeneralizedSequentialPatterns
Calculates the total number of extracted frequent sequences.
calcFullMargins(BayesNet) - Method in class weka.classifiers.bayes.net.MarginCalculator
 
calcGraph(int, int) - Method in class weka.gui.AttributeVisualizationPanel
Recalculates the barplot or histogram to display, required usually when the attribute is changed or the component is resized.
calcHeaderWidth(int) - Method in class weka.gui.JTableHelper
calcs the optimal header width of the given column
calcHeaderWidth(JTable, int) - Static method in class weka.gui.JTableHelper
Calculates the optimal width for the header of the given table.
calcMargins(BayesNet) - Method in class weka.classifiers.bayes.net.MarginCalculator
Calc marginal distributions of nodes in Bayesian network Note that a connected network is assumed.
calcNodeScore(int) - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
Calc Node Score for given parent set
calcOptimalWidth() - Method in class weka.gui.sql.ResultPanel
calculates the optimal column width for the current table
calcOutOfBagTipText() - Method in class weka.classifiers.meta.Bagging
Returns the tip text for this property
calcPivot(BallNode, BallNode, Instances) - Static method in class weka.core.neighboursearch.balltrees.BallNode
Calculates the centroid pivot of a node based on its two child nodes (if merging two nodes).
calcPivot(BottomUpConstructor.TempNode, BottomUpConstructor.TempNode, Instances) - Method in class weka.core.neighboursearch.balltrees.BottomUpConstructor
Calculates the centroid pivot of a node based on its two child nodes.
calcPivot(MiddleOutConstructor.TempNode, MiddleOutConstructor.TempNode, Instances) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
/** Calculates the centroid pivot of a node based on its two child nodes (if merging two nodes).
calcPivot(MiddleOutConstructor.MyIdxList, MiddleOutConstructor.MyIdxList, Instances) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Calculates the centroid pivot of a node based on the list of points that it contains (tbe two lists of its children are provided).
calcRadius(int[], Instances, Instance, DistanceFunction) - Static method in class weka.core.neighboursearch.balltrees.BallNode
Calculates the radius of node.
calcRadius(int, int, int[], Instances, Instance, DistanceFunction) - Static method in class weka.core.neighboursearch.balltrees.BallNode
Calculates the radius of a node.
calcRadius(BallNode, BallNode, Instance, DistanceFunction) - Static method in class weka.core.neighboursearch.balltrees.BallNode
Calculates the radius of a node based on its two child nodes (if merging two nodes).
calcRadius(BottomUpConstructor.TempNode, BottomUpConstructor.TempNode) - Method in class weka.core.neighboursearch.balltrees.BottomUpConstructor
Calculates the radius of a node based on its two child nodes.
calcRadius(MiddleOutConstructor.TempNode, MiddleOutConstructor.TempNode) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Calculates the radius of a node based on its two child nodes (if merging two nodes).
calcRadius(MiddleOutConstructor.MyIdxList, MiddleOutConstructor.MyIdxList, Instance, Instances) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Calculates the radius of a node based on the list of points that it contains (the two lists of its children are provided).
calcRowBC(int, int[][]) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
See Sugiyama et al.
calcScore(BayesNet) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
performCV returns the accuracy calculated using cross validation.
calcScoreOfCounts(int[], int, int, Instances) - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
utility function used by CalcScore and CalcNodeScore to determine the score based on observed frequencies.
calcScoreOfCounts2(int[][], int, int, Instances) - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
 
calcScoreWithExtraParent(int, int) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
Calc Node Score With Added Parent
calcScoreWithExtraParent(int, int) - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
Calc Node Score With AddedParent
calcScoreWithMissingParent(int, int) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
Calc Node Score With Parent Deleted
calcScoreWithMissingParent(int, int) - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
Calc Node Score With Parent Deleted
calcScoreWithReversedParent(int, int) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
Calc Node Score With Arrow reversed
calculate(Matrix, Matrix, double) - Method in class weka.core.matrix.LinearRegression
performs the actual regression.
calculateAlphas() - Method in class weka.classifiers.trees.lmt.LMTNode
Updates the alpha field for all nodes.
calculateAlphas() - Method in class weka.classifiers.trees.SimpleCart
Updates the alpha field for all nodes.
calculateBIC(int[], Instance, double, Instances) - Method in class weka.clusterers.XMeans
Returns the BIC-value for the given center and instances.
calculateBIC(int[][], Instances, double[]) - Method in class weka.clusterers.XMeans
Calculates the BIC for the given set of centers and instances.
calculateConfirmation() - Method in class weka.associations.tertius.Rule
Calculate the confirmation of this rule.
calculateCutPoints() - Method in class weka.filters.supervised.attribute.Discretize
Generate the cutpoints for each attribute
calculateCutPoints() - Method in class weka.filters.unsupervised.attribute.Discretize
Generate the cutpoints for each attribute
calculateCutPointsByEqualFrequencyBinning(int) - Method in class weka.filters.unsupervised.attribute.Discretize
Set cutpoints for a single attribute.
calculateCutPointsByEqualWidthBinning(int) - Method in class weka.filters.unsupervised.attribute.Discretize
Set cutpoints for a single attribute.
calculateCutPointsByMDL(int, Instances) - Method in class weka.filters.supervised.attribute.Discretize
Set cutpoints for a single attribute using MDL.
calculateDerived() - Method in class weka.experiment.PairedStats
Calculates the derived statistics (significance etc).
calculateDerived() - Method in class weka.experiment.PairedStatsCorrected
Calculates the derived statistics (significance etc).
calculateDerived() - Method in class weka.experiment.Stats
Tells the object to calculate any statistics that don't have their values automatically updated during add.
calculateDistance(Instances) - Method in class weka.classifiers.mi.MIOptimalBall
calculate the distances from each instance in a positive bag to each bag.
calculateMultiplier(Instance, int) - Method in class weka.filters.unsupervised.attribute.InterquartileRange
returns the mulitplier of the IQR the instance is off the median for this particular attribute.
calculateOptimistic() - Method in class weka.associations.tertius.Rule
Calculate the optimistic estimate of this rule.
calculatePriorSum(boolean, double) - Method in class weka.associations.PriorEstimation
calculates the numerator and the denominator of the prior equation
calculateStatistics(Instance, int, int, int) - Method in class weka.experiment.PairedCorrectedTTester
Computes a paired t-test comparison for a specified dataset between two resultsets.
calculateStatistics(Instance, int, int, int) - Method in class weka.experiment.PairedTTester
Computes a paired t-test comparison for a specified dataset between two resultsets.
calculateStatistics(Instance, int, int, int) - Method in interface weka.experiment.Tester
Computes a paired t-test comparison for a specified dataset between two resultsets.
calculateStdDevsTipText() - Method in class weka.experiment.AveragingResultProducer
Returns the tip text for this property
calculateTreshhold() - Method in class weka.attributeSelection.ScatterSearchV1
Calculate the treshold of a dataSet given an evaluator
canAcceptConnection(Class) - Method in class weka.gui.beans.MetaBean
Checks to see if any of the inputs to this group implements the supplied listener class
cancel() - Method in class weka.core.converters.AbstractFileSaver
Cancels the incremental saving process.
CANCEL - Static variable in class weka.core.converters.AbstractSaver
 
cancel() - Method in class weka.core.converters.AbstractSaver
Cancels the incremental saving process if the write mode is CANCEL.
cancel() - Method in class weka.core.converters.DatabaseSaver
Cancels the incremental saving process and tries to drop the table if the write mode is CANCEL.
CANCEL_OPTION - Static variable in class weka.gui.experiment.OutputFormatDialog
Signifies a cancelled property selection.
CANCEL_OPTION - Static variable in class weka.gui.ListSelectorDialog
Signifies a cancelled property selection
CANCEL_OPTION - Static variable in class weka.gui.PropertySelectorDialog
Signifies a cancelled property selection
CANCEL_OPTION - Static variable in class weka.gui.ViewerDialog
Signifies a cancelled property selection
cancelShapes() - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
Sets the list of shapes to empty and also cancels the current shape being drawn (if applicable).
candidateIsFullOwner(KDTreeNode, Instance, Instance) - Method in class weka.core.neighboursearch.KDTree
Returns true if candidate is a full owner in respect to a competitor.
canEstimate(CheckEstimator.AttrTypes, boolean, int) - Method in class weka.estimators.CheckEstimator
Checks basic estimation of one attribute of the scheme, for simple non-troublesome datasets.
canHandleClassAsNthAttribute(boolean, boolean, boolean, boolean, boolean, boolean, int, int) - Method in class weka.associations.CheckAssociator
Checks whether the scheme can handle class attributes as Nth attribute.
canHandleClassAsNthAttribute(boolean, boolean, boolean, boolean, boolean, boolean, int, int) - Method in class weka.attributeSelection.CheckAttributeSelection
Checks whether the scheme can handle class attributes as Nth attribute.
canHandleClassAsNthAttribute(boolean, boolean, boolean, boolean, boolean, boolean, int, int) - Method in class weka.classifiers.CheckClassifier
Checks whether the scheme can handle class attributes as Nth attribute.
canHandleClassAsNthAttribute(boolean, boolean, boolean, boolean, boolean, boolean, int, int) - Method in class weka.classifiers.functions.supportVector.CheckKernel
Checks whether the scheme can handle class attributes as Nth attribute.
canHandleClassAsNthAttribute(CheckEstimator.AttrTypes, int, int, int, int) - Method in class weka.estimators.CheckEstimator
Checks whether the scheme can handle class attributes as Nth attribute.
canHandleMissing(boolean, boolean, boolean, boolean, boolean, boolean, int, boolean, boolean, int) - Method in class weka.associations.CheckAssociator
Checks basic missing value handling of the scheme.
canHandleMissing(boolean, boolean, boolean, boolean, boolean, boolean, int, boolean, boolean, int) - Method in class weka.attributeSelection.CheckAttributeSelection
Checks basic missing value handling of the scheme.
canHandleMissing(boolean, boolean, boolean, boolean, boolean, boolean, int, boolean, boolean, int) - Method in class weka.classifiers.CheckClassifier
Checks basic missing value handling of the scheme.
canHandleMissing(boolean, boolean, boolean, boolean, boolean, boolean, int, boolean, boolean, int) - Method in class weka.classifiers.functions.supportVector.CheckKernel
Checks basic missing value handling of the scheme.
canHandleMissing(boolean, boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.clusterers.CheckClusterer
Checks basic missing value handling of the scheme.
canHandleMissing(CheckEstimator.AttrTypes, int, boolean, boolean, int) - Method in class weka.estimators.CheckEstimator
Checks basic missing value handling of the scheme.
canHandleNClasses(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.associations.CheckAssociator
Checks whether nominal schemes can handle more than two classes.
canHandleNClasses(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.attributeSelection.CheckAttributeSelection
Checks whether nominal schemes can handle more than two classes.
canHandleNClasses(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.classifiers.CheckClassifier
Checks whether nominal schemes can handle more than two classes.
canHandleNClasses(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.classifiers.functions.supportVector.CheckKernel
Checks whether nominal schemes can handle more than two classes.
canHandleNClasses(CheckEstimator.AttrTypes, int) - Method in class weka.estimators.CheckEstimator
Checks whether nominal schemes can handle more than two classes.
canHandleOnlyClass(boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.classifiers.CheckClassifier
Checks whether the scheme can handle data that contains only the class attribute.
canHandleZeroTraining(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.associations.CheckAssociator
Checks whether the scheme can handle zero training instances.
canHandleZeroTraining(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.attributeSelection.CheckAttributeSelection
Checks whether the scheme can handle zero training instances.
canHandleZeroTraining(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.classifiers.CheckClassifier
Checks whether the scheme can handle zero training instances.
canHandleZeroTraining(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.classifiers.functions.supportVector.CheckKernel
Checks whether the scheme can handle zero training instances.
canHandleZeroTraining(boolean, boolean, boolean, boolean, boolean, boolean) - Method in class weka.clusterers.CheckClusterer
Checks whether the scheme can handle zero training instances.
canHandleZeroTraining(CheckEstimator.AttrTypes, int) - Method in class weka.estimators.CheckEstimator
Checks whether the scheme can handle zero training instances.
canInstantiateClass() - Method in class weka.core.Javadoc
returns true if the class can be instantiated, i.e., has a default constructor.
canKeep(Instance, Literal) - Method in class weka.associations.tertius.Body
Test if an instance can be kept as a counter-instance, if a new literal is added to this body.
canKeep(Instance, Literal) - Method in class weka.associations.tertius.Head
Test if an instance can be kept as a counter-instance, if a new literal is added to this head.
canKeep(Instance, Literal) - Method in class weka.associations.tertius.LiteralSet
Test if an instance can be kept as a counter-instance, given a new literal.
canMoveDown(JList) - Static method in class weka.gui.JListHelper
checks whether the selected items can be moved down
canMoveUp(JList) - Static method in class weka.gui.JListHelper
checks whether the selected items can be moved up
canPredict(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.associations.CheckAssociator
Checks basic prediction of the scheme, for simple non-troublesome datasets.
canPredict(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.attributeSelection.CheckAttributeSelection
Checks basic prediction of the scheme, for simple non-troublesome datasets.
canPredict(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.classifiers.CheckClassifier
Checks basic prediction of the scheme, for simple non-troublesome datasets.
canPredict(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.classifiers.functions.supportVector.CheckKernel
Checks basic prediction of the scheme, for simple non-troublesome datasets.
canPredict(boolean, boolean, boolean, boolean, boolean, boolean) - Method in class weka.clusterers.CheckClusterer
Checks basic prediction of the scheme, for simple non-troublesome datasets.
canRedo() - Method in class weka.classifiers.bayes.net.EditableBayesNet
return whether there is something on the undo stack that can be performed
canSplitUpClass(CheckEstimator.AttrTypes, int) - Method in class weka.estimators.CheckEstimator
Checks basic estimation of one attribute of the scheme, for simple non-troublesome datasets.
canSplitUpClass(int, int) - Method in class weka.estimators.CheckEstimator
Checks basic estimation of one attribute of the scheme, for simple non-troublesome datasets.
canTakeOptions() - Method in class weka.associations.CheckAssociator
Checks whether the scheme can take command line options.
canTakeOptions() - Method in class weka.attributeSelection.CheckAttributeSelection
Checks whether the scheme can take command line options.
canTakeOptions() - Method in class weka.classifiers.CheckClassifier
Checks whether the scheme can take command line options.
canTakeOptions() - Method in class weka.classifiers.functions.supportVector.CheckKernel
Checks whether the scheme can take command line options.
canTakeOptions() - Method in class weka.clusterers.CheckClusterer
Checks whether the scheme can take command line options.
canTakeOptions() - Method in class weka.estimators.CheckEstimator
Checks whether the scheme can take command line options.
canUndo() - Method in class weka.classifiers.bayes.net.EditableBayesNet
return whether there is something on the undo stack that can be performed
canUndo() - Method in interface weka.core.Undoable
returns whether an undo is possible, i.e.
canUndo() - Method in class weka.gui.arffviewer.ArffPanel
returns whether an undo is possible
canUndo() - Method in class weka.gui.arffviewer.ArffSortedTableModel
returns whether an undo is possible, i.e.
canUndo() - Method in class weka.gui.arffviewer.ArffTableModel
returns whether an undo is possible, i.e.
Capabilities - Class in weka.core
A class that describes the capabilites (e.g., handling certain types of attributes, missing values, types of classes, etc.) of a specific classifier.
Capabilities(CapabilitiesHandler) - Constructor for class weka.core.Capabilities
initializes the capabilities for the given owner
capabilities() - Method in class weka.core.Capabilities
Returns an Iterator over the stored capabilities
Capabilities.Capability - Enum in weka.core
enumeration of all capabilities
capabilitiesFilterChanged(Explorer.CapabilitiesFilterChangeEvent) - Method in class weka.gui.explorer.AssociationsPanel
method gets called in case of a change event
capabilitiesFilterChanged(Explorer.CapabilitiesFilterChangeEvent) - Method in class weka.gui.explorer.AttributeSelectionPanel
method gets called in case of a change event
capabilitiesFilterChanged(Explorer.CapabilitiesFilterChangeEvent) - Method in class weka.gui.explorer.ClassifierPanel
method gets called in case of a change event
capabilitiesFilterChanged(Explorer.CapabilitiesFilterChangeEvent) - Method in class weka.gui.explorer.ClustererPanel
method gets called in case of a change event
capabilitiesFilterChanged(Explorer.CapabilitiesFilterChangeEvent) - Method in interface weka.gui.explorer.Explorer.CapabilitiesFilterChangeListener
method gets called in case of a change event
capabilitiesFilterChanged(Explorer.CapabilitiesFilterChangeEvent) - Method in class weka.gui.explorer.PreprocessPanel
method gets called in case of a change event
CapabilitiesFilterChangeEvent(Object, Capabilities) - Constructor for class weka.gui.explorer.Explorer.CapabilitiesFilterChangeEvent
Constructs a GOECapabilitiesFilterChangeEvent object.
CapabilitiesFilterDialog() - Constructor for class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
creates a dialog to choose Capabilities from.
CapabilitiesHandler - Interface in weka.core
Classes implementing this interface return their capabilities in regards to datasets.
CapabilitiesHelpDialog(Frame) - Constructor for class weka.gui.PropertySheetPanel.CapabilitiesHelpDialog
default constructor.
CapabilitiesHelpDialog(Dialog) - Constructor for class weka.gui.PropertySheetPanel.CapabilitiesHelpDialog
default constructor.
capacity() - Method in class weka.core.FastVector
Returns the capacity of the vector.
capacity() - Method in class weka.core.matrix.DoubleVector
Gets the capacity of the vector.
capacity() - Method in class weka.core.matrix.IntVector
Returns the capacity of the vector
cardinalityTipText() - Method in class weka.datagenerators.classifiers.classification.BayesNet
Returns the tip text for this property
caretUpdate(CaretEvent) - Method in class weka.gui.LogWindow
Called when the caret position is updated.
caretUpdate(CaretEvent) - Method in class weka.gui.sql.ConnectionPanel
Called when the caret position is updated.
caretUpdate(CaretEvent) - Method in class weka.gui.sql.QueryPanel
Called when the caret position is updated.
carTipText() - Method in class weka.associations.Apriori
Returns the tip text for this property
carTipText() - Method in class weka.associations.PredictiveApriori
Returns the tip text for this property
CaRuleGeneration - Class in weka.associations
Class implementing the rule generation procedure of the predictive apriori algorithm for class association rules.
CaRuleGeneration(ItemSet) - Constructor for class weka.associations.CaRuleGeneration
Constructor
CARuleMiner - Interface in weka.associations
Interface for learning class association rules.
cat(DoubleVector) - Method in class weka.core.matrix.DoubleVector
Combine two vectors together
cbind(PaceMatrix) - Method in class weka.classifiers.functions.pace.PaceMatrix
Returns a new matrix which binds two matrices with columns.
CEIL - Static variable in interface weka.core.mathematicalexpression.sym
 
CEIL - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
Center - Class in weka.filters.unsupervised.attribute
Centers all numeric attributes in the given dataset to have zero mean (apart from the class attribute, if set).
Center() - Constructor for class weka.filters.unsupervised.attribute.Center
 
centerDataTipText() - Method in class weka.attributeSelection.PrincipalComponents
Returns the tip text for this property
centerDataTipText() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
Returns the tip text for this property
centerHorizontal(FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet
center set of nodes half way between left and right most node in the list
centerInstances(Instances, int[], double) - Method in class weka.core.neighboursearch.KDTree
Assigns instances to centers using KDTree.
centerVertical(FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet
center set of nodes half way between top and bottom most node in the list
CfsSubsetEval - Class in weka.attributeSelection
CfsSubsetEval :

Evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them.

Subsets of features that are highly correlated with the class while having low intercorrelation are preferred.

For more information see:

M.
CfsSubsetEval() - Constructor for class weka.attributeSelection.CfsSubsetEval
Constructor
change() - Method in class weka.associations.RuleGeneration
Gets if the list fo the best rules has been changed
Change - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
This variable is used to keep track of change in the value of delta summation of r(i).
ChangeDateFormat - Class in weka.filters.unsupervised.attribute
Changes the date format used by a date attribute.
ChangeDateFormat() - Constructor for class weka.filters.unsupervised.attribute.ChangeDateFormat
 
changeInputNum(int, int) - Method in class weka.classifiers.functions.neural.NeuralConnection
Changes the connection value information for one of the connections.
changeLength(double) - Method in class weka.core.AlgVector
Changes the length of a vector.
changeOutputNum(int, int) - Method in class weka.classifiers.functions.neural.NeuralConnection
Changes the connection value information for one of the connections.
changeUID(long, long, String, String) - Static method in class weka.core.xml.SerialUIDChanger
changes the oldUID into newUID from the given file (binary/KOML) into the other one (binary/KOML).
CharacterDelimitedTokenizer - Class in weka.core.tokenizers
Abstract superclass for tokenizers that take characters as delimiters.
CharacterDelimitedTokenizer() - Constructor for class weka.core.tokenizers.CharacterDelimitedTokenizer
 
charSetTipText() - Method in class weka.core.converters.TextDirectoryLoader
the tip text for this property
ChartEvent - Class in weka.gui.beans
Event encapsulating info for plotting a data point on the StripChart
ChartEvent(Object, Vector, double, double, double[], boolean) - Constructor for class weka.gui.beans.ChartEvent
Creates a new ChartEvent instance.
ChartEvent(Object) - Constructor for class weka.gui.beans.ChartEvent
Creates a new ChartEvent instance.
ChartListener - Interface in weka.gui.beans
Interface to something that can process a ChartEvent
ChebyshevDistance - Class in weka.core
Implements the Chebyshev distance.
ChebyshevDistance() - Constructor for class weka.core.ChebyshevDistance
Constructs an Chebyshev Distance object, Instances must be still set.
ChebyshevDistance(Instances) - Constructor for class weka.core.ChebyshevDistance
Constructs an Chebyshev Distance object and automatically initializes the ranges.
check(double) - Method in class weka.classifiers.trees.j48.Distribution
Checks if at least two bags contain a minimum number of instances.
Check - Class in weka.core
Abstract general class for testing in Weka.
Check() - Constructor for class weka.core.Check
 
checkAgainstBackground(Color, Color) - Static method in class weka.gui.visualize.Plot2D
 
CheckAssociator - Class in weka.associations
Class for examining the capabilities and finding problems with associators.
CheckAssociator() - Constructor for class weka.associations.CheckAssociator
 
CheckAttributeSelection - Class in weka.attributeSelection
Class for examining the capabilities and finding problems with attribute selection schemes.
CheckAttributeSelection() - Constructor for class weka.attributeSelection.CheckAttributeSelection
 
CheckBoxList - Class in weka.gui
An extended JList that contains CheckBoxes.
CheckBoxList() - Constructor for class weka.gui.CheckBoxList
initializes the list with an empty CheckBoxListModel
CheckBoxList(CheckBoxList.CheckBoxListModel) - Constructor for class weka.gui.CheckBoxList
initializes the list with the given CheckBoxListModel
CheckBoxList.CheckBoxListItem - Class in weka.gui
represents an item in the CheckBoxListModel
CheckBoxList.CheckBoxListModel - Class in weka.gui
A specialized model.
CheckBoxList.CheckBoxListRenderer - Class in weka.gui
A specialized CellRenderer for the CheckBoxList
CheckBoxListItem(Object) - Constructor for class weka.gui.CheckBoxList.CheckBoxListItem
initializes the item with the given object and initially unchecked
CheckBoxListItem(Object, boolean) - Constructor for class weka.gui.CheckBoxList.CheckBoxListItem
initializes the item with the given object and whether it's checked initially
CheckBoxListModel() - Constructor for class weka.gui.CheckBoxList.CheckBoxListModel
initializes the model with no data.
CheckBoxListModel(Object[]) - Constructor for class weka.gui.CheckBoxList.CheckBoxListModel
Constructs a CheckBoxListModel from an array of objects and then applies setModel to it.
CheckBoxListModel(Vector) - Constructor for class weka.gui.CheckBoxList.CheckBoxListModel
Constructs a CheckBoxListModel from a Vector and then applies setModel to it.
CheckBoxListRenderer() - Constructor for class weka.gui.CheckBoxList.CheckBoxListRenderer
 
checkCanonicalUserOptions() - Method in class weka.core.CheckOptionHandler
checks whether the user-supplied options stay the same after settting, getting and re-setting again
CheckClassifier - Class in weka.classifiers
Class for examining the capabilities and finding problems with classifiers.
CheckClassifier() - Constructor for class weka.classifiers.CheckClassifier
 
checkClassifier() - Method in class weka.classifiers.functions.SMO.BinarySMO
Quick and dirty check whether the quadratic programming problem is solved.
checkClassifier() - Method in class weka.classifiers.mi.MISMO.BinaryMISMO
Quick and dirty check whether the quadratic programming problem is solved.
CheckClusterer - Class in weka.clusterers
Class for examining the capabilities and finding problems with clusterers.
CheckClusterer() - Constructor for class weka.clusterers.CheckClusterer
default constructor
checkCoverage() - Method in class weka.datagenerators.clusterers.SubspaceCluster
Checks, whether all attributes are covered by cluster definitions and returns TRUE in that case.
checkDefaultOptions() - Method in class weka.core.CheckOptionHandler
checks whether the default options can be processed completely or some invalid options are returned by the getOptions() method.
checkDimensions() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
checks whether the dimensions of filters and ranges fit together.
checkErrorRateTipText() - Method in class weka.classifiers.rules.JRip
Returns the tip text for this property
CheckEstimator - Class in weka.estimators
Class for examining the capabilities and finding problems with estimators.
CheckEstimator() - Constructor for class weka.estimators.CheckEstimator
 
CheckEstimator.AttrTypes - Class in weka.estimators
class that contains info about the attribute types the estimator can estimate estimator work on one attribute only
CheckEstimator.EstTypes - Class in weka.estimators
public class that contains info about the chosen attribute type estimator work on one attribute only
CheckEstimator.PostProcessor - Class in weka.estimators
a class for postprocessing the test-data
checkForAttributeType(int) - Method in class weka.core.Instances
Checks for attributes of the given type in the dataset
checkForDuplicateKeys(Object[]) - Method in class weka.experiment.AveragingResultProducer
Checks whether any duplicate results (with respect to a key template) were received.
checkForMissing(Instance, Instances) - Method in class weka.classifiers.functions.PaceRegression
Checks if an instance has a missing value.
checkForMultipleDifferences() - Method in class weka.experiment.AveragingResultProducer
Checks that the keys for a run only differ in one key field.
checkForNominalAttributes(Instances) - Method in class weka.clusterers.XMeans
Checks for nominal attributes in the dataset.
checkForRemainingOptions(String[]) - Static method in class weka.core.Utils
Checks if the given array contains any non-empty options.
checkForStringAttributes() - Method in class weka.core.Instances
Checks for string attributes in the dataset
checkGlobalInfo() - Method in class weka.core.CheckGOE
checks whether the object declares a globalInfo method.
CheckGOE - Class in weka.core
Simple command line checking of classes that are editable in the GOE.

Usage:

CheckGOE -W classname -- test options

Valid options are:

CheckGOE() - Constructor for class weka.core.CheckGOE
default constructor
checkIndices() - Method in class weka.datagenerators.ClusterGenerator
check if attribute types are not contradicting
checkIndicesList(MiddleOutConstructor.MyIdxList, int, int) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Checks whether if the points in an index list are in some specified of the master index array.
checkInstance(Instance) - Method in class weka.core.Instances
Checks if the given instance is compatible with this dataset.
checkInstances() - Method in class weka.clusterers.XMeans
Checks the instances.
CheckKernel - Class in weka.classifiers.functions.supportVector
Class for examining the capabilities and finding problems with kernels.
CheckKernel() - Constructor for class weka.classifiers.functions.supportVector.CheckKernel
 
checkKOML() - Static method in class weka.core.xml.SerialUIDChanger
checks whether KOML is present
checkListOptions() - Method in class weka.core.CheckOptionHandler
checks whether the listOptions method works
checkMinMax(Instances) - Method in class weka.classifiers.meta.RotationForest
Checks m_MinGroup and m_MaxGroup
checkMissing(Instances) - Method in class weka.core.neighboursearch.CoverTree
Checks if there is any instance with missing values.
checkMissing(Instances) - Method in class weka.core.neighboursearch.KDTree
Checks if there is any instance with missing values.
checkMissing(Instance) - Method in class weka.core.neighboursearch.KDTree
Checks if there is any missing value in the given instance.
checkModel() - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Checks if generated model is valid.
checkModel(int) - Method in class weka.classifiers.trees.lmt.ResidualSplit
Checks if there are at least 2 subsets that contain >= minNumInstances.
CheckOptionHandler - Class in weka.core
Simple command line checking of classes that implement OptionHandler.

Usage:

CheckOptionHandler -W optionHandlerClassName -- test options

Valid options are:

CheckOptionHandler() - Constructor for class weka.core.CheckOptionHandler
 
checkRemainingOptions() - Method in class weka.core.CheckOptionHandler
checks whether the user-supplied options can be processed completely or some "left-over" options remain
checkResettingOptions() - Method in class weka.core.CheckOptionHandler
checks whether the optionhandler can be re-setted again to default options after the user-supplied options have been set.
CheckScheme - Class in weka.core
Abstract general class for testing schemes in Weka.
CheckScheme() - Constructor for class weka.core.CheckScheme
 
CheckScheme.PostProcessor - Class in weka.core
a class for postprocessing the test-data
checkSetOptions() - Method in class weka.core.CheckOptionHandler
checks whether the user-supplied options can be processed at all
checkSorting(MiddleOutConstructor.MyIdxList) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList
Checks the sorting of a list.
CheckSource - Class in weka.classifiers
A simple class for checking the source generated from Classifiers implementing the weka.classifiers.Sourcable interface.
CheckSource() - Constructor for class weka.classifiers.CheckSource
 
CheckSource - Class in weka.filters
A simple class for checking the source generated from Filters implementing the weka.filters.Sourcable interface.
CheckSource() - Constructor for class weka.filters.CheckSource
 
checkStatus(Object) - Method in interface weka.experiment.Compute
Check on the status of a Task
checkStatus(Object) - Method in class weka.experiment.RemoteEngine
Returns status information on a particular task
checksTurnedOffTipText() - Method in class weka.classifiers.functions.SMO
Returns the tip text for this property
checksTurnedOffTipText() - Method in class weka.classifiers.functions.supportVector.Kernel
Returns the tip text for this property
checksTurnedOffTipText() - Method in class weka.classifiers.mi.MISMO
Returns the tip text for this property
checksTurnedOffTipText() - Method in class weka.filters.unsupervised.attribute.KernelFilter
Returns the tip text for this property
checkToolTips() - Method in class weka.core.CheckGOE
checks whether the object declares tip text method for all its properties.
ChildFrameMDI(Main, String) - Constructor for class weka.gui.Main.ChildFrameMDI
constructs a new internal frame that knows about its parent.
ChildFrameSDI(GUIChooser, String) - Constructor for class weka.gui.GUIChooser.ChildFrameSDI
constructs a new internal frame that knows about its parent.
ChildFrameSDI(Main, String) - Constructor for class weka.gui.Main.ChildFrameSDI
constructs a new internal frame that knows about its parent.
children() - Method in class weka.classifiers.trees.adtree.PredictionNode
Enumerates the children of this node.
children() - Method in class weka.classifiers.trees.LADTree.PredictionNode
 
childrenValues() - Method in class weka.gui.HierarchyPropertyParser
The value in the children nodes.
chisqDistribution - Static variable in class weka.core.matrix.Maths
Distribution type: chi-squared
ChisqMixture - Class in weka.classifiers.functions.pace
Class for manipulating chi-square mixture distributions.
ChisqMixture() - Constructor for class weka.classifiers.functions.pace.ChisqMixture
Contructs an empty ChisqMixture
chiSquared(double[][], boolean) - Static method in class weka.core.ContingencyTables
Returns chi-squared probability for a given matrix.
ChiSquaredAttributeEval - Class in weka.attributeSelection
ChiSquaredAttributeEval :

Evaluates the worth of an attribute by computing the value of the chi-squared statistic with respect to the class.

Valid options are:

ChiSquaredAttributeEval() - Constructor for class weka.attributeSelection.ChiSquaredAttributeEval
Constructor
chiSquaredProbability(double, double) - Static method in class weka.core.Statistics
Returns chi-squared probability for given value and degrees of freedom.
chiVal(double[][], boolean) - Static method in class weka.core.ContingencyTables
Computes chi-squared statistic for a contingency table.
chol() - Method in class weka.core.matrix.Matrix
Cholesky Decomposition
CholeskyDecomposition - Class in weka.core.matrix
Cholesky Decomposition.
CholeskyDecomposition(Matrix) - Constructor for class weka.core.matrix.CholeskyDecomposition
Cholesky algorithm for symmetric and positive definite matrix.
chooseButton - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
chooseIndex() - Method in class weka.classifiers.rules.part.ClassifierDecList
Method for choosing a subset to expand.
chooseLastIndex() - Method in class weka.classifiers.rules.part.ClassifierDecList
Choose last index (ie.
chooseRandomIndexBasedOnProportions(double[], Random) - Method in class weka.datagenerators.classifiers.classification.RandomRBF
returns a random index based on the given proportions
chunkSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
returns the chunk size used by the committee
CISearchAlgorithm - Class in weka.classifiers.bayes.net.search.ci
The CISearchAlgorithm class supports Bayes net structure search algorithms that are based on conditional independence test (as opposed to for example score based of cross validation based search algorithms).
CISearchAlgorithm() - Constructor for class weka.classifiers.bayes.net.search.ci.CISearchAlgorithm
 
CitationKNN - Class in weka.classifiers.mi
Modified version of the Citation kNN multi instance classifier.

For more information see:

Jun Wang, Zucker, Jean-Daniel: Solving Multiple-Instance Problem: A Lazy Learning Approach.
CitationKNN() - Constructor for class weka.classifiers.mi.CitationKNN
 
CLASS_FEATURENODE - Static variable in class weka.classifiers.functions.LibLINEAR
the svm_node classname
CLASS_IS_LAST - Static variable in class weka.core.TestInstances
can be used for settting the class attribute index to last
CLASS_LINEAR - Static variable in class weka.classifiers.functions.LibLINEAR
the svm classname
CLASS_MODEL - Static variable in class weka.classifiers.functions.LibLINEAR
the svm_model classname
CLASS_PARAMETER - Static variable in class weka.classifiers.functions.LibLINEAR
the svm_parameter classname
CLASS_PROBLEM - Static variable in class weka.classifiers.functions.LibLINEAR
the svm_problem classname
CLASS_PYTHONINERPRETER - Static variable in class weka.core.Jython
the classname of the Python interpreter
CLASS_PYTHONOBJECTINPUTSTREAM - Static variable in class weka.core.Jython
the classname of the Python ObjectInputStream
CLASS_SOLVERTYPE - Static variable in class weka.classifiers.functions.LibLINEAR
the svm_parameter classname
CLASS_SVM - Static variable in class weka.classifiers.functions.LibSVM
the svm classname
CLASS_SVMMODEL - Static variable in class weka.classifiers.functions.LibSVM
the svm_model classname
CLASS_SVMNODE - Static variable in class weka.classifiers.functions.LibSVM
the svm_node classname
CLASS_SVMPARAMETER - Static variable in class weka.classifiers.functions.LibSVM
the svm_parameter classname
CLASS_SVMPROBLEM - Static variable in class weka.classifiers.functions.LibSVM
the svm_problem classname
ClassAssigner - Class in weka.filters.unsupervised.attribute
Filter that can set and unset the class index.
ClassAssigner() - Constructor for class weka.filters.unsupervised.attribute.ClassAssigner
 
ClassAssigner - Class in weka.gui.beans
Bean that assigns a class attribute to a data set.
ClassAssigner() - Constructor for class weka.gui.beans.ClassAssigner
 
ClassAssignerBeanInfo - Class in weka.gui.beans
BeanInfo class for the class assigner bean
ClassAssignerBeanInfo() - Constructor for class weka.gui.beans.ClassAssignerBeanInfo
 
ClassAssignerCustomizer - Class in weka.gui.beans
GUI customizer for the class assigner bean
ClassAssignerCustomizer() - Constructor for class weka.gui.beans.ClassAssignerCustomizer
 
classAttribute() - Method in class weka.core.Instance
Returns class attribute.
classAttribute() - Method in class weka.core.Instances
Returns the class attribute.
classAttributeNames() - Method in class weka.classifiers.functions.SMO
 
classAttributeNames() - Method in class weka.classifiers.mi.MISMO
Returns the names of the class attributes.
ClassBalancedND - Class in weka.classifiers.meta.nestedDichotomies
A meta classifier for handling multi-class datasets with 2-class classifiers by building a random class-balanced tree structure.

For more info, check

Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.
ClassBalancedND() - Constructor for class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
Constructor.
classColumnTipText() - Method in class weka.gui.beans.ClassAssigner
Tool tip text for this property
ClassDiscovery - Class in weka.core
This class is used for discovering classes that implement a certain interface or a derived from a certain class.
ClassDiscovery() - Constructor for class weka.core.ClassDiscovery
 
ClassDiscovery.StringCompare - Class in weka.core
compares two strings.
classFirst(boolean) - Method in class weka.experiment.Experiment
Sets whether the first attribute is treated as the class for all datasets involved in the experiment.
classFlagTipText() - Method in class weka.datagenerators.ClusterGenerator
Returns the tip text for this property
ClassificationGenerator - Class in weka.datagenerators
Abstract class for data generators for classifiers.
ClassificationGenerator() - Constructor for class weka.datagenerators.ClassificationGenerator
initializes with default values
classificationTipText() - Method in class weka.associations.Tertius
Returns the tip text for this property.
ClassificationViaClustering - Class in weka.classifiers.meta
A simple meta-classifier that uses a clusterer for classification.
ClassificationViaClustering() - Constructor for class weka.classifiers.meta.ClassificationViaClustering
default constructor
ClassificationViaRegression - Class in weka.classifiers.meta
Class for doing classification using regression methods.
ClassificationViaRegression() - Constructor for class weka.classifiers.meta.ClassificationViaRegression
Default constructor.
Classifier - Class in weka.classifiers
Abstract classifier.
Classifier() - Constructor for class weka.classifiers.Classifier
 
Classifier - Class in weka.gui.beans
Bean that wraps around weka.classifiers
Classifier() - Constructor for class weka.gui.beans.Classifier
Creates a new Classifier instance.
Classifier.TrainingTask - Class in weka.gui.beans
 
ClassifierBeanInfo - Class in weka.gui.beans
BeanInfo class for the Classifier wrapper bean
ClassifierBeanInfo() - Constructor for class weka.gui.beans.ClassifierBeanInfo
 
ClassifierCustomizer - Class in weka.gui.beans
GUI customizer for the classifier wrapper bean
ClassifierCustomizer() - Constructor for class weka.gui.beans.ClassifierCustomizer
 
ClassifierDecList - Class in weka.classifiers.rules.part
Class for handling a rule (partial tree) for a decision list.
ClassifierDecList(ModelSelection, int) - Constructor for class weka.classifiers.rules.part.ClassifierDecList
Constructor - just calls constructor of class DecList.
ClassifierPanel - Class in weka.gui.explorer
0* This panel allows the user to select and configure a classifier, set the attribute of the current dataset to be used as the class, and evaluate the classifier using a number of testing modes (test on the training data, train/test on a percentage split, n-fold cross-validation, test on a separate split).
ClassifierPanel() - Constructor for class weka.gui.explorer.ClassifierPanel
Creates the classifier panel
ClassifierPerformanceEvaluator - Class in weka.gui.beans
A bean that evaluates the performance of batch trained classifiers
ClassifierPerformanceEvaluator() - Constructor for class weka.gui.beans.ClassifierPerformanceEvaluator
 
ClassifierPerformanceEvaluatorBeanInfo - Class in weka.gui.beans
Bean info class for the classifier performance evaluator
ClassifierPerformanceEvaluatorBeanInfo() - Constructor for class weka.gui.beans.ClassifierPerformanceEvaluatorBeanInfo
 
classifiers() - Method in class weka.classifiers.meta.LogitBoost
Returns the array of classifiers that have been built.
ClassifierSplitEvaluator - Class in weka.experiment
A SplitEvaluator that produces results for a classification scheme on a nominal class attribute.
ClassifierSplitEvaluator() - Constructor for class weka.experiment.ClassifierSplitEvaluator
No args constructor.
ClassifierSplitModel - Class in weka.classifiers.trees.j48
Abstract class for classification models that can be used recursively to split the data.
ClassifierSplitModel() - Constructor for class weka.classifiers.trees.j48.ClassifierSplitModel
 
classifiersTipText() - Method in class weka.classifiers.meta.MultiScheme
Returns the tip text for this property
classifiersTipText() - Method in class weka.classifiers.MultipleClassifiersCombiner
Returns the tip text for this property
ClassifierSubsetEval - Class in weka.attributeSelection
Classifier subset evaluator:

Evaluates attribute subsets on training data or a seperate hold out testing set.
ClassifierSubsetEval() - Constructor for class weka.attributeSelection.ClassifierSubsetEval
 
classifierTipText() - Method in class weka.attributeSelection.ClassifierSubsetEval
Returns the tip text for this property
classifierTipText() - Method in class weka.attributeSelection.WrapperSubsetEval
Returns the tip text for this property
classifierTipText() - Method in class weka.classifiers.SingleClassifierEnhancer
Returns the tip text for this property
classifierTipText() - Method in class weka.experiment.ClassifierSplitEvaluator
Returns the tip text for this property
classifierTipText() - Method in class weka.experiment.RegressionSplitEvaluator
Returns the tip text for this property
classifierTipText() - Method in class weka.filters.supervised.attribute.AddClassification
Returns the tip text for this property.
classifierTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Returns the tip text for this property
ClassifierTree - Class in weka.classifiers.trees.j48
Class for handling a tree structure used for classification.
ClassifierTree(ModelSelection) - Constructor for class weka.classifiers.trees.j48.ClassifierTree
Constructor.
CLASSIFY_CHILD - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
Asks for another learning scheme to classify this node.
classifyInstance(Instance) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Classifies the given instance using the Bayesian Logistic Regression function.
classifyInstance(Instance) - Method in class weka.classifiers.bayes.ComplementNaiveBayes
Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.Classifier
Classifies the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.functions.GaussianProcesses
Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.functions.IsotonicRegression
Generate a prediction for the supplied instance.
classifyInstance(Instance) - Method in class weka.classifiers.functions.LeastMedSq
Classify a given instance using the best generated LinearRegression Classifier.
classifyInstance(Instance) - Method in class weka.classifiers.functions.LinearRegression
Classifies the given instance using the linear regression function.
classifyInstance(Instance) - Method in class weka.classifiers.functions.PaceRegression
Classifies the given instance using the linear regression function.
classifyInstance(Instance) - Method in class weka.classifiers.functions.PLSClassifier
Classifies the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.functions.SimpleLinearRegression
Generate a prediction for the supplied instance.
classifyInstance(Instance) - Method in class weka.classifiers.functions.SMOreg
Classifies the given instance using the linear regression function.
classifyInstance(Instance) - Method in class weka.classifiers.functions.Winnow
Outputs the prediction for the given instance.
classifyInstance(Instance) - Method in class weka.classifiers.lazy.IB1
Classifies the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.meta.AdditiveRegression
Classify an instance.
classifyInstance(Instance) - Method in class weka.classifiers.meta.ClassificationViaClustering
Classifies the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.meta.GridSearch
Classifies the given instance.
classifyInstance(double[]) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
classifies an instance (given Fs values) with the committee
classifyInstance(Instance) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
classifies an instance with the committee
classifyInstance(Instance) - Method in class weka.classifiers.meta.RegressionByDiscretization
Returns a predicted class for the test instance.
classifyInstance(Instance) - Method in class weka.classifiers.meta.Vote
Classifies the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.mi.MINND
Use Kullback Leibler distance to find the nearest neighbours of the given exemplar.
classifyInstance(Instance) - Method in class weka.classifiers.rules.NNge
Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.rules.OneR
Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.rules.part.ClassifierDecList
Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.rules.PART
Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.rules.part.MakeDecList
Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.rules.Prism
Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.rules.Ridor
Classify the test instance with the rule learner
classifyInstance(Instance) - Method in class weka.classifiers.rules.ZeroR
Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.FT
Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.Id3
Classifies a given test instance using the decision tree.
classifyInstance(Instance) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.j48.ClassifierTree
Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.J48
Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.J48graft
Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.LMT
Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.m5.M5Base
Calculates a prediction for an instance using a set of rules or an M5 model tree
classifyInstance(Instance) - Method in class weka.classifiers.trees.m5.PreConstructedLinearModel
Predicts the class of the supplied instance using the linear model.
classifyInstance(Instance) - Method in class weka.classifiers.trees.m5.Rule
Calculates a prediction for an instance using this rule or M5 model tree
classifyInstance(Instance) - Method in class weka.classifiers.trees.m5.RuleNode
Classify an instance using this node.
classifyInstance(Instance) - Method in class weka.classifiers.trees.NBTree
Classifies an instance.
classifyInstanceMedian(Instance) - Method in class weka.classifiers.meta.Vote
Classifies the given test instance, returning the median from all classifiers.
ClassIndex - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
The class index from the training data
classIndex() - Method in class weka.core.Instance
Returns the class attribute's index.
classIndex() - Method in class weka.core.Instances
Returns the class attribute's index.
classIndexTipText() - Method in class weka.associations.Apriori
Returns the tip text for this property
classIndexTipText() - Method in class weka.associations.FilteredAssociator
Returns the tip text for this property
classIndexTipText() - Method in class weka.associations.PredictiveApriori
Returns the tip text for this property
classIndexTipText() - Method in class weka.associations.Tertius
Returns the tip text for this property.
classIndexTipText() - Method in class weka.core.converters.LibSVMSaver
Returns the tip text for this property
classIndexTipText() - Method in class weka.core.converters.SVMLightSaver
Returns the tip text for this property.
classIndexTipText() - Method in class weka.core.converters.XRFFSaver
Returns the tip text for this property
classIndexTipText() - Method in class weka.filters.unsupervised.attribute.ClassAssigner
Returns the tip text for this property.
classIndexTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Returns the tip text for this property
classIsMissing() - Method in class weka.core.Instance
Tests if an instance's class is missing.
ClassloaderUtil - Class in weka.core
Utility class that can add jar files to the classpath dynamically.
ClassloaderUtil() - Constructor for class weka.core.ClassloaderUtil
 
className(int) - Method in class weka.classifiers.evaluation.ConfusionMatrix
Gets the name of one of the classes.
classNameTipText() - Method in class weka.filters.unsupervised.attribute.NumericTransform
Returns the tip text for this property
ClassOrder - Class in weka.filters.supervised.attribute
Changes the order of the classes so that the class values are no longer of in the order specified in the header.
ClassOrder() - Constructor for class weka.filters.supervised.attribute.ClassOrder
 
classOrderTipText() - Method in class weka.filters.supervised.attribute.ClassOrder
Returns the tip text for this property
ClassPanel - Class in weka.gui.visualize
This panel displays coloured labels for nominal attributes and a spectrum for numeric attributes.
ClassPanel() - Constructor for class weka.gui.visualize.ClassPanel
 
ClassPanel(Color) - Constructor for class weka.gui.visualize.ClassPanel
 
classProb(int, Instance, int) - Method in class weka.classifiers.trees.j48.BinC45Split
Gets class probability for instance.
classProb(int, Instance, int) - Method in class weka.classifiers.trees.j48.C45Split
Gets class probability for instance.
classProb(int, Instance, int) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Gets class probability for instance.
classProb(int, Instance, int) - Method in class weka.classifiers.trees.j48.GraftSplit
returns the probability for instance for the specified class
classProb(int, Instance, int) - Method in class weka.classifiers.trees.j48.NBTreeNoSplit
Return the probability for a class value
classProb(int, Instance, int) - Method in class weka.classifiers.trees.j48.NBTreeSplit
Return the probability for a class value
classProbLaplace(int, Instance, int) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Gets class probability for instance.
classSelected(String) - Method in class weka.gui.GenericObjectEditor
Called when the user selects an class type to change to.
classSgn(double) - Static method in class weka.classifiers.bayes.BayesianLogisticRegression
This class is used to mask the internal class labels.
classValue() - Method in class weka.core.Instance
Returns an instance's class value in internal format.
ClassValuePicker - Class in weka.gui.beans
 
ClassValuePicker() - Constructor for class weka.gui.beans.ClassValuePicker
 
ClassValuePickerBeanInfo - Class in weka.gui.beans
BeanInfo class for the class value picker bean
ClassValuePickerBeanInfo() - Constructor for class weka.gui.beans.ClassValuePickerBeanInfo
 
ClassValuePickerCustomizer - Class in weka.gui.beans
 
ClassValuePickerCustomizer() - Constructor for class weka.gui.beans.ClassValuePickerCustomizer
 
classValueTipText() - Method in class weka.filters.supervised.instance.SMOTE
Returns the tip text for this property.
clean() - Method in class weka.classifiers.functions.supportVector.CachedKernel
Frees the cache used by the kernel.
clean() - Method in class weka.classifiers.functions.supportVector.Kernel
Frees the memory used by the kernel.
clean() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
Frees the memory used by the kernel.
clean() - Method in class weka.classifiers.functions.supportVector.StringKernel
Frees the memory used by the kernel.
cleanse(Instance) - Method in class weka.classifiers.mi.MINND
Cleanse the given exemplar according to the valid and noise data statistics
cleanup(Instances) - Method in class weka.classifiers.rules.part.ClassifierDecList
Cleanup in order to save memory.
cleanUp() - Method in class weka.classifiers.rules.RuleStats
Frees up memory after classifier has been built.
cleanup() - Method in class weka.classifiers.trees.ft.FTtree
Cleanup in order to save memory.
cleanup() - Method in class weka.classifiers.trees.j48.BinC45ModelSelection
Sets reference to training data to null.
cleanup() - Method in class weka.classifiers.trees.j48.C45ModelSelection
Sets reference to training data to null.
cleanup(Instances) - Method in class weka.classifiers.trees.j48.ClassifierTree
Cleanup in order to save memory.
cleanup() - Method in class weka.classifiers.trees.j48.NBTreeModelSelection
Sets reference to training data to null.
cleanup() - Method in class weka.classifiers.trees.lmt.LMTNode
Cleanup in order to save memory.
cleanup() - Method in class weka.classifiers.trees.lmt.LogisticBase
Cleanup in order to save memory.
cleanup() - Method in class weka.classifiers.trees.lmt.ResidualModelSelection
Method not in use
CLEANUPTIMEOUT - Static variable in class weka.experiment.RemoteEngine
Clean up interval (in ms)
clear() - Method in class weka.associations.tertius.SimpleLinkedList
 
clear(int) - Method in class weka.attributeSelection.GeneticSearch.GABitSet
unset a bit in the chromosome
clear() - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable
Clears this hashtable so that it contains no keys.
clear() - Method in class weka.classifiers.xml.XMLClassifier
generates internally a new XML document and clears also the IgnoreList and the mappings for the Read/Write-Methods
clear() - Method in class weka.core.neighboursearch.covertrees.Stack
Removes all the elements from the stack.
clear() - Method in class weka.core.ProtectedProperties
Overrides a method to prevent the properties from being modified.
clear() - Method in class weka.core.Stopwords
removes all stopwords
clear() - Method in class weka.core.Tee
removes all streams and places the default printstream, if any, again in the list.
clear() - Method in class weka.core.Trie
Removes all of the elements from this collection
clear() - Method in class weka.core.xml.MethodHandler
removes all mappings
clear() - Method in class weka.core.xml.XMLBasicSerialization
generates internally a new XML document and clears also the IgnoreList and the mappings for the Read/Write-Methods
clear() - Method in class weka.core.xml.XMLDocument
sets up an empty DOM document, with the current DOCTYPE and root node.
clear() - Method in class weka.core.xml.XMLSerialization
generates internally a new XML document and clears also the IgnoreList and the mappings for the Read/Write-Methods
clear() - Method in class weka.core.xml.XMLSerializationMethodHandler
removes all current methods and adds the methods according to the
clear() - Method in class weka.experiment.ResultMatrix
removes the stored data and the ordering, but retains the dimensions of the matrix
clear() - Method in class weka.experiment.ResultMatrixCSV
removes the stored data but retains the dimensions of the matrix
clear() - Method in class weka.experiment.ResultMatrixGnuPlot
removes the stored data but retains the dimensions of the matrix
clear() - Method in class weka.experiment.ResultMatrixHTML
removes the stored data but retains the dimensions of the matrix
clear() - Method in class weka.experiment.ResultMatrixLatex
removes the stored data but retains the dimensions of the matrix
clear() - Method in class weka.experiment.ResultMatrixPlainText
removes the stored data but retains the dimensions of the matrix
clear() - Method in class weka.experiment.ResultMatrixSignificance
removes the stored data but retains the dimensions of the matrix
clear() - Method in class weka.experiment.xml.XMLExperiment
generates internally a new XML document and clears also the IgnoreList and the mappings for the Read/Write-Methods
clear() - Method in class weka.gui.beans.xml.XMLBeans
generates internally a new XML document and clears also the IgnoreList and the mappings for the Read/Write-Methods
clear() - Method in class weka.gui.LogWindow
clears the output
clear() - Method in class weka.gui.sql.ConnectionPanel
sets the parameters back to standard.
clear() - Method in class weka.gui.sql.InfoPanel
clears the content of the panel
clear() - Method in class weka.gui.sql.QueryPanel
clears the textarea.
clear() - Method in class weka.gui.sql.ResultPanel
sets the parameters back to standard
clear() - Method in class weka.gui.sql.SqlViewer
calls the clear method of all sub-panels to set back to default values and free up memory.
clearBlacklist() - Static method in class weka.datagenerators.DataGenerator
removes all entries from the options blacklist
clearCache() - Static method in class weka.core.ClassDiscovery
clears the cache for class/classnames relation.
clearHeader() - Method in class weka.experiment.ResultMatrix
removes all the header information
clearLayout() - Method in class weka.gui.beans.KnowledgeFlowApp
 
clearRanking() - Method in class weka.experiment.ResultMatrix
clears the currently stored ranking data
clearRect(int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics
Draw a filled rectangle with the background color.
clearResults() - Method in class weka.gui.ResultHistoryPanel
Removes all of the result buffers from the history.
clearSearch() - Method in class weka.gui.arffviewer.ArffPanel
clears the search, i.e.
clearSearch() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
clears the search, i.e.
clearStatus() - Method in class weka.gui.beans.LogPanel
Clear the status area.
clearSummary() - Method in class weka.experiment.ResultMatrix
clears the current summary data
clearTemps_and_EdgesFromNodes() - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
This method removes the temporary nodes that were added to fill in the gaps, and removes all edges from all nodes in their edges[][] array
clearUndo() - Method in interface weka.core.Undoable
removes the undo history
clearUndo() - Method in class weka.gui.arffviewer.ArffPanel
removes the undo history
clearUndo() - Method in class weka.gui.arffviewer.ArffSortedTableModel
removes the undo history
clearUndo() - Method in class weka.gui.arffviewer.ArffTableModel
removes the undo history
clearUndoStack() - Method in class weka.classifiers.bayes.net.EditableBayesNet
remove all actions from the undo stack
clip(Shape) - Method in class weka.gui.visualize.PostscriptGraphics
 
clipRect(int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics
Not implemented
clipToInsideHrect(KDTreeNode, Instance) - Method in class weka.core.neighboursearch.KDTree
Finds the closest point in the hyper rectangle to a given point.
Clock() - Constructor for class weka.core.Debug.Clock
automatically starts the clock with FORMAT_SECONDS format and CPU time if available
Clock(int) - Constructor for class weka.core.Debug.Clock
automatically starts the clock with the given output format and CPU time if available
Clock(boolean) - Constructor for class weka.core.Debug.Clock
starts the clock depending on start immediately with the FORMAT_SECONDS output format and CPU time if available
Clock(boolean, int) - Constructor for class weka.core.Debug.Clock
starts the clock depending on start immediately, using CPU time if available
clone() - Method in class weka.associations.FPGrowth.FrequentBinaryItemSet
Make a copy of this item set.
clone() - Method in class weka.associations.gsp.Element
Returns a deep clone of an Element.
clone() - Method in class weka.associations.gsp.Sequence
Returns a deep clone of a Sequence.
clone() - Method in class weka.associations.tertius.LiteralSet
Returns a shallow copy of this set.
clone() - Method in class weka.associations.tertius.Rule
Returns a shallow copy of this rule.
clone() - Method in class weka.attributeSelection.GeneticSearch.GABitSet
makes a copy of this GABitSet
clone() - Method in class weka.attributeSelection.ScatterSearchV1.Subset
 
clone() - Method in class weka.classifiers.evaluation.ConfusionMatrix
Creates and returns a clone of this object.
clone() - Method in class weka.classifiers.functions.pace.DiscreteFunction
Clones the discrete function
clone() - Method in class weka.classifiers.functions.pace.PaceMatrix
Clone the PaceMatrix object.
clone() - Method in interface weka.classifiers.IterativeClassifier
Performs a deep copy of the classifier, and a reference copy of the training instances (or a deep copy if required).
clone() - Method in class weka.classifiers.trees.ADTree
Creates a clone that is identical to the current tree, but is independent.
clone() - Method in class weka.classifiers.trees.adtree.PredictionNode
Clones this node.
clone() - Method in class weka.classifiers.trees.adtree.Splitter
Clones this node.
clone() - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit
Clones this node.
clone() - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit
Clones this node.
clone() - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Allows to clone a model (shallow copy).
clone() - Method in class weka.classifiers.trees.j48.Distribution
Clones distribution (Deep copy of distribution).
clone() - Method in class weka.classifiers.trees.LADTree.PredictionNode
 
clone() - Method in class weka.classifiers.trees.LADTree.Splitter
 
clone() - Method in class weka.classifiers.trees.LADTree.TwoWayNominalSplit
 
clone() - Method in class weka.classifiers.trees.LADTree.TwoWayNumericSplit
 
clone() - Method in class weka.core.AlgVector
Creates and returns a clone of this object.
clone() - Method in class weka.core.Capabilities
Creates and returns a copy of this object.
clone() - Method in class weka.core.Matrix
Deprecated.
Creates and returns a clone of this object.
clone() - Method in class weka.core.matrix.DoubleVector
Clones the DoubleVector object.
clone() - Method in class weka.core.matrix.IntVector
Clones the IntVector object.
clone() - Method in class weka.core.matrix.Matrix
Clone the Matrix object.
clone() - Method in class weka.core.PropertyPath.PathElement
returns a clone of the current object
clone() - Method in class weka.core.TestInstances
creates a clone of the current object
clone() - Method in class weka.core.Trie
returns a deep copy of itself
clone() - Method in class weka.core.Trie.TrieNode
creates a deep copy of itself
clone(Estimator) - Static method in class weka.estimators.Estimator
Creates a deep copy of the given estimator using serialization.
CLOPE - Class in weka.clusterers
Yiling Yang, Xudong Guan, Jinyuan You: CLOPE: a fast and effective clustering algorithm for transactional data.
CLOPE() - Constructor for class weka.clusterers.CLOPE
the default constructor
close(ResultSet) - Method in class weka.experiment.DatabaseUtils
closes the ResultSet and the statement that generated the ResultSet to avoid memory leaks in JDBC drivers - in contrast to the JDBC specs, a lot of JDBC drives don't clean up correctly.
close() - Method in class weka.experiment.DatabaseUtils
closes the m_PreparedStatement to avoid memory leaks.
close() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
closes the window, i.e., if the parent is not null and implements the WindowListener interface it calls the windowClosing method
close() - Method in class weka.gui.LogWindow
closes the frame
close() - Method in class weka.gui.sql.ResultPanel
closes the current tab
closeAll() - Method in class weka.gui.sql.ResultPanel
closes all tabs
closeAllFiles() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
closes all open files
closeDialog() - Method in class weka.gui.FileEditor
Closes the dialog.
closeFile() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
closes the current tab
closeFile(boolean) - Method in class weka.gui.arffviewer.ArffViewerMainPanel
closes the current tab
closeFrame() - Method in class weka.gui.SetInstancesPanel
closes the frame, i.e., the visibility is set to false
closestPoint(Instance, Instances, int[]) - Method in class weka.core.EuclideanDistance
Returns the index of the closest point to the current instance.
closeToDefaultTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Returns the tip text for this property
closeToTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Returns the tip text for this property
closeToToleranceTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Returns the tip text for this property
ClusterDefinition - Class in weka.datagenerators
Ancestor to all ClusterDefinitions, i.e., subclasses that handle their own parameters that the cluster generator only passes on.
ClusterDefinition() - Constructor for class weka.datagenerators.ClusterDefinition
initializes the cluster, without a parent cluster (necessary for GOE)
ClusterDefinition(ClusterGenerator) - Constructor for class weka.datagenerators.ClusterDefinition
initializes the cluster
clusterDefinitionsTipText() - Method in class weka.datagenerators.clusterers.SubspaceCluster
Returns the tip text for this property
Clusterer - Interface in weka.clusterers
Interface for clusterers.
Clusterer - Class in weka.gui.beans
Bean that wraps around weka.clusterers
Clusterer() - Constructor for class weka.gui.beans.Clusterer
Creates a new Clusterer instance.
ClustererBeanInfo - Class in weka.gui.beans
BeanInfo class for the Clusterer wrapper bean
ClustererBeanInfo() - Constructor for class weka.gui.beans.ClustererBeanInfo
 
ClustererCustomizer - Class in weka.gui.beans
GUI customizer for the Clusterer wrapper bean
ClustererCustomizer() - Constructor for class weka.gui.beans.ClustererCustomizer
 
ClustererPanel - Class in weka.gui.explorer
This panel allows the user to select and configure a clusterer, and evaluate the clusterer using a number of testing modes (test on the training data, train/test on a percentage split, test on a separate split).
ClustererPanel() - Constructor for class weka.gui.explorer.ClustererPanel
Creates the clusterer panel
ClustererPerformanceEvaluator - Class in weka.gui.beans
A bean that evaluates the performance of batch trained clusterers
ClustererPerformanceEvaluator() - Constructor for class weka.gui.beans.ClustererPerformanceEvaluator
 
ClustererPerformanceEvaluatorBeanInfo - Class in weka.gui.beans
Bean info class for the clusterer performance evaluator
ClustererPerformanceEvaluatorBeanInfo() - Constructor for class weka.gui.beans.ClustererPerformanceEvaluatorBeanInfo
 
clustererTipText() - Method in class weka.classifiers.meta.ClassificationViaClustering
Returns the tip text for this property
clustererTipText() - Method in class weka.clusterers.MakeDensityBasedClusterer
Returns the tip text for this property
clustererTipText() - Method in class weka.clusterers.SingleClustererEnhancer
Returns the tip text for this property
clustererTipText() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
Returns the tip text for this property
clustererTipText() - Method in class weka.filters.unsupervised.attribute.AddCluster
Returns the tip text for this property
ClusterEvaluation - Class in weka.clusterers
Class for evaluating clustering models.

Valid options are:

-t name of the training file
Specify the training file.

ClusterEvaluation() - Constructor for class weka.clusterers.ClusterEvaluation
Constructor.
ClusterGenerator - Class in weka.datagenerators
Abstract class for cluster data generators.
ClusterGenerator() - Constructor for class weka.datagenerators.ClusterGenerator
initializes the generator
clusteringSeedTipText() - Method in class weka.classifiers.functions.RBFNetwork
Returns the tip text for this property
clusterInstance(Instance) - Method in class weka.clusterers.AbstractClusterer
Classifies a given instance.
clusterInstance(Instance) - Method in class weka.clusterers.CLOPE
Classifies a given instance.
clusterInstance(Instance) - Method in interface weka.clusterers.Clusterer
Classifies a given instance.
clusterInstance(Instance) - Method in class weka.clusterers.Cobweb
Classifies a given instance.
clusterInstance(Instance) - Method in class weka.clusterers.DBScan
Classifies a given instance.
clusterInstance(Instance) - Method in class weka.clusterers.FarthestFirst
Classifies a given instance.
clusterInstance(Instance) - Method in class weka.clusterers.HierarchicalClusterer
 
clusterInstance(Instance) - Method in class weka.clusterers.OPTICS
Classifies a given instance.
clusterInstance(Instance) - Method in class weka.clusterers.sIB
Cluster a given instance, this is the method defined in Clusterer interface do nothing but just return the cluster assigned to it
clusterInstance(Instance) - Method in class weka.clusterers.SimpleKMeans
Classifies a given instance.
clusterInstance(Instance) - Method in class weka.clusterers.XMeans
Classifies a given instance.
ClusterMembership - Class in weka.filters.unsupervised.attribute
A filter that uses a density-based clusterer to generate cluster membership values; filtered instances are composed of these values plus the class attribute (if set in the input data).
ClusterMembership() - Constructor for class weka.filters.unsupervised.attribute.ClusterMembership
 
clusterPriors() - Method in class weka.clusterers.AbstractDensityBasedClusterer
Returns the prior probability of each cluster.
clusterPriors() - Method in interface weka.clusterers.DensityBasedClusterer
Returns the prior probability of each cluster.
clusterPriors() - Method in class weka.clusterers.EM
Returns the cluster priors.
clusterPriors() - Method in class weka.clusterers.MakeDensityBasedClusterer
Returns the cluster priors.
clusterProcessedInstance(Instance) - Method in class weka.clusterers.FarthestFirst
clusters an instance that has been through the filters
clusterProcessedInstance(Instance, Instances) - Method in class weka.clusterers.XMeans
Clusters an instance.
clusterProcessedInstance(Instance) - Method in class weka.clusterers.XMeans
Clusters an instance that has been through the filters.
clusterResultsToString() - Method in class weka.clusterers.ClusterEvaluation
return the results of clustering.
clusters - Variable in class weka.clusterers.CLOPE
Array of clusters
clusterSubTypeTipText() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Returns the tip text for this property
clusterTypeTipText() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Returns the tip text for this property
Cobweb - Class in weka.clusterers
Class implementing the Cobweb and Classit clustering algorithms.

Note: the application of node operators (merging, splitting etc.) in terms of ordering and priority differs (and is somewhat ambiguous) between the original Cobweb and Classit papers.
Cobweb() - Constructor for class weka.clusterers.Cobweb
default constructor
cochransCriterion(double[][]) - Static method in class weka.core.ContingencyTables
Tests if Cochran's criterion is fullfilled for the given contingency table.
codingCost() - Method in class weka.classifiers.trees.j48.C45Split
Returns coding cost for split (used in rule learner).
codingCost() - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Returns coding costs of model.
coef0TipText() - Method in class weka.classifiers.functions.LibSVM
Returns the tip text for this property
coefficients() - Method in class weka.classifiers.functions.LinearRegression
Returns the coefficients for this linear model.
coefficients() - Method in class weka.classifiers.functions.Logistic
Returns the coefficients for this logistic model.
coefficients() - Method in class weka.classifiers.functions.PaceRegression
Returns the coefficients for this linear model.
coefficients() - Method in class weka.classifiers.trees.m5.PreConstructedLinearModel
Return the array of coefficients
collapse() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTree
Collapses a tree to a node if training error doesn't increase.
collapse() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG
Collapses a tree to a node if training error doesn't increase.
COLOR_STDERR - Static variable in class weka.gui.LogWindow
the Color of the style for stderr
COLOR_STDOUT - Static variable in class weka.gui.LogWindow
the color of the style for stdout
Colors - Class in weka.gui.treevisualizer
This class maintains a list that contains all the colornames from the dotty standard and what color (in RGB) they represent
Colors() - Constructor for class weka.gui.treevisualizer.Colors
 
colorToString(Color) - Method in class weka.gui.LogWindow
returns a string representation (#RGB) of the given color
column(int) - Method in class weka.classifiers.meta.GridSearch.Grid
returns an Enumeration over all pairs in the given column
columnAsVector(Matrix, int) - Method in class weka.filters.supervised.attribute.PLSFilter
returns the given column as a vector (actually a n x 1 matrix)
columnResponseExplanation(PaceMatrix, IntVector, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Returns the squared ks-th response value if the j-th column becomes the ks-th after orthogonal transformation.
COMBINATION_NOT_REDUCED - Static variable in class weka.attributeSelection.ScatterSearchV1
kind of combination
COMBINATION_REDUCED - Static variable in class weka.attributeSelection.ScatterSearchV1
 
combinationRuleTipText() - Method in class weka.classifiers.meta.Vote
Returns the tip text for this property
combinations(int, int) - Static method in class weka.classifiers.functions.LeastMedSq
Produces the combination nCr
combinationTipText() - Method in class weka.attributeSelection.ScatterSearchV1
Returns the tip text for this property
combinedDL(double, double) - Method in class weka.classifiers.rules.RuleStats
Compute the combined DL of the ruleset in this class, i.e.
CombineParents() - Method in class weka.attributeSelection.ScatterSearchV1
Combine all the posible pair solutions existing in the Population
COMBO_SIZE - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
combSort11(double[], int[]) - Static method in class weka.core.neighboursearch.NearestNeighbourSearch
sorts the two given arrays.
COMMA - Static variable in interface weka.core.mathematicalexpression.sym
 
COMMA - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
CommandlineCompletion() - Constructor for class weka.gui.SimpleCLIPanel.CommandlineCompletion
default constructor.
Committee(int) - Constructor for class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
constructor
committeeSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
returns the number of models in the committee
compactify() - Method in class weka.core.converters.ArffLoader.ArffReader
compactifies the data
compactify() - Method in class weka.core.Instances
Compactifies the set of instances.
compare(GridSearch.Performance, GridSearch.Performance) - Method in class weka.classifiers.meta.GridSearch.PerformanceComparator
Compares its two arguments for order.
compare(Object, Object) - Method in class weka.core.ClassDiscovery.StringCompare
Compares its two arguments for order.
compare(Object, Object) - Method in class weka.core.InstanceComparator
compares the two instances, returns -1 if o1 is smaller than o2, 0 if equal and +1 if greater.
compare(int, int, Stack<CoverTree.d_node>) - Method in class weka.core.neighboursearch.CoverTree
Returns the difference of two given nodes distance to the query.
compare(Instance, Instance) - Method in class weka.filters.CheckSource
compares two Instance
compare(Instances, Instances) - Method in class weka.filters.CheckSource
compares the two Instances objects
compareDatasets(Instances, Instances) - Method in class weka.core.CheckScheme
Compare two datasets to see if they differ.
compareDatasets(Instances, Instances) - Method in class weka.estimators.CheckEstimator
Compare two datasets to see if they differ.
compareOptions(String[], String[]) - Method in class weka.core.CheckOptionHandler
Compares the two given sets of options.
compareTo(FPGrowth.AssociationRule) - Method in class weka.associations.FPGrowth.AssociationRule
Compare this rule to the supplied rule.
compareTo(FPGrowth.BinaryItem) - Method in class weka.associations.FPGrowth.BinaryItem
Ensures that items will be sorted in descending order of frequency.
compareTo(Object) - Method in class weka.associations.RuleItem
compares two RuleItems and allows an ordering concerning expected predictive accuracy and time of generation Note: this class has a natural ordering that is inconsistent with equals
compareTo(Object) - Method in class weka.classifiers.trees.j48.GraftSplit
method needed for sorting a collection of GraftSplits by laplace value
compareTo(AttributeLocator) - Method in class weka.core.AttributeLocator
Compares this object with the specified object for order.
compareTo(Object) - Method in class weka.core.Version
checks the version of this class against the given version-string
compareTo(SortedTableModel.SortContainer) - Method in class weka.gui.SortedTableModel.SortContainer
Compares this object with the specified object for order.
comparisonString(int, Instances) - Method in class weka.classifiers.trees.adtree.Splitter
Gets the string describing the comparision the split depends on for a particular branch.
comparisonString(int, Instances) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit
Gets the string describing the comparision the split depends on for a particular branch.
comparisonString(int, Instances) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit
Gets the string describing the comparision the split depends on for a particular branch.
comparisonString(int) - Method in class weka.classifiers.trees.LADTree.Splitter
 
comparisonString(int) - Method in class weka.classifiers.trees.LADTree.TwoWayNominalSplit
 
comparisonString(int) - Method in class weka.classifiers.trees.LADTree.TwoWayNumericSplit
 
ComplementNaiveBayes - Class in weka.classifiers.bayes
Class for building and using a Complement class Naive Bayes classifier.

For more information see,

Jason D.
ComplementNaiveBayes() - Constructor for class weka.classifiers.bayes.ComplementNaiveBayes
 
complexityParameterTipText() - Method in class weka.attributeSelection.SVMAttributeEval
Returns a tip text for this property suitable for display in the GUI
ComponentHelper - Class in weka.gui
A helper class for some common tasks with Dialogs, Icons, etc.
ComponentHelper() - Constructor for class weka.gui.ComponentHelper
 
componentHidden(ComponentEvent) - Method in class weka.gui.hierarchyvisualizer.HierarchyVisualizer
 
componentMoved(ComponentEvent) - Method in class weka.gui.hierarchyvisualizer.HierarchyVisualizer
 
componentResized(ComponentEvent) - Method in class weka.gui.hierarchyvisualizer.HierarchyVisualizer
 
componentShown(ComponentEvent) - Method in class weka.gui.hierarchyvisualizer.HierarchyVisualizer
 
compressOutputTipText() - Method in class weka.core.converters.ArffSaver
Returns the tip text for this property
compressOutputTipText() - Method in class weka.core.converters.XRFFSaver
Returns the tip text for this property
Compute - Interface in weka.experiment
Interface to something that can accept remote connections and execute a task.
computeEntropy(double[], double) - Method in class weka.classifiers.trees.BFTree
Compute and return entropy for a given distribution of a node.
computeError(Instances) - Method in class weka.classifiers.meta.Decorate
Computes the error in classification on the given data.
computeFactor(double, double) - Method in class weka.classifiers.functions.supportVector.Puk
computes the factor for curve-fitting (see equation (13) in paper)
computeGini(double[], double) - Method in class weka.classifiers.trees.BFTree
Compute and return gini index for a given distribution of a node.
computeGini(double[], double) - Method in class weka.classifiers.trees.SimpleCart
Compute and return gini index for a given distribution of a node.
computeGiniGain(double[], double[][]) - Method in class weka.classifiers.trees.BFTree
Compute and return gini gain for given distributions of a node and its successor nodes.
computeGiniGain(double[], double[][]) - Method in class weka.classifiers.trees.SimpleCart
Compute and return gini gain for given distributions of a node and its successor nodes.
computeInfoGain(double[], double[][]) - Method in class weka.classifiers.trees.BFTree
Compute and return information gain for given distributions of a node and its successor nodes.
computeLoglikelihood(double[], Instances) - Method in class weka.classifiers.bayes.blr.GaussianPriorImpl
This method calls the log-likelihood implemented in the Prior abstract class.
computeLogLikelihood(double[], Instances) - Method in class weka.classifiers.bayes.blr.LaplacePriorImpl
Computes the log-likelihood values using the implementation in the Prior class.
computelogLikelihood(double[], Instances) - Method in class weka.classifiers.bayes.blr.Prior
Function computes the log-likelihood value: -sum{1 to n}{ln(1+exp(-Beta*x(i)*y(i))}
computeMinMaxAtts() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Set up the bounds of our graphic based by finding the smallest reasonable area in the instance space to surround our data points.
computePenalty(double[], double[]) - Method in class weka.classifiers.bayes.blr.GaussianPriorImpl
This function computes the penalty term specific to Gaussian distribution.
computePenalty(double[], double[]) - Method in class weka.classifiers.bayes.blr.LaplacePriorImpl
This function computes the penalty term specific to Laplacian distribution.
computePenalty(double[], double[]) - Method in class weka.classifiers.bayes.blr.Prior
Skeleton function to compute penalty terms.
computeRandomProjection(int, int, Instance) - Method in class weka.filters.unsupervised.attribute.RandomProjection
computes one random projection for a given instance (skip missing values)
computeSortedInfo(Instances, int[][], double[][], double[]) - Method in class weka.classifiers.trees.BFTree
Compute sorted indices, weights and class probabilities for a given dataset.
computeSortedInfo(Instances, int[][], double[][], double[]) - Method in class weka.classifiers.trees.SimpleCart
Compute sorted indices, weights and class probabilities for a given dataset.
computeSplitInfo(BFTree, Instances, int[][], double[][], double[][][], double[][], double[][], boolean, boolean) - Method in class weka.classifiers.trees.BFTree
Compute the best splitting attribute, split point or subset and the best gini gain or iformation gain for a given dataset.
computeStats(Instances) - Method in class weka.classifiers.meta.Decorate
Compute and store statistics required for generating artificial data.
computeThresholds(Instances) - Method in class weka.filters.unsupervised.attribute.InterquartileRange
computes the thresholds for outliers and extreme values
cond() - Method in class weka.core.matrix.Matrix
Matrix condition (2 norm)
cond() - Method in class weka.core.matrix.SingularValueDecomposition
Two norm condition number
ConditionalEstimator - Interface in weka.estimators
Interface for conditional probability estimators.
CONFIDENCE - Static variable in class weka.associations.Apriori
Metric type: Confidence
confidenceFactorTipText() - Method in class weka.classifiers.rules.PART
Returns the tip text for this property
confidenceFactorTipText() - Method in class weka.classifiers.trees.J48
Returns the tip text for this property
confidenceFactorTipText() - Method in class weka.classifiers.trees.J48graft
Returns the tip text for this property
confidenceForRule(AprioriItemSet, AprioriItemSet) - Static method in class weka.associations.AprioriItemSet
Outputs the confidence for a rule.
CONFIG - Static variable in class weka.core.Debug
the log level Vonfig
configureCurrentConverter(int) - Method in class weka.gui.ConverterFileChooser
configures the current converter
confirmationComparator - Static variable in class weka.associations.tertius.Rule
Comparator used to compare two rules according to their confirmation value.
confirmationThenObservedComparator - Static variable in class weka.associations.tertius.Rule
Comparator used to compare two rules according to their confirmation and then their observed number of counter-instances.
confirmationThresholdTipText() - Method in class weka.associations.Tertius
Returns the tip text for this property.
confirmationValuesTipText() - Method in class weka.associations.Tertius
Returns the tip text for this property.
confirmExit - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
ConfusionCell() - Constructor for class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell
 
ConfusionMatrix - Class in weka.classifiers.evaluation
Cells of this matrix correspond to counts of the number (or weight) of predictions for each actual value / predicted value combination.
confusionMatrix() - Method in class weka.classifiers.Evaluation
Returns a copy of the confusion matrix.
ConfusionMatrix(String[]) - Constructor for class weka.classifiers.evaluation.ConfusionMatrix
Creates the confusion matrix with the given class names.
ConjunctiveRule - Class in weka.classifiers.rules
This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels.

A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression.
ConjunctiveRule() - Constructor for class weka.classifiers.rules.ConjunctiveRule
 
connect(NeuralConnection, NeuralConnection) - Static method in class weka.classifiers.functions.neural.NeuralConnection
Connects two units together.
connect() - Method in class weka.gui.sql.ConnectionPanel
connects to the database, notifies the listeners.
CONNECT - Static variable in class weka.gui.sql.event.ConnectionEvent
it was a connect try
CONNECTED - Static variable in class weka.classifiers.functions.neural.NeuralConnection
This flag is set once the unit has a connection.
CONNECTING - Static variable in class weka.gui.beans.KnowledgeFlowApp
 
connectInput(NeuralConnection, int) - Method in class weka.classifiers.functions.neural.NeuralConnection
This will connect the specified unit to be an input to this unit.
connectInput(NeuralConnection, int) - Method in class weka.classifiers.functions.neural.NeuralNode
This will connect the specified unit to be an input to this unit.
CONNECTION_FAILED - Static variable in class weka.experiment.RemoteExperiment
status of the remote host: connection failed
CONNECTION_FAILED - Static variable in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
 
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.AbstractDataSink
Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.AbstractDataSink
Returns true if, at this time, the object will accept a connection according to the supplied event name
connectionAllowed(String) - Method in class weka.gui.beans.AbstractEvaluator
Returns true if, at this time, the object will accept a connection according to the supplied event name
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.AbstractEvaluator
Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.AbstractTestSetProducer
Returns true if, at this time, the object will accept a connection according to the supplied event name
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.AbstractTestSetProducer
Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
Returns true if, at this time, the object will accept a connection according to the supplied event name
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.AbstractTrainingSetProducer
Returns true if, at this time, the object will accept a connection according to the supplied event name
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.AbstractTrainingSetProducer
Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.Associator
Returns true if, at this time, the object will accept a connection with respect to the named event
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.Associator
Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(EventSetDescriptor) - Method in interface weka.gui.beans.BeanCommon
Returns true if, at this time, the object will accept a connection via the supplied EventSetDescriptor
connectionAllowed(String) - Method in interface weka.gui.beans.BeanCommon
Returns true if, at this time, the object will accept a connection via the named event
connectionAllowed(String) - Method in class weka.gui.beans.ClassAssigner
Returns true if, at this time, the object will accept a connection according to the supplied event name
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.ClassAssigner
Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.Classifier
Returns true if, at this time, the object will accept a connection with respect to the named event
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.Classifier
Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.ClassValuePicker
Returns true if, at this time, the object will accept a connection according to the supplied event name
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.ClassValuePicker
Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.Clusterer
Returns true if, at this time, the object will accept a connection with respect to the named event
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.Clusterer
Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.CostBenefitAnalysis
Returns true if, at this time, the object will accept a connection via the named event
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.CostBenefitAnalysis
Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.Filter
Returns true if, at this time, the object will accept a connection with respect to the supplied event name
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.Filter
Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.InstanceStreamToBatchMaker
Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.InstanceStreamToBatchMaker
Returns true if, at this time, the object will accept a connection with respect to the named event
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.Loader
Returns true if, at this time, the object will accept a connection via the supplied EventSetDescriptor.
connectionAllowed(String) - Method in class weka.gui.beans.Loader
Returns true if, at this time, the object will accept a connection via the named event
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.MetaBean
Returns true if, at this time, the object will accept a connection with respect to the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.MetaBean
 
connectionAllowed(String) - Method in class weka.gui.beans.PredictionAppender
Returns true if, at this time, the object will accept a connection according to the supplied event name
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.PredictionAppender
Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.SerializedModelSaver
Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor.
connectionAllowed(String) - Method in class weka.gui.beans.SerializedModelSaver
Returns true if, at this time, the object will accept a connection according to the supplied event name.
connectionAllowed(String) - Method in class weka.gui.beans.StripChart
Returns true if, at this time, the object will accept a connection via the named event
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.StripChart
Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.TextViewer
Returns true if, at this time, the object will accept a connection via the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.TextViewer
Returns true if, at this time, the object will accept a connection via the named event
connectionChange(ConnectionEvent) - Method in interface weka.gui.sql.event.ConnectionListener
This method gets called when the connection is either established or disconnected.
connectionChange(ConnectionEvent) - Method in class weka.gui.sql.QueryPanel
This method gets called when the connection is either established or disconnected.
connectionChange(ConnectionEvent) - Method in class weka.gui.sql.SqlViewer
This method gets called when the connection is either established or disconnected.
ConnectionEvent - Class in weka.gui.sql.event
An event that is generated when a connection is established or dropped.
ConnectionEvent(Object, int, DbUtils) - Constructor for class weka.gui.sql.event.ConnectionEvent
constructs the event
ConnectionEvent(Object, int, DbUtils, Exception) - Constructor for class weka.gui.sql.event.ConnectionEvent
constructs the event
ConnectionListener - Interface in weka.gui.sql.event
A listener for connect/disconnect events.
connectionNotification(String, Object) - Method in class weka.gui.beans.AbstractDataSink
Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.AbstractEvaluator
Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.AbstractTestSetProducer
Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.AbstractTrainingSetProducer
Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.Associator
Notify this object that it has been registered as a listener with a source with respect to the named event
connectionNotification(String, Object) - Method in interface weka.gui.beans.BeanCommon
Notify this object that it has been registered as a listener with a source for recieving events described by the named event This object is responsible for recording this fact.
connectionNotification(String, Object) - Method in class weka.gui.beans.ClassAssigner
Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.Classifier
Notify this object that it has been registered as a listener with a source with respect to the named event
connectionNotification(String, Object) - Method in class weka.gui.beans.ClassValuePicker
Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.Clusterer
Notify this object that it has been registered as a listener with a source with respect to the named event
connectionNotification(String, Object) - Method in interface weka.gui.beans.ConnectionNotificationConsumer
Notify this object that it has been registered as a listener with a source with respect to the supplied event name.
connectionNotification(String, Object) - Method in class weka.gui.beans.CostBenefitAnalysis
Notify this object that it has been registered as a listener with a source for recieving events described by the named event This object is responsible for recording this fact.
connectionNotification(String, Object) - Method in class weka.gui.beans.Filter
Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.InstanceStreamToBatchMaker
Notify this object that it has been registered as a listener with a source with respect to the named event
connectionNotification(String, Object) - Method in class weka.gui.beans.Loader
Notify this object that it has been registered as a listener with a source for receiving events described by the named event This object is responsible for recording this fact.
connectionNotification(String, Object) - Method in class weka.gui.beans.MetaBean
Notify this object that it has been registered as a listener with a source with respect to the named event.
connectionNotification(String, Object) - Method in class weka.gui.beans.PredictionAppender
Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.SerializedModelSaver
Notify this object that it has been registered as a listener with a source with respect to the supplied event name.
connectionNotification(String, Object) - Method in class weka.gui.beans.StripChart
Notify this object that it has been registered as a listener with a source for recieving events described by the named event This object is responsible for recording this fact.
connectionNotification(String, Object) - Method in class weka.gui.beans.TextViewer
Notify this object that it has been registered as a listener with a source for recieving events described by the named event This object is responsible for recording this fact.
ConnectionNotificationConsumer - Interface in weka.gui.beans
Interface for Beans that can receive (dis-)connection events generated when (dis-)connecting data processing nodes in the Weka KnowledgeFlow.
ConnectionPanel - Class in weka.gui.sql
Enables the user to insert a database URL, plus user/password to connect to this database.
ConnectionPanel(JFrame) - Constructor for class weka.gui.sql.ConnectionPanel
initializes the panel.
CONNECTIONS - Static variable in class weka.gui.beans.BeanConnection
The list of connections
connectOutput(NeuralConnection, int) - Method in class weka.classifiers.functions.neural.NeuralConnection
This will connect the specified unit to be an output to this unit.
connectToDatabase() - Method in class weka.core.converters.DatabaseLoader
Opens a connection to the database
connectToDatabase() - Method in class weka.core.converters.DatabaseSaver
Opens a connection to the database.
connectToDatabase() - Method in class weka.experiment.DatabaseUtils
Opens a connection to the database.
consequence() - Method in class weka.associations.RuleItem
Gets the consequence of a rule
conservativeForwardSelectionTipText() - Method in class weka.attributeSelection.GreedyStepwise
Returns the tip text for this property
ConsistencySubsetEval - Class in weka.attributeSelection
ConsistencySubsetEval :

Evaluates the worth of a subset of attributes by the level of consistency in the class values when the training instances are projected onto the subset of attributes.
ConsistencySubsetEval() - Constructor for class weka.attributeSelection.ConsistencySubsetEval
Constructor.
ConsistencySubsetEval.hashKey - Class in weka.attributeSelection
Class providing keys to the hash table.
ConsoleLogger - Class in weka.core.logging
A simple logger that outputs the logging information in the console.
ConsoleLogger() - Constructor for class weka.core.logging.ConsoleLogger
 
CONST_AUTOMATIC_SHAPE - Static variable in class weka.gui.visualize.Plot2D
 
CONSTANT - Static variable in class weka.classifiers.lazy.LWL
 
Constant - Class in weka.core.pmml
Class encapsulating a Constant Expression.
Constant(Element, FieldMetaInfo.Optype, ArrayList<Attribute>) - Constructor for class weka.core.pmml.Constant
Construct an new Constant Expression.
constructWithCopy(double[][]) - Static method in class weka.core.matrix.Matrix
Construct a matrix from a copy of a 2-D array.
containChildBallsTipText() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
Returns the tip text for this property.
containedBy(Instance) - Method in class weka.associations.ItemSet
Checks if an instance contains an item set.
contains(Literal) - Method in class weka.associations.tertius.LiteralSet
Test if this LiteralSet contains a given Literal.
contains(int) - Method in class weka.classifiers.bayes.net.ParentSet
test if node is contained in parent set
contains(int) - Method in class weka.classifiers.functions.supportVector.SMOset
Checks whether an element is in the set.
contains(int) - Method in class weka.classifiers.meta.nestedDichotomies.ND.NDTree
Checks whether an index is in the array.
contains(DataObject) - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database
Tests if the database contains the dataObject_Query
contains(DataObject) - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase
Tests if the database contains the dataObject_Query
contains(Object) - Method in class weka.core.FastVector
added by akibriya
contains(PrintStream) - Method in class weka.core.Tee
checks whether the given PrintStream is already in the list.
contains(Object) - Method in class weka.core.Trie
Returns true if this collection contains the specified element.
contains(String) - Method in class weka.core.Trie.TrieNode
checks whether a suffix can be found in its children
contains(String) - Method in class weka.core.xml.MethodHandler
checks whether a method is stored for the given property
contains(Class) - Method in class weka.core.xml.MethodHandler
checks whether a method is stored for the given class
contains(Object) - Method in class weka.gui.CheckBoxList.CheckBoxListModel
Tests whether the specified object is a component in this list.
contains(String) - Method in class weka.gui.HierarchyPropertyParser
Whether the HierarchyPropertyParser contains the given string
containsAll(Collection<?>) - Method in class weka.core.Trie
Returns true if this collection contains all of the elements in the specified collection.
containsEnvVariables(String) - Static method in class weka.core.Environment
Tests for the presence of environment variables.
containsItems(ArrayList<Attribute>, boolean) - Method in class weka.associations.FPGrowth.AssociationRule
 
containsKey(double) - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable
Tests if the specified double is a key in this hashtable.
containsKey(double) - Method in class weka.classifiers.lazy.kstar.KStarCache
Checks if the specified key maps with an entry in the cache table
containsOverOneEvent() - Method in class weka.associations.gsp.Element
Checks if an Element contains over one event.
containsPrefix(String) - Method in class weka.core.Trie
checks whether the given prefix is stored in the trie
containsValue(double) - Method in class weka.core.pmml.Discretize.DiscretizeBin
Returns true if there is an interval that contains the incoming value.
containsValue(double) - Method in class weka.core.pmml.FieldMetaInfo.Interval
Returns true if this interval contains the supplied value.
containsWindow(Class) - Method in class weka.gui.Main
checks, whether an instance of the given window class is already in the Window list.
containsWindow(String) - Method in class weka.gui.Main
checks, whether a window with the given title is already in the Window list.
contents(Object) - Method in class weka.core.Queue.QueueNode
Sets the contents of the node.
contents() - Method in class weka.core.Queue.QueueNode
Returns the contents in the node.
contents() - Method in class weka.experiment.PairedTTester.Dataset
Returns a vector containing the instances in the dataset
context() - Method in class weka.gui.HierarchyPropertyParser
The context of the current node, i.e.
ContingencyTables - Class in weka.core
Class implementing some statistical routines for contingency tables.
ContingencyTables() - Constructor for class weka.core.ContingencyTables
 
CONTINUOUS - Static variable in class weka.datagenerators.clusterers.SubspaceCluster
cluster subtype: continuous
ConverterFileChooser - Class in weka.gui
A specialized JFileChooser that lists all available file Loaders and Savers.
ConverterFileChooser() - Constructor for class weka.gui.ConverterFileChooser
onstructs a FileChooser pointing to the user's default directory.
ConverterFileChooser(File) - Constructor for class weka.gui.ConverterFileChooser
Constructs a FileChooser using the given File as the path.
ConverterFileChooser(String) - Constructor for class weka.gui.ConverterFileChooser
Constructs a FileChooser using the given path.
ConverterUtils - Class in weka.core.converters
Utility routines for the converter package.
ConverterUtils() - Constructor for class weka.core.converters.ConverterUtils
 
ConverterUtils.DataSink - Class in weka.core.converters
Helper class for saving data to files.
ConverterUtils.DataSource - Class in weka.core.converters
Helper class for loading data from files and URLs.
convertInfixToPostfix(String) - Method in class weka.core.AttributeExpression
Converts a string containing a mathematical expression in infix form to postfix form.
convertInstance(Instance) - Method in interface weka.attributeSelection.AttributeTransformer
Transforms an instance in the format of the original data to the transformed space
convertInstance(Instance) - Method in class weka.attributeSelection.LatentSemanticAnalysis
Transform an instance in original (unnormalized) format
convertInstance(Instance) - Method in class weka.attributeSelection.PrincipalComponents
Transform an instance in original (unormalized) format.
convertInstance(Instance, int) - Method in class weka.classifiers.meta.RotationForest
Transforms an instance for the i-th classifier.
convertInstance(Instance) - Method in class weka.filters.supervised.attribute.AttributeSelection
Convert a single instance over.
convertInstance(Instance) - Method in class weka.filters.supervised.attribute.Discretize
Convert a single instance over.
convertInstance(Instance) - Method in class weka.filters.unsupervised.attribute.AddCluster
Convert a single instance over.
convertInstance(Instance) - Method in class weka.filters.unsupervised.attribute.AddID
Convert a single instance over.
convertInstance(Instance) - Method in class weka.filters.unsupervised.attribute.ClusterMembership
Convert a single instance over.
convertInstance(Instance) - Method in class weka.filters.unsupervised.attribute.Discretize
Convert a single instance over.
convertInstance(Instance) - Method in class weka.filters.unsupervised.attribute.Normalize
Convert a single instance over.
convertInstance(Instance) - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
Transform an instance in original (unormalized) format.
convertInstance(Instance) - Method in class weka.filters.unsupervised.attribute.RandomProjection
converts a single instance to the required format
convertNewLines(String) - Static method in class weka.core.Utils
Converts carriage returns and new lines in a string into \r and \n.
convertNominalTipText() - Method in class weka.classifiers.trees.LMT
Returns the tip text for this property
convertNominalToBinaryTipText() - Method in class weka.classifiers.functions.LibLINEAR
Returns the tip text for this property
convertNumericAttToNominal(int, ArrayList<String>) - Method in class weka.core.pmml.MiningSchema
Convert a numeric attribute in the mining schema to nominal.
convertStringAttsToNominal() - Method in class weka.core.pmml.MiningSchema
Method to convert any string attributes in the mining schema Instances to nominal attributes.
convertToAttribX(double) - Method in class weka.gui.visualize.Plot2D
convert a Panel x coordinate to a raw x value.
convertToAttribY(double) - Method in class weka.gui.visualize.Plot2D
convert a Panel y coordinate to a raw y value.
convertToPanelX(double) - Method in class weka.gui.visualize.Plot2D
Convert an raw x value to Panel x coordinate.
convertToPanelY(double) - Method in class weka.gui.visualize.Plot2D
Convert an raw y value to Panel y coordinate.
convertToRelativePath(File) - Static method in class weka.core.Utils
Converts a File's absolute path to a path relative to the user (ie start) directory.
CONVICTION - Static variable in class weka.associations.Apriori
Metric type: Conviction
convictionForRule(AprioriItemSet, AprioriItemSet, int, int) - Method in class weka.associations.AprioriItemSet
Outputs the conviction for a rule.
copy() - Method in class weka.associations.tertius.IndividualInstance
 
copy(ParentSet) - Method in class weka.classifiers.bayes.net.ParentSet
Copy makes current parents set equal to other parent set
copy() - Method in class weka.classifiers.rules.JRip.Antd
Implements Copyable
copy() - Method in class weka.classifiers.rules.JRip.NominalAntd
Implements Copyable
copy() - Method in class weka.classifiers.rules.JRip.NumericAntd
Implements Copyable
copy() - Method in class weka.classifiers.rules.JRip.RipperRule
Get a shallow copy of this rule
copy() - Method in class weka.classifiers.rules.Rule
Get a shallow copy of this rule
copy() - Method in class weka.classifiers.trees.LADTree.LADInstance
 
copy() - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo
Makes a copy of this CorrelationSplitInfo object
copy() - Method in interface weka.classifiers.trees.m5.SplitEvaluate
makes a copy of the SplitEvaluate object
copy() - Method in class weka.classifiers.trees.m5.YongSplitInfo
Makes a copy of this SplitInfo object
copy() - Method in class weka.core.Attribute
Produces a shallow copy of this attribute.
copy(String) - Method in class weka.core.Attribute
Produces a shallow copy of this attribute with a new name.
copy() - Method in class weka.core.BinarySparseInstance
Produces a shallow copy of this instance.
copy() - Method in interface weka.core.Copyable
This method produces a shallow copy of an object.
copy() - Method in class weka.core.FastVector
Produces a shallow copy of this vector.
copy() - Method in class weka.core.Instance
Produces a shallow copy of this instance.
copy() - Method in class weka.core.matrix.DoubleVector
Makes a deep copy of the vector
copy() - Method in class weka.core.matrix.IntVector
Makes a deep copy of the vector
copy() - Method in class weka.core.matrix.Matrix
Make a deep copy of a matrix
copy() - Method in class weka.core.SparseInstance
Produces a shallow copy of this instance.
Copy - Class in weka.filters.unsupervised.attribute
An instance filter that copies a range of attributes in the dataset.
Copy() - Constructor for class weka.filters.unsupervised.attribute.Copy
 
copy2DArray(int[][], int[][]) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
Copies one array of type int[][] to another.
copy_cover_sets(CoverTree.CoverTreeNode, CoverTree.MyHeap, Stack<Stack<CoverTree.d_node>>, Stack<Stack<CoverTree.d_node>>, int, int) - Method in class weka.core.neighboursearch.CoverTree
Copies the contents of one set of cover sets to the other.
copy_zero_set(CoverTree.CoverTreeNode, CoverTree.MyHeap, Stack<CoverTree.d_node>, Stack<CoverTree.d_node>) - Method in class weka.core.neighboursearch.CoverTree
Copies the contents of one zero set to the other.
Copyable - Interface in weka.core
Interface implemented by classes that can produce "shallow" copies of their objects.
copyArea(int, int, int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics
Not implemented
copyContent() - Method in class weka.gui.arffviewer.ArffPanel
copies the content of the selection to the clipboard
copyContent() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
copies the content of the selection to the clipboard
copyElements() - Method in class weka.core.FastVector
Clones the vector and shallow copies all its elements.
copyInstances(int, Instances, int) - Method in class weka.core.Instances
Copies instances from one set to the end of another one.
copyInto(Object[]) - Method in class weka.gui.CheckBoxList.CheckBoxListModel
Copies the components of this list into the specified array.
copyMatrix(int[][], int[][]) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
Copies one Matrix of type int[][] to another.
copyObject(Object) - Method in class weka.gui.experiment.AlgorithmListPanel
Makes a copy of an object using serialization
copyObject(Object) - Method in class weka.gui.GenericObjectEditor.GOEPanel
Makes a copy of an object using serialization.
copyQuery() - Method in class weka.gui.sql.ResultPanel
copies the query of the current tab into the QueryPanel
copyRelationalValues(Instance, Instances, AttributeLocator) - Static method in class weka.core.RelationalLocator
Copies relational values contained in the instance copied to a new dataset.
copyRelationalValues(Instance, boolean, Instances, AttributeLocator, Instances, AttributeLocator) - Static method in class weka.core.RelationalLocator
Takes relational values referenced by an Instance and copies them from a source dataset to a destination dataset.
Copyright - Class in weka.core
A class for providing centralized Copyright information.
Copyright() - Constructor for class weka.core.Copyright
 
copyStringValues(Instance, Instances, AttributeLocator) - Static method in class weka.core.StringLocator
Copies string values contained in the instance copied to a new dataset.
copyStringValues(Instance, boolean, Instances, AttributeLocator, Instances, AttributeLocator) - Static method in class weka.core.StringLocator
Takes string values referenced by an Instance and copies them from a source dataset to a destination dataset.
copyToClipboard() - Method in class weka.gui.sql.InfoPanel
copies the currently selected error message to the clipboard
copyValues(Instance, boolean) - Method in class weka.filters.Filter
Copies string/relational values contained in the instance copied to a new dataset.
copyValues(Instance, boolean, Instances, Instances) - Method in class weka.filters.Filter
Takes string/relational values referenced by an Instance and copies them from a source dataset to a destination dataset.
CORE_FILE_LOADERS - Static variable in class weka.core.converters.ConverterUtils
the core loaders - hardcoded list necessary for RMI/Remote Experiments (comma-separated list).
CORE_FILE_SAVERS - Static variable in class weka.core.converters.ConverterUtils
the core savers - hardcoded list necessary for RMI/Remote Experiments (comma-separated list).
coreDistance(int, double, DataObject) - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database
Calculates the coreDistance for the specified DataObject.
coreDistance(int, double, DataObject) - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase
Calculates the coreDistance for the specified DataObject.
correct() - Method in class weka.classifiers.evaluation.ConfusionMatrix
Gets the number of correct classifications (that is, for which a correct prediction was made).
correct() - Method in class weka.classifiers.Evaluation
Gets the number of instances correctly classified (that is, for which a correct prediction was made).
correctBuildInitialisation(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.associations.CheckAssociator
Checks whether the scheme correctly initialises models when buildAssociations is called.
correctBuildInitialisation(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.classifiers.CheckClassifier
Checks whether the scheme correctly initialises models when buildClassifier is called.
correctBuildInitialisation(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.classifiers.functions.supportVector.CheckKernel
Checks whether the scheme correctly initialises models when buildKernel is called.
correctBuildInitialisation(boolean, boolean, boolean, boolean, boolean, boolean) - Method in class weka.clusterers.CheckClusterer
Checks whether the scheme correctly initialises models when buildClusterer is called.
correctBuildInitialisation(CheckEstimator.AttrTypes, int) - Method in class weka.estimators.CheckEstimator
Checks whether the scheme correctly initialises models when buildEstimator is called.
correctlyInitialized() - Method in class weka.core.neighboursearch.balltrees.BallSplitter
Checks whether if this ball splitter is correctly intialized or not (i.e.
correctlyInitialized() - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
Checks whether an object of this class has been correctly initialized.
correctSearchInitialisation(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.attributeSelection.CheckAttributeSelection
Checks whether the scheme correctly initialises models when ASSearch.search is called.
correlation(double[], double[], int) - Static method in class weka.core.Utils
Returns the correlation coefficient of two double vectors.
correlation - Variable in class weka.experiment.PairedStats
The correlation coefficient
correlationCoefficient() - Method in class weka.classifiers.Evaluation
Returns the correlation coefficient if the class is numeric.
CorrelationSplitInfo - Class in weka.classifiers.trees.m5
Finds split points using correlation.
CorrelationSplitInfo(int, int, int) - Constructor for class weka.classifiers.trees.m5.CorrelationSplitInfo
Constructs an object which contains the split information
COS - Static variable in interface weka.core.mathematicalexpression.sym
 
COS - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
CostBenefitAnalysis - Class in weka.gui.beans
Bean that aids in analyzing cost/benefit tradeoffs.
CostBenefitAnalysis() - Constructor for class weka.gui.beans.CostBenefitAnalysis
Constructor.
CostBenefitAnalysis.AnalysisPanel - Class in weka.gui.beans
Inner class for displaying the plots and all control widgets.
CostBenefitAnalysis.AnalysisPanel.ConfusionCell - Class in weka.gui.beans
Inner class for handling a single cell in the confusion matrix.
CostBenefitAnalysisBeanInfo - Class in weka.gui.beans
Bean info class for the cost/benefit analysis
CostBenefitAnalysisBeanInfo() - Constructor for class weka.gui.beans.CostBenefitAnalysisBeanInfo
 
CostCurve - Class in weka.classifiers.evaluation
Generates points illustrating probablity cost tradeoffs that can be obtained by varying the threshold value between classes.
CostCurve() - Constructor for class weka.classifiers.evaluation.CostCurve
 
CostMatrix - Class in weka.classifiers
Class for storing and manipulating a misclassification cost matrix.
CostMatrix(int) - Constructor for class weka.classifiers.CostMatrix
Creates a default cost matrix of a particular size.
CostMatrix(CostMatrix) - Constructor for class weka.classifiers.CostMatrix
Creates a cost matrix that is a copy of another.
CostMatrix(Reader) - Constructor for class weka.classifiers.CostMatrix
Reads a matrix from a reader.
CostMatrixEditor - Class in weka.gui
Class for editing CostMatrix objects.
CostMatrixEditor() - Constructor for class weka.gui.CostMatrixEditor
Constructs a new CostMatrixEditor.
costMatrixSourceTipText() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
 
costMatrixSourceTipText() - Method in class weka.classifiers.meta.CostSensitiveClassifier
 
costMatrixSourceTipText() - Method in class weka.classifiers.meta.MetaCost
Returns the tip text for this property
costMatrixTipText() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
 
costMatrixTipText() - Method in class weka.classifiers.meta.CostSensitiveClassifier
 
costMatrixTipText() - Method in class weka.classifiers.meta.MetaCost
Returns the tip text for this property
CostSensitiveASEvaluation - Class in weka.attributeSelection
Abstract base class for cost-sensitive subset and attribute evaluators.
CostSensitiveASEvaluation() - Constructor for class weka.attributeSelection.CostSensitiveASEvaluation
 
CostSensitiveAttributeEval - Class in weka.attributeSelection
A meta subset evaluator that makes its base subset evaluator cost-sensitive.
CostSensitiveAttributeEval() - Constructor for class weka.attributeSelection.CostSensitiveAttributeEval
Default constructor.
CostSensitiveClassifier - Class in weka.classifiers.meta
A metaclassifier that makes its base classifier cost-sensitive.
CostSensitiveClassifier() - Constructor for class weka.classifiers.meta.CostSensitiveClassifier
Default constructor.
CostSensitiveClassifierSplitEvaluator - Class in weka.experiment
SplitEvaluator that produces results for a classification scheme on a nominal class attribute, including weighted misclassification costs.
CostSensitiveClassifierSplitEvaluator() - Constructor for class weka.experiment.CostSensitiveClassifierSplitEvaluator
 
CostSensitiveSubsetEval - Class in weka.attributeSelection
A meta subset evaluator that makes its base subset evaluator cost-sensitive.
CostSensitiveSubsetEval() - Constructor for class weka.attributeSelection.CostSensitiveSubsetEval
Default constructor.
costTipText() - Method in class weka.classifiers.functions.LibLINEAR
Returns the tip text for this property
costTipText() - Method in class weka.classifiers.functions.LibSVM
Returns the tip text for this property
count() - Method in class weka.associations.RuleGeneration
Gets the actual maximum value of the generation time
count - Variable in class weka.experiment.PairedStats
The number of data points seen
count - Variable in class weka.experiment.Stats
The number of values seen
countBagCiters(Instance) - Method in class weka.classifiers.mi.CitationKNN
calculates the citers associated to a bag
countBagReferences(Instance) - Method in class weka.classifiers.mi.CitationKNN
Calculates the references of the exemplar bag
countData() - Method in class weka.classifiers.rules.RuleStats
Filter the data according to the ruleset and compute the basic stats: coverage/uncoverage, true/false positive/negatives of each rule
countData(int, Instances, double[][]) - Method in class weka.classifiers.rules.RuleStats
Count data from the position index in the ruleset assuming that given data are not covered by the rules in position 0...(index-1), and the statistics of these rules are provided.
This procedure is typically useful when a temporary object of RuleStats is constructed in order to efficiently calculate the relative DL of rule in position index, thus all other stuff is not needed.
counter() - Method in class weka.associations.ItemSet
Gets the counter
counterInstance(Instance, Instance) - Method in class weka.associations.tertius.LiteralSet
Test if an individual instance, given a part instance of this individual, is a counter-instance of this LiteralSet.
counterInstance(Instance) - Method in class weka.associations.tertius.LiteralSet
Test if an instance is a counter-instance of this LiteralSet.
counterInstance(Instance) - Method in class weka.associations.tertius.Rule
Test if an instance is a counter-instance of this rule.
countsForInstance(Instance) - Method in class weka.classifiers.bayes.BayesNet
Calculates the counts for Dirichlet distribution for the class membership probabilities for the given test instance.
cover - Variable in class weka.classifiers.rules.JRip.Antd
The coverage of this antecedent in the growing data
covers(Instance) - Method in class weka.classifiers.rules.JRip.Antd
 
covers(Instance) - Method in class weka.classifiers.rules.JRip.NominalAntd
Whether the instance is covered by this antecedent
covers(Instance) - Method in class weka.classifiers.rules.JRip.NumericAntd
Whether the instance is covered by this antecedent
covers(Instance) - Method in class weka.classifiers.rules.JRip.RipperRule
Whether the instance covered by this rule
covers(Instance) - Method in class weka.classifiers.rules.Rule
Whether the instance covered by this rule
CoverTree - Class in weka.core.neighboursearch
Class implementing the CoverTree datastructure.
The class is very much a translation of the c source code made available by the authors.

For more information and original source code see:

Alina Beygelzimer, Sham Kakade, John Langford: Cover trees for nearest neighbor.
CoverTree() - Constructor for class weka.core.neighboursearch.CoverTree
default constructor.
CoverTree.CoverTreeNode - Class in weka.core.neighboursearch
class representing a node of the cover tree.
CoverTree.MyHeap - Class in weka.core.neighboursearch
A class for a heap to store the nearest k neighbours to an instance.
CoverTree.MyHeapElement - Class in weka.core.neighboursearch
A class for storing data about a neighboring instance.
CoverTreeNode() - Constructor for class weka.core.neighboursearch.CoverTree.CoverTreeNode
Constructor for the class.
CoverTreeNode(Integer, double, double, Stack<CoverTree.CoverTreeNode>, int, int) - Constructor for class weka.core.neighboursearch.CoverTree.CoverTreeNode
Constructor.
CramersV(double[][]) - Static method in class weka.core.ContingencyTables
Computes Cramer's V for a contingency table.
create(Reader) - Method in class weka.gui.treevisualizer.TreeBuild
This will build A node structure from the dotty format passed.
create() - Method in class weka.gui.visualize.PostscriptGraphics
Clone a PostscriptGraphics object
createAnchorsHierarchy(Vector, int, int, int) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Creates an anchors hierarchy from a portion of master index array.
createAttribute(Element) - Method in class weka.core.xml.XMLInstances
creates an Attribute from the given XML node
createAttributes(Element, int[]) - Method in class weka.core.xml.XMLInstances
returns a list of generated attributes
createBeanConnection(int, int, String, boolean) - Method in class weka.gui.beans.xml.XMLBeans
generates a connection based on the given parameters
createChooseClassButton() - Method in class weka.gui.GenericObjectEditor
Creates a button that when clicked will enable the user to change the class of the object being edited.
createDefaultPanel() - Method in class weka.gui.PropertyPanel
Creates the default style of panel for editors that do not supply their own.
createDialog() - Method in class weka.gui.experiment.OutputFormatDialog
performs the creation of the dialog and all its components.
createDialog() - Method in class weka.gui.sql.SqlViewerDialog
builds the dialog and all its components
createDialog() - Method in class weka.gui.ViewerDialog
creates all the elements of the dialog
createExperimentIndex() - Method in class weka.experiment.DatabaseUtils
Attempts to create the experiment index table.
createExperimentIndexEntry(ResultProducer) - Method in class weka.experiment.DatabaseUtils
Attempts to insert a results entry for the table into the experiment index.
createFileChooser() - Method in class weka.gui.GenericObjectEditor.GOEPanel
Creates the file chooser the user will use to save/load files with.
createFrame() - Method in class weka.gui.arffviewer.ArffViewer
creates all the components in the frame
createFrame(GUIChooser, String, Component, LayoutManager, Object, int, int, JMenuBar, boolean, boolean) - Method in class weka.gui.GUIChooser
creates a frame and returns it.
createFrame() - Method in class weka.gui.LogWindow
creates the frame and all its components
createFrame(Main, String, Component, LayoutManager, Object, int, int, JMenuBar, boolean, boolean) - Method in class weka.gui.Main
creates a frame (depending on m_GUIType) and returns it.
createImage(JComponent, Rectangle) - Static method in class weka.gui.beans.KnowledgeFlowApp
Utility method to create an image of a region of the given component
createInstance(Instances, Element) - Method in class weka.core.xml.XMLInstances
creates an Instance from the given XML node
createInstances(Instances, Element) - Method in class weka.core.xml.XMLInstances
creates Instances from the given XML node
createLabels(Element) - Method in class weka.core.xml.XMLInstances
returns the labels listed underneath this (nominal) attribute in a FastVector
createMessage(String) - Method in class weka.core.Capabilities
Generates the message for, e.g., an exception.
createMetadata(Element) - Method in class weka.core.xml.XMLInstances
returns the metadata, if any available underneath this node, otherwise just null
createNewVisualizerWindow(Classifier, Instances) - Static method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
Creates a new GUI window with all of the BoundaryVisualizer trappings,
createNodes(DefaultMutableTreeNode) - Method in class weka.gui.PropertySelectorDialog
Creates the property tree below the current node.
createOptions() - Method in class weka.classifiers.meta.CVParameterSelection
Create the options array to pass to the classifier.
createPanel() - Method in class weka.gui.arffviewer.ArffPanel
creates all the components in the frame
createPanel() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
creates all the components in the panel
createPanel() - Method in class weka.gui.sql.ConnectionPanel
builds the panel with all its components.
createPanel() - Method in class weka.gui.sql.InfoPanel
inserts the components into the panel
createPanel() - Method in class weka.gui.sql.QueryPanel
creates the panel with all its components.
createPanel() - Method in class weka.gui.sql.ResultPanel
creates the panel with all its components
createPanel() - Method in class weka.gui.sql.SqlViewer
builds the interface.
CreatePopulation(int) - Method in class weka.attributeSelection.ScatterSearchV1
Create the initial Population
createRelativePath(File) - Static method in class weka.core.Utils
Converts a File's absolute path to a path relative to the user (ie start) directory.
createResultsTable(ResultProducer, String) - Method in class weka.experiment.DatabaseUtils
Creates a results table for the supplied result producer.
createSingleton(String[]) - Static method in class weka.gui.beans.KnowledgeFlowApp
Create the singleton instance of the KnowledgeFlow
createSingleton() - Static method in class weka.gui.GUIChooser
Create a singleton instance of the GUIChooser
createSingleton(String[]) - Static method in class weka.gui.Main
Create the singleton instance of the Main GUI.
createSubsample() - Method in class weka.filters.supervised.instance.Resample
Creates a subsample of the current set of input instances.
createSubsample() - Method in class weka.filters.unsupervised.instance.Resample
Creates a subsample of the current set of input instances.
createSubsample() - Method in class weka.filters.unsupervised.instance.ReservoirSample
Creates a subsample of the current set of input instances.
createSubsampleWithoutReplacement(Random, int, int, int, int[]) - Method in class weka.filters.supervised.instance.Resample
creates the subsample without replacement.
createSubsampleWithoutReplacement(Random, int, int) - Method in class weka.filters.unsupervised.instance.Resample
creates the subsample without replacement
createSubsampleWithReplacement(Random, int, int, int, int[]) - Method in class weka.filters.supervised.instance.Resample
creates the subsample with replacement.
createSubsampleWithReplacement(Random, int, int) - Method in class weka.filters.unsupervised.instance.Resample
creates the subsample with replacement
createTitle(String) - Method in class weka.gui.GUIChooser
creates and displays the title.
createTitle(String) - Method in class weka.gui.Main
creates and displays the title.
createTree(Hashtable) - Method in class weka.gui.GenericObjectEditor
Creates a JTree from an object heirarchy.
createWindowMenu() - Method in class weka.gui.Main
creates the menu of currently open windows.
CREATOR_FILE - Static variable in class weka.gui.GenericPropertiesCreator
The name of the properties file to use as a template.
criticalValueTipText() - Method in class weka.classifiers.bayes.AODEsr
Returns the tip text for this property
crossings(int[][]) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
Computes the number of edge crossings in the whole graph Takes as an argument levels of nodes.
crossoverProbTipText() - Method in class weka.attributeSelection.GeneticSearch
Returns the tip text for this property
crossValidate() - Method in class weka.classifiers.lazy.IBk
Select the best value for k by hold-one-out cross-validation.
crossValidate(NaiveBayesUpdateable, Instances, Random) - Static method in class weka.classifiers.trees.j48.NBTreeNoSplit
Utility method for fast 5-fold cross validation of a naive bayes model
CrossValidateAttributes() - Method in class weka.attributeSelection.AttributeSelection
Perform a cross validation for attribute selection.
crossValidateModel(Classifier, Instances, int, Random, Object...) - Method in class weka.classifiers.Evaluation
Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
crossValidateModel(String, Instances, int, String[], Random) - Method in class weka.classifiers.Evaluation
Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
crossValidateModel(DensityBasedClusterer, Instances, int, Random) - Static method in class weka.clusterers.ClusterEvaluation
Perform a cross-validation for DensityBasedClusterer on a set of instances.
crossValidateModel(String, Instances, int, String[], Random) - Static method in class weka.clusterers.ClusterEvaluation
Performs a cross-validation for a DensityBasedClusterer clusterer on a set of instances.
crossValidateTipText() - Method in class weka.classifiers.lazy.IBk
Returns the tip text for this property.
CrossValidationFoldMaker - Class in weka.gui.beans
Bean for splitting instances into training ant test sets according to a cross validation
CrossValidationFoldMaker() - Constructor for class weka.gui.beans.CrossValidationFoldMaker
 
CrossValidationFoldMakerBeanInfo - Class in weka.gui.beans
BeanInfo class for the cross validation fold maker bean
CrossValidationFoldMakerBeanInfo() - Constructor for class weka.gui.beans.CrossValidationFoldMakerBeanInfo
 
CrossValidationFoldMakerCustomizer - Class in weka.gui.beans
GUI Customizer for the cross validation fold maker bean
CrossValidationFoldMakerCustomizer() - Constructor for class weka.gui.beans.CrossValidationFoldMakerCustomizer
 
CrossValidationResultProducer - Class in weka.experiment
Generates for each run, carries out an n-fold cross-validation, using the set SplitEvaluator to generate some results.
CrossValidationResultProducer() - Constructor for class weka.experiment.CrossValidationResultProducer
 
crossValTipText() - Method in class weka.classifiers.rules.DecisionTable
Returns the tip text for this property
CSVLoader - Class in weka.core.converters
Reads a source that is in comma separated or tab separated format.
CSVLoader() - Constructor for class weka.core.converters.CSVLoader
default constructor.
CSVResultListener - Class in weka.experiment
Takes results from a result producer and assembles them into comma separated value form.
CSVResultListener() - Constructor for class weka.experiment.CSVResultListener
Sets temporary file.
CSVSaver - Class in weka.core.converters
Writes to a destination that is in csv format

Valid options are:

CSVSaver() - Constructor for class weka.core.converters.CSVSaver
Constructor
cTipText() - Method in class weka.classifiers.functions.SMO
Returns the tip text for this property
cTipText() - Method in class weka.classifiers.functions.SMOreg
Returns the tip text for this property
cTipText() - Method in class weka.classifiers.mi.MISMO
Returns the tip text for this property
cTipText() - Method in class weka.classifiers.mi.MISVM
Returns the tip text for this property
cumulate() - Method in class weka.core.matrix.DoubleVector
Returns a vector that stores the cumulated values of the original vector
cumulateInPlace() - Method in class weka.core.matrix.DoubleVector
Cumulates the original vector in place
cumulativeCV(BayesNet) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
CumulativeCV returns the accuracy calculated using cumulative cross validation.
currentLength() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.NeighborList
Gets the current length of the list.
CustomizerCloseRequester - Interface in weka.gui.beans
Customizers who want to be able to close the customizer window themselves can implement this window.
customizerClosing() - Method in class weka.gui.beans.ClassAssignerCustomizer
 
customizerClosing() - Method in class weka.gui.beans.ClassifierCustomizer
 
customizerClosing() - Method in class weka.gui.beans.ClassValuePickerCustomizer
 
customizerClosing() - Method in interface weka.gui.beans.CustomizerClosingListener
Customizer classes that want to know when they are being disposed of can implement this method.
CustomizerClosingListener - Interface in weka.gui.beans
 
CustomPanelSupplier - Interface in weka.gui
An interface for objects that are capable of supplying their own custom GUI components.
cutOffFactorTipText() - Method in class weka.clusterers.XMeans
Returns the tip text for this property.
cutoffTipText() - Method in class weka.clusterers.Cobweb
Returns the tip text for this property
cutpointsToString(double[], boolean[]) - Static method in class weka.estimators.EstimatorUtils
Returns a string representing the cutpoints
CV_BASED - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
 
CVBasedHyperparameter() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Method computes the best hyperparameter value by doing cross -validation on the training data and compute the likelihood.
CVParameter(String) - Constructor for class weka.classifiers.meta.CVParameterSelection.CVParameter
Constructs a CVParameter.
CVParameterSelection - Class in weka.classifiers.meta
Class for performing parameter selection by cross-validation for any classifier.

For more information, see:

R.
CVParameterSelection() - Constructor for class weka.classifiers.meta.CVParameterSelection
 
CVParameterSelection.CVParameter - Class in weka.classifiers.meta
A data structure to hold values associated with a single cross-validation search parameter
CVParametersTipText() - Method in class weka.classifiers.meta.CVParameterSelection
Returns the tip text for this property
CVResultsString() - Method in class weka.attributeSelection.AttributeSelection
returns a string summarizing the results of repeated attribute selection runs on splits of a dataset.
CVTypeTipText() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
 

D

D_CONVCHCLOSER - Static variable in class weka.clusterers.XMeans
have a closer look at converge children.
D_CURR - Static variable in class weka.clusterers.XMeans
for current debug.
D_FOLLOWSPLIT - Static variable in class weka.clusterers.XMeans
follows the splitting of the centers.
D_GENERAL - Static variable in class weka.clusterers.XMeans
general debugging.
D_ITERCOUNT - Static variable in class weka.clusterers.XMeans
follow iterations.
D_KDTREE - Static variable in class weka.clusterers.XMeans
check on kdtree.
D_METH_MISUSE - Static variable in class weka.clusterers.XMeans
functions were maybe misused.
D_PRINTCENTERS - Static variable in class weka.clusterers.XMeans
print the centers.
D_RANDOMVECTOR - Static variable in class weka.clusterers.XMeans
check on random vectors.
Dagging - Class in weka.classifiers.meta
This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier.
Dagging() - Constructor for class weka.classifiers.meta.Dagging
Constructor.
Database - Interface in weka.clusterers.forOPTICSAndDBScan.Databases
Database.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 20, 2004
Time: 1:03:43 PM
$ Revision 1.4 $
database_distanceTypeTipText() - Method in class weka.clusterers.DBScan
Returns the tip text for this property
database_distanceTypeTipText() - Method in class weka.clusterers.OPTICS
Returns the tip text for this property
database_TypeTipText() - Method in class weka.clusterers.DBScan
Returns the tip text for this property
database_TypeTipText() - Method in class weka.clusterers.OPTICS
Returns the tip text for this property
DatabaseConnection - Class in weka.core.converters
Connects to a database.
DatabaseConnection() - Constructor for class weka.core.converters.DatabaseConnection
Sets up the database drivers
DatabaseConnectionDialog - Class in weka.gui
A dialog to enter URL, username and password for a database connection.
DatabaseConnectionDialog(Frame) - Constructor for class weka.gui.DatabaseConnectionDialog
Create database connection dialog.
DatabaseConnectionDialog(Frame, String, String) - Constructor for class weka.gui.DatabaseConnectionDialog
Create database connection dialog.
DatabaseConnectionDialog(Frame, String, String, boolean) - Constructor for class weka.gui.DatabaseConnectionDialog
Create database connection dialog.
DatabaseConverter - Interface in weka.core.converters
Marker interface for a loader/saver that uses a database
databaseForName(String, Instances) - Method in class weka.clusterers.DBScan
Returns a new Class-Instance of the specified database
databaseForName(String, Instances) - Method in class weka.clusterers.OPTICS
Returns a new Class-Instance of the specified database
DatabaseLoader - Class in weka.core.converters
Reads Instances from a Database.
DatabaseLoader() - Constructor for class weka.core.converters.DatabaseLoader
Constructor
databaseOutputTipText() - Method in class weka.clusterers.OPTICS
Returns the tip text for this property.
DatabaseResultListener - Class in weka.experiment
Takes results from a result producer and sends them to a database.
DatabaseResultListener() - Constructor for class weka.experiment.DatabaseResultListener
Sets up the database drivers
DatabaseResultProducer - Class in weka.experiment
Examines a database and extracts out the results produced by the specified ResultProducer and submits them to the specified ResultListener.
DatabaseResultProducer() - Constructor for class weka.experiment.DatabaseResultProducer
Creates the DatabaseResultProducer, letting the parent constructor do it's thing.
DatabaseSaver - Class in weka.core.converters
Writes to a database (tested with MySQL, InstantDB, HSQLDB).
DatabaseSaver() - Constructor for class weka.core.converters.DatabaseSaver
Constructor.
databaseURLTipText() - Method in class weka.experiment.DatabaseUtils
Returns the tip text for this property.
DatabaseUtils - Class in weka.experiment
DatabaseUtils provides utility functions for accessing the experiment database.
DatabaseUtils() - Constructor for class weka.experiment.DatabaseUtils
Reads properties and sets up the database drivers.
dataDL(double, double, double, double, double) - Static method in class weka.classifiers.rules.RuleStats
The description length of data given the parameters of the data based on the ruleset.
dataFileLabel - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
DataFormatListener - Interface in weka.gui.beans
Listener interface that customizer classes that are interested in data format changes can implement.
dataFromXML(Instances) - Method in class weka.core.xml.XMLInstances
generates the complete dataset from the XML document
DataGenerator - Class in weka.datagenerators
Abstract superclass for data generators that generate data for classifiers and clusterers.
DataGenerator() - Constructor for class weka.datagenerators.DataGenerator
initializes with default settings.
DataGenerator - Interface in weka.gui.boundaryvisualizer
Interface to something that can generate new instances based on a set of input instances
DataGeneratorPanel - Class in weka.gui.explorer
A panel for generating artificial data via DataGenerators.
DataGeneratorPanel() - Constructor for class weka.gui.explorer.DataGeneratorPanel
creates the panel
DataNearBalancedND - Class in weka.classifiers.meta.nestedDichotomies
A meta classifier for handling multi-class datasets with 2-class classifiers by building a random data-balanced tree structure.

For more info, check

Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.
DataNearBalancedND() - Constructor for class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
Constructor.
DataObject - Interface in weka.clusterers.forOPTICSAndDBScan.DataObjects
DataObject.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 19, 2004
Time: 5:48:59 PM
$ Revision 1.4 $
dataObjectForName(String, Instance, String, Database) - Method in class weka.clusterers.DBScan
Returns a new Class-Instance of the specified database
dataObjectForName(String, Instance, String, Database) - Method in class weka.clusterers.OPTICS
Returns a new Class-Instance of the specified database
dataObjectIterator() - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database
Returns an iterator over all the dataObjects in the database
dataObjectIterator() - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase
Returns an iterator over all the dataObjects in the database
dataSeqIDTipText() - Method in class weka.associations.GeneralizedSequentialPatterns
Returns the dataSeqID option tip text for the Weka GUI.
dataset() - Method in class weka.core.Instance
Returns the dataset this instance has access to.
Dataset(Instance) - Constructor for class weka.experiment.PairedTTester.Dataset
Constructor
dataset(Instance) - Method in class weka.experiment.PairedTTester.Resultset
Returns a vector containing all instances belonging to one dataset.
DATASET_FIELD_NAME - Static variable in class weka.experiment.CrossValidationResultProducer
The name of the key field containing the dataset name
DATASET_FIELD_NAME - Static variable in class weka.experiment.RandomSplitResultProducer
The name of the key field containing the dataset name
DataSetEvent - Class in weka.gui.beans
Event encapsulating a data set
DataSetEvent(Object, Instances) - Constructor for class weka.gui.beans.DataSetEvent
 
datasetIntegrity(boolean, boolean, boolean, boolean, boolean, boolean, int, boolean, boolean) - Method in class weka.associations.CheckAssociator
Checks whether the scheme alters the training dataset during building.
datasetIntegrity(boolean, boolean, boolean, boolean, boolean, boolean, int, boolean, boolean) - Method in class weka.attributeSelection.CheckAttributeSelection
Checks whether the scheme alters the training dataset during training.
datasetIntegrity(boolean, boolean, boolean, boolean, boolean, boolean, int, boolean, boolean) - Method in class weka.classifiers.CheckClassifier
Checks whether the scheme alters the training dataset during training.
datasetIntegrity(boolean, boolean, boolean, boolean, boolean, boolean, int, boolean, boolean) - Method in class weka.classifiers.functions.supportVector.CheckKernel
Checks whether the scheme alters the training dataset during building.
datasetIntegrity(boolean, boolean, boolean, boolean, boolean, boolean, boolean) - Method in class weka.clusterers.CheckClusterer
Checks whether the scheme alters the training dataset during training.
datasetIntegrity(CheckEstimator.AttrTypes, int, boolean, boolean) - Method in class weka.estimators.CheckEstimator
Checks whether the scheme alters the training dataset during training.
DatasetListPanel - Class in weka.gui.experiment
This panel controls setting a list of datasets for an experiment to iterate over.
DatasetListPanel(Experiment) - Constructor for class weka.gui.experiment.DatasetListPanel
Creates the dataset list panel with the given experiment.
DatasetListPanel() - Constructor for class weka.gui.experiment.DatasetListPanel
Create the dataset list panel initially disabled.
DatasetSpecifiers() - Constructor for class weka.experiment.PairedTTester.DatasetSpecifiers
 
DataSink(String) - Constructor for class weka.core.converters.ConverterUtils.DataSink
initializes the sink to save the data to the given file.
DataSink(Saver) - Constructor for class weka.core.converters.ConverterUtils.DataSink
initializes the sink to save the data to the given Saver (expected to be fully configured).
DataSink(OutputStream) - Constructor for class weka.core.converters.ConverterUtils.DataSink
initializes the sink to save the data in the stream (always in ARFF format).
DataSink - Interface in weka.gui.beans
Indicator interface to something that can store instances to some destination
DataSource(String) - Constructor for class weka.core.converters.ConverterUtils.DataSource
Tries to load the data from the file.
DataSource(Instances) - Constructor for class weka.core.converters.ConverterUtils.DataSource
Initializes the datasource with the given dataset.
DataSource(Loader) - Constructor for class weka.core.converters.ConverterUtils.DataSource
Initializes the datasource with the given Loader.
DataSource(InputStream) - Constructor for class weka.core.converters.ConverterUtils.DataSource
Initializes the datasource with the given input stream.
DataSource - Interface in weka.gui.beans
Interface to something that is capable of being a source for data - either batch or incremental data
DataSourceListener - Interface in weka.gui.beans
Interface to something that can accept DataSetEvents
dataToXML() - Method in class weka.core.xml.XMLInstances
generates the XML structure from the rows
DATATYPE_LAYOUT - Static variable in class weka.gui.beans.xml.XMLBeans
the data that is about to be read/written contains a complete layout
DATATYPE_USERCOMPONENTS - Static variable in class weka.gui.beans.xml.XMLBeans
the data that is about to be read/written contains user-components, i.e., Metabeans
DataVisualizer - Class in weka.gui.beans
Bean that encapsulates weka.gui.visualize.VisualizePanel
DataVisualizer() - Constructor for class weka.gui.beans.DataVisualizer
 
DataVisualizerBeanInfo - Class in weka.gui.beans
Bean info class for the data visualizer
DataVisualizerBeanInfo() - Constructor for class weka.gui.beans.DataVisualizerBeanInfo
 
DATE - Static variable in class weka.core.Attribute
Constant set for attributes with date values.
DATE - Static variable in class weka.experiment.DatabaseUtils
Type mapping for DATE used for reading experiment results.
dateAttributesTipText() - Method in class weka.core.converters.CSVLoader
Returns the tip text for this property.
dateFormatTipText() - Method in class weka.core.converters.CSVLoader
Returns the tip text for this property.
dateFormatTipText() - Method in class weka.filters.unsupervised.attribute.Add
Returns the tip text for this property.
dateFormatTipText() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
 
DbConnectionDialog(String, String) - Method in class weka.gui.DatabaseConnectionDialog
Display the database connection dialog
DbConnectionDialog(String, String, boolean) - Method in class weka.gui.DatabaseConnectionDialog
Display the database connection dialog
DBO() - Constructor for class weka.core.Debug.DBO
 
DBScan - Class in weka.clusterers
Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise.
DBScan() - Constructor for class weka.clusterers.DBScan
 
DbUtils - Class in weka.gui.sql
A little bit extended DatabaseUtils class.
DbUtils() - Constructor for class weka.gui.sql.DbUtils
initializes the object.
dchisq(double) - Static method in class weka.core.matrix.Maths
Returns the density of the Chi-squared distribution.
dchisq(double, double) - Static method in class weka.core.matrix.Maths
Returns the density of the noncentral Chi-squared distribution.
dchisq(double, DoubleVector) - Static method in class weka.core.matrix.Maths
Returns the density of the noncentral Chi-squared distribution.
dchisqLog(double) - Static method in class weka.core.matrix.Maths
Returns the log-density of the noncentral Chi-square distribution.
dchisqLog(double, double) - Static method in class weka.core.matrix.Maths
Returns the log-density value of a noncentral Chi-square distribution.
dchisqLog(double, DoubleVector) - Static method in class weka.core.matrix.Maths
Returns the log-density of a set of noncentral Chi-squared distributions.
DDConditionalEstimator - Class in weka.estimators
Conditional probability estimator for a discrete domain conditional upon a discrete domain.
DDConditionalEstimator(int, int, boolean) - Constructor for class weka.estimators.DDConditionalEstimator
Constructor
Debug - Class in weka.core
A helper class for debug output, logging, clocking, etc.
Debug() - Constructor for class weka.core.Debug
default constructor, prints only to stdout
Debug(String) - Constructor for class weka.core.Debug
logs the output to the specified file (and stdout).
Debug(String, int, int) - Constructor for class weka.core.Debug
logs the output
DEBUG - Static variable in class weka.core.xml.XMLSerialization
for debugging purposes only
DEBUG - Static variable in class weka.gui.LogWindow
whether we're debugging - enables output on stdout
DEBUG - Static variable in class weka.gui.visualize.JComponentWriter
whether to print some debug information
DEBUG - Static variable in class weka.gui.visualize.PostscriptGraphics
whether to print some debug information
Debug.Clock - Class in weka.core
A little helper class for clocking and outputting times.
Debug.DBO - Class in weka.core
contains debug methods
Debug.Log - Class in weka.core
A helper class for logging stuff.
Debug.Random - Class in weka.core
This extended Random class enables one to print the generated random numbers etc., before they are returned.
Debug.SimpleLog - Class in weka.core
A little, simple helper class for logging stuff.
Debug.Timestamp - Class in weka.core
A class that can be used for timestamps in files, The toString() method simply returns the associated Date object in a timestamp format.
debugLevelTipText() - Method in class weka.clusterers.XMeans
Returns the tip text for this property.
debugTipText() - Method in class weka.associations.GeneralizedSequentialPatterns
Returns the tip text for this property
debugTipText() - Method in class weka.attributeSelection.RaceSearch
Returns the tip text for this property
debugTipText() - Method in class weka.attributeSelection.ScatterSearchV1
Returns the tip text for this property
debugTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns the tip text for this property
debugTipText() - Method in class weka.classifiers.Classifier
Returns the tip text for this property
debugTipText() - Method in class weka.classifiers.functions.LinearRegression
Returns the tip text for this property
debugTipText() - Method in class weka.classifiers.functions.Logistic
Returns the tip text for this property
debugTipText() - Method in class weka.classifiers.functions.PaceRegression
Returns the tip text for this property
debugTipText() - Method in class weka.classifiers.functions.supportVector.Kernel
Returns the tip text for this property
debugTipText() - Method in class weka.classifiers.meta.MultiScheme
Returns the tip text for this property
debugTipText() - Method in class weka.classifiers.rules.JRip
Returns the tip text for this property
debugTipText() - Method in class weka.clusterers.EM
Returns the tip text for this property
debugTipText() - Method in class weka.clusterers.HierarchicalClusterer
Returns the tip text for this property
debugTipText() - Method in class weka.clusterers.sIB
Returns the tip text for this property
debugTipText() - Method in class weka.core.converters.TextDirectoryLoader
the tip text for this property
debugTipText() - Method in class weka.datagenerators.DataGenerator
Returns the tip text for this property
debugTipText() - Method in class weka.estimators.Estimator
Returns the tip text for this property
debugTipText() - Method in class weka.experiment.DatabaseUtils
Returns the tip text for this property.
debugTipText() - Method in class weka.filters.SimpleFilter
Returns the tip text for this property
debugTipText() - Method in class weka.filters.unsupervised.attribute.AddExpression
Returns the tip text for this property
debugVectorsFileTipText() - Method in class weka.clusterers.XMeans
Returns the tip text for this property.
decayTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
decimal - Variable in class weka.core.matrix.FloatingPointFormat
 
decimalsTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Returns the tip text for this property
DecisionStump - Class in weka.classifiers.trees
Class for building and using a decision stump.
DecisionStump() - Constructor for class weka.classifiers.trees.DecisionStump
 
DecisionTable - Class in weka.classifiers.rules
Class for building and using a simple decision table majority classifier.

For more information see:

Ron Kohavi: The Power of Decision Tables.
DecisionTable() - Constructor for class weka.classifiers.rules.DecisionTable
Constructor for a DecisionTable
DecisionTableHashKey - Class in weka.classifiers.rules
Class providing hash table keys for DecisionTable
DecisionTableHashKey(Instance, int, boolean) - Constructor for class weka.classifiers.rules.DecisionTableHashKey
Constructor for a hashKey
DecisionTableHashKey(double[]) - Constructor for class weka.classifiers.rules.DecisionTableHashKey
Constructor for a hashKey
declaresSerialVersionUID() - Method in class weka.associations.CheckAssociator
tests for a serialVersionUID.
declaresSerialVersionUID() - Method in class weka.attributeSelection.CheckAttributeSelection
tests for a serialVersionUID.
declaresSerialVersionUID() - Method in class weka.classifiers.CheckClassifier
tests for a serialVersionUID.
declaresSerialVersionUID() - Method in class weka.classifiers.functions.supportVector.CheckKernel
tests for a serialVersionUID.
declaresSerialVersionUID() - Method in class weka.clusterers.CheckClusterer
tests for a serialVersionUID.
decompose() - Method in class weka.classifiers.BVDecompose
Carry out the bias-variance decomposition
decompose() - Method in class weka.classifiers.BVDecomposeSegCVSub
Carry out the bias-variance decomposition using the sub-sampled cross-validation method.
Decorate - Class in weka.classifiers.meta
DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples.
Decorate() - Constructor for class weka.classifiers.meta.Decorate
Constructor.
decreaseCount() - Method in class weka.gui.beans.FlowRunner
 
decreaseFrequency(int) - Method in class weka.associations.FPGrowth.BinaryItem
Decrease the frequency of this item.
decreaseFrequency() - Method in class weka.associations.FPGrowth.BinaryItem
Decrement the frequency of this item.
DEFAULT_COLORS - Static variable in class weka.gui.boundaryvisualizer.BoundaryPanel
default colours for classes
DEFAULT_FORMAT - Static variable in class weka.core.Debug.Timestamp
the default format
DEFAULT_FORMAT - Static variable in class weka.gui.SimpleDateFormatEditor
the default format
DEFAULT_HEIGHT - Static variable in class weka.gui.arffviewer.ArffViewerMainPanel
the default for height
DEFAULT_LEFT - Static variable in class weka.gui.arffviewer.ArffViewerMainPanel
the default for left
DEFAULT_NUM_PRECISION - Static variable in class weka.classifiers.bayes.NaiveBayes
The precision parameter used for numeric attributes
DEFAULT_SEPARATORS - Static variable in class weka.core.TestInstances
the default word separators used in strings
DEFAULT_SHAPE_SIZE - Static variable in class weka.gui.visualize.Plot2D
 
DEFAULT_TOP - Static variable in class weka.gui.arffviewer.ArffViewerMainPanel
the default for top
DEFAULT_WIDTH - Static variable in class weka.gui.arffviewer.ArffViewerMainPanel
the default for width
DEFAULT_WORDS - Static variable in class weka.core.TestInstances
the default list of words used in strings
defaultAmplitude() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
returns the default amplitude
defaultAssociatorString() - Method in class weka.associations.FilteredAssociator
String describing default associator.
defaultAssociatorString() - Method in class weka.associations.SingleAssociatorEnhancer
String describing default Associator.
defaultAttrIndexRange() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
returns the default attribute index range
defaultBalanceClass() - Method in class weka.datagenerators.classifiers.classification.Agrawal
returns the default for balancing the class
defaultCardinality() - Method in class weka.datagenerators.classifiers.classification.BayesNet
returns the default cardinality
defaultClassifierString() - Method in class weka.classifiers.lazy.LWL
String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.meta.AdaBoostM1
String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.meta.AdditiveRegression
String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.meta.Bagging
String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.meta.ClassificationViaRegression
String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.meta.CostSensitiveClassifier
String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.meta.Dagging
String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.meta.Decorate
String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.meta.END
String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.meta.FilteredClassifier
String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.meta.GridSearch
String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.meta.LogitBoost
String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.meta.MultiClassClassifier
String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.meta.nestedDichotomies.ND
String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.meta.OrdinalClassClassifier
String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.meta.RandomCommittee
String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.meta.RandomSubSpace
String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.meta.RegressionByDiscretization
String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.meta.RotationForest
String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.meta.ThresholdSelector
String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.SingleClassifierEnhancer
String describing default classifier.
defaultClustererString() - Method in class weka.classifiers.meta.ClassificationViaClustering
String describing default clusterer.
defaultClustererString() - Method in class weka.clusterers.MakeDensityBasedClusterer
String describing default clusterer.
defaultClustererString() - Method in class weka.clusterers.SingleClustererEnhancer
String describing default clusterer.
defaultClusterSubType() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
returns the default cluster sub type
defaultClusterType() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
returns the default cluster type
defaultDistMult() - Method in class weka.datagenerators.clusterers.BIRCHCluster
returns the default distance multiplier
defaultEvaluatorString() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
Return the name of the default evaluator.
defaultEvaluatorString() - Method in class weka.attributeSelection.CostSensitiveAttributeEval
Return the name of the default evaluator.
defaultExpression() - Method in class weka.datagenerators.classifiers.regression.Expression
returns the default expression
defaultFilter() - Method in class weka.classifiers.meta.RotationForest
Default projection method.
defaultFilterString() - Method in class weka.clusterers.FilteredClusterer
String describing default filter.
defaultFunction() - Method in class weka.datagenerators.classifiers.classification.Agrawal
returns the default function
defaultInputOrder() - Method in class weka.datagenerators.clusterers.BIRCHCluster
returns the default input order
defaultMaxInstNum() - Method in class weka.datagenerators.clusterers.BIRCHCluster
returns the default max number of instances
defaultMaxInstNum() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
returns the default max number of instances
defaultMaxRadius() - Method in class weka.datagenerators.clusterers.BIRCHCluster
returns the default max radius
defaultMaxRange() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
returns the default max range
defaultMaxRuleSize() - Method in class weka.datagenerators.classifiers.classification.RDG1
returns the default max size of rules
defaultMeanStddev() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
returns the default mean/stddev list
defaultMinInstNum() - Method in class weka.datagenerators.clusterers.BIRCHCluster
returns the default min number of instances
defaultMinInstNum() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
returns the default min number of instances
defaultMinRadius() - Method in class weka.datagenerators.clusterers.BIRCHCluster
returns the default min radius
defaultMinRange() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
returns the default min range
defaultMinRuleSize() - Method in class weka.datagenerators.classifiers.classification.RDG1
returns the default min size of rules
defaultNoisePercent() - Method in class weka.datagenerators.classifiers.classification.LED24
returns the default noise percentage
defaultNoiseRate() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
returns the default gaussian noise rate
defaultNoiseRate() - Method in class weka.datagenerators.clusterers.BIRCHCluster
returns the default noise rate
defaultNoiseRate() - Method in class weka.datagenerators.clusterers.SubspaceCluster
returns the default noise rate
defaultNoiseVariance() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
returns the default variance of the noise rate
defaultNumArcs() - Method in class weka.datagenerators.classifiers.classification.BayesNet
returns the default number of arcs
defaultNumAttributes() - Method in class weka.datagenerators.classifiers.classification.BayesNet
returns the default number of attributes
defaultNumAttributes() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
returns the default number of attributes
defaultNumAttributes() - Method in class weka.datagenerators.classifiers.classification.RDG1
returns the default number of attributes
defaultNumAttributes() - Method in class weka.datagenerators.clusterers.SubspaceCluster
returns the default number of attributes
defaultNumAttributes() - Method in class weka.datagenerators.ClusterGenerator
returns the default number of attributes
defaultNumCentroids() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
returns the default number of centroids
defaultNumClasses() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
returns the default number of classes
defaultNumClasses() - Method in class weka.datagenerators.classifiers.classification.RDG1
returns the default number of classes
defaultNumClusters() - Method in class weka.datagenerators.clusterers.BIRCHCluster
returns the default number of clusters
defaultNumCycles() - Method in class weka.datagenerators.clusterers.BIRCHCluster
returns the default number of cycles
defaultNumExamples() - Method in class weka.datagenerators.ClassificationGenerator
returns the default number of examples
defaultNumExamples() - Method in class weka.datagenerators.RegressionGenerator
returns the default number of examples
defaultNumExamplesAct() - Method in class weka.datagenerators.DataGenerator
returns the default number of actual examples
defaultNumIrrelevant() - Method in class weka.datagenerators.classifiers.classification.RDG1
returns the default number of irrelevant attributes
defaultNumNumeric() - Method in class weka.datagenerators.classifiers.classification.RDG1
returns the default number of numeric attributes
defaultOutput() - Method in class weka.datagenerators.DataGenerator
Gets the string writer, which is used for outputting to stdout.
defaultPattern() - Method in class weka.datagenerators.clusterers.BIRCHCluster
returns the default pattern
defaultPerturbationFraction() - Method in class weka.datagenerators.classifiers.classification.Agrawal
returns the default perturbation fraction
defaultRelationName() - Method in class weka.datagenerators.DataGenerator
returns a relation name based on the options
defaultSeed() - Method in class weka.datagenerators.DataGenerator
returns the default seed
defaultValuesList() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
returns the default values list
defaultWeightTipText() - Method in class weka.classifiers.functions.Winnow
Returns the tip text for this property
defineDataFormat() - Method in class weka.datagenerators.classifiers.classification.Agrawal
Initializes the format for the dataset produced.
defineDataFormat() - Method in class weka.datagenerators.classifiers.classification.BayesNet
Initializes the format for the dataset produced.
defineDataFormat() - Method in class weka.datagenerators.classifiers.classification.LED24
Initializes the format for the dataset produced.
defineDataFormat() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
Initializes the format for the dataset produced.
defineDataFormat() - Method in class weka.datagenerators.classifiers.classification.RDG1
Initializes the format for the dataset produced.
defineDataFormat() - Method in class weka.datagenerators.classifiers.regression.Expression
Initializes the format for the dataset produced.
defineDataFormat() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Initializes the format for the dataset produced.
defineDataFormat() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Initializes the format for the dataset produced.
defineDataFormat() - Method in class weka.datagenerators.clusterers.SubspaceCluster
Initializes the format for the dataset produced.
defineDataFormat() - Method in class weka.datagenerators.DataGenerator
Initializes the format for the dataset produced.
DefineFunction - Class in weka.core.pmml
Class encapsulating DefineFunction (used in TransformationDictionary).
DefineFunction(Element, TransformationDictionary) - Constructor for class weka.core.pmml.DefineFunction
 
DefineFunction.ParameterField - Class in weka.core.pmml
Inner class for handling Parameters
degreeTipText() - Method in class weka.classifiers.functions.LibSVM
Returns the tip text for this property
del(int, Instance) - Method in class weka.classifiers.trees.j48.Distribution
Deletes given instance from given bag.
delete(int) - Method in class weka.classifiers.functions.supportVector.SMOset
Deletes an element from the set.
delete() - Method in class weka.core.Instances
Removes all instances from the set.
delete(int) - Method in class weka.core.Instances
Removes an instance at the given position from the set.
deleteArc(String, String) - Method in class weka.classifiers.bayes.net.EditableBayesNet
Delete arc between two nodes.
deleteArc(int, int) - Method in class weka.classifiers.bayes.net.EditableBayesNet
Delete arc between two nodes.
deleteAttribute() - Method in class weka.gui.arffviewer.ArffPanel
deletes the currently selected attribute
deleteAttribute(boolean) - Method in class weka.gui.arffviewer.ArffViewerMainPanel
deletes the current selected Attribute or several chosen ones
deleteAttributeAt(int) - Method in class weka.core.Instance
Deletes an attribute at the given position (0 to numAttributes() - 1).
deleteAttributeAt(int) - Method in class weka.core.Instances
Deletes an attribute at the given position (0 to numAttributes() - 1).
deleteAttributeAt(int) - Method in class weka.gui.arffviewer.ArffSortedTableModel
deletes the attribute at the given col index
deleteAttributeAt(int) - Method in class weka.gui.arffviewer.ArffTableModel
deletes the attribute at the given col index.
deleteAttributeAt(int, boolean) - Method in class weka.gui.arffviewer.ArffTableModel
deletes the attribute at the given col index
deleteAttributes() - Method in class weka.gui.arffviewer.ArffPanel
deletes the chosen attributes
deleteAttributes(int[]) - Method in class weka.gui.arffviewer.ArffSortedTableModel
deletes the attributes at the given indices
deleteAttributes(int[]) - Method in class weka.gui.arffviewer.ArffTableModel
deletes the attributes at the given indices
deleteAttributeType(int) - Method in class weka.core.Instances
Deletes all attributes of the given type in the dataset.
deleteEmptyBinsTipText() - Method in class weka.classifiers.meta.RegressionByDiscretization
Returns the tip text for this property
deleteEvent(String) - Method in class weka.associations.gsp.Element
Deletes the first or last event of an Element.
deleteEvent(String) - Method in class weka.associations.gsp.Sequence
Deletes either the first or the last event/item of a Sequence.
deleteGraftedCases(Instances) - Method in class weka.classifiers.trees.j48.GraftSplit
deletes the cases in data that belong to leaf pointed to by the test (i.e.
deleteInfrequentSequences(FastVector, long) - Static method in class weka.associations.gsp.Sequence
Deletes Sequences of a given set which don't meet the minimum support count threshold.
deleteInstance() - Method in class weka.gui.arffviewer.ArffPanel
deletes the currently selected instance
deleteInstance(boolean) - Method in class weka.gui.arffviewer.ArffViewerMainPanel
deletes the current selected Instance or several chosen ones
deleteInstanceAt(int) - Method in class weka.gui.arffviewer.ArffSortedTableModel
deletes the instance at the given index
deleteInstanceAt(int) - Method in class weka.gui.arffviewer.ArffTableModel
deletes the instance at the given index
deleteInstanceAt(int, boolean) - Method in class weka.gui.arffviewer.ArffTableModel
deletes the instance at the given index
deleteInstances() - Method in class weka.gui.arffviewer.ArffPanel
deletes all the currently selected instances
deleteInstances(int[]) - Method in class weka.gui.arffviewer.ArffSortedTableModel
deletes the instances at the given positions
deleteInstances(int[]) - Method in class weka.gui.arffviewer.ArffTableModel
deletes the instances at the given positions
deleteItemSets(FastVector, int, int) - Static method in class weka.associations.ItemSet
Deletes all item sets that don't have minimum support.
deleteItemSets(FastVector, int, int) - Static method in class weka.associations.LabeledItemSet
Deletes all item sets that don't have minimum support and have more than maximum support
deleteLastParent(Instances) - Method in class weka.classifiers.bayes.net.ParentSet
Delete last added parent from parent set and update internals (specifically the cardinality of the parent set)
deleteNode(String) - Method in class weka.classifiers.bayes.net.EditableBayesNet
Delete node from the network, updating instances, parentsets, distributions Conditional distributions are condensed by taking the values for the target node to be its first value.
deleteNode(int) - Method in class weka.classifiers.bayes.net.EditableBayesNet
Delete node from the network, updating instances, parentsets, distributions Conditional distributions are condensed by taking the values for the target node to be its first value.
deleteParent(int, Instances) - Method in class weka.classifiers.bayes.net.ParentSet
delete node from parent set
deleteSelection(FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet
Delete nodes with indexes in selection from the network, updating instances, parentsets, distributions Conditional distributions are condensed by taking the values for the target node to be its first value.
deleteStringAttributes() - Method in class weka.core.Instances
Deletes all string attributes in the dataset.
deleteWithMissing(int) - Method in class weka.core.Instances
Removes all instances with missing values for a particular attribute from the dataset.
deleteWithMissing(Attribute) - Method in class weka.core.Instances
Removes all instances with missing values for a particular attribute from the dataset.
deleteWithMissingClass() - Method in class weka.core.Instances
Removes all instances with a missing class value from the dataset.
delimitersTipText() - Method in class weka.core.tokenizers.CharacterDelimitedTokenizer
Returns the tip text for this property
delNodeValue(int, String) - Method in class weka.classifiers.bayes.net.EditableBayesNet
Delete node value from a node.
delRange(int, Instances, int, int) - Method in class weka.classifiers.trees.j48.Distribution
Deletes all instances in given range from given bag.
Delta - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Trust Region Radius
Delta - Variable in class weka.classifiers.bayes.blr.Prior
 
DeltaBeta - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Array to store Regression Coefficient updates.
DeltaR - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
This vector is used to store the increments on the R(i).
deltaTipText() - Method in class weka.associations.Apriori
Returns the tip text for this property
deltaTipText() - Method in class weka.associations.FPGrowth
Returns the tip text for this property
deltaTipText() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
 
deltaTipText() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
 
DeltaUpdate - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Trust Region Radius Update
DeltaUpdate - Variable in class weka.classifiers.bayes.blr.Prior
 
DensityBasedClusterer - Interface in weka.clusterers
Interface for clusterers that can estimate the density for a given instance.
DensityBasedClustererSplitEvaluator - Class in weka.experiment
A SplitEvaluator that produces results for a density based clusterer.
DensityBasedClustererSplitEvaluator() - Constructor for class weka.experiment.DensityBasedClustererSplitEvaluator
 
densityBasedClustererTipText() - Method in class weka.filters.unsupervised.attribute.ClusterMembership
Returns a description of this option suitable for display as a tip text in the gui.
dependencies() - Method in class weka.core.Capabilities
Returns an Iterator over the stored dependencies
depth() - Method in class weka.gui.HierarchyPropertyParser
Get the depth of the tree, i.e.
DerivedFieldMetaInfo - Class in weka.core.pmml
 
DerivedFieldMetaInfo(Element, ArrayList<Attribute>, TransformationDictionary) - Constructor for class weka.core.pmml.DerivedFieldMetaInfo
 
descend(CoverTree.CoverTreeNode, CoverTree.MyHeap, int, int, Stack<Stack<CoverTree.d_node>>, Stack<CoverTree.d_node>) - Method in class weka.core.neighboursearch.CoverTree
This functions adds nodes for inspection at the next level during NN search.
descendantPopulationSizeTipText() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
 
descendantPopulationSizeTipText() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
 
description() - Method in class weka.associations.tertius.Predicate
 
description() - Method in class weka.core.Option
Returns the option's description.
deserialize(InputStream) - Static method in class weka.core.Jython
deserializes the Python Object from the stream
deSerialize(String) - Static method in class weka.core.xml.XStream
Deserializes an object from the supplied XML string
designatedClassTipText() - Method in class weka.classifiers.meta.ThresholdSelector
 
desiredSizeTipText() - Method in class weka.classifiers.meta.Decorate
Returns the tip text for this property
desiredWeightOfInstancesPerIntervalTipText() - Method in class weka.filters.unsupervised.attribute.Discretize
Returns the tip text for this property
dest - Variable in class weka.gui.graphvisualizer.GraphEdge
The index of target node in Nodes vector
DEST_ARFF_TEXT - Static variable in class weka.gui.experiment.SimpleSetupPanel
 
DEST_CSV_TEXT - Static variable in class weka.gui.experiment.SimpleSetupPanel
 
DEST_DATABASE_TEXT - Static variable in class weka.gui.experiment.SimpleSetupPanel
The strings used to identify the combo box choices
destLbl - Variable in class weka.gui.graphvisualizer.GraphEdge
Label of target node
det() - Method in class weka.core.matrix.LUDecomposition
Determinant
det() - Method in class weka.core.matrix.Matrix
Matrix determinant
detectionPerAttributeTipText() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Returns the tip text for this property
determineAssignments(KDTreeNode, Instances, int[], int[], double) - Method in class weka.core.neighboursearch.KDTree
Assigns instances to the current centers called candidates.
determineBestInGrid(GridSearch.Grid, Instances, int) - Method in class weka.classifiers.meta.GridSearch
determines the best values-pair for the given grid, using CV with specified number of folds.
determineBounds() - Method in class weka.gui.visualize.Plot2D
Determine the min and max values for axis and colouring attributes
determineClass(String) - Method in class weka.core.xml.XMLSerialization
returns the associated class for the given name
determineClass(double, double, int, int, int, int, double, int, double) - Method in interface weka.datagenerators.classifiers.classification.Agrawal.ClassFunction
returns a class value based on the given inputs
determineClassAttribute() - Method in class weka.core.converters.SVMLightLoader
Determines the class attribute, either a binary +1/-1 or numeric attribute.
determineColumnConstraints(ResultProducer) - Method in class weka.experiment.AveragingResultProducer
Determines if there are any constraints (imposed by the destination) on the result columns to be produced by resultProducers.
determineColumnConstraints(ResultProducer) - Method in class weka.experiment.CSVResultListener
Determines if there are any constraints (imposed by the destination) on the result columns to be produced by resultProducers.
determineColumnConstraints(ResultProducer) - Method in class weka.experiment.DatabaseResultListener
Determines if there are any constraints (imposed by the destination) on any additional measures produced by resultProducers.
determineColumnConstraints(ResultProducer) - Method in class weka.experiment.LearningRateResultProducer
Determines if there are any constraints (imposed by the destination) on the result columns to be produced by resultProducers.
determineColumnConstraints(ResultProducer) - Method in interface weka.experiment.ResultListener
Determines if there are any constraints (imposed by the destination) on additional result columns to be produced by resultProducers.
determineColumnNames(String, String, Instances) - Method in class weka.gui.experiment.ResultsPanel
Returns a vector with column names of the dataset, listed in "list".
determineCommonPrefix(String) - Method in class weka.core.Trie.TrieNode
determines the common prefix of the nodes.
determineDescriptor(String, String) - Method in class weka.core.xml.XMLSerialization
returns a property descriptor if possible, otherwise null
determineIndex(String, int) - Method in class weka.filters.unsupervised.attribute.Reorder
parses the index string and returns the corresponding int index
determineIndices(int) - Method in class weka.filters.unsupervised.attribute.Reorder
parses the range string and returns an array with the indices
determineNumAttributes(String, int) - Method in class weka.core.converters.LibSVMLoader
determines the number of attributes, if the number of attributes in the given row is greater than the current amount then this number will be returned, otherwise the current number.
determineNumAttributes(double[], int) - Method in class weka.core.converters.SVMLightLoader
determines the number of attributes, if the number of attributes in the given row is greater than the current amount then this number will be returned, otherwise the current number.
determineNumberOfClusters() - Method in class weka.clusterers.CLOPE
 
determineNumberOfClusters() - Method in class weka.clusterers.Cobweb
determines the number of clusters if necessary
determineOutputFormat(Instances) - Method in class weka.filters.MultiFilter
Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.SimpleFilter
Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.SimpleStreamFilter
Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.supervised.attribute.AddClassification
Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.supervised.attribute.PLSFilter
Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.ClassAssigner
Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.KernelFilter
Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.NumericToNominal
Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
Determines the output format based only on the full input dataset and returns this otherwise null is returned.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.RandomSubset
Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.RELAGGS
Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Wavelet
Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.instance.SubsetByExpression
Determines the output format based on the input format and returns this.
determineTemplate(int) - Method in class weka.experiment.AveragingResultProducer
Simulates a run to collect the keys the sub-resultproducer could generate.
determineUnusedIndices(Instances) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
determines the indices of unused attributes (ones that are not covered by any of the range).
determineValues(Instances) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
determines the values to retain, it is always at least 1 and up to the maximum number of distinct values
DIAMOND_SHAPE - Static variable in class weka.gui.visualize.Plot2D
 
difference(int, double, double) - Method in class weka.clusterers.FarthestFirst
Computes the difference between two given attribute values.
difference(int, String, String) - Method in class weka.core.AbstractStringDistanceFunction
Computes the difference between two given attribute values.
difference(int, double, double) - Method in class weka.core.NormalizableDistance
Computes the difference between two given attribute values.
differencesProbability - Variable in class weka.experiment.PairedStats
The probability of obtaining the observed differences
differencesSignificance - Variable in class weka.experiment.PairedStats
A significance indicator: 0 if the differences are not significant > 0 if x significantly greater than y < 0 if x significantly less than y
differencesStats - Variable in class weka.experiment.PairedStats
The stats associated with the paired differences
digits - Variable in class weka.core.matrix.ExponentialFormat
 
DIRECTED - Static variable in interface weka.gui.graphvisualizer.GraphConstants
Types of Edges
directionTipText() - Method in class weka.attributeSelection.BestFirst
Returns the tip text for this property
disable(Capabilities.Capability) - Method in class weka.core.Capabilities
disables the given capability Disabling NOMINAL_ATTRIBUTES also disables BINARY_ATTRIBUTES, UNARY_ATTRIBUTES and EMPTY_NOMINAL_ATTRIBUTES.
disable(Capabilities.Capability) - Method in class weka.core.FindWithCapabilities
disables the given capability.
disableAll() - Method in class weka.core.Capabilities
disables all attribute and class types (including dependencies)
disableAllAttributeDependencies() - Method in class weka.core.Capabilities
disables all attribute type dependencies
disableAllAttributes() - Method in class weka.core.Capabilities
disables all attribute types
disableAllClassDependencies() - Method in class weka.core.Capabilities
disables all class type dependencies
disableAllClasses() - Method in class weka.core.Capabilities
disables all class types
disabled_getEquivalent() - Method in class weka.associations.Tertius
Get the value of equivalent.
disabled_getPartFile() - Method in class weka.associations.Tertius
Get the value of partFile.
disabled_getSameClause() - Method in class weka.associations.Tertius
Get the value of sameClause.
disabled_getSubsumption() - Method in class weka.associations.Tertius
Get the value of subsumption.
disabled_setEquivalent(boolean) - Method in class weka.associations.Tertius
Set the value of equivalent.
disabled_setPartFile(File) - Method in class weka.associations.Tertius
Set the value of partFile.
disabled_setSameClause(boolean) - Method in class weka.associations.Tertius
Set the value of sameClause.
disabled_setSubsumption(boolean) - Method in class weka.associations.Tertius
Set the value of subsumption.
disableDependency(Capabilities.Capability) - Method in class weka.core.Capabilities
disables the dependency of the given capability Disabling NOMINAL_ATTRIBUTES also disables BINARY_ATTRIBUTES, UNARY_ATTRIBUTES and EMPTY_NOMINAL_ATTRIBUTES.
disableNot(Capabilities.Capability) - Method in class weka.core.FindWithCapabilities
disables the given "not to have" capability.
disconnect(NeuralConnection, NeuralConnection) - Static method in class weka.classifiers.functions.neural.NeuralConnection
Disconnects two units.
DISCONNECT - Static variable in class weka.gui.sql.event.ConnectionEvent
it was a disconnect
disconnectFromDatabase() - Method in class weka.experiment.DatabaseUtils
Closes the connection to the database.
disconnectInput(NeuralConnection, int) - Method in class weka.classifiers.functions.neural.NeuralConnection
This will disconnect the input with the specific connection number From this node (only on this end however).
disconnectInput(NeuralConnection, int) - Method in class weka.classifiers.functions.neural.NeuralNode
This will disconnect the input with the specific connection number From this node (only on this end however).
disconnectionNotification(String, Object) - Method in class weka.gui.beans.AbstractDataSink
Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.AbstractEvaluator
Notify this object that it has been deregistered as a listener with a source with respect to the supplied event named
disconnectionNotification(String, Object) - Method in class weka.gui.beans.AbstractTestSetProducer
Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.AbstractTrainingSetProducer
Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.Associator
Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in interface weka.gui.beans.BeanCommon
Notify this object that it has been deregistered as a listener with a source for named event.
disconnectionNotification(String, Object) - Method in class weka.gui.beans.ClassAssigner
Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.Classifier
Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.ClassValuePicker
Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.Clusterer
Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in interface weka.gui.beans.ConnectionNotificationConsumer
Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name This method should be implemented synchronized.
disconnectionNotification(String, Object) - Method in class weka.gui.beans.CostBenefitAnalysis
Notify this object that it has been deregistered as a listener with a source for named event.
disconnectionNotification(String, Object) - Method in class weka.gui.beans.Filter
Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.InstanceStreamToBatchMaker
Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.Loader
Notify this object that it has been deregistered as a listener with a source for named event.
disconnectionNotification(String, Object) - Method in class weka.gui.beans.MetaBean
Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name.
disconnectionNotification(String, Object) - Method in class weka.gui.beans.PredictionAppender
Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.SerializedModelSaver
Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name.
disconnectionNotification(String, Object) - Method in class weka.gui.beans.StripChart
Notify this object that it has been deregistered as a listener with a source for named event.
disconnectionNotification(String, Object) - Method in class weka.gui.beans.TextViewer
Notify this object that it has been deregistered as a listener with a source for named event.
disconnectOutput(NeuralConnection, int) - Method in class weka.classifiers.functions.neural.NeuralConnection
This will disconnect the output with the specific connection number From this node (only on this end however).
DiscreteEstimator - Class in weka.estimators
Simple symbolic probability estimator based on symbol counts.
DiscreteEstimator(int, boolean) - Constructor for class weka.estimators.DiscreteEstimator
Constructor
DiscreteEstimator(int, double) - Constructor for class weka.estimators.DiscreteEstimator
Constructor
DiscreteEstimatorBayes - Class in weka.classifiers.bayes.net.estimate
Symbolic probability estimator based on symbol counts and a prior.
DiscreteEstimatorBayes(int, double) - Constructor for class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes
Constructor
DiscreteEstimatorFullBayes - Class in weka.classifiers.bayes.net.estimate
Symbolic probability estimator based on symbol counts and a prior.
DiscreteEstimatorFullBayes(int, double, double, DiscreteEstimatorBayes, DiscreteEstimatorBayes, double) - Constructor for class weka.classifiers.bayes.net.estimate.DiscreteEstimatorFullBayes
Constructor
DiscreteFunction - Class in weka.classifiers.functions.pace
Class for handling discrete functions.
DiscreteFunction() - Constructor for class weka.classifiers.functions.pace.DiscreteFunction
Constructs an empty discrete function
DiscreteFunction(DoubleVector) - Constructor for class weka.classifiers.functions.pace.DiscreteFunction
Constructs a discrete function with the point values provides and the function values are all 1/n.
DiscreteFunction(DoubleVector, DoubleVector) - Constructor for class weka.classifiers.functions.pace.DiscreteFunction
Constructs a discrete function with both the point values and function values provided.
Discretize - Class in weka.core.pmml
Class encapsulating a Discretize Expression.
Discretize(Element, FieldMetaInfo.Optype, ArrayList<Attribute>) - Constructor for class weka.core.pmml.Discretize
Constructs a Discretize Expression
Discretize - Class in weka.filters.supervised.attribute
An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
Discretize() - Constructor for class weka.filters.supervised.attribute.Discretize
Constructor - initialises the filter
Discretize - Class in weka.filters.unsupervised.attribute
An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
Discretize() - Constructor for class weka.filters.unsupervised.attribute.Discretize
Constructor - initialises the filter
Discretize(String) - Constructor for class weka.filters.unsupervised.attribute.Discretize
Another constructor, sets the attribute indices immediately
Discretize.DiscretizeBin - Class in weka.core.pmml
Inner class to encapsulate DiscretizeBin elements
DiscretizeBin(Element) - Constructor for class weka.core.pmml.Discretize.DiscretizeBin
 
discretizeBinTipText() - Method in class weka.classifiers.mi.MIBoost
Returns the tip text for this property
displayModelInOldFormatTipText() - Method in class weka.classifiers.bayes.NaiveBayes
Returns the tip text for this property
displayModelInOldFormatTipText() - Method in class weka.clusterers.EM
Returns the tip text for this property
displayResultset(int) - Method in class weka.experiment.PairedTTester
Checks whether the resultset with the given index shall be displayed.
displayResultset(int) - Method in interface weka.experiment.Tester
Checks whether the resultset with the given index shall be displayed.
displayRulesTipText() - Method in class weka.classifiers.rules.DecisionTable
Returns the tip text for this property
displayStdDevsTipText() - Method in class weka.clusterers.SimpleKMeans
Returns the tip text for this property
dispose() - Method in class weka.gui.GUIChooser.ChildFrameSDI
de-registers the child frame with the parent first.
dispose() - Method in class weka.gui.Main.ChildFrameMDI
de-registers the child frame with the parent first.
dispose() - Method in class weka.gui.Main.ChildFrameSDI
de-registers the child frame with the parent first.
dispose() - Method in class weka.gui.visualize.PostscriptGraphics
Not implemented
disposeSplash() - Static method in class weka.gui.SplashWindow
Closes the splash window.
dist_of_scale(int) - Method in class weka.core.neighboursearch.CoverTree
Returns the distance/value of a given scale/level.
dist_split(Stack<CoverTree.DistanceNode>, Stack<CoverTree.DistanceNode>, CoverTree.DistanceNode, int) - Method in class weka.core.neighboursearch.CoverTree
Moves all the points in point_set covered by (the ball of) new_point into new_point_set, based on the given scale/level.
distance(Instance, Instance) - Method in class weka.classifiers.mi.CitationKNN
distance between two instances
distance(Instance, Instance) - Method in class weka.clusterers.FarthestFirst
Calculates the distance between two instances
distance(DataObject) - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
Calculates the distance between dataObject and this.dataObject
distance(DataObject) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
Calculates the euclidian-distance between dataObject and this.dataObject
distance(DataObject) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
Calculates the manhattan-distance between dataObject and this.dataObject
distance(Instance, Instance, double, PerformanceStats) - Method in class weka.core.AbstractStringDistanceFunction
Calculates the distance between two instances.
distance(Instance, Instance) - Method in interface weka.core.DistanceFunction
Calculates the distance between two instances.
distance(Instance, Instance, PerformanceStats) - Method in interface weka.core.DistanceFunction
Calculates the distance between two instances.
distance(Instance, Instance, double) - Method in interface weka.core.DistanceFunction
Calculates the distance between two instances.
distance(Instance, Instance, double, PerformanceStats) - Method in interface weka.core.DistanceFunction
Calculates the distance between two instances.
distance(Instance, Instance) - Method in class weka.core.EuclideanDistance
Calculates the distance between two instances.
distance(Instance, Instance, PerformanceStats) - Method in class weka.core.EuclideanDistance
Calculates the distance (or similarity) between two instances.
distance - Variable in class weka.core.neighboursearch.CoverTree.MyHeapElement
the distance.
distance - Variable in class weka.core.neighboursearch.NearestNeighbourSearch.MyHeapElement
the distance of this element.
distance(Instance, Instance) - Method in class weka.core.NormalizableDistance
Calculates the distance between two instances.
distance(Instance, Instance, PerformanceStats) - Method in class weka.core.NormalizableDistance
Calculates the distance between two instances.
distance(Instance, Instance, double) - Method in class weka.core.NormalizableDistance
Calculates the distance between two instances.
distance(Instance, Instance, double, PerformanceStats) - Method in class weka.core.NormalizableDistance
Calculates the distance between two instances.
distanceFTipText() - Method in class weka.clusterers.XMeans
Returns the tip text for this property.
DistanceFunction - Interface in weka.core
Interface for any class that can compute and return distances between two instances.
distanceFunctionTipText() - Method in class weka.clusterers.HierarchicalClusterer
 
distanceFunctionTipText() - Method in class weka.clusterers.SimpleKMeans
Returns the tip text for this property.
distanceFunctionTipText() - Method in class weka.core.neighboursearch.NearestNeighbourSearch
Returns the tip text for this property.
distanceIsBranchLengthTipText() - Method in class weka.clusterers.HierarchicalClusterer
 
distanceSet(Instance, Instance) - Method in class weka.classifiers.mi.CitationKNN
Calculates the distance between two instances
distanceToHrect(KDTreeNode, Instance) - Method in class weka.core.neighboursearch.KDTree
Returns the distance between a point and an hyperrectangle.
distanceWeightingTipText() - Method in class weka.classifiers.lazy.IBk
Returns the tip text for this property.
distinctCount - Variable in class weka.core.AttributeStats
The number of distinct values
distMultTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Returns the tip text for this property
distortion(int[][], Instances) - Method in class weka.clusterers.XMeans
Calculates the maximum likelihood estimate for the variance.
distributedExperimentSelected() - Method in class weka.gui.experiment.DistributeExperimentPanel
Returns true if the distribute experiment checkbox is selected
DistributeExperimentPanel - Class in weka.gui.experiment
This panel enables an experiment to be distributed to multiple hosts; it also allows remote host names to be specified.
DistributeExperimentPanel() - Constructor for class weka.gui.experiment.DistributeExperimentPanel
Constructor
DistributeExperimentPanel(Experiment) - Constructor for class weka.gui.experiment.DistributeExperimentPanel
Creates the panel with the supplied initial experiment.
distribution() - Method in class weka.classifiers.evaluation.NominalPrediction
Gets the predicted probabilities
distribution() - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Returns the distribution of class values induced by the model.
Distribution - Class in weka.classifiers.trees.j48
Class for handling a distribution of class values.
Distribution(int, int) - Constructor for class weka.classifiers.trees.j48.Distribution
Creates and initializes a new distribution.
Distribution(double[][]) - Constructor for class weka.classifiers.trees.j48.Distribution
Creates and initializes a new distribution using the given array.
Distribution(Instances) - Constructor for class weka.classifiers.trees.j48.Distribution
Creates a distribution with only one bag according to instances in source.
Distribution(Instances, ClassifierSplitModel) - Constructor for class weka.classifiers.trees.j48.Distribution
Creates a distribution according to given instances and split model.
Distribution(Distribution) - Constructor for class weka.classifiers.trees.j48.Distribution
Creates distribution with only one bag by merging all bags of given distribution.
Distribution(Distribution, int) - Constructor for class weka.classifiers.trees.j48.Distribution
Creates distribution with two bags by merging all bags apart of the indicated one.
distribution(double[][], double[][][], int, Instances) - Method in class weka.classifiers.trees.RandomTree
Computes class distribution for an attribute.
distribution(double[][], double[][][], int, int[], double[], double[][], Instances) - Method in class weka.classifiers.trees.REPTree.Tree
Computes class distribution for an attribute.
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.AODE
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.AODEsr
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.BayesNet
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.DMNBtext
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.DMNBtext.DNBBinary
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.HNB
Calculates the class membership probabilities for the given test instance
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.NaiveBayes
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.NaiveBayesMultinomial
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.NaiveBayesSimple
Calculates the class membership probabilities for the given test instance.
distributionForInstance(BayesNet, Instance) - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator
Calculates the class membership probabilities for the given test instance.
distributionForInstance(BayesNet, Instance) - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
Calculates the class membership probabilities for the given test instance.
distributionForInstance(BayesNet, Instance) - Method in class weka.classifiers.bayes.net.estimate.SimpleEstimator
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.WAODE
Calculates the class membership probabilities for the given test instance
distributionForInstance(Instance) - Method in class weka.classifiers.Classifier
Predicts the class memberships for a given instance.
distributionForInstance(Instance) - Method in class weka.classifiers.functions.LibLINEAR
Computes the distribution for a given instance.
distributionForInstance(Instance) - Method in class weka.classifiers.functions.LibSVM
Computes the distribution for a given instance.
distributionForInstance(Instance) - Method in class weka.classifiers.functions.Logistic
Computes the distribution for a given instance
distributionForInstance(Instance) - Method in class weka.classifiers.functions.MultilayerPerceptron
Call this function to predict the class of an instance once a classification model has been built with the buildClassifier call.
distributionForInstance(Instance) - Method in class weka.classifiers.functions.RBFNetwork
Computes the distribution for a given instance
distributionForInstance(Instance) - Method in class weka.classifiers.functions.SimpleLogistic
Returns class probabilities for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.functions.SMO
Estimates class probabilities for given instance.
distributionForInstance(Instance) - Method in class weka.classifiers.functions.SPegasos
Computes the distribution for a given instance
distributionForInstance(Instance) - Method in class weka.classifiers.functions.VotedPerceptron
Outputs the distribution for the given output.
distributionForInstance(Instance) - Method in class weka.classifiers.lazy.IBk
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.lazy.KStar
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.lazy.LBR
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.lazy.LWL
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.AdaBoostM1
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Classifies a given instance after attribute selection
distributionForInstance(Instance) - Method in class weka.classifiers.meta.Bagging
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.ClassificationViaRegression
Returns the distribution for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.CostSensitiveClassifier
Returns class probabilities.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.CVParameterSelection
Predicts the class distribution for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.Dagging
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.Decorate
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.END
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.FilteredClassifier
Classifies a given instance after filtering.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.Grading
Returns class probabilities for a given instance using the stacked classifier.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.LogitBoost
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.MetaCost
Classifies a given instance after filtering.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.MultiClassClassifier
Returns the distribution for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.MultiScheme
Returns class probabilities.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
Predicts the class distribution for a given instance
distributionForInstance(Instance) - Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
Predicts the class distribution for a given instance
distributionForInstance(Instance) - Method in class weka.classifiers.meta.nestedDichotomies.ND
Predicts the class distribution for a given instance
distributionForInstance(Instance, ND.NDTree) - Method in class weka.classifiers.meta.nestedDichotomies.ND
Predicts the class distribution for a given instance
distributionForInstance(Instance) - Method in class weka.classifiers.meta.OrdinalClassClassifier
Returns the distribution for an instance.
distributionForInstance(double[]) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
returns the distribution the committee generates for an instance (given Fs values)
distributionForInstance(Instance) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
returns the distribution the committee generates for an instance
distributionForInstance(Instance) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Computes class distribution of an instance using the best committee.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.RandomCommittee
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.RandomSubSpace
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.RotationForest
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.Stacking
Returns class probabilities.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.StackingC
Classifies a given instance using the stacked classifier.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.ThresholdSelector
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.Vote
Classifies a given instance using the selected combination rule.
distributionForInstance(Instance) - Method in class weka.classifiers.mi.CitationKNN
Computes the distribution for a given exemplar
distributionForInstance(Instance) - Method in class weka.classifiers.mi.MDD
Computes the distribution for a given exemplar
distributionForInstance(Instance) - Method in class weka.classifiers.mi.MIBoost
Computes the distribution for a given exemplar
distributionForInstance(Instance) - Method in class weka.classifiers.mi.MIDD
Computes the distribution for a given exemplar
distributionForInstance(Instance) - Method in class weka.classifiers.mi.MIEMDD
Computes the distribution for a given exemplar
distributionForInstance(Instance) - Method in class weka.classifiers.mi.MILR
Computes the distribution for a given exemplar
distributionForInstance(Instance) - Method in class weka.classifiers.mi.MIOptimalBall
Computes the distribution for a given multiple instance
distributionForInstance(Instance) - Method in class weka.classifiers.mi.MISMO
Estimates class probabilities for given instance.
distributionForInstance(Instance) - Method in class weka.classifiers.mi.MISVM
Computes the distribution for a given exemplar
distributionForInstance(Instance) - Method in class weka.classifiers.mi.MIWrapper
Computes the distribution for a given exemplar
distributionForInstance(Instance) - Method in class weka.classifiers.mi.SimpleMI
Computes the distribution for a given exemplar
distributionForInstance(Instance) - Method in class weka.classifiers.misc.HyperPipes
Classifies the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.misc.SerializedClassifier
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.misc.VFI
Classifies the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.pmml.consumer.GeneralRegression
Classifies the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.pmml.consumer.NeuralNetwork
Classifies the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.pmml.consumer.Regression
Classifies the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.rules.ConjunctiveRule
Computes class distribution for the given instance.
distributionForInstance(Instance) - Method in class weka.classifiers.rules.DecisionTable
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.rules.DTNB
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.rules.JRip
Classify the test instance with the rule learner and provide the class distributions
distributionForInstance(Instance) - Method in class weka.classifiers.rules.part.ClassifierDecList
Returns class probabilities for a weighted instance.
distributionForInstance(Instance) - Method in class weka.classifiers.rules.PART
Returns class probabilities for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.rules.part.MakeDecList
Returns the class distribution for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.rules.ZeroR
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.ADTree
Returns the class probability distribution for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.BFTree
Computes class probabilities for instance using the decision tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.DecisionStump
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.FT
Returns class probabilities for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.ft.FTInnerNode
Returns the class probabilities for an instance given by the Functional tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.ft.FTLeavesNode
Returns the class probabilities for an instance given by the Functional Leaves tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.ft.FTNode
Returns the class probabilities for an instance given by the Functional Tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.ft.FTtree
Returns the class probabilities for an instance given by the Functional tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.Id3
Computes class distribution for instance using decision tree.
distributionForInstance(Instance, boolean) - Method in class weka.classifiers.trees.j48.ClassifierTree
Returns class probabilities for a weighted instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.J48
Returns class probabilities for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.J48graft
Returns class probabilities for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.LADTree
Returns the class probability distribution for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.LMT
Returns class probabilities for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.lmt.LMTNode
Returns the class probabilities for an instance given by the logistic model tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.lmt.LogisticBase
Returns class probabilities for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.NBTree
Returns class probabilities for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.RandomForest
Returns the class probability distribution for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.RandomTree
Computes class distribution of an instance using the decision tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.REPTree
Computes class distribution of an instance using the tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.REPTree.Tree
Computes class distribution of an instance using the tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.SimpleCart
Computes class probabilities for instance using the decision tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.UserClassifier
Call this function to get a double array filled with the probability of how likely each class type is the class of the instance.
distributionForInstance(Instance) - Method in class weka.clusterers.AbstractClusterer
Predicts the cluster memberships for a given instance.
distributionForInstance(Instance) - Method in class weka.clusterers.AbstractDensityBasedClusterer
Returns the cluster probability distribution for an instance.
distributionForInstance(Instance) - Method in interface weka.clusterers.Clusterer
Predicts the cluster memberships for a given instance.
distributionForInstance(Instance) - Method in class weka.clusterers.FilteredClusterer
Classifies a given instance after filtering.
distributionForInstance(Instance) - Method in class weka.clusterers.HierarchicalClusterer
 
distributionForInstanceAverage(Instance) - Method in class weka.classifiers.meta.Vote
Classifies a given instance using the Average of Probabilities combination rule.
distributionForInstanceMajorityVoting(Instance) - Method in class weka.classifiers.meta.Vote
Classifies a given instance using the Majority Voting combination rule.
distributionForInstanceMax(Instance) - Method in class weka.classifiers.meta.Vote
Classifies a given instance using the Maximum Probability combination rule.
distributionForInstanceMin(Instance) - Method in class weka.classifiers.meta.Vote
Classifies a given instance using the Minimum Probability combination rule.
distributionForInstanceProduct(Instance) - Method in class weka.classifiers.meta.Vote
Classifies a given instance using the Product of Probabilities combination rule.
distributionsByOriginalIndex(double[]) - Method in class weka.filters.supervised.attribute.ClassOrder
Convert the given class distribution back to the distributions with the original internal class index
distributionSpreadTipText() - Method in class weka.filters.supervised.instance.SpreadSubsample
Returns the tip text for this property
distributionTipText() - Method in class weka.filters.unsupervised.attribute.RandomProjection
Returns the tip text for this property
divergence(BayesNet) - Method in class weka.classifiers.bayes.net.BIFReader
calculates the divergence between the probability distribution represented by this network and that of another, that is, \sum_{x\in X} P(x)log P(x)/Q(x) where X is the set of values the nodes in the network can take, P(x) the probability of this network for configuration x Q(x) the probability of the other network for configuration x
divide(Instances, boolean) - Static method in class weka.associations.LabeledItemSet
Splits the class attribute away.
dividedBy(DoubleVector) - Method in class weka.core.matrix.DoubleVector
Divided by another DoubleVector element by element
dividedByEquals(DoubleVector) - Method in class weka.core.matrix.DoubleVector
Divided by another DoubleVector element by element in place
DIVISION - Static variable in interface weka.core.mathematicalexpression.sym
 
DIVISION - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
DKConditionalEstimator - Class in weka.estimators
Conditional probability estimator for a discrete domain conditional upon a numeric domain.
DKConditionalEstimator(int, double) - Constructor for class weka.estimators.DKConditionalEstimator
Constructor
dl(int) - Method in class weka.core.Debug.DBO
Return true if the debug level is set same method as outpuTypeSet but better name
dloss(double) - Method in class weka.classifiers.functions.SPegasos
 
DMNBtext - Class in weka.classifiers.bayes
Class for building and using a Discriminative Multinomial Naive Bayes classifier.
DMNBtext() - Constructor for class weka.classifiers.bayes.DMNBtext
 
DMNBtext.DNBBinary - Class in weka.classifiers.bayes
 
DNBBinary() - Constructor for class weka.classifiers.bayes.DMNBtext.DNBBinary
 
DNConditionalEstimator - Class in weka.estimators
Conditional probability estimator for a discrete domain conditional upon a numeric domain.
DNConditionalEstimator(int, double) - Constructor for class weka.estimators.DNConditionalEstimator
Constructor
dnorm(double) - Static method in class weka.core.matrix.Maths
Returns the density of the standard normal.
dnorm(double, double, double) - Static method in class weka.core.matrix.Maths
Returns the density value of a standard normal.
dnorm(double, DoubleVector, double) - Static method in class weka.core.matrix.Maths
Returns the density values of a set of normal distributions with different means.
dnormLog(double) - Static method in class weka.core.matrix.Maths
Returns the log-density of the standard normal.
dnormLog(double, double, double) - Static method in class weka.core.matrix.Maths
Returns the log-density value of a standard normal.
dnormLog(double, DoubleVector, double) - Static method in class weka.core.matrix.Maths
Returns the log-density values of a set of normal distributions with different means.
do_action(int, lr_parser, Stack, int) - Method in class weka.core.mathematicalexpression.Parser
Invoke a user supplied parse action.
do_action(int, lr_parser, Stack, int) - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
Invoke a user supplied parse action.
doAverageResult(Object[]) - Method in class weka.experiment.AveragingResultProducer
Asks the resultlistener whether an average result is required, and if so, calculates it.
doCommandlineCompletion(KeyEvent) - Method in class weka.gui.SimpleCLIPanel
performs commandline completion on packages and classnames.
DOCTYPE - Static variable in class weka.core.xml.XMLInstances
the DTD
DOCTYPE - Static variable in class weka.core.xml.XMLOptions
the DTD for the XML file.
DOCTYPE - Static variable in class weka.core.xml.XMLSerialization
the DOCTYPE for the serialization
doesntUseTestClassVal(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.classifiers.CheckClassifier
Checks whether the classifier erroneously uses the class value of test instances (if provided).
doGrafting(Instances) - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG
Initializes variables for grafting.
doHistory(KeyEvent) - Method in class weka.gui.SimpleCLIPanel
Changes the currently displayed command line when certain keys are pressed.
doLayout() - Method in class weka.gui.beans.KnowledgeFlowApp.BeanLayout
 
doLog(Logger.Level, String, String, String, int) - Method in class weka.core.logging.ConsoleLogger
Performs the actual logging.
doLog(Logger.Level, String, String, String, int) - Method in class weka.core.logging.FileLogger
Performs the actual logging.
doLog(Logger.Level, String, String, String, int) - Method in class weka.core.logging.Logger
Performs the actual logging.
doLog(Logger.Level, String, String, String, int) - Method in class weka.core.logging.OutputLogger
Performs the actual logging.
doMarkovBlanketCorrection(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
for each node in the network make sure it is in the Markov blanket of the classifier node, and if not, add arrows so that it is.
doMetaConnection(BeanInstance, BeanInstance, EventSetDescriptor, JComponent) - Static method in class weka.gui.beans.BeanConnection
 
done() - Method in interface weka.classifiers.IterativeClassifier
Signal end of iterating, useful for any house-keeping/cleanup
done() - Method in class weka.classifiers.pmml.consumer.PMMLClassifier
Signal that a scoring run has been completed.
done() - Method in class weka.classifiers.trees.ADTree
Frees memory that is no longer needed for a final model - will no longer be able to increment the classifier after calling this.
done() - Method in class weka.classifiers.trees.LADTree
 
doNotOperateOnPerClassBasisTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Returns the tip text for this property.
doNotReplaceMissingValuesTipText() - Method in class weka.classifiers.functions.LibLINEAR
Returns the tip text for this property
doNotReplaceMissingValuesTipText() - Method in class weka.classifiers.functions.LibSVM
Returns the tip text for this property
dontFilterAfterFirstBatchTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Returns the tip text for this property.
dontNormalizeTipText() - Method in class weka.classifiers.functions.SPegasos
Returns the tip text for this property
dontNormalizeTipText() - Method in class weka.core.NormalizableDistance
Returns the tip text for this property.
dontReplaceMissingTipText() - Method in class weka.classifiers.functions.SPegasos
Returns the tip text for this property
dontReplaceMissingValuesTipText() - Method in class weka.clusterers.SimpleKMeans
Returns the tip text for this property
doRun(int) - Method in class weka.experiment.AveragingResultProducer
Gets the results for a specified run number.
doRun(int) - Method in class weka.experiment.CrossValidationResultProducer
Gets the results for a specified run number.
doRun(int) - Method in class weka.experiment.DatabaseResultProducer
Gets the results for a specified run number.
doRun(int) - Method in class weka.experiment.LearningRateResultProducer
Gets the results for a specified run number.
doRun(int) - Method in class weka.experiment.RandomSplitResultProducer
Gets the results for a specified run number.
doRun(int) - Method in interface weka.experiment.ResultProducer
Gets the results for a specified run number.
doRunKeys(int) - Method in class weka.experiment.AveragingResultProducer
Gets the keys for a specified run number.
doRunKeys(int) - Method in class weka.experiment.CrossValidationResultProducer
Gets the keys for a specified run number.
doRunKeys(int) - Method in class weka.experiment.DatabaseResultProducer
Gets the keys for a specified run number.
doRunKeys(int) - Method in class weka.experiment.LearningRateResultProducer
Gets the keys for a specified run number.
doRunKeys(int) - Method in class weka.experiment.RandomSplitResultProducer
Gets the keys for a specified run number.
doRunKeys(int) - Method in interface weka.experiment.ResultProducer
Gets the keys for a specified run number.
doSMOTE() - Method in class weka.filters.supervised.instance.SMOTE
The procedure implementing the SMOTE algorithm.
doTests() - Method in class weka.associations.CheckAssociator
Begin the tests, reporting results to System.out
doTests() - Method in class weka.attributeSelection.CheckAttributeSelection
Begin the tests, reporting results to System.out
doTests() - Method in class weka.classifiers.CheckClassifier
Begin the tests, reporting results to System.out
doTests() - Method in class weka.classifiers.functions.supportVector.CheckKernel
Begin the tests, reporting results to System.out
doTests() - Method in class weka.clusterers.CheckClusterer
Begin the tests, reporting results to System.out
doTests() - Method in class weka.core.Check
Begin the tests, reporting results to System.out
doTests() - Method in class weka.core.CheckGOE
Runs some diagnostic tests on the object.
doTests() - Method in class weka.core.CheckOptionHandler
Runs some diagnostic tests on an optionhandler object.
doTests() - Method in class weka.core.CheckScheme
Begin the tests, reporting results to System.out
doTests() - Method in class weka.estimators.CheckEstimator
Begin the tests, reporting results to System.out
dotMultiply(AlgVector) - Method in class weka.core.AlgVector
Returns the inner (or dot) product of two vectors
DotParser - Class in weka.gui.graphvisualizer
This class parses input in DOT format, and builds the datastructures that are passed to it.
DotParser(Reader, FastVector, FastVector) - Constructor for class weka.gui.graphvisualizer.DotParser
Dot parser Constructor
dotProd(Instance, double[], int) - Static method in class weka.classifiers.functions.SPegasos
 
dotProd(Instance, Instance) - Method in class weka.classifiers.functions.supportVector.CachedKernel
Calculates a dot product between two instances
DOUBLE - Static variable in class weka.experiment.DatabaseUtils
Type mapping for DOUBLE used for reading experiment results.
DOUBLE - Static variable in interface weka.gui.graphvisualizer.GraphConstants
Types of Edges
doubleToString(double, int) - Static method in class weka.core.Utils
Rounds a double and converts it into String.
doubleToString(double, int, int) - Static method in class weka.core.Utils
Rounds a double and converts it into a formatted decimal-justified String.
doubleToString(double, int) - Method in class weka.experiment.ResultMatrix
returns the given number as string rounded to the given number of decimals.
DoubleVector - Class in weka.core.matrix
A vector specialized on doubles.
DoubleVector() - Constructor for class weka.core.matrix.DoubleVector
Constructs a null vector.
DoubleVector(int) - Constructor for class weka.core.matrix.DoubleVector
Constructs an n-vector of zeros.
DoubleVector(int, double) - Constructor for class weka.core.matrix.DoubleVector
Constructs a constant n-vector.
DoubleVector(double[]) - Constructor for class weka.core.matrix.DoubleVector
Constructs a vector directly from a double array
downheap() - Method in class weka.core.neighboursearch.CoverTree.MyHeap
performs downheap operation for the heap to maintian its properties.
downheap() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.MyHeap
performs downheap operation for the heap to maintian its properties.
dp(String) - Method in class weka.core.Debug.DBO
prints out text if verbose is on.
dp(int, String) - Method in class weka.core.Debug.DBO
prints out text but only if debug level is set.
dpln(String) - Method in class weka.core.Debug.DBO
prints out text + endofline if verbose is on.
dpln(int, String) - Method in class weka.core.Debug.DBO
prints out text + endofline but only if parameter debug type is set.
draw(Shape) - Method in class weka.gui.visualize.PostscriptGraphics
 
draw3DRect(int, int, int, int, boolean) - Method in class weka.gui.visualize.PostscriptGraphics
Draw an outlined rectangle with 3D effect in current pen color.
Drawable - Interface in weka.core
Interface to something that can be drawn as a graph.
drawArc(int, int, int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics
Not implemented
drawBytes(byte[], int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics
simply calls drawString(String,int,int)
drawChars(char[], int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics
simply calls drawString(String,int,int)
drawDataPoint(double, double, double, double, int, int, Graphics) - Static method in class weka.gui.visualize.Plot2D
Draws a data point at a given set of panel coordinates at a given size and connects a line to the previous point.
drawDataPoint(double, double, int, int, Graphics) - Static method in class weka.gui.visualize.Plot2D
Draws a data point at a given set of panel coordinates at a given size.
drawGlyphVector(GlyphVector, float, float) - Method in class weka.gui.visualize.PostscriptGraphics
 
drawHighlight(Graphics, int, int) - Method in class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd
Call this function to draw the node highlighted.
drawHighlight(Graphics, int, int) - Method in class weka.classifiers.functions.neural.NeuralConnection
Call this function to draw the node highlighted.
drawImage(Image, int, int, Color, ImageObserver) - Method in class weka.gui.visualize.PostscriptGraphics
calls drawImage(Image,int,int,int,int,Color,ImageObserver)
drawImage(Image, int, int, ImageObserver) - Method in class weka.gui.visualize.PostscriptGraphics
calls drawImage(Image,int,int,Color,ImageObserver) with Color.WHITE as background color
drawImage(Image, int, int, int, int, Color, ImageObserver) - Method in class weka.gui.visualize.PostscriptGraphics
PS see http://astronomy.swin.edu.au/~pbourke/geomformats/postscript/ Java http://show.docjava.com:8086/book/cgij/doc/ip/graphics/SimpleImageFrame.java.html
drawImage(Image, int, int, int, int, ImageObserver) - Method in class weka.gui.visualize.PostscriptGraphics
calls drawImage(Image,int,int,int,int,Color,ImageObserver) with the color WHITE as background
drawImage(Image, int, int, int, int, int, int, int, int, Color, ImageObserver) - Method in class weka.gui.visualize.PostscriptGraphics
Not implemented
drawImage(Image, int, int, int, int, int, int, int, int, ImageObserver) - Method in class weka.gui.visualize.PostscriptGraphics
calls drawImage(Image,int,int,int,int,int,int,int,int,Color,ImageObserver) with Color.WHITE as background color
drawImage(BufferedImage, BufferedImageOp, int, int) - Method in class weka.gui.visualize.PostscriptGraphics
 
drawImage(Image, AffineTransform, ImageObserver) - Method in class weka.gui.visualize.PostscriptGraphics
 
drawInputLines(Graphics, int, int) - Method in class weka.classifiers.functions.neural.NeuralConnection
Call this function to draw the nodes input connections.
drawLine(int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics
Draw a line in current pen color.
drawNode(Graphics, int, int) - Method in class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd
This will draw the node id to the graphics context.
drawNode(Graphics, int, int) - Method in class weka.classifiers.functions.neural.NeuralConnection
Call this function to draw the node.
drawOutputLines(Graphics, int, int) - Method in class weka.classifiers.functions.neural.NeuralConnection
Call this function to draw the nodes output connections.
drawOval(int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics
Draw an Oval outline in current pen color.
drawPolygon(int[], int[], int) - Method in class weka.gui.visualize.PostscriptGraphics
Not implemented
drawPolyline(int[], int[], int) - Method in class weka.gui.visualize.PostscriptGraphics
Not implemented
drawRect(int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics
Draw an outlined rectangle in current pen color.
drawRenderableImage(RenderableImage, AffineTransform) - Method in class weka.gui.visualize.PostscriptGraphics
 
drawRenderedImage(RenderedImage, AffineTransform) - Method in class weka.gui.visualize.PostscriptGraphics
 
drawRoundRect(int, int, int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics
Not implemented
drawString(AttributedCharacterIterator, int, int) - Method in class weka.gui.visualize.PostscriptGraphics
Not implemented
drawString(String, int, int) - Method in class weka.gui.visualize.PostscriptGraphics
Draw text in current pen color.
drawString(AttributedCharacterIterator, float, float) - Method in class weka.gui.visualize.PostscriptGraphics
 
drawString(String, float, float) - Method in class weka.gui.visualize.PostscriptGraphics
 
DRIVERS - Variable in class weka.experiment.DatabaseUtils
Holds the jdbc drivers to be used (only to stop them being gc'ed).
DRIVERS_ERRORS - Static variable in class weka.experiment.DatabaseUtils
keeping track of drivers that couldn't be loaded.
DTD_ANY - Static variable in class weka.core.xml.XMLDocument
the ANY placeholder.
DTD_AT_LEAST_ONE - Static variable in class weka.core.xml.XMLDocument
the at least one marker.
DTD_ATTLIST - Static variable in class weka.core.xml.XMLDocument
the AttList definition.
DTD_CDATA - Static variable in class weka.core.xml.XMLDocument
the CDATA placeholder.
DTD_DOCTYPE - Static variable in class weka.core.xml.XMLDocument
the DocType definition.
DTD_ELEMENT - Static variable in class weka.core.xml.XMLDocument
the Element definition.
DTD_IMPLIED - Static variable in class weka.core.xml.XMLDocument
the #IMPLIED placeholder.
DTD_OPTIONAL - Static variable in class weka.core.xml.XMLDocument
the optional marker.
DTD_PCDATA - Static variable in class weka.core.xml.XMLDocument
the #PCDATA placeholder.
DTD_REQUIRED - Static variable in class weka.core.xml.XMLDocument
the #REQUIRED placeholder.
DTD_SEPARATOR - Static variable in class weka.core.xml.XMLDocument
the option separator.
DTD_ZERO_OR_MORE - Static variable in class weka.core.xml.XMLDocument
the zero or more marker.
DTNB - Class in weka.classifiers.rules
Class for building and using a decision table/naive bayes hybrid classifier.
DTNB() - Constructor for class weka.classifiers.rules.DTNB
 
DTNB.BackwardsWithDelete - Class in weka.classifiers.rules
Inner class implementing a special forwards search that looks for a good split of attributes between naive Bayes and the decision table.
DTNB.EvalWithDelete - Class in weka.classifiers.rules
 
dumpDistribution() - Method in class weka.classifiers.trees.j48.Distribution
Prints distribution.
dumpLabel(int, Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Prints label for subset index of instances (eg class).
dumpLabelG(int, Instances) - Method in class weka.classifiers.trees.j48.GraftSplit
Prints label for subset index of instances (eg class).
dumpModel(Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Prints the split model.
dumpTree(int, StringBuffer) - Method in class weka.classifiers.trees.ft.FTtree
Help method for printing tree structure.
dumpTree(int, StringBuffer) - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG
Help method for printing tree structure.
dumpTree(int, StringBuffer) - Method in class weka.classifiers.trees.lmt.LMTNode
Help method for printing tree structure.

E

EAST_CONNECTOR - Static variable in class weka.gui.beans.BeanVisual
 
Edge - Class in weka.gui.treevisualizer
This class is used in conjunction with the Node class to form a tree structure.
Edge(String, String, String) - Constructor for class weka.gui.treevisualizer.Edge
This constructs an Edge with the specified label and parent , child serial tags.
edgeAttrib(StreamTokenizer, GraphEdge) - Method in class weka.gui.graphvisualizer.DotParser
 
edgeStmt(StreamTokenizer, int) - Method in class weka.gui.graphvisualizer.DotParser
 
edit() - Method in class weka.gui.explorer.PreprocessPanel
edits the current instances object in the viewer
EditableBayesNet - Class in weka.classifiers.bayes.net
Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier.
EditableBayesNet() - Constructor for class weka.classifiers.bayes.net.EditableBayesNet
standard constructor *
EditableBayesNet(Instances) - Constructor for class weka.classifiers.bayes.net.EditableBayesNet
constructor, creates empty network with nodes based on the attributes in a data set
EditableBayesNet(BIFReader) - Constructor for class weka.classifiers.bayes.net.EditableBayesNet
constructor, copies Bayesian network structure from a Bayesian network encapsulated in a BIFReader
EditableBayesNet(boolean) - Constructor for class weka.classifiers.bayes.net.EditableBayesNet
constructor that potentially initializes instances as well
editableProperties() - Method in class weka.gui.PropertySheetPanel
Gets the number of editable properties for the current target.
EditDistance - Class in weka.core
Computes the Levenshtein edit distance between two strings.
EditDistance() - Constructor for class weka.core.EditDistance
 
EditDistance(Instances) - Constructor for class weka.core.EditDistance
 
EDITOR_PROPERTIES - Static variable in class weka.gui.GenericObjectEditor
Contains the editor properties.
eig() - Method in class weka.core.matrix.Matrix
Eigenvalue Decomposition
EigenvalueDecomposition - Class in weka.core.matrix
Eigenvalues and eigenvectors of a real matrix.
eigenvalueDecomposition(double[][], double[]) - Method in class weka.core.Matrix
Deprecated.
Performs Eigenvalue Decomposition using Householder QR Factorization Matrix must be symmetrical.
EigenvalueDecomposition(Matrix) - Constructor for class weka.core.matrix.EigenvalueDecomposition
Check for symmetry, then construct the eigenvalue decomposition
Element - Class in weka.associations.gsp
Class representing an Element, i.e., a set of events/items.
Element() - Constructor for class weka.associations.gsp.Element
Constructor
Element(int) - Constructor for class weka.associations.gsp.Element
Constructor accepting an initial size of the events Array as parameter.
element(int) - Method in class weka.core.neighboursearch.covertrees.Stack
Returns the ith element in the stack.
elementAt(int) - Method in class weka.core.FastVector
Returns the element at the given position.
elementAt(int) - Method in class weka.gui.CheckBoxList.CheckBoxListModel
Returns the component at the specified index.
elements() - Method in class weka.core.FastVector
Returns an enumeration of this vector.
elements(int) - Method in class weka.core.FastVector
Returns an enumeration of this vector, skipping the element with the given index.
elements - Variable in class weka.core.neighboursearch.covertrees.Stack
The elements inside the stack.
elements() - Method in class weka.core.Stopwords
Returns a sorted enumeration over all stored stopwords
eliminateColinearAttributesTipText() - Method in class weka.classifiers.functions.LinearRegression
Returns the tip text for this property
EM - Class in weka.clusterers
Simple EM (expectation maximisation) class.

EM assigns a probability distribution to each instance which indicates the probability of it belonging to each of the clusters.
EM() - Constructor for class weka.clusterers.EM
Constructor.
empiricalBayesEstimate(double) - Method in class weka.classifiers.functions.pace.NormalMixture
Returns the empirical Bayes estimate of a single value.
empiricalBayesEstimate(DoubleVector) - Method in class weka.classifiers.functions.pace.NormalMixture
Returns the empirical Bayes estimate of a vector.
empiricalProbability(DoubleVector, PaceMatrix) - Method in class weka.classifiers.functions.pace.MixtureDistribution
Computes the empirical probabilities of the data over a set of intervals.
empty() - Method in class weka.core.Queue
Checks if queue is empty.
enable(Capabilities.Capability) - Method in class weka.core.Capabilities
enables the given capability.
enable(Capabilities.Capability) - Method in class weka.core.FindWithCapabilities
enables the given capability.
enableAll() - Method in class weka.core.Capabilities
enables all attribute and class types (including dependencies)
enableAllAttributeDependencies() - Method in class weka.core.Capabilities
enables all attribute type dependencies
enableAllAttributes() - Method in class weka.core.Capabilities
enables all attribute types
enableAllClassDependencies() - Method in class weka.core.Capabilities
enables all class type dependencies
enableAllClasses() - Method in class weka.core.Capabilities
enables all class types
enableDependency(Capabilities.Capability) - Method in class weka.core.Capabilities
enables the dependency flag for the given capability Enabling NOMINAL_ATTRIBUTES also enables BINARY_ATTRIBUTES, UNARY_ATTRIBUTES and EMPTY_NOMINAL_ATTRIBUTES.
enableNot(Capabilities.Capability) - Method in class weka.core.FindWithCapabilities
enables the given "not to have" capability.
enclosureCharactersTipText() - Method in class weka.core.converters.CSVLoader
Returns the tip text for this property.
END - Class in weka.classifiers.meta
A meta classifier for handling multi-class datasets with 2-class classifiers by building an ensemble of nested dichotomies.

For more info, check

Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.
END() - Constructor for class weka.classifiers.meta.END
Constructor.
entropicAutoBlendTipText() - Method in class weka.classifiers.lazy.KStar
Returns the tip text for this property
ENTROPY - Static variable in interface weka.classifiers.bayes.net.search.local.Scoreable
 
entropy(double[][], double[][]) - Method in class weka.classifiers.trees.lmt.ResidualSplit
Helper function to compute entropy from Z/W values.
entropy(double[]) - Static method in class weka.core.ContingencyTables
Computes the entropy of the given array.
EntropyBasedSplitCrit - Class in weka.classifiers.trees.j48
"Abstract" class for computing splitting criteria based on the entropy of a class distribution.
EntropyBasedSplitCrit() - Constructor for class weka.classifiers.trees.j48.EntropyBasedSplitCrit
 
entropyConditionedOnColumns(double[][]) - Static method in class weka.core.ContingencyTables
Computes conditional entropy of the rows given the columns.
entropyConditionedOnRows(double[][]) - Static method in class weka.core.ContingencyTables
Computes conditional entropy of the columns given the rows.
entropyConditionedOnRows(double[][], double[][], double) - Static method in class weka.core.ContingencyTables
Computes conditional entropy of the columns given the rows of the test matrix with respect to the train matrix.
entropyGain() - Method in class weka.classifiers.trees.lmt.ResidualSplit
Computes entropy gain for current split.
entropyOverColumns(double[][]) - Static method in class weka.core.ContingencyTables
Computes the columns' entropy for the given contingency table.
entropyOverRows(double[][]) - Static method in class weka.core.ContingencyTables
Computes the rows' entropy for the given contingency table.
EntropySplitCrit - Class in weka.classifiers.trees.j48
Class for computing the entropy for a given distribution.
EntropySplitCrit() - Constructor for class weka.classifiers.trees.j48.EntropySplitCrit
 
enumerateAttributes() - Method in class weka.core.Instance
Returns an enumeration of all the attributes.
enumerateAttributes() - Method in class weka.core.Instances
Returns an enumeration of all the attributes.
enumerateInstances() - Method in class weka.core.Instances
Returns an enumeration of all instances in the dataset.
enumerateLiterals() - Method in class weka.associations.tertius.LiteralSet
Enumerate the literals contained in this set.
enumerateMeasures() - Method in class weka.classifiers.bayes.BayesNet
Returns an enumeration of the measure names.
enumerateMeasures() - Method in class weka.classifiers.functions.SimpleLogistic
Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.functions.SMOreg
Returns an enumeration of the measure names.
enumerateMeasures() - Method in class weka.classifiers.lazy.IBk
Returns an enumeration of the additional measure names produced by the neighbour search algorithm, plus the chosen K in case cross-validation is enabled.
enumerateMeasures() - Method in class weka.classifiers.lazy.LWL
Returns an enumeration of the additional measure names produced by the neighbour search algorithm.
enumerateMeasures() - Method in class weka.classifiers.meta.AdditiveRegression
Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.meta.Bagging
Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class weka.classifiers.meta.GridSearch
Returns an enumeration of the measure names.
enumerateMeasures() - Method in class weka.classifiers.rules.DecisionTable
Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.rules.DTNB
Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.rules.JRip
Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.rules.PART
Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.rules.Ridor
Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.trees.ADTree
Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class weka.classifiers.trees.BFTree
Return an enumeration of the measure names.
enumerateMeasures() - Method in class weka.classifiers.trees.FT
Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.trees.J48
Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.trees.J48graft
Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.trees.LADTree
Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class weka.classifiers.trees.LMT
Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.trees.m5.M5Base
Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.trees.NBTree
Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.trees.RandomForest
Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class weka.classifiers.trees.REPTree
Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class weka.classifiers.trees.SimpleCart
Return an enumeration of the measure names.
enumerateMeasures() - Method in interface weka.core.AdditionalMeasureProducer
Returns an enumeration of the measure names.
enumerateMeasures() - Method in class weka.core.neighboursearch.BallTree
Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class weka.core.neighboursearch.CoverTree
Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class weka.core.neighboursearch.KDTree
Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class weka.core.neighboursearch.NearestNeighbourSearch
Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class weka.core.neighboursearch.PerformanceStats
Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class weka.core.neighboursearch.TreePerformanceStats
Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class weka.experiment.AveragingResultProducer
Returns an enumeration of any additional measure names that might be in the result producer
enumerateMeasures() - Method in class weka.experiment.ClassifierSplitEvaluator
Returns an enumeration of any additional measure names that might be in the classifier
enumerateMeasures() - Method in class weka.experiment.CrossValidationResultProducer
Returns an enumeration of any additional measure names that might be in the SplitEvaluator
enumerateMeasures() - Method in class weka.experiment.DatabaseResultProducer
Returns an enumeration of any additional measure names that might be in the result producer
enumerateMeasures() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
Returns an enumeration of any additional measure names that might be in the classifier
enumerateMeasures() - Method in class weka.experiment.LearningRateResultProducer
Returns an enumeration of any additional measure names that might be in the result producer
enumerateMeasures() - Method in class weka.experiment.RandomSplitResultProducer
Returns an enumeration of any additional measure names that might be in the SplitEvaluator
enumerateMeasures() - Method in class weka.experiment.RegressionSplitEvaluator
Returns an enumeration of any additional measure names that might be in the classifier
enumerateRequests() - Method in class weka.gui.beans.Associator
Return an enumeration of requests that can be made by the user
enumerateRequests() - Method in class weka.gui.beans.AttributeSummarizer
Return an enumeration of actions that the user can ask this bean to perform
enumerateRequests() - Method in class weka.gui.beans.Classifier
Return an enumeration of requests that can be made by the user
enumerateRequests() - Method in class weka.gui.beans.ClassifierPerformanceEvaluator
Return an enumeration of user activated requests for this bean
enumerateRequests() - Method in class weka.gui.beans.Clusterer
Return an enumeration of requests that can be made by the user
enumerateRequests() - Method in class weka.gui.beans.ClustererPerformanceEvaluator
Return an enumeration of user activated requests for this bean
enumerateRequests() - Method in class weka.gui.beans.CostBenefitAnalysis
 
enumerateRequests() - Method in class weka.gui.beans.CrossValidationFoldMaker
Return an enumeration of user requests
enumerateRequests() - Method in class weka.gui.beans.DataVisualizer
Describe enumerateRequests method here.
enumerateRequests() - Method in class weka.gui.beans.Filter
Return an enumeration of user requests
enumerateRequests() - Method in class weka.gui.beans.GraphViewer
Return an enumeration of user requests
enumerateRequests() - Method in class weka.gui.beans.MetaBean
Return an enumeration of requests that can be made by the user
enumerateRequests() - Method in class weka.gui.beans.ModelPerformanceChart
Describe enumerateRequests method here.
enumerateRequests() - Method in class weka.gui.beans.StripChart
Describe enumerateRequests method here.
enumerateRequests() - Method in class weka.gui.beans.TextViewer
Get a list of user requests
enumerateRequests() - Method in class weka.gui.beans.TrainTestSplitMaker
Get list of user requests
enumerateRequests() - Method in interface weka.gui.beans.UserRequestAcceptor
Get a list of performable requests
enumerateValues() - Method in class weka.core.Attribute
Returns an enumeration of all the attribute's values if the attribute is nominal, string, or relation-valued, null otherwise.
enumToVector(Enumeration) - Method in class weka.datagenerators.DataGenerator
creates a vector out of the enumeration from the listOptions of the super class.
Environment - Class in weka.core
This class encapsulates a map of all environment and java system properties.
Environment() - Constructor for class weka.core.Environment
 
EnvironmentHandler - Interface in weka.core
Interface for something that can utilize environment variables.
EOF - Static variable in interface weka.core.mathematicalexpression.sym
 
EOF - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
EOF_sym() - Method in class weka.core.mathematicalexpression.Parser
EOF Symbol index.
EOF_sym() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
EOF Symbol index.
EPANECHNIKOV - Static variable in class weka.classifiers.lazy.LWL
 
epochsTipText() - Method in class weka.classifiers.functions.SPegasos
Returns the tip text for this property
EPSILON - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
 
epsilonParameterTipText() - Method in class weka.attributeSelection.SVMAttributeEval
Returns a tip text for this property suitable for display in the GUI
epsilonParameterTipText() - Method in class weka.classifiers.functions.supportVector.RegOptimizer
Returns the tip text for this property
EpsilonRange_ListElement - Class in weka.clusterers.forOPTICSAndDBScan.Utils
EpsilonRange_ListElement.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Sep 7, 2004
Time: 2:12:34 PM
$ Revision 1.4 $
EpsilonRange_ListElement(double, DataObject) - Constructor for class weka.clusterers.forOPTICSAndDBScan.Utils.EpsilonRange_ListElement
Constructs a new Element that is stored in the ArrayList which is built in the k_nextNeighbourQuery-method from a specified database.
epsilonRangeQuery(double, DataObject) - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database
Performs an epsilon range query for this dataObject
epsilonRangeQuery(double, DataObject) - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase
Performs an epsilon range query for this dataObject
epsilonTipText() - Method in class weka.classifiers.functions.SMO
Returns the tip text for this property
epsilonTipText() - Method in class weka.classifiers.functions.supportVector.RegSMO
Returns the tip text for this property
epsilonTipText() - Method in class weka.classifiers.mi.MISMO
Returns the tip text for this property
epsilonTipText() - Method in class weka.clusterers.DBScan
Returns the tip text for this property
epsilonTipText() - Method in class weka.clusterers.OPTICS
Returns the tip text for this property
epsTipText() - Method in class weka.classifiers.functions.LibLINEAR
Returns the tip text for this property
epsTipText() - Method in class weka.classifiers.functions.LibSVM
Returns the tip text for this property
EQ - Static variable in interface weka.core.mathematicalexpression.sym
 
eq(double, double) - Static method in class weka.core.Utils
Tests if a is equal to b.
EQ - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
equalCondset(Object) - Method in class weka.associations.LabeledItemSet
Compares two item sets
equalExemplars(Instance, Instance) - Method in class weka.classifiers.mi.CitationKNN
Wether the instances of two exemplars are or are not equal
equalHeaders(Instance) - Method in class weka.core.Instance
Tests if the headers of two instances are equivalent.
equalHeaders(Instances) - Method in class weka.core.Instances
Checks if two headers are equivalent.
equals(Object) - Method in class weka.associations.AssociatorEvaluation
Tests whether the current evaluation object is equal to another evaluation object
equals(Object) - Method in class weka.associations.FPGrowth.AssociationRule
Return true if this rule is equal to the supplied one.
equals(Object) - Method in class weka.associations.FPGrowth.BinaryItem
 
equals(Object) - Method in class weka.associations.gsp.Element
Checks if two Elements are equal.
equals(Object) - Method in class weka.associations.gsp.Sequence
Checks if two Sequences are equal.
equals(Object) - Method in class weka.associations.ItemSet
Tests if two item sets are equal.
equals(Object) - Method in class weka.associations.LabeledItemSet
Tests if two item sets are equal.
equals(Object) - Method in class weka.associations.RuleItem
returns whether two RuleItems are equal
equals(Object) - Method in class weka.attributeSelection.ConsistencySubsetEval.hashKey
Tests if two instances are equal
equals(Object) - Method in class weka.classifiers.Evaluation
Tests whether the current evaluation object is equal to another evaluation object
equals(Object) - Method in class weka.classifiers.functions.supportVector.KernelEvaluation
Tests whether the current evaluation object is equal to another evaluation object
equals(Object) - Method in class weka.classifiers.meta.GridSearch.Grid
Tests itself against the provided grid object
equals(Object) - Method in class weka.classifiers.meta.GridSearch.PerformanceComparator
Indicates whether some other object is "equal to" this Comparator.
equals(Object) - Method in class weka.classifiers.meta.GridSearch.PointDouble
Determines whether or not two points are equal.
equals(Object) - Method in class weka.classifiers.rules.DecisionTableHashKey
Tests if two instances are equal
equals(Object) - Method in class weka.clusterers.ClusterEvaluation
Tests whether the current evaluation object is equal to another evaluation object
equals(DataObject) - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
Compares two DataObjects in respect to their attribute-values
equals(DataObject) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
Compares two DataObjects in respect to their attribute-values
equals(DataObject) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
Compares two DataObjects in respect to their attribute-values
equals(Object) - Method in class weka.core.Attribute
Tests if given attribute is equal to this attribute.
equals(Object) - Method in class weka.core.AttributeLocator
Indicates whether some other object is "equal to" this one.
equals(Object) - Method in class weka.core.ClassDiscovery.StringCompare
Indicates whether some other object is "equal to" this Comparator.
equals(Object) - Method in class weka.core.SelectedTag
Returns true if this SelectedTag equals another object
equals(Object) - Method in class weka.core.SerializedObject
 
equals(Object) - Method in class weka.core.Trie
Compares the specified object with this collection for equality.
equals(Object) - Method in class weka.core.Trie.TrieNode
Indicates whether some other object is "equal to" this one.
equals(Object) - Method in class weka.core.Version
whether the given version string is equal to this version
equals(Object) - Method in class weka.estimators.Estimator
Tests whether the current estimation object is equal to another estimation object
equals(Object) - Method in class weka.gui.CheckBoxList.CheckBoxListItem
returns true if the "payload" objects of the current and the given CheckBoxListItem are the same.
equals(Object) - Method in class weka.gui.graphvisualizer.GraphEdge
 
equals(Object) - Method in class weka.gui.graphvisualizer.GraphNode
Returns true if passed in argument is an instance of GraphNode and is equal to this node.
equals(Object) - Method in class weka.gui.SortedTableModel.SortContainer
Indicates whether some other object is "equal to" this one.
equalTo(Splitter) - Method in class weka.classifiers.trees.adtree.Splitter
Tests whether two splitters are equivalent.
equalTo(Splitter) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit
Tests whether two splitters are equivalent.
equalTo(Splitter) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit
Tests whether two splitters are equivalent.
equalTo(LADTree.Splitter) - Method in class weka.classifiers.trees.LADTree.Splitter
 
equalTo(LADTree.Splitter) - Method in class weka.classifiers.trees.LADTree.TwoWayNominalSplit
 
equalTo(LADTree.Splitter) - Method in class weka.classifiers.trees.LADTree.TwoWayNumericSplit
 
equalTo(Test) - Method in class weka.datagenerators.Test
Compares the test with the test that is given as parameter.
equivalentTipText() - Method in class weka.associations.Tertius
Returns the tip text for this property.
equivalentTo(Rule) - Method in class weka.associations.tertius.Rule
Test if this rule is equivalent to another rule.
ERR - Static variable in class weka.core.neighboursearch.kdtrees.SlidingMidPointOfWidestSide
The floating point error to tolerate in finding the widest rectangular side.
errms(StreamTokenizer, String) - Static method in class weka.core.converters.ConverterUtils
Throws error message with line number and last token read.
error() - Method in class weka.classifiers.evaluation.NumericPrediction
Calculates the prediction error.
error - Static variable in interface weka.core.mathematicalexpression.sym
 
error - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
ERROR_SHAPE - Static variable in class weka.gui.visualize.Plot2D
 
error_sym() - Method in class weka.core.mathematicalexpression.Parser
error Symbol index.
error_sym() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
error Symbol index.
ErrorBasedMeritEvaluator - Interface in weka.attributeSelection
Interface for evaluators that calculate the "merit" of attributes/subsets as the error of a learning scheme
errorFunction(double) - Static method in class weka.core.Statistics
Returns the error function of the normal distribution.
errorFunctionComplemented(double) - Static method in class weka.core.Statistics
Returns the complementary Error function of the normal distribution.
errorMessage(String) - Method in class weka.core.converters.ArffLoader.ArffReader
Throws error message with line number and last token read.
errorOnProbabilitiesTipText() - Method in class weka.classifiers.functions.SimpleLogistic
Returns the tip text for this property
errorOnProbabilitiesTipText() - Method in class weka.classifiers.trees.FT
Returns the tip text for this property
errorOnProbabilitiesTipText() - Method in class weka.classifiers.trees.LMT
Returns the tip text for this property
errorRate() - Method in class weka.classifiers.evaluation.ConfusionMatrix
Returns the estimated error rate.
errorRate() - Method in class weka.classifiers.Evaluation
Returns the estimated error rate or the root mean squared error (if the class is numeric).
errorValue(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd
Call this to get the error value of this unit, which in this case is the difference between the predicted class, and the actual class.
errorValue(NeuralNode) - Method in class weka.classifiers.functions.neural.LinearUnit
This function calculates what the error value should be.
errorValue(boolean) - Method in class weka.classifiers.functions.neural.NeuralConnection
Call this to get the error value of this unit.
errorValue(NeuralNode) - Method in interface weka.classifiers.functions.neural.NeuralMethod
This function calculates what the error value should be.
errorValue(boolean) - Method in class weka.classifiers.functions.neural.NeuralNode
Call this to get the error value of this unit.
errorValue(NeuralNode) - Method in class weka.classifiers.functions.neural.SigmoidUnit
This function calculates what the error value should be.
ErrorVisualizePlugin - Interface in weka.gui.visualize.plugins
Interface implemented by classes loaded dynamically to visualize classifier errors in the explorer.
escape(String) - Method in class weka.gui.visualize.PostscriptGraphics
Escapes brackets in the string with backslashes.
estimateCPTs() - Method in class weka.classifiers.bayes.BayesNet
estimateCPTs estimates the conditional probability tables for the Bayes Net using the network structure.
estimateCPTs(BayesNet) - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator
estimateCPTs estimates the conditional probability tables for the Bayes Net using the network structure.
estimateCPTs(BayesNet) - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator
estimateCPTs estimates the conditional probability tables for the Bayes Net using the network structure.
estimateCPTs(BayesNet) - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
estimateCPTs estimates the conditional probability tables for the Bayes Net using the network structure.
estimateCPTs(BayesNet) - Method in class weka.classifiers.bayes.net.estimate.SimpleEstimator
estimateCPTs estimates the conditional probability tables for the Bayes Net using the network structure.
estimatePerformance(BitSet, int) - Method in class weka.classifiers.rules.DecisionTable
Evaluates a feature subset by cross validation
estimatePrior() - Method in class weka.associations.PriorEstimation
Method to estimate the prior probabilities
Estimator - Class in weka.estimators
Abstract class for all estimators.
Estimator() - Constructor for class weka.estimators.Estimator
 
estimatorTipText() - Method in class weka.classifiers.bayes.BayesNet
This will return a string describing the BayesNetEstimator.
estimatorTipText() - Method in class weka.classifiers.functions.PaceRegression
Returns the tip text for this property
EstimatorUtils - Class in weka.estimators
Contains static utility functions for Estimators.
EstimatorUtils() - Constructor for class weka.estimators.EstimatorUtils
 
EstTypes() - Constructor for class weka.estimators.CheckEstimator.EstTypes
Constructor
EstTypes(boolean, boolean, boolean) - Constructor for class weka.estimators.CheckEstimator.EstTypes
Constructor
EuclideanDistance - Class in weka.core
Implementing Euclidean distance (or similarity) function.

One object defines not one distance but the data model in which the distances between objects of that data model can be computed.

Attention: For efficiency reasons the use of consistency checks (like are the data models of the two instances exactly the same), is low.

For more information, see:

Wikipedia.
EuclideanDistance() - Constructor for class weka.core.EuclideanDistance
Constructs an Euclidean Distance object, Instances must be still set.
EuclideanDistance(Instances) - Constructor for class weka.core.EuclideanDistance
Constructs an Euclidean Distance object and automatically initializes the ranges.
EuclidianDataObject - Class in weka.clusterers.forOPTICSAndDBScan.DataObjects
EuclidianDataObject.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 19, 2004
Time: 5:50:22 PM
$ Revision 1.4 $
EuclidianDataObject(Instance, String, Database) - Constructor for class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
Constructs a new DataObject.
eval(int, int, Instance) - Method in class weka.classifiers.functions.supportVector.CachedKernel
Implements the abstract function of Kernel using the cache.
eval(int, int, Instance) - Method in class weka.classifiers.functions.supportVector.Kernel
Computes the result of the kernel function for two instances.
eval(int, int, Instance) - Method in class weka.classifiers.functions.supportVector.NormalizedPolyKernel
Computes the result of the kernel function for two instances.
eval(int, int, Instance) - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
 
eval(int, int, Instance) - Method in class weka.classifiers.functions.supportVector.StringKernel
Computes the result of the kernel function for two instances.
eval(String, QName) - Method in class weka.core.xml.XMLDocument
Returns the specified result of the XPath expression.
eval(HashMap) - Method in class weka.filters.unsupervised.attribute.MathExpression
Evaluates the symbols.
EVAL_ACCURACY - Static variable in class weka.classifiers.rules.DecisionTable
 
EVAL_AUC - Static variable in class weka.classifiers.rules.DecisionTable
 
EVAL_CROSS_VALIDATION - Static variable in class weka.classifiers.meta.ThresholdSelector
n-fold cross-validation
EVAL_DEFAULT - Static variable in class weka.classifiers.rules.DecisionTable
default is accuracy for discrete class and RMSE for numeric class
EVAL_MAE - Static variable in class weka.classifiers.rules.DecisionTable
 
EVAL_RMSE - Static variable in class weka.classifiers.rules.DecisionTable
 
EVAL_TRAINING_SET - Static variable in class weka.classifiers.meta.ThresholdSelector
entire training set
EVAL_TUNED_SPLIT - Static variable in class weka.classifiers.meta.ThresholdSelector
single tuned fold
evalBoolean(String) - Method in class weka.core.xml.XMLDocument
Evaluates and returns the boolean result of the XPath expression.
evalDouble(String) - Method in class weka.core.xml.XMLDocument
Evaluates and returns the double result of the XPath expression.
evalString(String) - Method in class weka.core.xml.XMLDocument
Evaluates and returns the boolean result of the XPath expression.
evaluate(String, String[]) - Static method in class weka.associations.AssociatorEvaluation
Evaluates an associator with the options given in an array of strings.
evaluate(Associator, String[]) - Static method in class weka.associations.AssociatorEvaluation
Evaluates the associator with the given commandline options and returns the evaluation string.
evaluate(Associator, Instances) - Method in class weka.associations.AssociatorEvaluation
Evaluates the associator with the given commandline options and returns the evaluation string.
evaluate(int, int, Instance) - Method in class weka.classifiers.functions.supportVector.CachedKernel
This method is overridden in subclasses to implement specific kernels.
evaluate(Kernel, String[]) - Static method in class weka.classifiers.functions.supportVector.KernelEvaluation
Evaluates the Kernel with the given commandline options and returns the evaluation string.
evaluate(String, String[]) - Static method in class weka.classifiers.functions.supportVector.KernelEvaluation
Evaluates a kernel with the options given in an array of strings.
evaluate(Kernel, Instances) - Method in class weka.classifiers.functions.supportVector.KernelEvaluation
Evaluates the Kernel with the given commandline options and returns the evaluation string.
evaluate(int, int, Instance) - Method in class weka.classifiers.functions.supportVector.PolyKernel
 
evaluate(int, int, Instance) - Method in class weka.classifiers.functions.supportVector.Puk
returns the dot product
evaluate(int, int, Instance) - Method in class weka.classifiers.functions.supportVector.RBFKernel
 
evaluate(double, boolean) - Method in class weka.classifiers.meta.GridSearch
evalutes the expression for the current iteration
evaluate(int, int, Instance) - Method in class weka.classifiers.mi.supportVector.MIPolyKernel
 
evaluate(int, int, Instance) - Method in class weka.classifiers.mi.supportVector.MIRBFKernel
 
evaluate(String, HashMap) - Static method in class weka.core.MathematicalExpression
Parses and evaluates the given expression.
evaluateAttribute(int) - Method in interface weka.attributeSelection.AttributeEvaluator
evaluates an individual attribute
evaluateAttribute(int) - Method in class weka.attributeSelection.AttributeSetEvaluator
evaluates an individual attribute
evaluateAttribute(int[], int[]) - Method in class weka.attributeSelection.AttributeSetEvaluator
Evaluates a set of attributes
evaluateAttribute(int) - Method in class weka.attributeSelection.ChiSquaredAttributeEval
evaluates an individual attribute by measuring its chi-squared value.
evaluateAttribute(int) - Method in class weka.attributeSelection.CostSensitiveAttributeEval
Evaluates an individual attribute.
evaluateAttribute(int) - Method in class weka.attributeSelection.FilteredAttributeEval
Evaluates an individual attribute by delegating to the base evaluator.
evaluateAttribute(int) - Method in class weka.attributeSelection.GainRatioAttributeEval
evaluates an individual attribute by measuring the gain ratio of the class given the attribute.
evaluateAttribute(int) - Method in class weka.attributeSelection.InfoGainAttributeEval
evaluates an individual attribute by measuring the amount of information gained about the class given the attribute.
evaluateAttribute(int) - Method in class weka.attributeSelection.LatentSemanticAnalysis
Evaluates the merit of a transformed attribute.
evaluateAttribute(int) - Method in class weka.attributeSelection.OneRAttributeEval
evaluates an individual attribute by measuring the amount of information gained about the class given the attribute.
evaluateAttribute(int) - Method in class weka.attributeSelection.PrincipalComponents
Evaluates the merit of a transformed attribute.
evaluateAttribute(int) - Method in class weka.attributeSelection.ReliefFAttributeEval
Evaluates an individual attribute using ReliefF's instance based approach.
evaluateAttribute(int) - Method in class weka.attributeSelection.SVMAttributeEval
Evaluates an attribute by returning the rank of the square of its coefficient in a linear support vector machine.
evaluateAttribute(int) - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
evaluates an individual attribute by measuring the symmetrical uncertainty between it and the class.
evaluateClusterer(Instances) - Method in class weka.clusterers.ClusterEvaluation
Evaluate the clusterer on a set of instances.
evaluateClusterer(Instances, String) - Method in class weka.clusterers.ClusterEvaluation
Evaluate the clusterer on a set of instances.
evaluateClusterer(Instances, String, boolean) - Method in class weka.clusterers.ClusterEvaluation
Evaluate the clusterer on a set of instances.
evaluateClusterer(Clusterer, String[]) - Static method in class weka.clusterers.ClusterEvaluation
Evaluates a clusterer with the options given in an array of strings.
evaluateExpression(Instance) - Method in class weka.core.AttributeExpression
Evaluate the expression using the supplied Instance.
evaluateExpression(double[]) - Method in class weka.core.AttributeExpression
Evaluate the expression using the supplied array of attribute values.
evaluateGradient(double[]) - Method in class weka.core.Optimization
Subclass should implement this procedure to evaluate gradient of the objective function
evaluateHessian(double[], int) - Method in class weka.core.Optimization
Subclass is recommended to override this procedure to evaluate second-order gradient of the objective function.
evaluateModel(String, String[]) - Static method in class weka.classifiers.Evaluation
Evaluates a classifier with the options given in an array of strings.
evaluateModel(Classifier, String[]) - Static method in class weka.classifiers.Evaluation
Evaluates a classifier with the options given in an array of strings.
evaluateModel(Classifier, Instances, Object...) - Method in class weka.classifiers.Evaluation
Evaluates the classifier on a given set of instances.
evaluateModelOnce(Classifier, Instance) - Method in class weka.classifiers.Evaluation
Evaluates the classifier on a single instance.
evaluateModelOnce(double[], Instance) - Method in class weka.classifiers.Evaluation
Evaluates the supplied distribution on a single instance.
evaluateModelOnce(double, Instance) - Method in class weka.classifiers.Evaluation
Evaluates the supplied prediction on a single instance.
evaluateModelOnceAndRecordPrediction(Classifier, Instance) - Method in class weka.classifiers.Evaluation
Evaluates the classifier on a single instance and records the prediction (if the class is nominal).
evaluateModelOnceAndRecordPrediction(double[], Instance) - Method in class weka.classifiers.Evaluation
Evaluates the supplied distribution on a single instance.
evaluateSubset(BitSet) - Method in class weka.attributeSelection.CfsSubsetEval
evaluates a subset of attributes
evaluateSubset(BitSet) - Method in class weka.attributeSelection.ClassifierSubsetEval
Evaluates a subset of attributes
evaluateSubset(BitSet, Instances) - Method in class weka.attributeSelection.ClassifierSubsetEval
Evaluates a subset of attributes with respect to a set of instances.
evaluateSubset(BitSet, Instance, boolean) - Method in class weka.attributeSelection.ClassifierSubsetEval
Evaluates a subset of attributes with respect to a single instance.
evaluateSubset(BitSet) - Method in class weka.attributeSelection.ConsistencySubsetEval
Evaluates a subset of attributes
evaluateSubset(BitSet) - Method in class weka.attributeSelection.CostSensitiveSubsetEval
Evaluates a subset of attributes.
evaluateSubset(BitSet) - Method in class weka.attributeSelection.FilteredSubsetEval
evaluates a subset of attributes
evaluateSubset(BitSet, Instances) - Method in class weka.attributeSelection.HoldOutSubsetEvaluator
Evaluates a subset of attributes with respect to a set of instances.
evaluateSubset(BitSet, Instance, boolean) - Method in class weka.attributeSelection.HoldOutSubsetEvaluator
Evaluates a subset of attributes with respect to a single instance.
evaluateSubset(BitSet) - Method in interface weka.attributeSelection.SubsetEvaluator
evaluates a subset of attributes
evaluateSubset(BitSet) - Method in class weka.attributeSelection.WrapperSubsetEval
Evaluates a subset of attributes
evaluateSubset(BitSet) - Method in class weka.classifiers.rules.DTNB.EvalWithDelete
 
evaluateSubsetDelete(BitSet, int) - Method in class weka.classifiers.rules.DTNB.EvalWithDelete
 
Evaluation - Class in weka.classifiers
Class for evaluating machine learning models.
Evaluation(Instances) - Constructor for class weka.classifiers.Evaluation
Initializes all the counters for the evaluation.
Evaluation(Instances, CostMatrix) - Constructor for class weka.classifiers.Evaluation
Initializes all the counters for the evaluation and also takes a cost matrix as parameter.
EVALUATION_ACC - Static variable in class weka.classifiers.meta.GridSearch
evaluation via: Accuracy
EVALUATION_CC - Static variable in class weka.classifiers.meta.GridSearch
evaluation via: Correlation coefficient
EVALUATION_COMBINED - Static variable in class weka.classifiers.meta.GridSearch
evaluation via: Combined = (1-CC) + RRSE + RAE
EVALUATION_KAPPA - Static variable in class weka.classifiers.meta.GridSearch
evaluation via: kappa statistic
EVALUATION_MAE - Static variable in class weka.classifiers.meta.GridSearch
evaluation via: Mean absolute error
EVALUATION_RAE - Static variable in class weka.classifiers.meta.GridSearch
evaluation via: Relative absolute error
EVALUATION_RMSE - Static variable in class weka.classifiers.meta.GridSearch
evaluation via: Root mean squared error
EVALUATION_RRSE - Static variable in class weka.classifiers.meta.GridSearch
evaluation via: Root relative squared error
evaluationMeasureTipText() - Method in class weka.classifiers.rules.DecisionTable
Returns the tip text for this property
evaluationModeTipText() - Method in class weka.classifiers.meta.ThresholdSelector
 
evaluationTipText() - Method in class weka.classifiers.meta.GridSearch
Returns the tip text for this property
EvaluationUtils - Class in weka.classifiers.evaluation
Contains utility functions for generating lists of predictions in various manners.
EvaluationUtils() - Constructor for class weka.classifiers.evaluation.EvaluationUtils
 
evaluatorTipText() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
Returns the tip text for this property
evaluatorTipText() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Returns the tip text for this property
evaluatorTipText() - Method in class weka.filters.supervised.attribute.AttributeSelection
Returns the tip text for this property
evalUsingTrainingDataTipText() - Method in class weka.attributeSelection.OneRAttributeEval
Returns a string for this option suitable for display in the gui as a tip text
EvalWithDelete() - Constructor for class weka.classifiers.rules.DTNB.EvalWithDelete
 
EventConstraints - Interface in weka.gui.beans
Interface for objects that want to be able to specify at any given time whether their current configuration allows a particular event to be generated.
eventGeneratable(EventSetDescriptor) - Method in class weka.gui.beans.Associator
Returns true, if at the current time, the event described by the supplied event descriptor could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.Associator
Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.ClassAssigner
Returns true, if at the current time, the named event could be generated.
eventGeneratable(EventSetDescriptor) - Method in class weka.gui.beans.Classifier
Returns true, if at the current time, the event described by the supplied event descriptor could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.Classifier
Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator
Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.ClassValuePicker
Returns true, if at the current time, the named event could be generated.
eventGeneratable(EventSetDescriptor) - Method in class weka.gui.beans.Clusterer
Returns true, if at the current time, the event described by the supplied event descriptor could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.Clusterer
Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.ClustererPerformanceEvaluator
Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.CrossValidationFoldMaker
Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in interface weka.gui.beans.EventConstraints
Returns true if, at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.Filter
Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.IncrementalClassifierEvaluator
Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.InstanceStreamToBatchMaker
Returns true if, at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.Loader
Returns true if the named event can be generated at this time
eventGeneratable(EventSetDescriptor) - Method in class weka.gui.beans.MetaBean
Returns true, if at the current time, the event described by the supplied event descriptor could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.MetaBean
Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.PredictionAppender
Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.TestSetMaker
Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.TextViewer
Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.TrainingSetMaker
Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.TrainTestSplitMaker
Returns true, if at the current time, the named event could be generated.
examineExample(int) - Method in class weka.classifiers.functions.SMO.BinarySMO
Examines instance.
examineExample(int) - Method in class weka.classifiers.functions.supportVector.RegSMO
examineExample method from pseudocode.
examineExample(int) - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
parameters correspond to pseudocode from paper.
examineExample(int) - Method in class weka.classifiers.mi.MISMO.BinaryMISMO
Examines instance.
EXCLUDE_CLASS - Static variable in class weka.gui.GenericPropertiesCreator
the prefix for an (exact) class exclusion
EXCLUDE_FILE - Static variable in class weka.gui.GenericPropertiesCreator
The name of the properties file that lists classes/interfaces/superclasses to exclude from being shown in the GUI.
EXCLUDE_INTERFACE - Static variable in class weka.gui.GenericPropertiesCreator
the prefix for an interface exclusion
EXCLUDE_SUPERCLASS - Static variable in class weka.gui.GenericPropertiesCreator
the prefix for a superclass exclusion
exclusiveTipText() - Method in class weka.classifiers.rules.ConjunctiveRule
Returns the tip text for this property
execute() - Method in class weka.classifiers.CheckSource
performs the comparison test
execute(String) - Method in class weka.experiment.DatabaseUtils
Executes a SQL query.
execute() - Method in class weka.experiment.RemoteExperimentSubTask
Run the experiment
execute() - Method in interface weka.experiment.Task
Execute this task.
execute() - Method in class weka.filters.CheckSource
performs the comparison test
execute() - Method in class weka.gui.beans.Classifier.TrainingTask
 
execute() - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
Perform the sub task
execute() - Method in class weka.gui.explorer.DataGeneratorPanel
generates the instances, returns TRUE if successful
execute() - Method in class weka.gui.GenericPropertiesCreator
generates the props-file for the GenericObjectEditor and stores it
execute(boolean) - Method in class weka.gui.GenericPropertiesCreator
generates the props-file for the GenericObjectEditor and stores it only if the the param store is TRUE.
execute() - Method in class weka.gui.sql.QueryPanel
executes the current query.
executeTask(Task) - Method in interface weka.experiment.Compute
Execute a task
executeTask(Task) - Method in class weka.experiment.RemoteEngine
Takes a task object and queues it for execution
ExhaustiveSearch - Class in weka.attributeSelection
ExhaustiveSearch :

Performs an exhaustive search through the space of attribute subsets starting from the empty set of attrubutes.
ExhaustiveSearch() - Constructor for class weka.attributeSelection.ExhaustiveSearch
Constructor
exists(TechnicalInformation.Field) - Method in class weka.core.TechnicalInformation
returns TRUE if the field is stored and has a value different from the empty string.
exitOnClose - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
EXP - Static variable in interface weka.core.mathematicalexpression.sym
 
exp - Variable in class weka.core.matrix.ExponentialFormat
 
EXP - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
EXP_INDEX_TABLE - Static variable in class weka.experiment.DatabaseUtils
The name of the table containing the index to experiments.
EXP_RESULT_COL - Static variable in class weka.experiment.DatabaseUtils
The name of the column containing the results table name.
EXP_RESULT_PREFIX - Static variable in class weka.experiment.DatabaseUtils
The prefix for result table names.
EXP_SETUP_COL - Static variable in class weka.experiment.DatabaseUtils
The name of the column containing the experiment setup (parameters).
EXP_TYPE_COL - Static variable in class weka.experiment.DatabaseUtils
The name of the column containing the experiment type (ResultProducer).
expectation(double, int, double[], Hashtable) - Static method in class weka.associations.RuleGeneration
calculates the expected predctive accuracy of a rule
expectedCosts(double[]) - Method in class weka.classifiers.CostMatrix
Calculates the expected misclassification cost for each possible class value, given class probability estimates.
expectedCosts(double[], Instance) - Method in class weka.classifiers.CostMatrix
Calculates the expected misclassification cost for each possible class value, given class probability estimates.
expectedResultsPerAverageTipText() - Method in class weka.experiment.AveragingResultProducer
Returns the tip text for this property
Experiment - Class in weka.experiment
Holds all the necessary configuration information for a standard type experiment.
Experiment() - Constructor for class weka.experiment.Experiment
 
Experimenter - Class in weka.gui.experiment
The main class for the experiment environment.
Experimenter(boolean) - Constructor for class weka.gui.experiment.Experimenter
Creates the experiment environment gui with no initial experiment
ExperimenterDefaults - Class in weka.gui.experiment
This class offers get methods for the default Experimenter settings in the props file weka/gui/experiment/Experimenter.props.
ExperimenterDefaults() - Constructor for class weka.gui.experiment.ExperimenterDefaults
 
experimentIndexExists() - Method in class weka.experiment.DatabaseUtils
Returns true if the experiment index exists.
EXPLICIT - Static variable in class weka.associations.Tertius
Way of handling missing values: min counterinstances
Explorer - Class in weka.gui.explorer
The main class for the Weka explorer.
Explorer() - Constructor for class weka.gui.explorer.Explorer
Creates the experiment environment gui with no initial experiment
Explorer.CapabilitiesFilterChangeEvent - Class in weka.gui.explorer
This event can be fired in case the capabilities filter got changed
Explorer.CapabilitiesFilterChangeListener - Interface in weka.gui.explorer
Interface for classes that listen for filter changes.
Explorer.ExplorerPanel - Interface in weka.gui.explorer
A common interface for panels to be displayed in the Explorer
Explorer.LogHandler - Interface in weka.gui.explorer
A common interface for panels in the explorer that can handle logs
ExplorerDefaults - Class in weka.gui.explorer
This class offers get methods for the default Explorer settings in the props file weka/gui/explorer/Explorer.props.
ExplorerDefaults() - Constructor for class weka.gui.explorer.ExplorerDefaults
 
ExponentialFormat - Class in weka.core.matrix
 
ExponentialFormat() - Constructor for class weka.core.matrix.ExponentialFormat
 
ExponentialFormat(int) - Constructor for class weka.core.matrix.ExponentialFormat
 
ExponentialFormat(int, boolean) - Constructor for class weka.core.matrix.ExponentialFormat
 
ExponentialFormat(int, int, boolean, boolean) - Constructor for class weka.core.matrix.ExponentialFormat
 
exponentTipText() - Method in class weka.classifiers.functions.supportVector.PolyKernel
Returns the tip text for this property
exponentTipText() - Method in class weka.classifiers.functions.VotedPerceptron
Returns the tip text for this property
Expression - Class in weka.core.pmml
 
Expression(FieldMetaInfo.Optype, ArrayList<Attribute>) - Constructor for class weka.core.pmml.Expression
 
Expression - Class in weka.datagenerators.classifiers.regression
A data generator for generating y according to a given expression out of randomly generated x.
E.g., the mexican hat can be generated like this:
sin(abs(a1)) / abs(a1)
In addition to this function, the amplitude can be changed and gaussian noise can be added.
Expression() - Constructor for class weka.datagenerators.classifiers.regression.Expression
initializes the generator
expressionTipText() - Method in class weka.datagenerators.classifiers.regression.Expression
Returns the tip text for this property
expressionTipText() - Method in class weka.filters.unsupervised.attribute.AddExpression
Returns the tip text for this property
expressionTipText() - Method in class weka.filters.unsupervised.attribute.MathExpression
Returns the tip text for this property
expressionTipText() - Method in class weka.filters.unsupervised.instance.SubsetByExpression
Returns the tip text for this property.
extend(GridSearch.PointDouble) - Method in class weka.classifiers.meta.GridSearch.Grid
returns an extended grid that encompasses the given point (won't be on the border of the grid).
ExtensionFileFilter - Class in weka.gui
Provides a file filter for FileChoosers that accepts or rejects files based on their extension.
ExtensionFileFilter(String, String) - Constructor for class weka.gui.ExtensionFileFilter
Creates the ExtensionFileFilter
ExtensionFileFilter(String[], String) - Constructor for class weka.gui.ExtensionFileFilter
Creates an ExtensionFileFilter that accepts files that have any of the extensions contained in the supplied array.
extraArcs(BayesNet) - Method in class weka.classifiers.bayes.net.BIFReader
Count nr of exta arcs from other network compared to current network Note that an arc is not 'extra' if it is reversed.
extract(RevisionHandler) - Static method in class weka.core.RevisionUtils
Extracts the revision string returned by the RevisionHandler.
extract(String) - Static method in class weka.core.RevisionUtils
Extracts the revision string.
extractDataSequences(Instances, int) - Method in class weka.associations.GeneralizedSequentialPatterns
Extracts the data sequences out of the original data set according to their sequence id attribute, which is removed after extraction.
extractFilterAttributes(String) - Method in class weka.associations.GeneralizedSequentialPatterns
Parses a given String containing attribute numbers which are used for result filtering.
extremeValuesAsOutliersTipText() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Returns the tip text for this property
extremeValuesFactorTipText() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Returns the tip text for this property

F

f(double) - Method in class weka.classifiers.functions.pace.ChisqMixture
Computes the value of f(x) given the mixture.
f(DoubleVector) - Method in class weka.classifiers.functions.pace.ChisqMixture
Computes the value of f(x) given the mixture, where x is a vector.
f(double) - Method in class weka.classifiers.functions.pace.NormalMixture
Computes the value of f(x) given the mixture.
f(DoubleVector) - Method in class weka.classifiers.functions.pace.NormalMixture
Computes the value of f(x) given the mixture, where x is a vector.
FAILED - Static variable in class weka.experiment.TaskStatusInfo
 
failed() - Method in class weka.gui.sql.event.ConnectionEvent
whether an exception happened and is stored
failed() - Method in class weka.gui.sql.event.QueryExecuteEvent
is TRUE in case the exception is not NULL, i.e.
FALLOUT_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
attribute name: Fallout
FALSE - Static variable in interface weka.core.mathematicalexpression.sym
 
FALSE - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
FALSE_NEG_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
attribute name: False Negatives
FALSE_POS_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
attribute name: False Positives
falseNegativeRate(int) - Method in class weka.classifiers.Evaluation
Calculate the false negative rate with respect to a particular class.
falsePositiveRate(int) - Method in class weka.classifiers.Evaluation
Calculate the false positive rate with respect to a particular class.
farthestAway(double[], boolean[]) - Method in class weka.clusterers.FarthestFirst
 
FarthestFirst - Class in weka.clusterers
Cluster data using the FarthestFirst algorithm.

For more information see:

Hochbaum, Shmoys (1985).
FarthestFirst() - Constructor for class weka.clusterers.FarthestFirst
 
fastRegressionTipText() - Method in class weka.classifiers.trees.LMT
Returns the tip text for this property
FastVector - Class in weka.core
Implements a fast vector class without synchronized methods.
FastVector() - Constructor for class weka.core.FastVector
Constructs an empty vector with initial capacity zero.
FastVector(int) - Constructor for class weka.core.FastVector
Constructs a vector with the given capacity.
FastVector.FastVectorEnumeration - Class in weka.core
Class for enumerating the vector's elements.
FastVectorEnumeration(FastVector) - Constructor for class weka.core.FastVector.FastVectorEnumeration
Constructs an enumeration.
FastVectorEnumeration(FastVector, int) - Constructor for class weka.core.FastVector.FastVectorEnumeration
Constructs an enumeration with a special element.
FieldMetaInfo - Class in weka.core.pmml
Abstract superclass for various types of field meta data.
FieldMetaInfo(Element) - Constructor for class weka.core.pmml.FieldMetaInfo
Construct a new FieldMetaInfo.
FieldMetaInfo.Interval - Class in weka.core.pmml
Inner class for an Interval.
FieldMetaInfo.Interval.Closure - Enum in weka.core.pmml
Enumerated type for the closure.
FieldMetaInfo.Optype - Enum in weka.core.pmml
Enumerated type for the Optype
FieldMetaInfo.Value - Class in weka.core.pmml
Inner class for Values
FieldMetaInfo.Value.Property - Enum in weka.core.pmml
Enumerated type for the property.
FieldRef - Class in weka.core.pmml
Class encapsulating a FieldRef Expression.
FieldRef(Element, FieldMetaInfo.Optype, ArrayList<Attribute>) - Constructor for class weka.core.pmml.FieldRef
 
fields() - Method in class weka.core.TechnicalInformation
returns an enumeration over all the stored fields
FILE_EXTENSION - Static variable in class weka.classifiers.CostMatrix
The deafult file extension for cost matrix files
FILE_EXTENSION - Static variable in class weka.core.converters.ArffLoader
the file extension
FILE_EXTENSION - Static variable in class weka.core.converters.C45Loader
the file extension
FILE_EXTENSION - Static variable in class weka.core.converters.CSVLoader
the file extension.
FILE_EXTENSION - Static variable in class weka.core.converters.LibSVMLoader
the file extension.
FILE_EXTENSION - Static variable in class weka.core.converters.LibSVMSaver
the file extension
FILE_EXTENSION - Static variable in class weka.core.converters.SerializedInstancesLoader
the file extension
FILE_EXTENSION - Static variable in class weka.core.converters.SVMLightLoader
the file extension.
FILE_EXTENSION - Static variable in class weka.core.converters.SVMLightSaver
the file extension.
FILE_EXTENSION - Static variable in class weka.core.converters.XRFFLoader
the file extension
FILE_EXTENSION - Static variable in class weka.core.Instances
The filename extension that should be used for arff files
FILE_EXTENSION - Static variable in class weka.core.xml.KOML
the extension for KOML files (including '.')
FILE_EXTENSION - Static variable in class weka.core.xml.XMLInstances
The filename extension that should be used for xrff files
FILE_EXTENSION - Static variable in class weka.core.xml.XStream
the extension for XStream files (including '.')
FILE_EXTENSION - Static variable in class weka.experiment.Experiment
The filename extension that should be used for experiment files
FILE_EXTENSION - Static variable in class weka.gui.beans.Classifier
the extension for serialized models (binary Java serialization)
FILE_EXTENSION - Static variable in class weka.gui.beans.KnowledgeFlowApp
the extension for the serialized setups (Java serialization)
FILE_EXTENSION - Static variable in class weka.gui.beans.SerializedModelSaver
the extension for serialized models (binary Java serialization)
FILE_EXTENSION_COMPRESSED - Static variable in class weka.core.converters.AbstractFileLoader
the extension for compressed files
FILE_EXTENSION_COMPRESSED - Static variable in class weka.core.converters.ArffLoader
 
FILE_EXTENSION_COMPRESSED - Static variable in class weka.core.converters.XRFFLoader
the extension for compressed files
FILE_EXTENSION_XML - Static variable in class weka.gui.beans.KnowledgeFlowApp
the extension for the serialized setups (Java serialization)
fileChooser - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
FileEditor - Class in weka.gui
A PropertyEditor for File objects that lets the user select a file.
FileEditor() - Constructor for class weka.gui.FileEditor
 
FileLogger - Class in weka.core.logging
A simple file logger, that just logs to a single file.
FileLogger() - Constructor for class weka.core.logging.FileLogger
 
FILENAME - Static variable in class weka.gui.SimpleCLIPanel
The filename of the properties file.
filePrefix() - Method in class weka.core.converters.AbstractFileSaver
Gets the file name prefix
filePrefix() - Method in class weka.core.converters.AbstractSaver
Default implementation throws an IOException.
filePrefix() - Method in interface weka.core.converters.Saver
Gets the file prefix This method is used in the KnowledgeFlow GUI.
FileSourcedConverter - Interface in weka.core.converters
Interface to a loader/saver that loads/saves from a file source.
fill(Shape) - Method in class weka.gui.visualize.PostscriptGraphics
 
fill3DRect(int, int, int, int, boolean) - Method in class weka.gui.visualize.PostscriptGraphics
Draw a filled rectangle with 3D effect in current pen color.
fillArc(int, int, int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics
Not implemented
fillCorrelation() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
Fill the correlation matrix.
fillCovariance() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
 
fillFrame(Component) - Method in interface weka.gui.MainMenuExtension
Fills the frame with life, like adding components, window listeners, setting size, location, etc.
fillIn(int[], boolean[][]) - Method in class weka.classifiers.bayes.net.MarginCalculator
Apply Tarjan and Yannakakis (1984) fill in algorithm for graph triangulation.
fillInnerNodes(Vector) - Method in class weka.classifiers.trees.SimpleCart
Fills a list with all inner nodes in the tree.
fillOval(int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics
Draw a filled Oval in current pen color.
fillPolygon(int[], int[], int) - Method in class weka.gui.visualize.PostscriptGraphics
Not implemented
fillPolygon(Polygon) - Method in class weka.gui.visualize.PostscriptGraphics
Not implemented
fillRect(int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics
Draw a filled rectangle in current pen color.
fillRoundRect(int, int, int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics
Not implemented
fillWithMissingTipText() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Returns the tip text for this property
Filter - Class in weka.filters
An abstract class for instance filters: objects that take instances as input, carry out some transformation on the instance and then output the instance.
Filter() - Constructor for class weka.filters.Filter
 
filter(String, Instances) - Static method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
Filters the input dataset against the provided expression.
Filter - Class in weka.gui.beans
A wrapper bean for Weka filters
Filter() - Constructor for class weka.gui.beans.Filter
 
FILTER_NONE - Static variable in class weka.classifiers.functions.GaussianProcesses
no filter
FILTER_NONE - Static variable in class weka.classifiers.functions.SMO
filter: No normalization/standardization
FILTER_NONE - Static variable in class weka.classifiers.functions.SMOreg
The filter to apply to the training data: None
FILTER_NONE - Static variable in class weka.classifiers.mi.MDD
No normalization/standardization
FILTER_NONE - Static variable in class weka.classifiers.mi.MIDD
No normalization/standardization
FILTER_NONE - Static variable in class weka.classifiers.mi.MIEMDD
No normalization/standardization
FILTER_NONE - Static variable in class weka.classifiers.mi.MIOptimalBall
No normalization/standardization
FILTER_NONE - Static variable in class weka.classifiers.mi.MISMO
No normalization/standardization
FILTER_NONE - Static variable in class weka.classifiers.mi.MISVM
No normalization/standardization
FILTER_NONE - Static variable in class weka.filters.unsupervised.attribute.StringToWordVector
normalization: No normalization.
FILTER_NORMALIZE - Static variable in class weka.classifiers.functions.GaussianProcesses
normalizes the data
FILTER_NORMALIZE - Static variable in class weka.classifiers.functions.SMO
filter: Normalize training data
FILTER_NORMALIZE - Static variable in class weka.classifiers.functions.SMOreg
The filter to apply to the training data: Normalzie
FILTER_NORMALIZE - Static variable in class weka.classifiers.mi.MDD
Normalize training data
FILTER_NORMALIZE - Static variable in class weka.classifiers.mi.MIDD
Normalize training data
FILTER_NORMALIZE - Static variable in class weka.classifiers.mi.MIEMDD
Normalize training data
FILTER_NORMALIZE - Static variable in class weka.classifiers.mi.MIOptimalBall
Normalize training data
FILTER_NORMALIZE - Static variable in class weka.classifiers.mi.MISMO
Normalize training data
FILTER_NORMALIZE - Static variable in class weka.classifiers.mi.MISVM
Normalize training data
FILTER_NORMALIZE_ALL - Static variable in class weka.filters.unsupervised.attribute.StringToWordVector
normalization: Normalize all data.
FILTER_NORMALIZE_TEST_ONLY - Static variable in class weka.filters.unsupervised.attribute.StringToWordVector
normalization: Normalize test data only.
FILTER_STANDARDIZE - Static variable in class weka.classifiers.functions.GaussianProcesses
standardizes the data
FILTER_STANDARDIZE - Static variable in class weka.classifiers.functions.SMO
filter: Standardize training data
FILTER_STANDARDIZE - Static variable in class weka.classifiers.functions.SMOreg
The filter to apply to the training data: Standardize
FILTER_STANDARDIZE - Static variable in class weka.classifiers.mi.MDD
Standardize training data
FILTER_STANDARDIZE - Static variable in class weka.classifiers.mi.MIDD
Standardize training data
FILTER_STANDARDIZE - Static variable in class weka.classifiers.mi.MIEMDD
Standardize training data
FILTER_STANDARDIZE - Static variable in class weka.classifiers.mi.MIOptimalBall
Standardize training data
FILTER_STANDARDIZE - Static variable in class weka.classifiers.mi.MISMO
Standardize training data
FILTER_STANDARDIZE - Static variable in class weka.classifiers.mi.MISVM
Standardize training data
filterAfterFirstBatchTipText() - Method in class weka.filters.unsupervised.instance.SubsetByExpression
Returns the tip text for this property.
filterAttributesTipText() - Method in class weka.associations.GeneralizedSequentialPatterns
Returns the filterAttributes option tip text for the Weka GUI.
FilterBeanInfo - Class in weka.gui.beans
Bean info class for the Filter bean
FilterBeanInfo() - Constructor for class weka.gui.beans.FilterBeanInfo
 
FilterCustomizer - Class in weka.gui.beans
GUI customizer for the filter bean
FilterCustomizer() - Constructor for class weka.gui.beans.FilterCustomizer
 
FilteredAssociator - Class in weka.associations
Class for running an arbitrary associator on data that has been passed through an arbitrary filter.
FilteredAssociator() - Constructor for class weka.associations.FilteredAssociator
Default constructor.
FilteredAttributeEval - Class in weka.attributeSelection
Class for running an arbitrary attribute evaluator on data that has been passed through an arbitrary filter (note: filters that alter the order or number of attributes are not allowed).
FilteredAttributeEval() - Constructor for class weka.attributeSelection.FilteredAttributeEval
 
FilteredClassifier - Class in weka.classifiers.meta
Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
FilteredClassifier() - Constructor for class weka.classifiers.meta.FilteredClassifier
Default constructor.
FilteredClusterer - Class in weka.clusterers
Class for running an arbitrary clusterer on data that has been passed through an arbitrary filter.
FilteredClusterer() - Constructor for class weka.clusterers.FilteredClusterer
Default constructor.
FilteredSubsetEval - Class in weka.attributeSelection
Class for running an arbitrary subset evaluator on data that has been passed through an arbitrary filter (note: filters that alter the order or number of attributes are not allowed).
FilteredSubsetEval() - Constructor for class weka.attributeSelection.FilteredSubsetEval
 
filterFile(Filter, String[]) - Static method in class weka.filters.Filter
Method for testing filters.
filterNonCoreLoaderFileFilters(Vector<ExtensionFileFilter>) - Method in class weka.gui.ConverterFileChooser
filters out all non-core loaders if only those should be displayed
filterNonCoreSaverFileFilters(Vector<ExtensionFileFilter>) - Method in class weka.gui.ConverterFileChooser
filters out all non-core savers if only those should be displayed
filterOutEmptyStrings() - Method in class weka.core.tokenizers.NGramTokenizer
filters out empty strings in m_SplitString and replaces m_SplitString with the cleaned version.
filterSaverFileFilters(Vector<ExtensionFileFilter>) - Method in class weka.gui.ConverterFileChooser
filters the list of file filters according to the currently set Capabilities
filtersTipText() - Method in class weka.filters.MultiFilter
Returns the tip text for this property
filtersTipText() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
Returns the tip text for this property.
filterSubset(List<ScatterSearchV1.Subset>, int) - Method in class weka.attributeSelection.ScatterSearchV1
Filter a given Lis of Subsets removing the equals subsets
filterTipText() - Method in class weka.associations.FilteredAssociator
Returns the tip text for this property
filterTipText() - Method in class weka.attributeSelection.FilteredAttributeEval
Returns the tip text for this property
filterTipText() - Method in class weka.attributeSelection.FilteredSubsetEval
Returns the tip text for this property
filterTipText() - Method in class weka.classifiers.functions.PLSClassifier
Returns the tip text for this property
filterTipText() - Method in class weka.classifiers.meta.FilteredClassifier
Returns the tip text for this property
filterTipText() - Method in class weka.classifiers.meta.GridSearch
Returns the tip text for this property
filterTipText() - Method in class weka.clusterers.FilteredClusterer
Returns the tip text for this property.
filterTipText() - Method in class weka.filters.unsupervised.attribute.Wavelet
Returns the tip text for this property
filterTypeTipText() - Method in class weka.attributeSelection.SVMAttributeEval
Returns a tip text for this property suitable for display in the GUI
filterTypeTipText() - Method in class weka.classifiers.functions.GaussianProcesses
Returns the tip text for this property
filterTypeTipText() - Method in class weka.classifiers.functions.SMO
Returns the tip text for this property
filterTypeTipText() - Method in class weka.classifiers.functions.SMOreg
Returns the tip text for this property
filterTypeTipText() - Method in class weka.classifiers.mi.MDD
Returns the tip text for this property
filterTypeTipText() - Method in class weka.classifiers.mi.MIDD
Returns the tip text for this property
filterTypeTipText() - Method in class weka.classifiers.mi.MIEMDD
Returns the tip text for this property
filterTypeTipText() - Method in class weka.classifiers.mi.MIOptimalBall
Returns the tip text for this property
filterTypeTipText() - Method in class weka.classifiers.mi.MISMO
Returns the tip text for this property
filterTypeTipText() - Method in class weka.classifiers.mi.MISVM
Returns the tip text for this property
finalize() - Method in class weka.gui.sql.ResultSetTable
frees up the memory
finalize() - Method in class weka.gui.sql.ResultSetTableModel
frees up the memory.
finalize() - Method in class weka.gui.visualize.PostscriptGraphics
Not implemented
find(boolean) - Method in class weka.core.AttributeLocator
returns the indices of the searched-for attributes (if TRUE) or the indices of AttributeLocator objects (if FALSE)
find(String, String[]) - Static method in class weka.core.ClassDiscovery
Checks the given packages for classes that inherited from the given class, in case it's a class, or implement this class, in case it's an interface.
find(String, String) - Static method in class weka.core.ClassDiscovery
Checks the given package for classes that inherited from the given class, in case it's a class, or implement this class, in case it's an interface.
find(Class, String[]) - Static method in class weka.core.ClassDiscovery
Checks the given packages for classes that inherited from the given class, in case it's a class, or implement this class, in case it's an interface.
find(Class, String) - Static method in class weka.core.ClassDiscovery
Checks the given package for classes that inherited from the given class, in case it's a class, or implement this class, in case it's an interface.
find() - Method in class weka.core.FindWithCapabilities
returns a list with all the classnames that fit the criteria.
find(Object, PropertyPath.Path) - Static method in class weka.core.PropertyPath
returns the property and object associated with the given path, null if a problem occurred.
find(String) - Method in class weka.core.Trie.TrieNode
returns the node with the given suffix
findAllRulesForSupportLevelTipText() - Method in class weka.associations.FPGrowth
Tip text for this property suitable for displaying in the GUI.
findArgmin(double[], double[][]) - Method in class weka.core.Optimization
Main algorithm.
findBest() - Method in class weka.classifiers.meta.GridSearch
returns the best values-pair in the grid
findBestLeaf(double[], RuleNode[]) - Method in class weka.classifiers.trees.m5.RuleNode
Find the leaf with greatest coverage
findCentralTendencies(double[]) - Method in class weka.classifiers.BVDecomposeSegCVSub
Finds the central tendency, given the classifications for an instance.
findFrequentSequences() - Method in class weka.associations.GeneralizedSequentialPatterns
The actual method for extracting frequent sequences.
findInstance(int, double[]) - Method in class weka.classifiers.mi.MIEMDD
given x, find the instance in ith bag with the most likelihood probability, which is most likely to responsible for the label of the bag For a positive bag, find the instance with the maximal probability of being positive For a negative bag, find the instance with the minimal probability of being negative
findInstance(Point) - Static method in class weka.gui.beans.BeanInstance
Looks for a bean (if any) whose bounds contain the supplied point
findInstances(Rectangle) - Static method in class weka.gui.beans.BeanInstance
Looks for all beans (if any) located within the supplied bounding box.
findIntervall(double) - Method in class weka.associations.PriorEstimation
searches the mid point of the interval a given confidence value falls into
findKeyIndex() - Method in class weka.experiment.AveragingResultProducer
Scans through the key field names of the result producer to find the index of the key field to average over.
findKNearest(Instance, int) - Method in class weka.core.neighboursearch.CoverTree
Performs k-NN serach for a single given query/test Instance.
findMinDistance(Instances, int) - Static method in class weka.estimators.EstimatorUtils
Find the minimum distance between values
findNearestNeighbours(Instance, KDTreeNode, int, NearestNeighbourSearch.MyHeap, double) - Method in class weka.core.neighboursearch.KDTree
Returns (in the supplied heap object) the k nearest neighbours of the given instance starting from the give tree node.
findNeighbors(Instance, int, Instances) - Method in class weka.classifiers.mi.CitationKNN
Build the list of nearest k neighbors to the given test instance.
findNodes(String) - Method in class weka.core.xml.XMLDocument
Returns the nodes that the given xpath expression will find in the document.
findNumBins(int) - Method in class weka.filters.unsupervised.attribute.Discretize
Optimizes the number of bins using leave-one-out cross-validation.
findNumBins(int) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Finds the number of bins to use and creates the cut points.
findNumBinsTipText() - Method in class weka.filters.unsupervised.attribute.Discretize
Returns the tip text for this property
findNumBinsTipText() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Returns the tip text for this property
findOptimalPointOnLine(int, double, double, double, int, double, double, double, double, double, double) - Method in class weka.classifiers.functions.supportVector.RegSMO
Finds optimal point on line constrained by first (i1) and second (i2) candidate.
findPackages() - Static method in class weka.core.ClassDiscovery
Lists all packages it can find in the classpath.
findParamsByCrossValidation(int, Instances, Random) - Method in class weka.classifiers.meta.CVParameterSelection
Finds the best parameter combination.
findRadius(Instances) - Method in class weka.classifiers.mi.MIOptimalBall
Find the maximum radius for the optimal ball.
findReadMethod(Object, String) - Static method in class weka.core.xml.XMLSerializationMethodHandler
returns the method with the given name that has the same signature as readFromXML() of the XMLSerialiation class.
findThreshold(FastVector) - Method in class weka.classifiers.meta.ThresholdSelector
Finds the best threshold, this implementation searches for the highest FMeasure.
findWeights(int, double[][]) - Method in class weka.classifiers.mi.MINND
Use gradient descent to distort the MU parameter for the exemplar.
FindWithCapabilities - Class in weka.core
Locates all classes with certain capabilities.
FindWithCapabilities() - Constructor for class weka.core.FindWithCapabilities
 
findWriteMethod(Object, String) - Static method in class weka.core.xml.XMLSerializationMethodHandler
returns the method with the given name that has the same signature as writeToXML() of the XMLSerialiation class.
FINE - Static variable in class weka.core.Debug
the log level Fine
FINER - Static variable in class weka.core.Debug
the log level Finer
FINEST - Static variable in class weka.core.Debug
the log level Finest
finished() - Method in class weka.experiment.OutputZipper
Closes the zip file.
FINISHED - Static variable in class weka.experiment.TaskStatusInfo
 
finished() - Method in class weka.gui.visualize.PostscriptGraphics
Finalizes output file.
fireLayoutCompleteEvent(LayoutCompleteEvent) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
Fires a LayoutCompleteEvent.
fireLayoutCompleteEvent(LayoutCompleteEvent) - Method in interface weka.gui.graphvisualizer.LayoutEngine
This fires a LayoutCompleteEvent once a layout has been completed.
FIRST - Static variable in class weka.filters.unsupervised.attribute.ClassAssigner
use the first attribute as class.
firstElement() - Method in class weka.core.FastVector
Returns the first element of the vector.
firstElement() - Method in class weka.gui.CheckBoxList.CheckBoxListModel
Returns the first component of this list.
firstInstance() - Method in class weka.core.Instances
Returns the first instance in the set.
FirstOrder - Class in weka.filters.unsupervised.attribute
This instance filter takes a range of N numeric attributes and replaces them with N-1 numeric attributes, the values of which are the difference between consecutive attribute values from the original instance.
FirstOrder() - Constructor for class weka.filters.unsupervised.attribute.FirstOrder
 
firstValueIndexTipText() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
 
firstValueIndexTipText() - Method in class weka.filters.unsupervised.attribute.SwapValues
 
fit(DoubleVector) - Method in class weka.classifiers.functions.pace.MixtureDistribution
Fits the mixture (or mixing) distribution to the data.
fit(DoubleVector, int) - Method in class weka.classifiers.functions.pace.MixtureDistribution
Fits the mixture (or mixing) distribution to the data.
fitForSingleCluster(DoubleVector, int) - Method in class weka.classifiers.functions.pace.MixtureDistribution
Fits the mixture (or mixing) distribution to the data.
fitLogistic(Instances, int, int, int, Random) - Method in class weka.classifiers.functions.SMO.BinarySMO
Fits logistic regression model to SVM outputs analogue to John Platt's method.
fitLogistic(Instances, int, int, int, Random) - Method in class weka.classifiers.mi.MISMO.BinaryMISMO
Fits logistic regression model to SVM outputs analogue to John Platt's method.
fittingIntervalLength - Variable in class weka.classifiers.functions.pace.ChisqMixture
 
fittingIntervalLength - Variable in class weka.classifiers.functions.pace.NormalMixture
 
fittingIntervals(DoubleVector) - Method in class weka.classifiers.functions.pace.ChisqMixture
Contructs the set of fitting intervals for mixture estimation.
fittingIntervals(DoubleVector) - Method in class weka.classifiers.functions.pace.MixtureDistribution
Contructs the set of fitting intervals for mixture estimation.
fittingIntervals(DoubleVector) - Method in class weka.classifiers.functions.pace.NormalMixture
Contructs the set of fitting intervals for mixture estimation.
fittingIntervalThreshold - Variable in class weka.classifiers.functions.pace.ChisqMixture
 
fitToScreen() - Method in class weka.gui.hierarchyvisualizer.HierarchyVisualizer
Fits the tree to the current screen size.
fitToScreen() - Method in class weka.gui.treevisualizer.TreeVisualizer
Fits the tree to the current screen size.
fixHyphens() - Method in class weka.core.xml.XMLOptions
pushes any options with type VAL_TYPE_HYPHENS to the end, i.e., the "--" are really added at the end.
FlexibleDecimalFormat - Class in weka.core.matrix
 
FlexibleDecimalFormat() - Constructor for class weka.core.matrix.FlexibleDecimalFormat
 
FlexibleDecimalFormat(int) - Constructor for class weka.core.matrix.FlexibleDecimalFormat
 
FlexibleDecimalFormat(int, boolean) - Constructor for class weka.core.matrix.FlexibleDecimalFormat
 
FlexibleDecimalFormat(int, boolean, boolean, boolean) - Constructor for class weka.core.matrix.FlexibleDecimalFormat
 
FlexibleDecimalFormat(double) - Constructor for class weka.core.matrix.FlexibleDecimalFormat
 
FLOAT - Static variable in class weka.experiment.DatabaseUtils
Type mapping for FLOAT used for reading experiment results.
floatingForwardSearch(int, BitSet, int[], int, boolean, int, Instances, SubsetEvaluator, boolean) - Method in class weka.attributeSelection.LFSMethods
Performs linear floating forward selection ( the stopping criteria cannot be changed to a specific size value )
FloatingPointFormat - Class in weka.core.matrix
Class for the format of floating point numbers
FloatingPointFormat() - Constructor for class weka.core.matrix.FloatingPointFormat
Default constructor
FloatingPointFormat(int) - Constructor for class weka.core.matrix.FloatingPointFormat
 
FloatingPointFormat(int, int) - Constructor for class weka.core.matrix.FloatingPointFormat
 
FloatingPointFormat(int, int, boolean) - Constructor for class weka.core.matrix.FloatingPointFormat
 
FLOOR - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
 
FLOOR - Static variable in interface weka.core.mathematicalexpression.sym
 
FLOOR - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
FLOOR1 - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
 
FlowRunner - Class in weka.gui.beans
Small utility class for executing KnowledgeFlow flows outside of the KnowledgeFlow application
FlowRunner() - Constructor for class weka.gui.beans.FlowRunner
Constructor
FlowRunner.SimpleLogger - Class in weka.gui.beans
 
flush() - Method in class weka.core.logging.OutputLogger.OutputPrintStream
ignored.
flush() - Method in class weka.core.Tee
flushes all the printstreams.
flush() - Method in class weka.gui.LogWindow.LogWindowPrintStream
flushes the printstream
flushInput() - Method in class weka.filters.Filter
This will remove all buffered instances from the inputformat dataset.
fMeasure(int) - Method in class weka.classifiers.Evaluation
Calculate the F-Measure with respect to a particular class.
FMEASURE - Static variable in class weka.classifiers.meta.ThresholdSelector
F-measure
FMEASURE_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
attribute name: FMeasure
FOLD_FIELD_NAME - Static variable in class weka.experiment.CrossValidationResultProducer
The name of the key field containing the fold number
foldsTipText() - Method in class weka.attributeSelection.OneRAttributeEval
Returns a string for this option suitable for display in the gui as a tip text
foldsTipText() - Method in class weka.attributeSelection.WrapperSubsetEval
Returns the tip text for this property
foldsTipText() - Method in class weka.classifiers.rules.ConjunctiveRule
Returns the tip text for this property
foldsTipText() - Method in class weka.classifiers.rules.JRip
Returns the tip text for this property
foldsTipText() - Method in class weka.classifiers.rules.Ridor
Returns the tip text for this property
foldsTipText() - Method in class weka.gui.beans.CrossValidationFoldMaker
Tip text for this property
foldsTypeTipText() - Method in class weka.attributeSelection.RaceSearch
Returns the tip text for this property
foldTipText() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Returns the tip text for this property
foldTipText() - Method in class weka.filters.unsupervised.instance.RemoveFolds
Returns the tip text for this property
forCapabilities(Capabilities) - Static method in class weka.core.TestInstances
returns a TestInstances instance setup already for the the given capabilities.
forCnt - Variable in class weka.classifiers.lazy.LBR
 
forInstances(Instances) - Static method in class weka.core.Capabilities
returns a Capabilities object specific for this data.
forInstances(Instances, boolean) - Static method in class weka.core.Capabilities
returns a Capabilities object specific for this data.
format() - Method in class weka.classifiers.functions.pace.PaceMatrix
Decimal format for converting a matrix into a string
format(int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Decimal format for converting a matrix into a string
format(int, boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix
Decimal format for converting a matrix into a string
format(int, int, int, int, boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix
Decimal format for converting a matrix into a string
format(int, int, int, int, int, boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix
Decimal format for converting a matrix into a string
format(double, StringBuffer, FieldPosition) - Method in class weka.core.matrix.ExponentialFormat
 
format(double, StringBuffer, FieldPosition) - Method in class weka.core.matrix.FlexibleDecimalFormat
 
format(double, StringBuffer, FieldPosition) - Method in class weka.core.matrix.FloatingPointFormat
 
FORMAT_AVAILABLE - Static variable in class weka.gui.beans.InstanceEvent
 
FORMAT_AVAILABLE - Static variable in class weka.gui.streams.InstanceEvent
Specifies that the instance format is available
FORMAT_HHMMSS - Static variable in class weka.core.Debug.Clock
the output format in hours:minutes:seconds, with fraction of msecs
FORMAT_MILLISECONDS - Static variable in class weka.core.Debug.Clock
the output format in milli-seconds
FORMAT_SECONDS - Static variable in class weka.core.Debug.Clock
the output format in seconds, with fraction of msecs
formatDate(double) - Method in class weka.core.Attribute
Returns the given amount of milliseconds formatted according to the current Date format.
formatString(String) - Method in class weka.core.matrix.FlexibleDecimalFormat
 
formatTipText() - Method in class weka.datagenerators.DataGenerator
Returns the tip text for this property
forName(String, String[]) - Static method in class weka.associations.AbstractAssociator
Creates a new instance of a associator given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.attributeSelection.ASEvaluation
Creates a new instance of an attribute/subset evaluator given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.attributeSelection.ASSearch
Creates a new instance of a search class given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.classifiers.Classifier
Creates a new instance of a classifier given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.classifiers.functions.supportVector.Kernel
Creates a new instance of a kernel given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.clusterers.AbstractClusterer
Creates a new instance of a clusterer given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, Class, String, String[]) - Method in class weka.core.Check
Tries to instantiate a new instance of the given class and checks whether it is an instance of the specified class.
forName(Class, String, String[]) - Static method in class weka.core.Utils
Creates a new instance of an object given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.estimators.Estimator
Creates a new instance of a estimatorr given it's class name and (optional) arguments to pass to it's setOptions method.
forward(PaceMatrix, IntVector, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Forward ordering of columns in terms of response explanation.
forwardSearch(int, BitSet, int[], int, boolean, int, int, Instances, SubsetEvaluator, boolean) - Method in class weka.attributeSelection.LFSMethods
Performs linear forward selection
forwardSelectionMethodTipText() - Method in class weka.attributeSelection.LinearForwardSelection
Returns the tip text for this property
foundUsefulAttribute() - Method in class weka.classifiers.functions.SimpleLinearRegression
Returns true if a usable attribute was found.
FP_RATE_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
attribute name: False Positive Rate"
FPGrowth - Class in weka.associations
Class implementing the FP-growth algorithm for finding large item sets without candidate generation.
FPGrowth() - Constructor for class weka.associations.FPGrowth
Construct a new FPGrowth object.
FPGrowth.AssociationRule - Class in weka.associations
 
FPGrowth.AssociationRule.METRIC_TYPE - Enum in weka.associations
Enum for holding different metric types
FPGrowth.BinaryItem - Class in weka.associations
Inner class that handles a single binary item
FPGrowth.FPTreeNode - Class in weka.associations
A node in the FP-tree.
FPGrowth.FrequentBinaryItemSet - Class in weka.associations
Class for maintaining a frequent item set.
FPGrowth.FrequentItemSets - Class in weka.associations
Maintains a list of frequent item sets.
FPGrowth.ShadowCounts - Class in weka.associations
This class holds the counts for projected tree nodes and header lists.
FProbability(double, int, int) - Static method in class weka.core.Statistics
Computes probability of F-ratio.
FPTreeNode(FPGrowth.FPTreeNode, FPGrowth.BinaryItem) - Constructor for class weka.associations.FPGrowth.FPTreeNode
Construct a new node with the given parent link and item.
frameTitle - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
freeNotCoveredInstances() - Method in class weka.classifiers.trees.m5.Rule
Free up memory consumed by the set of instances not covered by this rule.
FREQ_ASCEND - Static variable in class weka.filters.supervised.attribute.ClassOrder
The class values are sorted in ascending order based on their frequencies
FREQ_DESCEND - Static variable in class weka.filters.supervised.attribute.ClassOrder
The class values are sorted in descending order based on their frequencies
frequencyLimitTipText() - Method in class weka.classifiers.bayes.AODE
Returns the tip text for this property
frequencyLimitTipText() - Method in class weka.classifiers.bayes.AODEsr
Returns the tip text for this property
frequencyThresholdTipText() - Method in class weka.associations.Tertius
Returns the tip text for this property.
FrequentBinaryItemSet(ArrayList<FPGrowth.BinaryItem>, int) - Constructor for class weka.associations.FPGrowth.FrequentBinaryItemSet
Constructor
FrequentItemSets(int) - Constructor for class weka.associations.FPGrowth.FrequentItemSets
Constructor.
freshAttributeInfo() - Method in class weka.core.Instances
Replaces the attribute information by a clone of itself.
FromFile - Class in weka.classifiers.bayes.net.search.fixed
The FromFile reads the structure of a Bayes net from a file in BIFF format.
FromFile() - Constructor for class weka.classifiers.bayes.net.search.fixed.FromFile
 
fromXML(Document) - Method in class weka.core.xml.XMLSerialization
returns the given DOM document as an instance of the specified class
FT - Class in weka.classifiers.trees
Classifier for building 'Functional trees', which are classification trees that could have logistic regression functions at the inner nodes and/or leaves.
FT() - Constructor for class weka.classifiers.trees.FT
Creates an instance of FT with standard options
FTInnerNode - Class in weka.classifiers.trees.ft
Class for Functional Inner tree structure.
FTInnerNode(boolean, int, int, double, boolean) - Constructor for class weka.classifiers.trees.ft.FTInnerNode
Constructor for Functional Inner tree node.
FTLeavesNode - Class in weka.classifiers.trees.ft
Class for Functional Leaves tree version.
FTLeavesNode(boolean, int, int, double, boolean) - Constructor for class weka.classifiers.trees.ft.FTLeavesNode
Constructor for Functional Leaves tree node.
FTNode - Class in weka.classifiers.trees.ft
Class for Functional tree structure.
FTNode(boolean, int, int, double, boolean) - Constructor for class weka.classifiers.trees.ft.FTNode
Constructor for Functional tree node.
FTtree - Class in weka.classifiers.trees.ft
Abstract class for Functional tree structure.
FTtree() - Constructor for class weka.classifiers.trees.ft.FTtree
 
fullValue() - Method in class weka.gui.HierarchyPropertyParser
The full value of the current node, i.e.
Function - Class in weka.core.pmml
Abstract superclass for PMML built-in and DefineFunctions.
Function() - Constructor for class weka.core.pmml.Function
 
FUNCTION_1 - Static variable in class weka.datagenerators.classifiers.classification.Agrawal
function 1
FUNCTION_10 - Static variable in class weka.datagenerators.classifiers.classification.Agrawal
function 10
FUNCTION_2 - Static variable in class weka.datagenerators.classifiers.classification.Agrawal
function 2
FUNCTION_3 - Static variable in class weka.datagenerators.classifiers.classification.Agrawal
function 3
FUNCTION_4 - Static variable in class weka.datagenerators.classifiers.classification.Agrawal
function 4
FUNCTION_5 - Static variable in class weka.datagenerators.classifiers.classification.Agrawal
function 5
FUNCTION_6 - Static variable in class weka.datagenerators.classifiers.classification.Agrawal
function 6
FUNCTION_7 - Static variable in class weka.datagenerators.classifiers.classification.Agrawal
function 7
FUNCTION_8 - Static variable in class weka.datagenerators.classifiers.classification.Agrawal
function 8
FUNCTION_9 - Static variable in class weka.datagenerators.classifiers.classification.Agrawal
function 9
FUNCTION_TAGS - Static variable in class weka.datagenerators.classifiers.classification.Agrawal
the funtion tags
functionTipText() - Method in class weka.datagenerators.classifiers.classification.Agrawal
Returns the tip text for this property
fVector - Variable in class weka.classifiers.trees.LADTree.LADInstance
 

G

g1(double, double) - Method in class weka.classifiers.functions.pace.PaceMatrix
Constructs the Givens rotation
g2(double[], int, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Performs the Givens rotation
GABitSet() - Constructor for class weka.attributeSelection.GeneticSearch.GABitSet
Constructor
gain(double[][], double) - Method in class weka.classifiers.trees.RandomTree
Computes value of splitting criterion after split.
gain(double[][], double) - Method in class weka.classifiers.trees.REPTree.Tree
Computes value of splitting criterion after split.
gainRatio() - Method in class weka.classifiers.trees.j48.BinC45Split
Returns (C4.5-type) gain ratio for the generated split.
gainRatio() - Method in class weka.classifiers.trees.j48.C45Split
Returns (C4.5-type) gain ratio for the generated split.
gainRatio(double[][]) - Static method in class weka.core.ContingencyTables
Computes gain ratio for contingency table (split on rows).
GainRatioAttributeEval - Class in weka.attributeSelection
GainRatioAttributeEval :

Evaluates the worth of an attribute by measuring the gain ratio with respect to the class.

GainR(Class, Attribute) = (H(Class) - H(Class | Attribute)) / H(Attribute).

Valid options are:

GainRatioAttributeEval() - Constructor for class weka.attributeSelection.GainRatioAttributeEval
Constructor
GainRatioSplitCrit - Class in weka.classifiers.trees.j48
Class for computing the gain ratio for a given distribution.
GainRatioSplitCrit() - Constructor for class weka.classifiers.trees.j48.GainRatioSplitCrit
 
gamma(double) - Static method in class weka.core.Statistics
Returns the Gamma function of the argument.
gammaTipText() - Method in class weka.classifiers.functions.LibSVM
Returns the tip text for this property
gammaTipText() - Method in class weka.classifiers.functions.supportVector.RBFKernel
Returns the tip text for this property
GAUSS - Static variable in class weka.classifiers.lazy.LWL
 
GAUSSIAN - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
Distributions available
GAUSSIAN - Static variable in class weka.datagenerators.clusterers.SubspaceCluster
cluster type: gaussian
GAUSSIAN - Static variable in class weka.filters.unsupervised.attribute.RandomProjection
distribution type: gaussian
GaussianPriorImpl - Class in weka.classifiers.bayes.blr
Implementation of the Gaussian Prior update function based on CLG Algorithm with a certain Trust Region Update.
GaussianPriorImpl() - Constructor for class weka.classifiers.bayes.blr.GaussianPriorImpl
 
GaussianProcesses - Class in weka.classifiers.functions
Implements Gaussian Processes for regression without hyperparameter-tuning.
GaussianProcesses() - Constructor for class weka.classifiers.functions.GaussianProcesses
the default constructor
GE - Static variable in interface weka.core.mathematicalexpression.sym
 
GE - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
GeneralizedSequentialPatterns - Class in weka.associations
Class implementing a GSP algorithm for discovering sequential patterns in a sequential data set.
The attribute identifying the distinct data sequences contained in the set can be determined by the respective option.
GeneralizedSequentialPatterns() - Constructor for class weka.associations.GeneralizedSequentialPatterns
Constructor.
GeneralRegression - Class in weka.classifiers.pmml.consumer
Class implementing import of PMML General Regression model.
GeneralRegression(Element, Instances, MiningSchema) - Constructor for class weka.classifiers.pmml.consumer.GeneralRegression
Constructs a GeneralRegression classifier.
generate() - Method in class weka.classifiers.meta.GridSearch.PerformanceTable
generates the table
generate() - Method in class weka.core.Javadoc
generates either the plain Javadoc (if no filename specified) or the updated file (if a filename is specified).
generate() - Method in class weka.core.ListOptions
generates the options string.
generate() - Method in class weka.core.TestInstances
Generates a new dataset
generate(String) - Method in class weka.core.TestInstances
generates a new dataset.
generateArtificialData(int, Instances) - Method in class weka.classifiers.meta.Decorate
Generate artificial training examples.
generateAttribute(int, int, String) - Method in class weka.core.TestInstances
creates a new Attribute of the given type
generateAttributeValue(Instances, int, double) - Method in class weka.core.TestInstances
Generates a new value for the specified attribute.
generateClassValue(Instances) - Method in class weka.core.TestInstances
Generates the class value
generateDistribution() - Method in class weka.associations.PriorEstimation
Calculates the prior distribution.
generateExample() - Method in class weka.datagenerators.classifiers.classification.Agrawal
Generates one example of the dataset.
generateExample() - Method in class weka.datagenerators.classifiers.classification.BayesNet
Generates one example of the dataset.
generateExample() - Method in class weka.datagenerators.classifiers.classification.LED24
Generates one example of the dataset.
generateExample() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
Generates one example of the dataset.
generateExample() - Method in class weka.datagenerators.classifiers.classification.RDG1
Generate an example of the dataset dataset.
generateExample() - Method in class weka.datagenerators.classifiers.regression.Expression
Generates one example of the dataset.
generateExample() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Generates one example of the dataset.
generateExample() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Generate an example of the dataset.
generateExample() - Method in class weka.datagenerators.clusterers.SubspaceCluster
Generate an example of the dataset.
generateExample() - Method in class weka.datagenerators.DataGenerator
Generates one example of the dataset.
generateExamples() - Method in class weka.datagenerators.classifiers.classification.Agrawal
Generates all examples of the dataset.
generateExamples() - Method in class weka.datagenerators.classifiers.classification.BayesNet
Generates all examples of the dataset.
generateExamples() - Method in class weka.datagenerators.classifiers.classification.LED24
Generates all examples of the dataset.
generateExamples() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
Generates all examples of the dataset.
generateExamples() - Method in class weka.datagenerators.classifiers.classification.RDG1
Generate all examples of the dataset.
generateExamples(int, Random, Instances) - Method in class weka.datagenerators.classifiers.classification.RDG1
Generate all examples of the dataset.
generateExamples() - Method in class weka.datagenerators.classifiers.regression.Expression
Generates all examples of the dataset.
generateExamples() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Generates all examples of the dataset.
generateExamples() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Generate all examples of the dataset.
generateExamples(Random, Instances) - Method in class weka.datagenerators.clusterers.BIRCHCluster
Generate all examples of the dataset.
generateExamples() - Method in class weka.datagenerators.clusterers.SubspaceCluster
Generate all examples of the dataset.
generateExamples() - Method in class weka.datagenerators.DataGenerator
Generates all examples of the dataset.
generateFinished() - Method in class weka.datagenerators.classifiers.classification.Agrawal
Generates a comment string that documentats the data generator.
generateFinished() - Method in class weka.datagenerators.classifiers.classification.BayesNet
Generates a comment string that documentats the data generator.
generateFinished() - Method in class weka.datagenerators.classifiers.classification.LED24
Generates a comment string that documentats the data generator.
generateFinished() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
Generates a comment string that documentats the data generator.
generateFinished() - Method in class weka.datagenerators.classifiers.classification.RDG1
Compiles documentation about the data generation.
generateFinished() - Method in class weka.datagenerators.classifiers.regression.Expression
Generates a comment string that documentats the data generator.
generateFinished() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Generates a comment string that documentats the data generator.
generateFinished() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Compiles documentation about the data generation after the generation process
generateFinished() - Method in class weka.datagenerators.clusterers.SubspaceCluster
Compiles documentation about the data generation after the generation process
generateFinished() - Method in class weka.datagenerators.DataGenerator
Generates a comment string that documentates the data generator.
generateGroupsFromNumbers(Instances, Random) - Method in class weka.classifiers.meta.RotationForest
generates the groups of attributes, given their minimum and maximum numbers.
generateGroupsFromSizes(Instances, Random) - Method in class weka.classifiers.meta.RotationForest
generates the groups of attributes, given their minimum and maximum sizes.
generateHelp() - Method in class weka.core.Javadoc
generates a string to print as help on the console
generateHelp() - Method in class weka.core.ListOptions
generates a string to print as help on the console
generateID() - Method in class weka.core.TechnicalInformation
Generates an ID based on the current settings and returns it.
generateInstances() - Method in class weka.classifiers.bayes.net.BayesNetGenerator
GenerateInstances generates random instances sampling from the distribution represented by the Bayes network structure.
generateInstances(int[]) - Method in interface weka.gui.boundaryvisualizer.DataGenerator
Generate an instance.
generateInstances(int[]) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator
Generates a new instance using one kernel estimator.
generateInstances() - Method in class weka.gui.explorer.PreprocessPanel
sets Instances generated via DataGenerators (pops up a Dialog)
generateJavadoc(int) - Method in class weka.core.AllJavadoc
generates and returns the Javadoc for the specified start/end tag pair.
generateJavadoc(int) - Method in class weka.core.GlobalInfoJavadoc
generates and returns the Javadoc for the specified start/end tag pair.
generateJavadoc(int) - Method in class weka.core.Javadoc
generates and returns the Javadoc for the specified start/end tag pair.
generateJavadoc() - Method in class weka.core.Javadoc
generates and returns the Javadoc
generateJavadoc(int) - Method in class weka.core.OptionHandlerJavadoc
generates and returns the Javadoc for the specified start/end tag pair.
generateJavadoc(int) - Method in class weka.core.TechnicalInformationHandlerJavadoc
generates and returns the Javadoc for the specified start/end tag pair.
generateKCandidates(FastVector) - Static method in class weka.associations.gsp.Sequence
Generates candidate k-Sequences on the basis of a given (k-1)-Sequence set.
generateMetaLevel(Instances, Random) - Method in class weka.classifiers.meta.Grading
Generates the meta data
generateMetaLevel(Instances, Random) - Method in class weka.classifiers.meta.Stacking
Generates the meta data
generateMetaLevel(Instances, Random) - Method in class weka.classifiers.meta.StackingC
Method that builds meta level.
generateOutput() - Method in class weka.gui.visualize.BMPWriter
generates the actual output
generateOutput() - Method in class weka.gui.visualize.JComponentWriter
generates the actual output
generateOutput() - Method in class weka.gui.visualize.JPEGWriter
generates the actual output.
generateOutput() - Method in class weka.gui.visualize.PNGWriter
generates the actual output
generateOutput() - Method in class weka.gui.visualize.PostscriptWriter
generates the actual output
generateOutputProperties() - Method in class weka.gui.GenericPropertiesCreator
fills in all the classes (based on the packages in the input properties file) into the output properties file
generateRandomNetwork() - Method in class weka.classifiers.bayes.net.BayesNetGenerator
Generate random connected Bayesian network with discrete nodes having all the same cardinality.
generateRandomNetworkStructure(int, int) - Method in class weka.classifiers.bayes.net.BayesNetGenerator
GenerateRandomNetworkStructure generate random connected Bayesian network
generateRandomNumber(int) - Method in class weka.attributeSelection.ScatterSearchV1
 
generateRankingTipText() - Method in class weka.attributeSelection.GreedyStepwise
Returns the tip text for this property
generateRankingTipText() - Method in class weka.attributeSelection.RaceSearch
Returns the tip text for this property
generateRankingTipText() - Method in class weka.attributeSelection.Ranker
Returns the tip text for this property
GenerateReferenceSet(List<ScatterSearchV1.Subset>, int, int) - Method in class weka.attributeSelection.ScatterSearchV1
Generate the a ReferenceSet containing the n best solutions and the m most diverse solutions of the initial Population.
generateRuleItem(ItemSet, ItemSet, Instances, int, int, double[], Hashtable) - Method in class weka.associations.RuleItem
Constructs a new RuleItem if the support of the given rule is above the support threshold.
generateRules(double, FastVector, int) - Method in class weka.associations.AprioriItemSet
Generates all rules for an item set.
generateRules(int, double[], Hashtable, double, Instances, TreeSet, int) - Method in class weka.associations.CaRuleGeneration
Generates all rules for an item set.
generateRules(double, boolean) - Method in class weka.associations.LabeledItemSet
Generates rules out of item sets
generateRules(int, double[], Hashtable, double, Instances, TreeSet, int) - Method in class weka.associations.RuleGeneration
Generates all rules for an item set.
generateRulesBruteForce(double, int, FastVector, int, int, double) - Method in class weka.associations.AprioriItemSet
Generates all significant rules for an item set.
generateRulesBruteForce(FPGrowth.FrequentItemSets, FPGrowth.AssociationRule.METRIC_TYPE, double, int, int, int) - Static method in class weka.associations.FPGrowth.AssociationRule
Generate all association rules, from the supplied frequet item sets, that meet a given minimum metric threshold.
generateRulesTipText() - Method in class weka.classifiers.trees.m5.M5Base
Returns the tip text for this property
generateStart() - Method in class weka.datagenerators.classifiers.classification.Agrawal
Generates a comment string that documentates the data generator.
generateStart() - Method in class weka.datagenerators.classifiers.classification.BayesNet
Generates a comment string that documentates the data generator.
generateStart() - Method in class weka.datagenerators.classifiers.classification.LED24
Generates a comment string that documentates the data generator.
generateStart() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
Generates a comment string that documentates the data generator.
generateStart() - Method in class weka.datagenerators.classifiers.classification.RDG1
Generates a comment string that documentates the data generator.
generateStart() - Method in class weka.datagenerators.classifiers.regression.Expression
Generates a comment string that documentates the data generator.
generateStart() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Generates a comment string that documentates the data generator.
generateStart() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Compiles documentation about the data generation before the generation process
generateStart() - Method in class weka.datagenerators.clusterers.SubspaceCluster
Compiles documentation about the data generation before the generation process
generateStart() - Method in class weka.datagenerators.DataGenerator
Generates a comment string that documentates the data generator.
generateSubset(Instances, Range) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
generates a subset of the dataset with only the attributes from the range (class is always added if present).
GeneratorPropertyIteratorPanel - Class in weka.gui.experiment
This panel controls setting a list of values for an arbitrary resultgenerator property for an experiment to iterate over.
GeneratorPropertyIteratorPanel() - Constructor for class weka.gui.experiment.GeneratorPropertyIteratorPanel
Creates the property iterator panel initially disabled.
GeneratorPropertyIteratorPanel(Experiment) - Constructor for class weka.gui.experiment.GeneratorPropertyIteratorPanel
Creates the property iterator panel and sets the experiment.
GenericArrayEditor - Class in weka.gui
A PropertyEditor for arrays of objects that themselves have property editors.
GenericArrayEditor() - Constructor for class weka.gui.GenericArrayEditor
Sets up the array editor.
GenericObjectEditor - Class in weka.gui
A PropertyEditor for objects.
GenericObjectEditor() - Constructor for class weka.gui.GenericObjectEditor
Default constructor.
GenericObjectEditor(boolean) - Constructor for class weka.gui.GenericObjectEditor
Constructor that allows specifying whether it is possible to change the class within the editor dialog.
GenericObjectEditor.CapabilitiesFilterDialog - Class in weka.gui
A dialog for selecting Capabilities to look for in the GOE tree.
GenericObjectEditor.GOEPanel - Class in weka.gui
Handles the GUI side of editing values.
GenericObjectEditor.GOETreeNode - Class in weka.gui
A specialized TreeNode for supporting filtering via Capabilities.
GenericObjectEditor.JTreePopupMenu - Class in weka.gui
Creates a popup menu containing a tree that is aware of the screen dimensions.
GenericPropertiesCreator - Class in weka.gui
This class can generate the properties object that is normally loaded from the GenericObjectEditor.props file (= PROPERTY_FILE).
GenericPropertiesCreator() - Constructor for class weka.gui.GenericPropertiesCreator
initializes the creator, locates the props file with the Utils class.
GenericPropertiesCreator(String) - Constructor for class weka.gui.GenericPropertiesCreator
initializes the creator, the given file overrides the props-file search of the Utils class
GeneticSearch - Class in weka.attributeSelection
GeneticSearch:

Performs a search using the simple genetic algorithm described in Goldberg (1989).

For more information see:

David E.
GeneticSearch() - Constructor for class weka.attributeSelection.GeneticSearch
Constructor.
GeneticSearch - Class in weka.classifiers.bayes.net.search.global
This Bayes Network learning algorithm uses genetic search for finding a well scoring Bayes network structure.
GeneticSearch() - Constructor for class weka.classifiers.bayes.net.search.global.GeneticSearch
 
GeneticSearch - Class in weka.classifiers.bayes.net.search.local
This Bayes Network learning algorithm uses genetic search for finding a well scoring Bayes network structure.
GeneticSearch() - Constructor for class weka.classifiers.bayes.net.search.local.GeneticSearch
 
GeneticSearch.GABitSet - Class in weka.attributeSelection
A bitset for the genetic algorithm
get(int) - Method in class weka.attributeSelection.GeneticSearch.GABitSet
get the value of a bit in the chromosome
get(int, GridSearch.PointDouble) - Method in class weka.classifiers.meta.GridSearch.PerformanceCache
returns a cached performance object, null if not yet in the cache
get(int) - Method in class weka.core.matrix.DoubleVector
Gets a single element.
get(int) - Method in class weka.core.matrix.IntVector
Gets the value of an element.
get(int, int) - Method in class weka.core.matrix.Matrix
Get a single element.
get(int) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList
Returns an element at the specified index in the list.
get() - Method in class weka.core.neighboursearch.CoverTree.MyHeap
returns the first element and removes it from the heap.
get() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.MyHeap
returns the first element and removes it from the heap.
get(int) - Method in class weka.core.PropertyPath.Path
returns the element at the given index
get(int) - Method in class weka.core.Tee
returns the specified PrintStream from the list.
get(String) - Method in class weka.core.xml.MethodHandler
returns the stored method for the given property
get(Class) - Method in class weka.core.xml.MethodHandler
returns the stored method for the given class
get(int) - Method in class weka.gui.CheckBoxList.CheckBoxListModel
Returns the element at the specified position in this list.
get(String, String) - Static method in class weka.gui.experiment.ExperimenterDefaults
returns the value for the specified property, if non-existent then the default value.
get(String, String) - Static method in class weka.gui.explorer.ExplorerDefaults
returns the value for the specified property, if non-existent then the default value.
get_scale(double) - Method in class weka.core.neighboursearch.CoverTree
Finds the scale/level of a given value.
getAboutPanel() - Method in class weka.gui.PropertySheetPanel
Return the panel containing global info and help for the object being edited.
getAccu() - Method in class weka.classifiers.rules.JRip.Antd
 
getAccuRate() - Method in class weka.classifiers.rules.JRip.Antd
 
getActionListener(JFrame) - Method in interface weka.gui.MainMenuExtension
If the extension has a custom ActionListener for the menu item, then it must be returned here.
getActualClassifier() - Method in class weka.filters.supervised.attribute.AddClassification
Returns the actual classifier to use, either from the serialized model or the one specified by the user.
getActualIndex(int) - Method in class weka.core.AttributeLocator
returns actual index in the Instances object.
getActualRow(int) - Method in class weka.gui.SortedTableModel
Returns the actual underlying row the given visible one represents.
getAcuity() - Method in class weka.clusterers.Cobweb
get the acuity value
getAddress() - Static method in class weka.core.Copyright
returns the address of the owner
getAdjustWeights() - Method in class weka.filters.supervised.instance.SpreadSubsample
Returns true if instance weights will be adjusted to maintain total weight per class.
getADTree() - Method in class weka.classifiers.bayes.BayesNet
get ADTree strucrture containing efficient representation of counts.
getAdvanceDataSetFirst() - Method in class weka.experiment.Experiment
Get the value of m_DataSetFirstFirst.
getAlgorithm() - Method in class weka.filters.supervised.attribute.PLSFilter
Gets the type of algorithm to use
getAlgorithm() - Method in class weka.filters.unsupervised.attribute.Wavelet
Gets the type of algorithm to use
getAlgorithmStart() - Method in class weka.associations.GeneralizedSequentialPatterns
Returns the time/date string the algorithm was started
getAlgorithmType() - Method in class weka.classifiers.mi.MILR
Gets the type of algorithm.
getAllBits(List<ScatterSearchV1.Subset>) - Method in class weka.attributeSelection.ScatterSearchV1
Save in Bitset all the gens that are in many others subsets.
getAllowedIndices() - Method in class weka.core.AttributeLocator
returns the indices that are allowed to check for the attribute type
getAllowUnclassifiedInstances() - Method in class weka.classifiers.trees.RandomTree
Get the value of NumFolds.
getAllTheRules() - Method in class weka.associations.Apriori
returns all the rules
getAllTheRules() - Method in class weka.associations.PredictiveApriori
returns all the rules
getAlpha() - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator
Get prior used in probability table estimation
getAlpha() - Method in class weka.classifiers.functions.Winnow
Get the value of Alpha.
getAmplitude() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Gets the amplitude multiplier.
getAnimatedIcon() - Method in class weka.gui.beans.BeanVisual
Returns the animated icon
getAnimatedIconPath() - Method in class weka.gui.beans.BeanVisual
returns the path for the animated icon
getAntds() - Method in class weka.classifiers.rules.JRip.RipperRule
Return the antecedents
getAntiOperation(HillClimber.Operation) - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
getAntiOperation determines the Operation, which is needed to cancel oOperation
getAppendPredictedProbabilities() - Method in class weka.gui.beans.PredictionAppender
Return true if predicted probabilities are to be appended rather than class value
getArffFile() - Method in class weka.gui.streams.InstanceLoader
 
getArffFile() - Method in class weka.gui.streams.InstanceSavePanel
 
getArray() - Method in class weka.core.matrix.DoubleVector
Access the internal one-dimensional array.
getArray() - Method in class weka.core.matrix.IntVector
Access the internal one-dimensional array.
getArray() - Method in class weka.core.matrix.Matrix
Access the internal two-dimensional array.
getArrayClass(Class) - Static method in class weka.core.Utils
Returns the basic class of an array class (handles multi-dimensional arrays).
getArrayCopy() - Method in class weka.core.matrix.DoubleVector
Returns a copy of the DoubleVector usng a double array.
getArrayCopy() - Method in class weka.core.matrix.IntVector
Returns a copy of the internal one-dimensional array.
getArrayCopy() - Method in class weka.core.matrix.Matrix
Copy the internal two-dimensional array.
getArrayDimensions(Class) - Static method in class weka.core.Utils
Returns the dimensions of the given array.
getArrayDimensions(Object) - Static method in class weka.core.Utils
Returns the dimensions of the given array.
getArrayDimensions(Element) - Method in class weka.core.xml.XMLSerialization
returns an array with the dimensions of the array stored in XML
getArtificialSize() - Method in class weka.classifiers.meta.Decorate
Factor that determines number of artificial examples to generate.
getASCrossvalidationFolds() - Static method in class weka.gui.explorer.ExplorerDefaults
returns the default number of folds of the CV in the attribute selection panel.
getASEvaluator() - Static method in class weka.gui.explorer.ExplorerDefaults
returns the default attribute evalautor (fully configured) for the attribute selection panel.
getAsInstance(Instances, Random) - Method in class weka.core.AlgVector
Gets the elements of the vector as an instance.
getASRandomSeed() - Static method in class weka.gui.explorer.ExplorerDefaults
returns the default random seed value in the attribute selection panel.
getASSearch() - Static method in class weka.gui.explorer.ExplorerDefaults
returns the default attribute selection search scheme (fully configured) for the attribute selection panel.
getAssignments() - Method in class weka.clusterers.SimpleKMeans
Gets the assignments for each instance
getAssociatedConnections() - Method in class weka.gui.beans.MetaBean
 
getAssociationRules() - Method in class weka.associations.FPGrowth
Gets the list of mined association rules.
getAssociator() - Method in class weka.associations.CheckAssociator
Get the associator being tested
getAssociator() - Method in class weka.associations.SingleAssociatorEnhancer
Get the associator used as the base associator.
getAssociator() - Method in class weka.gui.beans.Associator
Get the associator currently set for this wrapper
getAssociator() - Static method in class weka.gui.explorer.ExplorerDefaults
returns the default associator (fully configured) for the associations panel.
getAssociatorSpec() - Method in class weka.associations.SingleAssociatorEnhancer
Gets the associator specification string, which contains the class name of the associator and any options to the associator
getASTestMode() - Static method in class weka.gui.explorer.ExplorerDefaults
returns the default attribute selection test mode for the attribute selection panel.
getAsText() - Method in class weka.gui.CostMatrixEditor
Some objects can be represented as text, but a cost matrix cannot.
getAsText() - Method in class weka.gui.GenericArrayEditor
Returns null as we don't support getting/setting values as text.
getAsText() - Method in class weka.gui.GenericObjectEditor
Returns null as we don't support getting/setting values as text.
getAsText() - Method in class weka.gui.SelectedTagEditor
Gets the current value as text.
getAsText() - Method in class weka.gui.SimpleDateFormatEditor
Returns the date format string.
getAttIndex(int) - Method in class weka.classifiers.lazy.LBR.Indexes
Returns the boolean value at the specified index in the Attribute Indexes array
getAttList_Irr() - Method in class weka.datagenerators.classifiers.classification.RDG1
Gets the array that defines which of the attributes are seen to be irrelevant.
getAttr() - Method in class weka.classifiers.rules.JRip.Antd
 
getAttribute() - Method in class weka.associations.FPGrowth.BinaryItem
Get the attribute that this item corresponds to.
getAttribute1() - Method in class weka.gui.visualize.VisualizePanelEvent
 
getAttribute2() - Method in class weka.gui.visualize.VisualizePanelEvent
 
getAttributeAt(int) - Method in class weka.gui.arffviewer.ArffSortedTableModel
returns the attribute at the given index, can be NULL if not an attribute column
getAttributeAt(int) - Method in class weka.gui.arffviewer.ArffTableModel
returns the attribute at the given index, can be NULL if not an attribute column
getAttributeCapabilities() - Method in class weka.core.Capabilities
returns all attribute capabilities
getAttributeColumn(String) - Method in class weka.gui.arffviewer.ArffSortedTableModel
returns the column of the given attribute name, -1 if not found
getAttributeColumn(String) - Method in class weka.gui.arffviewer.ArffTableModel
returns the column of the given attribute name, -1 if not found
getAttributeEvaluator() - Method in class weka.attributeSelection.FilteredAttributeEval
Get the attribute evaluator to use
getAttributeEvaluator() - Method in class weka.attributeSelection.RaceSearch
Get the attribute evaluator used to generate the ranking.
getAttributeEvaluator() - Method in class weka.attributeSelection.RankSearch
Get the attribute evaluator used to generate the ranking.
getAttributeID() - Method in class weka.experiment.ClassifierSplitEvaluator
Get the index of Attibute Identifying the instances
getAttributeIndex() - Method in class weka.classifiers.functions.SimpleLinearRegression
Returns the index of the attribute used in the regression.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.Add
Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.AddNoise
Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.AddValues
Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
Gets the index of the attribute converted.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.SwapValues
Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Get the index of the attribute used.
getAttributeIndexes() - Method in class weka.filters.unsupervised.attribute.NominalToString
Get the index of the attribute used.
getAttributeIndices() - Method in class weka.core.AttributeLocator
Returns the indices of the attributes.
getAttributeIndices() - Method in interface weka.core.DistanceFunction
Gets the range of attributes used in the calculation of the distance.
getAttributeIndices() - Method in class weka.core.NormalizableDistance
Gets the range of attributes used in the calculation of the distance.
getAttributeIndices() - Method in class weka.filters.supervised.attribute.Discretize
Gets the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Get the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.Copy
Get the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.Discretize
Gets the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.FirstOrder
Get the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Gets the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.NominalToBinary
Gets the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Gets the selection of the columns, e.g., first-last or first-3,5-last
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.NumericToNominal
Gets the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.NumericTransform
Get the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.Remove
Get the current range selection.
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.Reorder
Get the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Gets the current range selection.
getAttributeMaxValues() - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database
Returns the array of maximum-values for each attribute
getAttributeMaxValues() - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase
Returns the array of maximum-values for each attribute
getAttributeMinValues() - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database
Returns the array of minimum-values for each attribute
getAttributeMinValues() - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase
Returns the array of minimum-values for each attribute
getAttributeName() - Method in class weka.filters.unsupervised.attribute.Add
Get the name of the attribute to be created.
getAttributeName() - Method in class weka.filters.unsupervised.attribute.AddID
Get the name of the attribute to be created
getAttributeNamePrefix() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Get the attribute name prefix.
getAttributeRange() - Method in class weka.filters.unsupervised.attribute.StringToNominal
Get the range of indices of the attributes used.
getAttributes() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
 
getAttributes() - Method in class weka.gui.arffviewer.ArffPanel
returns a list with the attributes
getAttributeSelectionMethod() - Method in class weka.classifiers.functions.LinearRegression
Gets the method used to select attributes for use in the linear regression.
getAttributeType() - Method in class weka.filters.unsupervised.attribute.Add
Gets the type of attribute to generate.
getAttributeType() - Method in class weka.filters.unsupervised.attribute.RemoveType
Gets the attribute type to be deleted by the filter.
getAttributeTypes() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
Returns the current attribute - attribute-type relation in use.
getAttributeTypeString() - Method in class weka.filters.unsupervised.attribute.RemoveType
Gets the attribute type to be deleted by the filter as a string.
getAttrIndexRange() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
returns the attribute range(s).
getAttrValue() - Method in class weka.classifiers.rules.JRip.Antd
 
getAttsToEliminatePerIteration() - Method in class weka.attributeSelection.SVMAttributeEval
Get the constant rate of attribute elimination per iteration
getAuthors() - Method in class weka.core.TechnicalInformation
splits the authors on the " and " and returns a vector with the names
getAutoBuild() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
getAutoKeyGeneration() - Method in class weka.core.converters.DatabaseSaver
Gets whether or not a primary key will be generated automatically.
getAverage(int) - Method in class weka.experiment.ResultMatrix
returns the average of the mean at the given position, if the position is valid, otherwise 0
getBackground() - Method in class weka.gui.visualize.BMPWriter
returns the current background color
getBackground() - Method in class weka.gui.visualize.JPEGWriter
returns the current background color.
getBackground() - Method in class weka.gui.visualize.PNGWriter
returns the current background color
getBackground() - Method in class weka.gui.visualize.PostscriptGraphics
 
getBackup() - Method in class weka.gui.GenericObjectEditor
Returns the backup object (may be null if there is no backup.
getBagSizePercent() - Method in class weka.classifiers.meta.Bagging
Gets the size of each bag, as a percentage of the training set size.
getBagSizePercent() - Method in class weka.classifiers.meta.MetaCost
Gets the size of each bag, as a percentage of the training set size.
getBalanceClass() - Method in class weka.datagenerators.classifiers.classification.Agrawal
Gets whether the class is balanced.
getBalanced() - Method in class weka.classifiers.functions.Winnow
Get the value of Balanced.
getBallSplitter() - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor
Returns the BallSplitter algorithm set that would be used by the TopDown BallTree constructor.
getBallTreeConstructor() - Method in class weka.core.neighboursearch.BallTree
Returns the BallTreeConstructor currently in use.
getBase() - Method in class weka.core.neighboursearch.CoverTree
Returns the base in use for expansion constant.
getBaseExperiment() - Method in class weka.experiment.RemoteExperiment
Get the base experiment used by this remote experiment
getBean() - Method in class weka.gui.beans.BeanInstance
Gets the bean encapsulated in this instance
getBeanConnectionRelation(MetaBean) - Method in class weka.gui.beans.xml.XMLBeans
returns the relation for the given MetaBean, for the regular connections, null has to be used
getBeanContext() - Method in class weka.gui.beans.AbstractDataSource
Return the bean context (if any) that this bean is embedded in
getBeanContext() - Method in class weka.gui.beans.CostBenefitAnalysis
 
getBeanContext() - Method in class weka.gui.beans.DataVisualizer
Return the bean context (if any) that this bean is embedded in
getBeanContext() - Method in class weka.gui.beans.GraphViewer
Return the bean context (if any) that this bean is embedded in
getBeanContext() - Method in class weka.gui.beans.ModelPerformanceChart
Return the bean context (if any) that this bean is embedded in
getBeanContext() - Method in class weka.gui.beans.TextViewer
Return the bean context (if any) that this bean is embedded in
getBeanDescriptor() - Method in class weka.gui.beans.AssociatorBeanInfo
Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.ClassAssignerBeanInfo
 
getBeanDescriptor() - Method in class weka.gui.beans.ClassifierBeanInfo
Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.ClassValuePickerBeanInfo
 
getBeanDescriptor() - Method in class weka.gui.beans.ClustererBeanInfo
Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.CrossValidationFoldMakerBeanInfo
Return the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.FilterBeanInfo
Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.IncrementalClassifierEvaluatorBeanInfo
Return the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.LoaderBeanInfo
Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.PredictionAppenderBeanInfo
Return the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.SaverBeanInfo
Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.SerializedModelSaverBeanInfo
Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.StripChartBeanInfo
Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.TrainTestSplitMakerBeanInfo
Get the bean descriptor for this bean
getBeanInfoInputs() - Method in class weka.gui.beans.MetaBean
 
getBeanInfoOutputs() - Method in class weka.gui.beans.MetaBean
 
getBeanInfoSubFlow() - Method in class weka.gui.beans.MetaBean
 
getBeanInstances() - Static method in class weka.gui.beans.BeanInstance
Return the list of displayed beans
getBeanInstancesForIDs(Vector) - Method in class weka.gui.beans.xml.XMLBeans
returns a vector with references to BeanInstances according to the IDs in the given Vector.
getBeansInInputs() - Method in class weka.gui.beans.MetaBean
Return all the beans in the inputs
getBeansInOutputs() - Method in class weka.gui.beans.MetaBean
Return all the beans in the outputs
getBeansInSubFlow() - Method in class weka.gui.beans.MetaBean
Return all the beans in the sub flow
getBestClassifier() - Method in class weka.classifiers.meta.GridSearch
returns the best Classifier setup
getBestClassifierIndex() - Method in class weka.classifiers.meta.MultiScheme
Get the index of the classifier that was determined as best during cross-validation.
getBestClassifierOptions() - Method in class weka.classifiers.meta.CVParameterSelection
Returns (a copy of) the best options found for the classifier.
getBestCommitteeChunkSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Get the best committee chunk size
getBestCommitteeErrorEstimate() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Get the best committee's error on the validation data
getBestCommitteeLLEstimate() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Get the best committee's log likelihood on the validation data
getBestCommitteeSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Get the number of members in the best committee
getBestFilter() - Method in class weka.classifiers.meta.GridSearch
returns the best filter setup
getBestgen(ScatterSearchV1.Subset, BitSet) - Method in class weka.attributeSelection.ScatterSearchV1
Evaluate each gen of a BitSet inserted in a Subset and get the most significant for that Subset
getBestGroup() - Method in class weka.attributeSelection.LFSMethods
 
getBestGroupOfSize(int) - Method in class weka.attributeSelection.LFSMethods
 
getBestIteration(double[], int) - Method in class weka.classifiers.trees.lmt.LogisticBase
Helper function to find the minimum in an array of error values.
getBestMerit() - Method in class weka.attributeSelection.LFSMethods
 
getBeta() - Method in class weka.classifiers.functions.Winnow
Get the value of Beta.
getBias() - Method in class weka.classifiers.BVDecompose
Get the calculated bias squared
getBias() - Method in class weka.classifiers.functions.LibLINEAR
Returns bias term value (default 1) No bias term is added if value < 0
getBias() - Method in class weka.classifiers.misc.VFI
Get the value of the bias parameter
getBiasToUniformClass() - Method in class weka.filters.supervised.instance.Resample
Gets the bias towards a uniform class.
getBIFFile() - Method in class weka.classifiers.bayes.BayesNet
Get name of network in BIF file to compare with
getBIFFile() - Method in class weka.classifiers.bayes.net.search.fixed.FromFile
Get name of network in BIF file to read structure from
getBIFHeader() - Method in class weka.classifiers.bayes.BayesNet
 
getBinarizeNumericAttributes() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
get whether numeric attributes are just being binarized.
getBinarizeNumericAttributes() - Method in class weka.attributeSelection.InfoGainAttributeEval
get whether numeric attributes are just being binarized.
getBinaryAttributesNominal() - Method in class weka.filters.supervised.attribute.NominalToBinary
Gets if binary attributes are to be treated as nominal ones.
getBinaryAttributesNominal() - Method in class weka.filters.unsupervised.attribute.NominalToBinary
Gets if binary attributes are to be treated as nominal ones.
getBinarySplits() - Method in class weka.classifiers.rules.PART
Get the value of binarySplits.
getBinarySplits() - Method in class weka.classifiers.trees.J48
Get the value of binarySplits.
getBinarySplits() - Method in class weka.classifiers.trees.J48graft
Get the value of binarySplits.
getBins() - Method in class weka.filters.unsupervised.attribute.Discretize
Gets the number of bins numeric attributes will be divided into
getBins() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Ignored
getBinSplit() - Method in class weka.classifiers.trees.FT
Get the value of binarySplits.
getBinValue() - Method in class weka.clusterers.XMeans
Gets value that represents true in a new numeric attribute.
getBinValue() - Method in class weka.core.pmml.Discretize.DiscretizeBin
Get the bin value for this DiscretizeBin
getBooleanCols() - Method in class weka.datagenerators.ClusterGenerator
returns the range of boolean attributes.
getBuilder() - Method in class weka.core.xml.XMLDocument
returns the DocumentBuilder.
getBuildLogisticModels() - Method in class weka.classifiers.functions.SMO
Get the value of buildLogisticModels.
getBuildLogisticModels() - Method in class weka.classifiers.mi.MISMO
Get the value of buildLogisticModels.
getBuildRegressionTree() - Method in class weka.classifiers.trees.m5.M5Base
Get the value of regressionTree.
getC() - Method in class weka.classifiers.functions.SMO
Get the value of C.
getC() - Method in class weka.classifiers.functions.SMOreg
Get the value of C.
getC() - Method in class weka.classifiers.mi.MISMO
Get the value of C.
getC() - Method in class weka.classifiers.mi.MISVM
Get the value of C.
getCache(Class, String) - Static method in class weka.core.ClassDiscovery
returns the list of classnames associated with this class and package, if available, otherwise null.
getCacheHits() - Method in class weka.classifiers.functions.supportVector.RegOptimizer
return the number of kernel cache hits
getCacheKeyName() - Method in class weka.experiment.DatabaseResultListener
Get the value of CacheKeyName.
getCacheSize() - Method in class weka.classifiers.functions.LibSVM
Gets cache memory size in MB
getCacheSize() - Method in class weka.classifiers.functions.supportVector.CachedKernel
Gets the size of the cache
getCacheSize() - Method in class weka.classifiers.functions.supportVector.StringKernel
Gets the size of the cache
getCacheValues(double) - Method in class weka.classifiers.lazy.kstar.KStarCache
Returns the values in the cache mapped by the specified key
getCalcOutOfBag() - Method in class weka.classifiers.meta.Bagging
Get whether the out of bag error is calculated.
getCalculatedNumToSelect() - Method in class weka.attributeSelection.GreedyStepwise
Gets the calculated number of attributes to retain.
getCalculatedNumToSelect() - Method in class weka.attributeSelection.RaceSearch
Gets the calculated number of attributes to retain.
getCalculatedNumToSelect() - Method in interface weka.attributeSelection.RankedOutputSearch
Gets the calculated number of attributes to retain.
getCalculatedNumToSelect() - Method in class weka.attributeSelection.Ranker
Gets the calculated number to select.
getCalculateStdDevs() - Method in class weka.experiment.AveragingResultProducer
Get the value of CalculateStdDevs.
getCanChangeClassInDialog() - Method in class weka.gui.GenericObjectEditor
Returns whether the user can change the class in the dialog.
getCapabilities() - Method in class weka.associations.AbstractAssociator
Returns the Capabilities of this associator.
getCapabilities() - Method in class weka.associations.Apriori
Returns default capabilities of the classifier.
getCapabilities() - Method in interface weka.associations.Associator
Returns the Capabilities of this associator.
getCapabilities() - Method in class weka.associations.FilteredAssociator
Returns default capabilities of the associator.
getCapabilities() - Method in class weka.associations.FPGrowth
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.associations.GeneralizedSequentialPatterns
Returns the Capabilities of the algorithm.
getCapabilities() - Method in class weka.associations.PredictiveApriori
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.associations.SingleAssociatorEnhancer
Returns default capabilities of the base associator.
getCapabilities() - Method in class weka.associations.Tertius
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.attributeSelection.ASEvaluation
Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.CfsSubsetEval
Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.ClassifierSubsetEval
Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.ConsistencySubsetEval
Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.attributeSelection.FilteredAttributeEval
Returns default capabilities of the evaluator.
getCapabilities() - Method in class weka.attributeSelection.FilteredSubsetEval
Returns default capabilities of the evaluator.
getCapabilities() - Method in class weka.attributeSelection.GainRatioAttributeEval
Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.InfoGainAttributeEval
Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.LatentSemanticAnalysis
Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.OneRAttributeEval
Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.PrincipalComponents
Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.ReliefFAttributeEval
Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.SVMAttributeEval
Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.WrapperSubsetEval
Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.classifiers.bayes.AODE
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.bayes.AODEsr
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
This method tests what kind of data this classifier can handle.
getCapabilities() - Method in class weka.classifiers.bayes.BayesNet
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.bayes.ComplementNaiveBayes
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.bayes.DMNBtext
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.bayes.HNB
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.bayes.NaiveBayes
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.bayes.NaiveBayesMultinomial
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.bayes.NaiveBayesSimple
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.bayes.WAODE
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.Classifier
Returns the Capabilities of this classifier.
getCapabilities() - Method in class weka.classifiers.functions.GaussianProcesses
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.IsotonicRegression
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.LeastMedSq
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.LibLINEAR
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.LibSVM
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.LinearRegression
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.Logistic
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.MultilayerPerceptron
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.PaceRegression
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.PLSClassifier
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.RBFNetwork
Returns default capabilities of the classifier, i.e., and "or" of Logistic and LinearRegression.
getCapabilities() - Method in class weka.classifiers.functions.SimpleLinearRegression
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.SimpleLogistic
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.SMO
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.SMOreg
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.SPegasos
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.supportVector.Kernel
Returns the Capabilities of this kernel.
getCapabilities() - Method in class weka.classifiers.functions.supportVector.PolyKernel
Returns the Capabilities of this kernel.
getCapabilities() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
Returns the Capabilities of this kernel.
getCapabilities() - Method in class weka.classifiers.functions.supportVector.Puk
Returns the Capabilities of this kernel.
getCapabilities() - Method in class weka.classifiers.functions.supportVector.RBFKernel
Returns the Capabilities of this kernel.
getCapabilities() - Method in class weka.classifiers.functions.supportVector.StringKernel
Returns the Capabilities of this kernel.
getCapabilities() - Method in class weka.classifiers.functions.VotedPerceptron
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.Winnow
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.lazy.IB1
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.lazy.IBk
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.lazy.KStar
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.lazy.LBR
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.lazy.LWL
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.AdaBoostM1
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.AdditiveRegression
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.ClassificationViaClustering
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.ClassificationViaRegression
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.CostSensitiveClassifier
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.CVParameterSelection
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.Decorate
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.END
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.FilteredClassifier
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.GridSearch
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.LogitBoost
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.MetaCost
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.MultiClassClassifier
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.nestedDichotomies.ND
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.OrdinalClassClassifier
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.RegressionByDiscretization
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.Stacking
Returns combined capabilities of the base classifiers, i.e., the capabilities all of them have in common.
getCapabilities() - Method in class weka.classifiers.meta.ThresholdSelector
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.Vote
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.CitationKNN
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.MDD
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.MIBoost
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.MIDD
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.MIEMDD
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.MILR
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.MINND
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.MIOptimalBall
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.MISMO
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.MISVM
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.MIWrapper
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.SimpleMI
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.supportVector.MIPolyKernel
Returns the Capabilities of this kernel.
getCapabilities() - Method in class weka.classifiers.mi.supportVector.MIRBFKernel
Returns the Capabilities of this kernel.
getCapabilities() - Method in class weka.classifiers.misc.HyperPipes
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.misc.SerializedClassifier
Returns default capabilities of the base classifier.
getCapabilities() - Method in class weka.classifiers.misc.VFI
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.MultipleClassifiersCombiner
Returns combined capabilities of the base classifiers, i.e., the capabilities all of them have in common.
getCapabilities() - Method in class weka.classifiers.rules.ConjunctiveRule
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.rules.DecisionTable
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.rules.DTNB
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.rules.JRip
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.rules.NNge
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.rules.OneR
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.rules.PART
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.rules.part.MakeDecList
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.rules.Prism
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.rules.Ridor
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.rules.ZeroR
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.SingleClassifierEnhancer
Returns default capabilities of the base classifier.
getCapabilities() - Method in class weka.classifiers.trees.ADTree
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.BFTree
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.DecisionStump
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.FT
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.Id3
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTree
Returns default capabilities of the classifier tree.
getCapabilities() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG
Returns default capabilities of the classifier tree.
getCapabilities() - Method in class weka.classifiers.trees.j48.ClassifierTree
Returns default capabilities of the classifier tree.
getCapabilities() - Method in class weka.classifiers.trees.J48
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.j48.NBTreeClassifierTree
Returns default capabilities of the classifier tree.
getCapabilities() - Method in class weka.classifiers.trees.j48.PruneableClassifierTree
Returns default capabilities of the classifier tree.
getCapabilities() - Method in class weka.classifiers.trees.J48graft
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.LADTree
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.LMT
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.m5.M5Base
Returns default capabilities of the classifier, i.e., of LinearRegression.
getCapabilities() - Method in class weka.classifiers.trees.NBTree
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.RandomForest
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.RandomTree
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.REPTree
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.SimpleCart
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.UserClassifier
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.clusterers.AbstractClusterer
Returns the Capabilities of this clusterer.
getCapabilities() - Method in class weka.clusterers.CLOPE
Returns default capabilities of the clusterer.
getCapabilities() - Method in interface weka.clusterers.Clusterer
Returns the Capabilities of this clusterer.
getCapabilities() - Method in class weka.clusterers.Cobweb
Returns default capabilities of the clusterer.
getCapabilities() - Method in class weka.clusterers.DBScan
Returns default capabilities of the clusterer.
getCapabilities() - Method in class weka.clusterers.EM
Returns default capabilities of the clusterer (i.e., the ones of SimpleKMeans).
getCapabilities() - Method in class weka.clusterers.FarthestFirst
Returns default capabilities of the clusterer.
getCapabilities() - Method in class weka.clusterers.FilteredClusterer
Returns default capabilities of the clusterer.
getCapabilities() - Method in class weka.clusterers.HierarchicalClusterer
 
getCapabilities() - Method in class weka.clusterers.MakeDensityBasedClusterer
Returns default capabilities of the clusterer (i.e., of the wrapper clusterer).
getCapabilities() - Method in class weka.clusterers.OPTICS
Returns default capabilities of the clusterer.
getCapabilities() - Method in class weka.clusterers.sIB
Returns default capabilities of the clusterer.
getCapabilities() - Method in class weka.clusterers.SimpleKMeans
Returns default capabilities of the clusterer.
getCapabilities() - Method in class weka.clusterers.SingleClustererEnhancer
Returns default capabilities of the clusterer.
getCapabilities() - Method in class weka.clusterers.XMeans
Returns default capabilities of the clusterer.
getCapabilities() - Method in interface weka.core.CapabilitiesHandler
Returns the capabilities of this object.
getCapabilities() - Method in class weka.core.converters.AbstractSaver
Returns the Capabilities of this saver.
getCapabilities() - Method in class weka.core.converters.ArffSaver
Returns the Capabilities of this saver.
getCapabilities() - Method in class weka.core.converters.C45Saver
Returns the Capabilities of this saver.
getCapabilities() - Method in class weka.core.converters.CSVSaver
Returns the Capabilities of this saver.
getCapabilities() - Method in class weka.core.converters.DatabaseSaver
Returns the Capabilities of this saver.
getCapabilities() - Method in class weka.core.converters.LibSVMSaver
Returns the Capabilities of this saver.
getCapabilities() - Method in class weka.core.converters.SerializedInstancesSaver
Returns the Capabilities of this saver.
getCapabilities() - Method in class weka.core.converters.SVMLightSaver
Returns the Capabilities of this saver.
getCapabilities() - Method in class weka.core.converters.XRFFSaver
Returns the Capabilities of this saver.
getCapabilities() - Method in class weka.core.FindWithCapabilities
The capabilities to search for.
getCapabilities() - Method in class weka.estimators.DiscreteEstimator
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.estimators.Estimator
Returns the Capabilities of this Estimator.
getCapabilities() - Method in class weka.estimators.KernelEstimator
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.estimators.MahalanobisEstimator
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.estimators.NormalEstimator
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.estimators.PoissonEstimator
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.filters.AllFilter
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.Filter
Returns the Capabilities of this filter.
getCapabilities(Instances) - Method in class weka.filters.Filter
Returns the Capabilities of this filter, customized based on the data.
getCapabilities() - Method in class weka.filters.MultiFilter
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.supervised.attribute.AddClassification
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.supervised.attribute.AttributeSelection
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.supervised.attribute.ClassOrder
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.supervised.attribute.Discretize
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.supervised.attribute.NominalToBinary
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.supervised.attribute.PLSFilter
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.supervised.instance.Resample
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.supervised.instance.SMOTE
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.supervised.instance.SpreadSubsample
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.Add
Returns the Capabilities of this filter.
getCapabilities(Instances) - Method in class weka.filters.unsupervised.attribute.AddCluster
Returns the Capabilities of this filter, makes sure that the class is never set (for the clusterer).
getCapabilities() - Method in class weka.filters.unsupervised.attribute.AddCluster
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.AddExpression
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.AddID
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.AddNoise
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.AddValues
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.Center
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.ClassAssigner
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.ClusterMembership
Returns the Capabilities of this filter.
getCapabilities(Instances) - Method in class weka.filters.unsupervised.attribute.ClusterMembership
Returns the Capabilities of this filter, makes sure that the class is never set (for the clusterer).
getCapabilities() - Method in class weka.filters.unsupervised.attribute.Copy
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.Discretize
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.FirstOrder
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.KernelFilter
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.MathExpression
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.NominalToBinary
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.NominalToString
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.Normalize
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.NumericToBinary
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.NumericToNominal
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.NumericTransform
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.Obfuscate
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.RandomProjection
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.RandomSubset
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.RELAGGS
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.Remove
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.RemoveType
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.RemoveUseless
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.Reorder
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.Standardize
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.StringToNominal
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.SwapValues
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.TimeSeriesDelta
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.Wavelet
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.NonSparseToSparse
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.Normalize
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.Randomize
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.RemoveFolds
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.RemovePercentage
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.RemoveRange
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.Resample
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.ReservoirSample
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.SparseToNonSparse
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.SubsetByExpression
Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
returns the currently selected capabilities.
getCapabilitiesFilter() - Method in class weka.gui.ConverterFileChooser
returns the capabilities filter for the savers, can be null if all are listed.
getCapabilitiesFilter() - Method in class weka.gui.GenericObjectEditor
Returns the current Capabilities filter, can be null.
getCar() - Method in class weka.associations.Apriori
Gets whether class association ruels are mined
getCar() - Method in class weka.associations.PredictiveApriori
Gets whether class association ruels are mined
getCardinality(int) - Method in class weka.classifiers.bayes.BayesNet
get number of values a node can take
getCardinality() - Method in class weka.datagenerators.classifiers.classification.BayesNet
Gets the cardinality of the attributes (incl class attribute)
getCardinalityOfParents() - Method in class weka.classifiers.bayes.net.ParentSet
returns cardinality of parents
getCell(int, int) - Method in class weka.classifiers.CostMatrix
Return the contents of a particular cell.
getCellEditor(int, int) - Method in class weka.gui.arffviewer.ArffTable
returns the cell editor for the given cell
getCellEditorValue() - Method in class weka.gui.arffviewer.ArffTable.RelationalCellEditor
Returns the value contained in the editor.
getCells() - Method in class weka.gui.sql.ResultSetHelper
returns an 2-dimensional array with the content of the resultset, the first dimension is the row, the second the column (i.e., getCells()[y][x]).
getCenter() - Method in class weka.gui.treevisualizer.Node
Get the value of center.
getCenterData() - Method in class weka.attributeSelection.PrincipalComponents
Get whether to center (rather than standardize) the data.
getCenterData() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
Get whether to center (rather than standardize) the data.
getCenteredLeft() - Method in class weka.gui.arffviewer.ArffViewer
returns the left coordinate if the frame would be centered
getCenteredTop() - Method in class weka.gui.arffviewer.ArffViewer
returns the top coordinate if the frame would be centered
getChangeInWeights() - Method in class weka.classifiers.functions.neural.NeuralNode
call this function to get the chnage in weights array.
getChar() - Method in class weka.core.Trie.TrieNode
returns the stored character
getCharSet() - Method in class weka.core.converters.TextDirectoryLoader
Get the character set to use when reading text files.
getChecked() - Method in class weka.gui.CheckBoxList.CheckBoxListItem
returns the checked state of the item
getChecked(int) - Method in class weka.gui.CheckBoxList.CheckBoxListModel
returns the checked state of the element at the given index
getChecked(int) - Method in class weka.gui.CheckBoxList
returns the checked state of the element at the given index
getCheckedIndices() - Method in class weka.gui.CheckBoxList
returns an array with the indices of all checked items
getCheckErrorRate() - Method in class weka.classifiers.rules.JRip
Gets whether to check for error rate is in stopping criterion
getChecksTurnedOff() - Method in class weka.classifiers.functions.SMO
Returns whether the checks are turned off or not.
getChecksTurnedOff() - Method in class weka.classifiers.functions.supportVector.Kernel
Returns whether the checks are turned off or not.
getChecksTurnedOff() - Method in class weka.classifiers.mi.MISMO
Returns whether the checks are turned off or not.
getChecksTurnedOff() - Method in class weka.filters.unsupervised.attribute.KernelFilter
Returns whether the checks are turned off or not.
getChild(int) - Method in class weka.gui.treevisualizer.Node
Get the Edge for the child number 'i'.
getChildForBranch(int) - Method in class weka.classifiers.trees.adtree.Splitter
Gets the child for a branch of the split.
getChildForBranch(int) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit
Gets the child for a branch of the split.
getChildForBranch(int) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit
Gets the child for a branch of the split.
getChildForBranch(int) - Method in class weka.classifiers.trees.LADTree.Splitter
 
getChildForBranch(int) - Method in class weka.classifiers.trees.LADTree.TwoWayNominalSplit
 
getChildForBranch(int) - Method in class weka.classifiers.trees.LADTree.TwoWayNumericSplit
 
getChildren(int) - Method in class weka.classifiers.bayes.net.EditableBayesNet
return list of children of a node
getChildren() - Method in class weka.classifiers.trees.adtree.PredictionNode
Gets the children of this node.
getChildren() - Method in class weka.classifiers.trees.LADTree.PredictionNode
 
getChildTags(Node) - Static method in class weka.core.xml.XMLDocument
returns all non tag-children from the given node.
getChildTags(Node, String) - Static method in class weka.core.xml.XMLDocument
returns all non tag-children from the given node.
getChooseClassPopupMenu() - Method in class weka.gui.GenericObjectEditor
Returns a popup menu that allows the user to change the class of object.
getChromosome() - Method in class weka.attributeSelection.GeneticSearch.GABitSet
get the chromosome
getCindex() - Method in class weka.gui.visualize.PlotData2D
Get the currently set colouring index of the data
getCIndex() - Method in class weka.gui.visualize.VisualizePanel
Get the index of the attribute selected for coloring
getClassAttribute() - Method in class weka.gui.beans.ThresholdDataEvent
Return the class attribute for which the threshold data was generated for.
getClassCapabilities() - Method in class weka.core.Capabilities
returns all class capabilities
getClassColumn() - Method in class weka.gui.beans.ClassAssigner
 
getClassCounts() - Method in class weka.filters.supervised.attribute.ClassOrder
Get the class distribution of the sorted class values.
getClassesFromProperties() - Method in class weka.gui.GenericObjectEditor
Called when the class of object being edited changes.
getClassesToClusters() - Method in class weka.clusterers.ClusterEvaluation
Return the array (ordered by cluster number) of minimum error class to cluster mappings
getClassFlag() - Method in class weka.datagenerators.ClusterGenerator
Gets the class flag.
getClassForIRStatistics() - Method in class weka.experiment.ClassifierSplitEvaluator
Get the value of ClassForIRStatistics.
getClassification() - Method in class weka.associations.Tertius
Get the value of classification.
getClassifier() - Method in class weka.attributeSelection.ClassifierSubsetEval
Get the classifier used as the base learner.
getClassifier() - Method in class weka.attributeSelection.WrapperSubsetEval
Get the classifier used as the base learner.
getClassifier() - Method in class weka.classifiers.BVDecompose
Gets the name of the classifier being analysed
getClassifier() - Method in class weka.classifiers.BVDecomposeSegCVSub
Gets the name of the classifier being analysed
getClassifier() - Method in class weka.classifiers.CheckClassifier
Get the classifier used as the classifier
getClassifier() - Method in class weka.classifiers.CheckSource
Gets the classifier being used for the tests, can be null.
getClassifier(int) - Method in class weka.classifiers.meta.MultiScheme
Gets a single classifier from the set of available classifiers.
getClassifier(int) - Method in class weka.classifiers.MultipleClassifiersCombiner
Gets a single classifier from the set of available classifiers.
getClassifier() - Method in class weka.classifiers.SingleClassifierEnhancer
Get the classifier used as the base learner.
getClassifier() - Method in class weka.experiment.ClassifierSplitEvaluator
Get the value of Classifier.
getClassifier() - Method in class weka.experiment.RegressionSplitEvaluator
Get the value of Classifier.
getClassifier() - Method in class weka.filters.supervised.attribute.AddClassification
Gets the classifier used by the filter.
getClassifier() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Gets the classifier used by the filter.
getClassifier() - Method in class weka.gui.beans.BatchClassifierEvent
Get the classifier
getClassifier() - Method in class weka.gui.beans.Classifier
Get the classifier currently set for this wrapper
getClassifier() - Method in class weka.gui.beans.IncrementalClassifierEvent
Get the classifier
getClassifier() - Static method in class weka.gui.explorer.ExplorerDefaults
returns the default classifier (fully configured) for the classify panel.
getClassifierCostSensitiveEval() - Static method in class weka.gui.explorer.ExplorerDefaults
returns whether the evaluation of the classifier is done cost-sensitively.
getClassifierCrossvalidationFolds() - Static method in class weka.gui.explorer.ExplorerDefaults
returns the default number of folds of the CV in the classify panel.
getClassifierOutputAdditionalAttributes() - Static method in class weka.gui.explorer.ExplorerDefaults
returns the string with the additional indices to output alongside the predictions.
getClassifierOutputConfusionMatrix() - Static method in class weka.gui.explorer.ExplorerDefaults
returns whether the confusion matrix for the classifier is output.
getClassifierOutputEntropyEvalMeasures() - Static method in class weka.gui.explorer.ExplorerDefaults
returns whether entropy-based evaluation meastures of the classifier are output.
getClassifierOutputModel() - Static method in class weka.gui.explorer.ExplorerDefaults
returns whether the built model is output.
getClassifierOutputPerClassStats() - Static method in class weka.gui.explorer.ExplorerDefaults
returns whether additional per-class stats of the classifier are output.
getClassifierOutputPredictions() - Static method in class weka.gui.explorer.ExplorerDefaults
returns whether the predictions of the classifier are output as well.
getClassifierOutputSourceCode() - Static method in class weka.gui.explorer.ExplorerDefaults
returns whether the source of a sourcable Classifier is output in the classify tab.
getClassifierPercentageSplit() - Static method in class weka.gui.explorer.ExplorerDefaults
returns the default classifier test mode for the classify panel (0-99).
getClassifierPreserveOrder() - Static method in class weka.gui.explorer.ExplorerDefaults
returns whether the order is preserved in case of the percentage split in the classify tab.
getClassifierRandomSeed() - Static method in class weka.gui.explorer.ExplorerDefaults
returns the default random seed value for the classifier for the classify panel.
getClassifiers() - Method in class weka.classifiers.meta.MultiScheme
Gets the list of possible classifers to choose from.
getClassifiers() - Method in class weka.classifiers.MultipleClassifiersCombiner
Gets the list of possible classifers to choose from.
getClassifierSourceCodeClass() - Static method in class weka.gui.explorer.ExplorerDefaults
returns the default classname for a sourcable Classifier in the classify tab.
getClassifierSpec() - Method in class weka.classifiers.meta.CostSensitiveClassifier
Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
getClassifierSpec() - Method in class weka.classifiers.meta.MetaCost
Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
getClassifierSpec(int) - Method in class weka.classifiers.meta.MultiScheme
Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
getClassifierSpec(int) - Method in class weka.classifiers.MultipleClassifiersCombiner
Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
getClassifierSpec() - Method in class weka.classifiers.SingleClassifierEnhancer
Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
getClassifierSpec() - Method in class weka.filters.supervised.attribute.AddClassification
Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier.
getClassifierSpec() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier.
getClassifierStorePredictionsForVis() - Static method in class weka.gui.explorer.ExplorerDefaults
returns whether the predictions of the classifier are stored for visualization.
getClassifierTemplate() - Method in class weka.gui.beans.Classifier
Return the classifier template currently in use.
getClassifierTestMode() - Static method in class weka.gui.explorer.ExplorerDefaults
returns the default classifier test mode for the classify panel.
getClassifyIterations() - Method in class weka.classifiers.BVDecomposeSegCVSub
Gets the number of times an instance is classified
getClassIndex() - Method in class weka.associations.Apriori
Gets the class index
getClassIndex() - Method in class weka.associations.FilteredAssociator
Gets the class index
getClassIndex() - Method in class weka.associations.PredictiveApriori
Gets the index of the class attribute
getClassIndex() - Method in class weka.associations.Tertius
Get the value of classIndex.
getClassIndex() - Method in class weka.classifiers.BVDecompose
Get the index (starting from 1) of the attribute used as the class.
getClassIndex() - Method in class weka.classifiers.BVDecomposeSegCVSub
Get the index (starting from 1) of the attribute used as the class.
getClassIndex() - Method in class weka.classifiers.CheckSource
Gets the class index of the dataset.
getClassIndex() - Method in class weka.core.converters.LibSVMSaver
Get the index of the class attribute.
getClassIndex() - Method in class weka.core.converters.SVMLightSaver
Get the index of the class attribute.
getClassIndex() - Method in class weka.core.converters.XRFFSaver
Get the index of the class attribute.
getClassIndex() - Method in class weka.core.FindWithCapabilities
returns the current current class index, -1 if no class attribute.
getClassIndex() - Method in class weka.core.TestInstances
returns the current class index (0-based), -1 is last attribute
getClassIndex() - Method in class weka.filters.CheckSource
Gets the class index of the dataset.
getClassIndex() - Method in class weka.filters.unsupervised.attribute.ClassAssigner
returns the class index.
getClassIndex() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Gets the attribute on which misclassifications are based.
getClassMatches(String) - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion
returns all the class/package matches with the partial search string.
getClassname() - Method in class weka.core.Javadoc
returns the current classname
getClassname() - Method in class weka.core.ListOptions
returns the current classname
getClassName() - Method in class weka.filters.unsupervised.attribute.NumericTransform
Get the class containing the transformation method.
getClassname(String) - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion
returns the classname part of the partial classname.
getClassnameFromPath(TreePath) - Method in class weka.gui.GenericObjectEditor
creates a classname from the given path.
getClassnames(String) - Static method in class weka.gui.GenericObjectEditor
Returns the available classnames for a certain property in the props file.
getClassOrder() - Method in class weka.filters.supervised.attribute.ClassOrder
Get the wanted class order
getClassPriors() - Method in class weka.classifiers.Evaluation
Get the current weighted class counts
getClassType() - Method in class weka.core.TestInstances
returns the current class type
getClassValue() - Method in class weka.filters.supervised.instance.SMOTE
Gets the index of the class value to which SMOTE should be applied.
getClassValue() - Method in class weka.gui.beans.ClassValuePicker
Gets the class value considered to be the "positive" class value.
getClearEachDataset() - Method in class weka.gui.streams.InstanceViewer
 
getClip() - Method in class weka.gui.visualize.PostscriptGraphics
Not implemented
getClipBounds() - Method in class weka.gui.visualize.PostscriptGraphics
This returns the full current drawing area
getClipBounds(Rectangle) - Method in class weka.gui.visualize.PostscriptGraphics
This returns the full current drawing area
getClipRect() - Method in class weka.gui.visualize.PostscriptGraphics
Not implemented
getClock() - Method in class weka.core.Debug
returns the instance of the Clock that is internally used
getClosestConnections(Point, int) - Static method in class weka.gui.beans.BeanConnection
Return a list of connections within some delta of a point
getClosestConnectorPoint(Point) - Method in class weka.gui.beans.BeanVisual
Returns the coordinates of the closest "connector" point to the supplied point.
getCloseTo() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Get the "close to" number.
getCloseToDefault() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Get the "close to" default.
getCloseToTolerance() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Get the "close to" Tolerance.
getClusterAssignments() - Method in class weka.clusterers.ClusterEvaluation
Return an array of cluster assignments corresponding to the most recent set of instances clustered.
getClusterCenters() - Method in class weka.clusterers.XMeans
Return the centers of the clusters as an Instances object
getClusterCentroids() - Method in class weka.clusterers.SimpleKMeans
Gets the the cluster centroids
getClusterDefinitions() - Method in class weka.datagenerators.clusterers.SubspaceCluster
returns the currently set clusters
getClusterer() - Method in class weka.attributeSelection.UnsupervisedSubsetEvaluator
Get the clusterer
getClusterer() - Method in class weka.classifiers.meta.ClassificationViaClustering
Get the clusterer used as the base learner.
getClusterer() - Method in class weka.clusterers.CheckClusterer
Get the clusterer used as the clusterer
getClusterer() - Method in class weka.clusterers.MakeDensityBasedClusterer
Gets the clusterer being wrapped.
getClusterer() - Method in class weka.clusterers.SingleClustererEnhancer
Get the clusterer used as the base clusterer.
getClusterer() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
Get the value of clusterer
getClusterer() - Method in class weka.filters.unsupervised.attribute.AddCluster
Gets the clusterer used by the filter.
getClusterer() - Method in class weka.gui.beans.BatchClustererEvent
Get the clusterer
getClusterer() - Method in class weka.gui.beans.Clusterer
Get the clusterer currently set for this wrapper
getClusterer() - Static method in class weka.gui.explorer.ExplorerDefaults
returns the default clusterer (fully configured) for the clusterer panel.
getClustererSpec() - Method in class weka.clusterers.SingleClustererEnhancer
Gets the clusterer specification string, which contains the class name of the clusterer and any options to the clusterer
getClustererSpec() - Method in class weka.filters.unsupervised.attribute.AddCluster
Gets the clusterer specification string, which contains the class name of the clusterer and any options to the clusterer.
getClustererStoreClustersForVis() - Static method in class weka.gui.explorer.ExplorerDefaults
returns whether the clusters are storeed for visualization purposes in the cluster panel.
getClustererTestMode() - Static method in class weka.gui.explorer.ExplorerDefaults
returns the default cluster test mode for the cluster panel.
getClusteringSeed() - Method in class weka.classifiers.functions.RBFNetwork
Get the random seed used by K-means.
getClusterLabel() - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
Returns the clusterID, to which this DataObject belongs to
getClusterLabel() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
Returns the clusterID, to which this DataObject belongs to
getClusterLabel() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
Returns the clusterID, to which this DataObject belongs to
getClusterModelsNumericAtts() - Method in class weka.clusterers.EM
Return the normal distributions for the cluster models
getClusterNominalCounts() - Method in class weka.clusterers.SimpleKMeans
Returns for each cluster the frequency counts for the values of each nominal attribute
getClusterPriors() - Method in class weka.clusterers.EM
Return the priors for the clusters
getClusters() - Method in class weka.datagenerators.clusterers.SubspaceCluster
returns the current cluster definitions, if necessary initializes them
getClusterSizes() - Method in class weka.clusterers.SimpleKMeans
Gets the number of instances in each cluster
getClusterStandardDevs() - Method in class weka.clusterers.SimpleKMeans
Gets the standard deviations of the numeric attributes in each cluster
getClusterSubType() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Gets the cluster sub type.
getClusterType() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Gets the cluster type.
getCoef0() - Method in class weka.classifiers.functions.LibSVM
Gets coef
getCoefficients() - Method in class weka.classifiers.trees.ft.FTtree
Returns an array containing the coefficients of the logistic regression function at this node.
getCoefficients() - Method in class weka.classifiers.trees.lmt.LMTNode
Returns an array containing the coefficients of the logistic regression function at this node.
getCoefficients() - Method in class weka.classifiers.trees.lmt.LogisticBase
Returns an array holding the coefficients of the logistic model.
getCoefficients() - Method in class weka.core.matrix.LinearRegression
returns the calculated coefficients
getColCount() - Method in class weka.experiment.ResultMatrix
returns the number of columns
getColHidden(int) - Method in class weka.experiment.ResultMatrix
returns the hidden status of the column, if the index is valid, otherwise false
getColName(int) - Method in class weka.experiment.ResultMatrix
returns the name of the row, if the index is valid, otherwise null.
getColNameWidth() - Method in class weka.experiment.ResultMatrix
returns the current width for the column names
getColor() - Method in class weka.gui.treevisualizer.Node
Get the value of color.
getColor(String) - Method in class weka.gui.treevisualizer.TreeVisualizer
Processes the color string.
getColor() - Method in class weka.gui.visualize.PostscriptGraphics
Get current pen color.
getColorBox() - Method in class weka.gui.AttributeVisualizationPanel
Returns the class selection combo box if the parent component wants to place it in itself or in some component other than this component.
getColOrder() - Method in class weka.experiment.ResultMatrix
returns the current order of the columns, null means the default order
getColoringIndex() - Method in class weka.gui.AttributeVisualizationPanel
Get the coloring (class) index for the plot
getColoringIndex() - Method in class weka.gui.beans.AttributeSummarizer
Return the coloring index for the attribute summary plots
getColors() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Get the current vector of Color objects used for the classes
getColSize(String[][], int) - Method in class weka.experiment.ResultMatrix
returns the length of the longest cell in the given column
getColSize(String[][], int, boolean, boolean) - Method in class weka.experiment.ResultMatrix
returns the length of the longest cell in the given column
getColumn(int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Return a DoubleVector that stores a column of the matrix
getColumn(int, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Return a DoubleVector that stores some elements of a column of the matrix
getColumn(int) - Method in class weka.core.Matrix
Deprecated.
Gets a column of the matrix and returns it as a double array.
getColumn() - Static method in class weka.gui.experiment.ExperimenterDefaults
the comma-separated list of attribute names that identify a column
getColumnClass(int) - Method in class weka.gui.arffviewer.ArffTableModel
returns the most specific superclass for all the cell values in the column (always String)
getColumnClass(int) - Method in class weka.gui.SortedTableModel
Returns the most specific superclass for all the cell values in the column.
getColumnClass(int) - Method in class weka.gui.sql.ResultSetTableModel
returns the most specific superclass for all the cell values in the column (always String).
getColumnClasses() - Method in class weka.gui.sql.ResultSetHelper
returns the classes for the columns.
getColumnCount() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.ResultVectorTableModel
Returns the number of columns of this model.
getColumnCount() - Method in class weka.gui.arffviewer.ArffTableModel
returns the number of columns in the model
getColumnCount() - Method in class weka.gui.SortedTableModel
Returns the number of columns in the model
getColumnCount() - Method in class weka.gui.sql.ResultSetHelper
returns the number of columns in the resultset.
getColumnCount() - Method in class weka.gui.sql.ResultSetTableModel
returns the number of columns in the model.
getColumnDimension() - Method in class weka.core.matrix.Matrix
Get column dimension.
getColumnName(int) - Method in class weka.gui.arffviewer.ArffTableModel
returns the name of the column at columnIndex
getColumnName(int) - Method in class weka.gui.SortedTableModel
Returns the name of the column at columnIndex
getColumnName(int) - Method in class weka.gui.sql.ResultSetTableModel
returns the name of the column at columnIndex.
getColumnNames() - Method in class weka.gui.sql.ResultSetHelper
returns an array with the names of the columns in the resultset.
getColumnPackedCopy() - Method in class weka.core.matrix.Matrix
Make a one-dimensional column packed copy of the internal array.
getCombination() - Method in class weka.attributeSelection.ScatterSearchV1
Get the combination
getCombinationRule() - Method in class weka.classifiers.meta.Vote
Gets the combination rule used
getCommand() - Method in class weka.gui.treevisualizer.TreeDisplayEvent
 
getComment() - Method in enum weka.core.TechnicalInformation.Field
returns the comment string
getComment() - Method in enum weka.core.TechnicalInformation.Type
returns the comment string
getCommonPrefix() - Method in class weka.core.Trie
returns the common prefix for all the nodes
getCommonPrefix() - Method in class weka.core.Trie.TrieNode
returns the common prefix for all the nodes starting with this node.
getCommonPrefix(String) - Method in class weka.core.Trie.TrieNode
returns the common prefix for all the nodes starting with the node for the specified prefix.
getCommonPrefix(Vector<String>) - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion
returns the common prefix for all the items in the list.
getComparisonField() - Static method in class weka.gui.experiment.ExperimenterDefaults
returns the name of the field used for comparison
getCompatibilityState() - Method in class weka.experiment.AveragingResultProducer
Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState() - Method in class weka.experiment.CrossValidationResultProducer
Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState() - Method in class weka.experiment.DatabaseResultProducer
Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState() - Method in class weka.experiment.LearningRateResultProducer
Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState() - Method in class weka.experiment.RandomSplitResultProducer
Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState() - Method in interface weka.experiment.ResultProducer
Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getComplexityParameter() - Method in class weka.attributeSelection.SVMAttributeEval
Get the value of C used with SMO
getComponent() - Method in class weka.gui.visualize.JComponentWriter
returns the component that is stored in the output format
getComponent() - Method in class weka.gui.visualize.PrintableComponent
returns the GUI component this print dialog is part of.
getComposite() - Method in class weka.gui.visualize.PostscriptGraphics
 
getCompressOutput() - Method in class weka.core.converters.ArffSaver
Gets whether the output data is compressed.
getCompressOutput() - Method in class weka.core.converters.XRFFSaver
Gets whether the output data is compressed.
getConfidenceFactor() - Method in class weka.classifiers.rules.PART
Get the value of CF.
getConfidenceFactor() - Method in class weka.classifiers.trees.J48
Get the value of CF.
getConfidenceFactor() - Method in class weka.classifiers.trees.J48graft
Get the value of CF.
getConfirmation() - Method in class weka.associations.tertius.Rule
Get the confirmation value of this rule.
getConfirmationThreshold() - Method in class weka.associations.Tertius
Get the value of confirmationThreshold.
getConfirmationValues() - Method in class weka.associations.Tertius
Get the value of confirmationValues.
getConfirmExit() - Method in class weka.gui.arffviewer.ArffViewer
returns the setting of whether to display a confirm messagebox or not on exit
getConfirmExit() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
returns the setting of whether to display a confirm messagebox or not on exit
getConfusionMatrix() - Method in class weka.classifiers.evaluation.TwoClassStats
Generates a ConfusionMatrix representing the current two-class statistics, using class names "negative" and "positive".
getConnectedFormat() - Method in class weka.gui.beans.ClassAssigner
Returns the structure of the incoming instances (if any)
getConnectedFormat() - Method in class weka.gui.beans.ClassValuePicker
Returns the structure of the incoming instances (if any)
getConnection() - Method in class weka.gui.sql.DbUtils
returns the current database connection.
getConnections() - Static method in class weka.gui.beans.BeanConnection
Returns the list of connections
getConnectorPoint(int) - Method in class weka.gui.beans.BeanVisual
Returns the coordinates of the connector point given a compass point
getConsequence() - Method in class weka.associations.FPGrowth.AssociationRule
Get the consequence of this rule.
getConsequenceSupport() - Method in class weka.associations.FPGrowth.AssociationRule
Get the support for the consequence.
getConsequent() - Method in class weka.classifiers.rules.JRip.RipperRule
Gets the internal representation of the class label to be predicted
getConsequent() - Method in class weka.classifiers.rules.Rule
Get the consequent of this rule, i.e.
getConservativeForwardSelection() - Method in class weka.attributeSelection.GreedyStepwise
Gets whether conservative selection has been enabled
getConstError(double[]) - Method in class weka.classifiers.trees.ft.FTtree
 
getContainChildBalls() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
Gets whether if a parent ball should completely enclose its two child balls.
getContent(Element) - Method in class weka.classifiers.bayes.net.BIFReader
Returns all TEXT children of the given node in one string.
getContent(Element) - Method in class weka.classifiers.bayes.net.EditableBayesNet
XML helper function.
getContent(Element) - Static method in class weka.core.xml.XMLDocument
returns the text between the opening and closing tag of a node (performs a trim() on the result).
getContent() - Method in class weka.gui.CheckBoxList.CheckBoxListItem
returns the content object
getControlPanel() - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
This method returns a handle to the extra controls panel, so that the visualizing class can add it to some of it's own gui panel.
getControlPanel() - Method in interface weka.gui.graphvisualizer.LayoutEngine
This method returns the extra controls panel for the LayoutEngine, if there is any.
getConverterForExtension(String, Hashtable<String, String>) - Static method in class weka.core.converters.ConverterUtils
tries to determine the loader to use for this kind of extension, returns null if none can be found.
getConverterForFile(String, Hashtable<String, String>) - Static method in class weka.core.converters.ConverterUtils
tries to determine the converter to use for this kind of file, returns null if none can be found in the given hashtable.
getConverters(Hashtable<String, String>) - Static method in class weka.core.converters.ConverterUtils
returns a vector with the classnames of all the loaders from the given hashtable.
getConvertNominal() - Method in class weka.classifiers.trees.LMT
Get the value of convertNominal.
getConvertNominalToBinary() - Method in class weka.classifiers.functions.LibLINEAR
Gets whether conversion of nominal to binary is turned on.
getCopy(String[]) - Method in class weka.core.CheckOptionHandler
creates a copy of the given options
getCoreConvertersOnly() - Method in class weka.gui.ConverterFileChooser
Returns whether only the hardcoded core converters are displayed.
getCoreDistance() - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
Returns the coreDistance for this dataObject
getCoreDistance() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
Returns the coreDistance for this dataObject
getCoreDistance() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
Returns the coreDistance for this dataObject
getCost() - Method in class weka.classifiers.functions.LibLINEAR
Returns the cost parameter C
getCost() - Method in class weka.classifiers.functions.LibSVM
Sets the parameter C of C-SVC, epsilon-SVR, and nu-SVR
getCostMatrix() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
Gets the misclassification cost matrix.
getCostMatrix() - Method in class weka.classifiers.meta.CostSensitiveClassifier
Gets the misclassification cost matrix.
getCostMatrix() - Method in class weka.classifiers.meta.MetaCost
Gets the misclassification cost matrix.
getCostMatrixSource() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
Gets the source location method of the cost matrix.
getCostMatrixSource() - Method in class weka.classifiers.meta.CostSensitiveClassifier
Gets the source location method of the cost matrix.
getCostMatrixSource() - Method in class weka.classifiers.meta.MetaCost
Gets the source location method of the cost matrix.
getCount(int) - Method in class weka.associations.FPGrowth.ShadowCounts
Get the count at the specified recursion depth.
getCount(double) - Method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes
Get a counts for a value
getCount(double) - Method in class weka.estimators.DiscreteEstimator
Get the count for a value
getCount(int) - Method in class weka.experiment.ResultMatrix
returns the count for the row.
getCount(Node, int) - Static method in class weka.gui.treevisualizer.Node
Recursively finds the number of visible nodes there are (this may accidentally count some of the invis nodes).
getCounterInstancesFrequency() - Method in class weka.associations.tertius.LiteralSet
Get the frequency of counter-instances of this LiteralSet in the data.
getCounterInstancesNumber() - Method in class weka.associations.tertius.LiteralSet
Get the number of counter-instances of this LiteralSet.
getCounts(int[], int[], int[], int, int, boolean) - Method in class weka.classifiers.bayes.net.ADNode
get counts for specific instantiation of a set of nodes
getCounts(int[], int[], int[], int, int, ADNode, boolean) - Method in class weka.classifiers.bayes.net.VaryNode
get counts for specific instantiation of a set of nodes
getCountWidth() - Method in class weka.experiment.ResultMatrix
returns the current width for the counts
getCover() - Method in class weka.classifiers.rules.JRip.Antd
 
getCoverSet(int, Stack<Stack<CoverTree.d_node>>) - Method in class weka.core.neighboursearch.CoverTree
Returns a cover set for a given level/scale.
getCreatorApplication() - Method in class weka.classifiers.pmml.consumer.PMMLClassifier
Get the name of the application that created this model
getCreatorApplication() - Method in interface weka.core.pmml.PMMLModel
Get the name of the application that created this model.
getCriticalValue() - Method in class weka.classifiers.bayes.AODEsr
Gets the critical value.
getCrossoverProb() - Method in class weka.attributeSelection.GeneticSearch
get the probability of crossover
getCrossVal() - Method in class weka.classifiers.rules.DecisionTable
Gets the number of folds for cross validation
getCrossValidate() - Method in class weka.classifiers.lazy.IBk
Gets whether hold-one-out cross-validation will be used to select the best k value.
getCurrent() - Method in class weka.core.Memory
returns the current memory consumption
getCurrentDatasetNumber() - Method in class weka.experiment.Experiment
When an experiment is running, this returns the current dataset number.
getCurrentDir() - Static method in class weka.core.Debug
returns the current working directory of the user
getCurrentFilename() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
returns the filename of the current tab
getCurrentIndex() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
returns the currently selected tab index
getCurrentInstance() - Method in class weka.gui.beans.IncrementalClassifierEvent
Get the current instance
getCurrentModel() - Method in class weka.classifiers.misc.SerializedClassifier
Gets the currently loaded model (can be null).
getCurrentPanel() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
returns the currently selected panel
getCurrentPropertyNumber() - Method in class weka.experiment.Experiment
When an experiment is running, this returns the index of the current custom property value.
getCurrentRunNumber() - Method in class weka.experiment.Experiment
When an experiment is running, this returns the current run number.
getCurrentTable() - Method in class weka.gui.sql.ResultPanel
returns the table of the current tab, can be NULL
getCurrentTime() - Method in class weka.core.Debug.Clock
returns the current time in msec
getCurve(FastVector) - Method in class weka.classifiers.evaluation.CostCurve
Calculates the performance stats for the default class and return results as a set of Instances.
getCurve(FastVector, int) - Method in class weka.classifiers.evaluation.CostCurve
Calculates the performance stats for the desired class and return results as a set of Instances.
getCurve(FastVector) - Method in class weka.classifiers.evaluation.MarginCurve
Calculates the cumulative margin distribution for the set of predictions, returning the result as a set of Instances.
getCurve(FastVector) - Method in class weka.classifiers.evaluation.ThresholdCurve
Calculates the performance stats for the default class and return results as a set of Instances.
getCurve(FastVector, int) - Method in class weka.classifiers.evaluation.ThresholdCurve
Calculates the performance stats for the desired class and return results as a set of Instances.
getCustomEditor() - Method in class weka.gui.CostMatrixEditor
Gets a GUI component with which the user can edit the cost matrix.
getCustomEditor() - Method in class weka.gui.FileEditor
Gets the custom editor component.
getCustomEditor() - Method in class weka.gui.GenericArrayEditor
Returns the array editing component.
getCustomEditor() - Method in class weka.gui.GenericObjectEditor
Returns the array editing component.
getCustomEditor() - Method in class weka.gui.SimpleDateFormatEditor
Gets a GUI component with which the user can edit the date format.
getCustomHeight() - Method in class weka.gui.visualize.JComponentWriter
gets the custom height currently used
getCustomName() - Method in class weka.gui.beans.Associator
Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in interface weka.gui.beans.BeanCommon
Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.ClassAssigner
Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.Classifier
Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.ClassifierPerformanceEvaluator
Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.ClassValuePicker
Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.Clusterer
Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.ClustererPerformanceEvaluator
Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.CostBenefitAnalysis
Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.CrossValidationFoldMaker
Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.Filter
Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.IncrementalClassifierEvaluator
Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.InstanceStreamToBatchMaker
Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.Loader
Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.MetaBean
Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.PredictionAppender
Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.Saver
Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.SerializedModelSaver
Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.StripChart
Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.TestSetMaker
Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.TextViewer
Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.TrainingSetMaker
Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.TrainTestSplitMaker
Get the custom (descriptive) name for this bean (if one has been set)
getCustomPanel() - Method in interface weka.gui.CustomPanelSupplier
Gets the custom panel for the object.
getCustomPanel() - Method in class weka.gui.GenericObjectEditor
Gets the custom panel used for editing the object.
getCustomWidth() - Method in class weka.gui.visualize.JComponentWriter
gets the custom width currently used
getCutoff() - Method in class weka.clusterers.Cobweb
get the cutoff
getCutOffFactor() - Method in class weka.clusterers.XMeans
Gets the cutoff factor.
getCutPoints(int) - Method in class weka.filters.supervised.attribute.Discretize
Gets the cut points for an attribute
getCutPoints(int) - Method in class weka.filters.unsupervised.attribute.Discretize
Gets the cut points for an attribute
getCVisible() - Method in class weka.gui.treevisualizer.Node
Get If this node's childs are visible.
getCVParameter(int) - Method in class weka.classifiers.meta.CVParameterSelection
Gets the scheme paramter with the given index.
getCVParameters() - Method in class weka.classifiers.meta.CVParameterSelection
Get method for CVParameters.
getCVPredictions(Classifier, Instances, int) - Method in class weka.classifiers.evaluation.EvaluationUtils
Generate a bunch of predictions ready for processing, by performing a cross-validation on the supplied dataset.
getCVType() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
get cross validation strategy to be used in searching for networks.
getCycleEnd() - Method in class weka.associations.GeneralizedSequentialPatterns
Returns the time/date string the cycle ended
getCycleStart() - Method in class weka.associations.GeneralizedSequentialPatterns
Returns the time/date string the cycle was started
getD() - Method in class weka.core.matrix.EigenvalueDecomposition
Return the block diagonal eigenvalue matrix
getData() - Method in class weka.attributeSelection.BestFirst.Link2
Get a group
getData() - Method in class weka.attributeSelection.LFSMethods.Link2
Get a group
getData() - Method in class weka.classifiers.rules.RuleStats
Get the data of the stats
getData() - Method in class weka.core.AttributeLocator
returns the underlying data
getData() - Method in class weka.core.converters.ArffLoader.ArffReader
Returns the data that was read
getData() - Method in class weka.core.TestInstances
returns the current dataset, can be null
getDatabase_distanceType() - Method in class weka.clusterers.DBScan
Returns the distance-type
getDatabase_distanceType() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject
Returns the distance-type
getDatabase_distanceType() - Method in class weka.clusterers.OPTICS
Returns the distance-type
getDatabase_Type() - Method in class weka.clusterers.DBScan
Returns the type of the used index (database)
getDatabase_Type() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject
Returns the type of the used index (database)
getDatabase_Type() - Method in class weka.clusterers.OPTICS
Returns the type of the used index (database)
getDatabaseOutput() - Method in class weka.clusterers.OPTICS
Returns the file to save the database to - if directory, database is not saved.
getDatabaseSize() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject
Returns the database's size
getDatabaseURL() - Method in class weka.experiment.DatabaseUtils
Get the value of DatabaseURL.
getDataDictionary() - Method in class weka.classifiers.pmml.consumer.PMMLClassifier
Get the data dictionary.
getDataDictionaryAsInstances(Document) - Static method in class weka.core.pmml.PMMLFactory
Get the data dictionary as an Instances object
getDataFileName() - Method in class weka.classifiers.BVDecompose
Get the name of the data file used for the decomposition
getDataFileName() - Method in class weka.classifiers.BVDecomposeSegCVSub
Get the name of the data file used for the decomposition
getDataObject(String) - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database
Select a dataObject from the database
getDataObject(String) - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase
Select a dataObject from the database
getDataObject() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.EpsilonRange_ListElement
Returns this dataObject
getDataPoint() - Method in class weka.gui.beans.ChartEvent
Get the data point
getDataSeqID() - Method in class weka.associations.GeneralizedSequentialPatterns
Returns the attribute representing the data sequence ID.
getDataset() - Method in class weka.classifiers.CheckSource
Gets the dataset to use for testing, can be null.
getDataSet() - Method in class weka.core.converters.AbstractLoader
 
getDataSet() - Method in class weka.core.converters.ArffLoader
Return the full data set.
getDataSet() - Method in class weka.core.converters.C45Loader
Return the full data set.
getDataSet() - Method in class weka.core.converters.ConverterUtils.DataSource
returns the full dataset, can be null in case of an error.
getDataSet(int) - Method in class weka.core.converters.ConverterUtils.DataSource
returns the full dataset with the specified class index set, can be null in case of an error.
getDataSet() - Method in class weka.core.converters.CSVLoader
Return the full data set.
getDataSet() - Method in class weka.core.converters.DatabaseLoader
Return the full data set in batch mode (header and all intances at once).
getDataSet() - Method in class weka.core.converters.LibSVMLoader
Return the full data set.
getDataSet() - Method in interface weka.core.converters.Loader
Return the full data set.
getDataSet() - Method in class weka.core.converters.SerializedInstancesLoader
Return the full data set.
getDataSet() - Method in class weka.core.converters.SVMLightLoader
Return the full data set.
getDataSet() - Method in class weka.core.converters.TextDirectoryLoader
Return the full data set.
getDataSet() - Method in class weka.core.converters.XRFFLoader
Return the full data set.
getDataset() - Method in class weka.filters.CheckSource
Gets the dataset to use for testing, can be null.
getDataSet() - Method in class weka.gui.beans.DataSetEvent
Return the instances of the data set
getDataSet() - Method in class weka.gui.beans.ThresholdDataEvent
Return the instances of the data set
getDataSet() - Method in class weka.gui.beans.VisualizableErrorEvent
Return the instances of the data set
getDatasetFormat() - Method in class weka.datagenerators.DataGenerator
Gets the format of the dataset that is to be generated.
getDatasetKeyColumns() - Method in class weka.experiment.PairedTTester
Get the value of DatasetKeyColumns.
getDatasetKeyColumns() - Method in interface weka.experiment.Tester
Get the value of DatasetKeyColumns.
getDatasets() - Method in class weka.experiment.Experiment
Gets the datasets in the experiment.
getDatasetsFirst() - Static method in class weka.gui.experiment.ExperimenterDefaults
whether datasets or algorithms are iterated first
getDataType() - Method in class weka.gui.beans.xml.XMLBeans
returns the type of data that is to be read/written
getDateAttributes() - Method in class weka.core.converters.CSVLoader
Returns the current attribute range to be forced to type date.
getDateFormat() - Method in class weka.core.Attribute
Returns the Date format pattern in case this attribute is of type DATE, otherwise an empty string.
getDateFormat() - Method in class weka.core.converters.CSVLoader
Get the format to use for parsing date values.
getDateFormat() - Method in class weka.filters.unsupervised.attribute.Add
Get the date format, complying to ISO-8601.
getDateFormat() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
Get the date format used in output.
getDbUtils() - Method in class weka.gui.sql.event.ConnectionEvent
returns the DbUtils instance that is responsible for the connect/disconnect.
getDbUtils() - Method in class weka.gui.sql.event.QueryExecuteEvent
returns the DbUtils instance that was executed the query
getDebug() - Method in class weka.associations.GeneralizedSequentialPatterns
Get whether debugging is turned on.
getDebug() - Method in class weka.attributeSelection.RaceSearch
Get whether output is to be verbose
getDebug() - Method in class weka.attributeSelection.ScatterSearchV1
Get whether output is to be verbose
getDebug() - Method in class weka.classifiers.BVDecompose
Gets whether debugging is turned on
getDebug() - Method in class weka.classifiers.BVDecomposeSegCVSub
Gets whether debugging is turned on
getDebug() - Method in class weka.classifiers.Classifier
Get whether debugging is turned on.
getDebug() - Method in class weka.classifiers.functions.LeastMedSq
Returns whether or not debugging output shouild be printed
getDebug() - Method in class weka.classifiers.functions.LinearRegression
Controls whether debugging output will be printed
getDebug() - Method in class weka.classifiers.functions.Logistic
Gets whether debugging output will be printed.
getDebug() - Method in class weka.classifiers.functions.PaceRegression
Controls whether debugging output will be printed
getDebug() - Method in class weka.classifiers.functions.supportVector.Kernel
Gets whether debugging output is turned on or not.
getDebug() - Method in class weka.classifiers.meta.MultiScheme
Get whether debugging is turned on
getDebug() - Method in class weka.classifiers.rules.JRip
Gets whether debug information is output to the console
getDebug() - Method in class weka.clusterers.EM
Get debug mode
getDebug() - Method in class weka.clusterers.HierarchicalClusterer
Get whether debugging is turned on.
getDebug() - Method in class weka.clusterers.sIB
Get debug mode
getDebug() - Method in class weka.core.Check
Get whether debugging is turned on
getDebug() - Method in class weka.core.converters.TextDirectoryLoader
Gets whether additional debug information is printed.
getDebug() - Method in class weka.core.Debug.Random
returns whether to print the generated random values or not
getDebug() - Method in class weka.datagenerators.DataGenerator
Gets the debug flag.
getDebug() - Method in class weka.estimators.CheckEstimator
Get whether debugging is turned on
getDebug() - Method in class weka.estimators.Estimator
Get whether debugging is turned on.
getDebug() - Method in class weka.experiment.DatabaseUtils
Gets whether there should be printed some debugging output to stderr or not.
getDebug() - Method in class weka.filters.SimpleFilter
Returns the current debugging mode state.
getDebug() - Method in class weka.filters.unsupervised.attribute.AddExpression
Gets whether debug is set
getDebug() - Method in class weka.gui.DatabaseConnectionDialog
Returns the debug flag
getDebug() - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion
returns whether debug mode is on.
getDebug() - Method in class weka.gui.streams.InstanceCounter
 
getDebug() - Method in class weka.gui.streams.InstanceJoiner
 
getDebug() - Method in class weka.gui.streams.InstanceLoader
 
getDebug() - Method in class weka.gui.streams.InstanceSavePanel
 
getDebug() - Method in class weka.gui.streams.InstanceTable
 
getDebug() - Method in class weka.gui.streams.InstanceViewer
 
getDebugLevel() - Method in class weka.clusterers.XMeans
Gets the debug level.
getDebugVectorsFile() - Method in class weka.clusterers.XMeans
Gets the file name for a file that has the random vectors stored.
getDecay() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
getDecimals() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Get the number of decimals to round to.
getDefault() - Method in class weka.core.Tee
returns the default printstrean, can be NULL.
getDefaultHandler() - Method in class weka.core.CheckOptionHandler
returns a new instance of the OptionHandler's class
getDefaultOptions() - Method in class weka.core.CheckOptionHandler
returns the default options the default OptionHandler will return
getDefaultValue() - Method in class weka.core.pmml.TargetMetaInfo
Get the default value (numeric target)
getDefaultWeight() - Method in class weka.classifiers.functions.Winnow
Get the value of defaultWeight.
getDegree() - Method in class weka.classifiers.functions.LibSVM
Gets the degree of the kernel
getDegreesOfFreedom() - Method in class weka.experiment.PairedStats
Gets the degrees of freedom.
getDeletedList() - Method in class weka.classifiers.rules.DTNB.EvalWithDelete
 
getDeleteEmptyBins() - Method in class weka.classifiers.meta.RegressionByDiscretization
Gets the number of bins numeric attributes will be divided into
getDelimiters() - Method in class weka.core.tokenizers.CharacterDelimitedTokenizer
Get the value of delimiters (not backquoted).
getDelta() - Method in class weka.associations.Apriori
Get the value of delta.
getDelta() - Method in class weka.associations.FPGrowth
Get the value of delta.
getDelta() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
 
getDelta() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
 
getDensityBasedClusterer() - Method in class weka.filters.unsupervised.attribute.ClusterMembership
Get the clusterer used by this filter
getDerivedFields() - Method in class weka.core.pmml.MiningSchema
 
getDerivedValue(double[]) - Method in class weka.core.pmml.DerivedFieldMetaInfo
Get the derived field value for the given incoming vector of values.
getDescendantPopulationSize() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
 
getDescendantPopulationSize() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
 
getDescription() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERFileFilter
The description of this filter.
getDescription() - Method in class weka.gui.ExtensionFileFilter
Gets the description of accepted files.
getDescription() - Method in class weka.gui.visualize.BMPWriter
returns the name of the writer, to display in the FileChooser.
getDescription() - Method in class weka.gui.visualize.JComponentWriter
returns the name of the writer, to display in the FileChooser.
getDescription() - Method in class weka.gui.visualize.JPEGWriter
returns the name of the writer, to display in the FileChooser.
getDescription() - Method in class weka.gui.visualize.PNGWriter
returns the name of the writer, to display in the FileChooser.
getDescription() - Method in class weka.gui.visualize.PostscriptWriter
returns the name of the writer, to display in the FileChooser.
getDescriptor() - Method in class weka.core.PropertyPath.PropertyContainer
returns the stored descriptor
getDescriptorByName(Object, String) - Method in class weka.core.xml.XMLSerialization
returns a descriptor for a given objet by providing the name
getDescriptors(Object) - Method in class weka.core.xml.XMLSerialization
returns a hashtable with PropertyDescriptors that have "get" and "set" methods indexed by the property name.
getDesignatedClass() - Method in class weka.classifiers.meta.ThresholdSelector
Gets the method to determine which class value to optimize.
getDesignVersion() - Method in interface weka.gui.visualize.plugins.ErrorVisualizePlugin
Get the specific version of Weka the class is designed for.
getDesignVersion() - Method in interface weka.gui.visualize.plugins.GraphVisualizePlugin
Get the specific version of Weka the class is designed for.
getDesignVersion() - Method in interface weka.gui.visualize.plugins.TreeVisualizePlugin
Get the specific version of Weka the class is designed for.
getDesignVersion() - Method in interface weka.gui.visualize.plugins.VisualizePlugin
Get the specific version of Weka the class is designed for.
getDesiredSize() - Method in class weka.classifiers.meta.Decorate
Gets the desired size of the committee.
getDesiredWeightOfInstancesPerInterval() - Method in class weka.filters.unsupervised.attribute.Discretize
Get the DesiredWeightOfInstancesPerInterval value.
getDestination() - Static method in class weka.gui.experiment.ExperimenterDefaults
returns the default destination
getDetectionPerAttribute() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Gets whether an Outlier/ExtremeValue attribute pair is generated for each numeric attribute ("true") or just one pair for all numeric attributes together ("false").
getDeviceConfiguration() - Method in class weka.gui.visualize.PostscriptGraphics
 
getDir() - Method in class weka.core.Javadoc
returns the current dir containing the class to update.
getDir() - Method in class weka.gui.Loader
returns the dir prefix
getDirection() - Method in class weka.attributeSelection.BestFirst
Get the search direction
getDirectory() - Method in class weka.core.converters.TextDirectoryLoader
get the Dir specified as the source
getDirectory() - Method in class weka.gui.beans.SerializedModelSaver
Get the directory that the model(s) will be saved into
getDiscretizeBin() - Method in class weka.classifiers.mi.MIBoost
Get the number of bins in discretization
getDiscretizer() - Method in class weka.classifiers.trees.j48.NBTreeNoSplit
Return the discretizer used at this node
getDisplay() - Method in enum weka.core.TechnicalInformation.Field
returns the display string
getDisplay() - Method in enum weka.core.TechnicalInformation.Type
returns the display string
getDisplayCol(int) - Method in class weka.experiment.ResultMatrix
returns the displayed index of the given col, depending on the order of columns, returns -1 if index out of bounds
getDisplayedResultsets() - Method in class weka.experiment.PairedTTester
Gets the indices of the the datasets that are displayed (if null then all are displayed).
getDisplayedResultsets() - Method in interface weka.experiment.Tester
Gets the indices of the the datasets that are displayed (if null then all are displayed).
getDisplayModelInOldFormat() - Method in class weka.classifiers.bayes.NaiveBayes
Get whether to display model output in the old, original format.
getDisplayModelInOldFormat() - Method in class weka.clusterers.EM
Get whether to display model output in the old, original format.
getDisplayName() - Method in class weka.experiment.PairedCorrectedTTester
returns the name of the tester
getDisplayName() - Method in class weka.experiment.PairedTTester
returns the name of the tester
getDisplayName() - Method in class weka.experiment.ResultMatrix
returns the name of the output format
getDisplayName() - Method in class weka.experiment.ResultMatrixCSV
returns the name of the output format
getDisplayName() - Method in class weka.experiment.ResultMatrixGnuPlot
returns the name of the output format
getDisplayName() - Method in class weka.experiment.ResultMatrixHTML
returns the name of the output format
getDisplayName() - Method in class weka.experiment.ResultMatrixLatex
returns the name of the output format
getDisplayName() - Method in class weka.experiment.ResultMatrixPlainText
returns the name of the output format
getDisplayName() - Method in class weka.experiment.ResultMatrixSignificance
returns the name of the output format
getDisplayName() - Method in interface weka.experiment.Tester
returns the name of the testing algorithm
getDisplayRow(int) - Method in class weka.experiment.ResultMatrix
returns the displayed index of the given row, depending on the order of rows, returns -1 if index out of bounds
getDisplayRules() - Method in class weka.classifiers.rules.DecisionTable
Gets whether rules are being printed
getDisplayStdDevs() - Method in class weka.clusterers.SimpleKMeans
Gets whether standard deviations and nominal count Should be displayed in the clustering output
getDisplayValue() - Method in class weka.core.pmml.FieldMetaInfo.Value
 
getDistance() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.EpsilonRange_ListElement
Returns the distance that was calulcated for this dataObject (The distance between this dataObject and the dataObject for which an epsilon-range-query was performed.)
getDistanceF() - Method in class weka.clusterers.XMeans
Gets the distance function.
getDistanceFSpec() - Method in class weka.clusterers.XMeans
Gets the distance function specification string, which contains the class name of the distance function class and any options to it.
getDistanceFunction() - Method in class weka.clusterers.HierarchicalClusterer
 
getDistanceFunction() - Method in class weka.clusterers.SimpleKMeans
returns the distance function currently in use.
getDistanceFunction() - Method in class weka.core.neighboursearch.KDTree
returns the distance function currently in use.
getDistanceFunction() - Method in class weka.core.neighboursearch.NearestNeighbourSearch
returns the distance function currently in use.
getDistanceIsBranchLength() - Method in class weka.clusterers.HierarchicalClusterer
 
getDistances() - Method in class weka.core.neighboursearch.BallTree
Returns the distances of the k nearest neighbours.
getDistances() - Method in class weka.core.neighboursearch.CoverTree
Returns the distances of the (k)-NN(s) found earlier by kNearestNeighbours()/nearestNeighbour().
getDistances() - Method in class weka.core.neighboursearch.KDTree
Returns the distances to the kNearest or 1 nearest neighbour currently found with either the kNearestNeighbours or the nearestNeighbour method.
getDistances() - Method in class weka.core.neighboursearch.LinearNNSearch
Returns the distances of the k nearest neighbours.
getDistances() - Method in class weka.core.neighboursearch.NearestNeighbourSearch
Returns the distances of the k nearest neighbours.
getDistanceWeighting() - Method in class weka.classifiers.lazy.IBk
Gets the distance weighting method used.
getDistMult() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Gets the distance multiplier.
getDistribution(String) - Method in class weka.classifiers.bayes.net.EditableBayesNet
returns distribution of a node in matrix form with matrix representing distribution with P[i][j] = P(node = j | parent configuration = i)
getDistribution(int) - Method in class weka.classifiers.bayes.net.EditableBayesNet
returns distribution of a node in matrix form with matrix representing distribution with P[i][j] = P(node = j | parent configuration = i)
getDistribution() - Method in class weka.filters.unsupervised.attribute.RandomProjection
Returns the current distribution that'll be used for calculating the random matrix
getDistributions() - Method in class weka.classifiers.bayes.BayesNet
Get full set of estimators.
getDistributions(int) - Method in class weka.classifiers.rules.RuleStats
Get the class distribution predicted by the rule in given position
getDistributionSpread() - Method in class weka.filters.supervised.instance.SpreadSubsample
Gets the value for the distribution spread
getDocType() - Method in class weka.core.xml.XMLDocument
returns the current DOCTYPE, can be null.
getDocument() - Method in class weka.core.xml.XMLDocument
returns the parsed DOM document.
getDocument() - Method in class weka.core.xml.XMLOptions
returns the parsed DOM document.
getDominantEigenVector(Matrix) - Method in class weka.filters.supervised.attribute.PLSFilter
determines the dominant eigenvector for the given matrix and returns it
getDoNotOperateOnPerClassBasis() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Get the DoNotOperateOnPerClassBasis value.
getDoNotReplaceMissingValues() - Method in class weka.classifiers.functions.LibLINEAR
Gets whether automatic replacement of missing values is disabled.
getDoNotReplaceMissingValues() - Method in class weka.classifiers.functions.LibSVM
Gets whether automatic replacement of missing values is disabled.
getDontFilterAfterFirstBatch() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Get whether to apply the filter to instances that arrive once the first (training) batch has been seen.
getDontNormalize() - Method in class weka.classifiers.functions.SPegasos
Get whether normalization has been turned off.
getDontNormalize() - Method in class weka.core.NormalizableDistance
Gets whether if the attribute values are to be normazlied in distance calculation.
getDontReplaceMissing() - Method in class weka.classifiers.functions.SPegasos
Get whether global replacement of missing values has been disabled.
getDontReplaceMissingValues() - Method in class weka.clusterers.SimpleKMeans
Gets whether missing values are to be replaced
getDoublePivot() - Method in class weka.core.matrix.LUDecomposition
Return pivot permutation vector as a one-dimensional double array
getEditor() - Method in class weka.gui.PropertyDialog
Gets the current property editor.
getEditorActive() - Method in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Returns true if the editor is currently in an active status---that is the array is active and able to be edited.
getElapsedTime() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject
Returns the elapsed-time
getElement(int, int) - Method in class weka.classifiers.CostMatrix
Return the value of a cell as a double (for legacy code)
getElement(int, int, Instance) - Method in class weka.classifiers.CostMatrix
Return the value of a cell as a double.
getElement(int) - Method in class weka.core.AlgVector
Returns the value of a cell in the matrix.
getElement(int, int) - Method in class weka.core.Matrix
Deprecated.
Returns the value of a cell in the matrix.
getElementAt(int) - Method in class weka.gui.CheckBoxList.CheckBoxListModel
Returns the component at the specified index.
getElements() - Method in class weka.associations.gsp.Sequence
Returns the Elements of the Sequence.
getElements() - Method in class weka.core.AlgVector
Gets the elements of the vector and returns them as double array.
getEliminateColinearAttributes() - Method in class weka.classifiers.functions.LinearRegression
Get the value of EliminateColinearAttributes.
getEnabled() - Method in class weka.core.Debug
returns whether the logging is enabled
getEnclosureCharacters() - Method in class weka.core.converters.CSVLoader
Get the character(s) to use/recognize as string enclosures
getEntropicAutoBlend() - Method in class weka.classifiers.lazy.KStar
Get whether entropic blending being used
getEntry(double) - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable
Returns the table entry to which the specified key is mapped in this hashtable.
getEnumerateColNames() - Method in class weka.experiment.ResultMatrix
returns whether column names or numbers instead are enumerateed
getEnumerateRowNames() - Method in class weka.experiment.ResultMatrix
returns whether row names or numbers instead are enumerateed
getEnvironment() - Method in class weka.gui.beans.FlowRunner
Get the environment variables that are in use.
getEpochs() - Method in class weka.classifiers.functions.SPegasos
Get current number of epochs
getEps() - Method in class weka.classifiers.functions.LibLINEAR
Gets tolerance of termination criterion
getEps() - Method in class weka.classifiers.functions.LibSVM
Gets tolerance of termination criterion
getEpsilon() - Method in class weka.classifiers.functions.SMO
Get the value of epsilon.
getEpsilon() - Method in class weka.classifiers.functions.supportVector.RegSMO
Get the value of epsilon.
getEpsilon() - Method in class weka.classifiers.mi.MISMO
Get the value of epsilon.
getEpsilon() - Method in class weka.clusterers.DBScan
Returns the value of epsilon
getEpsilon() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject
Returns the value of epsilon
getEpsilon() - Method in class weka.clusterers.OPTICS
Returns the value of epsilon
getEpsilonParameter() - Method in class weka.attributeSelection.SVMAttributeEval
Get the value of P used with SMO
getEpsilonParameter() - Method in class weka.classifiers.functions.supportVector.RegOptimizer
Get the value of epsilon parameter of the epsilon insensitive loss function.
getError() - Method in class weka.classifiers.BVDecompose
Get the calculated error rate
getError() - Method in class weka.classifiers.BVDecomposeSegCVSub
Get the calculated error rate
getErrorOnProbabilities() - Method in class weka.classifiers.functions.SimpleLogistic
Get the value of errorOnProbabilities.
getErrorOnProbabilities() - Method in class weka.classifiers.trees.FT
Get the value of errorOnProbabilities.
getErrorOnProbabilities() - Method in class weka.classifiers.trees.LMT
Get the value of errorOnProbabilities.
getErrorRate(Instances) - Method in class weka.classifiers.trees.lmt.LogisticBase
Returns the misclassification error of the current model on a set of instances.
getErrors() - Method in class weka.classifiers.trees.j48.NBTreeNoSplit
Return the errors made by the naive bayes model at this node
getErrors() - Method in class weka.classifiers.trees.j48.NBTreeSplit
Return the errors made by the naive bayes models arising from this split.
getEstimatedErrors() - Method in class weka.classifiers.trees.ft.FTtree
Computes estimated errors for tree.
getEstimatedErrorsForBranch(Instances) - Method in class weka.classifiers.trees.ft.FTtree
Computes estimated errors for one branch.
getEstimatedErrorsForDistribution(Distribution) - Method in class weka.classifiers.trees.ft.FTtree
Computes estimated errors for leaf.
getEstimatedErrorsForLeaf() - Method in class weka.classifiers.rules.part.C45PruneableDecList
Computes estimated errors for leaf.
getEstimator() - Method in class weka.classifiers.bayes.BayesNet
Get the BayesNetEstimator used for calculating the CPTs
getEstimator() - Method in class weka.classifiers.functions.PaceRegression
Gets the estimator
getEstimator() - Method in class weka.estimators.CheckEstimator
Get the estimator used as the estimator
getEstimator(double) - Method in interface weka.estimators.ConditionalEstimator
Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.DDConditionalEstimator
Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.DKConditionalEstimator
Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.DNConditionalEstimator
Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.KDConditionalEstimator
Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.KKConditionalEstimator
Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.NDConditionalEstimator
Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.NNConditionalEstimator
Get a probability estimator for a value
getEtimateConstModel(Distribution) - Method in class weka.classifiers.trees.ft.FTtree
Computes estimated errors for Constructor Model.
getEvaluation() - Method in class weka.classifiers.meta.GridSearch
Gets the criterion used for evaluating the classifier performance.
getEvaluation() - Method in class weka.classifiers.meta.GridSearch.PerformanceComparator
returns the performance measure that's used to compare the objects
getEvaluationMeasure() - Method in class weka.classifiers.rules.DecisionTable
Gets the currently set performance evaluation measure used for selecting attributes for the decision table
getEvaluationMode() - Method in class weka.classifiers.meta.ThresholdSelector
Gets the evaluation mode used.
getEvaluator() - Method in class weka.attributeSelection.CheckAttributeSelection
Get the current evaluator
getEvaluator() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
Get the evaluator used as the base evaluator.
getEvaluator() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Gets the attribute evaluator used
getEvaluator() - Method in class weka.filters.supervised.attribute.AttributeSelection
Get the name of the attribute/subset evaluator
getEvaluatorSpec() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
Gets the evaluator specification string, which contains the class name of the evaluator and any options to the evaluator
getEvaluatorSpec() - Method in class weka.attributeSelection.FilteredAttributeEval
Get the evaluator + options as a string
getEvaluatorSpec() - Method in class weka.attributeSelection.FilteredSubsetEval
Get the evaluator + options as a string
getEvaluatorSpec() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Gets the evaluator specification string, which contains the class name of the attribute evaluator and any options to it
getEvalUsingTrainingData() - Method in class weka.attributeSelection.OneRAttributeEval
Returns true if the training data is to be used for evaluation
getEventName() - Method in class weka.gui.beans.BeanConnection
Returns the name of the event for this conncetion
getEvents() - Method in class weka.associations.gsp.Element
Returns the events Array of an Element.
getEventSetDescriptors() - Method in class weka.gui.beans.AbstractDataSinkBeanInfo
Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.AbstractDataSourceBeanInfo
Get the event set descriptors pertinent to data sources
getEventSetDescriptors() - Method in class weka.gui.beans.AbstractTestSetProducerBeanInfo
 
getEventSetDescriptors() - Method in class weka.gui.beans.AbstractTrainAndTestSetProducerBeanInfo
 
getEventSetDescriptors() - Method in class weka.gui.beans.AbstractTrainingSetProducerBeanInfo
Returns event set descriptors for this type of bean
getEventSetDescriptors() - Method in class weka.gui.beans.AssociatorBeanInfo
 
getEventSetDescriptors() - Method in class weka.gui.beans.AttributeSummarizerBeanInfo
Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.ClassAssignerBeanInfo
Returns the event set descriptors
getEventSetDescriptors() - Method in class weka.gui.beans.ClassifierBeanInfo
 
getEventSetDescriptors() - Method in class weka.gui.beans.ClassifierPerformanceEvaluatorBeanInfo
 
getEventSetDescriptors() - Method in class weka.gui.beans.ClassValuePickerBeanInfo
Returns the event set descriptors
getEventSetDescriptors() - Method in class weka.gui.beans.ClustererBeanInfo
 
getEventSetDescriptors() - Method in class weka.gui.beans.ClustererPerformanceEvaluatorBeanInfo
 
getEventSetDescriptors() - Method in class weka.gui.beans.CostBenefitAnalysisBeanInfo
Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.DataVisualizerBeanInfo
Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.FilterBeanInfo
Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.GraphViewerBeanInfo
Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.IncrementalClassifierEvaluatorBeanInfo
Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.InstanceStreamToBatchMakerBeanInfo
Returns the event set descriptors
getEventSetDescriptors() - Method in class weka.gui.beans.ModelPerformanceChartBeanInfo
Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.PredictionAppenderBeanInfo
Get the event set descriptors pertinent to data sources
getEventSetDescriptors() - Method in class weka.gui.beans.ScatterPlotMatrixBeanInfo
Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.SerializedModelSaverBeanInfo
Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.StripChartBeanInfo
Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.TextViewerBeanInfo
Get the event set descriptors for this bean
getEvidence(int) - Method in class weka.classifiers.bayes.net.EditableBayesNet
get evidence state of a node.
getException() - Method in class weka.gui.sql.event.ConnectionEvent
returns the stored exception, if any (can be NULL)
getException() - Method in class weka.gui.sql.event.QueryExecuteEvent
returns the exception, if one happened, otherwise NULL
getExclusive() - Method in class weka.classifiers.rules.ConjunctiveRule
Returns whether exclusive expressions for nominal attributes splits are considered
getExecutionSlots() - Method in class weka.gui.beans.Classifier
Get the number of execution slots (threads) used to train models.
getExecutionStatus() - Method in class weka.experiment.TaskStatusInfo
Get the execution status of this Task.
getExitIfNoWindowsOpen() - Static method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
Gets whether System.exit gets called after the last window gets closed
getExitOnClose() - Method in class weka.gui.arffviewer.ArffViewer
returns TRUE if a System.exit(0) is done on a close
getExitOnClose() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
returns TRUE if a System.exit(0) is done on a close
getExpectedFrequency() - Method in class weka.associations.tertius.Rule
Get the expected frequency of counter-instances of this rule.
getExpectedNumber() - Method in class weka.associations.tertius.Rule
 
getExpectedResultsPerAverage() - Method in class weka.experiment.AveragingResultProducer
Get the value of ExpectedResultsPerAverage.
getExperiment() - Method in class weka.experiment.RemoteExperimentSubTask
Get the experiment for this sub task
getExperiment() - Method in class weka.gui.experiment.SetupModePanel
Gets the currently configured experiment.
getExperiment() - Method in class weka.gui.experiment.SetupPanel
Gets the currently configured experiment.
getExperiment() - Method in class weka.gui.experiment.SimpleSetupPanel
Gets the currently configured experiment.
getExperimentType() - Static method in class weka.gui.experiment.ExperimenterDefaults
returns the default experiment type
getExplicitPropsFile() - Method in class weka.gui.GenericPropertiesCreator
returns TRUE, if a file is loaded and not the Utils class used for locating the props file.
getExplorer() - Method in class weka.gui.explorer.AssociationsPanel
returns the parent Explorer frame
getExplorer() - Method in class weka.gui.explorer.AttributeSelectionPanel
returns the parent Explorer frame
getExplorer() - Method in class weka.gui.explorer.ClassifierPanel
returns the parent Explorer frame
getExplorer() - Method in class weka.gui.explorer.ClustererPanel
returns the parent Explorer frame
getExplorer() - Method in interface weka.gui.explorer.Explorer.ExplorerPanel
returns the parent Explorer frame
getExplorer() - Method in class weka.gui.explorer.PreprocessPanel
returns the parent Explorer frame
getExplorer() - Method in class weka.gui.explorer.VisualizePanel
returns the parent Explorer frame
getExponent() - Method in class weka.classifiers.functions.supportVector.PolyKernel
Gets the exponent value.
getExponent() - Method in class weka.classifiers.functions.VotedPerceptron
Get the value of exponent.
getExpression(Node, FieldMetaInfo.Optype, ArrayList<Attribute>, TransformationDictionary) - Static method in class weka.core.pmml.Expression
Static factory method that returns a subclass of Expression that encapsulates the type of expression contained in the Element supplied.
getExpression(String, Node, FieldMetaInfo.Optype, ArrayList<Attribute>, TransformationDictionary) - Static method in class weka.core.pmml.Expression
Static factory method that returns a subclass of Expression that encapsulates the type of expression supplied as an argument.
getExpression() - Method in class weka.datagenerators.classifiers.regression.Expression
Gets the mathematical expression for generating y out of x
getExpression() - Method in class weka.filters.unsupervised.attribute.AddExpression
Get the expression
getExpression() - Method in class weka.filters.unsupervised.attribute.MathExpression
Get the expression
getExpression() - Method in class weka.filters.unsupervised.instance.SubsetByExpression
Returns the expression used for filtering.
getExtension() - Static method in class weka.gui.experiment.ExperimenterDefaults
returns the default experiment extension
getExtension() - Method in class weka.gui.visualize.BMPWriter
returns the extension (incl.
getExtension() - Method in class weka.gui.visualize.JComponentWriter
returns the extension (incl.
getExtension() - Method in class weka.gui.visualize.JPEGWriter
returns the extension (incl.
getExtension() - Method in class weka.gui.visualize.PNGWriter
returns the extension (incl.
getExtension() - Method in class weka.gui.visualize.PostscriptWriter
returns the extension (incl.
getExtensions() - Method in class weka.gui.ExtensionFileFilter
Returns a copy of the acceptable extensions.
getExtremeValuesAsOutliers() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Get whether extreme values are also tagged as outliers.
getExtremeValuesFactor() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Gets the factor for determining the thresholds for extreme values.
getFactory() - Method in class weka.core.xml.XMLDocument
returns the DocumentBuilderFactory.
getFailReason() - Method in class weka.core.Capabilities
returns the reason why the tests failed, is null if tests succeeded
getFallout() - Method in class weka.classifiers.evaluation.TwoClassStats
Calculate the fallout.
getFalseNegative() - Method in class weka.classifiers.evaluation.TwoClassStats
Gets the number of positive instances predicted as negative
getFalsePositive() - Method in class weka.classifiers.evaluation.TwoClassStats
Gets the number of negative instances predicted as positive
getFalsePositiveRate() - Method in class weka.classifiers.evaluation.TwoClassStats
Calculate the false positive rate.
getFastRegression() - Method in class weka.classifiers.trees.LMT
Get the value of fastRegression.
getField(Object, String) - Method in class weka.classifiers.functions.LibLINEAR
returns the current value of the specified field
getField(Object, String) - Method in class weka.classifiers.functions.LibSVM
returns the current value of the specified field
getFieldAsAttribute() - Method in class weka.core.pmml.DefineFunction.ParameterField
 
getFieldAsAttribute() - Method in class weka.core.pmml.DerivedFieldMetaInfo
Get this derived field as an Attribute.
getFieldAsAttribute() - Method in class weka.core.pmml.FieldMetaInfo
Return this field as an Attribute.
getFieldAsAttribute() - Method in class weka.core.pmml.MiningFieldMetaInfo
Return this mining field as an Attribute.
getFieldAsAttribute() - Method in class weka.core.pmml.TargetMetaInfo
Return this field as an Attribute.
getFieldDef(String) - Method in class weka.core.pmml.Expression
Return the named attribute from the list of reference fields.
getFieldDefIndex(String) - Method in class weka.core.pmml.Expression
 
getFieldName() - Method in class weka.core.pmml.FieldMetaInfo
Get the name of this field.
getFieldsAsInstances() - Method in class weka.core.pmml.MiningSchema
Get the all the fields (both mining schema and derived) as Instances.
getFieldsMappingString() - Method in class weka.classifiers.pmml.consumer.PMMLClassifier
Get a textual description of the mapping between mining schema fields and incoming data fields.
getFieldsMappingString() - Method in class weka.core.pmml.MappingInfo
Get a textual description of them mapping between mining schema fields and incoming data fields.
getFile() - Method in class weka.gui.visualize.JComponentWriter
returns the file being used for storing the output
getFileConverters(String, String[]) - Static method in class weka.core.converters.ConverterUtils
returns a hashtable with the association "file extension <-> converter classname" for the comma-separated list of converter classnames.
getFileConverters(Vector, String[]) - Static method in class weka.core.converters.ConverterUtils
returns a hashtable with the association "file extension <-> converter classname" for the list of converter classnames.
getFileDescription() - Method in class weka.core.converters.AbstractFileSaver
to be pverridden
getFileDescription() - Method in class weka.core.converters.ArffLoader
Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.ArffSaver
Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.C45Loader
Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.C45Saver
Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.CSVLoader
Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.CSVSaver
Returns a description of the file type.
getFileDescription() - Method in interface weka.core.converters.FileSourcedConverter
Get a one line description of the type of file
getFileDescription() - Method in class weka.core.converters.LibSVMLoader
Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.LibSVMSaver
Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.SerializedInstancesLoader
Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.SerializedInstancesSaver
Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.SVMLightLoader
Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.SVMLightSaver
Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.TextDirectoryLoader
Returns a description of the file type, actually it's directories.
getFileDescription() - Method in class weka.core.converters.XRFFLoader
Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.XRFFSaver
Returns a description of the file type.
getFileExtension() - Method in class weka.core.converters.AbstractFileSaver
Gets ihe file extension.
getFileExtension() - Method in class weka.core.converters.AbstractSaver
Default implementation throws an IOException.
getFileExtension() - Method in class weka.core.converters.ArffLoader
Get the file extension used for arff files
getFileExtension() - Method in class weka.core.converters.C45Loader
Get the file extension used for arff files
getFileExtension() - Method in class weka.core.converters.CSVLoader
Get the file extension used for arff files.
getFileExtension() - Method in interface weka.core.converters.FileSourcedConverter
Get the file extension used for this type of file
getFileExtension() - Method in class weka.core.converters.LibSVMLoader
Get the file extension used for libsvm files.
getFileExtension() - Method in interface weka.core.converters.Saver
Gets the file extension
getFileExtension() - Method in class weka.core.converters.SerializedInstancesLoader
Get the file extension used for arff files
getFileExtension() - Method in class weka.core.converters.SVMLightLoader
Get the file extension used for svm light files.
getFileExtension() - Method in class weka.core.converters.XRFFLoader
Get the file extension used for libsvm files
getFileExtensions() - Method in class weka.core.converters.AbstractFileSaver
Gets all the file extensions used for this type of file
getFileExtensions() - Method in class weka.core.converters.ArffLoader
Gets all the file extensions used for this type of file
getFileExtensions() - Method in class weka.core.converters.ArffSaver
Gets all the file extensions used for this type of file
getFileExtensions() - Method in class weka.core.converters.C45Loader
Gets all the file extensions used for this type of file
getFileExtensions() - Method in class weka.core.converters.CSVLoader
Gets all the file extensions used for this type of file.
getFileExtensions() - Method in interface weka.core.converters.FileSourcedConverter
Gets all the file extensions used for this type of file
getFileExtensions() - Method in class weka.core.converters.LibSVMLoader
Gets all the file extensions used for this type of file.
getFileExtensions() - Method in class weka.core.converters.SerializedInstancesLoader
Gets all the file extensions used for this type of file
getFileExtensions() - Method in class weka.core.converters.SVMLightLoader
Gets all the file extensions used for this type of file.
getFileExtensions() - Method in class weka.core.converters.XRFFLoader
Gets all the file extensions used for this type of file
getFileExtensions() - Method in class weka.core.converters.XRFFSaver
Gets all the file extensions used for this type of file
getFileFormat() - Method in class weka.gui.beans.SerializedModelSaver
Get the file format to use for saving.
getFileLoaders() - Static method in class weka.core.converters.ConverterUtils
returns a vector with the classnames of all the file loaders.
getFileMatches(String) - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion
returns all the file/dir matches with the partial search string.
getFileMustExist() - Method in class weka.gui.ConverterFileChooser
Returns whether the selected file must exist (only open dialog).
getFileName() - Method in class weka.classifiers.bayes.net.BIFReader
returns the current filename
getFilename() - Method in class weka.core.Debug.Log
returns the filename of the log, can be null
getFilename() - Method in class weka.core.Debug.SimpleLog
returns the filename of the log, can be null
getFilename() - Method in class weka.core.FindWithCapabilities
returns the current filename for the dataset to base the capabilities on.
getFilename() - Method in class weka.gui.arffviewer.ArffPanel
returns the filename
getFilename(int) - Method in class weka.gui.arffviewer.ArffViewerMainPanel
returns the filename of the specified panel
getFileSavers() - Static method in class weka.core.converters.ConverterUtils
returns a vector with the classnames of all the file savers.
getFilesRecursively(File, Vector) - Method in class weka.gui.experiment.DatasetListPanel
Gets all the files in the given directory that match the currently selected extension.
getFillWithMissing() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Gets whether missing values should be used rather than removing instances where the translated value is not known (due to border effects).
getFilter() - Method in class weka.associations.FilteredAssociator
Gets the filter used.
getFilter() - Method in class weka.attributeSelection.FilteredAttributeEval
Get the filter to use
getFilter() - Method in class weka.attributeSelection.FilteredSubsetEval
Get the filter to use
getFilter() - Method in class weka.classifiers.functions.PLSClassifier
Get the PLS filter.
getFilter() - Method in class weka.classifiers.meta.FilteredClassifier
Gets the filter used.
getFilter() - Method in class weka.classifiers.meta.GridSearch
Get the kernel filter.
getFilter() - Method in class weka.clusterers.FilteredClusterer
Gets the filter used.
getFilter() - Method in class weka.filters.CheckSource
Gets the filter being used for the tests, can be null.
getFilter(int) - Method in class weka.filters.MultiFilter
Gets a single filter from the set of available filters.
getFilter(int) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
Gets a single filter from the set of available filters.
getFilter() - Method in class weka.filters.unsupervised.attribute.Wavelet
Get the preprocessing filter.
getFilter() - Method in class weka.gui.beans.Filter
 
getFilter() - Method in class weka.gui.explorer.Explorer.CapabilitiesFilterChangeEvent
returns the associated Capabilities filter
getFilter() - Static method in class weka.gui.explorer.ExplorerDefaults
returns the default filter (fully configured) for the preprocess panel.
getFilterAfterFirstBatch() - Method in class weka.filters.unsupervised.instance.SubsetByExpression
Get whether to apply the filter to instances that arrive once the first (training) batch has been seen.
getFilterAttributes() - Method in class weka.associations.GeneralizedSequentialPatterns
Returns the String containing the attributes which are used for output filtering.
getFiltered(int) - Method in class weka.classifiers.rules.RuleStats
Get the data after filtering the given rule
getFilters() - Method in class weka.filters.MultiFilter
Gets the list of possible filters to choose from.
getFilters() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
Gets the list of possible filters to choose from.
getFilterSpec() - Method in class weka.associations.FilteredAssociator
Gets the filter specification string, which contains the class name of the filter and any options to the filter
getFilterSpec() - Method in class weka.attributeSelection.FilteredAttributeEval
Get the filter + options as a string
getFilterSpec() - Method in class weka.attributeSelection.FilteredSubsetEval
Get the filter + options as a string
getFilterSpec() - Method in class weka.classifiers.meta.FilteredClassifier
Gets the filter specification string, which contains the class name of the filter and any options to the filter
getFilterSpec() - Method in class weka.clusterers.FilteredClusterer
Gets the filter specification string, which contains the class name of the filter and any options to the filter.
getFilterSpec(Filter) - Method in class weka.filters.MultiFilter
returns the filter classname and the options as one string
getFilterSpec(Filter) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
returns the filter classname and the options as one string.
getFilterType() - Method in class weka.attributeSelection.SVMAttributeEval
Get the filtering mode passed to SMO
getFilterType() - Method in class weka.classifiers.functions.GaussianProcesses
Gets how the training data will be transformed.
getFilterType() - Method in class weka.classifiers.functions.SMO
Gets how the training data will be transformed.
getFilterType() - Method in class weka.classifiers.functions.SMOreg
Gets how the training data will be transformed.
getFilterType() - Method in class weka.classifiers.mi.MDD
Gets how the training data will be transformed.
getFilterType() - Method in class weka.classifiers.mi.MIDD
Gets how the training data will be transformed.
getFilterType() - Method in class weka.classifiers.mi.MIEMDD
Gets how the training data will be transformed.
getFilterType() - Method in class weka.classifiers.mi.MIOptimalBall
Gets how the training data will be transformed.
getFilterType() - Method in class weka.classifiers.mi.MISMO
Gets how the training data will be transformed.
getFilterType() - Method in class weka.classifiers.mi.MISVM
Gets how the training data will be transformed.
getFindAllRulesForSupportLevel() - Method in class weka.associations.FPGrowth
Get whether all rules meeting the lower bound on min support and the minimum metric threshold are to be found.
getFindNumBins() - Method in class weka.filters.unsupervised.attribute.Discretize
Get the value of FindNumBins.
getFindNumBins() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Get the value of FindNumBins.
getFirst() - Method in class weka.associations.tertius.SimpleLinkedList
 
getFirst() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList
Returns the first element in the list.
getFirst() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.NeighborList
returns the first element in the list.
getFirstToken() - Method in class weka.core.converters.ArffLoader.ArffReader
Gets next token, skipping empty lines.
getFirstToken(StreamTokenizer) - Static method in class weka.core.converters.ConverterUtils
Gets token, skipping empty lines.
getFirstValueIndex() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
Get the index of the first value used.
getFirstValueIndex() - Method in class weka.filters.unsupervised.attribute.SwapValues
Get the index of the first value used.
getFitness() - Method in class weka.attributeSelection.GeneticSearch.GABitSet
gets the scaled fitness
getFlag(char, String[]) - Static method in class weka.core.Utils
Checks if the given array contains the flag "-Char".
getFlag(String, String[]) - Static method in class weka.core.Utils
Checks if the given array contains the flag "-String".
getFlow() - Method in class weka.gui.beans.KnowledgeFlowApp
Gets the current flow being edited.
getFlows() - Method in class weka.gui.beans.FlowRunner
Get the vector holding the flow(s)
getFMeasure() - Method in class weka.classifiers.evaluation.TwoClassStats
Calculate the F-Measure.
getFold() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Gets the fold which is selected.
getFold() - Method in class weka.filters.unsupervised.instance.RemoveFolds
Gets the fold which is selected.
getFoldColumn() - Method in class weka.experiment.PairedTTester
Get the value of FoldColumn.
getFoldColumn() - Method in interface weka.experiment.Tester
Get the value of FoldColumn.
getFolds() - Method in class weka.attributeSelection.OneRAttributeEval
Get the number of folds used for cross validation
getFolds() - Method in class weka.attributeSelection.WrapperSubsetEval
Get the number of folds used for accuracy estimation
getFolds() - Method in class weka.classifiers.rules.ConjunctiveRule
returns the current number of folds
getFolds() - Method in class weka.classifiers.rules.JRip
Gets the number of folds
getFolds() - Method in class weka.classifiers.rules.Ridor
 
getFolds() - Method in class weka.gui.beans.CrossValidationFoldMaker
Get the currently set number of folds
getFolds() - Static method in class weka.gui.experiment.ExperimenterDefaults
returns the number of folds used for cross-validation
getFoldsType() - Method in class weka.attributeSelection.RaceSearch
Get the xfold type
getFont() - Method in class weka.gui.visualize.PostscriptGraphics
Get current font.
getFontMetrics(Font) - Method in class weka.gui.visualize.PostscriptGraphics
Get Font metrics
getFontRenderContext() - Method in class weka.gui.visualize.PostscriptGraphics
START overridden Graphics2D methods
getFormat() - Method in class weka.core.Debug.Timestamp
returns the current timestamp format
getForwardSelectionMethod() - Method in class weka.attributeSelection.LinearForwardSelection
Get the search direction
getFPRate() - Method in class weka.associations.tertius.Rule
Get the rate of False Positive instances of this rule.
getFrameLocation() - Method in class weka.gui.MemoryUsagePanel
Returns the default position for the dialog.
getFrameTitle() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
returns the title (incl.
getFrequency() - Method in class weka.associations.FPGrowth.BinaryItem
Get the frequency of this item.
getFrequencyLimit() - Method in class weka.classifiers.bayes.AODE
Gets the frequency limit.
getFrequencyLimit() - Method in class weka.classifiers.bayes.AODEsr
Gets the frequency limit.
getFrequencyThreshold() - Method in class weka.associations.Tertius
Get the value of frequencyThreshold.
getFreshCardinalityOfParents(Instances) - Method in class weka.classifiers.bayes.net.ParentSet
returns cardinality of parents after recalculation
getFromYear() - Static method in class weka.core.Copyright
returns the start year of the copyright
getFs(Instance) - Method in class weka.classifiers.trees.ft.FTtree
Computes the F-values of LogitBoost for an instance from the current logistic model at the node Note that this also takes into account the (partial) logistic model fit at higher levels in the tree.
getFs(Instance) - Method in class weka.classifiers.trees.lmt.LMTNode
Computes the F-values of LogitBoost for an instance from the current logistic model at the node Note that this also takes into account the (partial) logistic model fit at higher levels in the tree.
getFs(Instance) - Method in class weka.classifiers.trees.lmt.LogisticBase
Computes the F-values for a single instance.
getFs(Instances) - Method in class weka.classifiers.trees.lmt.LogisticBase
Computes the F-values for a set of instances.
getFunction(String) - Static method in class weka.core.pmml.Function
Get a built-in PMML Function.
getFunction(String, TransformationDictionary) - Static method in class weka.core.pmml.Function
Get either a function.
getFunction() - Method in class weka.datagenerators.classifiers.classification.Agrawal
Gets the function for generating the data.
getFunctionValue(int) - Method in class weka.classifiers.functions.pace.DiscreteFunction
Gets a particular function value
getFunctionValues() - Method in class weka.classifiers.functions.pace.DiscreteFunction
Gets all function values
getFurthestFromMeanAnchor(int, int) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Returns an anchor point which is furthest from the mean point for a given set of points (instances) (The anchor instance is chosen from the given set of points).
getGamma() - Method in class weka.classifiers.functions.LibSVM
Gets gamma
getGamma() - Method in class weka.classifiers.functions.supportVector.RBFKernel
Gets the gamma value.
getGCount(Node, int) - Static method in class weka.gui.treevisualizer.Node
Recursively finds the number of visible groups of siblings there are.
getGenerateRanking() - Method in class weka.attributeSelection.GreedyStepwise
Gets whether ranking has been requested.
getGenerateRanking() - Method in class weka.attributeSelection.RaceSearch
Gets whether ranking has been requested.
getGenerateRanking() - Method in interface weka.attributeSelection.RankedOutputSearch
Gets whether the user has opted to see a ranked list of attributes rather than the normal result of the search
getGenerateRanking() - Method in class weka.attributeSelection.Ranker
This is a dummy method.
getGenerateRules() - Method in class weka.classifiers.trees.m5.M5Base
get whether rules are being generated rather than a tree
getGenerator() - Method in class weka.datagenerators.classifiers.classification.BayesNet
returns the actual datagenerator
getGenerator() - Method in class weka.gui.explorer.DataGeneratorPanel
returns the currently selected DataGenerator
getGeneratorSamplesBase() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Get the base used for computing the number of samples to obtain from each generator
getGlobalBlend() - Method in class weka.classifiers.lazy.KStar
Get the value of the global blend parameter
getGlobalInfo(Classifier) - Static method in class weka.classifiers.Evaluation
Return the global info (if it exists) for the supplied classifier
getGlobalInfo(Clusterer) - Static method in class weka.clusterers.ClusterEvaluation
Return the global info (if it exists) for the supplied clusterer
getGlobalInfo(Object) - Static method in class weka.gui.beans.KnowledgeFlowApp
Utility method for grabbing the global info help (if it exists) from an arbitrary object
getGlobalModel() - Method in class weka.classifiers.trees.j48.NBTreeSplit
Return the global naive bayes model for this node
getGoodOperations(BayesNet, Instances, int) - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
getGoodOperations determines the nrOfGoodOperations best Operations, which are considered for the calculation of an optimal operationsequence
getGraphString() - Method in class weka.gui.beans.GraphEvent
Return the dot string for the graph
getGraphTitle() - Method in class weka.gui.beans.GraphEvent
Return the graph title
getGraphType() - Method in class weka.gui.beans.GraphEvent
Return the graph type
getGrid() - Method in class weka.classifiers.meta.GridSearch.PerformanceTable
returns the corresponding grid
getGridExtensionsPerformed() - Method in class weka.classifiers.meta.GridSearch
returns the number of grid extensions that took place during the search (only applicable if the grid was extendable).
getGridIsExtendable() - Method in class weka.classifiers.meta.GridSearch
Get whether the grid can be extended dynamically.
getGridWidth() - Method in class weka.gui.beans.AttributeSummarizer
Get the width of the grid of plots
getGroupIdentifier() - Method in class weka.gui.beans.BatchClassifierEvent
 
getGUI() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
getGUIType() - Method in class weka.gui.Main
Gets the currently set type of GUI to display.
getH() - Method in class weka.core.matrix.QRDecomposition
Return the Householder vectors
getHandler() - Method in class weka.core.FindWithCapabilities
returns the current set CapabilitiesHandler to generate the dataset for, can be null.
getHandler() - Method in class weka.core.TestInstances
returns the current set CapabilitiesHandler to generate the dataset for, can be null
getHashtable(FastVector, int) - Static method in class weka.associations.ItemSet
Return a hashtable filled with the given item sets.
getHashtable(FastVector, int) - Static method in class weka.associations.LabeledItemSet
Return a hashtable filled with the given item sets.
getHDRank() - Method in class weka.classifiers.mi.CitationKNN
Returns the rank associated to the Hausdorff distance
getHeader(String) - Method in class weka.experiment.ResultMatrix
returns the value associated with the given key, null if if cannot be found
getHeight() - Method in class weka.gui.beans.BeanInstance
Gets the height of this bean
getHeight(Node, int) - Static method in class weka.gui.treevisualizer.Node
Recursively finds the number of visible levels there are.
getHeuristic() - Method in class weka.classifiers.trees.BFTree
Get if use heuristic search for nominal attributes in multi-class problems.
getHeuristic() - Method in class weka.classifiers.trees.SimpleCart
Get if use heuristic search for nominal attributes in multi-class problems.
getHeuristicStop() - Method in class weka.classifiers.functions.SimpleLogistic
Get the value of heuristicStop.
getHiddenLayers() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
getHistory() - Method in class weka.gui.sql.ConnectionPanel
returns the history.
getHistory() - Method in class weka.gui.sql.event.HistoryChangedEvent
returns the history model
getHistory() - Method in class weka.gui.sql.QueryPanel
returns the history.
getHistoryFilename() - Method in class weka.gui.sql.SqlViewer
returns the filename of the history file.
getHistoryName() - Method in class weka.gui.sql.event.HistoryChangedEvent
returns the name of the history
getHoldOutFile() - Method in class weka.attributeSelection.ClassifierSubsetEval
Gets the file that holds hold out/test instances.
getHomeDir() - Static method in class weka.core.Debug
returns the home directory of the user
getHornClauses() - Method in class weka.associations.Tertius
Get the value of hornClauses.
getHyperparameterRange() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Get the range of hyperparameter values to consider during CV-based selection.
getHyperparameterSelection() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Get the method used to select the hyperparameter
getHyperparameterValue() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Get the hyperparameter value.
getIconPath() - Method in class weka.gui.beans.BeanVisual
returns the path for the icon
getId() - Method in class weka.classifiers.functions.neural.NeuralConnection
 
getID(int, GridSearch.PointDouble) - Method in class weka.classifiers.meta.GridSearch.PerformanceCache
returns the ID string for a cache item
getID() - Method in class weka.core.Debug.Random
returns the unique ID of this number generator
getID() - Method in class weka.core.Tag
Gets the numeric ID of the Tag.
getID() - Method in class weka.core.TechnicalInformation
returns the unique ID (either the one used in creating this instance or the automatically generated one)
getID() - Method in class weka.gui.streams.InstanceEvent
Get the event type
getID() - Method in class weka.gui.treevisualizer.TreeDisplayEvent
 
getIDFTransform() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Sets whether if the word frequencies in a document should be transformed into:
fij*log(num of Docs/num of Docs with word i)
where fij is the frequency of word i in document(instance) j.
getIDIndex() - Method in class weka.filters.unsupervised.attribute.AddID
Get the index of the attribute used.
getIDsForBeanInstances(Vector) - Method in class weka.gui.beans.xml.XMLBeans
returns the IDs for the given BeanInstances, i.e., the stored IDs in m_BeanInstancesID, based on m_BeanInstances
getIDStr() - Method in class weka.core.Tag
Gets the string ID of the Tag.
getIgnoreClass() - Method in class weka.filters.unsupervised.attribute.PotentialClassIgnorer
Gets the IgnoreClass value.
getIgnoredAttributeIndices() - Method in class weka.filters.unsupervised.attribute.AddCluster
Gets ranges of attributes to be ignored.
getIgnoredAttributeIndices() - Method in class weka.filters.unsupervised.attribute.ClusterMembership
Gets ranges of attributes to be ignored.
getIgnoredProperties() - Method in class weka.core.CheckGOE
Get the ignored properties used in checkToolTips() as comma-separated list (sorted).
getIgnoreRange() - Method in class weka.filters.unsupervised.attribute.MathExpression
Get the current range selection.
getImage(String, String) - Static method in class weka.gui.ComponentHelper
returns the Image for a given directory and filename, NULL if not successful
getImage(String) - Static method in class weka.gui.ComponentHelper
returns the Image for a given filename, NULL if not successful
getImageIcon(String, String) - Static method in class weka.gui.ComponentHelper
returns the ImageIcon for a given filename and directory, NULL if not successful
getImageIcon(String) - Static method in class weka.gui.ComponentHelper
returns the ImageIcon for a given filename, NULL if not successful
getImagEigenvalues() - Method in class weka.core.matrix.EigenvalueDecomposition
Return the imaginary parts of the eigenvalues
getIncludeClass() - Method in class weka.core.InstanceComparator
returns TRUE if the class is included in the comparison
getIncludeClass() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Gets whether the class is included in the cleaning process or always skipped.
getIndentionLength(String) - Method in class weka.core.Javadoc
determines the number of indention strings that have to be inserted to generated the given indention string.
getIndentionString(String) - Method in class weka.core.Javadoc
determines the base string of the given indention string, whether it's either only spaces (one space will be retured) or mixed mode (tabs and spaces, in that case the same string will be returned)
getIndex() - Method in class weka.associations.tertius.Predicate
 
getIndex() - Method in class weka.core.converters.ArffLoader.ArffReader
Gets index, checking for a premature and of line.
getIndex() - Method in class weka.core.PropertyPath.PathElement
returns the index of the property, -1 if the property is not an index-based one
getIndex() - Method in class weka.core.SingleIndex
Gets the selected index
getIndex() - Method in class weka.gui.SortedTableModel.SortContainer
Returns the original index of the item.
getIndexofBiggest(List<Integer>) - Method in class weka.attributeSelection.ScatterSearchV1
get the index in a List where this have the biggest number
getIndices() - Method in class weka.classifiers.meta.nestedDichotomies.ND.NDTree
Gets the indices in an array of ints.
getInitAsNaiveBayes() - Method in class weka.classifiers.bayes.net.search.global.HillClimber
Gets whether to init as naive bayes
getInitAsNaiveBayes() - Method in class weka.classifiers.bayes.net.search.global.K2
Gets whether to init as naive bayes
getInitAsNaiveBayes() - Method in class weka.classifiers.bayes.net.search.local.HillClimber
Gets whether to init as naive bayes
getInitAsNaiveBayes() - Method in class weka.classifiers.bayes.net.search.local.K2
Gets whether to init as naive bayes
getInitFile() - Method in class weka.filters.unsupervised.attribute.KernelFilter
Gets the file to initialize the filter with, can be null.
getInitFileClassIndex() - Method in class weka.filters.unsupervised.attribute.KernelFilter
Gets the class index of the file to initialize the filter with.
getInitGenericObjectEditorFilter() - Static method in class weka.gui.explorer.ExplorerDefaults
returns if the GOEs in the Explorer will be initialized based on the data that is loaded into the Explorer.
getInitial() - Method in class weka.core.Memory
returns the initial size of the JVM
getInitialDatasetsDirectory() - Static method in class weka.gui.experiment.ExperimenterDefaults
returns the initial directory for the datasets (if empty, it returns the user's home directory)
getInitialDirectory() - Static method in class weka.gui.explorer.ExplorerDefaults
Returns the initial directory for the file chooser used for opening datasets.
getInnerNodes() - Method in class weka.classifiers.trees.SimpleCart
Return a list of all inner nodes in the tree.
getInputCenterFile() - Method in class weka.clusterers.XMeans
Gets the file to read the list of centers from.
getInputFilename() - Method in class weka.gui.GenericPropertiesCreator
returns the name of the input file
getInputFormat() - Method in class weka.filters.Filter
Gets the currently set inputformat instances.
getInputNums() - Method in class weka.classifiers.functions.neural.NeuralConnection
Use this to get easy access to the input numbers.
getInputOrder() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Gets the input order.
getInputProperties() - Method in class weka.gui.GenericPropertiesCreator
returns the input properties object (template containing the packages)
getInputs() - Method in class weka.classifiers.functions.neural.NeuralConnection
Use this to get easy access to the inputs.
getInputs() - Method in class weka.gui.beans.MetaBean
 
getInputStream(String, String) - Static method in class weka.gui.Loader
returns an InputStream for the given dir and filename, can be NULL if it fails
getInputStream(String) - Method in class weka.gui.Loader
returns an InputStream for the given filename, can be NULL if it fails
getInstalledLookAndFeels() - Static method in class weka.gui.LookAndFeel
returns an array with the classnames of all the installed LnFs
getInstance() - Static method in class weka.associations.gsp.Messages
getInstance.
getInstance() - Static method in class weka.associations.Messages
getInstance.
getInstance() - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
Returns the original instance
getInstance() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
Returns the original instance
getInstance() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
Returns the original instance
getInstance(Instances, boolean) - Method in class weka.core.converters.ArffLoader.ArffReader
Reads a single instance using the tokenizer and returns it.
getInstance() - Method in class weka.core.Javadoc
Returns a new instance of the class
getInstance() - Static method in class weka.gui.arffviewer.Messages
getInstance.
getInstance() - Method in class weka.gui.beans.InstanceEvent
Get the instance
getInstance() - Static method in class weka.gui.beans.Messages
getInstance.
getInstance() - Static method in class weka.gui.beans.xml.Messages
getInstance.
getInstance() - Static method in class weka.gui.boundaryvisualizer.Messages
getInstance.
getInstance() - Static method in class weka.gui.experiment.Messages
getInstance.
getInstance() - Static method in class weka.gui.explorer.Messages
getInstance.
getInstance() - Static method in class weka.gui.graphvisualizer.Messages
getInstance.
getInstance() - Static method in class weka.gui.hierarchyvisualizer.Messages
getInstance.
getInstance() - Static method in class weka.gui.Messages
getInstance.
getInstance() - Static method in class weka.gui.sql.event.Messages
getInstance.
getInstance() - Static method in class weka.gui.sql.Messages
getInstance.
getInstance() - Static method in class weka.gui.streams.Messages
getInstance.
getInstance() - Static method in class weka.gui.treevisualizer.Messages
getInstance.
getInstance() - Static method in class weka.gui.visualize.Messages
getInstance.
getInstanceFull(boolean) - Method in class weka.core.converters.ArffLoader.ArffReader
Reads a single instance using the tokenizer and returns it.
getInstanceIndex(int) - Method in class weka.classifiers.lazy.LBR.Indexes
Returns the boolean value at the specified index in the Instance Index array
getInstanceRange() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Gets the number of instances forward to translate values between.
getInstances() - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database
Returns the original instances delivered from WEKA
getInstances() - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase
Returns the original instances delivered from WEKA
getInstances() - Method in class weka.core.converters.AbstractSaver
Gets instances that should be stored.
getInstances() - Method in interface weka.core.DistanceFunction
returns the instances currently set.
getInstances() - Method in class weka.core.neighboursearch.NearestNeighbourSearch
returns the instances currently set.
getInstances() - Method in class weka.core.NormalizableDistance
returns the instances currently set.
getInstances() - Method in class weka.core.xml.XMLInstances
returns the current instances, either the ones that were set or the ones that were generated from the XML structure.
getInstances() - Method in class weka.experiment.PairedTTester
Get the value of Instances.
getInstances() - Method in interface weka.experiment.Tester
Get the value of Instances.
getInstances() - Method in class weka.gui.arffviewer.ArffPanel
returns the instances of the panel, if none then NULL
getInstances() - Method in class weka.gui.arffviewer.ArffSortedTableModel
returns the data
getInstances() - Method in class weka.gui.arffviewer.ArffTableModel
returns the data
getInstances() - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
Get the training instances
getInstances() - Method in class weka.gui.explorer.DataGeneratorPanel
returns the generated instances, null if the process was cancelled.
getInstances() - Method in class weka.gui.explorer.PreprocessPanel
Gets the working set of instances.
getInstances() - Method in class weka.gui.SetInstancesPanel
Gets the set of instances currently held by the panel
getInstances() - Method in class weka.gui.treevisualizer.Node
This will return the Instances object related to this node.
getInstances() - Method in class weka.gui.ViewerDialog
returns the currently displayed instances
getInstances() - Method in class weka.gui.visualize.VisualizePanel
Get the master plot's instances
getInstances1() - Method in class weka.gui.visualize.VisualizePanelEvent
 
getInstances2() - Method in class weka.gui.visualize.VisualizePanelEvent
 
getInstancesAt(int, int) - Method in class weka.gui.arffviewer.ArffTable.RelationalCellEditor
returns the underlying instances at the given position
getInstancesFromClass(Instances, int, int, double, Instances) - Static method in class weka.estimators.EstimatorUtils
Returns a dataset that contains all instances of a certain class value.
getInstancesFromClass(Instances, int, double) - Static method in class weka.estimators.EstimatorUtils
Returns a dataset that contains of all instances of a certain class value.
getInstancesFromValue(Instances, int, double) - Static method in class weka.estimators.EstimatorUtils
Returns a dataset that contains of all instances of a certain value for the given attribute.
getInstancesIndices() - Method in class weka.filters.unsupervised.instance.RemoveRange
Gets ranges of instances selected.
getInstancesNoClass() - Method in class weka.associations.Apriori
Gets the instances without the class atrribute.
getInstancesNoClass() - Method in interface weka.associations.CARuleMiner
Gets the instances without the class attribute
getInstancesNoClass() - Method in class weka.associations.PredictiveApriori
Gets the instances without the class attribute
getInstancesOnlyClass() - Method in class weka.associations.Apriori
Gets only the class attribute of the instances.
getInstancesOnlyClass() - Method in interface weka.associations.CARuleMiner
Gets the class attribute and its values for all instances
getInstancesOnlyClass() - Method in class weka.associations.PredictiveApriori
Gets the class attribute of all instances
getInstanceSparse(boolean) - Method in class weka.core.converters.ArffLoader.ArffReader
Reads a single instance using the tokenizer and returns it.
getInstancesValueAt(int, int) - Method in class weka.gui.arffviewer.ArffSortedTableModel
returns the double value of the underlying Instances object at the given position, -1 if out of bounds
getInstancesValueAt(int, int) - Method in class weka.gui.arffviewer.ArffTableModel
returns the double value of the underlying Instances object at the given position, -1 if out of bounds
getInstanceWeight() - Method in class weka.core.converters.ArffLoader.ArffReader
Gets the value of an instance's weight (if one exists)
getInstNums() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Gets the upper and lower boundary for instances per cluster.
getInstNums() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Get a string with the upper and lower boundary for the number of instances for this cluster.
getIntercept() - Method in class weka.classifiers.functions.SimpleLinearRegression
Returns the intercept of the function.
getInternalCacheSize() - Method in class weka.classifiers.functions.supportVector.StringKernel
Gets the size of the internal cache
getInternals() - Method in class weka.classifiers.bayes.WAODE
Gets whether more internals of the classifier are printed.
getInterpreter() - Method in class weka.core.Jython
returns the currently used Python Interpreter
getInterval() - Method in class weka.gui.MemoryUsagePanel.MemoryMonitor
Returns the refresh interval in msecs.
getInvert() - Method in class weka.core.Range
Gets whether the range sense is inverted, i.e.
getInvert() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Get whether selection is inverted.
getInvertSelection() - Method in interface weka.core.DistanceFunction
Gets whether the matching sense of attribute indices is inverted or not.
getInvertSelection() - Method in class weka.core.NormalizableDistance
Gets whether the matching sense of attribute indices is inverted or not.
getInvertSelection() - Method in class weka.filters.supervised.attribute.Discretize
Gets whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.supervised.instance.Resample
Gets whether selection is inverted (only if instances are drawn WIHTOUT replacement).
getInvertSelection() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Gets if selection is to be inverted.
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Get whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.Copy
Get whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.Discretize
Gets whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.MathExpression
Get whether the supplied columns are to be select or unselect
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.NominalToBinary
Gets whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Gets whether the selection of the columns is inverted
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.NumericToNominal
Gets whether the supplied columns are to be worked on or the others.
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.NumericTransform
Get whether the supplied columns are to be transformed or not
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.RELAGGS
Gets whether the supplied columns are to be processed or skipped
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.Remove
Get whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.RemoveType
Get whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Gets whether the supplied columns are to be processed or skipped.
getInvertSelection() - Method in class weka.filters.unsupervised.instance.RemoveFolds
Gets if selection is to be inverted.
getInvertSelection() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Get whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.instance.RemovePercentage
Gets if selection is to be inverted.
getInvertSelection() - Method in class weka.filters.unsupervised.instance.RemoveRange
Gets if selection is to be inverted.
getInvertSelection() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Get whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.instance.Resample
Gets whether selection is inverted (only if instances are drawn WIHTOUT replacement).
getItem() - Method in class weka.associations.FPGrowth.FPTreeNode
Get the item at this node.
getItem(int) - Method in class weka.associations.FPGrowth.FrequentBinaryItemSet
Get a particular item from this item set.
getItems() - Method in class weka.associations.FPGrowth.FrequentBinaryItemSet
Get the items in this item set.
getItemSet(int) - Method in class weka.associations.FPGrowth.FrequentItemSets
Get an item set.
getJavaInitializationString() - Method in class weka.gui.CostMatrixEditor
Returns the Java code that generates an object the same as the one being edited.
getJavaInitializationString() - Method in class weka.gui.FileEditor
Returns a representation of the current property value as java source.
getJavaInitializationString() - Method in class weka.gui.GenericArrayEditor
Supposedly returns an initialization string to create a classifier identical to the current one, including it's state, but this doesn't appear possible given that the initialization string isn't supposed to contain multiple statements.
getJavaInitializationString() - Method in class weka.gui.GenericObjectEditor
Supposedly returns an initialization string to create a Object identical to the current one, including it's state, but this doesn't appear possible given that the initialization string isn't supposed to contain multiple statements.
getJavaInitializationString() - Method in class weka.gui.SelectedTagEditor
Returns a description of the property value as java source.
getJavaInitializationString() - Method in class weka.gui.SimpleDateFormatEditor
Returns the Java code that generates an object the same as the one being edited.
getJTable() - Method in class weka.gui.JTableHelper
returns the JTable
getKDTree() - Method in class weka.clusterers.XMeans
Gets the KDTree class.
getKDTreeSpec() - Method in class weka.clusterers.XMeans
Gets the KDTree specification string, which contains the class name of the KDTree class and any options to the KDTree.
getKernel() - Method in class weka.classifiers.functions.GaussianProcesses
Gets the kernel to use.
getKernel() - Method in class weka.classifiers.functions.SMO.BinarySMO
Returns the kernel to use
getKernel() - Method in class weka.classifiers.functions.SMO
Returns the kernel to use
getKernel() - Method in class weka.classifiers.functions.SMOreg
Returns the kernel to use
getKernel() - Method in class weka.classifiers.functions.supportVector.CheckKernel
Get the kernel being tested
getKernel() - Method in class weka.classifiers.mi.MISMO.BinaryMISMO
Returns the kernel to use
getKernel() - Method in class weka.classifiers.mi.MISMO
Gets the kernel to use.
getKernel() - Method in class weka.classifiers.mi.MISVM
Gets the kernel to use.
getKernel() - Method in class weka.filters.unsupervised.attribute.KernelFilter
Gets the kernel to use.
getKernelBandwidth() - Method in class weka.gui.boundaryvisualizer.KDDataGenerator
Get the kernel bandwidth
getKernelEvaluations() - Method in class weka.classifiers.functions.supportVector.RegOptimizer
returns the number of kernel evaluations
getKernelFactorExpression() - Method in class weka.filters.unsupervised.attribute.KernelFilter
Gets the expression for the kernel.
getKernelMatrixFile() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
Gets the file containing the kernel matrix.
getKernelType() - Method in class weka.classifiers.functions.LibSVM
Gets type of kernel function
getKey() - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
Returns the key for this DataObject
getKey() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
Returns the key for this DataObject
getKey() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
Returns the key for this DataObject
getKey() - Method in class weka.experiment.ClassifierSplitEvaluator
Gets the key describing the current SplitEvaluator.
getKey() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
Gets the key describing the current SplitEvaluator.
getKey() - Method in class weka.experiment.RegressionSplitEvaluator
Gets the key describing the current SplitEvaluator.
getKey() - Method in interface weka.experiment.SplitEvaluator
Gets the key describing the current SplitEvaluator.
getKeyFieldName() - Method in class weka.experiment.AveragingResultProducer
Get the value of KeyFieldName.
getKeyNames() - Method in class weka.experiment.AveragingResultProducer
Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.ClassifierSplitEvaluator
Gets the names of each of the key columns produced for a single run.
getKeyNames() - Method in class weka.experiment.CrossValidationResultProducer
Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.DatabaseResultProducer
Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
Gets the names of each of the key columns produced for a single run.
getKeyNames() - Method in class weka.experiment.LearningRateResultProducer
Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.RandomSplitResultProducer
Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.RegressionSplitEvaluator
Gets the names of each of the key columns produced for a single run.
getKeyNames() - Method in interface weka.experiment.ResultProducer
Gets the names of each of the key columns produced for a single run.
getKeyNames() - Method in interface weka.experiment.SplitEvaluator
Gets the names of each of the key columns produced for a single run.
getKeys() - Method in class weka.core.converters.DatabaseLoader
Gets the key columns' name
getKeyTypes() - Method in class weka.experiment.AveragingResultProducer
Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.ClassifierSplitEvaluator
Gets the data types of each of the key columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.CrossValidationResultProducer
Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.DatabaseResultProducer
Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
Gets the data types of each of the key columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.LearningRateResultProducer
Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.RandomSplitResultProducer
Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.RegressionSplitEvaluator
Gets the data types of each of the key columns produced for a single run.
getKeyTypes() - Method in interface weka.experiment.ResultProducer
Gets the data types of each of the key columns produced for a single run.
getKeyTypes() - Method in interface weka.experiment.SplitEvaluator
Gets the data types of each of the key columns produced for a single run.
getKeywords() - Method in class weka.experiment.DatabaseUtils
Returns the currently stored keywords (as comma-separated list).
getKeywordsMaskChar() - Method in class weka.experiment.DatabaseUtils
Returns the currently set mask character.
getKNN() - Method in class weka.classifiers.lazy.IBk
Gets the number of neighbours the learner will use.
getKNN() - Method in class weka.classifiers.lazy.LWL
Gets the number of neighbours used for kernel bandwidth setting.
getKthNearest() - Method in class weka.core.neighboursearch.CoverTree.MyHeap
returns the kth nearest element or null if none there.
getKthNearest() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.MyHeap
returns the kth nearest element or null if none there.
getKValue() - Method in class weka.classifiers.trees.RandomTree
Get the value of K.
getKWBias() - Method in class weka.classifiers.BVDecomposeSegCVSub
Get the calculated bias squared according to the Kohavi and Wolpert definition
getKWSigma() - Method in class weka.classifiers.BVDecomposeSegCVSub
Get the calculated sigma according to the Kohavi and Wolpert definition
getKWVariance() - Method in class weka.classifiers.BVDecomposeSegCVSub
Get the calculated variance according to the Kohavi and Wolpert definition
getL() - Method in class weka.core.matrix.CholeskyDecomposition
Return triangular factor.
getL() - Method in class weka.core.Matrix
Deprecated.
Returns the L part of the matrix.
getL() - Method in class weka.core.matrix.LUDecomposition
Return lower triangular factor
getLabel() - Method in class weka.gui.treevisualizer.Edge
Get the value of label.
getLabel() - Method in class weka.gui.treevisualizer.Node
Get the value of label.
getLabels() - Method in class weka.filters.unsupervised.attribute.AddValues
Get the comma-separated list of labels that are added.
getLabelX() - Method in class weka.classifiers.meta.GridSearch.Grid
returns the label for the X axis
getLabelY() - Method in class weka.classifiers.meta.GridSearch.Grid
returns the label for the Y axis
getLambda() - Method in class weka.classifiers.functions.SPegasos
Get the current value of lambda
getLambda() - Method in class weka.classifiers.functions.supportVector.StringKernel
Gets the lambda constant used in the string kernel
getLast() - Method in class weka.associations.tertius.SimpleLinkedList
 
getLast() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.NeighborList
returns the last element in the list.
getLastLiteral() - Method in class weka.associations.tertius.LiteralSet
Give the last literal added to this set.
getLastToken(boolean) - Method in class weka.core.converters.ArffLoader.ArffReader
Gets token and checks if its end of line.
getLeaf() - Method in class weka.classifiers.trees.j48.GraftSplit
 
getLearningRate() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
getLegendText() - Method in class weka.gui.beans.ChartEvent
Get the legend text vector
getLevel() - Method in class weka.gui.HierarchyPropertyParser
Get the level of current node.
getLikelihoodThreshold() - Method in class weka.classifiers.meta.LogitBoost
Get the value of Precision.
getLine(int) - Method in class weka.gui.treevisualizer.Edge
Returns line number n
getLine(int) - Method in class weka.gui.treevisualizer.Node
Returns the text String for the specfied line.
getLineNo() - Method in class weka.core.converters.ArffLoader.ArffReader
returns the current line number
getLink() - Method in class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd
 
getLinkAt(int) - Method in class weka.attributeSelection.BestFirst.LinkedList2
returns the element (Link) at a specific index from the list.
getLinkAt(int) - Method in class weka.attributeSelection.LFSMethods.LinkedList2
returns the element (Link) at a specific index from the list.
getLinkType() - Method in class weka.clusterers.HierarchicalClusterer
 
getList() - Method in class weka.gui.ResultHistoryPanel
Gets the JList used by the results list
getListCellRendererComponent(JList, Object, int, boolean, boolean) - Method in class weka.gui.CheckBoxList.CheckBoxListRenderer
Return a component that has been configured to display the specified value.
getListCellRendererComponent(JList, Object, int, boolean, boolean) - Method in class weka.gui.experiment.AlgorithmListPanel.ObjectCellRenderer
Return a component that has been configured to display the specified value.
getListCellRendererComponent(JList, Object, int, boolean, boolean) - Method in class weka.gui.sql.InfoPanelCellRenderer
Return a component that has been configured to display the specified value.
getLiteral(int) - Method in class weka.associations.tertius.Predicate
 
getLNorm() - Method in class weka.filters.unsupervised.instance.Normalize
Get the L Norm used.
getLoader() - Method in class weka.core.converters.ConverterUtils.DataSource
returns the determined loader, null if the DataSource was initialized with data alone and not a file/URL.
getLoader() - Method in class weka.gui.beans.Loader
Get the loader
getLoader() - Method in class weka.gui.ConverterFileChooser
returns the loader that was chosen by the user, can be null in case the user aborted the dialog or the save dialog was shown
getLoader() - Method in class weka.gui.SetInstancesPanel
Gets the currently used Loader
getLoaderForExtension(String) - Static method in class weka.core.converters.ConverterUtils
tries to determine the loader to use for this kind of extension, returns null if none can be found.
getLoaderForFile(String) - Static method in class weka.core.converters.ConverterUtils
tries to determine the loader to use for this kind of file, returns null if none can be found.
getLoaderForFile(File) - Static method in class weka.core.converters.ConverterUtils
tries to determine the loader to use for this kind of file, returns null if none can be found.
getLocallyPredictive() - Method in class weka.attributeSelection.CfsSubsetEval
Return true if including locally predictive attributes
getLocation(GridSearch.PointDouble) - Method in class weka.classifiers.meta.GridSearch.Grid
returns the closest index pair for the given value pair in the grid.
getLocation() - Static method in class weka.core.logging.Logger
Returns the location the logging happened.
getLocator(int) - Method in class weka.core.AttributeLocator
Returns the AttributeLocator at the given index.
getLocatorIndices() - Method in class weka.core.AttributeLocator
Returns the indices of the AttributeLocator objects.
getLog() - Method in class weka.classifiers.pmml.consumer.PMMLClassifier
Get the logger.
getLog() - Method in class weka.core.Debug.Random
the currently used log, if null then stdout is used for outputting the debugging information
getLog() - Method in interface weka.core.pmml.PMMLModel
Get the logger.
getLogFile() - Method in class weka.classifiers.meta.GridSearch
Gets current log file.
getLogFile() - Method in class weka.core.logging.FileLogger
Returns the log file to use.
getLogger() - Method in class weka.core.Debug.Log
initializes and returns the logger if necessary (e.g., due to serialization).
getLoglikeliHood(double[], Instances) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
 
getLoglikelihood() - Method in class weka.classifiers.bayes.blr.Prior
 
getLogLikelihood() - Method in class weka.clusterers.ClusterEvaluation
Return the log likelihood corresponding to the most recent set of instances clustered.
getLogPosterior() - Method in class weka.classifiers.bayes.blr.Prior
 
getLogProbForTargetClass(Instance) - Method in class weka.classifiers.bayes.DMNBtext.DNBBinary
Calculates the class membership probabilities for the given test instance.
getLookupCacheSize() - Method in class weka.attributeSelection.BestFirst
Return the maximum size of the evaluated subset cache (expressed as a multiplier for the number of attributes in a data set.
getLookupCacheSize() - Method in class weka.attributeSelection.LinearForwardSelection
Return the maximum size of the evaluated subset cache (expressed as a multiplier for the number of attributes in a data set.
getLookupCacheSize() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Return the maximum size of the evaluated subset cache (expressed as a multiplier for the number of attributes in a data set.
getLoss() - Method in class weka.classifiers.functions.LibSVM
Gets the epsilon in loss function of epsilon-SVR
getLossFunction() - Method in class weka.classifiers.functions.SPegasos
Get the current loss function.
getLower() - Method in class weka.gui.experiment.RunNumberPanel
Gets the current lower run number.
getLowerBoundMinSupport() - Method in class weka.associations.Apriori
Get the value of lowerBoundMinSupport.
getLowerBoundMinSupport() - Method in class weka.associations.FPGrowth
Get the value of lowerBoundMinSupport.
getLowerCaseTokens() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Gets whether if the tokens are to be downcased or not.
getLowerNumericBound() - Method in class weka.core.Attribute
Returns the lower bound of a numeric attribute.
getLowerSize() - Method in class weka.experiment.LearningRateResultProducer
Get the value of LowerSize.
getM5RootNode() - Method in class weka.classifiers.trees.m5.M5Base
 
getM5RootNode() - Method in class weka.classifiers.trees.m5.Rule
 
getMainPanel() - Method in class weka.gui.arffviewer.ArffViewer
returns the main panel
getMajorityClass() - Method in class weka.classifiers.rules.Ridor
 
getMakeBinary() - Method in class weka.filters.supervised.attribute.Discretize
Gets whether binary attributes should be made for discretized ones.
getMakeBinary() - Method in class weka.filters.unsupervised.attribute.Discretize
Gets whether binary attributes should be made for discretized ones.
getManualThresholdValue() - Method in class weka.classifiers.meta.ThresholdSelector
Returns the value of the manual threshold.
getMargin(int) - Method in class weka.classifiers.bayes.net.EditableBayesNet
return marginal distibution for a node
getMargin(int) - Method in class weka.classifiers.bayes.net.MarginCalculator
 
getMarkovBlanketClassifier() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
 
getMarkovBlanketClassifier() - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
 
getMarkovBlanketClassifier() - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
 
getMasterPlot() - Method in class weka.gui.visualize.Plot2D
Get the master plot
getMatches() - Method in class weka.core.FindWithCapabilities
returns the matches from the last find call.
getMatches(String) - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion
returns all the matches with the partial search string, files or classes.
getMatchMissingValues() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Gets whether missing values are counted as a match.
getMatrix() - Method in class weka.core.Matrix
Deprecated.
returns the internal matrix
getMatrix(int, int, int, int) - Method in class weka.core.matrix.Matrix
Get a submatrix.
getMatrix(int[], int[]) - Method in class weka.core.matrix.Matrix
Get a submatrix.
getMatrix(int, int, int[]) - Method in class weka.core.matrix.Matrix
Get a submatrix.
getMatrix(int[], int, int) - Method in class weka.core.matrix.Matrix
Get a submatrix.
getMax() - Method in class weka.classifiers.meta.GridSearch.PerformanceTable
the maximum performance
getMax() - Method in class weka.core.Memory
returns the maximum amount of memory that can be assigned
getMax() - Method in class weka.gui.beans.ChartEvent
Get the max y value
getMaxArray() - Method in class weka.filters.unsupervised.attribute.Normalize
Returns the calculated maximum values for the attributes in the data.
getMaxBoostingIterations() - Method in class weka.classifiers.functions.SimpleLogistic
Get the value of maxBoostingIterations.
getMaxC() - Method in class weka.gui.visualize.Plot2D
Return the current max value of the colouring attribute
getMaxCardinality() - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm
returns the max cardinality
getMaxCardinality() - Method in class weka.filters.unsupervised.attribute.RELAGGS
Gets the maximum number of values allowed for nominal attributes, before they're skipped.
getMaxChunkSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Get the maximum chunk size
getMaxCoordsPerPoint() - Method in class weka.core.neighboursearch.PerformanceStats
Returns the maximum of coords per point.
getMaxCost(int) - Method in class weka.classifiers.CostMatrix
Gets the maximum cost for a particular class value.
getMaxCost(int, Instance) - Method in class weka.classifiers.CostMatrix
Gets the maximum cost for a particular class value.
getMaxCount() - Method in class weka.filters.supervised.instance.SpreadSubsample
Gets the value for the max count
getMaxDefault() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Get the maximum default.
getMaxDepth() - Method in class weka.classifiers.trees.RandomForest
Get the maximum depth of trh tree, 0 for unlimited.
getMaxDepth() - Method in class weka.classifiers.trees.RandomTree
Get the maximum depth of trh tree, 0 for unlimited.
getMaxDepth() - Method in class weka.classifiers.trees.REPTree
Get the value of MaxDepth.
getMaxDepth() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
Returns the depth of the built tree.
getMaxGenerations() - Method in class weka.attributeSelection.GeneticSearch
get the number of generations
getMaxGridExtensions() - Method in class weka.classifiers.meta.GridSearch
Gets the maximum number of grid extensions, -1 for unlimited.
getMaxGroup() - Method in class weka.classifiers.meta.RotationForest
Gets the maximum size of a group.
getMaximumAttributeNames() - Method in class weka.attributeSelection.LatentSemanticAnalysis
Gets maximum number of attributes to include in transformed attribute names.
getMaximumAttributeNames() - Method in class weka.attributeSelection.PrincipalComponents
Gets maximum number of attributes to include in transformed attribute names.
getMaximumAttributeNames() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
Gets maximum number of attributes to include in transformed attribute names.
getMaximumAttributes() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
Gets maximum number of PC attributes to retain.
getMaximumVariancePercentageAllowed() - Method in class weka.filters.unsupervised.attribute.RemoveUseless
Gets the maximum variance attributes are allowed to have before they are deleted by the filter.
getMaxInfoGain() - Method in class weka.classifiers.rules.JRip.Antd
 
getMaxInstancesInLeaf() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
Returns the maximum number of instances allowed in a leaf.
getMaxInstInLeaf() - Method in class weka.core.neighboursearch.KDTree
Get the maximum number of instances in a leaf.
getMaxInstNum() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Gets the upper boundary for instances per cluster.
getMaxInstNum() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Gets the upper boundary for instances per cluster.
getMaxIntNodesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats
returns the maximum of internal nodes visited.
getMaxIterations() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Get the maximum number of iterations to perform
getMaxIterations() - Method in class weka.classifiers.mi.MIBoost
Get the maximum number of boost iterations
getMaxIterations() - Method in class weka.classifiers.mi.MISVM
Gets the maximum number of iterations.
getMaxIterations() - Method in class weka.classifiers.trees.lmt.LogisticBase
Returns the maxIterations parameter.
getMaxIterations() - Method in class weka.clusterers.EM
Get the maximum number of iterations
getMaxIterations() - Method in class weka.clusterers.sIB
Get the max number of iterations
getMaxIterations() - Method in class weka.clusterers.SimpleKMeans
gets the number of maximum iterations to be executed
getMaxIterations() - Method in class weka.clusterers.XMeans
Gets the maximum number of iterations.
getMaxIterations() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Gets the maximum number of cleansing iterations performed
getMaxIts() - Method in class weka.classifiers.functions.Logistic
Get the value of MaxIts.
getMaxIts() - Method in class weka.classifiers.functions.RBFNetwork
Get the value of MaxIts.
getMaxK() - Method in class weka.classifiers.functions.VotedPerceptron
Get the value of maxK.
getMaxKMeans() - Method in class weka.clusterers.XMeans
Gets the maximum number of iterations in KMeans.
getMaxKMeansForChildren() - Method in class weka.clusterers.XMeans
Gets the maximum number of iterations in KMeans.
getMaxLeavesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats
Returns the maximum number of leaves visited.
getMaxNrOfParents() - Method in class weka.classifiers.bayes.net.search.global.HillClimber
Gets the max number of parents.
getMaxNrOfParents() - Method in class weka.classifiers.bayes.net.search.global.K2
Gets the max number of parents.
getMaxNrOfParents() - Method in class weka.classifiers.bayes.net.search.local.HillClimber
Gets the max number of parents.
getMaxNrOfParents() - Method in class weka.classifiers.bayes.net.search.local.K2
Gets the max number of parents.
getMaxNrOfParents() - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
Gets the max number of parents.
getMaxNumberOfItems() - Method in class weka.associations.FPGrowth
Gets the maximum number of items to be included in large item sets.
getMaxNumClusters() - Method in class weka.clusterers.XMeans
Gets the maximum number of clusters to generate.
getMaxPlots() - Method in class weka.gui.beans.AttributeSummarizer
Get the number of plots to display
getMaxPointsVisited() - Method in class weka.core.neighboursearch.PerformanceStats
Returns the maximum of points visited.
getMaxRadius() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Gets the upper boundary for the radiuses of the clusters.
getMaxRange() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Gets the upper boundary for the range of x
getMaxRelativeLeafRadius() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
Returns the maximum relative radius of a leaf node.
getMaxRelativeNodeWidth(double[][], double[][]) - Method in class weka.core.neighboursearch.KDTree
Returns the maximum attribute width of instances/points in a KDTreeNode relative to the whole dataset.
getMaxRows() - Method in class weka.gui.sql.event.QueryExecuteEvent
returns the maximum number of rows to retrieve.
getMaxRows() - Method in class weka.gui.sql.QueryPanel
returns the current value for the maximum number of rows.
getMaxRows() - Method in class weka.gui.sql.ResultSetHelper
the maximum number of rows to retrieve, less than 1 means unlimited.
getMaxRuleSize() - Method in class weka.datagenerators.classifiers.classification.RDG1
Gets the maximum number of tests in rules.
getMaxRunNumber() - Method in class weka.gui.beans.BatchClassifierEvent
Get the maximum run number
getMaxRunNumber() - Method in class weka.gui.beans.TestSetEvent
Get the maximum number of runs.
getMaxRunNumber() - Method in class weka.gui.beans.TrainingSetEvent
Get the maximum number of runs.
getMaxSetNumber() - Method in class weka.gui.beans.BatchClassifierEvent
Get the maximum set number (ie the total number of training and testing sets in the series).
getMaxSetNumber() - Method in class weka.gui.beans.BatchClustererEvent
Get the maximum set number (ie the total number of training and testing sets in the series).
getMaxSetNumber() - Method in class weka.gui.beans.TestSetEvent
Get the maximum set number
getMaxSetNumber() - Method in class weka.gui.beans.TrainingSetEvent
Get the maximum set number
getMaxSubsequenceLength() - Method in class weka.classifiers.functions.supportVector.StringKernel
Returns the maximum length of the subsequence
getMaxThreshold() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Get the maximum threshold.
getMaxValue() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
 
getMaxVersion() - Method in interface weka.gui.visualize.plugins.ErrorVisualizePlugin
Get the maximum version of Weka, exclusive, the class is designed to work with.
getMaxVersion() - Method in interface weka.gui.visualize.plugins.GraphVisualizePlugin
Get the maximum version of Weka, exclusive, the class is designed to work with.
getMaxVersion() - Method in interface weka.gui.visualize.plugins.TreeVisualizePlugin
Get the maximum version of Weka, exclusive, the class is designed to work with.
getMaxVersion() - Method in interface weka.gui.visualize.plugins.VisualizePlugin
Get the maximum version of Weka, exclusive, the class is designed to work with.
getMaxX() - Method in class weka.classifiers.meta.GridSearch.Grid
returns the right border
getMaxX() - Method in class weka.gui.visualize.Plot2D
Return the current max value of the attribute plotted on the x axis
getMaxXBound() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Gets the maximum x-coordinate bound, in training-instance units (not mouse coordinates).
getMaxY() - Method in class weka.classifiers.meta.GridSearch.Grid
returns the top border
getMaxY() - Method in class weka.gui.visualize.Plot2D
Return the current max value of the attribute plotted on the y axis
getMaxYBound() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Gets the maximum x-coordinate bound, in training-instance units (not mouse coordinates).
getMean() - Method in class weka.estimators.NormalEstimator
Return the value of the mean of this normal estimator.
getMean(int, int) - Method in class weka.experiment.ResultMatrix
returns the mean at the given position, if the position is valid, otherwise 0
getMeanAbsoluteError(Instances) - Method in class weka.classifiers.trees.lmt.LogisticBase
Returns the error of the probability estimates for the current model on a set of instances.
getMeanCoordsPerPoint() - Method in class weka.core.neighboursearch.PerformanceStats
Returns the mean of coords per point.
getMeanIntNodesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats
Returns the mean of internal nodes visited.
getMeanLeavesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats
Returns the mean of number of leaves visited.
getMeanPointsVisited() - Method in class weka.core.neighboursearch.PerformanceStats
Returns the mean of points visited.
getMeanPrec() - Method in class weka.experiment.ResultMatrix
returns the current precision for the means
getMeanPrec() - Method in class weka.gui.experiment.OutputFormatDialog
Gets the precision used for printing the mean.
getMeanPrecision() - Static method in class weka.gui.experiment.ExperimenterDefaults
returns the default precision for the mean
getMeans() - Method in class weka.estimators.KernelEstimator
Return the means of the kernels.
getMeanSquared() - Method in class weka.classifiers.lazy.IBk
Gets whether the mean squared error is used rather than mean absolute error when doing cross-validation.
getMeanStddev() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
returns the current mean/stddev setup
getMeanValue() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
 
getMeanWidth() - Method in class weka.experiment.ResultMatrix
returns the current width for the mean
getMeasure(String) - Method in class weka.classifiers.bayes.BayesNet
Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.functions.SimpleLogistic
Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.functions.SMOreg
Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.lazy.IBk
Returns the value of the named measure from the neighbour search algorithm, plus the chosen K in case cross-validation is enabled.
getMeasure(String) - Method in class weka.classifiers.lazy.LWL
Returns the value of the named measure from the neighbour search algorithm.
getMeasure(String) - Method in class weka.classifiers.meta.AdditiveRegression
Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.meta.Bagging
Returns the value of the named measure.
getMeasure(String) - Method in class weka.classifiers.meta.GridSearch
Returns the value of the named measure
getMeasure() - Method in class weka.classifiers.meta.ThresholdSelector
get measure used for determining threshold
getMeasure(String) - Method in class weka.classifiers.rules.DecisionTable
Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.rules.DTNB
Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.rules.JRip
Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.rules.PART
Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.rules.Ridor
Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.trees.ADTree
Returns the value of the named measure.
getMeasure(String) - Method in class weka.classifiers.trees.BFTree
Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.trees.FT
Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.trees.J48
Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.trees.J48graft
Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.trees.LADTree
Returns the value of the named measure.
getMeasure(String) - Method in class weka.classifiers.trees.LMT
Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.trees.m5.M5Base
Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.trees.NBTree
Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.trees.RandomForest
Returns the value of the named measure.
getMeasure(String) - Method in class weka.classifiers.trees.REPTree
Returns the value of the named measure.
getMeasure(String) - Method in class weka.classifiers.trees.SimpleCart
Returns the value of the named measure.
getMeasure(String) - Method in interface weka.core.AdditionalMeasureProducer
Returns the value of the named measure
getMeasure(String) - Method in class weka.core.neighboursearch.BallTree
Returns the value of the named measure.
getMeasure(String) - Method in class weka.core.neighboursearch.CoverTree
Returns the value of the named measure.
getMeasure(String) - Method in class weka.core.neighboursearch.KDTree
Returns the value of the named measure.
getMeasure(String) - Method in class weka.core.neighboursearch.NearestNeighbourSearch
Returns the value of the named measure.
getMeasure(String) - Method in class weka.core.neighboursearch.PerformanceStats
Returns the value of the named measure.
getMeasure(String) - Method in class weka.core.neighboursearch.TreePerformanceStats
Returns the value of the named measure.
getMeasure(String) - Method in class weka.experiment.AveragingResultProducer
Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.ClassifierSplitEvaluator
Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.CrossValidationResultProducer
Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.DatabaseResultProducer
Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.LearningRateResultProducer
Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.RandomSplitResultProducer
Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.RegressionSplitEvaluator
Returns the value of the named measure
getMeasurePerformance() - Method in class weka.core.neighboursearch.NearestNeighbourSearch
Gets whether performance statistics are being calculated or not.
getMenu() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
returns the menu bar to be added in a frame
getMenuBar() - Method in class weka.classifiers.bayes.net.GUI
Get the menu bar for this application.
getMenuTitle() - Method in interface weka.gui.MainMenuExtension
Returns the name of the menu item.
getMestWeight() - Method in class weka.classifiers.bayes.AODEsr
Gets the weight used in m-estimate
getMetaClassifier() - Method in class weka.classifiers.meta.Stacking
Gets the meta classifier.
getMetadata() - Method in class weka.core.Attribute
Returns the properties supplied for this attribute.
getMetaData() - Method in class weka.core.converters.DatabaseConnection
Gets meta data for the database connection object.
getMethod() - Method in class weka.classifiers.functions.neural.NeuralNode
 
getMethod() - Method in class weka.classifiers.meta.MultiClassClassifier
Gets the method used.
getMethod() - Method in class weka.classifiers.mi.MIWrapper
Get the method used in testing.
getMethodName() - Method in class weka.filters.unsupervised.attribute.NumericTransform
Get the transformation method.
getMetricType() - Method in class weka.associations.Apriori
Get the metric type
getMetricType() - Method in class weka.associations.FPGrowth.AssociationRule
Get the metric type of this rule (e.g.
getMetricType() - Method in class weka.associations.FPGrowth
Get the metric type to use.
getMetricValue() - Method in class weka.associations.FPGrowth.AssociationRule
Get the value of the metric for this rule.
getMiddle(double[]) - Method in class weka.core.EuclideanDistance
Returns value in the middle of the two parameter values.
getMidPoints() - Method in class weka.associations.PriorEstimation
returns an ordered array of all mid points
getMin() - Method in class weka.classifiers.meta.GridSearch.PerformanceTable
the minimum performance
getMin() - Method in class weka.gui.beans.ChartEvent
Get the min y value
getMinArray() - Method in class weka.filters.unsupervised.attribute.Normalize
Returns the calculated minimum values for the attributes in the data.
getMinBoxRelWidth() - Method in class weka.core.neighboursearch.KDTree
Gets the minimum relative box width.
getMinBucketSize() - Method in class weka.classifiers.rules.OneR
Get the value of minBucketSize.
getMinC() - Method in class weka.gui.visualize.Plot2D
Return the current min value of the colouring attribute
getMinChange() - Method in class weka.clusterers.sIB
get the minimum number of changes
getMinChunkSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Get the minimum chunk size
getMinCoordsPerPoint() - Method in class weka.core.neighboursearch.PerformanceStats
Returns the minimum of coords per point.
getMinDefault() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Get the minimum default.
getMinFunction() - Method in class weka.core.Optimization
Get the minimal function value
getMinGroup() - Method in class weka.classifiers.meta.RotationForest
Gets the minimum size of a group.
getMinimax() - Method in class weka.classifiers.mi.MISMO
Check if the MIMinimax feature space is to be used.
getMinimizeExpectedCost() - Method in class weka.classifiers.meta.CostSensitiveClassifier
Gets the value of MinimizeExpectedCost.
getMinimumBucketSize() - Method in class weka.attributeSelection.OneRAttributeEval
Get the minimum bucket size used by oneR
getMinimumMaximum(Instances, int) - Method in class weka.estimators.CheckEstimator
Gets the minimum and maximum of the values a the first attribute of the given data set
getMinimumNumberInstances() - Method in class weka.core.Capabilities
returns the minimum number of instances that have to be in the dataset
getMiningFields() - Method in class weka.core.pmml.MiningSchema
 
getMiningSchema() - Method in class weka.classifiers.pmml.consumer.PMMLClassifier
Get the mining schema for this model.
getMiningSchema() - Method in interface weka.core.pmml.PMMLModel
Get the mining schema.
getMiningSchemaAsInstances() - Method in class weka.core.pmml.MiningSchema
Get the mining schema fields as an Instances object.
getMiningSchemaAsInstances(Element, Instances) - Static method in class weka.core.pmml.PMMLFactory
Deprecated.
Use the MiningSchema class instead
getMinInstNum() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Gets the lower boundary for instances per cluster.
getMinInstNum() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Gets the lower boundary for instances per cluster.
getMinIntNodesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats
Returns the minimum of internal nodes visited.
getMinLeavesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats
Returns the minimum number of leaves visited.
getMinLevel() - Method in class weka.core.logging.Logger
Returns the minimum level log messages must have in order to appear in the log.
getMinMax(Instances, int, double[]) - Static method in class weka.estimators.CheckEstimator
Find the minimum and the maximum of the attribute and return it in the last parameter..
getMinMax(Instances, int, double[]) - Static method in class weka.estimators.EstimatorUtils
Find the minimum and the maximum of the attribute and return it in the last parameter..
getMinMetric() - Method in class weka.associations.Apriori
Get the value of minConfidence.
getMinMetric() - Method in class weka.associations.FPGrowth
Get the value of minConfidence.
getMinNo() - Method in class weka.classifiers.rules.ConjunctiveRule
Gets the minimum total weight of the instances in a rule
getMinNo() - Method in class weka.classifiers.rules.JRip
Gets the minimum total weight of the instances in a rule
getMinNo() - Method in class weka.classifiers.rules.Ridor
 
getMinNum() - Method in class weka.classifiers.trees.RandomTree
Get the value of MinNum.
getMinNum() - Method in class weka.classifiers.trees.REPTree
Get the value of MinNum.
getMinNumClusters() - Method in class weka.clusterers.XMeans
Gets the minimum number of clusters to generate.
getMinNumInstances() - Method in class weka.classifiers.trees.FT
Get the value of minNumInstances.
getMinNumInstances() - Method in class weka.classifiers.trees.LMT
Get the value of minNumInstances.
getMinNumInstances() - Method in class weka.classifiers.trees.m5.M5Base
Get the minimum number of instances to allow at a leaf node
getMinNumInstances() - Method in class weka.classifiers.trees.m5.Rule
Get the minimum number of instances to allow at a leaf node
getMinNumInstances() - Method in class weka.classifiers.trees.m5.RuleNode
Get the minimum number of instances to allow at a leaf node
getMinNumObj() - Method in class weka.classifiers.rules.PART
Get the value of minNumObj.
getMinNumObj() - Method in class weka.classifiers.trees.BFTree
Get minimal number of instances at the terminal nodes.
getMinNumObj() - Method in class weka.classifiers.trees.J48
Get the value of minNumObj.
getMinNumObj() - Method in class weka.classifiers.trees.J48graft
Get the value of minNumObj.
getMinNumObj() - Method in class weka.classifiers.trees.SimpleCart
Get minimal number of instances at the terminal nodes.
getMinPoints() - Method in class weka.clusterers.DBScan
Returns the value of minPoints
getMinPoints() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject
Returns the number of minPoints
getMinPoints() - Method in class weka.clusterers.OPTICS
Returns the value of minPoints
getMinPointsVisited() - Method in class weka.core.neighboursearch.PerformanceStats
Returns the minimum of points visited.
getMinRadius() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Gets the lower boundary for the radiuses of the clusters.
getMinRange() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Gets the lower boundary for the range of x
getMinRuleSize() - Method in class weka.datagenerators.classifiers.classification.RDG1
Gets the minimum number of tests in rules.
getMinStdDev() - Method in class weka.classifiers.functions.RBFNetwork
Get the MinStdDev value.
getMinStdDev() - Method in class weka.clusterers.EM
Get the minimum allowable standard deviation.
getMinStdDev() - Method in class weka.clusterers.MakeDensityBasedClusterer
Get the minimum allowable standard deviation.
getMinSupport() - Method in class weka.associations.GeneralizedSequentialPatterns
Returns the minimum support threshold.
getMinTermFreq() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Get the MinTermFreq value.
getMinThreshold() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Get the minimum threshold.
getMinValue() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
 
getMinVarianceProp() - Method in class weka.classifiers.trees.REPTree
Get the value of MinVarianceProp.
getMinVersion() - Method in interface weka.gui.visualize.plugins.ErrorVisualizePlugin
Get the minimum version of Weka, inclusive, the class is designed to work with.
getMinVersion() - Method in interface weka.gui.visualize.plugins.GraphVisualizePlugin
Get the minimum version of Weka, inclusive, the class is designed to work with.
getMinVersion() - Method in interface weka.gui.visualize.plugins.TreeVisualizePlugin
Get the minimum version of Weka, inclusive, the class is designed to work with.
getMinVersion() - Method in interface weka.gui.visualize.plugins.VisualizePlugin
Get the minimum version of Weka, inclusive, the class is designed to work with.
getMinX() - Method in class weka.classifiers.meta.GridSearch.Grid
returns the left border
getMinX() - Method in class weka.gui.visualize.Plot2D
Return the current min value of the attribute plotted on the x axis
getMinXBound() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Gets the minimum x-coordinate bound, in training-instance units (not mouse coordinates).
getMinY() - Method in class weka.classifiers.meta.GridSearch.Grid
returns the bottom border
getMinY() - Method in class weka.gui.visualize.Plot2D
Return the current min value of the attribute plotted on the y axis
getMinYBound() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Gets the minimum y-coordinate bound, in training-instance units (not mouse coordinates).
getMisses() - Method in class weka.core.FindWithCapabilities
returns the misses from the last find call.
getMissingMerge() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
get whether missing values are being distributed or not
getMissingMerge() - Method in class weka.attributeSelection.GainRatioAttributeEval
get whether missing values are being distributed or not
getMissingMerge() - Method in class weka.attributeSelection.InfoGainAttributeEval
get whether missing values are being distributed or not
getMissingMerge() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
get whether missing values are being distributed or not
getMissingMode() - Method in class weka.classifiers.lazy.KStar
Gets the method to use for handling missing values.
getMissingSeparate() - Method in class weka.attributeSelection.CfsSubsetEval
Return true is missing is treated as a separate value
getMissingValue() - Method in class weka.core.converters.CSVLoader
Returns the current placeholder for missing values.
getMissingValues() - Method in class weka.associations.Tertius
Get the value of missingValues.
getMissingValueTreatmentMethod() - Method in class weka.core.pmml.MiningFieldMetaInfo
Get the missing value treatment method for this field.
getMixingDistribution() - Method in class weka.classifiers.functions.pace.MixtureDistribution
Gets the mixing distribution
getModel() - Method in class weka.classifiers.functions.LibLINEAR
 
getModel() - Method in class weka.classifiers.trees.m5.RuleNode
Get the linear model at this node
getModel() - Method in class weka.gui.SortedTableModel
returns the current model, can be null
getModelElement(Document, PMMLFactory.ModelType) - Static method in class weka.core.pmml.PMMLFactory
Get the Element that contains the pmml model
getModelFile() - Method in class weka.classifiers.misc.SerializedClassifier
Gets the file containing the serialized model.
getModelInstance(Document, PMMLFactory.ModelType, Element, Instances, MiningSchema) - Static method in class weka.core.pmml.PMMLFactory
Get an instance of a PMMLModel from the supplied Document
getModelParameters() - Method in class weka.classifiers.trees.ft.FTtree
Returns a string describing the number of LogitBoost iterations performed at this node, the total number of LogitBoost iterations performed (including iterations at higher levels in the tree), and the number of training examples at this node.
getModelParameters() - Method in class weka.classifiers.trees.lmt.LMTNode
Returns a string describing the number of LogitBoost iterations performed at this node, the total number of LogitBoost iterations performed (including iterations at higher levels in the tree), and the number of training examples at this node.
getModelType() - Method in class weka.classifiers.trees.FT
Get the type of functional tree model being used.
getModelType(Document) - Static method in class weka.core.pmml.PMMLFactory
Get the type of model
getModelValueAt(int, int) - Method in class weka.gui.arffviewer.ArffSortedTableModel
returns the value at the given position
getModifyHeader() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Gets whether the header will be modified when selecting on nominal attributes.
getModifyHeader() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Gets whether the header will be modified when selecting on nominal attributes.
getMomentum() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
getMultiInstance() - Method in class weka.core.TestInstances
Gets whether multi-instance data (with a fixed structure) is generated
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.CitationKNN
Returns the capabilities of this multi-instance classifier for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MDD
Returns the capabilities of this multi-instance classifier for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MIBoost
Returns the capabilities of this multi-instance classifier for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MIDD
Returns the capabilities of this multi-instance classifier for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MIEMDD
Returns the capabilities of this multi-instance classifier for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MILR
Returns the capabilities of this multi-instance classifier for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MINND
Returns the capabilities of this multi-instance classifier for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MIOptimalBall
Returns the capabilities of this multi-instance classifier for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MISMO
Returns the capabilities of this multi-instance classifier for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MISVM
Returns the capabilities of this multi-instance classifier for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MIWrapper
Returns the capabilities of this multi-instance classifier for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.SimpleMI
Returns the capabilities of this multi-instance classifier for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.supportVector.MIPolyKernel
Returns the capabilities of this multi-instance kernel for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.supportVector.MIRBFKernel
Returns the capabilities of this multi-instance kernel for the relational data.
getMultiInstanceCapabilities() - Method in interface weka.core.MultiInstanceCapabilitiesHandler
Returns the capabilities of this multi-instance classifier for the relational data (i.e., the bags).
getMultiInstanceCapabilities() - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
Returns the capabilities of this multi-instance filter for the relational data (i.e., the bags).
getMultinomialWord() - Method in class weka.classifiers.bayes.DMNBtext
Gets whether use binary text representation
getMutationProb() - Method in class weka.attributeSelection.GeneticSearch
get the probability of mutation
getNaiveBayesModel() - Method in class weka.classifiers.trees.j48.NBTreeNoSplit
Get the naive bayes model at this node
getName() - Method in class weka.classifiers.bayes.BayesNet
get name of the Bayes network
getName() - Method in class weka.core.pmml.Function
 
getName() - Method in class weka.core.pmml.MiningFieldMetaInfo
Get the name of this field.
getName() - Method in class weka.core.PropertyPath.PathElement
returns the name of the property
getName() - Method in class weka.filters.unsupervised.attribute.AddExpression
Returns the name of the new attribute
getName() - Method in class weka.gui.visualize.VisualizePanel
Returns the name associated with this plot.
getNameAtIndex(int) - Method in class weka.gui.ResultHistoryPanel
Gets the name of theitem in the list at the specified index
getNamedBuffer(String) - Method in class weka.gui.ResultHistoryPanel
Gets the named buffer
getNamedObject(String) - Method in class weka.gui.ResultHistoryPanel
Get the named object from the list
getNearestNeighbors() - Method in class weka.filters.supervised.instance.SMOTE
Gets the number of nearest neighbors to use.
getNearestNeighbourSearchAlgorithm() - Method in class weka.classifiers.lazy.IBk
Returns the current nearestNeighbourSearch algorithm in use.
getNearestNeighbourSearchAlgorithm() - Method in class weka.classifiers.lazy.LWL
Returns the current nearestNeighbourSearch algorithm in use.
getNegation() - Method in class weka.associations.Tertius
Get the value of negation.
getNegation() - Method in class weka.associations.tertius.Literal
 
getNewDecList(Instances, boolean) - Method in class weka.classifiers.rules.part.C45PruneableDecList
Returns a newly created tree.
getNewDecList(Instances, boolean) - Method in class weka.classifiers.rules.part.ClassifierDecList
Returns a newly created tree.
getNewDecList(Instances, Instances, boolean) - Method in class weka.classifiers.rules.part.PruneableDecList
Returns a newly created tree.
getNewTree(Instances) - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTree
Returns a newly created tree.
getNewTree(Instances) - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG
Returns a newly created tree.
getNewTree(Instances) - Method in class weka.classifiers.trees.j48.ClassifierTree
Returns a newly created tree.
getNewTree(Instances, Instances) - Method in class weka.classifiers.trees.j48.ClassifierTree
Returns a newly created tree.
getNewTree(Instances) - Method in class weka.classifiers.trees.j48.NBTreeClassifierTree
Returns a newly created tree.
getNewTree(Instances, Instances) - Method in class weka.classifiers.trees.j48.NBTreeClassifierTree
Returns a newly created tree.
getNewTree(Instances, Instances) - Method in class weka.classifiers.trees.j48.PruneableClassifierTree
Returns a newly created tree.
getNext(int) - Method in class weka.classifiers.functions.supportVector.SMOset
Gets the next element in the set.
getNextDebugVectorsInstance(Instances) - Method in class weka.clusterers.XMeans
Read an instance from debug vectors file.
getNextInstance(Instances) - Method in class weka.core.converters.AbstractLoader
 
getNextInstance(Instances) - Method in class weka.core.converters.ArffLoader
Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get.
getNextInstance(Instances) - Method in class weka.core.converters.C45Loader
Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get.
getNextInstance(Instances) - Method in class weka.core.converters.CSVLoader
CSVLoader is unable to process a data set incrementally.
getNextInstance(Instances) - Method in class weka.core.converters.DatabaseLoader
Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get.
getNextInstance(Instances) - Method in class weka.core.converters.LibSVMLoader
LibSVmLoader is unable to process a data set incrementally.
getNextInstance(Instances) - Method in interface weka.core.converters.Loader
Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get.
getNextInstance(Instances) - Method in class weka.core.converters.SerializedInstancesLoader
Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get.
getNextInstance(Instances) - Method in class weka.core.converters.SVMLightLoader
SVMLightLoader is unable to process a data set incrementally.
getNextInstance(Instances) - Method in class weka.core.converters.TextDirectoryLoader
TextDirectoryLoader is unable to process a data set incrementally.
getNextInstance(Instances) - Method in class weka.core.converters.XRFFLoader
XRFFLoader is unable to process a data set incrementally.
getNextTabName() - Method in class weka.gui.sql.ResultPanel
returns the next name for a tab "QueryXYZ'
getNextToken() - Method in class weka.core.converters.ArffLoader.ArffReader
Gets next token, checking for a premature and of line.
getNGramMaxSize() - Method in class weka.core.tokenizers.NGramTokenizer
Gets the max N of the NGram.
getNGramMinSize() - Method in class weka.core.tokenizers.NGramTokenizer
Gets the min N of the NGram.
getNoClass() - Method in class weka.core.TestInstances
whether no class attribute is generated
getNode(String) - Method in class weka.classifiers.bayes.net.BIFReader
getNode finds the index of the node with name sNodeName and throws an exception if no such node can be found.
getNode(String) - Method in class weka.classifiers.bayes.net.EditableBayesNet
returns index of node with given name.
getNode(String) - Method in class weka.classifiers.bayes.net.MarginCalculator
 
getNode(String) - Method in class weka.core.xml.XMLDocument
Returns the node represented by the XPath expression.
getNode2(String) - Method in class weka.classifiers.bayes.net.EditableBayesNet
returns index of node with given name, or -1 if no such node exists
getNodeName(int) - Method in class weka.classifiers.bayes.BayesNet
get name of a node in the Bayes network
getNodes() - Method in class weka.classifiers.trees.ft.FTtree
Return a list of all inner nodes in the tree
getNodes(Vector) - Method in class weka.classifiers.trees.ft.FTtree
Fills a list with all inner nodes in the tree
getNodes() - Method in class weka.classifiers.trees.lmt.LMTNode
Return a list of all inner nodes in the tree
getNodes(Vector) - Method in class weka.classifiers.trees.lmt.LMTNode
Fills a list with all inner nodes in the tree
getNodes() - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
give access to set of graph nodes
getNodes() - Method in interface weka.gui.graphvisualizer.LayoutEngine
give access to set of graph nodes
getNodeSplitter() - Method in class weka.core.neighboursearch.KDTree
Returns the splitting method currently in use to split the nodes of the KDTree.
getNodeValue(int, int) - Method in class weka.classifiers.bayes.BayesNet
get name of a particular value of a node
getNoise() - Method in class weka.classifiers.functions.GaussianProcesses
Get the value of noise.
getNoisePercent() - Method in class weka.datagenerators.classifiers.classification.LED24
Gets the noise percentage.
getNoiseRate() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Gets the gaussian noise rate.
getNoiseRate() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Gets the percentage of noise set.
getNoiseRate() - Method in class weka.datagenerators.clusterers.SubspaceCluster
Gets the percentage of noise set.
getNoiseThreshold() - Method in class weka.associations.Tertius
Get the value of noiseThreshold.
getNoiseVariance() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Gets the noise variance
getNominalAttributes() - Method in class weka.core.converters.CSVLoader
Returns the current attribute range to be forced to type nominal.
getNominalCols() - Method in class weka.datagenerators.ClusterGenerator
returns the range of nominal attributes
getNominalIndices() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Get the set of nominal value indices that will be used for selection
getNominalLabels() - Method in class weka.filters.unsupervised.attribute.Add
Get the list of labels for nominal attribute creation.
getNominalToBinaryFilter() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
getNoPruning() - Method in class weka.classifiers.trees.REPTree
Get the value of NoPruning.
getNoReplacement() - Method in class weka.filters.supervised.instance.Resample
Gets whether instances are drawn with or without replacement.
getNoReplacement() - Method in class weka.filters.unsupervised.instance.Resample
Gets whether instances are drawn with or without replacement.
getNorm() - Method in class weka.filters.unsupervised.instance.Normalize
Get the instance's Norm.
getNormalize() - Method in class weka.attributeSelection.LatentSemanticAnalysis
Gets whether or not input data is to be normalized
getNormalize() - Method in class weka.classifiers.functions.LibLINEAR
whether to normalize input data
getNormalize() - Method in class weka.classifiers.functions.LibSVM
whether to normalize input data
getNormalizeAttributes() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
getNormalizeDimWidths() - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
Whether we are normalizing the widths(ranges) of the dimensions (attributes) or not.
getNormalizeDocLength() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Gets whether if the word frequencies for a document (instance) should be normalized or not.
getNormalizeNodeWidth() - Method in class weka.core.neighboursearch.KDTree
Gets the normalize flag.
getNormalizeNumericClass() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
getNormalizeWordWeights() - Method in class weka.classifiers.bayes.ComplementNaiveBayes
Returns true if the word weights for each class are to be normalized
getNot() - Method in class weka.datagenerators.Test
Negates the test.
getNotCapabilities() - Method in class weka.core.FindWithCapabilities
The "not to have" capabilities to search for.
getNotes() - Method in class weka.experiment.Experiment
Get the user notes.
getNotUnifyNorm() - Method in class weka.clusterers.sIB
Get whether to normalize instances to unify prior probability before building the clusterer
getNPointPrecision(Instances, int) - Static method in class weka.classifiers.evaluation.ThresholdCurve
Calculates the n point precision result, which is the precision averaged over n evenly spaced (w.r.t recall) samples of the curve.
getNrOfGoodOperations() - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
Gets the number of "good operations"
getNrOfLookAheadSteps() - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
Gets the number of look-ahead steps
getNrOfNodes() - Method in class weka.classifiers.bayes.BayesNet
get number of nodes in the Bayes network
getNrOfParents(int) - Method in class weka.classifiers.bayes.BayesNet
get number of parents of a node in the network structure
getNrOfParents() - Method in class weka.classifiers.bayes.net.ParentSet
returns number of parents
getNu() - Method in class weka.classifiers.functions.LibSVM
Gets nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
getNumAntds() - Method in class weka.classifiers.rules.ConjunctiveRule
Gets the number of antecedants
getNumArcs() - Method in class weka.datagenerators.classifiers.classification.BayesNet
Gets the number of arcs for the bayesian net
getNumAttemptsOfGeneOption() - Method in class weka.classifiers.rules.NNge
Gets the number of attempts for generalisation.
getNumAttributes() - Method in class weka.classifiers.lazy.LBR.Indexes
Returns the number of attributes in the dataset
getNumAttributes() - Method in class weka.core.TestInstances
returns the overall number of attributes (incl.
getNumAttributes() - Method in class weka.datagenerators.classifiers.classification.BayesNet
Gets the number of attributes that should be produced.
getNumAttributes() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
Gets the number of attributes that should be produced.
getNumAttributes() - Method in class weka.datagenerators.classifiers.classification.RDG1
Gets the number of attributes that should be produced.
getNumAttributes() - Method in class weka.datagenerators.ClusterGenerator
Gets the number of attributes that should be produced.
getNumAttributes() - Method in class weka.filters.unsupervised.attribute.RandomSubset
Get the number of attributes (< 1 percentage, >= 1 absolute number).
getNumAttributesSet() - Method in class weka.classifiers.lazy.LBR.Indexes
Returns the number of attributes "in use"
getNumberLiterals() - Method in class weka.associations.Tertius
Get the value of numberLiterals.
getNumberOfAttributes() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject
Returns the number of Attributes of the specified database
getNumberOfAttributes() - Method in class weka.filters.unsupervised.attribute.RandomProjection
Gets the current number of attributes (dimensionality) to which the data will be reduced to.
getNumberOfGeneratedClusters() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject
Returns the number of generated clusters
getNumberOfGroups() - Method in class weka.classifiers.meta.RotationForest
Get whether minGroup and maxGroup refer to the number of groups or their size
getNumberOfTransactions() - Method in class weka.associations.FPGrowth.FrequentItemSets
Get the total number of transactions in the data that the item sets were derived from.
getNumBins() - Method in class weka.classifiers.meta.RegressionByDiscretization
Gets the number of bins numeric attributes will be divided into
getNumBoostingIterations() - Method in class weka.classifiers.functions.SimpleLogistic
Get the value of numBoostingIterations.
getNumBoostingIterations() - Method in class weka.classifiers.trees.FT
Get the value of numBoostingIterations.
getNumBoostingIterations() - Method in class weka.classifiers.trees.LMT
Get the value of numBoostingIterations.
getNumCentroids() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
Gets the number of centroids.
getNumCiters() - Method in class weka.classifiers.mi.CitationKNN
Returns the number of citers considered to estimate the class prediction of tests bags
getNumClasses() - Method in class weka.core.TestInstances
returns the current number of classes
getNumClasses() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
Gets the number of classes the dataset should have.
getNumClasses() - Method in class weka.datagenerators.classifiers.classification.RDG1
Gets the number of classes the dataset should have.
getNumClusters() - Method in class weka.attributeSelection.UnsupervisedSubsetEvaluator
Return the number of clusters used by the subset evaluator
getNumClusters() - Method in class weka.classifiers.functions.RBFNetwork
Return the number of clusters to generate.
getNumClusters() - Method in class weka.clusterers.ClusterEvaluation
Return the number of clusters found for the most recent call to evaluateClusterer
getNumClusters() - Method in class weka.clusterers.EM
Get the number of clusters
getNumClusters() - Method in class weka.clusterers.FarthestFirst
gets the number of clusters to generate
getNumClusters() - Method in class weka.clusterers.HierarchicalClusterer
 
getNumClusters() - Method in class weka.clusterers.sIB
Get the number of clusters
getNumClusters() - Method in class weka.clusterers.SimpleKMeans
gets the number of clusters to generate
getNumClusters() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Gets the number of clusters the dataset should have.
getNumComponents() - Method in class weka.filters.supervised.attribute.PLSFilter
returns the maximum number of attributes to use.
getNumCycles() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Gets the number of cycles.
getNumDatasets() - Method in class weka.experiment.PairedTTester
Gets the number of datasets in the resultsets
getNumDatasets() - Method in interface weka.experiment.Tester
Gets the number of datasets in the resultsets
getNumDate() - Method in class weka.core.CheckScheme
returns the current number of date attributes
getNumDate() - Method in class weka.core.TestInstances
returns the current number of date attributes
getNumeric() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
Check if new attribute is to be numeric.
getNumericColumns() - Method in class weka.gui.sql.ResultSetHelper
returns an array that indicates whether a column is numeric or nor.
getNumericData(Instances) - Method in class weka.classifiers.trees.ft.FTtree
Returns a numeric version of a set of instances.
getNumericData(Instances) - Method in class weka.classifiers.trees.lmt.LMTNode
Returns a numeric version of a set of instances.
getNumericData(Instances) - Method in class weka.classifiers.trees.lmt.LogisticBase
Converts training data to numeric version.
getNumEvalsCached() - Method in class weka.attributeSelection.LFSMethods
 
getNumEvalsTotal() - Method in class weka.attributeSelection.LFSMethods
 
getNumExamples() - Method in class weka.datagenerators.ClassificationGenerator
Gets the number of examples, given by option.
getNumExamples() - Method in class weka.datagenerators.classifiers.classification.BayesNet
Gets the number of examples, given by option.
getNumExamples() - Method in class weka.datagenerators.RegressionGenerator
Gets the number of examples, given by option.
getNumExamplesAct() - Method in class weka.datagenerators.DataGenerator
Gets the number of examples the dataset should have.
getNumFeatures() - Method in class weka.classifiers.trees.RandomForest
Get the number of features used in random selection.
getNumFiles() - Method in class weka.core.Debug.Log
returns the number of files being used
getNumFoldersMIOption() - Method in class weka.classifiers.rules.NNge
Gets the number of folder for mutual information.
getNumFolds() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Return the number of folds for CV-based hyperparameter selection
getNumFolds() - Method in class weka.classifiers.functions.SMO
Get the value of numFolds.
getNumFolds() - Method in class weka.classifiers.meta.CVParameterSelection
Gets the number of folds for the cross-validation.
getNumFolds() - Method in class weka.classifiers.meta.Dagging
Gets the number of folds to use for splitting the training set.
getNumFolds() - Method in class weka.classifiers.meta.LogitBoost
Get the value of NumFolds.
getNumFolds() - Method in class weka.classifiers.meta.MultiScheme
Gets the number of folds for cross-validation.
getNumFolds() - Method in class weka.classifiers.meta.Stacking
Gets the number of folds for the cross-validation.
getNumFolds() - Method in class weka.classifiers.mi.MISMO
Get the value of numFolds.
getNumFolds() - Method in class weka.classifiers.rules.PART
Get the value of numFolds.
getNumFolds() - Method in class weka.classifiers.trees.J48
Get the value of numFolds.
getNumFolds() - Method in class weka.classifiers.trees.RandomTree
Get the value of NumFolds.
getNumFolds() - Method in class weka.classifiers.trees.REPTree
Get the value of NumFolds.
getNumFolds() - Method in class weka.experiment.CrossValidationResultProducer
Get the value of NumFolds.
getNumFolds() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Gets the number of folds in which dataset is to be split into.
getNumFolds() - Method in class weka.filters.unsupervised.instance.RemoveFolds
Gets the number of folds in which dataset is to be split into.
getNumFolds() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Gets the number of cross-validation folds used by the filter.
getNumFoldsPruning() - Method in class weka.classifiers.trees.BFTree
Set number of folds in internal cross-validation.
getNumFoldsPruning() - Method in class weka.classifiers.trees.SimpleCart
Set number of folds in internal cross-validation.
getNumGeneratingModels() - Method in interface weka.gui.boundaryvisualizer.DataGenerator
Returns the number of generating models used by this DataGenerator
getNumGeneratingModels() - Method in class weka.gui.boundaryvisualizer.KDDataGenerator
Return the number of kernels (there is one per training instance)
getNumInnerNodes() - Method in class weka.classifiers.trees.ft.FTtree
Method to count the number of inner nodes in the tree
getNumInnerNodes() - Method in class weka.classifiers.trees.lmt.LMTNode
Method to count the number of inner nodes in the tree
getNumInputs() - Method in class weka.classifiers.functions.neural.NeuralConnection
 
getNumInstances() - Method in class weka.classifiers.lazy.LBR.Indexes
Returns the number of instances in the dataset
getNumInstances() - Method in class weka.classifiers.trees.m5.RuleNode
Return the number of instances that reach this node.
getNumInstances() - Method in class weka.core.CheckScheme
Gets the current number of instances to use for the datasets.
getNumInstances() - Method in class weka.core.TestInstances
returns the current number of instances to produce
getNumInstances() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
 
getNumInstances() - Method in class weka.estimators.CheckEstimator
Gets the current number of instances to use for the datasets.
getNumInstancesRelational() - Method in class weka.core.CheckScheme
returns the current number of instances in relational/bag attributes to produce
getNumInstancesRelational() - Method in class weka.core.TestInstances
returns the current number of instances in relational/bag attributes to produce
getNumInstancesSet() - Method in class weka.classifiers.lazy.LBR.Indexes
Returns the number of instances "in use"
getNumIrrelevant() - Method in class weka.datagenerators.classifiers.classification.RDG1
Gets the number of irrelevant attributes.
getNumIterations() - Method in class weka.classifiers.bayes.DMNBtext
Gets the number of iterations to be performed
getNumIterations() - Method in class weka.classifiers.functions.VotedPerceptron
Get the value of NumIterations.
getNumIterations() - Method in class weka.classifiers.functions.Winnow
Get the value of numIterations.
getNumIterations() - Method in class weka.classifiers.IteratedSingleClassifierEnhancer
Gets the number of bagging iterations
getNumIterations() - Method in class weka.classifiers.meta.MetaCost
Gets the number of bagging iterations
getNumKernels() - Method in class weka.estimators.KernelEstimator
Return the number of kernels in this kernel estimator
getNumLeaves() - Method in class weka.classifiers.trees.ft.FTtree
Returns the number of leaves in the tree.
getNumLeaves() - Method in class weka.classifiers.trees.lmt.LMTNode
Returns the number of leaves in the tree.
getNumLeaves() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
Returns the number of leaves in the built tree.
getNumNeighbours() - Method in class weka.attributeSelection.ReliefFAttributeEval
Get the number of nearest neighbours
getNumNeighbours() - Method in class weka.classifiers.mi.MINND
Returns the number of nearest neighbours to estimate the class prediction of tests bags
getNumNodes() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
Returns the number of nodes (internal + leaf) in the built tree.
getNumNominal() - Method in class weka.core.CheckScheme
returns the current number of nominal attributes
getNumNominal() - Method in class weka.core.TestInstances
returns the current number of nominal attributes
getNumNominalValues() - Method in class weka.core.TestInstances
returns the current number of values for nominal attributes
getNumNumeric() - Method in class weka.core.CheckScheme
returns the current number of numeric attributes
getNumNumeric() - Method in class weka.core.TestInstances
returns the current number of numeric attributes
getNumNumeric() - Method in class weka.datagenerators.classifiers.classification.RDG1
Gets the number of numerical attributes.
getNumOfBoostingIterations() - Method in class weka.classifiers.trees.ADTree
Gets the number of boosting iterations.
getNumOfBoostingIterations() - Method in class weka.classifiers.trees.LADTree
Gets the number of boosting iterations.
getNumOfBranches() - Method in class weka.classifiers.trees.adtree.Splitter
Gets the number of branches of the split.
getNumOfBranches() - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit
Gets the number of branches of the split.
getNumOfBranches() - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit
Gets the number of branches of the split.
getNumOfBranches() - Method in class weka.classifiers.trees.LADTree.Splitter
 
getNumOfBranches() - Method in class weka.classifiers.trees.LADTree.TwoWayNominalSplit
 
getNumOfBranches() - Method in class weka.classifiers.trees.LADTree.TwoWayNumericSplit
 
getNumOutputs() - Method in class weka.classifiers.functions.neural.NeuralConnection
 
getNumQueries() - Method in class weka.core.neighboursearch.PerformanceStats
Returns the number of queries.
getNumReferences() - Method in class weka.classifiers.mi.CitationKNN
Returns the number of references considered to estimate the class prediction of tests bags
getNumRegressions() - Method in class weka.classifiers.functions.SimpleLogistic
Get the number of LogitBoost iterations performed (= the number of regression functions fit by LogitBoost).
getNumRegressions() - Method in class weka.classifiers.trees.lmt.LogisticBase
The number of LogitBoost iterations performed (= the number of simple regression functions fit).
getNumRelational() - Method in class weka.core.CheckScheme
returns the current number of relational attributes
getNumRelational() - Method in class weka.core.TestInstances
returns the current number of relational attributes
getNumRelationalDate() - Method in class weka.core.TestInstances
returns the current number of date attributes in a relational attribute
getNumRelationalNominal() - Method in class weka.core.TestInstances
returns the current number of nominal attributes in a relational attribute
getNumRelationalNominalValues() - Method in class weka.core.TestInstances
returns the current number of values for nominal attributes in a relational attribute
getNumRelationalNumeric() - Method in class weka.core.TestInstances
returns the current number of numeric attributes in a relational attribute
getNumRelationalString() - Method in class weka.core.TestInstances
returns the current number of string attributes in a relational attribute
getNumRestarts() - Method in class weka.clusterers.sIB
Get the number of restarts
getNumResultsets() - Method in class weka.experiment.PairedTTester
Gets the number of resultsets in the data.
getNumResultsets() - Method in interface weka.experiment.Tester
Gets the number of resultsets in the data.
getNumRules() - Method in class weka.associations.Apriori
Get the value of numRules.
getNumRules() - Method in class weka.associations.PredictiveApriori
Get the value of the number of required rules.
getNumRulesToFind() - Method in class weka.associations.FPGrowth
Get the number of rules to find.
getNumRuns() - Method in class weka.classifiers.meta.LogitBoost
Get the value of NumRuns.
getNumSamplesPerRegion() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Get the number of points to sample from a region (fixed dimensions).
getNumString() - Method in class weka.core.CheckScheme
returns the current number of string attributes
getNumString() - Method in class weka.core.TestInstances
returns the current number of string attributes
getNumSubCmtys() - Method in class weka.classifiers.meta.MultiBoostAB
Get the number of sub committees to use
getNumSubsetSizeCVFolds() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Get the number of cross validation folds for subset size determination (default = 5).
getNumSymbols() - Method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes
Gets the number of symbols this estimator operates with
getNumSymbols() - Method in class weka.estimators.DiscreteEstimator
Gets the number of symbols this estimator operates with
getNumTestingNoises() - Method in class weka.classifiers.mi.MINND
Returns The number of nearest neighbour instances in the selection of noises in the test data
getNumToSelect() - Method in class weka.attributeSelection.GreedyStepwise
Gets the number of attributes to be retained.
getNumToSelect() - Method in class weka.attributeSelection.RaceSearch
Gets the number of attributes to be retained.
getNumToSelect() - Method in interface weka.attributeSelection.RankedOutputSearch
Gets the user specified number of attributes to be retained.
getNumToSelect() - Method in class weka.attributeSelection.Ranker
Gets the number of attributes to be retained.
getNumTraining() - Method in class weka.classifiers.lazy.IBk
Get the number of training instances the classifier is currently using.
getNumTrainingNoises() - Method in class weka.classifiers.mi.MINND
Returns the number of nearest neighbour instances in the selection of noises in the training data
getNumTrees() - Method in class weka.classifiers.trees.RandomForest
Get the value of numTrees.
getNumUsedAttributes() - Method in class weka.attributeSelection.LinearForwardSelection
Get the number of top-ranked attributes that taken into account by the search process.
getNumUsedAttributes() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Get the number of top-ranked attributes that taken into account by the search process.
getNumValues() - Method in class weka.datagenerators.clusterers.SubspaceCluster
returns array that stores the number of values for a nominal attribute.
getNumValues() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Gets how many values are retained
getNumXValFolds() - Method in class weka.classifiers.meta.ThresholdSelector
Get the number of folds used for cross-validation.
getObject() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueueElement
Returns the object
getObject() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueueElement
Returns the object
getObject() - Method in class weka.core.CheckGOE
Get the object used in the tests.
getObject() - Method in class weka.core.PropertyPath.PropertyContainer
returns the stored object
getObject() - Method in class weka.core.SerializedObject
Returns a serialized object.
getObject(String, String) - Static method in class weka.gui.explorer.ExplorerDefaults
Tries to instantiate the class stored for this property, optional options will be set as well.
getObject(String, String, Class) - Static method in class weka.gui.explorer.ExplorerDefaults
Tries to instantiate the class stored for this property, optional options will be set as well.
getObjective() - Method in class weka.attributeSelection.GeneticSearch.GABitSet
gets the objective merit
getObjectKey() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueueElement
Returns the key
getObservedFrequency() - Method in class weka.associations.tertius.Rule
Get the observed frequency of counter-instances of this rule in the dataset.
getObservedNumber() - Method in class weka.associations.tertius.Rule
Get the observed number of counter-instances of this rule in the dataset.
getOmega() - Method in class weka.classifiers.functions.supportVector.Puk
Gets the omega value.
getOnDemandDirectory() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
Returns the directory that will be searched for cost files when loading on demand.
getOnDemandDirectory() - Method in class weka.classifiers.meta.CostSensitiveClassifier
Returns the directory that will be searched for cost files when loading on demand.
getOnDemandDirectory() - Method in class weka.classifiers.meta.MetaCost
Returns the directory that will be searched for cost files when loading on demand.
getOnDemandDirectory() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
Returns the directory that will be searched for cost files when loading on demand.
getOneElements(Instances) - Static method in class weka.associations.gsp.Element
Returns all events of the given data set as Elements containing a single event.
getOptimalOperations(BayesNet, Instances, int, int) - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
getOptimalOperations determines an optimal operationsequence in respect of the parameters nrOfLookAheadSteps and nrOfGoodOperations
getOptimistic() - Method in class weka.associations.tertius.Rule
Get the optimistic estimate of the confirmation obtained by refining this rule.
getOptimizations() - Method in class weka.classifiers.rules.JRip
Gets the the number of optimization runs
getOption(char, String[]) - Static method in class weka.core.Utils
Gets an option indicated by a flag "-Char" from the given array of strings.
getOption(String, String[]) - Static method in class weka.core.Utils
Gets an option indicated by a flag "-String" from the given array of strings.
getOptionHandler() - Method in class weka.core.CheckOptionHandler
Get the OptionHandler used in the tests.
getOptionPos(char, String[]) - Static method in class weka.core.Utils
Gets the index of an option or flag indicated by a flag "-Char" from the given array of strings.
getOptionPos(String, String[]) - Static method in class weka.core.Utils
Gets the index of an option or flag indicated by a flag "-String" from the given array of strings.
getOptions() - Method in class weka.associations.Apriori
Gets the current settings of the Apriori object.
getOptions() - Method in class weka.associations.CheckAssociator
Gets the current settings of the CheckAssociator.
getOptions() - Method in class weka.associations.FilteredAssociator
Gets the current settings of the Associator.
getOptions() - Method in class weka.associations.FPGrowth
Gets the current settings of the classifier.
getOptions() - Method in class weka.associations.GeneralizedSequentialPatterns
Returns an Array containing the current options settings.
getOptions() - Method in class weka.associations.PredictiveApriori
Gets the current settings of the PredictiveApriori object.
getOptions() - Method in class weka.associations.SingleAssociatorEnhancer
Gets the current settings of the associator.
getOptions() - Method in class weka.associations.Tertius
Gets the current settings of the Tertius object.
getOptions() - Method in class weka.attributeSelection.BestFirst
Gets the current settings of BestFirst.
getOptions() - Method in class weka.attributeSelection.CfsSubsetEval
Gets the current settings of CfsSubsetEval
getOptions() - Method in class weka.attributeSelection.CheckAttributeSelection
Gets the current settings of the CheckAttributeSelection.
getOptions() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
Gets the current settings.
getOptions() - Method in class weka.attributeSelection.ClassifierSubsetEval
Gets the current settings of ClassifierSubsetEval
getOptions() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
Gets the current settings of the subset evaluator.
getOptions() - Method in class weka.attributeSelection.ExhaustiveSearch
Gets the current settings of RandomSearch.
getOptions() - Method in class weka.attributeSelection.FilteredAttributeEval
Gets the current settings of the subset evaluator.
getOptions() - Method in class weka.attributeSelection.FilteredSubsetEval
Gets the current settings of the subset evaluator.
getOptions() - Method in class weka.attributeSelection.GainRatioAttributeEval
Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.GeneticSearch
Gets the current settings of ReliefFAttributeEval.
getOptions() - Method in class weka.attributeSelection.GreedyStepwise
Gets the current settings of ReliefFAttributeEval.
getOptions() - Method in class weka.attributeSelection.InfoGainAttributeEval
Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.LatentSemanticAnalysis
Gets the current settings of LatentSemanticAnalysis
getOptions() - Method in class weka.attributeSelection.LinearForwardSelection
Gets the current settings of LinearForwardSelection.
getOptions() - Method in class weka.attributeSelection.OneRAttributeEval
returns the current setup.
getOptions() - Method in class weka.attributeSelection.PrincipalComponents
Gets the current settings of PrincipalComponents
getOptions() - Method in class weka.attributeSelection.RaceSearch
Gets the current settings of BestFirst.
getOptions() - Method in class weka.attributeSelection.RandomSearch
Gets the current settings of RandomSearch.
getOptions() - Method in class weka.attributeSelection.Ranker
Gets the current settings of ReliefFAttributeEval.
getOptions() - Method in class weka.attributeSelection.RankSearch
Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.ReliefFAttributeEval
Gets the current settings of ReliefFAttributeEval.
getOptions() - Method in class weka.attributeSelection.ScatterSearchV1
Gets the current settings of ScatterSearchV1.
getOptions() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Gets the current settings of LinearForwardSelection.
getOptions() - Method in class weka.attributeSelection.SVMAttributeEval
Gets the current settings of SVMAttributeEval
getOptions() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.WrapperSubsetEval
Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.classifiers.bayes.AODE
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.AODEsr
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
 
getOptions() - Method in class weka.classifiers.bayes.BayesNet
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.ComplementNaiveBayes
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.DMNBtext
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.NaiveBayes
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.net.BayesNetGenerator
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.bayes.net.search.fixed.FromFile
Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.global.HillClimber
Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.global.K2
Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.global.TAN
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.local.HillClimber
Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.local.K2
Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.local.TAN
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.bayes.WAODE
Gets the current settings of the filter.
getOptions() - Method in class weka.classifiers.BVDecompose
Gets the current settings of the CheckClassifier.
getOptions() - Method in class weka.classifiers.BVDecomposeSegCVSub
Gets the current settings of the CheckClassifier.
getOptions() - Method in class weka.classifiers.CheckClassifier
Gets the current settings of the CheckClassifier.
getOptions() - Method in class weka.classifiers.CheckSource
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.Classifier
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.functions.GaussianProcesses
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.LeastMedSq
Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.classifiers.functions.LibLINEAR
Returns the current options
getOptions() - Method in class weka.classifiers.functions.LibSVM
Returns the current options
getOptions() - Method in class weka.classifiers.functions.LinearRegression
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.Logistic
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.MultilayerPerceptron
Gets the current settings of NeuralNet.
getOptions() - Method in class weka.classifiers.functions.PaceRegression
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.PLSClassifier
returns the options of the current setup
getOptions() - Method in class weka.classifiers.functions.RBFNetwork
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.SimpleLogistic
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.functions.SMO
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.SMOreg
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.SPegasos
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.supportVector.CachedKernel
Gets the current settings of the Kernel.
getOptions() - Method in class weka.classifiers.functions.supportVector.CheckKernel
Gets the current settings of the CheckKernel.
getOptions() - Method in class weka.classifiers.functions.supportVector.Kernel
Gets the current settings of the Kernel.
getOptions() - Method in class weka.classifiers.functions.supportVector.PolyKernel
Gets the current settings of the Kernel.
getOptions() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
Gets the current settings of the Kernel.
getOptions() - Method in class weka.classifiers.functions.supportVector.Puk
Gets the current settings of the Kernel.
getOptions() - Method in class weka.classifiers.functions.supportVector.RBFKernel
Gets the current settings of the Kernel.
getOptions() - Method in class weka.classifiers.functions.supportVector.RegOptimizer
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.supportVector.RegSMO
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
Gets the current settings of the object.
getOptions() - Method in class weka.classifiers.functions.supportVector.StringKernel
Gets the current settings of the Kernel.
getOptions() - Method in class weka.classifiers.functions.VotedPerceptron
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.Winnow
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.IteratedSingleClassifierEnhancer
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.lazy.IBk
Gets the current settings of IBk.
getOptions() - Method in class weka.classifiers.lazy.KStar
Gets the current settings of K*.
getOptions() - Method in class weka.classifiers.lazy.LWL
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.meta.AdaBoostM1
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.AdditiveRegression
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.Bagging
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.ClassificationViaClustering
returns the options of the current setup
getOptions() - Method in class weka.classifiers.meta.CostSensitiveClassifier
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.CVParameterSelection
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.Dagging
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.Decorate
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.FilteredClassifier
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.GridSearch
returns the options of the current setup
getOptions() - Method in class weka.classifiers.meta.LogitBoost
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.MetaCost
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.MultiBoostAB
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.MultiClassClassifier
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.MultiScheme
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.OrdinalClassClassifier
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.RandomSubSpace
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.RegressionByDiscretization
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.RotationForest
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.Stacking
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.ThresholdSelector
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.Vote
Gets the current settings of Vote.
getOptions() - Method in class weka.classifiers.mi.CitationKNN
Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.classifiers.mi.MDD
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.mi.MIBoost
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.mi.MIDD
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.mi.MIEMDD
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.mi.MILR
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.mi.MINND
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.mi.MIOptimalBall
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.mi.MISMO
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.mi.MISVM
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.mi.MIWrapper
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.mi.SimpleMI
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.misc.SerializedClassifier
returns the options of the current setup
getOptions() - Method in class weka.classifiers.misc.VFI
Gets the current settings of VFI
getOptions() - Method in class weka.classifiers.MultipleClassifiersCombiner
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.RandomizableClassifier
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.RandomizableMultipleClassifiersCombiner
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.RandomizableSingleClassifierEnhancer
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.rules.ConjunctiveRule
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.rules.DecisionTable
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.rules.DTNB
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.rules.JRip
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.rules.NNge
Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.classifiers.rules.OneR
Gets the current settings of the OneR classifier.
getOptions() - Method in class weka.classifiers.rules.PART
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.rules.Ridor
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.SingleClassifierEnhancer
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.trees.ADTree
Gets the current settings of ADTree.
getOptions() - Method in class weka.classifiers.trees.BFTree
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.trees.FT
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.trees.J48
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.trees.J48graft
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.trees.LADTree
Gets the current settings of ADTree.
getOptions() - Method in class weka.classifiers.trees.LMT
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.trees.m5.M5Base
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.trees.M5P
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.trees.RandomForest
Gets the current settings of the forest.
getOptions() - Method in class weka.classifiers.trees.RandomTree
Gets options from this classifier.
getOptions() - Method in class weka.classifiers.trees.REPTree
Gets options from this classifier.
getOptions() - Method in class weka.classifiers.trees.SimpleCart
Gets the current settings of the classifier.
getOptions() - Method in class weka.clusterers.CheckClusterer
Gets the current settings of the CheckClusterer.
getOptions() - Method in class weka.clusterers.CLOPE
Gets the current settings of CLOPE
getOptions() - Method in class weka.clusterers.Cobweb
Gets the current settings of Cobweb.
getOptions() - Method in class weka.clusterers.DBScan
Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.clusterers.EM
Gets the current settings of EM.
getOptions() - Method in class weka.clusterers.FarthestFirst
Gets the current settings of FarthestFirst
getOptions() - Method in class weka.clusterers.FilteredClusterer
Gets the current settings of the clusterer.
getOptions() - Method in class weka.clusterers.HierarchicalClusterer
Gets the current settings of the clusterer.
getOptions() - Method in class weka.clusterers.MakeDensityBasedClusterer
Gets the current settings of the clusterer.
getOptions() - Method in class weka.clusterers.OPTICS
Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.clusterers.RandomizableClusterer
Gets the current settings of the classifier.
getOptions() - Method in class weka.clusterers.RandomizableDensityBasedClusterer
Gets the current settings of the classifier.
getOptions() - Method in class weka.clusterers.RandomizableSingleClustererEnhancer
Gets the current settings of the classifier.
getOptions() - Method in class weka.clusterers.sIB
Gets the current settings.
getOptions() - Method in class weka.clusterers.SimpleKMeans
Gets the current settings of SimpleKMeans
getOptions() - Method in class weka.clusterers.SingleClustererEnhancer
Gets the current settings of the clusterer.
getOptions() - Method in class weka.clusterers.XMeans
Gets the current settings of SimpleKMeans.
getOptions() - Method in class weka.core.Check
Gets the current settings of the CheckClassifier.
getOptions() - Method in class weka.core.CheckGOE
Gets the current settings of the object.
getOptions() - Method in class weka.core.CheckOptionHandler
Gets the current settings of the CheckClassifier.
getOptions() - Method in class weka.core.CheckScheme
Gets the current settings of the CheckClassifier.
getOptions() - Method in class weka.core.converters.AbstractFileSaver
Gets the current settings of the Saver object.
getOptions() - Method in class weka.core.converters.ArffSaver
returns the options of the current setup
getOptions() - Method in class weka.core.converters.C45Saver
Gets the current settings of the C45Saver object.
getOptions() - Method in class weka.core.converters.CSVLoader
Gets the current settings of the Classifier.
getOptions() - Method in class weka.core.converters.DatabaseLoader
Gets the setting
getOptions() - Method in class weka.core.converters.DatabaseSaver
Gets the setting.
getOptions() - Method in class weka.core.converters.LibSVMSaver
returns the options of the current setup
getOptions() - Method in class weka.core.converters.SVMLightSaver
returns the options of the current setup.
getOptions() - Method in class weka.core.converters.TextDirectoryLoader
Gets the setting
getOptions() - Method in class weka.core.converters.XRFFSaver
returns the options of the current setup
getOptions() - Method in class weka.core.FindWithCapabilities
Gets the current settings of this object.
getOptions() - Method in class weka.core.Javadoc
Gets the current settings of this object.
getOptions() - Method in class weka.core.ListOptions
Gets the current settings of this object.
getOptions() - Method in class weka.core.neighboursearch.BallTree
Gets the current settings of KDtree.
getOptions() - Method in class weka.core.neighboursearch.balltrees.BallSplitter
Gets the current settings of the object.
getOptions() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
Gets the current settings.
getOptions() - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
Gets the current settings of the object.
getOptions() - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
Gets the current settings.
getOptions() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Gets the current settings of this BallTree MiddleOutConstructor.
getOptions() - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor
Gets the current settings of KDtree.
getOptions() - Method in class weka.core.neighboursearch.CoverTree
Gets the current settings of KDtree.
getOptions() - Method in class weka.core.neighboursearch.KDTree
Gets the current settings of KDtree.
getOptions() - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
Gets the current settings of the object.
getOptions() - Method in class weka.core.neighboursearch.LinearNNSearch
Gets the current settings.
getOptions() - Method in class weka.core.neighboursearch.NearestNeighbourSearch
Gets the current settings.
getOptions() - Method in class weka.core.NormalizableDistance
Gets the current settings.
getOptions() - Method in interface weka.core.OptionHandler
Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.core.OptionHandlerJavadoc
Gets the current settings of this object.
getOptions() - Method in class weka.core.stemmers.SnowballStemmer
Gets the current settings of the classifier.
getOptions() - Method in class weka.core.TechnicalInformationHandlerJavadoc
Gets the current settings of this object.
getOptions() - Method in class weka.core.TestInstances
Gets the current settings of this object.
getOptions() - Method in class weka.core.tokenizers.CharacterDelimitedTokenizer
Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.core.tokenizers.NGramTokenizer
Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.core.tokenizers.Tokenizer
Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.datagenerators.ClassificationGenerator
Gets the current settings of the classifier.
getOptions() - Method in class weka.datagenerators.classifiers.classification.Agrawal
Gets the current settings of the datagenerator.
getOptions() - Method in class weka.datagenerators.classifiers.classification.BayesNet
Gets the current settings of the datagenerator.
getOptions() - Method in class weka.datagenerators.classifiers.classification.LED24
Gets the current settings of the datagenerator.
getOptions() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
Gets the current settings of the datagenerator.
getOptions() - Method in class weka.datagenerators.classifiers.classification.RDG1
Gets the current settings of the datagenerator RDG1.
getOptions() - Method in class weka.datagenerators.classifiers.regression.Expression
Gets the current settings of the datagenerator BIRCHCluster.
getOptions() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Gets the current settings of the datagenerator BIRCHCluster.
getOptions() - Method in class weka.datagenerators.ClusterDefinition
Gets the current settings of the datagenerator BIRCHCluster.
getOptions() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Gets the current settings of the datagenerator BIRCHCluster.
getOptions() - Method in class weka.datagenerators.clusterers.SubspaceCluster
Gets the current settings of the datagenerator.
getOptions() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Gets the current settings of the datagenerator BIRCHCluster.
getOptions() - Method in class weka.datagenerators.ClusterGenerator
Gets the current settings of the classifier.
getOptions() - Method in class weka.datagenerators.DataGenerator
Gets the current settings of the datagenerator RDG1.
getOptions() - Method in class weka.datagenerators.RegressionGenerator
Gets the current settings of the classifier.
getOptions() - Method in class weka.estimators.CheckEstimator
Gets the current settings of the CheckEstimator.
getOptions() - Method in class weka.estimators.Estimator
Gets the current settings of the Estimator.
getOptions() - Method in class weka.experiment.AveragingResultProducer
Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.ClassifierSplitEvaluator
Gets the current settings of the Classifier.
getOptions() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
Gets the current settings of the Classifier.
getOptions() - Method in class weka.experiment.CrossValidationResultProducer
Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.CSVResultListener
Gets the current settings of the Classifier.
getOptions() - Method in class weka.experiment.DatabaseResultProducer
Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
Gets the current settings of the Classifier.
getOptions() - Method in class weka.experiment.Experiment
Gets the current settings of the experiment iterator.
getOptions() - Method in class weka.experiment.InstanceQuery
Gets the current settings of InstanceQuery
getOptions() - Method in class weka.experiment.LearningRateResultProducer
Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.PairedTTester
Gets current settings of the PairedTTester.
getOptions() - Method in class weka.experiment.RandomSplitResultProducer
Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.RegressionSplitEvaluator
Gets the current settings of the Classifier.
getOptions() - Method in class weka.filters.CheckSource
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.MultiFilter
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.SimpleFilter
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.supervised.attribute.AddClassification
Gets the current settings of the classifier.
getOptions() - Method in class weka.filters.supervised.attribute.AttributeSelection
Gets the current settings for the attribute selection (search, evaluator) etc.
getOptions() - Method in class weka.filters.supervised.attribute.ClassOrder
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.supervised.attribute.Discretize
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.supervised.attribute.NominalToBinary
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.supervised.attribute.PLSFilter
returns the options of the current setup
getOptions() - Method in class weka.filters.supervised.instance.Resample
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.supervised.instance.SMOTE
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.supervised.instance.SpreadSubsample
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.Add
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.AddCluster
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.AddExpression
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.AddID
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.AddNoise
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.AddValues
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.ClassAssigner
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.ClusterMembership
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.Copy
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.Discretize
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.FirstOrder
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.KernelFilter
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.MathExpression
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
Gets the current settings of the classifier.
getOptions() - Method in class weka.filters.unsupervised.attribute.NominalToBinary
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.NominalToString
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.Normalize
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.NumericToNominal
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.NumericTransform
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.PotentialClassIgnorer
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
Gets the current settings of the classifier.
getOptions() - Method in class weka.filters.unsupervised.attribute.RandomProjection
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.RandomSubset
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.RELAGGS
Gets the current settings of the classifier.
getOptions() - Method in class weka.filters.unsupervised.attribute.Remove
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.RemoveType
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.RemoveUseless
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.Reorder
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.StringToNominal
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.SwapValues
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.Wavelet
returns the options of the current setup
getOptions() - Method in class weka.filters.unsupervised.instance.Normalize
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.Randomize
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.RemoveFolds
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.RemovePercentage
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.RemoveRange
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.Resample
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.ReservoirSample
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.SubsetByExpression
Gets the current settings of the filter.
getOptions() - Method in class weka.gui.Main
returns the options of the current setup.
getOptype() - Method in class weka.core.pmml.Expression
Get the optype of the result of applying this Expression.
getOptype() - Method in class weka.core.pmml.FieldMetaInfo
Get the optype.
getOrder() - Method in enum weka.core.logging.Logger.Level
Returns the order of this level.
getOrderedFlag() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Gets the ordered flag (option O).
getOriginalCoords() - Method in class weka.gui.beans.MetaBean
returns the vector containing the original coordinates (instances of class Point) for the inputs
getOtherCapabilities() - Method in class weka.core.Capabilities
returns all other capabilities, besides class and attribute related ones
getOtherLeaf() - Method in class weka.classifiers.trees.j48.GraftSplit
 
getOutlierFactor() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Gets the factor for determining the thresholds for outliers.
getOutlierTreatmentMethod() - Method in class weka.core.pmml.MiningFieldMetaInfo
Get the outlier treatment method used for this field.
getOutput() - Method in class weka.datagenerators.DataGenerator
Gets the print writer.
getOutput() - Method in class weka.gui.explorer.DataGeneratorPanel
returns the generated output as text
getOutputCenterFile() - Method in class weka.clusterers.XMeans
Gets the file to write the list of centers to.
getOutputClassification() - Method in class weka.filters.supervised.attribute.AddClassification
Get whether the classifiction of the classifier is output.
getOutputDef() - Method in class weka.core.pmml.BuiltInArithmetic
Get the structure of the result produced by this function.
getOutputDef() - Method in class weka.core.pmml.BuiltInMath
Get the structure of the result produced by this function.
getOutputDef() - Method in class weka.core.pmml.BuiltInString
Get the structure of the result produced by this function.
getOutputDef() - Method in class weka.core.pmml.Constant
Return the structure of the result of applying this Expression as an Attribute.
getOutputDef() - Method in class weka.core.pmml.DefineFunction
Get the structure of the result produced by this function.
getOutputDef() - Method in class weka.core.pmml.Discretize
Return the structure of the result of applying this Expression as an Attribute.
getOutputDef() - Method in class weka.core.pmml.Expression
Return the structure of the result of applying this Expression as an Attribute.
getOutputDef() - Method in class weka.core.pmml.FieldRef
Return the structure of the result of applying this Expression as an Attribute.
getOutputDef() - Method in class weka.core.pmml.Function
Get the structure of the result produced by this function.
getOutputDef() - Method in class weka.core.pmml.NormContinuous
Return the structure of the result of applying this Expression as an Attribute.
getOutputDef() - Method in class weka.core.pmml.NormDiscrete
Return the structure of the result of applying this Expression as an Attribute.
getOutputDistribution() - Method in class weka.filters.supervised.attribute.AddClassification
Get whether the classifiction of the classifier is output.
getOutputErrorFlag() - Method in class weka.filters.supervised.attribute.AddClassification
Get whether the classifiction of the classifier is output.
getOutputFile() - Method in class weka.experiment.CrossValidationResultProducer
Get the value of OutputFile.
getOutputFile() - Method in class weka.experiment.CSVResultListener
Get the value of OutputFile.
getOutputFile() - Method in class weka.experiment.RandomSplitResultProducer
Get the value of OutputFile.
getOutputFilename() - Method in class weka.core.converters.TextDirectoryLoader
Gets whether the filename will be stored as an extra attribute.
getOutputFilename() - Method in class weka.gui.GenericPropertiesCreator
returns the name of the output file
getOutputFormat() - Method in class weka.core.Debug.Clock
returns the output format
getOutputFormat() - Method in class weka.filters.Filter
Gets the format of the output instances.
getOutputFormat() - Method in class weka.filters.unsupervised.attribute.PotentialClassIgnorer
Gets the format of the output instances.
getOutputFormat() - Static method in class weka.gui.experiment.ExperimenterDefaults
returns the classname of the ResultMatrix class, responsible for the output format
getOutputItemSets() - Method in class weka.associations.Apriori
Gets whether itemsets are output as well
getOutputNums() - Method in class weka.classifiers.functions.neural.NeuralConnection
Use this to get easy access to the output numbers.
getOutputOffsetMultiplier() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Gets whether an additional attribute "Offset" is generated per Outlier/ExtremeValue attribute pair that lists the multiplier the value is off the median: value = median + 'multiplier' * IQR.
getOutputPerClassInfoRetrievalStats() - Method in class weka.gui.beans.IncrementalClassifierEvaluator
Get whether per-class information retrieval stats are to be output.
getOutputProperties() - Method in class weka.gui.GenericPropertiesCreator
returns the output properties object (structure like the template, but filled with classes instead of packages)
getOutputs() - Method in class weka.classifiers.functions.neural.NeuralConnection
Use this to get easy access to the outputs.
getOutputs() - Method in class weka.gui.beans.MetaBean
 
getOutputTypes() - Method in class weka.core.Debug.DBO
Gets the current output type selection
getOutputWordCounts() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Gets whether output instances contain 0 or 1 indicating word presence, or word counts.
getOverwriteWarning() - Method in class weka.gui.ConverterFileChooser
Returns whether a popup appears with a warning that the file already exists (only save dialog).
getOwner() - Method in class weka.core.Capabilities
returns the owner of this capabilities object
getOwner() - Static method in class weka.core.Copyright
returns the entity owning the copyright
getP() - Method in class weka.classifiers.BVDecomposeSegCVSub
Get the proportion of instances that are common between two training sets.
getPackage(String) - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion
returns the packages part of the partial classname.
getPadding() - Method in class weka.filters.unsupervised.attribute.Wavelet
Gets the type of Padding to use
getPaint() - Method in class weka.gui.visualize.PostscriptGraphics
 
getPanel(int) - Method in class weka.gui.arffviewer.ArffViewerMainPanel
returns the specified panel, null if index is out of bounds
getPanelCount() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
returns the number of panels currently open
getPanels() - Method in class weka.gui.explorer.Explorer
returns all the panels, apart from the PreprocessPanel
getParameterNames() - Method in class weka.core.pmml.BuiltInArithmetic
Returns an array of the names of the parameters expected as input by this function
getParameterNames() - Method in class weka.core.pmml.BuiltInMath
Returns an array of the names of the parameters expected as input by this function.
getParameterNames() - Method in class weka.core.pmml.BuiltInString
Returns an array of the names of the parameters expected as input by this function.
getParameterNames() - Method in class weka.core.pmml.DefineFunction
Returns an array of the names of the parameters expected as input by this function.
getParameterNames() - Method in class weka.core.pmml.Function
Returns an array of the names of the parameters expected as input by this function.
getParameters() - Method in class weka.classifiers.functions.LibLINEAR
transfers the local variables into a svm_parameter object
getParameters() - Method in class weka.classifiers.functions.LibSVM
transfers the local variables into a svm_parameter object
getParent() - Method in class weka.associations.FPGrowth.FPTreeNode
Get the parent node.
getParent(int, int) - Method in class weka.classifiers.bayes.BayesNet
get node index of a parent of a node in the network structure
getParent(int) - Method in class weka.classifiers.bayes.net.ParentSet
returns index parent of parent specified by index
getParent() - Method in class weka.datagenerators.ClusterDefinition
returns the parent datagenerator this cluster belongs to
getParent(int) - Method in class weka.gui.treevisualizer.Node
Get the parent edge.
getParentCardinality(int) - Method in class weka.classifiers.bayes.BayesNet
get number of values the collection of parents of a node can take
getParentDialog(Container) - Static method in class weka.gui.PropertyDialog
Tries to determine the dialog this panel is part of.
getParentFrame() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
returns the parent frame, if it's a JFrame, otherwise null
getParentFrame() - Method in class weka.gui.GUIChooser.ChildFrameSDI
returns the parent frame, can be null.
getParentFrame() - Method in class weka.gui.Main.ChildFrameMDI
returns the parent frame, can be null.
getParentFrame() - Method in class weka.gui.Main.ChildFrameSDI
returns the parent frame, can be null.
getParentFrame(Container) - Static method in class weka.gui.PropertyDialog
Tries to determine the frame this panel is part of.
getParentFrame() - Method in class weka.gui.SetInstancesPanel
Returns the current frame the panel knows of, that it resides in.
getParentInternalFrame() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
returns the parent frame, if it's a JInternalFrame, otherwise null
getParents() - Method in class weka.classifiers.bayes.net.ParentSet
 
getParentSet(int) - Method in class weka.classifiers.bayes.BayesNet
get the parent set of a node
getParentSets() - Method in class weka.classifiers.bayes.BayesNet
Get full set of parent sets.
getParts() - Method in class weka.associations.tertius.IndividualInstance
 
getPassword() - Method in class weka.core.converters.DatabaseLoader
Returns the database password
getPassword() - Method in class weka.core.converters.DatabaseSaver
Returns the database password.
getPassword() - Method in class weka.experiment.DatabaseUtils
Get the database password.
getPassword() - Method in class weka.gui.DatabaseConnectionDialog
Returns password from dialog
getPassword() - Method in class weka.gui.sql.ConnectionPanel
returns the current Password.
getPassword() - Method in class weka.gui.sql.event.ResultChangedEvent
returns the password that produced the table model
getPassword() - Method in class weka.gui.sql.ResultSetTable
returns the password that produced the table model
getPassword() - Method in class weka.gui.sql.SqlViewer
returns the password from the currently active tab in the ResultPanel, otherwise an empty string.
getPassword() - Method in class weka.gui.sql.SqlViewerDialog
returns the chosen password, if any
getPath(Element) - Method in class weka.core.xml.XMLSerialization
returns the path of the "name" attribute from the root down to this node (including it).
getPath() - Method in class weka.gui.PropertySelectorDialog
Gets the path of property nodes to the selected property.
getPattern() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Gets the pattern type.
getPenalty() - Method in class weka.classifiers.bayes.blr.Prior
 
getPercent() - Method in class weka.filters.unsupervised.attribute.AddNoise
Gets the size of noise data as a percentage of the original set.
getPercent() - Method in class weka.filters.unsupervised.attribute.RandomProjection
Gets the percent the attributes (dimensions) of the data will be reduced to
getPercentage() - Method in class weka.filters.supervised.instance.SMOTE
Gets the percentage of SMOTE instances to create.
getPercentage() - Method in class weka.filters.unsupervised.instance.RemovePercentage
Gets the percentage of instances to select.
getPercentCompleted() - Method in class weka.gui.boundaryvisualizer.RemoteResult
Return the progress for this row
getPercentThreshold() - Method in class weka.attributeSelection.SVMAttributeEval
Get the threshold below which percentage elimination reverts to constant elimination.
getPercentToEliminatePerIteration() - Method in class weka.attributeSelection.SVMAttributeEval
Get the percentage rate of attribute elimination per iteration
getPerformance(int) - Method in class weka.classifiers.meta.GridSearch.Performance
returns the performance measure
getPerformances() - Method in class weka.classifiers.meta.GridSearch.PerformanceTable
returns the underlying performances
getPerformanceStats() - Method in class weka.core.neighboursearch.NearestNeighbourSearch
Gets the class object that contains the performance statistics of the search method.
getPerformPrediction() - Method in class weka.filters.supervised.attribute.PLSFilter
Gets whether the class attribute is updated with the predicted value.
getPerformRanking() - Method in class weka.attributeSelection.LinearForwardSelection
Get boolean if initial ranking should be performed to select the top-ranked attributes
getPerformRanking() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Get boolean if initial ranking should be performed to select the top-ranked attributes
getPeriodicPruning() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Gets the rate at which the dictionary is periodically pruned, as a percentage of the dataset size.
getPerturbationFraction() - Method in class weka.datagenerators.classifiers.classification.Agrawal
Gets the perturbation fraction.
getPivot() - Method in class weka.core.matrix.LUDecomposition
Return pivot permutation vector
getPivot() - Method in class weka.core.neighboursearch.balltrees.BallNode
Returns the pivot/centre of the node's ball.
getPlainColumnName(int) - Method in class weka.gui.arffviewer.ArffTable
returns the basically the attribute name of the column and not the HTML column name via getColumnName(int)
getPlotInstances() - Method in class weka.gui.visualize.PlotData2D
Returns the instances for this plot
getPlotName() - Method in class weka.gui.visualize.PlotData2D
Get the name of this plot
getPlotNameHTML() - Method in class weka.gui.visualize.PlotData2D
Get the name of the plot for use in a tool tip text.
getPlotPanel() - Method in class weka.gui.visualize.VisualizePanel
Returns the underlying plot panel.
getPlots() - Method in class weka.gui.visualize.Plot2D
Return the list of plots
getPlotTrainingData() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Returns true if training data is to be superimposed
getPMMLModel(String) - Static method in class weka.core.pmml.PMMLFactory
Read and return a PMML model.
getPMMLModel(File) - Static method in class weka.core.pmml.PMMLFactory
Read and return a PMML model.
getPMMLModel(InputStream) - Static method in class weka.core.pmml.PMMLFactory
Read and return a PMML model.
getPMMLModel(String, Logger) - Static method in class weka.core.pmml.PMMLFactory
Read and return a PMML model.
getPMMLModel(File, Logger) - Static method in class weka.core.pmml.PMMLFactory
Read and return a PMML model.
getPMMLModel(InputStream, Logger) - Static method in class weka.core.pmml.PMMLFactory
Read and return a PMML model.
getPMMLVersion() - Method in class weka.classifiers.pmml.consumer.PMMLClassifier
Get the PMML version used for this model.
getPMMLVersion() - Method in interface weka.core.pmml.PMMLModel
Get the version of PMML used to encode this model.
getPointValue(int) - Method in class weka.classifiers.functions.pace.DiscreteFunction
Gets a particular point value
getPointValues() - Method in class weka.classifiers.functions.pace.DiscreteFunction
Gets all point values
getPopulationSize() - Method in class weka.attributeSelection.GeneticSearch
get the size of the population
getPopulationSize() - Method in class weka.attributeSelection.ScatterSearchV1
Get the population size
getPopulationSize() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
 
getPopulationSize() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
 
getPopup() - Method in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
returns the currently set JPopupMenu.
getPositionX(int) - Method in class weka.classifiers.bayes.net.EditableBayesNet
get x position of a node
getPositionY(int) - Method in class weka.classifiers.bayes.net.EditableBayesNet
get y position of a node
getPositiveIndex() - Method in class weka.associations.FPGrowth
Get the index of the attribute value to consider as positive for binary attributes in normal dense instances.
getPostFixExpression() - Method in class weka.core.AttributeExpression
Return the postfix expression
getPostProcessor() - Method in class weka.core.CheckScheme
returns the current PostProcessor, can be null
getPostProcessor() - Method in class weka.estimators.CheckEstimator
returns the current PostProcessor, can be null
getPrecision() - Method in class weka.classifiers.evaluation.TwoClassStats
Calculate the precision.
getPrecision() - Method in class weka.estimators.KernelEstimator
Return the precision of this kernel estimator.
getPrecision() - Method in class weka.estimators.NormalEstimator
Return the value of the precision of this normal estimator.
getPredicate() - Method in class weka.associations.tertius.Literal
 
getPrediction(Classifier, Instance) - Method in class weka.classifiers.evaluation.EvaluationUtils
Generate a single prediction for a test instance given the pre-trained classifier.
getPredictions(Instances, int, int) - Method in class weka.classifiers.meta.ThresholdSelector
Collects the classifier predictions using the specified evaluation method.
getPredTargetColumn() - Method in class weka.experiment.ClassifierSplitEvaluator
 
getPreferredScrollableViewportSize() - Method in class weka.gui.AttributeSelectionPanel
 
getPrefix() - Method in class weka.gui.beans.SerializedModelSaver
Get the prefix to prepend to the model file names.
getPremise() - Method in class weka.associations.FPGrowth.AssociationRule
Get the premise of this rule.
getPremiseSupport() - Method in class weka.associations.FPGrowth.AssociationRule
Get the support for the premise.
getPreprocessing() - Method in class weka.filters.supervised.attribute.PLSFilter
Gets the type of preprocessing to use
getPreprocessing() - Method in class weka.filters.unsupervised.attribute.KernelFilter
Gets the filter used for preprocessing
getPreprocessPanel() - Method in class weka.gui.explorer.Explorer
returns the instance of the PreprocessPanel being used in this instance of the Explorer
getPreserveInstancesOrder() - Method in class weka.clusterers.SimpleKMeans
Gets whether order of instances must be preserved
getPrimitive(Element) - Method in class weka.core.xml.XMLSerialization
returns an Object representing the primitive described by the given node.
getPrintColNames() - Method in class weka.experiment.ResultMatrix
returns whether column names or numbers instead are printed
getPrintNewick() - Method in class weka.clusterers.HierarchicalClusterer
 
getPrintRowNames() - Method in class weka.experiment.ResultMatrix
returns whether row names or numbers instead are printed
getPriorClass() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Get the type of prior to use.
getPriority(int) - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueue
Returns the priority for the object at the specified index
getPriority() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueueElement
Returns the priority for this object
getPriority(int) - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueue
Returns the priority for the object at the specified index
getPriority() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueueElement
Returns the priority for this object
getPriorProbability(String) - Method in class weka.core.pmml.TargetMetaInfo
Get the prior probability for the supplied value.
getProbabilities() - Method in class weka.gui.boundaryvisualizer.RemoteResult
Return the probability distributions for this row in the visualization
getProbability(int, int, int) - Method in class weka.classifiers.bayes.BayesNet
get particular probability of the conditional probability distribtion of a node given its parents.
getProbability(double) - Method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes
Get a probability estimate for a value
getProbability(double, double) - Method in interface weka.estimators.ConditionalEstimator
Get a probability for a value conditional on another value
getProbability(double, double) - Method in class weka.estimators.DDConditionalEstimator
Get a probability estimate for a value
getProbability(double) - Method in class weka.estimators.DiscreteEstimator
Get a probability estimate for a value
getProbability(double, double) - Method in class weka.estimators.DKConditionalEstimator
Get a probability estimate for a value
getProbability(double, double) - Method in class weka.estimators.DNConditionalEstimator
Get a probability estimate for a value
getProbability(double) - Method in class weka.estimators.Estimator
Get a probability estimate for a value.
getProbability(double, double) - Method in class weka.estimators.KDConditionalEstimator
Get a probability estimate for a value
getProbability(double) - Method in class weka.estimators.KernelEstimator
Get a probability estimate for a value.
getProbability(double, double) - Method in class weka.estimators.KKConditionalEstimator
Get a probability estimate for a value
getProbability(double) - Method in class weka.estimators.MahalanobisEstimator
Get a probability estimate for a value
getProbability(double, double) - Method in class weka.estimators.NDConditionalEstimator
Get a probability estimate for a value
getProbability(double, double) - Method in class weka.estimators.NNConditionalEstimator
Get a probability estimate for a value
getProbability(double) - Method in class weka.estimators.NormalEstimator
Get a probability estimate for a value
getProbability(double) - Method in class weka.estimators.PoissonEstimator
Get a probability estimate for a value
getProbabilityEstimates() - Method in class weka.classifiers.functions.LibLINEAR
Sets whether to generate probability estimates instead of -1/+1 for classification problems.
getProbabilityEstimates() - Method in class weka.classifiers.functions.LibSVM
Sets whether to generate probability estimates instead of -1/+1 for classification problems.
getProblem(List<Object>, List<Integer>, int) - Method in class weka.classifiers.functions.LibLINEAR
returns the svm_problem
getProblem(Vector, Vector) - Method in class weka.classifiers.functions.LibSVM
returns the svm_problem
getProbs(double[][]) - Method in class weka.classifiers.trees.lmt.LogisticBase
Computes the p-values (probabilities for the different classes) from the F-values for a set of instances.
getProgressBar() - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
Returns a handle to the progressBar of this LayoutEngine.
getProgressBar() - Method in interface weka.gui.graphvisualizer.LayoutEngine
This method returns the progress bar for the LayoutEngine, which shows the progress of the layout process, if it takes a while to layout the graph
getProjectedCount(int) - Method in class weka.associations.FPGrowth.FPTreeNode
Get the projected count at the given recursion level for this node.
getProjectionFilter() - Method in class weka.classifiers.meta.RotationForest
Gets the filter used to project the data.
getProjectionFilterSpec() - Method in class weka.classifiers.meta.RotationForest
Gets the filter specification string, which contains the class name of the filter and any options to the filter
getProlog() - Method in class weka.core.OptionHandlerJavadoc
whether "Valid options are..." prolog is included in the Javadoc
getProlog() - Method in class weka.core.TechnicalInformationHandlerJavadoc
whether "Valid options are..." prolog is included in the Javadoc
getProperties() - Static method in class weka.gui.experiment.ExperimenterDefaults
returns the associated properties file
getProperties() - Static method in class weka.gui.explorer.ExplorerDefaults
returns the associated properties file.
getProperty() - Method in class weka.core.pmml.FieldMetaInfo.Value
 
getPropertyArray() - Method in class weka.experiment.Experiment
Gets the array of values to set the custom property to.
getPropertyArrayLength() - Method in class weka.experiment.Experiment
Gets the number of custom iterator values that have been defined for the experiment.
getPropertyArrayValue(int) - Method in class weka.experiment.Experiment
Gets a specified value from the custom property iterator array.
getPropertyDescriptor(Object, PropertyPath.Path) - Static method in class weka.core.PropertyPath
returns the property associated with the given path, null if a problem occurred.
getPropertyDescriptor(Object, String) - Static method in class weka.core.PropertyPath
returns the property associated with the given path
getPropertyDescriptors() - Method in class weka.gui.beans.ClassAssignerBeanInfo
Returns the property descriptors
getPropertyDescriptors() - Method in class weka.gui.beans.ClassValuePickerBeanInfo
Returns the property descriptors
getPropertyDescriptors() - Method in class weka.gui.beans.CrossValidationFoldMakerBeanInfo
Return the property descriptors for this bean
getPropertyDescriptors() - Method in class weka.gui.beans.IncrementalClassifierEvaluatorBeanInfo
Return the property descriptors for this bean
getPropertyDescriptors() - Method in class weka.gui.beans.PredictionAppenderBeanInfo
Return the property descriptors for this bean
getPropertyDescriptors() - Method in class weka.gui.beans.StripChartBeanInfo
Get the property descriptors for this bean
getPropertyDescriptors() - Method in class weka.gui.beans.TrainTestSplitMakerBeanInfo
Get the property descriptors for this bean
getPropertyPath() - Method in class weka.experiment.Experiment
Gets the path of properties taken to get to the custom property to iterate over.
getPruningMethod() - Method in class weka.classifiers.functions.supportVector.StringKernel
Gets the method used for pruning.
getPruningStrategy() - Method in class weka.classifiers.trees.BFTree
Gets the pruning strategy.
getPruningType() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Get the pruning type
getQ() - Method in class weka.core.matrix.QRDecomposition
Generate and return the (economy-sized) orthogonal factor
getQuality() - Method in class weka.gui.visualize.JPEGWriter
returns the quality the JPEG will be stored in.
getQuery() - Method in class weka.core.converters.DatabaseLoader
Gets the query to execute against the database
getQuery() - Method in class weka.experiment.InstanceQuery
Get the query to execute against the database
getQuery() - Method in class weka.gui.sql.event.QueryExecuteEvent
returns the query that was executed
getQuery() - Method in class weka.gui.sql.event.ResultChangedEvent
returns the query that was executed
getQuery() - Method in class weka.gui.sql.QueryPanel
returns the currently displayed query.
getQuery() - Method in class weka.gui.sql.ResultSetTable
returns the query that produced the table model
getQuery() - Method in class weka.gui.sql.SqlViewer
returns the query from the currently active tab in the ResultPanel, otherwise an empty string.
getQuery() - Method in class weka.gui.sql.SqlViewerDialog
returns the chosen query, if any
getQueryPanel() - Method in class weka.gui.sql.ResultPanel
returns the currently set QueryPanel, can be NULL
getR() - Method in class weka.core.matrix.QRDecomposition
Return the upper triangular factor
getRaceType() - Method in class weka.attributeSelection.RaceSearch
Get the race type
getRadius() - Method in class weka.core.neighboursearch.balltrees.BallNode
Returns the radius of the node's ball.
getRadiuses() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Gets the upper and lower boundary for the radius of the clusters.
getRandom(int) - Method in class weka.classifiers.trees.ADTree
Gets the next random value.
getRandom() - Method in class weka.datagenerators.DataGenerator
Gets the random generator.
getRandomAnchor(int, int) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Returns a random anchor point/instance from a given set of points/instances.
getRandomize() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
Gets whether the order of the generated is randomized
getRandomizeData() - Method in class weka.experiment.RandomSplitResultProducer
Get if dataset is to be randomized
getRandomNumberGenerator(long) - Method in class weka.core.Instances
Returns a random number generator.
getRandomOrder() - Method in class weka.classifiers.bayes.net.search.global.K2
Get random order flag
getRandomOrder() - Method in class weka.classifiers.bayes.net.search.local.K2
Get random order flag
getRandomSeed() - Method in class weka.classifiers.functions.LeastMedSq
get the seed for the random number generator
getRandomSeed() - Method in class weka.classifiers.functions.SMO
Get the value of randomSeed.
getRandomSeed() - Method in class weka.classifiers.mi.MISMO
Get the value of randomSeed.
getRandomSeed() - Method in class weka.classifiers.trees.ADTree
Gets random seed for a random walk.
getRandomSeed() - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
Returns the seed value of random number generator.
getRandomSeed() - Method in class weka.filters.supervised.instance.Resample
Gets the random number seed.
getRandomSeed() - Method in class weka.filters.supervised.instance.SMOTE
Gets the random number seed.
getRandomSeed() - Method in class weka.filters.supervised.instance.SpreadSubsample
Gets the random number seed.
getRandomSeed() - Method in class weka.filters.unsupervised.attribute.AddNoise
Gets the random number seed.
getRandomSeed() - Method in class weka.filters.unsupervised.attribute.RandomProjection
Gets the random seed of the random number generator
getRandomSeed() - Method in class weka.filters.unsupervised.instance.Randomize
Get the random number generator seed value.
getRandomSeed() - Method in class weka.filters.unsupervised.instance.Resample
Gets the random number seed.
getRandomSeed() - Method in class weka.filters.unsupervised.instance.ReservoirSample
Gets the random number seed.
getRandomWidthFactor() - Method in class weka.classifiers.meta.MultiClassClassifier
Gets the multiplier when generating random codes.
getRange() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Gets the upper and lower boundary for the range of x
getRange(int) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
Gets a single Range from the set of available Ranges.
getRangeCorrection() - Method in class weka.classifiers.meta.ThresholdSelector
Gets the confidence range correction mode used.
getRanges() - Method in class weka.core.NormalizableDistance
Method to get the ranges.
getRanges() - Method in class weka.core.Range
Gets the string representing the selected range of values
getRanges() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
Gets the list of possible Ranges to choose from.
getRank() - Method in class weka.attributeSelection.LatentSemanticAnalysis
Gets the desired matrix rank (or coverage proportion) for feature-space reduction
getRawOutput() - Method in class weka.experiment.CrossValidationResultProducer
Get if raw split evaluator output is to be saved
getRawOutput() - Method in class weka.experiment.RandomSplitResultProducer
Get if raw split evaluator output is to be saved
getRawResultOutput() - Method in class weka.experiment.ClassifierSplitEvaluator
Gets the raw output from the classifier
getRawResultOutput() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
Gets the raw output from the classifier
getRawResultOutput() - Method in class weka.experiment.RegressionSplitEvaluator
Gets the raw output from the classifier
getRawResultOutput() - Method in interface weka.experiment.SplitEvaluator
Returns the raw output for the most recent call to getResult.
getReachabilityDistance() - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
Returns the reachabilityDistance for this dataObject
getReachabilityDistance() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
Returns the reachabilityDistance for this dataObject
getReachabilityDistance() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
Returns the reachabilityDistance for this dataObject
getReadable() - Method in class weka.core.Tag
Gets the string description of the Tag.
getReader(String, String) - Static method in class weka.gui.Loader
returns a Reader for the given filename and dir, can be NULL if it fails
getReader(String) - Method in class weka.gui.Loader
returns a Reader for the given filename, can be NULL if it fails
getReadIncrementally() - Method in class weka.gui.SetInstancesPanel
Gets whether instances are to be read incrementally or not
getRealEigenvalues() - Method in class weka.core.matrix.EigenvalueDecomposition
Return the real parts of the eigenvalues
getRecall() - Method in class weka.classifiers.evaluation.TwoClassStats
Calculate the recall.
getReducedErrorPruning() - Method in class weka.classifiers.rules.PART
Get the value of reducedErrorPruning.
getReducedErrorPruning() - Method in class weka.classifiers.trees.J48
Get the value of reducedErrorPruning.
getRefer() - Method in class weka.gui.treevisualizer.Node
Get the value of refer.
getRefreshFreq() - Method in class weka.gui.beans.StripChart
Get the refresh frequency
getRegOptimizer() - Method in class weka.classifiers.functions.SMOreg
returns the learning algorithm
getRegressionTree() - Method in class weka.classifiers.trees.m5.Rule
Get the value of regressionTree.
getRegressionTree() - Method in class weka.classifiers.trees.m5.RuleNode
Get the value of regressionTree.
getRelabel() - Method in class weka.classifiers.trees.J48graft
Get the value of relabelling
getRelation() - Method in class weka.core.TestInstances
returns the current name of the relation
getRelationalClassFormat() - Method in class weka.core.TestInstances
returns the current strcuture of the relational class attribute, can be null
getRelationalFormat(int) - Method in class weka.core.TestInstances
returns the format for the specified relational attribute, can be null
getRelationForTableName() - Method in class weka.core.converters.DatabaseSaver
Gets whether or not the relation name is used as name of the table.
getRelationName() - Method in class weka.datagenerators.DataGenerator
Gets the relation name the dataset should have.
getRelationNameForFilename() - Method in class weka.gui.beans.Saver
Get whether the relation name is the primary part of the filename.
getRelationNameToUse() - Method in class weka.datagenerators.DataGenerator
returns the relation name to use, i.e., in case the currently set relation name is empty, a generic one is returned.
getRemoteHosts() - Method in class weka.experiment.RemoteExperiment
Get the list of remote host names
getRemoveAllMissingCols() - Method in class weka.associations.Apriori
Returns whether columns containing all missing values are to be removed
getRemoveClassColumn() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
Get whether the class column is to be removed.
getRemovedPercentage() - Method in class weka.classifiers.meta.RotationForest
Gets the percentage of instances to be removed
getRemoveFilterClassnames() - Static method in class weka.gui.experiment.ExperimenterDefaults
whether the filter classnames in the dataset names are removed by default
getRemoveFilterName() - Method in class weka.experiment.ResultMatrix
returns whether the filter classname is removed from the dataset name
getRemoveFilterName() - Method in class weka.gui.experiment.OutputFormatDialog
returns whether the filter classname is removed from the dataset name.
getRemoveOldClass() - Method in class weka.filters.supervised.attribute.AddClassification
Get whether the old class attribute is removed.
getRemoveUnused() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
Gets whether unused attributes (ones that are not covered by any of the ranges) are removed from the output.
getRenderingHint(RenderingHints.Key) - Method in class weka.gui.visualize.PostscriptGraphics
 
getRenderingHints() - Method in class weka.gui.visualize.PostscriptGraphics
 
getRepeatLiterals() - Method in class weka.associations.Tertius
Get the value of repeatLiterals.
getRepetitions() - Static method in class weka.gui.experiment.ExperimenterDefaults
returns the number of repetitions to use
getReplaceMissing() - Method in class weka.filters.supervised.attribute.PLSFilter
Gets whether missing values are replace.
getReplaceMissingValues() - Method in class weka.filters.unsupervised.attribute.RandomProjection
Gets the current setting for using ReplaceMissingValues filter
getReportFrequency() - Method in class weka.attributeSelection.GeneticSearch
get how often repports are generated
getRepulsion() - Method in class weka.clusterers.CLOPE
gets the repulsion
getReset() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
getReset() - Method in class weka.gui.beans.ChartEvent
get the value of the reset flag
getResult() - Method in class weka.core.mathematicalexpression.Parser
Returns the result of the evaluation.
getResult(double[]) - Method in class weka.core.pmml.BuiltInArithmetic
Get the result of applying this function.
getResult(double[]) - Method in class weka.core.pmml.BuiltInMath
Get the result of applying this function.
getResult(double[]) - Method in class weka.core.pmml.BuiltInString
Get the result of applying this function.
getResult(double[]) - Method in class weka.core.pmml.Constant
Get the result of evaluating the expression.
getResult(double[]) - Method in class weka.core.pmml.DefineFunction
Get the result of applying this function.
getResult(double[]) - Method in class weka.core.pmml.Discretize
Get the result of evaluating the expression.
getResult(double[]) - Method in class weka.core.pmml.Expression
Get the result of evaluating the expression.
getResult(double[]) - Method in class weka.core.pmml.FieldRef
 
getResult(double[]) - Method in class weka.core.pmml.Function
Get the result of applying this function.
getResult(double[]) - Method in class weka.core.pmml.NormContinuous
Get the result of evaluating the expression.
getResult(double[]) - Method in class weka.core.pmml.NormDiscrete
Get the result of evaluating the expression.
getResult(Instances, Instances) - Method in class weka.experiment.ClassifierSplitEvaluator
Gets the results for the supplied train and test datasets.
getResult(Instances, Instances) - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
Gets the results for the supplied train and test datasets.
getResult(Instances, Instances) - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
Gets the results for the supplied train and test datasets.
getResult(Instances, Instances) - Method in class weka.experiment.RegressionSplitEvaluator
Gets the results for the supplied train and test datasets.
getResult(Instances, Instances) - Method in interface weka.experiment.SplitEvaluator
Gets the results for the supplied train and test datasets.
getResult() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
Returns the result of the evaluation.
getResult() - Method in class weka.gui.experiment.OutputFormatDialog
the result from the last display of the dialog, the same is returned from showDialog.
getResultCategorical(double[]) - Method in class weka.core.pmml.Constant
Gets the result of evaluating the expression when the optype is categorical or ordinal as the actual String value.
getResultCategorical(double[]) - Method in class weka.core.pmml.Discretize
Gets the result of evaluating the expression when the optype is categorical or ordinal as the actual String value.
getResultCategorical(double[]) - Method in class weka.core.pmml.Expression
Gets the result of evaluating the expression when the optype is categorical or ordinal as the actual String value.
getResultCategorical(double[]) - Method in class weka.core.pmml.FieldRef
 
getResultCategorical(double[]) - Method in class weka.core.pmml.NormContinuous
Always throws an Exception since the result of NormContinuous must be continuous.
getResultCategorical(double[]) - Method in class weka.core.pmml.NormDiscrete
Always throws an Exception since the result of NormDiscrete must be continuous.
getResultContinuous(double[]) - Method in class weka.core.pmml.Expression
Get the result of evaluating the expression for continuous optype.
getResultFromTable(String, ResultProducer, Object[]) - Method in class weka.experiment.DatabaseUtils
Executes a database query to extract a result for the supplied key from the database.
getResultInverse(double[]) - Method in class weka.core.pmml.NormContinuous
Compute the inverse of the normalization (i.e.
getResultListener() - Method in class weka.experiment.Experiment
Gets the result listener where results will be sent.
getResultMatrix() - Method in class weka.experiment.PairedTTester
Gets the instance that produces the output.
getResultMatrix() - Method in interface weka.experiment.Tester
Gets the instance that produces the output.
getResultMatrix() - Method in class weka.gui.experiment.OutputFormatDialog
Gets the currently selected output format result matrix.
getResultNames() - Method in class weka.experiment.AveragingResultProducer
Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.ClassifierSplitEvaluator
Gets the names of each of the result columns produced for a single run.
getResultNames() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
Gets the names of each of the result columns produced for a single run.
getResultNames() - Method in class weka.experiment.CrossValidationResultProducer
Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.DatabaseResultProducer
Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
Gets the names of each of the result columns produced for a single run.
getResultNames() - Method in class weka.experiment.LearningRateResultProducer
Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.RandomSplitResultProducer
Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.RegressionSplitEvaluator
Gets the names of each of the result columns produced for a single run.
getResultNames() - Method in interface weka.experiment.ResultProducer
Gets the names of each of the result columns produced for a single run.
getResultNames() - Method in interface weka.experiment.SplitEvaluator
Gets the names of each of the result columns produced for a single run.
getResultProducer() - Method in class weka.experiment.AveragingResultProducer
Get the ResultProducer.
getResultProducer() - Method in class weka.experiment.DatabaseResultProducer
Get the ResultProducer.
getResultProducer() - Method in class weka.experiment.Experiment
Get the result producer used for the current experiment.
getResultProducer() - Method in class weka.experiment.LearningRateResultProducer
Get the ResultProducer.
getResults() - Method in class weka.associations.Tertius
returns the results
getResultSet() - Method in class weka.experiment.DatabaseUtils
Gets the results generated by a previous query.
getResultSet() - Method in class weka.gui.sql.event.QueryExecuteEvent
returns the resultset that was produced, can be null in case the query failed
getResultSet() - Method in class weka.gui.sql.ResultSetHelper
the underlying resultset.
getResultsetKeyColumns() - Method in class weka.experiment.PairedTTester
Get the value of ResultsetKeyColumns.
getResultsetKeyColumns() - Method in interface weka.experiment.Tester
Get the value of ResultsetKeyColumns.
getResultsetName(int) - Method in class weka.experiment.PairedTTester
Gets a string descriptive of the specified resultset.
getResultsetName(int) - Method in interface weka.experiment.Tester
Gets a string descriptive of the specified resultset.
getResultsTableName(ResultProducer) - Method in class weka.experiment.DatabaseUtils
Gets the name of the experiment table that stores results from a particular ResultProducer.
getResultTypes() - Method in class weka.experiment.AveragingResultProducer
Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.ClassifierSplitEvaluator
Gets the data types of each of the result columns produced for a single run.
getResultTypes() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
Gets the data types of each of the result columns produced for a single run.
getResultTypes() - Method in class weka.experiment.CrossValidationResultProducer
Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.DatabaseResultProducer
Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
Gets the data types of each of the result columns produced for a single run.
getResultTypes() - Method in class weka.experiment.LearningRateResultProducer
Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.RandomSplitResultProducer
Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.RegressionSplitEvaluator
Gets the data types of each of the result columns produced for a single run.
getResultTypes() - Method in interface weka.experiment.ResultProducer
Gets the data types of each of the result columns produced for a single run.
getResultTypes() - Method in interface weka.experiment.SplitEvaluator
Gets the data types of each of the result columns produced for a single run.
getResultVector() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject
Returns the resultVector
getResultVector() - Method in class weka.clusterers.OPTICS
Returns the resultVector
getRetrieval() - Method in class weka.core.converters.AbstractLoader
Gets the retrieval mode.
getRetrieval() - Method in class weka.core.converters.AbstractSaver
Gets the retrieval mode.
getReturnValue(int) - Method in class weka.classifiers.functions.supportVector.StringKernel
Recursion-ending function that is called at the end of each recursion branch.
getReturnValue() - Method in class weka.gui.DatabaseConnectionDialog
Returns which of OK or cancel was clicked from dialog
getReturnValue() - Method in class weka.gui.sql.SqlViewerDialog
returns whether the user clicked OK (JOptionPane.OK_OPTION) or whether he cancelled the dialog (JOptionPane.CANCEL_OPTION)
getRevision() - Method in class weka.associations.AbstractAssociator
Returns the revision string.
getRevision() - Method in class weka.associations.Apriori
Returns the revision string.
getRevision() - Method in class weka.associations.AprioriItemSet
Returns the revision string.
getRevision() - Method in class weka.associations.AssociatorEvaluation
Returns the revision string.
getRevision() - Method in class weka.associations.CaRuleGeneration
Returns the revision string.
getRevision() - Method in class weka.associations.CheckAssociator
Returns the revision string.
getRevision() - Method in class weka.associations.FilteredAssociator
Returns the revision string.
getRevision() - Method in class weka.associations.FPGrowth
Returns the revision string.
getRevision() - Method in class weka.associations.GeneralizedSequentialPatterns
Returns the revision string.
getRevision() - Method in class weka.associations.gsp.Element
Returns the revision string.
getRevision() - Method in class weka.associations.gsp.Sequence
Returns the revision string.
getRevision() - Method in class weka.associations.ItemSet
Returns the revision string.
getRevision() - Method in class weka.associations.LabeledItemSet
Returns the revision string.
getRevision() - Method in class weka.associations.PredictiveApriori
Returns the revision string.
getRevision() - Method in class weka.associations.PriorEstimation
Returns the revision string.
getRevision() - Method in class weka.associations.RuleGeneration
Returns the revision string.
getRevision() - Method in class weka.associations.RuleItem
Returns the revision string.
getRevision() - Method in class weka.associations.tertius.AttributeValueLiteral
Returns the revision string.
getRevision() - Method in class weka.associations.tertius.Body
Returns the revision string.
getRevision() - Method in class weka.associations.Tertius
Returns the revision string.
getRevision() - Method in class weka.associations.tertius.Head
Returns the revision string.
getRevision() - Method in class weka.associations.tertius.IndividualInstance
Returns the revision string.
getRevision() - Method in class weka.associations.tertius.IndividualInstances
Returns the revision string.
getRevision() - Method in class weka.associations.tertius.IndividualLiteral
Returns the revision string.
getRevision() - Method in class weka.associations.tertius.Predicate
Returns the revision string.
getRevision() - Method in class weka.associations.tertius.Rule
Returns the revision string.
getRevision() - Method in class weka.associations.tertius.SimpleLinkedList
Returns the revision string.
getRevision() - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListInverseIterator
Returns the revision string.
getRevision() - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListIterator
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.ASEvaluation
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.ASSearch
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.AttributeSelection
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.BestFirst
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.BestFirst.Link2
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.BestFirst.LinkedList2
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.CfsSubsetEval
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.CheckAttributeSelection
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.ClassifierSubsetEval
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.ConsistencySubsetEval
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.ConsistencySubsetEval.hashKey
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.CostSensitiveAttributeEval
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.CostSensitiveSubsetEval
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.ExhaustiveSearch
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.FilteredAttributeEval
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.FilteredSubsetEval
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.GainRatioAttributeEval
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.GeneticSearch.GABitSet
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.GeneticSearch
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.GreedyStepwise
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.InfoGainAttributeEval
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.LatentSemanticAnalysis
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.LFSMethods
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.LFSMethods.Link2
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.LFSMethods.LinkedList2
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.LinearForwardSelection
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.OneRAttributeEval
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.PrincipalComponents
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.RaceSearch
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.RandomSearch
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.Ranker
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.RankSearch
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.ReliefFAttributeEval
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.ScatterSearchV1
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.SVMAttributeEval
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Returns the revision string.
getRevision() - Method in class weka.attributeSelection.WrapperSubsetEval
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.AODE
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.AODEsr
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.BayesNet
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.blr.GaussianPriorImpl
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.blr.LaplacePriorImpl
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.ComplementNaiveBayes
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.DMNBtext
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.HNB
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.NaiveBayes
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.NaiveBayesMultinomial
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.NaiveBayesSimple
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.NaiveBayesUpdateable
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.ADNode
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.BayesNetGenerator
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.BIFReader
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.EditableBayesNet
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorFullBayes
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.estimate.SimpleEstimator
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.MarginCalculator
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeSeparator
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.ParentSet
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.ci.CISearchAlgorithm
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.fixed.FromFile
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.fixed.NaiveBayes
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.global.HillClimber
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.global.K2
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.global.TAN
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.local.HillClimber
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.local.K2
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.local.TAN
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.VaryNode
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.WAODE
Returns the revision string.
getRevision() - Method in class weka.classifiers.BVDecompose
Returns the revision string.
getRevision() - Method in class weka.classifiers.BVDecomposeSegCVSub
Returns the revision string.
getRevision() - Method in class weka.classifiers.CheckClassifier
Returns the revision string.
getRevision() - Method in class weka.classifiers.CheckSource
Returns the revision string.
getRevision() - Method in class weka.classifiers.Classifier
Returns the revision string.
getRevision() - Method in class weka.classifiers.CostMatrix
Returns the revision string.
getRevision() - Method in class weka.classifiers.evaluation.ConfusionMatrix
Returns the revision string.
getRevision() - Method in class weka.classifiers.evaluation.CostCurve
Returns the revision string.
getRevision() - Method in class weka.classifiers.evaluation.EvaluationUtils
Returns the revision string.
getRevision() - Method in class weka.classifiers.Evaluation
Returns the revision string.
getRevision() - Method in class weka.classifiers.evaluation.MarginCurve
Returns the revision string.
getRevision() - Method in class weka.classifiers.evaluation.NominalPrediction
Returns the revision string.
getRevision() - Method in class weka.classifiers.evaluation.NumericPrediction
Returns the revision string.
getRevision() - Method in class weka.classifiers.evaluation.ThresholdCurve
Returns the revision string.
getRevision() - Method in class weka.classifiers.evaluation.TwoClassStats
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.GaussianProcesses
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.IsotonicRegression
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.LeastMedSq
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.LibLINEAR
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.LibSVM
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.LinearRegression
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.Logistic
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.MultilayerPerceptron
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.neural.LinearUnit
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.neural.NeuralNode
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.neural.SigmoidUnit
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.pace.ChisqMixture
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.pace.DiscreteFunction
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.pace.NormalMixture
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.pace.PaceMatrix
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.PaceRegression
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.PLSClassifier
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.RBFNetwork
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.SimpleLinearRegression
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.SimpleLogistic
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.SMO.BinarySMO
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.SMO
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.SMOreg
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.SPegasos
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.CheckKernel
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.Kernel
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.KernelEvaluation
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.NormalizedPolyKernel
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.PolyKernel
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.Puk
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.RBFKernel
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.RegOptimizer
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.RegSMO
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.SMOset
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.StringKernel
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.VotedPerceptron
Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.Winnow
Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.IB1
Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.IBk
Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.KStar
Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable
Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.kstar.KStarCache
Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.kstar.KStarCache.TableEntry
Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.kstar.KStarNominalAttribute
Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute
Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.kstar.KStarWrapper
Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.LBR
Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.LBR.Indexes
Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.LWL
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.AdaBoostM1
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.AdditiveRegression
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.Bagging
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.ClassificationViaClustering
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.ClassificationViaRegression
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.CostSensitiveClassifier
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.CVParameterSelection.CVParameter
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.CVParameterSelection
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.Dagging
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.Decorate
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.END
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.FilteredClassifier
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.Grading
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.GridSearch
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.GridSearch.Grid
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.GridSearch.Performance
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.GridSearch.PerformanceCache
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.GridSearch.PerformanceComparator
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.GridSearch.PerformanceTable
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.GridSearch.PointDouble
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.GridSearch.PointInt
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.LogitBoost
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.MetaCost
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.MultiBoostAB
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.MultiClassClassifier
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.MultiScheme
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.nestedDichotomies.ND
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.nestedDichotomies.ND.NDTree
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.OrdinalClassClassifier
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.RandomCommittee
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.RandomSubSpace
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.RegressionByDiscretization
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.RotationForest
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.Stacking
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.StackingC
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.ThresholdSelector
Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.Vote
Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.CitationKNN
Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.MDD
Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.MIBoost
Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.MIDD
Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.MIEMDD
Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.MILR
Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.MINND
Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.MIOptimalBall
Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.MISMO.BinaryMISMO
Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.MISMO
Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.MISVM
Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.MIWrapper
Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.SimpleMI
Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.supportVector.MIPolyKernel
Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.supportVector.MIRBFKernel
Returns the revision string.
getRevision() - Method in class weka.classifiers.misc.HyperPipes
Returns the revision string.
getRevision() - Method in class weka.classifiers.misc.SerializedClassifier
Returns the revision string.
getRevision() - Method in class weka.classifiers.misc.VFI
Returns the revision string.
getRevision() - Method in class weka.classifiers.pmml.consumer.GeneralRegression
 
getRevision() - Method in class weka.classifiers.pmml.consumer.NeuralNetwork
 
getRevision() - Method in class weka.classifiers.pmml.consumer.Regression
 
getRevision() - Method in class weka.classifiers.rules.ConjunctiveRule
Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.DecisionTable
Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.DecisionTableHashKey
Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.DTNB.BackwardsWithDelete
Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.DTNB.EvalWithDelete
Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.DTNB
Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.JRip.Antd
Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.JRip
Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.JRip.NominalAntd
Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.JRip.NumericAntd
Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.JRip.RipperRule
Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.M5Rules
Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.NNge
Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.OneR
Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.part.C45PruneableDecList
Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.part.ClassifierDecList
Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.PART
Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.part.MakeDecList
Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.part.PruneableDecList
Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.Prism
Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.Ridor
Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.RuleStats
Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.ZeroR
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.ADTree
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.adtree.PredictionNode
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.adtree.ReferenceInstances
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.BFTree
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.DecisionStump
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.ft.FTInnerNode
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.ft.FTLeavesNode
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.ft.FTNode
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.ft.FTtree
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.FT
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.Id3
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.BinC45ModelSelection
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.BinC45Split
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.C45ModelSelection
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTree
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.C45Split
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.ClassifierTree
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.Distribution
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.EntropySplitCrit
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.GainRatioSplitCrit
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.J48
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.GraftSplit
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.InfoGainSplitCrit
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.NBTreeClassifierTree
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.NBTreeModelSelection
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.NBTreeNoSplit
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.NBTreeSplit
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.NoSplit
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.PruneableClassifierTree
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.Stats
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.J48graft
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.LADTree
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.LMT
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.lmt.LMTNode
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.lmt.LogisticBase
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.lmt.ResidualModelSelection
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.lmt.ResidualSplit
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.m5.Impurity
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.m5.PreConstructedLinearModel
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.m5.Rule
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.m5.RuleNode
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.m5.Values
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.m5.YongSplitInfo
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.M5P
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.NBTree
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.RandomForest
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.RandomTree
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.REPTree
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.REPTree.Tree
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.SimpleCart
Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.UserClassifier
Returns the revision string.
getRevision() - Method in class weka.classifiers.xml.XMLClassifier
Returns the revision string.
getRevision() - Method in class weka.clusterers.AbstractClusterer
Returns the revision string.
getRevision() - Method in class weka.clusterers.CheckClusterer
Returns the revision string.
getRevision() - Method in class weka.clusterers.CLOPE
Returns the revision string.
getRevision() - Method in class weka.clusterers.ClusterEvaluation
Returns the revision string.
getRevision() - Method in class weka.clusterers.Cobweb
Returns the revision string.
getRevision() - Method in class weka.clusterers.DBScan
Returns the revision string.
getRevision() - Method in class weka.clusterers.EM
Returns the revision string.
getRevision() - Method in class weka.clusterers.FarthestFirst
Returns the revision string.
getRevision() - Method in class weka.clusterers.FilteredClusterer
Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase
Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel
Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.OPTICS_Visualizer
Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.ResultVectorTableModel
Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERFileFilter
Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject
Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.EpsilonRange_ListElement
Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueue
Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueueElement
Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueue
Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueueElement
Returns the revision string.
getRevision() - Method in class weka.clusterers.HierarchicalClusterer
Returns the revision string.
getRevision() - Method in class weka.clusterers.MakeDensityBasedClusterer
Returns the revision string.
getRevision() - Method in class weka.clusterers.OPTICS
Returns the revision string.
getRevision() - Method in class weka.clusterers.sIB
Returns the revision string.
getRevision() - Method in class weka.clusterers.SimpleKMeans
Returns the revision string.
getRevision() - Method in class weka.clusterers.XMeans
Returns the revision string.
getRevision() - Method in class weka.core.AlgVector
Returns the revision string.
getRevision() - Method in class weka.core.AllJavadoc
Returns the revision string.
getRevision() - Method in class weka.core.Attribute
Returns the revision string.
getRevision() - Method in class weka.core.AttributeExpression
Returns the revision string.
getRevision() - Method in class weka.core.AttributeLocator
Returns the revision string.
getRevision() - Method in class weka.core.AttributeStats
Returns the revision string.
getRevision() - Method in class weka.core.BinarySparseInstance
Returns the revision string.
getRevision() - Method in class weka.core.Capabilities
Returns the revision string.
getRevision() - Method in class weka.core.ChebyshevDistance
Returns the revision string.
getRevision() - Method in class weka.core.CheckGOE
Returns the revision string.
getRevision() - Method in class weka.core.CheckOptionHandler
Returns the revision string.
getRevision() - Method in class weka.core.CheckScheme.PostProcessor
Returns the revision string.
getRevision() - Method in class weka.core.ClassDiscovery
Returns the revision string.
getRevision() - Method in class weka.core.ClassDiscovery.StringCompare
Returns the revision string.
getRevision() - Method in class weka.core.ClassloaderUtil
Returns the revision string.
getRevision() - Method in class weka.core.ContingencyTables
Returns the revision string.
getRevision() - Method in class weka.core.converters.ArffLoader.ArffReader
Returns the revision string.
getRevision() - Method in class weka.core.converters.ArffLoader
Returns the revision string.
getRevision() - Method in class weka.core.converters.ArffSaver
Returns the revision string.
getRevision() - Method in class weka.core.converters.C45Loader
Returns the revision string.
getRevision() - Method in class weka.core.converters.C45Saver
Returns the revision string.
getRevision() - Method in class weka.core.converters.ConverterUtils.DataSink
Returns the revision string.
getRevision() - Method in class weka.core.converters.ConverterUtils.DataSource
Returns the revision string.
getRevision() - Method in class weka.core.converters.ConverterUtils
Returns the revision string.
getRevision() - Method in class weka.core.converters.CSVLoader
Returns the revision string.
getRevision() - Method in class weka.core.converters.CSVSaver
Returns the revision string.
getRevision() - Method in class weka.core.converters.DatabaseConnection
Returns the revision string.
getRevision() - Method in class weka.core.converters.DatabaseLoader
Returns the revision string.
getRevision() - Method in class weka.core.converters.DatabaseSaver
Returns the revision string.
getRevision() - Method in class weka.core.converters.LibSVMLoader
Returns the revision string.
getRevision() - Method in class weka.core.converters.LibSVMSaver
Returns the revision string.
getRevision() - Method in class weka.core.converters.SerializedInstancesLoader
Returns the revision string.
getRevision() - Method in class weka.core.converters.SerializedInstancesSaver
Returns the revision string.
getRevision() - Method in class weka.core.converters.SVMLightLoader
Returns the revision string.
getRevision() - Method in class weka.core.converters.SVMLightSaver
Returns the revision string.
getRevision() - Method in class weka.core.converters.TextDirectoryLoader
Returns the revision string.
getRevision() - Method in class weka.core.converters.XRFFLoader
Returns the revision string.
getRevision() - Method in class weka.core.converters.XRFFSaver
Returns the revision string.
getRevision() - Method in class weka.core.Debug.Clock
Returns the revision string.
getRevision() - Method in class weka.core.Debug.DBO
Returns the revision string.
getRevision() - Method in class weka.core.Debug
Returns the revision string.
getRevision() - Method in class weka.core.Debug.Log
Returns the revision string.
getRevision() - Method in class weka.core.Debug.Random
Returns the revision string.
getRevision() - Method in class weka.core.Debug.SimpleLog
Returns the revision string.
getRevision() - Method in class weka.core.Debug.Timestamp
Returns the revision string.
getRevision() - Method in class weka.core.EditDistance
Returns the revision string.
getRevision() - Method in class weka.core.Environment
Returns the revision string.
getRevision() - Method in class weka.core.EuclideanDistance
Returns the revision string.
getRevision() - Method in class weka.core.FastVector.FastVectorEnumeration
Returns the revision string.
getRevision() - Method in class weka.core.FastVector
Returns the revision string.
getRevision() - Method in class weka.core.FindWithCapabilities
Returns the revision string.
getRevision() - Method in class weka.core.GlobalInfoJavadoc
Returns the revision string.
getRevision() - Method in class weka.core.Instance
Returns the revision string.
getRevision() - Method in class weka.core.InstanceComparator
Returns the revision string.
getRevision() - Method in class weka.core.Instances
Returns the revision string.
getRevision() - Method in class weka.core.Jython
Returns the revision string.
getRevision() - Method in class weka.core.ListOptions
Returns the revision string.
getRevision() - Method in class weka.core.logging.ConsoleLogger
Returns the revision string.
getRevision() - Method in class weka.core.logging.FileLogger
Returns the revision string.
getRevision() - Method in class weka.core.logging.OutputLogger
Returns the revision string.
getRevision() - Method in class weka.core.ManhattanDistance
Returns the revision string.
getRevision() - Method in class weka.core.MathematicalExpression
Returns the revision string.
getRevision() - Method in class weka.core.matrix.CholeskyDecomposition
Returns the revision string.
getRevision() - Method in class weka.core.matrix.DoubleVector
Returns the revision string.
getRevision() - Method in class weka.core.matrix.EigenvalueDecomposition
Returns the revision string.
getRevision() - Method in class weka.core.matrix.ExponentialFormat
Returns the revision string.
getRevision() - Method in class weka.core.matrix.FlexibleDecimalFormat
Returns the revision string.
getRevision() - Method in class weka.core.matrix.FloatingPointFormat
Returns the revision string.
getRevision() - Method in class weka.core.Matrix
Deprecated.
Returns the revision string.
getRevision() - Method in class weka.core.matrix.IntVector
Returns the revision string.
getRevision() - Method in class weka.core.matrix.LinearRegression
Returns the revision string.
getRevision() - Method in class weka.core.matrix.LUDecomposition
Returns the revision string.
getRevision() - Method in class weka.core.matrix.Maths
Returns the revision string.
getRevision() - Method in class weka.core.matrix.Matrix
Returns the revision string.
getRevision() - Method in class weka.core.matrix.QRDecomposition
Returns the revision string.
getRevision() - Method in class weka.core.matrix.SingularValueDecomposition
Returns the revision string.
getRevision() - Method in class weka.core.Memory
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.BallTree
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.balltrees.BallNode
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.balltrees.BallSplitter
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.balltrees.BottomUpConstructor
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.balltrees.BottomUpConstructor.TempNode
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor.ListNode
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor.TempNode
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.balltrees.PointsClosestToFurthestChildren
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.CoverTree.CoverTreeNode
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.CoverTree
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.CoverTree.MyHeap
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.CoverTree.MyHeapElement
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.covertrees.Stack
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.KDTree
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.kdtrees.KDTreeNode
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.kdtrees.KMeansInpiredMethod
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.kdtrees.MedianOfWidestDimension
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.kdtrees.MidPointOfWidestDimension
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.kdtrees.SlidingMidPointOfWidestSide
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.LinearNNSearch
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.MyHeap
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.MyHeapElement
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.NeighborList
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.NeighborNode
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.PerformanceStats
Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.TreePerformanceStats
Returns the revision string.
getRevision() - Method in class weka.core.Option
Returns the revision string.
getRevision() - Method in class weka.core.OptionHandlerJavadoc
Returns the revision string.
getRevision() - Method in class weka.core.PropertyPath
Returns the revision string.
getRevision() - Method in class weka.core.PropertyPath.Path
Returns the revision string.
getRevision() - Method in class weka.core.PropertyPath.PathElement
Returns the revision string.
getRevision() - Method in class weka.core.PropertyPath.PropertyContainer
Returns the revision string.
getRevision() - Method in class weka.core.ProtectedProperties
Returns the revision string.
getRevision() - Method in class weka.core.Queue
Returns the revision string.
getRevision() - Method in class weka.core.Queue.QueueNode
Returns the revision string.
getRevision() - Method in class weka.core.RandomVariates
Returns the revision string.
getRevision() - Method in class weka.core.Range
Returns the revision string.
getRevision() - Method in class weka.core.RelationalLocator
Returns the revision string.
getRevision() - Method in interface weka.core.RevisionHandler
Returns the revision string.
getRevision() - Method in class weka.core.SelectedTag
Returns the revision string.
getRevision() - Method in class weka.core.SerializationHelper
Returns the revision string.
getRevision() - Method in class weka.core.SerializedObject
Returns the revision string.
getRevision() - Method in class weka.core.SingleIndex
Returns the revision string.
getRevision() - Method in class weka.core.SparseInstance
Returns the revision string.
getRevision() - Method in class weka.core.SpecialFunctions
Returns the revision string.
getRevision() - Method in class weka.core.Statistics
Returns the revision string.
getRevision() - Method in class weka.core.stemmers.IteratedLovinsStemmer
Returns the revision string.
getRevision() - Method in class weka.core.stemmers.LovinsStemmer
Returns the revision string.
getRevision() - Method in class weka.core.stemmers.NullStemmer
Returns the revision string.
getRevision() - Method in class weka.core.stemmers.SnowballStemmer
Returns the revision string.
getRevision() - Method in class weka.core.stemmers.Stemming
Returns the revision string.
getRevision() - Method in class weka.core.Stopwords
Returns the revision string.
getRevision() - Method in class weka.core.StringLocator
Returns the revision string.
getRevision() - Method in class weka.core.SystemInfo
Returns the revision string.
getRevision() - Method in class weka.core.Tag
Returns the revision string.
getRevision() - Method in class weka.core.TechnicalInformation
Returns the revision string.
getRevision() - Method in class weka.core.TechnicalInformationHandlerJavadoc
Returns the revision string.
getRevision() - Method in class weka.core.Tee
Returns the revision string.
getRevision() - Method in class weka.core.TestInstances
Returns the revision string.
getRevision() - Method in class weka.core.tokenizers.AlphabeticTokenizer
Returns the revision string.
getRevision() - Method in class weka.core.tokenizers.NGramTokenizer
Returns the revision string.
getRevision() - Method in class weka.core.tokenizers.WordTokenizer
Returns the revision string.
getRevision() - Method in class weka.core.Trie
Returns the revision string.
getRevision() - Method in class weka.core.Trie.TrieIterator
Returns the revision string.
getRevision() - Method in class weka.core.Trie.TrieNode
Returns the revision string.
getRevision() - Method in class weka.core.Utils
Returns the revision string.
getRevision() - Method in class weka.core.Version
Returns the revision string.
getRevision() - Method in class weka.core.xml.KOML
Returns the revision string.
getRevision() - Method in class weka.core.xml.MethodHandler
Returns the revision string.
getRevision() - Method in class weka.core.xml.PropertyHandler
Returns the revision string.
getRevision() - Method in class weka.core.xml.SerialUIDChanger
Returns the revision string.
getRevision() - Method in class weka.core.xml.XMLBasicSerialization
Returns the revision string.
getRevision() - Method in class weka.core.xml.XMLDocument
Returns the revision string.
getRevision() - Method in class weka.core.xml.XMLInstances
Returns the revision string.
getRevision() - Method in class weka.core.xml.XMLOptions
Returns the revision string.
getRevision() - Method in class weka.core.xml.XMLSerialization
Returns the revision string.
getRevision() - Method in class weka.core.xml.XMLSerializationMethodHandler
Returns the revision string.
getRevision() - Method in class weka.core.xml.XStream
Returns the revision string.
getRevision() - Method in class weka.datagenerators.classifiers.classification.Agrawal
Returns the revision string.
getRevision() - Method in class weka.datagenerators.classifiers.classification.BayesNet
Returns the revision string.
getRevision() - Method in class weka.datagenerators.classifiers.classification.LED24
Returns the revision string.
getRevision() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
Returns the revision string.
getRevision() - Method in class weka.datagenerators.classifiers.classification.RDG1
Returns the revision string.
getRevision() - Method in class weka.datagenerators.classifiers.regression.Expression
Returns the revision string.
getRevision() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Returns the revision string.
getRevision() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Returns the revision string.
getRevision() - Method in class weka.datagenerators.clusterers.SubspaceCluster
Returns the revision string.
getRevision() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Returns the revision string.
getRevision() - Method in class weka.datagenerators.Test
Returns the revision string.
getRevision() - Method in class weka.estimators.CheckEstimator.AttrTypes
Returns the revision string.
getRevision() - Method in class weka.estimators.CheckEstimator.EstTypes
Returns the revision string.
getRevision() - Method in class weka.estimators.CheckEstimator
Returns the revision string.
getRevision() - Method in class weka.estimators.CheckEstimator.PostProcessor
Returns the revision string.
getRevision() - Method in class weka.estimators.DDConditionalEstimator
Returns the revision string.
getRevision() - Method in class weka.estimators.DiscreteEstimator
Returns the revision string.
getRevision() - Method in class weka.estimators.DKConditionalEstimator
Returns the revision string.
getRevision() - Method in class weka.estimators.DNConditionalEstimator
Returns the revision string.
getRevision() - Method in class weka.estimators.EstimatorUtils
Returns the revision string.
getRevision() - Method in class weka.estimators.KDConditionalEstimator
Returns the revision string.
getRevision() - Method in class weka.estimators.KernelEstimator
Returns the revision string.
getRevision() - Method in class weka.estimators.KKConditionalEstimator
Returns the revision string.
getRevision() - Method in class weka.estimators.MahalanobisEstimator
Returns the revision string.
getRevision() - Method in class weka.estimators.NDConditionalEstimator
Returns the revision string.
getRevision() - Method in class weka.estimators.NNConditionalEstimator
Returns the revision string.
getRevision() - Method in class weka.estimators.NormalEstimator
Returns the revision string.
getRevision() - Method in class weka.estimators.PoissonEstimator
Returns the revision string.
getRevision() - Method in class weka.experiment.AveragingResultProducer
Returns the revision string.
getRevision() - Method in class weka.experiment.ClassifierSplitEvaluator
Returns the revision string.
getRevision() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
Returns the revision string.
getRevision() - Method in class weka.experiment.CrossValidationResultProducer
Returns the revision string.
getRevision() - Method in class weka.experiment.CSVResultListener
Returns the revision string.
getRevision() - Method in class weka.experiment.DatabaseResultListener
Returns the revision string.
getRevision() - Method in class weka.experiment.DatabaseResultProducer
Returns the revision string.
getRevision() - Method in class weka.experiment.DatabaseUtils
Returns the revision string.
getRevision() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
Returns the revision string.
getRevision() - Method in class weka.experiment.Experiment
Returns the revision string.
getRevision() - Method in class weka.experiment.InstanceQuery
Returns the revision string.
getRevision() - Method in class weka.experiment.InstancesResultListener
Returns the revision string.
getRevision() - Method in class weka.experiment.LearningRateResultProducer
Returns the revision string.
getRevision() - Method in class weka.experiment.OutputZipper
Returns the revision string.
getRevision() - Method in class weka.experiment.PairedCorrectedTTester
Returns the revision string.
getRevision() - Method in class weka.experiment.PairedStats
Returns the revision string.
getRevision() - Method in class weka.experiment.PairedStatsCorrected
Returns the revision string.
getRevision() - Method in class weka.experiment.PairedTTester.Dataset
Returns the revision string.
getRevision() - Method in class weka.experiment.PairedTTester.DatasetSpecifiers
Returns the revision string.
getRevision() - Method in class weka.experiment.PairedTTester
Returns the revision string.
getRevision() - Method in class weka.experiment.PairedTTester.Resultset
Returns the revision string.
getRevision() - Method in class weka.experiment.PropertyNode
Returns the revision string.
getRevision() - Method in class weka.experiment.RandomSplitResultProducer
Returns the revision string.
getRevision() - Method in class weka.experiment.RegressionSplitEvaluator
Returns the revision string.
getRevision() - Method in class weka.experiment.RemoteEngine
Returns the revision string.
getRevision() - Method in class weka.experiment.RemoteExperiment
Returns the revision string.
getRevision() - Method in class weka.experiment.RemoteExperimentSubTask
Returns the revision string.
getRevision() - Method in class weka.experiment.ResultMatrixCSV
Returns the revision string.
getRevision() - Method in class weka.experiment.ResultMatrixGnuPlot
Returns the revision string.
getRevision() - Method in class weka.experiment.ResultMatrixHTML
Returns the revision string.
getRevision() - Method in class weka.experiment.ResultMatrixLatex
Returns the revision string.
getRevision() - Method in class weka.experiment.ResultMatrixPlainText
Returns the revision string.
getRevision() - Method in class weka.experiment.ResultMatrixSignificance
Returns the revision string.
getRevision() - Method in class weka.experiment.Stats
Returns the revision string.
getRevision() - Method in class weka.experiment.TaskStatusInfo
Returns the revision string.
getRevision() - Method in class weka.experiment.xml.XMLExperiment
Returns the revision string.
getRevision() - Method in class weka.filters.AllFilter
Returns the revision string.
getRevision() - Method in class weka.filters.CheckSource
Returns the revision string.
getRevision() - Method in class weka.filters.Filter
Returns the revision string.
getRevision() - Method in class weka.filters.MultiFilter
Returns the revision string.
getRevision() - Method in class weka.filters.supervised.attribute.AddClassification
Returns the revision string.
getRevision() - Method in class weka.filters.supervised.attribute.AttributeSelection
Returns the revision string.
getRevision() - Method in class weka.filters.supervised.attribute.ClassOrder
Returns the revision string.
getRevision() - Method in class weka.filters.supervised.attribute.Discretize
Returns the revision string.
getRevision() - Method in class weka.filters.supervised.attribute.NominalToBinary
Returns the revision string.
getRevision() - Method in class weka.filters.supervised.attribute.PLSFilter
Returns the revision string.
getRevision() - Method in class weka.filters.supervised.instance.Resample
Returns the revision string.
getRevision() - Method in class weka.filters.supervised.instance.SMOTE
Returns the revision string.
getRevision() - Method in class weka.filters.supervised.instance.SpreadSubsample
Returns the revision string.
getRevision() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.Add
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.AddCluster
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.AddExpression
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.AddID
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.AddNoise
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.AddValues
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.Center
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.ClassAssigner
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.ClusterMembership
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.Copy
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.Discretize
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.FirstOrder
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.KernelFilter
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.MathExpression
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.NominalToBinary
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.NominalToString
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.Normalize
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.NumericToBinary
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.NumericToNominal
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.NumericTransform
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.Obfuscate
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.RandomProjection
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.RandomSubset
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.RELAGGS
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.Remove
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.RemoveType
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.RemoveUseless
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.Reorder
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.Standardize
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.StringToNominal
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.SwapValues
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.TimeSeriesDelta
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.Wavelet
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.NonSparseToSparse
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.Normalize
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.Randomize
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.RemoveFolds
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.RemovePercentage
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.RemoveRange
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.Resample
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.ReservoirSample
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.SparseToNonSparse
Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.SubsetByExpression
Returns the revision string.
getRevision() - Method in class weka.gui.beans.FlowRunner
 
getRevision() - Method in class weka.gui.sql.DbUtils
Returns the revision string.
getRidge() - Method in class weka.classifiers.functions.LinearRegression
Get the value of Ridge.
getRidge() - Method in class weka.classifiers.functions.Logistic
Gets the ridge in the log-likelihood.
getRidge() - Method in class weka.classifiers.functions.RBFNetwork
Gets the ridge value.
getRidge() - Method in class weka.classifiers.mi.MILR
Gets the ridge in the log-likelihood.
getRocAnalysis() - Method in class weka.associations.Tertius
Get the value of rocAnalysis.
getROCArea(Instances) - Static method in class weka.classifiers.evaluation.ThresholdCurve
Calculates the area under the ROC curve as the Wilcoxon-Mann-Whitney statistic.
getROCString() - Method in class weka.gui.visualize.ThresholdVisualizePanel
This extracts the ROC area string
getRoot() - Method in class weka.core.Trie
returns the root node of the trie
getRoot() - Method in class weka.gui.treevisualizer.Node
Get the value of root.
getRootFromClass(String, String) - Static method in class weka.gui.GenericObjectEditor
returns the name of the root element of the given class name, null if it doesn't contain the separator.
getRootNode() - Method in class weka.core.xml.XMLDocument
returns the current root node.
getRow(int) - Method in class weka.core.Matrix
Deprecated.
Gets a row of the matrix and returns it as double array.
getRow() - Static method in class weka.gui.experiment.ExperimenterDefaults
the comma-separated list of attribute names that identify a row
getRowCount() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.ResultVectorTableModel
Returns the number of rows of this model.
getRowCount() - Method in class weka.experiment.ResultMatrix
returns the number of rows
getRowCount() - Method in class weka.gui.arffviewer.ArffTableModel
returns the number of rows in the model
getRowCount() - Method in class weka.gui.SortedTableModel
Returns the number of rows in the model.
getRowCount() - Method in class weka.gui.sql.ResultSetHelper
returns the number of rows in the resultset.
getRowCount() - Method in class weka.gui.sql.ResultSetTableModel
returns the number of rows in the model.
getRowDimension() - Method in class weka.core.matrix.Matrix
Get row dimension.
getRowHidden(int) - Method in class weka.experiment.ResultMatrix
returns the hidden status of the row, if the index is valid, otherwise false
getRowName(int) - Method in class weka.experiment.ResultMatrix
returns the name of the row, if the index is valid, otherwise null.
getRowNameWidth() - Method in class weka.experiment.ResultMatrix
returns the current width for the row names
getRowOrder() - Method in class weka.experiment.ResultMatrix
returns the current order of the rows, null means the default order
getRowPackedCopy() - Method in class weka.core.matrix.Matrix
Make a one-dimensional row packed copy of the internal array.
getRsource() - Method in class weka.gui.treevisualizer.Edge
Get the value of rsource.
getRtarget() - Method in class weka.gui.treevisualizer.Edge
Get the value of rtarget.
getRuleset() - Method in class weka.classifiers.rules.JRip
Get the ruleset generated by Ripper
getRuleset() - Method in class weka.classifiers.rules.RuleStats
Get the ruleset of the stats
getRulesetSize() - Method in class weka.classifiers.rules.RuleStats
Get the size of the ruleset in the stats
getRulesMustContain() - Method in class weka.associations.FPGrowth
Get the comma separated list of items that rules must contain in order to be output.
getRuleStats(int) - Method in class weka.classifiers.rules.JRip
Get the statistics of the ruleset in the given position
getRunColumn() - Method in class weka.experiment.PairedTTester
Get the value of RunColumn.
getRunColumn() - Method in interface weka.experiment.Tester
Get the value of RunColumn.
getRunLower() - Method in class weka.experiment.Experiment
Get the lower run number for the experiment.
getRunNumber() - Method in class weka.gui.beans.BatchClassifierEvent
Get the run number.
getRunNumber() - Method in class weka.gui.beans.TestSetEvent
Get the run number that this training set belongs to.
getRunNumber() - Method in class weka.gui.beans.TrainingSetEvent
Get the run number that this training set belongs to.
getRuns() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
 
getRuns() - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
Returns the number of runs
getRuns() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
 
getRuns() - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
 
getRuns() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
 
getRuns() - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
 
getRuns() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
 
getRuns() - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
 
getRunUpper() - Method in class weka.experiment.Experiment
Get the upper run number for the experiment.
getS() - Method in class weka.core.matrix.SingularValueDecomposition
Return the diagonal matrix of singular values
getSampleSize() - Method in class weka.attributeSelection.ReliefFAttributeEval
Get the number of instances used for estimating attributes
getSampleSize() - Method in class weka.classifiers.functions.LeastMedSq
gets number of samples
getSampleSize() - Method in class weka.filters.unsupervised.instance.ReservoirSample
Gets the subsample size.
getSampleSizePercent() - Method in class weka.classifiers.meta.GridSearch
Gets the sample size for the initial grid search.
getSampleSizePercent() - Method in class weka.filters.supervised.instance.Resample
Gets the subsample size as a percentage of the original set.
getSampleSizePercent() - Method in class weka.filters.unsupervised.instance.Resample
Gets the subsample size as a percentage of the original set.
getSaveDialogTitle() - Method in class weka.gui.visualize.PrintableComponent
returns the title for the save dialog.
getSaveDialogTitle() - Method in interface weka.gui.visualize.PrintableHandler
returns the title for the save dialog
getSaveDialogTitle() - Method in class weka.gui.visualize.PrintablePanel
returns the title for the save dialog
getSaveInstanceData() - Method in class weka.classifiers.trees.ADTree
Gets whether the tree is to save instance data.
getSaveInstanceData() - Method in class weka.classifiers.trees.J48
Check whether instance data is to be saved.
getSaveInstanceData() - Method in class weka.classifiers.trees.J48graft
Check whether instance data is to be saved.
getSaveInstanceData() - Method in class weka.clusterers.Cobweb
Get the value of saveInstances.
getSaveInstances() - Method in class weka.classifiers.trees.M5P
Get whether instance data is being save.
getSaver() - Method in class weka.gui.ConverterFileChooser
returns the saver that was chosen by the user, can be null in case the user aborted the dialog or the open dialog was shown
getSaverForExtension(String) - Static method in class weka.core.converters.ConverterUtils
tries to determine the saver to use for this kind of extension, returns null if none can be found.
getSaverForFile(String) - Static method in class weka.core.converters.ConverterUtils
tries to determine the saver to use for this kind of file, returns null if none can be found.
getSaverForFile(File) - Static method in class weka.core.converters.ConverterUtils
tries to determine the saver to use for this kind of file, returns null if none can be found.
getSaverTemplate() - Method in class weka.gui.beans.Saver
Get the saver
getScale() - Method in class weka.filters.unsupervised.attribute.Normalize
Get the scaling factor.
getScalingEnabled() - Method in class weka.gui.visualize.JComponentWriter
whether scaling is enabled or ignored
getScore() - Method in class weka.classifiers.functions.supportVector.RegOptimizer
Compute the value of the objective function.
getScoreType() - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
get quality measure to be used in searching for networks.
getSearch() - Method in class weka.attributeSelection.CheckAttributeSelection
Get the current search method
getSearch() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Gets the search method used
getSearch() - Method in class weka.classifiers.rules.DecisionTable
Gets the current search method
getSearch() - Method in class weka.classifiers.rules.DTNB
Gets the current search method
getSearch() - Method in class weka.filters.supervised.attribute.AttributeSelection
Get the name of the search method
getSearchAlgorithm() - Method in class weka.classifiers.bayes.BayesNet
Get the SearchAlgorithm used as the search algorithm
getSearchBackwards() - Method in class weka.attributeSelection.GreedyStepwise
Get whether to search backwards
getSearchPath() - Method in class weka.classifiers.trees.ADTree
Gets the method of searching the tree for a new insertion.
getSearchPercent() - Method in class weka.attributeSelection.RandomSearch
get the percentage of the search space to consider
getSearchSpec() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Gets the search specification string, which contains the class name of the search method and any options to it
getSearchSpec() - Method in class weka.classifiers.rules.DecisionTable
Gets the search specification string, which contains the class name of the search method and any options to it
getSearchString() - Method in class weka.gui.arffviewer.ArffTable
returns the search string, can be NULL if no search string is set
getSearchTermination() - Method in class weka.attributeSelection.BestFirst
Get the termination criterion (number of non-improving nodes).
getSearchTermination() - Method in class weka.attributeSelection.LinearForwardSelection
Get the termination criterion (number of non-improving nodes).
getSecondValueIndex() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
Get the index of the second value used.
getSecondValueIndex() - Method in class weka.filters.unsupervised.attribute.SwapValues
Get the index of the second value used.
getSeed() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
Gets the seed for the random number generations.
getSeed() - Method in class weka.attributeSelection.GeneticSearch
get the value of the random number generator's seed
getSeed() - Method in class weka.attributeSelection.OneRAttributeEval
Get the random number seed
getSeed() - Method in class weka.attributeSelection.RandomSearch
Get the random seed to use
getSeed() - Method in class weka.attributeSelection.ReliefFAttributeEval
Get the seed used for randomly sampling instances.
getSeed() - Method in class weka.attributeSelection.ScatterSearchV1
get the value of the random number generator's seed
getSeed() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Seed for cross validation subset size determination.
getSeed() - Method in class weka.attributeSelection.WrapperSubsetEval
Get the random number seed used for cross validation
getSeed() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Get the seed for randomizing the instances for CV-based hyperparameter selection
getSeed() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
 
getSeed() - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
Returns the random seed
getSeed() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
 
getSeed() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
 
getSeed() - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
 
getSeed() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
 
getSeed() - Method in class weka.classifiers.BVDecompose
Gets the random number seed
getSeed() - Method in class weka.classifiers.BVDecomposeSegCVSub
Gets the random number seed
getSeed() - Method in class weka.classifiers.evaluation.EvaluationUtils
Gets the seed for randomization during cross-validation
getSeed() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
getSeed() - Method in class weka.classifiers.functions.supportVector.RegOptimizer
Gets the current seed value for the random number generator
getSeed() - Method in class weka.classifiers.functions.VotedPerceptron
Get the value of Seed.
getSeed() - Method in class weka.classifiers.functions.Winnow
Get the value of Seed.
getSeed() - Method in class weka.classifiers.meta.MultiScheme
Gets the random number seed.
getSeed() - Method in class weka.classifiers.RandomizableClassifier
Gets the seed for the random number generations
getSeed() - Method in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
Gets the seed for the random number generations
getSeed() - Method in class weka.classifiers.RandomizableMultipleClassifiersCombiner
Gets the seed for the random number generations
getSeed() - Method in class weka.classifiers.RandomizableSingleClassifierEnhancer
Gets the seed for the random number generations
getSeed() - Method in class weka.classifiers.rules.ConjunctiveRule
returns the current seed value for randomizing the data
getSeed() - Method in class weka.classifiers.rules.JRip
Gets the current seed value to use in randomizing the data
getSeed() - Method in class weka.classifiers.rules.PART
Get the value of Seed.
getSeed() - Method in class weka.classifiers.rules.Ridor
 
getSeed() - Method in class weka.classifiers.trees.J48
Get the value of Seed.
getSeed() - Method in class weka.classifiers.trees.RandomForest
Gets the seed for the random number generations
getSeed() - Method in class weka.classifiers.trees.RandomTree
Gets the seed for the random number generations
getSeed() - Method in class weka.classifiers.trees.REPTree
Get the value of Seed.
getSeed() - Method in class weka.clusterers.RandomizableClusterer
Gets the seed for the random number generations
getSeed() - Method in class weka.clusterers.RandomizableDensityBasedClusterer
Gets the seed for the random number generations
getSeed() - Method in class weka.clusterers.RandomizableSingleClustererEnhancer
Gets the seed for the random number generations
getSeed() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Returns the seed for random number generator.
getSeed() - Method in interface weka.core.Randomizable
Gets the seed for the random number generations
getSeed() - Method in class weka.core.TestInstances
returns the current seed value
getSeed() - Method in class weka.datagenerators.DataGenerator
Gets the random number seed.
getSeed() - Method in class weka.filters.supervised.attribute.ClassOrder
Get the current randomization seed
getSeed() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Gets the random number seed used for shuffling the dataset.
getSeed() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
Returns the current seed value for randomizing the order of the generated data
getSeed() - Method in class weka.filters.unsupervised.attribute.RandomSubset
Get the seed value for the random number generator.
getSeed() - Method in class weka.filters.unsupervised.instance.RemoveFolds
Gets the random number seed used for shuffling the dataset.
getSeed() - Method in class weka.gui.beans.CrossValidationFoldMaker
Get the currently set seed
getSeed() - Method in class weka.gui.beans.TrainTestSplitMaker
Get the value of the random seed
getSelectedAttributes() - Method in class weka.gui.AttributeSelectionPanel
Gets an array containing the indices of all selected attributes.
getSelectedBuffer() - Method in class weka.gui.ResultHistoryPanel
Gets the buffer associated with the currently selected item in the list.
getSelectedName() - Method in class weka.gui.ResultHistoryPanel
Get the name of the currently selected item in the list
getSelectedObject() - Method in class weka.gui.ResultHistoryPanel
Gets the object associated with the currently selected item in the list.
getSelectedRange() - Method in class weka.filters.unsupervised.attribute.RELAGGS
Gets the current range selection.
getSelectedRange() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Get the value of m_SelectedRange.
getSelectedTag() - Method in class weka.core.SelectedTag
Gets the selected Tag.
getSelection() - Method in class weka.core.Range
Gets an array containing all the selected values, in the order that they were selected (or ascending order if range inversion is on)
getSelectionModel() - Method in class weka.gui.AttributeListPanel
Gets the selection model used by the table.
getSelectionModel() - Method in class weka.gui.AttributeSelectionPanel
Gets the selection model used by the table.
getSelectionModel() - Method in class weka.gui.ResultHistoryPanel
Gets the selection model used by the results list.
getSelectionThreshold() - Method in class weka.attributeSelection.RaceSearch
Returns the threshold so that the AttributeSelection module can discard attributes from the ranking.
getSeparatingThreshold() - Method in class weka.classifiers.functions.pace.ChisqMixture
Gets the separating threshold value.
getSeparatingThreshold() - Method in class weka.classifiers.functions.pace.NormalMixture
Gets the separating threshold value.
getSeperator() - Method in class weka.gui.HierarchyPropertyParser
Get the seperator between levels.
getSequentialAttIndex(int) - Method in class weka.classifiers.lazy.LBR.Indexes
Returns the boolean value at the specified index in the Sequential Attribute Indexes array
getSequentialInstanceIndex(int) - Method in class weka.classifiers.lazy.LBR.Indexes
Returns the boolean value at the specified index in the Sequential Instance Indexes array
getSequentialNumAttributes() - Method in class weka.classifiers.lazy.LBR.Indexes
Returns the number of attributes in the Sequential array
getSequentialNumInstances() - Method in class weka.classifiers.lazy.LBR.Indexes
Returns the number of instances in the Sequential array
getSerializedClassifierFile() - Method in class weka.filters.supervised.attribute.AddClassification
Gets the file pointing to a serialized, trained classifier.
getSERObject() - Method in class weka.clusterers.OPTICS
Returns the internal database
getSetNumber() - Method in class weka.gui.beans.BatchClassifierEvent
Get the set number (ie which fold this is)
getSetNumber() - Method in class weka.gui.beans.BatchClustererEvent
Get the set number (ie which fold this is)
getSetNumber() - Method in class weka.gui.beans.TestSetEvent
Get the test set number (eg.
getSetNumber() - Method in class weka.gui.beans.TrainingSetEvent
Get the set number (eg.
getShape() - Method in class weka.gui.treevisualizer.Node
Get the value of shape.
getShapes() - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
 
getShowAttBars() - Method in class weka.gui.visualize.VisualizePanel
Gets whether or not attribute bars are being displayed.
getShowAverage() - Method in class weka.experiment.ResultMatrix
returns whether average per column is displayed or not
getShowAverage() - Static method in class weka.gui.experiment.ExperimenterDefaults
returns whether the Average is shown by default
getShowAverage() - Method in class weka.gui.experiment.OutputFormatDialog
returns whether the average for each column is displayed.
getShowClassPanel() - Method in class weka.gui.visualize.VisualizePanel
Gets whether or not the class panel is being displayed.
getShowGUI() - Method in class weka.clusterers.OPTICS
Returns the flag for showing the OPTICS visualizer GUI.
getShowStdDev() - Method in class weka.experiment.ResultMatrix
returns whether std deviations are displayed or not
getShowStdDevs() - Method in class weka.experiment.PairedTTester
Returns true if standard deviations have been requested.
getShowStdDevs() - Method in interface weka.experiment.Tester
Returns true if standard deviations have been requested.
getShowStdDevs() - Static method in class weka.gui.experiment.ExperimenterDefaults
returns whether StdDevs are shown by default
getShrinkage() - Method in class weka.classifiers.meta.AdditiveRegression
Get the shrinkage rate.
getShrinkage() - Method in class weka.classifiers.meta.LogitBoost
Get the value of Shrinkage.
getShrinking() - Method in class weka.classifiers.functions.LibSVM
whether to use the shrinking heuristics
getShuffle() - Method in class weka.classifiers.rules.Ridor
 
getSigma() - Method in class weka.attributeSelection.ReliefFAttributeEval
Get the value of sigma.
getSigma() - Method in class weka.classifiers.BVDecompose
Get the calculated sigma squared
getSigma() - Method in class weka.classifiers.functions.supportVector.Puk
Gets the sigma value.
getSignificance(int, int) - Method in class weka.experiment.ResultMatrix
returns the significance at the given position, if the position is valid, otherwise SIGNIFICANCE_ATIE
getSignificance() - Static method in class weka.gui.experiment.ExperimenterDefaults
returns the default significance
getSignificanceCount(int, int) - Method in class weka.experiment.ResultMatrix
counts the occurrences of the given significance type in the given column.
getSignificanceLevel() - Method in class weka.associations.Apriori
Get the value of significanceLevel.
getSignificanceLevel() - Method in class weka.attributeSelection.RaceSearch
Get the significance level
getSignificanceLevel() - Method in class weka.experiment.PairedTTester
Get the value of SignificanceLevel.
getSignificanceLevel() - Method in interface weka.experiment.Tester
Get the value of SignificanceLevel.
getSignificanceWidth() - Method in class weka.experiment.ResultMatrix
returns the current width for the significance
getSilent() - Method in class weka.core.Check
Get whether silent mode is turned on
getSilent() - Method in class weka.core.Javadoc
whether output in the console is suppressed
getSilent() - Method in class weka.estimators.CheckEstimator
Get whether silent mode is turned on
getSimpleStats(int) - Method in class weka.classifiers.rules.RuleStats
Get the simple stats of one rule, including 6 parameters: 0: coverage; 1:uncoverage; 2: true positive; 3: true negatives; 4: false positives; 5: false negatives
getSIndex() - Method in class weka.gui.visualize.VisualizePanel
Get the index of the shape selected for creating splits.
getSingleIndex() - Method in class weka.core.SingleIndex
Gets the string representing the selected range of values
getSingleModeFlag() - Method in class weka.datagenerators.classifiers.classification.Agrawal
Return if single mode is set for the given data generator mode depends on option setting and or generator type.
getSingleModeFlag() - Method in class weka.datagenerators.classifiers.classification.BayesNet
Return if single mode is set for the given data generator mode depends on option setting and or generator type.
getSingleModeFlag() - Method in class weka.datagenerators.classifiers.classification.LED24
Return if single mode is set for the given data generator mode depends on option setting and or generator type.
getSingleModeFlag() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
Return if single mode is set for the given data generator mode depends on option setting and or generator type.
getSingleModeFlag() - Method in class weka.datagenerators.classifiers.classification.RDG1
Gets the single mode flag.
getSingleModeFlag() - Method in class weka.datagenerators.classifiers.regression.Expression
Return if single mode is set for the given data generator mode depends on option setting and or generator type.
getSingleModeFlag() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Return if single mode is set for the given data generator mode depends on option setting and or generator type.
getSingleModeFlag() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Gets the single mode flag.
getSingleModeFlag() - Method in class weka.datagenerators.clusterers.SubspaceCluster
Gets the single mode flag.
getSingleModeFlag() - Method in class weka.datagenerators.DataGenerator
Return if single mode is set for the given data generator mode depends on option setting and or generator type.
getSingleton() - Static method in class weka.core.logging.Logger
Returns the singleton instance of the logger.
getSingleton() - Static method in class weka.gui.beans.KnowledgeFlowApp
Return the singleton instance of the KnowledgeFlow
getSingleton() - Static method in class weka.gui.GUIChooser
Get the singleton instance of the GUIChooser
getSingleton() - Static method in class weka.gui.Main
Return the singleton instance of the Main GUI.
getSingletons(Instances) - Method in class weka.associations.FPGrowth
Get the singleton items in the data
getSingularValues() - Method in class weka.core.matrix.SingularValueDecomposition
Return the one-dimensional array of singular values
getSize() - Method in class weka.core.Debug.Log
returns the size of the files
getSizeOfBranch() - Method in class weka.classifiers.rules.part.ClassifierDecList
Returns the number of instances covered by a branch
getSizePer() - Method in class weka.classifiers.trees.BFTree
Get training set size.
getSizePer() - Method in class weka.classifiers.trees.SimpleCart
Get training set size.
getSkipIdentical() - Method in class weka.core.neighboursearch.LinearNNSearch
Gets whether if identical instances are skipped from the neighbourhood.
getSlope() - Method in class weka.classifiers.functions.SimpleLinearRegression
Returns the slope of the function.
getSmoothing() - Method in class weka.classifiers.trees.m5.Rule
Get whether or not smoothing has been turned on
getSmoothingParameter() - Method in class weka.classifiers.bayes.ComplementNaiveBayes
Gets the smoothing value to be used to avoid zero WordGivenClass probabilities.
getSort() - Method in class weka.filters.unsupervised.attribute.AddValues
Gets whether the labels are sorted or not.
getSortColumn() - Method in class weka.experiment.PairedTTester
Returns the column to sort on, -1 means the default sorting.
getSortColumn() - Method in interface weka.experiment.Tester
Returns the column to sort on, -1 means the default sorting.
getSortColumnName() - Method in class weka.experiment.PairedTTester
Returns the name of the column to sort on.
getSortColumnName() - Method in interface weka.experiment.Tester
Returns the name of the column to sort on.
getSorting() - Static method in class weka.gui.experiment.ExperimenterDefaults
returns the default sorting (empty string means none)
getSource() - Method in class weka.gui.beans.BeanConnection
returns the source BeanInstance for this connection
getSource() - Method in class weka.gui.treevisualizer.Edge
Get the value of source.
getSourceCode() - Method in class weka.classifiers.CheckSource
Gets the class to test.
getSourceCode() - Method in class weka.filters.CheckSource
Gets the class to test.
getSourceEventSetDescriptor() - Method in class weka.gui.beans.BeanConnection
Returns the event set descriptor for the event generated by the source for this connection
getSparseData() - Method in class weka.experiment.InstanceQuery
Gets whether data is to be returned as a set of sparse instances
getSplitByDataSet() - Method in class weka.experiment.RemoteExperiment
Returns true if sub experiments are to be created on the basis of data set..
getSplitDim() - Method in class weka.core.neighboursearch.kdtrees.KDTreeNode
Gets the splitting dimension.
getSplitEvaluator() - Method in class weka.experiment.CrossValidationResultProducer
Get the SplitEvaluator.
getSplitEvaluator() - Method in class weka.experiment.RandomSplitResultProducer
Get the SplitEvaluator.
getSplitOnResiduals() - Method in class weka.classifiers.trees.LMT
Get the value of splitOnResiduals.
getSplitPoint() - Method in class weka.classifiers.rules.JRip.NumericAntd
Get split point of this numeric antecedent
getSplitPoint() - Method in class weka.classifiers.trees.lmt.ResidualSplit
Selects split point for numeric attribute.
getSplitPoint() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Get the split point used for numeric selection
getSplitValue() - Method in class weka.core.neighboursearch.kdtrees.KDTreeNode
Gets the splitting value.
getSquaredError() - Method in class weka.clusterers.SimpleKMeans
Gets the squared error for all clusters
getStamp() - Method in class weka.core.Debug.Timestamp
returns the associated date/time
getStandardDeviation(Instance) - Method in class weka.classifiers.functions.GaussianProcesses
Gives the variance of the prediction at the given instance
getStart() - Method in class weka.core.Debug.Clock
returns the start time
getStartMessage() - Method in class weka.gui.beans.Loader
Gets a string that describes the start action.
getStartMessage() - Method in interface weka.gui.beans.Startable
Gets a string that describes the start action.
getStartPoint() - Method in class weka.attributeSelection.RankSearch
Get the point at which to start evaluating the ranking
getStartSequentially() - Method in class weka.gui.beans.FlowRunner
Gets whether Startable beans will be launched sequentially or all in parallel.
getStartSet() - Method in class weka.attributeSelection.BestFirst
Returns a list of attributes (and or attribute ranges) as a String
getStartSet() - Method in class weka.attributeSelection.GeneticSearch
Returns a list of attributes (and or attribute ranges) as a String
getStartSet() - Method in class weka.attributeSelection.GreedyStepwise
Returns a list of attributes (and or attribute ranges) as a String
getStartSet() - Method in class weka.attributeSelection.LinearForwardSelection
Returns a list of attributes (and or attribute ranges) as a String
getStartSet() - Method in class weka.attributeSelection.RandomSearch
Returns a list of attributes (and or attribute ranges) as a String
getStartSet() - Method in class weka.attributeSelection.Ranker
Returns a list of attributes (and or attribute ranges) as a String
getStartSet() - Method in interface weka.attributeSelection.StartSetHandler
Returns a list of attributes (and or attribute ranges) as a String
getStaticIcon() - Method in class weka.gui.beans.BeanVisual
Returns the static icon
getStats() - Method in class weka.core.neighboursearch.PerformanceStats
Returns a string representation of the statistics.
getStats() - Method in class weka.core.neighboursearch.TreePerformanceStats
Returns a string representation of the statistics.
getStatus() - Method in class weka.gui.beans.IncrementalClassifierEvent
Get the status
getStatus() - Method in class weka.gui.beans.InstanceEvent
Get the status
getStatusFrequency() - Method in class weka.gui.beans.IncrementalClassifierEvaluator
Get how often progress is reported to the status bar.
getStatusMessage() - Method in class weka.experiment.TaskStatusInfo
Get the status message.
getStatusTable() - Method in class weka.gui.beans.LogPanel
The JTable used for the status messages (in case clients want to attach listeners etc.)
getStdDev() - Method in class weka.estimators.KernelEstimator
Return the standard deviation of this kernel estimator.
getStdDev() - Method in class weka.estimators.NormalEstimator
Return the value of the standard deviation of this normal estimator.
getStdDev(int, int) - Method in class weka.experiment.ResultMatrix
returns the std deviation at the given position, if the position is valid, otherwise 0
getStdDevCoordsPerPoint() - Method in class weka.core.neighboursearch.PerformanceStats
Returns the standard deviation of coords per point.
getStdDevIntNodesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats
Returns the standard deviation of internal nodes visited.
getStdDevLeavesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats
Returns the standard deviation of leaves visited.
getStdDevPointsVisited() - Method in class weka.core.neighboursearch.PerformanceStats
Returns the standard deviation of points visited.
getStdDevPrec() - Method in class weka.experiment.ResultMatrix
returns the current standard deviation precision
getStdDevPrec() - Method in class weka.gui.experiment.OutputFormatDialog
Gets the precision used for printing the std.
getStdDevPrecision() - Static method in class weka.gui.experiment.ExperimenterDefaults
returns the default precision for the stddevs
getStddevValue() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
 
getStdDevWidth() - Method in class weka.experiment.ResultMatrix
returns the current width for the std dev
getStemmer() - Method in class weka.core.stemmers.SnowballStemmer
returns the name of the current stemmer, null if none is set.
getStemmer() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Returns the current stemming algorithm, null if none is used.
getStepSize() - Method in class weka.attributeSelection.RankSearch
Get the number of attributes to add from the rankining in each iteration
getStepSize() - Method in class weka.experiment.LearningRateResultProducer
Get the value of StepSize.
getStepX() - Method in class weka.classifiers.meta.GridSearch.Grid
returns the step size on the X axis
getStepY() - Method in class weka.classifiers.meta.GridSearch.Grid
returns the step size on the Y axis
getStop() - Method in class weka.core.Debug.Clock
returns the stop time or, if still running, the current time
getStopwords() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
returns the file used for obtaining the stopwords, if the file represents a directory then the default ones are used.
getString(String) - Static method in class weka.associations.gsp.Messages
getString.
getString(String, Locale) - Static method in class weka.associations.gsp.Messages
getString.
getString(String) - Static method in class weka.associations.Messages
getString.
getString(String, Locale) - Static method in class weka.associations.Messages
getString.
getString(int[]) - Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
Returns the list of indices as a string.
getString(int[]) - Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
Returns the list of indices as a string.
getString() - Method in class weka.classifiers.meta.nestedDichotomies.ND.NDTree
Returns the list of indices as a string.
getString() - Method in class weka.core.Trie.TrieNode
returns the full string up to the root
getString(String) - Static method in class weka.gui.arffviewer.Messages
getString.
getString(String, Locale) - Static method in class weka.gui.arffviewer.Messages
getString.
getString(String) - Static method in class weka.gui.beans.Messages
getString.
getString(String, Locale) - Static method in class weka.gui.beans.Messages
getString.
getString(String) - Static method in class weka.gui.beans.xml.Messages
getString.
getString(String, Locale) - Static method in class weka.gui.beans.xml.Messages
getString.
getString(String) - Static method in class weka.gui.boundaryvisualizer.Messages
getString.
getString(String, Locale) - Static method in class weka.gui.boundaryvisualizer.Messages
getString.
getString(String) - Static method in class weka.gui.experiment.Messages
getString.
getString(String, Locale) - Static method in class weka.gui.experiment.Messages
getString.
getString(String) - Static method in class weka.gui.explorer.Messages
getString.
getString(String, Locale) - Static method in class weka.gui.explorer.Messages
getString.
getString(String) - Static method in class weka.gui.graphvisualizer.Messages
getString.
getString(String, Locale) - Static method in class weka.gui.graphvisualizer.Messages
getString.
getString(String) - Static method in class weka.gui.hierarchyvisualizer.Messages
getString.
getString(String, Locale) - Static method in class weka.gui.hierarchyvisualizer.Messages
getString.
getString(String) - Static method in class weka.gui.Messages
getString.
getString(String, Locale) - Static method in class weka.gui.Messages
getString.
getString(String) - Static method in class weka.gui.sql.event.Messages
getString.
getString(String, Locale) - Static method in class weka.gui.sql.event.Messages
getString.
getString(String) - Static method in class weka.gui.sql.Messages
getString.
getString(String, Locale) - Static method in class weka.gui.sql.Messages
getString.
getString(String) - Static method in class weka.gui.streams.Messages
getString.
getString(String, Locale) - Static method in class weka.gui.streams.Messages
getString.
getString(String) - Static method in class weka.gui.treevisualizer.Messages
getString.
getString(String, Locale) - Static method in class weka.gui.treevisualizer.Messages
getString.
getString(String) - Static method in class weka.gui.visualize.Messages
getString.
getString(String, Locale) - Static method in class weka.gui.visualize.Messages
getString.
getStringAttributes() - Method in class weka.core.converters.CSVLoader
Returns the current attribute range to be forced to type string.
getStringSelection() - Method in class weka.gui.arffviewer.ArffTable
returns the selected content in a StringSelection that can be copied to the clipboard and used in Excel, if nothing is selected the whole table is copied to the clipboard
getStroke() - Method in class weka.gui.visualize.PostscriptGraphics
 
getStructure() - Method in class weka.core.converters.AbstractLoader
 
getStructure() - Method in class weka.core.converters.ArffLoader.ArffReader
Returns the header format
getStructure() - Method in class weka.core.converters.ArffLoader
Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in class weka.core.converters.C45Loader
Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in class weka.core.converters.ConverterUtils.DataSource
returns the structure of the data.
getStructure(int) - Method in class weka.core.converters.ConverterUtils.DataSource
returns the structure of the data, with the defined class index.
getStructure() - Method in class weka.core.converters.CSVLoader
Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in class weka.core.converters.DatabaseLoader
Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in class weka.core.converters.LibSVMLoader
Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in interface weka.core.converters.Loader
Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in class weka.core.converters.SerializedInstancesLoader
Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in class weka.core.converters.SVMLightLoader
Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in class weka.core.converters.TextDirectoryLoader
Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in class weka.core.converters.XRFFLoader
Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure(String) - Method in class weka.gui.beans.ClassAssigner
Get the structure of the output encapsulated in the named event.
getStructure(String) - Method in class weka.gui.beans.ClassValuePicker
 
getStructure() - Method in class weka.gui.beans.ClassValuePicker
 
getStructure() - Method in class weka.gui.beans.IncrementalClassifierEvent
Get the instances structure (may be null if this is not a NEW_BATCH event)
getStructure() - Method in class weka.gui.beans.InstanceEvent
Get the instances structure (may be null if this is not a FORMAT_AVAILABLE event)
getStructure(String) - Method in class weka.gui.beans.Loader
Get the structure of the output encapsulated in the named event.
getStructure(String) - Method in interface weka.gui.beans.StructureProducer
Get the structure of the output encapsulated in the named event.
getSubDirectories(String, File, HashSet) - Static method in class weka.core.ClassDiscovery
adds all the sub-directories recursively to the list.
getSubFlow() - Method in class weka.gui.beans.MetaBean
 
getSubmenuTitle() - Method in interface weka.gui.MainMenuExtension
Returns the name of the submenu.
getSubsequenceLength() - Method in class weka.classifiers.functions.supportVector.StringKernel
Returns the length of the subsequence
getSubsetEvaluator() - Method in class weka.attributeSelection.FilteredSubsetEval
Get the subset evaluator to use
getSubsetSizeEvaluator() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Get the subset evaluator used for subset size determination.
getSubSpaceSize() - Method in class weka.classifiers.meta.RandomSubSpace
Gets the size of each subSpace, as a percentage of the training set size.
getSubtreeRaising() - Method in class weka.classifiers.trees.J48
Get the value of subtreeRaising.
getSubtreeRaising() - Method in class weka.classifiers.trees.J48graft
Get the value of subtreeRaising.
getSuccess() - Method in class weka.core.CheckGOE
returns the success of the tests
getSuccess() - Method in class weka.core.CheckOptionHandler
returns the success of the tests
getSuitableTargets(EventSetDescriptor) - Method in class weka.gui.beans.MetaBean
Return a list of input beans capable of receiving the supplied event
getSummary() - Method in class weka.gui.SetInstancesPanel
Gets the instances summary panel associated with this panel
getSummaryTitle(int) - Method in class weka.experiment.ResultMatrix
returns the character representation of the given column
getSumOfCounts() - Method in class weka.estimators.DiscreteEstimator
Get the sum of all the counts
getSumOfWeights() - Method in class weka.estimators.NormalEstimator
Return the sum of the weights for this normal estimator.
getSupport() - Method in class weka.associations.FPGrowth.FrequentBinaryItemSet
Get the support of this item set.
getSupportCount() - Method in class weka.associations.gsp.Sequence
Returns the support count of the Sequence.
getSupportedCursorScrollType() - Method in class weka.experiment.DatabaseUtils
Returns the type of scrolling that the cursor supports, -1 if not supported or not connected.
getSVMType() - Method in class weka.classifiers.functions.LibLINEAR
Gets type of SVM
getSVMType() - Method in class weka.classifiers.functions.LibSVM
Gets type of SVM
getSymbols() - Method in class weka.core.mathematicalexpression.Parser
Returns the current variable - value relation in use.
getSymbols() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
Returns the current variable - value relation in use.
getSystemInfo() - Method in class weka.core.SystemInfo
returns a copy of the system info.
getSystemLookAndFeel() - Static method in class weka.gui.LookAndFeel
returns the system LnF classname
getSystemWide() - Static method in class weka.core.Environment
Get the singleton system-wide (visible to every class in the running VM) set of environment variables.
getTabbedPane() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
returns the tabbedpane instance
getTabbedPane() - Method in class weka.gui.explorer.Explorer
returns the tabbed pane of the Explorer
getTable() - Method in class weka.classifiers.meta.GridSearch.PerformanceTable
returns the generated table
getTable() - Method in class weka.gui.arffviewer.ArffPanel
returns the table component
getTableCellEditorComponent(JTable, Object, boolean, int, int) - Method in class weka.gui.arffviewer.ArffTable.RelationalCellEditor
Sets an initial value for the editor.
getTableCellRendererComponent(JTable, Object, boolean, boolean, int, int) - Method in class weka.gui.arffviewer.ArffTableCellRenderer
Returns the default table cell renderer.
getTableCellRendererComponent(JTable, Object, boolean, boolean, int, int) - Method in class weka.gui.sql.ResultSetTableCellRenderer
Returns the default table cell renderer.
getTableModel() - Method in class weka.gui.AttributeSelectionPanel
Get the table model in use (or null if no instances have been set yet).
getTableName() - Method in class weka.core.converters.DatabaseSaver
Gets the table's name.
getTabs() - Static method in class weka.gui.explorer.ExplorerDefaults
returns an array with the classnames of all the additional panels to display as tabs in the Explorer.
getTabTitle() - Method in class weka.gui.explorer.AssociationsPanel
Returns the title for the tab in the Explorer
getTabTitle() - Method in class weka.gui.explorer.AttributeSelectionPanel
Returns the title for the tab in the Explorer
getTabTitle() - Method in class weka.gui.explorer.ClassifierPanel
Returns the title for the tab in the Explorer
getTabTitle() - Method in class weka.gui.explorer.ClustererPanel
Returns the title for the tab in the Explorer
getTabTitle() - Method in interface weka.gui.explorer.Explorer.ExplorerPanel
Returns the title for the tab in the Explorer
getTabTitle() - Method in class weka.gui.explorer.PreprocessPanel
Returns the title for the tab in the Explorer
getTabTitle() - Method in class weka.gui.explorer.VisualizePanel
Returns the title for the tab in the Explorer
getTabTitleToolTip() - Method in class weka.gui.explorer.AssociationsPanel
Returns the tooltip for the tab in the Explorer
getTabTitleToolTip() - Method in class weka.gui.explorer.AttributeSelectionPanel
Returns the tooltip for the tab in the Explorer
getTabTitleToolTip() - Method in class weka.gui.explorer.ClassifierPanel
Returns the tooltip for the tab in the Explorer
getTabTitleToolTip() - Method in class weka.gui.explorer.ClustererPanel
Returns the tooltip for the tab in the Explorer
getTabTitleToolTip() - Method in interface weka.gui.explorer.Explorer.ExplorerPanel
Returns the tooltip for the tab in the Explorer
getTabTitleToolTip() - Method in class weka.gui.explorer.PreprocessPanel
Returns the tooltip for the tab in the Explorer
getTabTitleToolTip() - Method in class weka.gui.explorer.VisualizePanel
Returns the tooltip for the tab in the Explorer
getTabuList() - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
 
getTabuList() - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
 
getTags() - Method in class weka.core.SelectedTag
Gets the set of all valid Tags.
getTags() - Method in class weka.gui.CostMatrixEditor
Some objects can return tags, but a cost matrix cannot.
getTags() - Method in class weka.gui.GenericArrayEditor
Returns null as we don't support getting values as tags.
getTags() - Method in class weka.gui.GenericObjectEditor
Returns null as we don't support getting values as tags.
getTags() - Method in class weka.gui.SelectedTagEditor
Gets the list of tags that can be selected from.
getTags() - Method in class weka.gui.SimpleDateFormatEditor
Some objects can return tags, but a date format cannot.
getTarget() - Method in class weka.gui.beans.BeanConnection
Returns the target BeanInstance for this connection
getTarget() - Method in class weka.gui.treevisualizer.Edge
Get the value of target.
getTargetClass() - Method in class weka.classifiers.bayes.DMNBtext.DNBBinary
Gets the Target Class
getTargetMetaData() - Method in class weka.core.pmml.MiningSchema
Get the Target meta data.
getTaskResult() - Method in class weka.experiment.TaskStatusInfo
Get the returnable result of this task.
getTaskStatus() - Method in class weka.experiment.RemoteExperimentSubTask
 
getTaskStatus() - Method in interface weka.experiment.Task
Clients should be able to call this method at any time to obtain information on a current task.
getTaskStatus() - Method in class weka.gui.beans.Classifier.TrainingTask
 
getTaskStatus() - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
Return status information for this sub task
getTechnicalInformation() - Method in class weka.associations.Apriori
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.associations.FPGrowth
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.associations.GeneralizedSequentialPatterns
Returns TechnicalInformation about the paper related to the algorithm.
getTechnicalInformation() - Method in class weka.associations.PredictiveApriori
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.associations.Tertius
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.attributeSelection.CfsSubsetEval
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.attributeSelection.ConsistencySubsetEval
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.attributeSelection.GeneticSearch
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.attributeSelection.LinearForwardSelection
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.attributeSelection.RaceSearch
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.attributeSelection.RandomSearch
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.attributeSelection.RankSearch
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.attributeSelection.ReliefFAttributeEval
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.attributeSelection.ScatterSearchV1
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.attributeSelection.SVMAttributeEval
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.attributeSelection.WrapperSubsetEval
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.AODE
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.AODEsr
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.ComplementNaiveBayes
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.DMNBtext
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.HNB
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.NaiveBayes
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.NaiveBayesMultinomial
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.NaiveBayesSimple
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.NaiveBayesUpdateable
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.net.ADNode
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.net.BIFReader
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.net.search.global.K2
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.net.search.global.TAN
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.net.search.local.K2
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.net.search.local.TAN
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.WAODE
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.BVDecompose
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.BVDecomposeSegCVSub
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.GaussianProcesses
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.LeastMedSq
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.LibLINEAR
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.LibSVM
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.Logistic
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.pace.MixtureDistribution
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.PaceRegression
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.SimpleLogistic
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.SMO
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.SMOreg
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.SPegasos
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.supportVector.Puk
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.supportVector.RegSMO
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.supportVector.StringKernel
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.VotedPerceptron
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.Winnow
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.lazy.IB1
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.lazy.IBk
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.lazy.KStar
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.lazy.LBR
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.lazy.LWL
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.AdaBoostM1
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.AdditiveRegression
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.Bagging
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.ClassificationViaRegression
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.CVParameterSelection
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.Dagging
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.Decorate
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.END
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.Grading
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.LogitBoost
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.MetaCost
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.MultiBoostAB
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.nestedDichotomies.ND
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.OrdinalClassClassifier
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.RandomSubSpace
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.RotationForest
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.Stacking
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.StackingC
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.Vote
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.mi.CitationKNN
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.mi.MDD
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.mi.MIBoost
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.mi.MIDD
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.mi.MIEMDD
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.mi.MINND
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.mi.MIOptimalBall
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.mi.MISMO
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.mi.MISVM
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.mi.MIWrapper
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.misc.VFI
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.rules.DecisionTable
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.rules.DTNB
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.rules.JRip
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.rules.M5Rules
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.rules.NNge
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.rules.OneR
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.rules.PART
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.rules.Prism
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.ADTree
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.BFTree
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.FT
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.Id3
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.J48
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.J48graft
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.LADTree
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.LMT
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.m5.M5Base
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.NBTree
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.RandomForest
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.SimpleCart
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.UserClassifier
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.clusterers.CLOPE
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.clusterers.Cobweb
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.clusterers.DBScan
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.clusterers.FarthestFirst
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.clusterers.OPTICS
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.clusterers.sIB
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.clusterers.XMeans
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.ChebyshevDistance
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.EuclideanDistance
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.ManhattanDistance
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.BallTree
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.balltrees.BottomUpConstructor
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.balltrees.PointsClosestToFurthestChildren
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.CoverTree
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.KDTree
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.kdtrees.KMeansInpiredMethod
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.kdtrees.MedianOfWidestDimension
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.kdtrees.MidPointOfWidestDimension
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.kdtrees.SlidingMidPointOfWidestSide
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.Optimization
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.stemmers.LovinsStemmer
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in interface weka.core.TechnicalInformationHandler
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.datagenerators.classifiers.classification.Agrawal
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.datagenerators.classifiers.classification.LED24
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.experiment.PairedCorrectedTTester
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.filters.supervised.attribute.Discretize
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.filters.supervised.attribute.NominalToBinary
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.filters.supervised.attribute.PLSFilter
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.filters.supervised.instance.SMOTE
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.filters.unsupervised.attribute.KernelFilter
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.filters.unsupervised.attribute.RandomProjection
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.filters.unsupervised.attribute.RELAGGS
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.filters.unsupervised.attribute.Wavelet
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTempDir() - Static method in class weka.core.Debug
returns the system temp directory
getTester() - Static method in class weka.gui.experiment.ExperimenterDefaults
returns the display name of the preferred Tester algorithm
getTestEvaluator() - Method in class weka.attributeSelection.CheckAttributeSelection
Gets whether the evaluator is being tested or the search method.
getTestObject() - Method in class weka.attributeSelection.CheckAttributeSelection
returns either the evaluator or the search method.
getTestOrTrain() - Method in class weka.gui.beans.BatchClustererEvent
Get whether the set of instances is a test or a training set
getTestPredictions(Classifier, Instances) - Method in class weka.classifiers.evaluation.EvaluationUtils
Generate a bunch of predictions ready for processing, by performing a evaluation on a test set assuming the classifier is already trained.
getTestSet() - Method in class weka.gui.beans.BatchClassifierEvent
Get the test set
getTestSet() - Method in class weka.gui.beans.BatchClustererEvent
Get the training/test set
getTestSet() - Method in class weka.gui.beans.TestSetEvent
Get the test set instances
getText() - Method in class weka.gui.beans.BeanVisual
Get the visual's label
getText() - Method in class weka.gui.beans.TextEvent
Describe getText method here.
getTextTitle() - Method in class weka.gui.beans.TextEvent
Describe getTextTitle method here.
getTFTransform() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Gets whether if the word frequencies should be transformed into log(1+fij) where fij is the frequency of word i in document(instance) j.
getThreadMonitor() - Method in class weka.core.Debug.Clock
Returns a new thread monitor if the current one is null (e.g., due to serialization) or the currently set one.
getThreshold() - Method in class weka.attributeSelection.GreedyStepwise
Returns the threshold so that the AttributeSelection module can discard attributes from the ranking.
getThreshold() - Method in class weka.attributeSelection.RaceSearch
Get the threshold
getThreshold() - Method in interface weka.attributeSelection.RankedOutputSearch
Gets the threshold by which attributes can be discarded.
getThreshold() - Method in class weka.attributeSelection.Ranker
Returns the threshold so that the AttributeSelection module can discard attributes from the ranking.
getThreshold() - Method in class weka.attributeSelection.ScatterSearchV1
Get the treshold
getThreshold() - Method in class weka.attributeSelection.WrapperSubsetEval
Get the value of the threshold
getThreshold() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Return the threshold being used.
getThreshold() - Method in class weka.classifiers.functions.PaceRegression
Gets the threshold for olsc estimator
getThreshold() - Method in class weka.classifiers.functions.Winnow
Get the value of Threshold.
getThreshold() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Gets the threshold for the max error when predicting a numeric class.
getThresholdInstance(Instances, double) - Static method in class weka.classifiers.evaluation.ThresholdCurve
Gets the index of the instance with the closest threshold value to the desired target
getTimeAndDate() - Method in class weka.associations.GeneralizedSequentialPatterns
Returns the current time and date.
getTimestamp() - Static method in class weka.experiment.CrossValidationResultProducer
Gets a Double representing the current date and time.
getTimestamp() - Static method in class weka.experiment.RandomSplitResultProducer
Gets a Double representing the current date and time.
getTimestamp() - Static method in class weka.gui.LogPanel
Gets a string containing current date and time.
getTimestamp() - Static method in class weka.gui.SysErrLog
Gets a string containing current date and time.
getTitle() - Method in class weka.gui.arffviewer.ArffPanel
returns the title for the Tab, i.e.
getToken(StreamTokenizer) - Static method in class weka.core.converters.ConverterUtils
Gets token.
getTokenizer() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Returns the current tokenizer algorithm.
getTolerance() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Get the tolerance value
getTolerance() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
returns the current tolerance
getToleranceParameter() - Method in class weka.attributeSelection.SVMAttributeEval
Get the value of T used with SMO
getToleranceParameter() - Method in class weka.classifiers.functions.SMO
Get the value of tolerance parameter.
getToleranceParameter() - Method in class weka.classifiers.mi.MISMO
Get the value of tolerance parameter.
getToolTipText() - Method in class weka.experiment.PairedCorrectedTTester
returns a string that is displayed as tooltip on the "perform test" button in the experimenter
getToolTipText() - Method in class weka.experiment.PairedTTester
returns a string that is displayed as tooltip on the "perform test" button in the experimenter
getToolTipText() - Method in interface weka.experiment.Tester
returns a string that is displayed as tooltip on the "perform test" button in the experimenter
getToolTipText(MouseEvent) - Method in class weka.gui.AttributeVisualizationPanel
Returns "<nominal value> [<nominal value count>]" if displaying a bar plot and mouse is on some bar.
getToolTipText(PrintableComponent) - Static method in class weka.gui.visualize.PrintableComponent
Returns a tooltip only if the user wants it.
getTop() - Method in class weka.gui.treevisualizer.Node
Get the value of top.
getTotalCoordsPerPoint() - Method in class weka.core.neighboursearch.PerformanceStats
Returns the total sum of coords per point.
getTotalCount(Node, int) - Static method in class weka.gui.treevisualizer.Node
Recursively finds the total number of nodes there are.
getTotalGCount(Node, int) - Static method in class weka.gui.treevisualizer.Node
Recursively finds the total number of groups of siblings there are.
getTotalHeight(Node, int) - Static method in class weka.gui.treevisualizer.Node
Recursively finds the total number of levels there are.
getTotalIntNodesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats
Returns the total number of internal nodes visited.
getTotalLeavesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats
Returns the total number of leaves visited.
getTotalPointsVisited() - Method in class weka.core.neighboursearch.PerformanceStats
Returns the total number of points visited.
getTotalSupport() - Method in class weka.associations.FPGrowth.AssociationRule
Get the total support for this rule (premise + consequence).
getTotalTransactions() - Method in class weka.associations.FPGrowth.AssociationRule
Get the total number of transactions in the data.
getToYear() - Static method in class weka.core.Copyright
returns the end year of the copyright (i.e., current year)
getTPRate() - Method in class weka.associations.tertius.Rule
Get the rate of True Positive instances of this rule.
getTrainingSet() - Method in class weka.gui.beans.TrainingSetEvent
Get the training instances
getTrainingTime() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
getTrainIterations() - Method in class weka.classifiers.BVDecompose
Gets the maximum number of boost iterations
getTrainPercent() - Method in class weka.experiment.RandomSplitResultProducer
Get the value of TrainPercent.
getTrainPercent() - Method in class weka.gui.beans.TrainTestSplitMaker
Get the percentage of the data that will be in the training portion of the split
getTrainPercentage() - Static method in class weka.gui.experiment.ExperimenterDefaults
returns the training percentage in case of splits
getTrainPoolSize() - Method in class weka.classifiers.BVDecompose
Get the number of instances in the training pool.
getTrainSet() - Method in class weka.gui.beans.BatchClassifierEvent
Get the train set
getTrainSize() - Method in class weka.classifiers.BVDecomposeSegCVSub
Get the training size
getTrainTestPredictions(Classifier, Instances, Instances) - Method in class weka.classifiers.evaluation.EvaluationUtils
Generate a bunch of predictions ready for processing, by performing a evaluation on a test set after training on the given training set.
getTransactionsMustContain() - Method in class weka.associations.FPGrowth
Gets the comma separated list of items that transactions must contain in order to be considered for large item sets and rules.
getTransform() - Method in class weka.gui.visualize.PostscriptGraphics
 
getTransformAllValues() - Method in class weka.filters.supervised.attribute.NominalToBinary
Gets if all nominal values are turned into new attributes, not only if there are more than 2.
getTransformAllValues() - Method in class weka.filters.unsupervised.attribute.NominalToBinary
Gets if all nominal values are turned into new attributes, not only if there are more than 2.
getTransformationDictionary() - Method in class weka.core.pmml.MiningSchema
Get the transformation dictionary .
getTransformationDictionary(Document, Instances) - Static method in class weka.core.pmml.PMMLFactory
Get the transformation dictionary (if there is one).
getTransformBackToOriginal() - Method in class weka.attributeSelection.PrincipalComponents
Gets whether the data is to be transformed back to the original space.
getTransformMethod() - Method in class weka.classifiers.mi.SimpleMI
Get the method used in transformation.
getTranslation() - Method in class weka.filters.unsupervised.attribute.Normalize
Get the translation.
getTraversal() - Method in class weka.classifiers.meta.GridSearch
Gets the type of traversal for the grid.
getTrimingThreshold() - Method in class weka.classifiers.functions.pace.ChisqMixture
Gets the triming thresholding value.
getTrimingThreshold() - Method in class weka.classifiers.functions.pace.NormalMixture
Gets the triming thresholding value.
getTrueNegative() - Method in class weka.classifiers.evaluation.TwoClassStats
Gets the number of negative instances predicted as negative
getTruePositive() - Method in class weka.classifiers.evaluation.TwoClassStats
Gets the number of positive instances predicted as positive
getTruePositiveRate() - Method in class weka.classifiers.evaluation.TwoClassStats
Calculate the true positive rate.
getTStart() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
 
getTStart() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
 
getTwoClassStats(int) - Method in class weka.classifiers.evaluation.ConfusionMatrix
Gets the performance with respect to one of the classes as a TwoClassStats object.
getType() - Method in class weka.associations.tertius.IndividualLiteral
 
getType() - Method in class weka.associations.tertius.LiteralSet
Give the type of properties in this set (individual or part properties).
getType() - Method in class weka.attributeSelection.LinearForwardSelection
Get the type
getType() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Get the type
getType() - Method in class weka.classifiers.functions.neural.NeuralConnection
 
getType() - Method in class weka.classifiers.meta.GridSearch.PerformanceTable
returns the type of performance
getType() - Method in class weka.core.AttributeLocator
returns the type of attribute that is located
getType(RevisionHandler) - Static method in class weka.core.RevisionUtils
Determines the type of a (sanitized) revision string returned by the RevisionHandler.
getType(String) - Static method in class weka.core.RevisionUtils
Determines the type of a (sanitized) revision string.
getType() - Method in class weka.core.TechnicalInformation
returns the type of this technical information
getType(int) - Method in class weka.gui.arffviewer.ArffSortedTableModel
returns the TYPE of the attribute at the given position
getType(int, int) - Method in class weka.gui.arffviewer.ArffSortedTableModel
returns the TYPE of the attribute at the given position
getType(int) - Method in class weka.gui.arffviewer.ArffTableModel
returns the TYPE of the attribute at the given position
getType(int, int) - Method in class weka.gui.arffviewer.ArffTableModel
returns the TYPE of the attribute at the given position
getType() - Method in class weka.gui.sql.event.ConnectionEvent
returns the type of this event, CONNECT or DISCONNECT
getU() - Method in class weka.core.Matrix
Deprecated.
Returns the U part of the matrix.
getU() - Method in class weka.core.matrix.LUDecomposition
Return upper triangular factor
getU() - Method in class weka.core.matrix.SingularValueDecomposition
Return the left singular vectors
getUID(String) - Static method in class weka.core.SerializationHelper
reads or creates the serialVersionUID for the given class.
getUID(Class) - Static method in class weka.core.SerializationHelper
reads or creates the serialVersionUID for the given class.
getUnpruned() - Method in class weka.classifiers.rules.PART
Get the value of unpruned.
getUnpruned() - Method in class weka.classifiers.trees.J48
Get the value of unpruned.
getUnpruned() - Method in class weka.classifiers.trees.J48graft
Get the value of unpruned.
getUnpruned() - Method in class weka.classifiers.trees.m5.M5Base
Get whether unpruned tree/rules are being generated
getUnpruned() - Method in class weka.classifiers.trees.m5.Rule
Get whether unpruned tree/rules are being generated
getUpdateCount() - Method in class weka.core.converters.DatabaseConnection
Dewtermines if the current query retrieves a result set or updates a table
getUpdateIncrementalClassifier() - Method in class weka.gui.beans.Classifier
Get whether an incremental classifier will be updated on the incoming instance stream.
getUpper() - Method in class weka.gui.experiment.RunNumberPanel
Gets the current upper run number.
getUpperBoundMinSupport() - Method in class weka.associations.Apriori
Get the value of upperBoundMinSupport.
getUpperBoundMinSupport() - Method in class weka.associations.FPGrowth
Get the value of upperBoundMinSupport.
getUpperCase() - Method in class weka.core.converters.DatabaseConnection
Check if the property checkUpperCaseNames in the DatabaseUtils file is set to true or false.
getUpperNumericBound() - Method in class weka.core.Attribute
Returns the upper bound of a numeric attribute.
getUpperSize() - Method in class weka.experiment.LearningRateResultProducer
Get the value of UpperSize.
getURL(String, String) - Static method in class weka.core.ClassDiscovery
If the given package can be found in this part of the classpath then an URL object is returned, otherwise null.
getUrl() - Method in interface weka.core.converters.DatabaseConverter
 
getUrl() - Method in class weka.core.converters.DatabaseLoader
Gets the URL
getUrl() - Method in class weka.core.converters.DatabaseSaver
Gets the database URL.
getURL() - Static method in class weka.core.Copyright
returns the URL of the owner
getURL() - Method in class weka.gui.DatabaseConnectionDialog
Returns URL from dialog
getURL(String, String) - Static method in class weka.gui.Loader
returns a URL for the given filename, can be NULL if it fails
getURL(String) - Method in class weka.gui.Loader
returns a URL for the given filename, can be NULL if it fails
getURL() - Method in class weka.gui.sql.ConnectionPanel
returns the current URL.
getURL() - Method in class weka.gui.sql.event.ResultChangedEvent
returns the database URL that produced the table model
getURL() - Method in class weka.gui.sql.ResultSetTable
returns the database URL that produced the table model
getURL() - Method in class weka.gui.sql.SqlViewer
returns the database URL from the currently active tab in the ResultPanel, otherwise an empty string.
getURL() - Method in class weka.gui.sql.SqlViewerDialog
returns the chosen URL, if any
getURLFileLoaders() - Static method in class weka.core.converters.ConverterUtils
returns a vector with the classnames of all the URL file loaders.
getURLLoaderForExtension(String) - Static method in class weka.core.converters.ConverterUtils
tries to determine the URL loader to use for this kind of extension, returns null if none can be found.
getURLLoaderForFile(String) - Static method in class weka.core.converters.ConverterUtils
tries to determine the URL loader to use for this kind of file, returns null if none can be found.
getURLLoaderForFile(File) - Static method in class weka.core.converters.ConverterUtils
tries to determine the URL loader to use for this kind of file, returns null if none can be found.
getUsageType() - Method in class weka.core.pmml.MiningFieldMetaInfo
Get the usage type of this field.
getUseADTree() - Method in class weka.classifiers.bayes.BayesNet
Method declaration
getUseAIC() - Method in class weka.classifiers.functions.SimpleLogistic
Get the value of useAIC.
getUseAIC() - Method in class weka.classifiers.trees.FT
Get the value of useAIC.
getUseAIC() - Method in class weka.classifiers.trees.LMT
Get the value of useAIC.
getUseAIC() - Method in class weka.classifiers.trees.lmt.LogisticBase
Get the value of useAIC.
getUseArcReversal() - Method in class weka.classifiers.bayes.net.search.global.HillClimber
get use the arc reversal operation
getUseArcReversal() - Method in class weka.classifiers.bayes.net.search.local.HillClimber
get use the arc reversal operation
getUseBetterEncoding() - Method in class weka.filters.supervised.attribute.Discretize
Gets whether better encoding is to be used for MDL.
getUseClassification() - Static method in class weka.gui.experiment.ExperimenterDefaults
whether classification or regression is used
getUseCpuTime() - Method in class weka.core.Debug.Clock
returns whether the use of CPU is time is enabled/disabled (regardless whether the system supports it or not)
getUseCrossOver() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
 
getUseCrossOver() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
 
getUseCrossValidation() - Method in class weka.classifiers.functions.SimpleLogistic
Get the value of useCrossValidation.
getUseCustomDimensions() - Method in class weka.gui.visualize.JComponentWriter
whether custom dimensions are to used for the size of the image
getUsedAttributes() - Method in class weka.classifiers.trees.lmt.LogisticBase
Returns an array of the indices of the attributes used in the logistic model.
getUseEqualFrequency() - Method in class weka.classifiers.meta.RegressionByDiscretization
Get the value of UseEqualFrequency.
getUseEqualFrequency() - Method in class weka.filters.unsupervised.attribute.Discretize
Get the value of UseEqualFrequency.
getUseEqualFrequency() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Get the value of UseEqualFrequency.
getUseErrorRate() - Method in class weka.classifiers.trees.BFTree
Get if use error rate in internal cross-validation.
getUseGini() - Method in class weka.classifiers.trees.BFTree
Get if use Gini index as splitting criterion.
getUseGUI() - Method in class weka.core.Memory
whether to display a dialog in case of a problem (= TRUE) or just print on stderr (= FALSE)
getUseIBk() - Method in class weka.classifiers.rules.DecisionTable
Gets whether IBk is being used instead of the majority class
getUseKDTree() - Method in class weka.clusterers.XMeans
Gets whether the KDTree is used or not.
getUseKernelEstimator() - Method in class weka.classifiers.bayes.NaiveBayes
Gets if kernel estimator is being used.
getUseKononenko() - Method in class weka.filters.supervised.attribute.Discretize
Gets whether Kononenko's MDL criterion is to be used.
getUseLaplace() - Method in class weka.classifiers.bayes.AODEsr
Gets if laplace correction is being used.
getUseLaplace() - Method in class weka.classifiers.trees.J48
Get the value of useLaplace.
getUseLaplace() - Method in class weka.classifiers.trees.J48graft
Get the value of useLaplace.
getUseLeastValues() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Gets whether to use values with least or most instances
getUseLowerOrder() - Method in class weka.classifiers.functions.supportVector.PolyKernel
Gets whether lower-order terms are used.
getUseMEstimates() - Method in class weka.classifiers.bayes.AODE
Gets if m-estimaces is being used.
getUseMissing() - Method in class weka.filters.unsupervised.attribute.AddNoise
Gets the flag if missing values are treated as extra values.
getUseMutation() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
 
getUseMutation() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
 
getUseNormalization() - Method in class weka.classifiers.functions.supportVector.StringKernel
Returns whether normalization is used.
getUseOneSE() - Method in class weka.classifiers.trees.BFTree
Get if use the 1SE rule to choose final model.
getUseOneSE() - Method in class weka.classifiers.trees.SimpleCart
Get if use the 1SE rule to choose final model.
getUseORForMustContainList() - Method in class weka.associations.FPGrowth
Gets whether OR is to be used rather than AND when considering must contain lists.
getUsePairwiseCoupling() - Method in class weka.classifiers.meta.MultiClassClassifier
Gets whether to use pairwise coupling with 1-vs-1 classification to improve probability estimates.
getUseProb() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
 
getUsePropertyIterator() - Method in class weka.experiment.Experiment
Gets whether the custom property iterator should be used.
getUsePrune() - Method in class weka.classifiers.trees.SimpleCart
Get if use minimal cost-complexity pruning.
getUsePruning() - Method in class weka.classifiers.rules.JRip
Gets whether pruning is performed
getUser() - Method in interface weka.core.converters.DatabaseConverter
 
getUser() - Method in class weka.core.converters.DatabaseLoader
Gets the user name
getUser() - Method in class weka.core.converters.DatabaseSaver
Gets the database user.
getUser() - Method in class weka.gui.sql.ConnectionPanel
returns the current User.
getUser() - Method in class weka.gui.sql.event.ResultChangedEvent
returns the user that produced the table model
getUser() - Method in class weka.gui.sql.ResultSetTable
returns the user that produced the table model
getUser() - Method in class weka.gui.sql.SqlViewer
returns the user from the currently active tab in the ResultPanel, otherwise an empty string.
getUser() - Method in class weka.gui.sql.SqlViewerDialog
returns the chosen user, if any
getUseRelativePath() - Method in class weka.core.converters.AbstractFileLoader
Gets whether relative paths are to be used
getUseRelativePath() - Method in class weka.core.converters.AbstractFileSaver
Gets whether relative paths are to be used
getUseRelativePath() - Method in interface weka.core.converters.FileSourcedConverter
Gets whether relative paths are to be used
getUseRelativePath() - Method in class weka.gui.beans.SerializedModelSaver
Get whether to use relative paths for the directory.
getUseRelativePaths() - Static method in class weka.gui.experiment.ExperimenterDefaults
whether relative paths are used by default
getUseResampling() - Method in class weka.classifiers.meta.AdaBoostM1
Get whether resampling is turned on
getUseResampling() - Method in class weka.classifiers.meta.LogitBoost
Get whether resampling is turned on
getUseResampling() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Get whether resampling is turned on
getUsername() - Method in class weka.experiment.DatabaseUtils
Get the database username.
getUsername() - Method in class weka.gui.DatabaseConnectionDialog
Returns Username from dialog
getUserOptions() - Method in class weka.classifiers.functions.supportVector.KernelEvaluation
returns the options the user supplied for the kernel
getUserOptions() - Method in class weka.core.CheckOptionHandler
Gets the current user-supplied options (creates a copy)
getUseStars() - Method in class weka.core.Javadoc
whether the Javadoc is prefixed with "*"
getUseStoplist() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Gets whether if the words on the stoplist are to be ignored (The stoplist is in weka.core.StopWords).
getUseSupervisedDiscretization() - Method in class weka.classifiers.bayes.NaiveBayes
Get whether supervised discretization is to be used.
getUseTournamentSelection() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
 
getUseTournamentSelection() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
 
getUseTraining() - Method in class weka.attributeSelection.ClassifierSubsetEval
Get if training data is to be used instead of hold out/test data
getUseTree() - Method in class weka.classifiers.trees.m5.Rule
get whether an m5 tree is being used rather than rules
getUseUnsmoothed() - Method in class weka.classifiers.trees.m5.M5Base
Get whether or not smoothing is being used
getV() - Method in class weka.core.matrix.EigenvalueDecomposition
Return the eigenvector matrix
getV() - Method in class weka.core.matrix.SingularValueDecomposition
Return the right singular vectors
getValidating() - Method in class weka.core.xml.XMLDocument
returns whether a validating parser is used.
getValidating() - Method in class weka.core.xml.XMLOptions
returns whether a validating parser is used.
getValidationChunkSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Get the validation chunk size
getValidationSetSize() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
getValidationThreshold() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
getValue() - Method in class weka.classifiers.trees.adtree.PredictionNode
Gets the prediction value of the node.
getValue() - Method in class weka.core.pmml.FieldMetaInfo.Value
 
getValue(Object, PropertyPath.Path) - Static method in class weka.core.PropertyPath
returns the value specified by the given path from the object
getValue(Object, String) - Static method in class weka.core.PropertyPath
returns the value specified by the given path from the object
getValue(TechnicalInformation.Field) - Method in class weka.core.TechnicalInformation
returns the value associated with the given field, or empty if field is not currently stored.
getValue(Instance, int) - Static method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
Returns either a String object for nominal attributes or a Double for numeric ones.
getValue() - Method in class weka.gui.CostMatrixEditor
Gets the cost matrix that is being edited.
getValue() - Method in class weka.gui.GenericArrayEditor
Gets the current object array.
getValue() - Method in class weka.gui.GenericObjectEditor
Gets the current Object.
getValue() - Method in class weka.gui.HierarchyPropertyParser
Get the value of current node
getValue() - Method in class weka.gui.SimpleDateFormatEditor
Gets the date format that is being edited.
getValue() - Method in class weka.gui.SortedTableModel.SortContainer
Returns the value to sort on.
getValueAt(int, int) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.ResultVectorTableModel
Returns the value for the JTable for a given position.
getValueAt(int, int) - Method in class weka.gui.arffviewer.ArffTableModel
returns the value for the cell at columnindex and rowIndex
getValueAt(int, int) - Method in class weka.gui.SortedTableModel
Returns the value for the cell at columnIndex and rowIndex.
getValueAt(int, int) - Method in class weka.gui.sql.ResultSetTableModel
returns the value for the cell at columnindex and rowIndex.
getValueIndex() - Method in class weka.associations.FPGrowth.BinaryItem
Get the value index for this item.
getValueIndices() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
Get the indices of the indicator values.
getValueName(int, int) - Method in class weka.classifiers.bayes.net.EditableBayesNet
returns value of a node
getValueRange() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
Get the range containing the indicator values.
getValues(String) - Method in class weka.classifiers.bayes.net.EditableBayesNet
returns array of values of a node
getValues(int) - Method in class weka.classifiers.bayes.net.EditableBayesNet
returns array of values of a node
getValues() - Method in class weka.classifiers.meta.GridSearch
returns the parameter pair that was found to work best
getValues(int, int) - Method in class weka.classifiers.meta.GridSearch.Grid
returns the values at the given point in the grid
getValues() - Method in class weka.classifiers.meta.GridSearch.Performance
returns the values-pair for this performance
getValues() - Method in class weka.classifiers.trees.LADTree.PredictionNode
 
getValues() - Method in class weka.core.pmml.TargetMetaInfo
Get the values (discrete case only) for this Target.
getValues() - Method in class weka.gui.visualize.VisualizePanelEvent
 
getValuesList() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
returns the range for each attribute as string
getValuesOutput() - Method in class weka.associations.Tertius
Get the value of valuesOutput.
getVarbValues() - Method in class weka.core.Optimization
Get the variable values.
getVariableNames() - Method in class weka.core.Environment
Get the names of the variables (keys) stored in the internal map.
getVariableValue(String) - Method in class weka.core.Environment
Get the value for a particular variable.
getVariance() - Method in class weka.classifiers.BVDecompose
Get the calculated variance
getVarianceCovered() - Method in class weka.attributeSelection.PrincipalComponents
Gets the proportion of total variance to account for when retaining principal components
getVarianceCovered() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
Gets the proportion of total variance to account for when retaining principal components.
getVector(Matrix, int) - Method in class weka.filters.supervised.attribute.PLSFilter
returns the (column) vector of the matrix at the specified index
getVectorOfAttrTypes() - Method in class weka.estimators.CheckEstimator.AttrTypes
 
getVerbose() - Method in class weka.associations.Apriori
Gets whether algorithm is run in verbose mode
getVerbose() - Method in class weka.attributeSelection.ExhaustiveSearch
get whether or not output is verbose
getVerbose() - Method in class weka.attributeSelection.LinearForwardSelection
Get whether output is to be verbose
getVerbose() - Method in class weka.attributeSelection.RandomSearch
get whether or not output is verbose
getVerbose() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Get whether output is to be verbose
getVerbose() - Method in class weka.classifiers.meta.Dagging
Gets the verbose state
getVersion() - Method in class weka.core.xml.XMLSerialization
returns the WEKA version with which the serialized object was created
getVisible() - Method in class weka.gui.treevisualizer.Node
Get the value of visible.
getVisibleColCount() - Method in class weka.experiment.ResultMatrix
returns the number of visible columns
getVisibleRowCount() - Method in class weka.experiment.ResultMatrix
returns the number of visible rows
getVisual() - Method in class weka.gui.beans.AbstractDataSink
Get the visual being used by this data source.
getVisual() - Method in class weka.gui.beans.AbstractDataSource
Get the visual being used by this data source.
getVisual() - Method in class weka.gui.beans.AbstractEvaluator
Get the visual
getVisual() - Method in class weka.gui.beans.AbstractTestSetProducer
Get the visual for this bean
getVisual() - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
Get the visual for this bean
getVisual() - Method in class weka.gui.beans.AbstractTrainingSetProducer
Get the visual for this bean
getVisual() - Method in class weka.gui.beans.Associator
Gets the visual appearance of this wrapper bean
getVisual() - Method in class weka.gui.beans.ClassAssigner
 
getVisual() - Method in class weka.gui.beans.Classifier
Gets the visual appearance of this wrapper bean
getVisual() - Method in class weka.gui.beans.ClassValuePicker
 
getVisual() - Method in class weka.gui.beans.Clusterer
Gets the visual appearance of this wrapper bean
getVisual() - Method in class weka.gui.beans.CostBenefitAnalysis
 
getVisual() - Method in class weka.gui.beans.DataVisualizer
Return the visual appearance of this bean
getVisual() - Method in class weka.gui.beans.Filter
Get the visual appearance of this bean
getVisual() - Method in class weka.gui.beans.GraphViewer
Get the visual appearance of this bean
getVisual() - Method in class weka.gui.beans.InstanceStreamToBatchMaker
Gets the visual appearance of this wrapper bean
getVisual() - Method in class weka.gui.beans.MetaBean
Gets the visual appearance of this wrapper bean
getVisual() - Method in class weka.gui.beans.ModelPerformanceChart
Return the visual appearance of this bean
getVisual() - Method in class weka.gui.beans.PredictionAppender
Get the visual being used by this data source.
getVisual() - Method in class weka.gui.beans.SerializedModelSaver
Get the visual being used by this data source.
getVisual() - Method in class weka.gui.beans.StripChart
Get the visual appearance of this bean
getVisual() - Method in class weka.gui.beans.TextViewer
Get the visual appearance of this bean
getVisual() - Method in interface weka.gui.beans.Visible
Get the visual representation
getVisualizeMenuItem(Instances) - Method in interface weka.gui.visualize.plugins.ErrorVisualizePlugin
Get a JMenu or JMenuItem which contain action listeners that perform the visualization of the classifier errors.
getVisualizeMenuItem(String, String) - Method in interface weka.gui.visualize.plugins.GraphVisualizePlugin
Get a JMenu or JMenuItem which contain action listeners that perform the visualization of the graph in XML BIF format.
getVisualizeMenuItem(String, String) - Method in interface weka.gui.visualize.plugins.TreeVisualizePlugin
Get a JMenu or JMenuItem which contain action listeners that perform the visualization of the tree in GraphViz's dotty format.
getVisualizeMenuItem(FastVector, Attribute) - Method in interface weka.gui.visualize.plugins.VisualizePlugin
Get a JMenu or JMenuItem which contain action listeners that perform the visualization, using some but not necessarily all of the data.
getVoteFlag() - Method in class weka.datagenerators.classifiers.classification.RDG1
Gets the vote flag.
getWBias() - Method in class weka.classifiers.BVDecomposeSegCVSub
Get the calculated bias according to the Webb definition
getWeight() - Method in class weka.classifiers.bayes.AODE
Gets the weight used in m-estimate
getWeightByConfidence() - Method in class weka.classifiers.misc.VFI
Get whether feature intervals are being weighted by confidence
getWeightByDistance() - Method in class weka.attributeSelection.ReliefFAttributeEval
Get whether nearest neighbours are being weighted by distance
getWeightingKernel() - Method in class weka.classifiers.lazy.LWL
Gets the kernel weighting method to use.
getWeightMethod() - Method in class weka.classifiers.mi.MIWrapper
Returns the current weighting method for instances.
getWeightMethod() - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
Returns the current weighting method for instances.
getWeights() - Method in class weka.classifiers.functions.LibLINEAR
Gets the parameters C of class i to weight[i]*C (default 1).
getWeights() - Method in class weka.classifiers.functions.LibSVM
Gets the parameters C of class i to weight[i]*C, for C-SVC (default 1).
getWeights() - Method in class weka.classifiers.functions.neural.NeuralNode
call this function to get the weights array.
getWeights() - Method in class weka.estimators.KernelEstimator
Return the weights of the kernels.
getWeights() - Method in interface weka.gui.boundaryvisualizer.DataGenerator
Get weights
getWeights() - Method in class weka.gui.boundaryvisualizer.KDDataGenerator
 
getWeightThreshold() - Method in class weka.classifiers.meta.AdaBoostM1
Get the degree of weight thresholding
getWeightThreshold() - Method in class weka.classifiers.meta.LogitBoost
Get the degree of weight thresholding
getWeightTrimBeta() - Method in class weka.classifiers.functions.SimpleLogistic
Get the value of weightTrimBeta.
getWeightTrimBeta() - Method in class weka.classifiers.trees.FT
Get the value of weightTrimBeta.
getWeightTrimBeta() - Method in class weka.classifiers.trees.LMT
Get the value of weightTrimBeta.
getWeightTrimBeta() - Method in class weka.classifiers.trees.lmt.LogisticBase
Get the value of weightTrimBeta.
getWholeDataErr() - Method in class weka.classifiers.rules.Ridor
 
getWidth() - Method in class weka.gui.beans.BeanInstance
Gets the width of this bean
getWindow(Class) - Method in class weka.gui.Main
returns the first instance of the given window class, null if none can be found.
getWindow(String) - Method in class weka.gui.Main
returns the first window with the given title, null if none can be found.
getWindowList() - Method in class weka.gui.Main
returns all currently open frames.
getWindowSize() - Method in class weka.classifiers.lazy.IBk
Gets the maximum number of instances allowed in the training pool.
getWithPrefix(String) - Method in class weka.core.Trie
returns all stored strings that match the given prefix
getWords() - Method in class weka.core.CheckScheme
returns the words used for assembling strings in a comma-separated list.
getWords() - Method in class weka.core.TestInstances
returns the words used for assembling strings in a comma-separated list.
getWordSeparators() - Method in class weka.core.CheckScheme
returns the word separators (chars) to use for assembling strings.
getWordSeparators() - Method in class weka.core.TestInstances
returns the word separators (chars) to use for assembling strings.
getWordsToKeep() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Gets the number of words (per class if there is a class attribute assigned) to attempt to keep.
getWrappedAlgorithm() - Method in class weka.gui.beans.Associator
Returns the wrapped associator
getWrappedAlgorithm() - Method in class weka.gui.beans.Classifier
Returns the wrapped classifier
getWrappedAlgorithm() - Method in class weka.gui.beans.Clusterer
Returns the wrapped clusterer
getWrappedAlgorithm() - Method in class weka.gui.beans.Filter
Get the filter wrapped by this bean
getWrappedAlgorithm() - Method in class weka.gui.beans.Loader
Get the loader
getWrappedAlgorithm() - Method in class weka.gui.beans.Saver
Get the saver
getWrappedAlgorithm() - Method in interface weka.gui.beans.WekaWrapper
Get the algorithm
getWriteMode() - Method in class weka.core.converters.AbstractSaver
Gets the write mode.
getWriteMode() - Method in interface weka.core.converters.Saver
Gets the write mode
getWriteOPTICSresults() - Method in class weka.clusterers.OPTICS
Returns the flag for writing actions
getWriter() - Method in class weka.core.converters.AbstractFileSaver
Gets the writer
getWriter(String) - Method in class weka.gui.visualize.PrintableComponent
returns the JComponentWriter associated with the given name, is null if not found.
getWriter() - Method in class weka.gui.visualize.PrintableComponent.JComponentWriterFileFilter
returns the associated writer.
getWriter(String) - Method in interface weka.gui.visualize.PrintableHandler
returns the JComponentWriter associated with the given name, is null if not found
getWriter(String) - Method in class weka.gui.visualize.PrintablePanel
returns the JComponentWriter associated with the given name, is null if not found
getWriters() - Method in class weka.gui.visualize.PrintableComponent
returns a Hashtable with the current available JComponentWriters in the save dialog.
getWriters() - Method in interface weka.gui.visualize.PrintableHandler
returns a Hashtable with the current available JComponentWriters in the save dialog.
getWriters() - Method in class weka.gui.visualize.PrintablePanel
returns a Hashtable with the current available JComponentWriters in the save dialog.
getWs(double[][], double[][]) - Method in class weka.classifiers.trees.lmt.LogisticBase
Computes the LogitBoost weights from an array of y/p values (actual/estimated class probabilities).
getWVariance() - Method in class weka.classifiers.BVDecomposeSegCVSub
Get the calculated variance according to the Webb definition
getX() - Method in class weka.classifiers.functions.neural.NeuralConnection
 
getX(Instances) - Method in class weka.filters.supervised.attribute.PLSFilter
returns the data minus the class column as matrix
getX(Instance) - Method in class weka.filters.supervised.attribute.PLSFilter
returns the data minus the class column as matrix
getX() - Method in class weka.gui.beans.BeanInstance
Gets the x coordinate of this bean
getXBase() - Method in class weka.classifiers.meta.GridSearch
Get the value of the base for X.
getXExpression() - Method in class weka.classifiers.meta.GridSearch
Get the expression for the X value.
getXindex() - Method in class weka.gui.visualize.PlotData2D
Get the currently set x index of the data
getXIndex() - Method in class weka.gui.visualize.VisualizePanel
Get the index of the attribute on the x axis
getXLabelFreq() - Method in class weka.gui.beans.StripChart
Get the frequency by which x axis values are printed
getXMax() - Method in class weka.classifiers.meta.GridSearch
Get the value of the Maximum of X.
getXMin() - Method in class weka.classifiers.meta.GridSearch
Get the value of the minimum of X.
getXMLDocument() - Method in class weka.core.xml.XMLOptions
returns the handler of the XML document.
getXProperty() - Method in class weka.classifiers.meta.GridSearch
Get the X property to test (normally the filter).
getXScale() - Method in class weka.gui.visualize.JComponentWriter
returns the scale factor for the x-axis
getXScale() - Method in class weka.gui.visualize.PrintableComponent
returns the scale factor for the x-axis.
getXScale() - Method in interface weka.gui.visualize.PrintableHandler
returns the scale factor for the x-axis
getXScale() - Method in class weka.gui.visualize.PrintablePanel
returns the scale factor for the x-axis
getXStep() - Method in class weka.classifiers.meta.GridSearch
Get the value of the step size for X.
getY() - Method in class weka.classifiers.functions.neural.NeuralConnection
 
getY(Instances) - Method in class weka.filters.supervised.attribute.PLSFilter
returns the data class column as matrix
getY(Instance) - Method in class weka.filters.supervised.attribute.PLSFilter
returns the data class column as matrix
getY() - Method in class weka.gui.beans.BeanInstance
Gets the y coordinate of this bean
getYBase() - Method in class weka.classifiers.meta.GridSearch
Get the value of the base for Y.
getYExpression() - Method in class weka.classifiers.meta.GridSearch
Get the expression for the Y value.
getYindex() - Method in class weka.gui.visualize.PlotData2D
Get the currently set y index of the data
getYIndex() - Method in class weka.gui.visualize.VisualizePanel
Get the index of the attribute on the y axis
getYMax() - Method in class weka.classifiers.meta.GridSearch
Get the value of the Maximum of Y.
getYMin() - Method in class weka.classifiers.meta.GridSearch
Get the value of the minimum of Y.
getYProperty() - Method in class weka.classifiers.meta.GridSearch
Get the Y property (normally the classifier).
getYs(Instances) - Method in class weka.classifiers.trees.lmt.LogisticBase
Computes the Y-values (actual class probabilities) for a set of instances.
getYScale() - Method in class weka.gui.visualize.JComponentWriter
returns the scale factor for the y-axis
getYScale() - Method in class weka.gui.visualize.PrintableComponent
returns the scale factor for the y-axis.
getYScale() - Method in interface weka.gui.visualize.PrintableHandler
returns the scale factor for the y-axis
getYScale() - Method in class weka.gui.visualize.PrintablePanel
returns the scale factor for the y-axis
getYStep() - Method in class weka.classifiers.meta.GridSearch
Get the value of the step size for Y.
getZ(double, double) - Method in class weka.classifiers.trees.lmt.LogisticBase
Computes the LogitBoost response variable from y/p values (actual/estimated class probabilities).
getZs(double[][], double[][]) - Method in class weka.classifiers.trees.lmt.LogisticBase
Computes the LogitBoost response for an array of y/p values (actual/estimated class probabilities).
globalBlendTipText() - Method in class weka.classifiers.lazy.KStar
Returns the tip text for this property
globalInfo() - Method in class weka.associations.Apriori
Returns a string describing this associator
globalInfo() - Method in class weka.associations.FilteredAssociator
Returns a string describing this Associator
globalInfo() - Method in class weka.associations.FPGrowth
Returns a string describing this associator
globalInfo() - Method in class weka.associations.GeneralizedSequentialPatterns
Returns global information about the algorithm.
globalInfo() - Method in class weka.associations.PredictiveApriori
Returns a string describing this associator
globalInfo() - Method in class weka.associations.Tertius
Returns a string describing this associator.
globalInfo() - Method in class weka.attributeSelection.BestFirst
Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.CfsSubsetEval
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.ClassifierSubsetEval
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.ConsistencySubsetEval
Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
 
globalInfo() - Method in class weka.attributeSelection.ExhaustiveSearch
Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.FilteredAttributeEval
 
globalInfo() - Method in class weka.attributeSelection.FilteredSubsetEval
 
globalInfo() - Method in class weka.attributeSelection.GainRatioAttributeEval
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.GeneticSearch
Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.GreedyStepwise
Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.InfoGainAttributeEval
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.LatentSemanticAnalysis
Returns a string describing this attribute transformer
globalInfo() - Method in class weka.attributeSelection.LinearForwardSelection
Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.OneRAttributeEval
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.PrincipalComponents
Returns a string describing this attribute transformer
globalInfo() - Method in class weka.attributeSelection.RaceSearch
Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.RandomSearch
Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.Ranker
Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.RankSearch
Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.ReliefFAttributeEval
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.ScatterSearchV1
Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.SVMAttributeEval
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.WrapperSubsetEval
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.classifiers.bayes.AODE
Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.bayes.AODEsr
Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
 
globalInfo() - Method in class weka.classifiers.bayes.BayesNet
This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.ComplementNaiveBayes
Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.bayes.DMNBtext
Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.bayes.HNB
Returns a string describing this classifier.
globalInfo() - Method in class weka.classifiers.bayes.NaiveBayes
Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.bayes.NaiveBayesMultinomial
Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable
Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.bayes.NaiveBayesSimple
Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.bayes.NaiveBayesUpdateable
Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.bayes.net.BIFReader
This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator
This will return a string describing the class.
globalInfo() - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator
Returns a string describing this object
globalInfo() - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
Returns a string describing this object
globalInfo() - Method in class weka.classifiers.bayes.net.estimate.SimpleEstimator
Returns a string describing this object
globalInfo() - Method in class weka.classifiers.bayes.net.search.ci.CISearchAlgorithm
Returns a string describing this object
globalInfo() - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm
This will return a string describing the search algorithm.
globalInfo() - Method in class weka.classifiers.bayes.net.search.fixed.FromFile
Returns a string describing this object
globalInfo() - Method in class weka.classifiers.bayes.net.search.fixed.NaiveBayes
Returns a string describing this object
globalInfo() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
This will return a string describing the search algorithm.
globalInfo() - Method in class weka.classifiers.bayes.net.search.global.HillClimber
This will return a string describing the search algorithm.
globalInfo() - Method in class weka.classifiers.bayes.net.search.global.K2
This will return a string describing the search algorithm.
globalInfo() - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.net.search.global.TAN
This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.net.search.local.HillClimber
This will return a string describing the search algorithm.
globalInfo() - Method in class weka.classifiers.bayes.net.search.local.K2
This will return a string describing the search algorithm.
globalInfo() - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
This will return a string describing the search algorithm.
globalInfo() - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
This will return a string describing the search algorithm.
globalInfo() - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.net.search.local.TAN
This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.WAODE
Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.BVDecompose
Returns a string describing this object
globalInfo() - Method in class weka.classifiers.BVDecomposeSegCVSub
Returns a string describing this object
globalInfo() - Method in class weka.classifiers.functions.GaussianProcesses
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.IsotonicRegression
Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.functions.LeastMedSq
Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.functions.LibLINEAR
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.LibSVM
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.LinearRegression
Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.functions.Logistic
Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.functions.MultilayerPerceptron
This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.functions.PaceRegression
Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.functions.PLSClassifier
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.RBFNetwork
Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.functions.SimpleLinearRegression
Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.functions.SimpleLogistic
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.SMO
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.SMOreg
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.SPegasos
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.supportVector.Kernel
Returns a string describing the kernel
globalInfo() - Method in class weka.classifiers.functions.supportVector.NormalizedPolyKernel
Returns a string describing the kernel
globalInfo() - Method in class weka.classifiers.functions.supportVector.PolyKernel
Returns a string describing the kernel
globalInfo() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
Returns a string describing the kernel
globalInfo() - Method in class weka.classifiers.functions.supportVector.Puk
Returns a string describing the kernel
globalInfo() - Method in class weka.classifiers.functions.supportVector.RBFKernel
Returns a string describing the kernel
globalInfo() - Method in class weka.classifiers.functions.supportVector.RegSMO
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
Returns a string describing the object
globalInfo() - Method in class weka.classifiers.functions.supportVector.StringKernel
Returns a string describing the kernel
globalInfo() - Method in class weka.classifiers.functions.VotedPerceptron
Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.functions.Winnow
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.lazy.IB1
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.lazy.IBk
Returns a string describing classifier.
globalInfo() - Method in class weka.classifiers.lazy.KStar
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.lazy.LBR
 
globalInfo() - Method in class weka.classifiers.lazy.LWL
Returns a string describing classifier.
globalInfo() - Method in class weka.classifiers.meta.AdaBoostM1
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.AdditiveRegression
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Returns a string describing this search method
globalInfo() - Method in class weka.classifiers.meta.Bagging
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.ClassificationViaClustering
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.ClassificationViaRegression
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.CostSensitiveClassifier
 
globalInfo() - Method in class weka.classifiers.meta.CVParameterSelection
Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.meta.Dagging
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.Decorate
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.END
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.FilteredClassifier
Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.meta.Grading
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.GridSearch
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.LogitBoost
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.MetaCost
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.MultiBoostAB
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.MultiClassClassifier
 
globalInfo() - Method in class weka.classifiers.meta.MultiScheme
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
 
globalInfo() - Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
 
globalInfo() - Method in class weka.classifiers.meta.nestedDichotomies.ND
 
globalInfo() - Method in class weka.classifiers.meta.OrdinalClassClassifier
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
 
globalInfo() - Method in class weka.classifiers.meta.RandomCommittee
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.RandomSubSpace
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.RegressionByDiscretization
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.RotationForest
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.Stacking
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.StackingC
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.ThresholdSelector
 
globalInfo() - Method in class weka.classifiers.meta.Vote
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.mi.CitationKNN
Returns a string describing this filter
globalInfo() - Method in class weka.classifiers.mi.MDD
Returns a string describing this filter
globalInfo() - Method in class weka.classifiers.mi.MIBoost
Returns a string describing this filter
globalInfo() - Method in class weka.classifiers.mi.MIDD
Returns a string describing this filter
globalInfo() - Method in class weka.classifiers.mi.MIEMDD
Returns a string describing this filter
globalInfo() - Method in class weka.classifiers.mi.MILR
Returns the tip text for this property
globalInfo() - Method in class weka.classifiers.mi.MINND
Returns a string describing this filter
globalInfo() - Method in class weka.classifiers.mi.MIOptimalBall
Returns a string describing this filter
globalInfo() - Method in class weka.classifiers.mi.MISMO
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.mi.MISVM
Returns a string describing this filter
globalInfo() - Method in class weka.classifiers.mi.MIWrapper
Returns a string describing this filter
globalInfo() - Method in class weka.classifiers.mi.SimpleMI
Returns a string describing this filter
globalInfo() - Method in class weka.classifiers.misc.HyperPipes
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.misc.SerializedClassifier
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.misc.VFI
Returns a string describing this search method
globalInfo() - Method in class weka.classifiers.rules.ConjunctiveRule
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.DecisionTable
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.DTNB.BackwardsWithDelete
 
globalInfo() - Method in class weka.classifiers.rules.DTNB
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.JRip
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.M5Rules
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.NNge
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.OneR
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.PART
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.Prism
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.Ridor
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.ZeroR
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.ADTree
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.BFTree
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.DecisionStump
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.FT
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.Id3
Returns a string describing the classifier.
globalInfo() - Method in class weka.classifiers.trees.J48
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.J48graft
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.LADTree
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.LMT
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.m5.M5Base
returns information about the classifier
globalInfo() - Method in class weka.classifiers.trees.NBTree
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.RandomForest
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.RandomTree
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.REPTree
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.SimpleCart
Return a description suitable for displaying in the explorer/experimenter.
globalInfo() - Method in class weka.classifiers.trees.UserClassifier
This will return a string describing the classifier.
globalInfo() - Method in class weka.clusterers.CLOPE
Returns a string describing this DataMining-Algorithm
globalInfo() - Method in class weka.clusterers.Cobweb
Returns a string describing this clusterer
globalInfo() - Method in class weka.clusterers.DBScan
Returns a string describing this DataMining-Algorithm
globalInfo() - Method in class weka.clusterers.EM
Returns a string describing this clusterer
globalInfo() - Method in class weka.clusterers.FarthestFirst
Returns a string describing this clusterer
globalInfo() - Method in class weka.clusterers.FilteredClusterer
Returns a string describing this clusterer.
globalInfo() - Method in class weka.clusterers.HierarchicalClusterer
This will return a string describing the clusterer.
globalInfo() - Method in class weka.clusterers.MakeDensityBasedClusterer
Returns a string describing classifier
globalInfo() - Method in class weka.clusterers.OPTICS
Returns a string describing this DataMining-Algorithm
globalInfo() - Method in class weka.clusterers.sIB
Returns a string describing this clusterer
globalInfo() - Method in class weka.clusterers.SimpleKMeans
Returns a string describing this clusterer
globalInfo() - Method in class weka.clusterers.XMeans
Returns a string describing this clusterer.
globalInfo() - Method in class weka.core.ChebyshevDistance
Returns a string describing this object.
globalInfo() - Method in class weka.core.converters.ArffLoader
Returns a string describing this Loader
globalInfo() - Method in class weka.core.converters.ArffSaver
Returns a string describing this Saver
globalInfo() - Method in class weka.core.converters.C45Loader
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.core.converters.C45Saver
Returns a string describing this Saver
globalInfo() - Method in class weka.core.converters.CSVLoader
Returns a string describing this attribute evaluator.
globalInfo() - Method in class weka.core.converters.CSVSaver
Returns a string describing this Saver
globalInfo() - Method in class weka.core.converters.DatabaseLoader
Returns a string describing this Loader
globalInfo() - Method in class weka.core.converters.DatabaseSaver
Returns a string describing this Saver.
globalInfo() - Method in class weka.core.converters.LibSVMLoader
Returns a string describing this Loader.
globalInfo() - Method in class weka.core.converters.LibSVMSaver
Returns a string describing this Saver
globalInfo() - Method in class weka.core.converters.SerializedInstancesLoader
Returns a string describing this object
globalInfo() - Method in class weka.core.converters.SerializedInstancesSaver
Returns a string describing this Saver.
globalInfo() - Method in class weka.core.converters.SVMLightLoader
Returns a string describing this Loader.
globalInfo() - Method in class weka.core.converters.SVMLightSaver
Returns a string describing this Saver.
globalInfo() - Method in class weka.core.converters.TextDirectoryLoader
Returns a string describing this loader
globalInfo() - Method in class weka.core.converters.XRFFLoader
Returns a string describing this Loader
globalInfo() - Method in class weka.core.converters.XRFFSaver
Returns a string describing this Saver
globalInfo() - Method in class weka.core.EditDistance
Returns a string describing this object.
globalInfo() - Method in class weka.core.EuclideanDistance
Returns a string describing this object.
globalInfo() - Method in class weka.core.ManhattanDistance
Returns a string describing this object.
globalInfo() - Method in class weka.core.neighboursearch.BallTree
Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.balltrees.BottomUpConstructor
Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.balltrees.PointsClosestToFurthestChildren
Returns a string describing this object.
globalInfo() - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor
Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.CoverTree
Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.KDTree
Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.kdtrees.KMeansInpiredMethod
Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.kdtrees.MedianOfWidestDimension
Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.kdtrees.MidPointOfWidestDimension
Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.kdtrees.SlidingMidPointOfWidestSide
Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.LinearNNSearch
Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.NearestNeighbourSearch
Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.NormalizableDistance
Returns a string describing this object.
globalInfo() - Method in class weka.core.stemmers.IteratedLovinsStemmer
Returns a string describing the stemmer
globalInfo() - Method in class weka.core.stemmers.LovinsStemmer
Returns a string describing the stemmer
globalInfo() - Method in class weka.core.stemmers.NullStemmer
Returns a string describing the stemmer
globalInfo() - Method in class weka.core.stemmers.SnowballStemmer
Returns a string describing the stemmer.
globalInfo() - Method in class weka.core.tokenizers.AlphabeticTokenizer
Returns a string describing the stemmer
globalInfo() - Method in class weka.core.tokenizers.NGramTokenizer
Returns a string describing the stemmer
globalInfo() - Method in class weka.core.tokenizers.Tokenizer
Returns a string describing the stemmer
globalInfo() - Method in class weka.core.tokenizers.WordTokenizer
Returns a string describing the stemmer
globalInfo() - Method in class weka.datagenerators.classifiers.classification.Agrawal
Returns a string describing this data generator.
globalInfo() - Method in class weka.datagenerators.classifiers.classification.BayesNet
Returns a string describing this data generator.
globalInfo() - Method in class weka.datagenerators.classifiers.classification.LED24
Returns a string describing this data generator.
globalInfo() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
Returns a string describing this data generator.
globalInfo() - Method in class weka.datagenerators.classifiers.classification.RDG1
Returns a string describing this data generator.
globalInfo() - Method in class weka.datagenerators.classifiers.regression.Expression
Returns a string describing this data generator.
globalInfo() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Returns a string describing this data generator.
globalInfo() - Method in class weka.datagenerators.ClusterDefinition
Returns a string describing this data generator.
globalInfo() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Returns a string describing this data generator.
globalInfo() - Method in class weka.datagenerators.clusterers.SubspaceCluster
Returns a string describing this data generator.
globalInfo() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Returns a string describing this data generator.
globalInfo() - Method in class weka.experiment.AveragingResultProducer
Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.ClassifierSplitEvaluator
Returns a string describing this split evaluator
globalInfo() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
Returns a string describing this split evaluator
globalInfo() - Method in class weka.experiment.CrossValidationResultProducer
Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.CSVResultListener
Returns a string describing this result listener
globalInfo() - Method in class weka.experiment.DatabaseResultListener
Returns a string describing this result listener
globalInfo() - Method in class weka.experiment.DatabaseResultProducer
Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
Returns a string describing this split evaluator
globalInfo() - Method in class weka.experiment.InstancesResultListener
Returns a string describing this result listener
globalInfo() - Method in class weka.experiment.LearningRateResultProducer
Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.RandomSplitResultProducer
Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.RegressionSplitEvaluator
Returns a string describing this split evaluator
globalInfo() - Method in class weka.filters.AllFilter
Returns a string describing this filter
globalInfo() - Method in class weka.filters.MultiFilter
Returns a string describing this filter
globalInfo() - Method in class weka.filters.SimpleFilter
Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.supervised.attribute.AddClassification
Returns a string describing this filter.
globalInfo() - Method in class weka.filters.supervised.attribute.AttributeSelection
Returns a string describing this filter
globalInfo() - Method in class weka.filters.supervised.attribute.ClassOrder
Returns a string describing this filter
globalInfo() - Method in class weka.filters.supervised.attribute.Discretize
Returns a string describing this filter
globalInfo() - Method in class weka.filters.supervised.attribute.NominalToBinary
Returns a string describing this filter
globalInfo() - Method in class weka.filters.supervised.attribute.PLSFilter
Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.supervised.instance.Resample
Returns a string describing this filter.
globalInfo() - Method in class weka.filters.supervised.instance.SMOTE
Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.supervised.instance.SpreadSubsample
Returns a string describing this filter
globalInfo() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Add
Returns a string describing this filter.
globalInfo() - Method in class weka.filters.unsupervised.attribute.AddCluster
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.AddExpression
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.AddID
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.AddNoise
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.AddValues
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Center
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.ClassAssigner
Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.unsupervised.attribute.ClusterMembership
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Copy
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Discretize
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.FirstOrder
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.KernelFilter
Returns a string describing this filter.
globalInfo() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
 
globalInfo() - Method in class weka.filters.unsupervised.attribute.MathExpression
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.NominalToBinary
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.NominalToString
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Normalize
Returns a string describing this filter.
globalInfo() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Returns a string describing this filter.
globalInfo() - Method in class weka.filters.unsupervised.attribute.NumericToBinary
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.NumericToNominal
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.NumericTransform
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Obfuscate
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
Returns a string describing this filter.
globalInfo() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
Returns a string describing this filter.
globalInfo() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.RandomProjection
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.RandomSubset
Returns a string describing this filter.
globalInfo() - Method in class weka.filters.unsupervised.attribute.RELAGGS
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Remove
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.RemoveType
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.RemoveUseless
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Reorder
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Standardize
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.StringToNominal
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Returns a string describing this filter.
globalInfo() - Method in class weka.filters.unsupervised.attribute.SwapValues
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.TimeSeriesDelta
Returns a string describing this classifier
globalInfo() - Method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate
Returns a string describing this classifier
globalInfo() - Method in class weka.filters.unsupervised.attribute.Wavelet
Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.unsupervised.instance.NonSparseToSparse
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.Normalize
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.Randomize
Returns a string describing this classifier
globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveFolds
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.RemovePercentage
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveRange
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Returns a string describing this classifier
globalInfo() - Method in class weka.filters.unsupervised.instance.Resample
Returns a string describing this classifier
globalInfo() - Method in class weka.filters.unsupervised.instance.ReservoirSample
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.SparseToNonSparse
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.SubsetByExpression
Returns a string describing this filter.
globalInfo() - Method in class weka.gui.beans.Associator
Global info (if it exists) for the wrapped classifier
globalInfo() - Method in class weka.gui.beans.AttributeSummarizer
Global info for this bean
globalInfo() - Method in class weka.gui.beans.ClassAssigner
Global info for this bean
globalInfo() - Method in class weka.gui.beans.Classifier
Global info (if it exists) for the wrapped classifier
globalInfo() - Method in class weka.gui.beans.ClassifierPerformanceEvaluator
Global info for this bean
globalInfo() - Method in class weka.gui.beans.ClassValuePicker
Global info for this bean
globalInfo() - Method in class weka.gui.beans.Clusterer
Global info (if it exists) for the wrapped classifier
globalInfo() - Method in class weka.gui.beans.ClustererPerformanceEvaluator
Global info for this bean
globalInfo() - Method in class weka.gui.beans.CostBenefitAnalysis
Global info for this bean
globalInfo() - Method in class weka.gui.beans.CrossValidationFoldMaker
Global info for this bean
globalInfo() - Method in class weka.gui.beans.DataVisualizer
Global info for this bean
globalInfo() - Method in class weka.gui.beans.Filter
Global info (if it exists) for the wrapped filter
globalInfo() - Method in class weka.gui.beans.GraphViewer
Global info for this bean
globalInfo() - Method in class weka.gui.beans.IncrementalClassifierEvaluator
Global info for this bean
globalInfo() - Method in class weka.gui.beans.Loader
Global info (if it exists) for the wrapped loader
globalInfo() - Method in class weka.gui.beans.ModelPerformanceChart
Global info for this bean
globalInfo() - Method in class weka.gui.beans.PredictionAppender
Global description of this bean
globalInfo() - Method in class weka.gui.beans.Saver
Global info (if it exists) for the wrapped loader
globalInfo() - Method in class weka.gui.beans.ScatterPlotMatrix
Global info for this bean
globalInfo() - Method in class weka.gui.beans.SerializedModelSaver
Global info for this bean.
globalInfo() - Method in class weka.gui.beans.StripChart
Global info for this bean
globalInfo() - Method in class weka.gui.beans.TestSetMaker
Global info for this bean
globalInfo() - Method in class weka.gui.beans.TextViewer
Global info for this bean
globalInfo() - Method in class weka.gui.beans.TrainingSetMaker
Global info for this bean
globalInfo() - Method in class weka.gui.beans.TrainTestSplitMaker
Global info for this bean
globalInfo() - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
Returns a string describing this tool
GLOBALINFO_ENDTAG - Static variable in class weka.core.GlobalInfoJavadoc
the end comment tag for inserting the generated Javadoc
GLOBALINFO_METHOD - Static variable in class weka.core.GlobalInfoJavadoc
the globalInfo method name
GLOBALINFO_STARTTAG - Static variable in class weka.core.GlobalInfoJavadoc
the start comment tag for inserting the generated Javadoc
GlobalInfoJavadoc - Class in weka.core
Generates Javadoc comments from the class's globalInfo method.
GlobalInfoJavadoc() - Constructor for class weka.core.GlobalInfoJavadoc
default constructor
GlobalScoreSearchAlgorithm - Class in weka.classifiers.bayes.net.search.global
This Bayes Network learning algorithm uses cross validation to estimate classification accuracy.
GlobalScoreSearchAlgorithm() - Constructor for class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
 
goDown(String) - Method in class weka.gui.HierarchyPropertyParser
Go to a certain node of the tree down from the current node according to the specified relative path.
GOEPanel() - Constructor for class weka.gui.GenericObjectEditor.GOEPanel
Creates the GUI editor component.
GOETreeNode() - Constructor for class weka.gui.GenericObjectEditor.GOETreeNode
Creates a tree node that has no parent and no children, but which allows children.
GOETreeNode(Object) - Constructor for class weka.gui.GenericObjectEditor.GOETreeNode
Creates a tree node with no parent, no children, but which allows children, and initializes it with the specified user object.
GOETreeNode(Object, boolean) - Constructor for class weka.gui.GenericObjectEditor.GOETreeNode
Creates a tree node with no parent, no children, initialized with the specified user object, and that allows children only if specified.
goTo(String) - Method in class weka.gui.HierarchyPropertyParser
Go to a certain node of the tree according to the specified path Note that the path must be absolute path from the root.
goToChild(String) - Method in class weka.gui.HierarchyPropertyParser
Go to one child node from the current position in the tree according to the given value
If the child node with the given value cannot be found it returns false, true otherwise.
goToChild(int) - Method in class weka.gui.HierarchyPropertyParser
Go to one child node from the current position in the tree according to the given position
goToParent() - Method in class weka.gui.HierarchyPropertyParser
Go to the parent from the current position in the tree If the current position is the root, it stays there and does not move
goToRoot() - Method in class weka.gui.HierarchyPropertyParser
Go to the root of the tree
gr(double, double) - Static method in class weka.core.Utils
Tests if a is greater than b.
Grading - Class in weka.classifiers.meta
Implements Grading.
Grading() - Constructor for class weka.classifiers.meta.Grading
 
GraftSplit - Class in weka.classifiers.trees.j48
Class implementing a split for nodes added to a tree during grafting.
GraftSplit(int, double, int, double, double) - Constructor for class weka.classifiers.trees.j48.GraftSplit
constructor
GraftSplit(int, double, int, double, double[][]) - Constructor for class weka.classifiers.trees.j48.GraftSplit
constructor
graph(FPGrowth.FPTreeRoot) - Method in class weka.associations.FPGrowth
Assemble a dot graph representation of the FP-tree.
graph() - Method in class weka.classifiers.bayes.BayesNet
Returns a BayesNet graph in XMLBIF ver 0.3 format.
graph() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Returns graph describing the classifier (if possible).
graph() - Method in class weka.classifiers.meta.CostSensitiveClassifier
Returns graph describing the classifier (if possible).
graph() - Method in class weka.classifiers.meta.CVParameterSelection
Returns graph describing the classifier (if possible).
graph() - Method in class weka.classifiers.meta.FilteredClassifier
Returns graph describing the classifier (if possible).
graph() - Method in class weka.classifiers.meta.ThresholdSelector
Returns graph describing the classifier (if possible).
graph() - Method in class weka.classifiers.trees.ADTree
Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.ft.FTtree
Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.FT
Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.j48.ClassifierTree
Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.J48
Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.j48.NBTreeClassifierTree
Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.J48graft
Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.LADTree
Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.LMT
Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.lmt.LMTNode
Returns graph describing the tree.
graph(StringBuffer) - Method in class weka.classifiers.trees.m5.RuleNode
Assign a unique identifier to each node in the tree and then calls graphTree
graph() - Method in class weka.classifiers.trees.M5P
Return a dot style String describing the tree.
graph() - Method in class weka.classifiers.trees.NBTree
Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.RandomTree
Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.REPTree
Outputs the decision tree as a graph
graph() - Method in class weka.classifiers.trees.UserClassifier
 
graph() - Method in class weka.clusterers.Cobweb
Generates the graph string of the Cobweb tree
graph() - Method in class weka.clusterers.HierarchicalClusterer
 
graph() - Method in interface weka.core.Drawable
Returns a string that describes a graph representing the object.
graph(StreamTokenizer) - Method in class weka.gui.graphvisualizer.DotParser
Following methods parse the DOT input and mimic the DOT language's grammar in their structure
GraphConstants - Interface in weka.gui.graphvisualizer
GraphConstants.java
GraphEdge - Class in weka.gui.graphvisualizer
This class represents an edge in the graph
GraphEdge(int, int, int) - Constructor for class weka.gui.graphvisualizer.GraphEdge
 
GraphEdge(int, int, int, String, String) - Constructor for class weka.gui.graphvisualizer.GraphEdge
 
GraphEvent - Class in weka.gui.beans
Event for graphs
GraphEvent(Object, String, String, int) - Constructor for class weka.gui.beans.GraphEvent
Creates a new GraphEvent instance.
graphFPTree(StringBuffer) - Method in class weka.associations.FPGrowth.FPTreeNode
Generate a dot graph description string for the tree.
graphID - Variable in class weka.gui.graphvisualizer.GraphVisualizer
String containing graph's name
GraphListener - Interface in weka.gui.beans
Describe interface TextListener here.
graphMatrix - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
Interconnection matrix for the graph
graphName - Variable in class weka.gui.graphvisualizer.BIFParser
This holds the name of the graph (i.e.
GraphNode - Class in weka.gui.graphvisualizer
This class represents a node in the Graph.
GraphNode(String, String) - Constructor for class weka.gui.graphvisualizer.GraphNode
Constructor
GraphNode(String, String, int) - Constructor for class weka.gui.graphvisualizer.GraphNode
Constructor
GraphPanel - Class in weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI
GraphPanel.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht
Date: Sep 16, 2004
Time: 10:28:19 AM
$ Revision 1.4 $
GraphPanel(FastVector, int, boolean, boolean) - Constructor for class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel
 
graphTraverse(PredictionNode, StringBuffer, int, int, Instances) - Method in class weka.classifiers.trees.ADTree
Traverses the tree, graphing each node.
graphTraverse(LADTree.PredictionNode, StringBuffer, int, int) - Method in class weka.classifiers.trees.LADTree
Traverses the tree, graphing each node.
graphTree(StringBuffer) - Method in class weka.classifiers.trees.ft.FTtree
Helper function for graph description of tree
graphTree(StringBuffer) - Method in class weka.classifiers.trees.m5.RuleNode
Return a dotty style string describing the tree
graphType() - Method in class weka.classifiers.bayes.BayesNet
Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.meta.CostSensitiveClassifier
Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.meta.CVParameterSelection
Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.meta.FilteredClassifier
Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.meta.ThresholdSelector
Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.ADTree
Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.FT
Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.j48.ClassifierTree
Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.J48
Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.J48graft
Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.LADTree
Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.LMT
Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.M5P
Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.NBTree
Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.RandomTree
Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.REPTree
Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.UserClassifier
Returns the type of graph this classifier represents.
graphType() - Method in class weka.clusterers.Cobweb
Returns the type of graphs this class represents
graphType() - Method in class weka.clusterers.HierarchicalClusterer
 
graphType() - Method in interface weka.core.Drawable
Returns the type of graph representing the object.
GraphViewer - Class in weka.gui.beans
A bean encapsulating weka.gui.treevisualize.TreeVisualizer
GraphViewer() - Constructor for class weka.gui.beans.GraphViewer
 
GraphViewerBeanInfo - Class in weka.gui.beans
Bean info class for the graph viewer
GraphViewerBeanInfo() - Constructor for class weka.gui.beans.GraphViewerBeanInfo
 
GraphVisualizePlugin - Interface in weka.gui.visualize.plugins
Interface implemented by classes loaded dynamically to visualize graphs in the explorer.
GraphVisualizer - Class in weka.gui.graphvisualizer
This class displays the graph we want to visualize.
GraphVisualizer() - Constructor for class weka.gui.graphvisualizer.GraphVisualizer
Constructor
Sets up the gui and initializes all the other previously uninitialized variables.
GreedyStepwise - Class in weka.attributeSelection
GreedyStepwise :

Performs a greedy forward or backward search through the space of attribute subsets.
GreedyStepwise() - Constructor for class weka.attributeSelection.GreedyStepwise
Constructor
Grid(double, double, double, double, double, double) - Constructor for class weka.classifiers.meta.GridSearch.Grid
initializes the grid
Grid(double, double, double, String, double, double, double, String) - Constructor for class weka.classifiers.meta.GridSearch.Grid
initializes the grid
GRID - Static variable in class weka.datagenerators.clusterers.BIRCHCluster
Constant set for choice of pattern.
gridIsExtendableTipText() - Method in class weka.classifiers.meta.GridSearch
Returns the tip text for this property
GridSearch - Class in weka.classifiers.meta
Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting.

The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy).
GridSearch() - Constructor for class weka.classifiers.meta.GridSearch
the default constructor
GridSearch.Grid - Class in weka.classifiers.meta
for generating the parameter pairs in a grid
GridSearch.Performance - Class in weka.classifiers.meta
A helper class for storing the performance of a values-pair.
GridSearch.PerformanceCache - Class in weka.classifiers.meta
Represents a simple cache for performance objects.
GridSearch.PerformanceComparator - Class in weka.classifiers.meta
A concrete Comparator for the Performance class.
GridSearch.PerformanceTable - Class in weka.classifiers.meta
Generates a 2-dim array for the performances from a grid for a certain type.
GridSearch.PointDouble - Class in weka.classifiers.meta
a serializable version of Point2D.Double
GridSearch.PointInt - Class in weka.classifiers.meta
a serializable version of Point
grOrEq(double, double) - Static method in class weka.core.Utils
Tests if a is greater or equal to b.
grouping(boolean) - Method in class weka.core.matrix.FlexibleDecimalFormat
 
grow(Instances) - Method in class weka.classifiers.rules.JRip.RipperRule
Build one rule using the growing data
grow(Instances) - Method in class weka.classifiers.rules.Rule
Build this rule
GT - Static variable in interface weka.core.mathematicalexpression.sym
 
GT - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
GUI - Class in weka.classifiers.bayes.net
GUI interface to Bayesian Networks.
GUI() - Constructor for class weka.classifiers.bayes.net.GUI
Constructor
Sets up the gui and initializes all the other previously uninitialized variables.
GUI_MDI - Static variable in class weka.gui.Main
displays the GUI as MDI.
GUI_SDI - Static variable in class weka.gui.Main
displays the GUI as SDI.
GUIChooser - Class in weka.gui
The main class for the Weka GUIChooser.
GUIChooser() - Constructor for class weka.gui.GUIChooser
Creates the experiment environment gui with no initial experiment
GUIChooser.ChildFrameSDI - Class in weka.gui
Specialized JFrame class.
GUIEDITORS_PROPERTY_FILE - Static variable in class weka.gui.GenericObjectEditor
the properties files containing the class/editor mappings.
GUITipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
 

H

h(double) - Method in class weka.classifiers.functions.pace.ChisqMixture
Computes the value of h(x) given the mixture.
h(DoubleVector) - Method in class weka.classifiers.functions.pace.ChisqMixture
Computes the value of h(x) given the mixture, where x is a vector.
h(double) - Method in class weka.classifiers.functions.pace.NormalMixture
Computes the value of h(x) given the mixture.
h(DoubleVector) - Method in class weka.classifiers.functions.pace.NormalMixture
Computes the value of h(x) given the mixture, where x is a vector.
h1(int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Constructs single Householder transformation for a column
h2(int, int, double, PaceMatrix, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Performs single Householder transformation on one column of a matrix
halfsort(Stack<CoverTree.d_node>) - Method in class weka.core.neighboursearch.CoverTree
Half-sorts a cover set, so that nodes nearer to the query are at the front.
handleCostOption(String, int) - Static method in class weka.classifiers.Evaluation
Attempts to load a cost matrix.
handles(Capabilities.Capability) - Method in class weka.core.Capabilities
returns true if the classifier handler has the specified capability
handles(Capabilities.Capability) - Method in class weka.core.FindWithCapabilities
returns true if the given capability can be handled.
hasAdditional() - Method in class weka.core.TechnicalInformation
returns true if there are further technical informations stored in this
hasAntds() - Method in class weka.classifiers.rules.ConjunctiveRule
Whether this rule has antecedents, i.e.
hasAntds() - Method in class weka.classifiers.rules.JRip.RipperRule
Whether this rule has antecedents, i.e.
hasAntds() - Method in class weka.classifiers.rules.Rule
Whether this rule has antecedents, i.e.
hasClasspathProblems() - Method in class weka.core.CheckScheme
returns TRUE if the classifier returned a "not in classpath" Exception
hasClasspathProblems() - Method in class weka.estimators.CheckEstimator
returns TRUE if the estimator returned a "not in classpath" Exception
hasDependencies() - Method in class weka.core.Capabilities
Checks whether there are any dependencies at all
hasDependency(Capabilities.Capability) - Method in class weka.core.Capabilities
returns true if the classifier handler has a dependency for the specified capability
hasFalseHead() - Method in class weka.associations.tertius.Rule
Test if the head of the rule is false.
hash - Variable in class weka.classifiers.lazy.kstar.KStarCache.TableEntry
attribute value hash code
hashCode() - Method in class weka.associations.FPGrowth.BinaryItem
 
hashCode() - Method in class weka.associations.ItemSet
Produces a hash code for a item set.
hashCode() - Method in class weka.attributeSelection.ConsistencySubsetEval.hashKey
Calculates a hash code
hashCode() - Method in class weka.classifiers.rules.DecisionTableHashKey
Calculates a hash code
hashCode() - Method in class weka.core.SerializedObject
Returns a hashcode for this object.
hashCode() - Method in class weka.core.Trie
Returns the hash code value for this collection.
hashKey(Instance, int) - Constructor for class weka.attributeSelection.ConsistencySubsetEval.hashKey
Constructor for a hashKey
hashKey(double[]) - Constructor for class weka.attributeSelection.ConsistencySubsetEval.hashKey
Constructor for a hashKey
hasImmediateOutputFormat() - Method in class weka.filters.MultiFilter
Returns true if the output format is immediately available after the input format has been set and not only after all the data has been seen (see batchFinished()).
hasImmediateOutputFormat() - Method in class weka.filters.SimpleBatchFilter
returns true if the output format is immediately available after the input format has been set and not only after all the data has been seen (see batchFinished())
hasImmediateOutputFormat() - Method in class weka.filters.SimpleFilter
returns true if the output format is immediately available after the input format has been set and not only after all the data has been seen (see batchFinished())
hasImmediateOutputFormat() - Method in class weka.filters.SimpleStreamFilter
Returns true if the output format is immediately available after the input format has been set and not only after all the data has been seen (see batchFinished()).
hasIncomingBatchInstances() - Method in class weka.gui.beans.Classifier
Returns true if this classifier has an incoming connection that is a batch set of instances
hasIncomingBatchInstances() - Method in class weka.gui.beans.Clusterer
Returns true if this clusterer has an incoming connection that is a batch set of instances
hasIncomingStreamInstances() - Method in class weka.gui.beans.Classifier
Returns true if this classifier has an incoming connection that is an instance stream
hasIndex() - Method in class weka.core.PropertyPath.PathElement
returns whether the property is an index-based one
hasInterface(String, String) - Static method in class weka.core.ClassDiscovery
Checks whether the given class implements the given interface.
hasInterface(Class, Class) - Static method in class weka.core.ClassDiscovery
Checks whether the given class implements the given interface.
hasMaxCounterInstances() - Method in class weka.associations.tertius.LiteralSet
Test if all the intances are counter-instances.
hasMaxRows() - Method in class weka.gui.sql.ResultSetHelper
whether a limit on the rows to retrieve was set.
hasMissingValue() - Method in class weka.core.Instance
Tests whether an instance has a missing value.
hasModels() - Method in class weka.classifiers.trees.ft.FTtree
Returns true if the logistic regression model at this node has changed compared to the one at the parent node.
hasModels() - Method in class weka.classifiers.trees.lmt.LMTNode
Returns true if the logistic regression model at this node has changed compared to the one at the parent node.
hasMoreElements(Instances) - Method in class weka.core.converters.ConverterUtils.DataSource
returns whether there are more Instance objects in the data.
hasMoreElements() - Method in class weka.core.FastVector.FastVectorEnumeration
Tests if there are any more elements to enumerate.
hasMoreElements() - Method in class weka.core.tokenizers.AlphabeticTokenizer
returns whether there are more elements still
hasMoreElements() - Method in class weka.core.tokenizers.NGramTokenizer
returns true if there's more elements available
hasMoreElements() - Method in class weka.core.tokenizers.Tokenizer
Tests if this enumeration contains more elements.
hasMoreElements() - Method in class weka.core.tokenizers.WordTokenizer
Tests if this enumeration contains more elements.
hasMoreIterations() - Method in class weka.experiment.Experiment
Returns true if there are more iterations to carry out in the experiment.
hasNext() - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListIterator
 
hasNext() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueue
Tests, if the queue has some more elements left
hasNext() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueue
Tests, if the queue has some more elements left
hasNext() - Method in class weka.core.Trie.TrieIterator
Returns true if the iteration has more elements.
hasPrevious() - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListInverseIterator
 
hasResult() - Method in class weka.gui.sql.event.QueryExecuteEvent
whether a ResultSet was produced, e.g.
hasTargetMetaData() - Method in class weka.core.pmml.MiningSchema
Returns true if there is Target meta data.
hasTrueBody() - Method in class weka.associations.tertius.Rule
Test if the body of the rule is true.
hasUID(String) - Static method in class weka.core.SerializationHelper
checks whether the given class contains a serialVersionUID.
hasUID(Class) - Static method in class weka.core.SerializationHelper
checks whether the given class contains a serialVersionUID.
hasZeropoint() - Method in class weka.core.Attribute
Returns whether the attribute has a zeropoint and may be added meaningfully.
HDRankTipText() - Method in class weka.classifiers.mi.CitationKNN
Returns the tip text for this property
Head - Class in weka.associations.tertius
Class representing the head of a rule.
Head() - Constructor for class weka.associations.tertius.Head
Constructor without storing the counter-instances.
Head(Instances) - Constructor for class weka.associations.tertius.Head
Constructor storing the counter-instances.
headContains(Literal) - Method in class weka.associations.tertius.Rule
Test if the head of the rule contains a literal.
header(int) - Method in class weka.experiment.PairedTTester
Creates a "header" string describing the current resultsets.
header(int) - Method in interface weka.experiment.Tester
Creates a "header" string describing the current resultsets.
headerFromXML() - Method in class weka.core.xml.XMLInstances
generates the header from the XML document
headerKeys() - Method in class weka.experiment.ResultMatrix
returns an enumeration of the header keys
headerToXML() - Method in class weka.core.xml.XMLInstances
generates the XML structure for the header
height() - Method in class weka.classifiers.meta.GridSearch.Grid
returns the number of points in the grid on the Y axis (incl.
HEIGHT - Static variable in class weka.gui.arffviewer.ArffViewerMainPanel
default height
height - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
HEIGHT - Static variable in class weka.gui.sql.SqlViewer
the height property in the history file.
heuristicStopTipText() - Method in class weka.classifiers.functions.SimpleLogistic
Returns the tip text for this property
heuristicTipText() - Method in class weka.classifiers.trees.BFTree
Returns the tip text for this property
heuristicTipText() - Method in class weka.classifiers.trees.SimpleCart
Returns the tip text for this property
hf(double) - Method in class weka.classifiers.functions.pace.ChisqMixture
Computes the value of h(x) / f(x) given the mixture.
hf(double) - Method in class weka.classifiers.functions.pace.NormalMixture
Computes the value of h(x) / f(x) given the mixture.
hiddenLayersTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
HierarchicalBCEngine - Class in weka.gui.graphvisualizer
This class lays out the vertices of a graph in a hierarchy of vertical levels, with a number of nodes in each level.
HierarchicalBCEngine(FastVector, FastVector, int, int) - Constructor for class weka.gui.graphvisualizer.HierarchicalBCEngine
Constructor - takes in FastVectors of nodes and edges, and the initial width and height of a node
HierarchicalBCEngine(FastVector, FastVector, int, int, boolean) - Constructor for class weka.gui.graphvisualizer.HierarchicalBCEngine
Constructor - takes in FastVectors of nodes and edges, the initial width and height of a node, and a boolean value to indicate if the edges should be concentrated.
HierarchicalBCEngine() - Constructor for class weka.gui.graphvisualizer.HierarchicalBCEngine
SimpleConstructor If we want to instantiate the class first, and if information for nodes and edges is not available.
HierarchicalClusterer - Class in weka.clusterers
Hierarchical clustering class.
HierarchicalClusterer() - Constructor for class weka.clusterers.HierarchicalClusterer
 
HierarchyPropertyParser - Class in weka.gui
This class implements a parser to read properties that have a hierarchy(i.e.
HierarchyPropertyParser() - Constructor for class weka.gui.HierarchyPropertyParser
Default constructor
HierarchyPropertyParser(String, String) - Constructor for class weka.gui.HierarchyPropertyParser
Constructor that builds a tree from the given property with the given delimitor
HierarchyVisualizer - Class in weka.gui.hierarchyvisualizer
 
HierarchyVisualizer(String) - Constructor for class weka.gui.hierarchyvisualizer.HierarchyVisualizer
 
HillClimber - Class in weka.classifiers.bayes.net.search.global
This Bayes Network learning algorithm uses a hill climbing algorithm adding, deleting and reversing arcs.
HillClimber() - Constructor for class weka.classifiers.bayes.net.search.global.HillClimber
 
HillClimber - Class in weka.classifiers.bayes.net.search.local
This Bayes Network learning algorithm uses a hill climbing algorithm adding, deleting and reversing arcs.
HillClimber() - Constructor for class weka.classifiers.bayes.net.search.local.HillClimber
 
HINGE - Static variable in class weka.classifiers.functions.SPegasos
 
HISTORY_FILE - Static variable in class weka.gui.sql.SqlViewer
the name of the history file (in the home directory).
HISTORY_NAME - Static variable in class weka.gui.sql.ConnectionPanel
the name of the history.
HISTORY_NAME - Static variable in class weka.gui.sql.QueryPanel
the name of the history.
historyChanged(HistoryChangedEvent) - Method in interface weka.gui.sql.event.HistoryChangedListener
This method gets called when a history is modified.
historyChanged(HistoryChangedEvent) - Method in class weka.gui.sql.SqlViewer
This method gets called when a history is modified.
HistoryChangedEvent - Class in weka.gui.sql.event
An event that is generated when a history is modified.
HistoryChangedEvent(Object, String, DefaultListModel) - Constructor for class weka.gui.sql.event.HistoryChangedEvent
constructs the event
HistoryChangedListener - Interface in weka.gui.sql.event
A listener for changes in a history.
historyInput(Instance) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Adds an instance to the history buffer.
historyRightClickPopup(String, int, int) - Method in class weka.gui.explorer.AssociationsPanel
Handles constructing a popup menu with visualization options.
hit(Rectangle, Shape, boolean) - Method in class weka.gui.visualize.PostscriptGraphics
 
HLINE - Static variable in class weka.gui.visualize.VisualizePanelEvent
 
HNB - Class in weka.classifiers.bayes
Contructs Hidden Naive Bayes classification model with high classification accuracy and AUC.

For more information refer to:

H.
HNB() - Constructor for class weka.classifiers.bayes.HNB
 
holdOutFileTipText() - Method in class weka.attributeSelection.ClassifierSubsetEval
Returns the tip text for this property
HoldOutSubsetEvaluator - Class in weka.attributeSelection
Abstract attribute subset evaluator capable of evaluating subsets with respect to a data set that is distinct from that used to initialize/ train the subset evaluator.
HoldOutSubsetEvaluator() - Constructor for class weka.attributeSelection.HoldOutSubsetEvaluator
 
hornClausesTipText() - Method in class weka.associations.Tertius
Returns the tip text for this property.
HostListPanel - Class in weka.gui.experiment
This panel controls setting a list of hosts for a RemoteExperiment to use.
HostListPanel(RemoteExperiment) - Constructor for class weka.gui.experiment.HostListPanel
Creates the host list panel with the given experiment.
HostListPanel() - Constructor for class weka.gui.experiment.HostListPanel
Create the host list panel initially disabled.
Hyperparameter - Variable in class weka.classifiers.bayes.blr.Prior
 
HyperparameterRange - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
CV Hyperparameter Range
hyperparameterRangeTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns the tip text for this property
Hyperparameters - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Array to store Hyperparameter values for each feature.
HyperparameterSelection - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Hyperparameter selection method
hyperparameterSelectionTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns the tip text for this property
HyperparameterValue - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Best hyperparameter for test phase
hyperparameterValueTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns the tip text for this property
HyperPipes - Class in weka.classifiers.misc
Class implementing a HyperPipe classifier.
HyperPipes() - Constructor for class weka.classifiers.misc.HyperPipes
 
hypot(double, double) - Static method in class weka.core.Matrix
Deprecated.
Returns sqrt(a^2 + b^2) without under/overflow.
hypot(double, double) - Static method in class weka.core.matrix.Maths
sqrt(a^2 + b^2) without under/overflow.

I

I0 - Static variable in class weka.classifiers.functions.supportVector.RegSMOImproved
 
I0a - Static variable in class weka.classifiers.functions.supportVector.RegSMOImproved
 
I0b - Static variable in class weka.classifiers.functions.supportVector.RegSMOImproved
 
I1 - Static variable in class weka.classifiers.functions.supportVector.RegSMOImproved
 
I2 - Static variable in class weka.classifiers.functions.supportVector.RegSMOImproved
 
I3 - Static variable in class weka.classifiers.functions.supportVector.RegSMOImproved
 
IB1 - Class in weka.classifiers.lazy
Nearest-neighbour classifier.
IB1() - Constructor for class weka.classifiers.lazy.IB1
 
IBk - Class in weka.classifiers.lazy
K-nearest neighbours classifier.
IBk(int) - Constructor for class weka.classifiers.lazy.IBk
IBk classifier.
IBk() - Constructor for class weka.classifiers.lazy.IBk
IB1 classifer.
ICON_PATH - Static variable in class weka.gui.beans.BeanVisual
 
ICSSearchAlgorithm - Class in weka.classifiers.bayes.net.search.ci
This Bayes Network learning algorithm uses conditional independence tests to find a skeleton, finds V-nodes and applies a set of rules to find the directions of the remaining arrows.
ICSSearchAlgorithm() - Constructor for class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm
 
Id3 - Class in weka.classifiers.trees
Class for constructing an unpruned decision tree based on the ID3 algorithm.
Id3() - Constructor for class weka.classifiers.trees.Id3
 
identity(int, int) - Static method in class weka.core.matrix.Matrix
Generate identity matrix
IDFTransformTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Returns the tip text for this property.
IDIndexTipText() - Method in class weka.filters.unsupervised.attribute.AddID
Returns the tip text for this property
IDLE - Static variable in class weka.gui.beans.BeanInstance
 
IFELSE - Static variable in interface weka.core.mathematicalexpression.sym
 
ignoreClassTipText() - Method in class weka.filters.unsupervised.attribute.PotentialClassIgnorer
Returns the tip text for this property
ignored() - Method in class weka.core.xml.PropertyHandler
returns an enumeration of the stored display names and classes of properties to ignore.
NOTE: String and Class Objects are mixed in this enumeration, depending whether it is a global property to ignore or just one for a certain class!
ignoredAttributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.AddCluster
Returns the tip text for this property
ignoredAttributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.ClusterMembership
Returns the tip text for this property
ignoreRangeTipText() - Method in class weka.filters.unsupervised.attribute.MathExpression
Returns the tip text for this property
il2 - Variable in class weka.core.neighboursearch.CoverTree
if we have base 2 then this can be viewed as 1/ln(2), which can be used later on to do il2*ln(d) instead of ln(d)/ln(2), to get log2(d), in get_scale method.
IMAGES - Static variable in class weka.gui.ComponentHelper
the default directories for images
IMPLICIT - Static variable in class weka.associations.Tertius
Way of handling missing values: max counterinstances
ImproveSolutions() - Method in class weka.attributeSelection.ScatterSearchV1
Improve the solutions previously combined by adding the attributes that improve that solution
Impurity - Class in weka.classifiers.trees.m5
Class for handling the impurity values when spliting the instances
Impurity(int, int, Instances, int) - Constructor for class weka.classifiers.trees.m5.Impurity
Constructs an Impurity object containing the impurity values of partitioning the instances using an attribute
IN_USE - Static variable in class weka.experiment.RemoteExperiment
status of the remote host: in use
IN_USE - Static variable in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
 
includeClassTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Returns the tip text for this property
incompleteBeta(double, double, double) - Static method in class weka.core.Statistics
Returns the Incomplete Beta Function evaluated from zero to xx.
incompleteBetaFraction1(double, double, double) - Static method in class weka.core.Statistics
Continued fraction expansion #1 for incomplete beta integral.
incompleteBetaFraction2(double, double, double) - Static method in class weka.core.Statistics
Continued fraction expansion #2 for incomplete beta integral.
incompleteGamma(double, double) - Static method in class weka.core.Statistics
Returns the Incomplete Gamma function.
incompleteGammaComplement(double, double) - Static method in class weka.core.Statistics
Returns the Complemented Incomplete Gamma function.
incorrect() - Method in class weka.classifiers.evaluation.ConfusionMatrix
Gets the number of incorrect classifications (that is, for which an incorrect prediction was made).
incorrect() - Method in class weka.classifiers.Evaluation
Gets the number of instances incorrectly classified (that is, for which an incorrect prediction was made).
incrCoordCount() - Method in class weka.core.neighboursearch.PerformanceStats
Increments the coordinate count (number of coordinates/attributes looked at).
increaseCount(int, int) - Method in class weka.associations.FPGrowth.ShadowCounts
Increase the count at a given recursion level.
increaseFrequency(int) - Method in class weka.associations.FPGrowth.BinaryItem
Increase the frequency of this item.
increaseFrequency() - Method in class weka.associations.FPGrowth.BinaryItem
Increment the frequency of this item.
increaseProjectedCount(int, int) - Method in class weka.associations.FPGrowth.FPTreeNode
Increase the projected count at the given recursion level at this node
incremental(double, int) - Method in class weka.classifiers.trees.m5.Impurity
Incrementally computes the impurirty values
INCREMENTAL - Static variable in interface weka.core.converters.Loader
 
INCREMENTAL - Static variable in interface weka.core.converters.Saver
 
IncrementalClassifierEvaluator - Class in weka.gui.beans
Bean that evaluates incremental classifiers
IncrementalClassifierEvaluator() - Constructor for class weka.gui.beans.IncrementalClassifierEvaluator
 
IncrementalClassifierEvaluatorBeanInfo - Class in weka.gui.beans
Bean info class for the incremental classifier evaluator bean
IncrementalClassifierEvaluatorBeanInfo() - Constructor for class weka.gui.beans.IncrementalClassifierEvaluatorBeanInfo
 
IncrementalClassifierEvaluatorCustomizer - Class in weka.gui.beans
GUI Customizer for the incremental classifier evaluator bean
IncrementalClassifierEvaluatorCustomizer() - Constructor for class weka.gui.beans.IncrementalClassifierEvaluatorCustomizer
 
IncrementalClassifierEvent - Class in weka.gui.beans
Class encapsulating an incrementally built classifier and current instance
IncrementalClassifierEvent(Object, Classifier, Instance, int) - Constructor for class weka.gui.beans.IncrementalClassifierEvent
Creates a new IncrementalClassifierEvent instance.
IncrementalClassifierEvent(Object, Classifier, Instances) - Constructor for class weka.gui.beans.IncrementalClassifierEvent
Creates a new incremental classifier event that encapsulates header information and classifier.
IncrementalClassifierEvent(Object) - Constructor for class weka.gui.beans.IncrementalClassifierEvent
 
IncrementalClassifierListener - Interface in weka.gui.beans
Interface to something that can process a IncrementalClassifierEvent
IncrementalConverter - Interface in weka.core.converters
Marker interface for a loader/saver that can retrieve instances incrementally
incrementalEstimator() - Method in class weka.estimators.CheckEstimator
Checks whether the scheme can build models incrementally.
IncrementalEstimator - Interface in weka.estimators
Interface for an incremental probability estimators.
incrementFailed(int) - Method in class weka.experiment.RemoteExperiment
Increment the overall number of failures and the number of failures for a particular host
incrementFailed(int) - Method in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
Increment the overall number of failures and the number of failures for a particular host
incrementFinished() - Method in class weka.experiment.RemoteExperiment
Increment the number of successfully completed sub experiments
incrementFinished() - Method in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
Increment the number of successfully completed sub experiments
incrementingEquality(CheckEstimator.AttrTypes, int) - Method in class weka.estimators.CheckEstimator
Checks whether an incremental scheme produces the same model when trained incrementally as when batch trained.
incrIntNodeCount() - Method in class weka.core.neighboursearch.TreePerformanceStats
Increments the internal node count.
incrLeafCount() - Method in class weka.core.neighboursearch.TreePerformanceStats
Increments the leaf count.
incrPointCount() - Method in class weka.core.neighboursearch.PerformanceStats
Increments the point count (number of datapoints looked at).
indent(String, int, String) - Method in class weka.core.Javadoc
indents the given string by a given number of indention strings
indeX - Variable in class weka.classifiers.rules.part.ClassifierDecList
Which son to expand?
index() - Method in class weka.core.Attribute
Returns the index of this attribute.
index(int) - Method in class weka.core.Instance
Returns the index of the attribute stored at the given position.
index - Variable in class weka.core.neighboursearch.NearestNeighbourSearch.MyHeapElement
the index of this element.
index(int) - Method in class weka.core.SparseInstance
Returns the index of the attribute stored at the given position.
INDEX_BEANCONNECTIONS - Static variable in class weka.gui.beans.xml.XMLBeans
the index in the Vector, where the BeanConnections are stored (Instances and Connections are stored in a Vector and then serialized)
INDEX_BEANINSTANCES - Static variable in class weka.gui.beans.xml.XMLBeans
the index in the Vector, where the BeanInstances are stored (Instances and Connections are stored in a Vector and then serialized)
Indexes(int, int, boolean, int) - Constructor for class weka.classifiers.lazy.LBR.Indexes
constructor
Indexes(LBR.Indexes) - Constructor for class weka.classifiers.lazy.LBR.Indexes
constructor
indexOf(Literal) - Method in class weka.associations.tertius.Predicate
 
indexOf(Object) - Method in class weka.core.FastVector
Searches for the first occurence of the given argument, testing for equality using the equals method.
indexOf(Object) - Method in class weka.gui.CheckBoxList.CheckBoxListModel
Searches for the first occurrence of elem.
indexOf(Object, int) - Method in class weka.gui.CheckBoxList.CheckBoxListModel
Searches for the first occurrence of elem, beginning the search at index.
indexOfMax() - Method in class weka.core.matrix.DoubleVector
Returns the index of the maximum.
indexOfValue(String) - Method in class weka.core.Attribute
Returns the index of a given attribute value.
indexToString(int) - Static method in class weka.core.SingleIndex
Creates a string representation of the given index.
indicesToRangeList(int[]) - Static method in class weka.core.Range
Creates a string representation of the indices in the supplied array.
INDIVIDUAL_PROPERTY - Static variable in class weka.associations.tertius.IndividualLiteral
 
IndividualInstance - Class in weka.associations.tertius
 
IndividualInstance(Instance, Instances) - Constructor for class weka.associations.tertius.IndividualInstance
 
IndividualInstance(IndividualInstance) - Constructor for class weka.associations.tertius.IndividualInstance
 
IndividualInstances - Class in weka.associations.tertius
 
IndividualInstances(Instances, Instances) - Constructor for class weka.associations.tertius.IndividualInstances
 
IndividualLiteral - Class in weka.associations.tertius
 
IndividualLiteral(Predicate, String, int, int, int, int) - Constructor for class weka.associations.tertius.IndividualLiteral
 
individualPredictions(Instance) - Method in class weka.classifiers.meta.MultiClassClassifier
Returns the individual predictions of the base classifiers for an instance.
INFO - Static variable in class weka.core.Debug
the log level Info
info(int[]) - Static method in class weka.core.Utils
Computes entropy for an array of integers.
infoGain() - Method in class weka.classifiers.trees.j48.BinC45Split
Returns (C4.5-type) information gain for the generated split.
infoGain() - Method in class weka.classifiers.trees.j48.C45Split
Returns (C4.5-type) information gain for the generated split.
InfoGainAttributeEval - Class in weka.attributeSelection
InfoGainAttributeEval :

Evaluates the worth of an attribute by measuring the information gain with respect to the class.

InfoGain(Class,Attribute) = H(Class) - H(Class | Attribute).

Valid options are:

InfoGainAttributeEval() - Constructor for class weka.attributeSelection.InfoGainAttributeEval
Constructor
InfoGainSplitCrit - Class in weka.classifiers.trees.j48
Class for computing the information gain for a given distribution.
InfoGainSplitCrit() - Constructor for class weka.classifiers.trees.j48.InfoGainSplitCrit
 
InfoPanel - Class in weka.gui.sql
A simple panel for displaying information, e.g.
InfoPanel(JFrame) - Constructor for class weka.gui.sql.InfoPanel
creates the panel
InfoPanelCellRenderer - Class in weka.gui.sql
A specialized renderer that takes care of JLabels in a JList.
InfoPanelCellRenderer() - Constructor for class weka.gui.sql.InfoPanelCellRenderer
the constructor
Init(int, int) - Method in class weka.classifiers.bayes.net.BayesNetGenerator
Init defines a minimal Bayes net with no arcs
init(Instances) - Method in class weka.classifiers.functions.supportVector.RegOptimizer
initializes the algorithm
init(Instances) - Method in class weka.classifiers.functions.supportVector.RegSMO
initialize various variables before starting the actual optimizer
init(Instances) - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
initialize various variables before starting the actual optimizer
init() - Method in class weka.classifiers.lazy.IBk
Initialise scheme variables.
init() - Method in class weka.core.Debug.Clock
initializes the clocking, ensure to get the correct thread ID.
init_actions() - Method in class weka.core.mathematicalexpression.Parser
Action encapsulation object initializer.
init_actions() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
Action encapsulation object initializer.
initAsNaiveBayesTipText() - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
 
initAssignments(int[]) - Method in class weka.clusterers.XMeans
Set array of int, used to store assignments, to -1.
initAssignments(int) - Method in class weka.clusterers.XMeans
Creates and initializes integer array, used to store assignments.
initBatchBuffer() - Method in class weka.core.converters.ConverterUtils.DataSource
initializes the batch buffer if necessary, i.e., for non-incremental loaders.
initBuffers() - Method in class weka.core.converters.ArffLoader.ArffReader
initializes the buffers for sparse instances to be read
initCache() - Static method in class weka.core.ClassDiscovery
initializes the cache for the classnames.
initCapabilities() - Method in class weka.gui.GenericObjectEditor.GOETreeNode
generates if necessary a Capabilities object for the given leaf.
initClassifier(Instances) - Method in class weka.classifiers.bayes.DMNBtext.DNBBinary
 
initClassifier(Instances) - Method in interface weka.classifiers.IterativeClassifier
Inits an iterative classifier.
initClassifier(Instances) - Method in class weka.classifiers.trees.ADTree
Sets up the tree ready to be trained, using two-class optimized method.
initClassifier(Instances) - Method in class weka.classifiers.trees.LADTree
Sets up the tree ready to be trained.
initCPTs() - Method in class weka.classifiers.bayes.BayesNet
initializes the conditional probabilities
initCPTs(BayesNet) - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator
initCPTs reserves space for CPTs and set all counts to zero
initCPTs(BayesNet) - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator
initCPTs reserves space for CPTs and set all counts to zero
initCPTs(BayesNet) - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
initCPTs reserves space for CPTs and set all counts to zero
initCPTs(BayesNet) - Method in class weka.classifiers.bayes.net.estimate.SimpleEstimator
initCPTs reserves space for CPTs and set all counts to zero
initDebugVectorsInput() - Method in class weka.clusterers.XMeans
Initialises the debug vector input.
initFileChooser() - Method in class weka.gui.visualize.PrintableComponent
initializes the filechooser, i.e.
initFileClassIndexTipText() - Method in class weka.filters.unsupervised.attribute.KernelFilter
Returns the tip text for this property
initFileTipText() - Method in class weka.filters.unsupervised.attribute.KernelFilter
Returns the tip text for this property
initFilter(Instances) - Method in class weka.filters.unsupervised.attribute.KernelFilter
initializes the filter with the given dataset, i.e., the kernel gets built.
initFilters(boolean, Vector<String>) - Static method in class weka.gui.ConverterFileChooser
initializes the ExtensionFileFilters
initGUI(int) - Method in class weka.gui.ConverterFileChooser
initializes the GUI
initGUI() - Method in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
sets up the GUI.
initGUI() - Method in class weka.gui.Main
initializes the GUI.
INITIAL_STEP - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
 
initialAnchorRandomTipText() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Returns the tip text for this property.
initialize() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
(1)Initialize m_Beta[j] to 0.
initialize() - Method in class weka.classifiers.CostMatrix
Initializes the matrix
initialize() - Method in class weka.classifiers.trees.j48.Distribution
Sets all counts to zero.
initialize(int, int, int) - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo
Resets the object of split information
initialize(int, int, int) - Method in class weka.classifiers.trees.m5.YongSplitInfo
Resets the object of split information
initialize() - Method in class weka.core.AlgVector
Resets the elements to the default value which is 0.0.
initialize(Random) - Method in class weka.core.AlgVector
Initializes the values with random numbers between 0 and 1.
initialize(Instances, int, int[]) - Method in class weka.core.AttributeLocator
initializes the AttributeLocator
initialize(Instances, int) - Method in class weka.core.Instances
initializes with the header information of the given dataset and sets the capacity of the set of instances.
initialize() - Method in class weka.core.logging.FileLogger
Initializes the logger.
initialize() - Method in class weka.core.logging.Logger
Initializes the logger.
initialize() - Method in class weka.core.logging.OutputLogger
Initializes the logger.
initialize() - Method in class weka.core.NormalizableDistance
initializes the ranges and the attributes being used.
initialize() - Method in class weka.experiment.Experiment
Prepares an experiment for running, initializing current iterator settings.
initialize() - Method in class weka.experiment.RemoteExperiment
Prepares a remote experiment for running, creates sub experiments
initialize() - Method in class weka.gui.arffviewer.ArffPanel
any member variables are initialized here
initialize() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Set up the off screen bitmap for rendering to
initialize() - Method in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
 
initialize(PropertyEditor, int, int) - Method in class weka.gui.PropertyDialog
Initializes the dialog.
initialize() - Method in class weka.gui.PropertySheetPanel.CapabilitiesHelpDialog
Initializes the dialog.
initialize() - Method in class weka.gui.sql.ResultSetHelper
initializes, i.e.
initialize() - Method in class weka.gui.treevisualizer.TreeVisualizer
Performs some initialization.
initialize() - Method in class weka.gui.visualize.BMPWriter
further initialization
initialize() - Method in class weka.gui.visualize.JComponentWriter
further initialization can take place here
initialize() - Method in class weka.gui.visualize.JPEGWriter
further initialization.
initialize() - Method in class weka.gui.visualize.PNGWriter
further initialization
initializeAttributeIndices() - Method in class weka.core.NormalizableDistance
initializes the attribute indices.
initializeDown(boolean) - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode
 
initializeIndices() - Method in class weka.gui.SortedTableModel
(re-)initializes the indices
initializeRanges(int) - Method in class weka.core.Debug.DBO
Initialize ranges, upper limit must be set
initializeRanges() - Method in class weka.core.NormalizableDistance
Initializes the ranges using all instances of the dataset.
initializeRanges(int[]) - Method in class weka.core.NormalizableDistance
Initializes the ranges of a subset of the instances of this dataset.
initializeRanges(int[], int, int) - Method in class weka.core.NormalizableDistance
Initializes the ranges of a subset of the instances of this dataset.
initializeRangesEmpty(int, double[][]) - Method in class weka.core.NormalizableDistance
Used to initialize the ranges.
initializeUp() - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode
 
initInputLocators(Instances, int[]) - Method in class weka.filters.Filter
Initializes the input attribute locators.
initInternalFields() - Method in class weka.gui.visualize.MatrixPanel
Initializes internal data fields, i.e.
initMinMax(Instances) - Method in class weka.clusterers.FarthestFirst
 
initModel() - Method in class weka.classifiers.misc.SerializedClassifier
loads the serialized model if necessary, throws an Exception if the derserialization fails.
initOutputLocators(Instances, int[]) - Method in class weka.filters.Filter
Initializes the output attribute locators.
InitPopulation(int) - Method in class weka.attributeSelection.ScatterSearchV1
Creating space for introducing the population
initRegressions() - Method in class weka.classifiers.trees.lmt.LogisticBase
Helper function to initialize m_regressions.
initResultMatrix() - Method in class weka.experiment.PairedTTester
clears the content and fills the column and row names according to the given sorting
initStructure() - Method in class weka.classifiers.bayes.BayesNet
Init structure initializes the structure to an empty graph or a Naive Bayes graph (depending on the -N flag).
initTokenizer() - Method in class weka.core.converters.ArffLoader.ArffReader
Initializes the StreamTokenizer used for reading the ARFF file.
initVars(Instances) - Method in class weka.classifiers.functions.supportVector.CachedKernel
initializes variables etc.
initVars(Instances) - Method in class weka.classifiers.functions.supportVector.Kernel
initializes variables etc.
initVars(Instances) - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
initializes variables etc.
initVars(Instances) - Method in class weka.classifiers.functions.supportVector.Puk
initializes variables etc.
initVars(Instances) - Method in class weka.classifiers.functions.supportVector.RBFKernel
initializes variables etc.
initVars(Instances) - Method in class weka.classifiers.functions.supportVector.StringKernel
initializes variables etc.
initVars(Instances) - Method in class weka.classifiers.mi.supportVector.MIRBFKernel
initializes variables etc.
innerProduct(DoubleVector) - Method in class weka.core.matrix.DoubleVector
Returns the inner product of two DoubleVectors
INPUT - Static variable in class weka.classifiers.functions.neural.NeuralConnection
This unit is an input unit.
input(Instance) - Method in class weka.filters.AllFilter
Input an instance for filtering.
input(Instance) - Method in class weka.filters.Filter
Input an instance for filtering.
input(Instance) - Method in class weka.filters.SimpleBatchFilter
Input an instance for filtering.
input(Instance) - Method in class weka.filters.SimpleStreamFilter
Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.attribute.AttributeSelection
Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.attribute.ClassOrder
Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.attribute.Discretize
Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.attribute.NominalToBinary
Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.instance.Resample
Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.instance.SMOTE
Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.instance.SpreadSubsample
Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Add
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.AddCluster
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.AddExpression
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.AddID
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.AddNoise
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.AddValues
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Center
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.ClusterMembership
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Copy
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Discretize
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.FirstOrder
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.MakeIndicator
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.MathExpression
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.NominalToBinary
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.NominalToString
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Normalize
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.NumericToBinary
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.NumericTransform
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Obfuscate
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.RandomProjection
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Remove
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.RemoveType
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.RemoveUseless
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Reorder
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Standardize
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.StringToNominal
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.SwapValues
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.NonSparseToSparse
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.Normalize
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.Randomize
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.RemoveFolds
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.RemovePercentage
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.RemoveRange
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.Resample
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.ReservoirSample
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.SparseToNonSparse
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.SubsetByExpression
Input an instance for filtering.
input(Instance) - Method in class weka.gui.streams.InstanceCounter
 
input(Instance) - Method in class weka.gui.streams.InstanceJoiner
 
input(Instance) - Method in class weka.gui.streams.InstanceSavePanel
 
input(Instance) - Method in class weka.gui.streams.InstanceTable
 
input(Instance) - Method in class weka.gui.streams.InstanceViewer
 
inputCenterFileTipText() - Method in class weka.clusterers.XMeans
Returns the tip text for this property.
inputFormat(Instances) - Method in class weka.gui.streams.InstanceCounter
 
inputFormat(Instances) - Method in class weka.gui.streams.InstanceJoiner
Sets the format of the input instances.
inputFormat(Instances) - Method in class weka.gui.streams.InstanceSavePanel
 
inputFormat(Instances) - Method in class weka.gui.streams.InstanceTable
 
inputFormat(Instances) - Method in class weka.gui.streams.InstanceViewer
 
inputFormatPeek() - Method in class weka.filters.Filter
Returns a reference to the current input format without copying it.
InputHyperparameterValues - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
Set of values to be used as hyperparameter values during Cross-Validation.
inputOrderTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Returns the tip text for this property
inputs(Vector) - Static method in class weka.gui.beans.BeanConnection
Returns a vector of BeanInstances that can be considered as inputs (or the left-hand side of a sub-flow)
inputsContains(BeanInstance) - Method in class weka.gui.beans.MetaBean
 
inRanges(Instance, double[][]) - Method in class weka.core.NormalizableDistance
Test if an instance is within the given ranges.
insert(int) - Method in class weka.classifiers.functions.supportVector.SMOset
Inserts an element into the set.
insert(double, double, double) - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable
Inserts a new entry in the hashtable using the specified key.
insert(DataObject) - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database
Inserts a new dataObject into the database
insert(DataObject) - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase
Inserts a new dataObject into the database
insertAttributeAt(int) - Method in class weka.core.Instance
Inserts an attribute at the given position (0 to numAttributes()).
insertAttributeAt(Attribute, int) - Method in class weka.core.Instances
Inserts an attribute at the given position (0 to numAttributes()) and sets all values to be missing.
insertClassIndex(int) - Method in class weka.classifiers.meta.nestedDichotomies.ND.NDTree
Inserts a class index into the tree.
insertClassIndexAtNode(int) - Method in class weka.classifiers.meta.nestedDichotomies.ND.NDTree
Inserts the class index at a given node.
insertElementAt(Object, int) - Method in class weka.core.FastVector
Inserts an element at the given position.
insertElementAt(Object, int) - Method in class weka.gui.CheckBoxList.CheckBoxListModel
Inserts the specified object as a component in this list at the specified index.
insertHoldOutInstance(Instance, double, REPTree.Tree) - Method in class weka.classifiers.trees.REPTree.Tree
Inserts an instance from the hold-out set into the tree.
insertHoldOutSet(Instances) - Method in class weka.classifiers.trees.REPTree.Tree
Inserts hold-out set into tree.
insertMenuItem(JMenu, JMenuItem) - Method in class weka.gui.GUIChooser
insert the menu item in a sorted fashion.
insertMenuItem(JMenu, JMenuItem, int) - Method in class weka.gui.GUIChooser
insert the menu item in a sorted fashion.
insertMenuItem(JMenu, JMenuItem) - Method in class weka.gui.Main
insert the menu item in a sorted fashion.
insertMenuItem(JMenu, JMenuItem, int) - Method in class weka.gui.Main
insert the menu item in a sorted fashion.
insertNewAttr(Instances) - Method in class weka.classifiers.trees.ft.FTtree
Inserts new attributes in current dataset or instance
insertReverseSorted(int, double) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList
Inserts an element in reverse sorted order in the list.
insertSorted(double, Instance) - Method in class weka.core.neighboursearch.NearestNeighbourSearch.NeighborList
Inserts an instance neighbor into the list, maintaining the list sorted by distance.
inSplit(Instance) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
This will check if an instance is inside or outside of the current shapes.
installLinearModels() - Method in class weka.classifiers.trees.m5.RuleNode
Traverses the tree and installs linear models at each node.
installSmoothedModels() - Method in class weka.classifiers.trees.m5.RuleNode
 
Instance - Class in weka.core
Class for handling an instance.
Instance(Instance) - Constructor for class weka.core.Instance
Constructor that copies the attribute values and the weight from the given instance.
Instance(double, double[]) - Constructor for class weka.core.Instance
Constructor that inititalizes instance variable with given values.
Instance(int) - Constructor for class weka.core.Instance
Constructor of an instance that sets weight to one, all values to be missing, and the reference to the dataset to null.
Instance() - Constructor for class weka.core.Instance
Private constructor for subclasses.
instance(int) - Method in class weka.core.Instances
Returns the instance at the given position.
INSTANCE_AVAILABLE - Static variable in class weka.gui.beans.InstanceEvent
 
INSTANCE_AVAILABLE - Static variable in class weka.gui.streams.InstanceEvent
Specifies that an instance is available
InstanceComparator - Class in weka.core
A comparator for the Instance class.
InstanceComparator() - Constructor for class weka.core.InstanceComparator
initializes the comparator and includes the class in the comparison
InstanceComparator(boolean) - Constructor for class weka.core.InstanceComparator
initializes the comparator
InstanceCounter - Class in weka.gui.streams
A bean that counts instances streamed to it.
InstanceCounter() - Constructor for class weka.gui.streams.InstanceCounter
 
InstanceEvent - Class in weka.gui.beans
Event that encapsulates a single instance or header information only
InstanceEvent(Object, Instance, int) - Constructor for class weka.gui.beans.InstanceEvent
Creates a new InstanceEvent instance that encapsulates a single instance only.
InstanceEvent(Object, Instances) - Constructor for class weka.gui.beans.InstanceEvent
Creates a new InstanceEvent instance which encapsulates header information only.
InstanceEvent(Object) - Constructor for class weka.gui.beans.InstanceEvent
 
InstanceEvent - Class in weka.gui.streams
An event encapsulating an instance stream event.
InstanceEvent(Object, int) - Constructor for class weka.gui.streams.InstanceEvent
Constructs an InstanceEvent with the specified source object and event type
InstanceJoiner - Class in weka.gui.streams
A bean that joins two streams of instances into one.
InstanceJoiner() - Constructor for class weka.gui.streams.InstanceJoiner
Setup the initial states of the member variables
InstanceListener - Interface in weka.gui.beans
Interface to something that can accept instance events
InstanceListener - Interface in weka.gui.streams
An interface for objects interested in listening to streams of instances.
InstanceLoader - Class in weka.gui.streams
A bean that produces a stream of instances from a file.
InstanceLoader() - Constructor for class weka.gui.streams.InstanceLoader
 
instanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceCounter
 
instanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceJoiner
 
instanceProduced(InstanceEvent) - Method in interface weka.gui.streams.InstanceListener
 
instanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceSavePanel
 
instanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceTable
 
instanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceViewer
 
InstanceProducer - Interface in weka.gui.streams
An interface for objects capable of producing streams of instances.
InstanceQuery - Class in weka.experiment
Convert the results of a database query into instances.
InstanceQuery() - Constructor for class weka.experiment.InstanceQuery
Sets up the database drivers
instanceRangeTipText() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Returns the tip text for this property
Instances - Class in weka.core
Class for handling an ordered set of weighted instances.
Instances(Reader) - Constructor for class weka.core.Instances
Reads an ARFF file from a reader, and assigns a weight of one to each instance.
Instances(Reader, int) - Constructor for class weka.core.Instances
Deprecated.
instead of using this method in conjunction with the readInstance(Reader) method, one should use the ArffLoader or DataSource class instead.
Instances(Instances) - Constructor for class weka.core.Instances
Constructor copying all instances and references to the header information from the given set of instances.
Instances(Instances, int) - Constructor for class weka.core.Instances
Constructor creating an empty set of instances.
Instances(Instances, int, int) - Constructor for class weka.core.Instances
Creates a new set of instances by copying a subset of another set.
Instances(String, FastVector, int) - Constructor for class weka.core.Instances
Creates an empty set of instances.
instancesAndWeights() - Method in class weka.core.Instances
Returns string including all instances, their weights and their indices in the original dataset.
InstanceSavePanel - Class in weka.gui.streams
A bean that saves a stream of instances to a file.
InstanceSavePanel() - Constructor for class weka.gui.streams.InstanceSavePanel
 
instancesDownBranch(int, Instances) - Method in class weka.classifiers.trees.adtree.Splitter
Gets the subset of instances that apply to a particluar branch of the split.
instancesDownBranch(int, Instances) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit
Gets the subset of instances that apply to a particluar branch of the split.
instancesDownBranch(int, Instances) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit
Gets the subset of instances that apply to a particluar branch of the split.
instancesDownBranch(int, Instances) - Method in class weka.classifiers.trees.LADTree.Splitter
 
instancesDownBranch(int, Instances) - Method in class weka.classifiers.trees.LADTree.TwoWayNominalSplit
 
instancesDownBranch(int, Instances) - Method in class weka.classifiers.trees.LADTree.TwoWayNumericSplit
 
instancesIndicesTipText() - Method in class weka.filters.unsupervised.instance.RemoveRange
Returns the tip text for this property
InstancesResultListener - Class in weka.experiment
Outputs the received results in arff format to a Writer.
InstancesResultListener() - Constructor for class weka.experiment.InstancesResultListener
Sets temporary file.
InstancesSummaryPanel - Class in weka.gui
This panel just displays relation name, number of instances, and number of attributes.
InstancesSummaryPanel() - Constructor for class weka.gui.InstancesSummaryPanel
Creates the instances panel with no initial instances.
InstanceStreamToBatchMaker - Class in weka.gui.beans
Bean that converts an instance stream into a (batch) data set.
InstanceStreamToBatchMaker() - Constructor for class weka.gui.beans.InstanceStreamToBatchMaker
 
InstanceStreamToBatchMakerBeanInfo - Class in weka.gui.beans
BeanInfo class for the InstanceStreamToBatchMaker bean
InstanceStreamToBatchMakerBeanInfo() - Constructor for class weka.gui.beans.InstanceStreamToBatchMakerBeanInfo
 
InstanceTable - Class in weka.gui.streams
A bean that takes a stream of instances and displays in a table.
InstanceTable() - Constructor for class weka.gui.streams.InstanceTable
 
instanceToArray(Instance) - Method in class weka.classifiers.functions.LibLINEAR
returns an instance into a sparse liblinear array
instanceToArray(Instance) - Method in class weka.classifiers.functions.LibSVM
returns an instance into a sparse libsvm array
instanceToLibsvm(Instance) - Method in class weka.core.converters.LibSVMSaver
turns the instance into a libsvm row
instanceToSchema(Instance, MiningSchema) - Method in class weka.core.pmml.MappingInfo
Convert an Instance to an array of values that matches the format of the mining schema.
instanceToString(Instance) - Method in class weka.core.converters.CSVSaver
turns an instance into a string.
instanceToSvmlight(Instance) - Method in class weka.core.converters.SVMLightSaver
turns the instance into a svm light row.
InstanceViewer - Class in weka.gui.streams
This is a very simple instance viewer - just displays the dataset as text output as it would be written to a file.
InstanceViewer() - Constructor for class weka.gui.streams.InstanceViewer
 
instanceWeights(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.associations.CheckAssociator
Checks whether the associator can handle instance weights.
instanceWeights(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.attributeSelection.CheckAttributeSelection
Checks whether the scheme can handle instance weights.
instanceWeights(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.classifiers.CheckClassifier
Checks whether the classifier can handle instance weights.
instanceWeights(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.classifiers.functions.supportVector.CheckKernel
Checks whether the kernel can handle instance weights.
instanceWeights(boolean, boolean, boolean, boolean, boolean, boolean) - Method in class weka.clusterers.CheckClusterer
Checks whether the clusterer can handle instance weights.
instanceWeights(CheckEstimator.AttrTypes, int) - Method in class weka.estimators.CheckEstimator
Checks whether the estimator can handle instance weights.
instNumsTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Returns the tip text for this property
instNumsTipText() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Returns the tip text for this property
inStream - Variable in class weka.gui.graphvisualizer.BIFParser
This holds the InputStream to be parsed
inString - Variable in class weka.gui.graphvisualizer.BIFParser
This holds the string to be parsed
intCount - Variable in class weka.core.AttributeStats
The number of int-like values
INTEGER - Static variable in class weka.datagenerators.clusterers.SubspaceCluster
cluster subtype: integer
INTEGER - Static variable in class weka.experiment.DatabaseUtils
Type mapping for INTEGER used for reading experiment results.
intercept() - Method in class weka.classifiers.trees.m5.PreConstructedLinearModel
Return the intercept
internal_batch_nearest_neighbor(int, CoverTree.CoverTreeNode, Stack<Stack<CoverTree.d_node>>, Stack<CoverTree.d_node>, int, int, CoverTree.MyHeap, Stack<NearestNeighbourSearch.NeighborList>) - Method in class weka.core.neighboursearch.CoverTree
Performs a recursive k-NN search for a given batch of queries provided in the form of a cover tree.
internalCacheSizeTipText() - Method in class weka.classifiers.functions.supportVector.StringKernel
Returns the tip text for this property
internalsTipText() - Method in class weka.classifiers.bayes.WAODE
Returns the tip text for this property
InterquartileRange - Class in weka.filters.unsupervised.attribute
A filter for detecting outliers and extreme values based on interquartile ranges.
InterquartileRange() - Constructor for class weka.filters.unsupervised.attribute.InterquartileRange
 
intersectSubsets(ScatterSearchV1.Subset, ScatterSearchV1.Subset) - Method in class weka.attributeSelection.ScatterSearchV1
Intersects two subsets
Interval(Element) - Constructor for class weka.core.pmml.FieldMetaInfo.Interval
Construct an interval.
IntervalEstimator - Interface in weka.classifiers
Interface for classifiers that can output confidence intervals
IntVector - Class in weka.core.matrix
A vector specialized on integers.
IntVector() - Constructor for class weka.core.matrix.IntVector
Constructs a null vector.
IntVector(int) - Constructor for class weka.core.matrix.IntVector
Constructs an n-vector of zeros.
IntVector(int, int) - Constructor for class weka.core.matrix.IntVector
Constructs an n-vector of a constant
IntVector(int[]) - Constructor for class weka.core.matrix.IntVector
Constructs a vector given an int array
invalidate() - Method in class weka.core.NormalizableDistance
invalidates all initializations.
INVERSE - Static variable in class weka.classifiers.lazy.LWL
 
inverse() - Method in class weka.core.matrix.Matrix
Matrix inverse or pseudoinverse
inverseIterator() - Method in class weka.associations.tertius.SimpleLinkedList
 
inverseLabel(double[]) - Method in class weka.classifiers.meta.Decorate
Select class label such that the probability of selection is inversely proportional to the ensemble's predictions.
invertSelectionTipText() - Method in class weka.core.NormalizableDistance
Returns the tip text for this property.
invertSelectionTipText() - Method in class weka.filters.supervised.attribute.Discretize
Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.supervised.instance.Resample
Returns the tip text for this property.
invertSelectionTipText() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.Copy
Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.Discretize
Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.MathExpression
Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.NominalToBinary
Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.NumericToNominal
Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.NumericTransform
Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.RELAGGS
Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.Remove
Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.RemoveType
Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Returns the tip text for this property.
invertSelectionTipText() - Method in class weka.filters.unsupervised.instance.RemoveFolds
Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.instance.RemovePercentage
Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.instance.RemoveRange
Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.instance.Resample
Returns the tip text for this property
invertTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Returns the tip text for this property
invoke(String, Class[], Object[]) - Method in class weka.core.Jython
executes the specified method on the current interpreter and returns the result, if any
invoke(Object, String, Class[], Object[]) - Static method in class weka.core.Jython
executes the specified method and returns the result, if any
invokeMain(String, String[]) - Static method in class weka.gui.SplashWindow
Invokes the main method of the provided class name.
invokeMethod(Object, String, Class[], Object[]) - Method in class weka.classifiers.functions.LibLINEAR
executes the specified method and returns the result, if any
invokeMethod(Object, String, Class[], Object[]) - Method in class weka.classifiers.functions.LibSVM
executes the specified method and returns the result, if any
invokeMethod(String, String, String[]) - Static method in class weka.gui.SplashWindow
Invokes the named method of the provided class name.
invokeReadFromXML(Element) - Method in class weka.core.xml.XMLSerialization
either invokes a custom method to read a specific property/class or the standard method readFromXML(Element)
invokeWriteToXML(Element, Object, String) - Method in class weka.core.xml.XMLSerialization
either invokes a custom method to write a specific property/class or the standard method writeToXML(Element,Object,String)
is(String) - Method in class weka.core.Stopwords
Returns true if the given string is a stop word.
IS - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
isALeaf() - Method in class weka.core.neighboursearch.balltrees.BallNode
Returns true if the node is a leaf node (if both its left and right child are null).
isALeaf() - Method in class weka.core.neighboursearch.CoverTree.CoverTreeNode
Returns whether if the node is a leaf or not.
isALeaf() - Method in class weka.core.neighboursearch.kdtrees.KDTreeNode
Checks if node is a leaf.
isAllowed(Class, String) - Method in class weka.core.xml.PropertyHandler
returns whether the given property (display name) is allowed for the given class.
isAllowed(Object, String) - Method in class weka.core.xml.PropertyHandler
returns whether the given property (display name) is allowed for the given object .
isArc(BayesNet, int, int) - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
IsArc checks whether the arc from iAttributeTail to iAttributeHead already exists
isArff(String) - Static method in class weka.core.converters.ConverterUtils.DataSource
returns whether the extension of the location is likely to be of ARFF format, i.e., ending in ".arff" or ".arff.gz" (case-insensitive).
isAttribute() - Method in enum weka.core.Capabilities.Capability
returns true if the capability is an attribute
isAttributeCapability() - Method in enum weka.core.Capabilities.Capability
returns true if the capability is an attribute capability
isAveragable() - Method in class weka.core.Attribute
Returns whether the attribute can be averaged meaningfully.
isAverage(int) - Method in class weka.experiment.ResultMatrix
returns true if the row index (in the array produced by toArray(boolean)) contains the average row
isBoolean(int) - Method in class weka.datagenerators.clusterers.SubspaceCluster
Returns true if attribute is boolean
isBusy() - Method in class weka.gui.beans.Associator
Returns true if.
isBusy() - Method in interface weka.gui.beans.BeanCommon
Returns true if.
isBusy() - Method in class weka.gui.beans.ClassAssigner
Returns true if.
isBusy() - Method in class weka.gui.beans.Classifier
Returns true if.
isBusy() - Method in class weka.gui.beans.ClassifierPerformanceEvaluator
Returns true if.
isBusy() - Method in class weka.gui.beans.ClassValuePicker
Returns true if.
isBusy() - Method in class weka.gui.beans.Clusterer
Returns true if.
isBusy() - Method in class weka.gui.beans.ClustererPerformanceEvaluator
Returns true if.
isBusy() - Method in class weka.gui.beans.CostBenefitAnalysis
Returns true if.
isBusy() - Method in class weka.gui.beans.CrossValidationFoldMaker
Returns true if.
isBusy() - Method in class weka.gui.beans.Filter
Returns true if.
isBusy() - Method in class weka.gui.beans.IncrementalClassifierEvaluator
Returns true if.
isBusy() - Method in class weka.gui.beans.InstanceStreamToBatchMaker
Returns true if.
isBusy() - Method in class weka.gui.beans.Loader
Returns true if.
isBusy() - Method in class weka.gui.beans.MetaBean
Returns true if.
isBusy() - Method in class weka.gui.beans.PredictionAppender
Returns true if.
isBusy() - Method in class weka.gui.beans.Saver
Returns true if.
isBusy() - Method in class weka.gui.beans.SerializedModelSaver
Returns true if.
isBusy() - Method in class weka.gui.beans.StripChart
Returns true if.
isBusy() - Method in class weka.gui.beans.TestSetMaker
Returns true if.
isBusy() - Method in class weka.gui.beans.TextViewer
Returns true if.
isBusy() - Method in class weka.gui.beans.TrainingSetMaker
Returns true if.
isBusy() - Method in class weka.gui.beans.TrainTestSplitMaker
Returns true if.
isCached(int, GridSearch.PointDouble) - Method in class weka.classifiers.meta.GridSearch.PerformanceCache
checks whether the point was already calculated ones
isCacheValid(Object[]) - Method in class weka.experiment.DatabaseResultListener
Checks whether the current cache contents are valid for the supplied key.
isCellEditable(int, int) - Method in class weka.gui.arffviewer.ArffTableModel
returns true if the cell at rowindex and columnindexis editable
isCellEditable(int, int) - Method in class weka.gui.SortedTableModel
Returns true if the cell at rowIndex and columnIndex is editable.
isCellEditable(int, int) - Method in class weka.gui.sql.ResultSetTableModel
returns true if the cell at rowindex and columnindexis editable.
isChanged() - Method in class weka.classifiers.bayes.net.EditableBayesNet
return true when current state differs from the state the network was last saved
isChanged() - Method in class weka.gui.arffviewer.ArffPanel
returns whether the content of the panel was changed
isChanged() - Method in class weka.gui.ViewerDialog
returns whether the data has been changed
isClass() - Method in class weka.associations.tertius.Predicate
 
isClass() - Method in enum weka.core.Capabilities.Capability
returns true if the capability is a class
isClassCapability() - Method in enum weka.core.Capabilities.Capability
returns true if the capability is a other capability
isClassname(String) - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion
tests whether the given partial string is the name of a class with classpath - it basically tests, whether the string consists only of alphanumeric literals, underscores and dots.
isConditionalIndependent(int, int, int[], int) - Method in class weka.classifiers.bayes.net.search.ci.CISearchAlgorithm
IsConditionalIndependent tests whether two nodes X and Y are independent given a set of variables Z.
isConnected() - Method in class weka.experiment.DatabaseUtils
Returns true if a database connection is active.
isConnected() - Method in class weka.gui.sql.event.ConnectionEvent
returns whether the connection is still open.
isContainedBy(Instance) - Method in class weka.associations.gsp.Element
Checks if an Element is contained by a given Instance.
isContinuous() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
checks, whether cluster sub type is continuous
isCoreFileLoader(String) - Static method in class weka.core.converters.ConverterUtils
checks whether the given class is one of the hardcoded core file loaders.
isCoreFileSaver(String) - Static method in class weka.core.converters.ConverterUtils
checks whether the given class is one of the hardcoded core file savers.
isCover(Instance) - Method in class weka.classifiers.rules.ConjunctiveRule
Whether the instance covered by this rule
isCpuTime() - Method in class weka.core.Debug.Clock
whether the measurement is based on the msecs returned from the System class or on the more accurate CPU time.
isCursorScrollable() - Method in class weka.experiment.DatabaseUtils
Checks whether cursors are scrollable in general, false otherwise (also if not connected).
isCursorScrollSensitive() - Method in class weka.experiment.DatabaseUtils
Returns whether the cursors only support forward movement or are scroll sensitive (with ResultSet.CONCUR_READ_ONLY concurrency).
isDate() - Method in class weka.core.Attribute
Tests if the attribute is a date type.
isDebug() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns true if debug is turned on.
isEmpty() - Method in class weka.associations.gsp.Element
Checks if the Element contains any events.
isEmpty() - Method in class weka.associations.tertius.LiteralSet
Test if this set is empty.
isEmpty() - Method in class weka.associations.tertius.Rule
Test if this rule is empty.
isEmpty() - Method in class weka.associations.tertius.SimpleLinkedList
 
isEmpty() - Method in class weka.classifiers.functions.pace.DiscreteFunction
Returns true if it is empty.
isEmpty() - Method in class weka.classifiers.functions.pace.PaceMatrix
Check if the matrix is empty
isEmpty() - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable
Tests if this hashtable maps no keys to values.
isEmpty() - Method in class weka.core.matrix.DoubleVector
Checks if it is an empty vector
isEmpty() - Method in class weka.core.matrix.IntVector
Returns true if the vector is empty
isEmpty() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.NeighborList
Gets whether the list is empty.
isEmpty() - Method in class weka.core.Trie
Returns true if this collection contains no elements.
isEnabled(Capabilities.Capability) - Method in class weka.core.FindWithCapabilities
whether the given capability is enabled.
isEnabled() - Method in class weka.core.Memory
returns whether the memory management is enabled
isEnabledNot(Capabilities.Capability) - Method in class weka.core.FindWithCapabilities
whether the given "not to have" capability is enabled.
isEqual(ScatterSearchV1.Subset) - Method in class weka.attributeSelection.ScatterSearchV1.Subset
 
isExtremeValue(Instance, int) - Method in class weka.filters.unsupervised.attribute.InterquartileRange
returns whether the instance has an extreme value in the specified attribute or not
isExtremeValue(Instance) - Method in class weka.filters.unsupervised.attribute.InterquartileRange
returns whether the instance is an extreme value or not
isFirstBatchDone() - Method in class weka.filters.Filter
Returns true if the first batch of instances got processed.
isFullRank() - Method in class weka.core.matrix.QRDecomposition
Is the matrix full rank?
isGaussian() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
checks, whether cluster type is gaussian
isHidden() - Method in class weka.gui.beans.BeanConnection
Returns true if this connection is invisible
isHierachic(String) - Method in class weka.gui.HierarchyPropertyParser
Whether the given string has a hierachy structure with the seperators
isIgnored(String) - Method in class weka.core.xml.PropertyHandler
checks whether the given display name is an ignored property
isIgnored(Class, String) - Method in class weka.core.xml.PropertyHandler
checks whether the given display name of a certain class is an ignored property.
isIgnored(Object, String) - Method in class weka.core.xml.PropertyHandler
checks whether the given display name of a given object is an ignored property.
isIncludedIn(Rule) - Method in class weka.associations.tertius.Body
Test if this Body is included in a rule.
isIncludedIn(Rule) - Method in class weka.associations.tertius.Head
Test if this Head is included in a rule.
isIncludedIn(Rule) - Method in class weka.associations.tertius.LiteralSet
Test if this LiteralSet is included in a rule.
isIncremental() - Method in class weka.core.converters.ConverterUtils.DataSource
returns whether the loader is an incremental one.
isInitialAnchorRandom() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Gets whether if the initial anchor is chosen randomly.
isInitialized() - Method in class weka.gui.SortedTableModel
whether the model is initialized
isInRange(double) - Method in class weka.core.Attribute
Determines whether a value lies within the bounds of the attribute.
isInRange(int) - Method in class weka.core.Range
Gets whether the supplied cardinal number is included in the current range.
isInteger() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
checks, whether cluster sub type is integer
isKeyInCache(ResultProducer, Object[]) - Method in class weka.experiment.DatabaseResultListener
Returns true if the supplied key is in the key cache (and thus we do not need to execute a database query).
isKeyInTable(String, ResultProducer, Object[]) - Method in class weka.experiment.DatabaseUtils
Executes a database query to see whether a result for the supplied key is already in the database.
isKeyword(String) - Method in class weka.experiment.DatabaseUtils
Checks whether the given string is a reserved keyword.
isKOML(String) - Static method in class weka.core.xml.SerialUIDChanger
checks whether the given filename ends with ".koml"
isLeaf() - Method in class weka.classifiers.trees.m5.RuleNode
Return true if this node is a leaf
isLeafReached() - Method in class weka.gui.HierarchyPropertyParser
Whether the current position is a leaf
isMean(int) - Method in class weka.experiment.ResultMatrix
returns true if the index (in the array produced by toArray(boolean)) contains a mean
isMissing(int) - Method in class weka.core.Instance
Tests if a specific value is "missing".
isMissing(Attribute) - Method in class weka.core.Instance
Tests if a specific value is "missing".
isMissing(int) - Method in class weka.core.SparseInstance
Tests if a specific value is "missing".
ISMISSING - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
isMissingAt(int, int) - Method in class weka.gui.arffviewer.ArffSortedTableModel
checks whether the value at the given position is missing
isMissingAt(int, int) - Method in class weka.gui.arffviewer.ArffTableModel
checks whether the value at the given position is missing
isMissingSparse(int) - Method in class weka.core.Instance
Tests if a specific value is "missing".
isMissingValue(double) - Static method in class weka.core.Instance
Tests if the given value codes "missing".
isMonitoring() - Method in class weka.gui.MemoryUsagePanel
Returns whether the thread is still running.
isMonitoring() - Method in class weka.gui.MemoryUsagePanel.MemoryMonitor
Returns whether the thread is still running.
isNewBatch() - Method in class weka.filters.Filter
Returns true if the a new batch was started, either a new instance of the filter was created or the batchFinished() method got called.
isNewer(Object) - Method in class weka.core.Version
checks whether this version is newer than the one from the given version string
isNominal() - Method in class weka.core.Attribute
Test if the attribute is nominal.
isNominal(int) - Method in class weka.datagenerators.clusterers.SubspaceCluster
Returns true if attribute is nominal
isNominal() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Returns true if selection attribute is nominal.
isNominal() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Returns true if selection attribute is nominal.
isNonsingular() - Method in class weka.core.matrix.LUDecomposition
Is the matrix nonsingular?
isNormalizeData() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns true if the data is to be normalized first
isNotificationEnabled() - Method in class weka.gui.arffviewer.ArffSortedTableModel
returns whether the notification of changes is enabled
isNotificationEnabled() - Method in class weka.gui.arffviewer.ArffTableModel
returns whether the notification of changes is enabled
isNullAt(int, int) - Method in class weka.gui.sql.ResultSetTableModel
checks whether the value of the cell is NULL.
isNumeric() - Method in class weka.core.Attribute
Tests if the attribute is numeric.
isNumeric() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Returns true if selection attribute is numeric.
isNumericAt(int) - Method in class weka.gui.sql.ResultSetTableModel
returns whether the column at the given index is numeric.
isOlder(Object) - Method in class weka.core.Version
checks whether this version is older than the one from the given version string
isOnBlacklist(String) - Static method in class weka.datagenerators.DataGenerator
checks, whether the given option is in the blacklist of options not to be output by makeOptionString
isOnBorder(GridSearch.PointDouble) - Method in class weka.classifiers.meta.GridSearch.Grid
checks whether the given values are on the border of the grid
isOnBorder(GridSearch.PointInt) - Method in class weka.classifiers.meta.GridSearch.Grid
checks whether the given location is on the border of the grid
isOpticsOutputs() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject
Returns the flag for writing actions
isort(int[], float[]) - Static method in class weka.gui.graphvisualizer.HierarchicalBCEngine
This methods sorts the vertices in level[] according to their barycenters in BC[], using insertion sort.
isOtherCapability() - Method in enum weka.core.Capabilities.Capability
returns true if the capability is a class capability
IsotonicRegression - Class in weka.classifiers.functions
Learns an isotonic regression model.
IsotonicRegression() - Constructor for class weka.classifiers.functions.IsotonicRegression
 
isOutlier(Instance, int) - Method in class weka.filters.unsupervised.attribute.InterquartileRange
returns whether the instance has an outlier in the specified attribute or not
isOutlier(Instance) - Method in class weka.filters.unsupervised.attribute.InterquartileRange
returns whether the instance is an outlier or not
isOutOfMemory() - Method in class weka.core.Memory
checks if there's still enough memory left.
isOutputFormatDefined() - Method in class weka.filters.Filter
Returns whether the output format is ready to be collected
isOutputFormatDefined() - Method in class weka.filters.unsupervised.attribute.RemoveType
Returns whether the output format is ready to be collected
isPaintable() - Method in class weka.gui.CostMatrixEditor
Indicates whether the object can be represented graphically.
isPaintable() - Method in class weka.gui.FileEditor
Returns true since this editor is paintable.
isPaintable() - Method in class weka.gui.GenericArrayEditor
Returns true to indicate that we can paint a representation of the string array.
isPaintable() - Method in class weka.gui.GenericObjectEditor
Returns true to indicate that we can paint a representation of the Object.
isPaintable() - Method in class weka.gui.SimpleDateFormatEditor
Indicates whether the object can be represented graphically.
isPanelSelected() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
checks whether a panel is currently selected
isPresent() - Static method in class weka.classifiers.functions.LibLINEAR
returns whether the liblinear classes are present or not, i.e.
isPresent() - Static method in class weka.classifiers.functions.LibSVM
returns whether the libsvm classes are present or not, i.e.
isPresent() - Static method in class weka.core.Jython
returns whether the Jython classes are present or not, i.e.
isPresent() - Static method in class weka.core.stemmers.SnowballStemmer
returns whether Snowball is present or not, i.e.
isPresent() - Static method in class weka.core.xml.KOML
returns whether KOML is present or not, i.e.
isPresent() - Static method in class weka.core.xml.XStream
returns whether XStream is present or not, i.e.
isPrimitiveArray(Class) - Method in class weka.core.xml.XMLSerialization
checks whether the innermost class is a primitive class (handles multi-dimensional arrays)
isProcessed() - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
Gives information about the status of a dataObject
isProcessed() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
Gives information about the status of a dataObject
isProcessed() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
Gives information about the status of a dataObject
isRandom() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
checks, whether cluster type is random
isReadOnly() - Method in class weka.gui.arffviewer.ArffPanel
returns whether the model is read-only
isReadOnly() - Method in class weka.gui.arffviewer.ArffSortedTableModel
returns whether the model is read-only
isReadOnly() - Method in class weka.gui.arffviewer.ArffTable
returns whether the model is read-only
isReadOnly() - Method in class weka.gui.arffviewer.ArffTableModel
returns whether the model is read-only
isRegular() - Method in class weka.core.Attribute
Returns whether the attribute values are equally spaced.
isRelationValued() - Method in class weka.core.Attribute
Tests if the attribute is relation valued.
isResultRequired(ResultProducer, Object[]) - Method in class weka.experiment.AveragingResultProducer
Determines whether the results for a specified key must be generated.
isResultRequired(ResultProducer, Object[]) - Method in class weka.experiment.CSVResultListener
Always says a result is required.
isResultRequired(ResultProducer, Object[]) - Method in class weka.experiment.DatabaseResultListener
Always says a result is required.
isResultRequired(ResultProducer, Object[]) - Method in class weka.experiment.DatabaseResultProducer
Determines whether the results for a specified key must be generated.
isResultRequired(ResultProducer, Object[]) - Method in class weka.experiment.LearningRateResultProducer
Determines whether the results for a specified key must be generated.
isResultRequired(ResultProducer, Object[]) - Method in interface weka.experiment.ResultListener
Determines whether the results for a specified key must be generated.
isRootReached() - Method in class weka.gui.HierarchyPropertyParser
Whether the current position is the root
isRowName(int) - Method in class weka.experiment.ResultMatrix
returns true if the index (in the array produced by toArray(boolean)) is the row name
isRunning() - Method in class weka.core.Debug.Clock
whether the time is still being clocked
isSaved() - Method in class weka.classifiers.bayes.net.EditableBayesNet
indicate the network state was saved
isSequentialAttIndexValid() - Method in class weka.classifiers.lazy.LBR.Indexes
Returns whether or not the Sequential Attribute Index requires rebuilding due to a change
isSequentialInstanceIndexValid() - Method in class weka.classifiers.lazy.LBR.Indexes
Returns whether or not the Sequential Instance Index requires rebuilding due to a change
isSerializable(String) - Static method in class weka.core.SerializationHelper
checks whether a class is serializable.
isSerializable(Class) - Static method in class weka.core.SerializationHelper
checks whether a class is serializable.
isShowCoreDistances() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel
Returns the flag for showCoreDistances
isShowReachabilityDistances() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel
Returns the flag for showReachabilityDistances
isSignificance(int) - Method in class weka.experiment.ResultMatrix
returns true if the index (in the array produced by toArray(boolean)) contains a significance column
isSorted() - Method in class weka.gui.SortedTableModel
returns whether the table was sorted
isSPD() - Method in class weka.core.matrix.CholeskyDecomposition
Is the matrix symmetric and positive definite?
isSquare() - Method in class weka.core.matrix.Matrix
returns whether the matrix is a square matrix or not.
isStdDev(int) - Method in class weka.experiment.ResultMatrix
returns true if the index (in the array produced by toArray(boolean)) contains a std deviation
isStopword(String) - Static method in class weka.core.Stopwords
Returns true if the given string is a stop word.
isStreamableFilter() - Method in class weka.filters.MultiFilter
tests whether all the enclosed filters are streamable
isString() - Method in class weka.core.Attribute
Tests if the attribute is a string.
isStructureOnly() - Method in class weka.gui.beans.DataSetEvent
Returns true if the encapsulated instances contain just header information
isStructureOnly() - Method in class weka.gui.beans.TestSetEvent
Returns true if the encapsulated instances contain just header information
isStructureOnly() - Method in class weka.gui.beans.TrainingSetEvent
Returns true if the encapsulated instances contain just header information
isSubclass(String, String) - Static method in class weka.core.ClassDiscovery
Checks whether the "otherclass" is a subclass of the given "superclass".
isSubclass(Class, Class) - Static method in class weka.core.ClassDiscovery
Checks whether the "otherclass" is a subclass of the given "superclass".
isSubsequenceOf(Instances) - Method in class weka.associations.gsp.Sequence
Checks if the Sequence is subsequence of a given data sequence.
isSymmetric() - Method in class weka.core.Matrix
Deprecated.
Returns true if the matrix is symmetric.
isSymmetric() - Method in class weka.core.matrix.Matrix
Returns true if the matrix is symmetric.
isUndoEnabled() - Method in interface weka.core.Undoable
returns whether undo support is enabled
isUndoEnabled() - Method in class weka.gui.arffviewer.ArffPanel
returns whether undo support is enabled
isUndoEnabled() - Method in class weka.gui.arffviewer.ArffSortedTableModel
returns whether undo support is enabled
isUndoEnabled() - Method in class weka.gui.arffviewer.ArffTableModel
returns whether undo support is enabled
isUniform() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
checks, whether cluster type is uniform
isUseK2Prior() - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator
Returns whether K2 prior is used
isUseK2Prior() - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
 
isUseVariant1() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
Whether variant 1 is used
isValidClassname(String) - Method in class weka.gui.GenericPropertiesCreator
checks whether the classname is a valid one, i.e., from a public class
isValidClassname(String, String) - Method in class weka.gui.GenericPropertiesCreator
Checks whether the classname is a valid one for the given key.
isValidRange(String) - Method in class weka.core.Range
Determines if a string represents a valid index or simple range.
itemAt(int) - Method in class weka.associations.ItemSet
Gest the index of the value of the specified attribute
items() - Method in class weka.associations.ItemSet
Gest the item set as an int array
ItemSet - Class in weka.associations
Class for storing a set of items.
ItemSet(int) - Constructor for class weka.associations.ItemSet
Constructor
ItemSet(int, int[]) - Constructor for class weka.associations.ItemSet
Constructor
ItemSet(int[]) - Constructor for class weka.associations.ItemSet
Contsructor
itemStateChanged(ItemEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
Performs the action associated with the ItemEvent.
IteratedLovinsStemmer - Class in weka.core.stemmers
An iterated version of the Lovins stemmer.
IteratedLovinsStemmer() - Constructor for class weka.core.stemmers.IteratedLovinsStemmer
 
IteratedSingleClassifierEnhancer - Class in weka.classifiers
Abstract utility class for handling settings common to meta classifiers that build an ensemble from a single base learner.
IteratedSingleClassifierEnhancer() - Constructor for class weka.classifiers.IteratedSingleClassifierEnhancer
 
iterationCounter - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Iteration counter
IterativeClassifier - Interface in weka.classifiers
Interface for classifiers that can induce models of growing complexity one step at a time.
iterator() - Method in class weka.associations.FPGrowth.FrequentItemSets
Get an iterator that can be used to access all the item sets.
iterator() - Method in class weka.associations.tertius.SimpleLinkedList
 
iterator() - Method in class weka.core.Trie
Returns an iterator over the elements in this collection.

J

J48 - Class in weka.classifiers.trees
Class for generating a pruned or unpruned C4.5 decision tree.
J48() - Constructor for class weka.classifiers.trees.J48
 
J48graft - Class in weka.classifiers.trees
Class for generating a grafted (pruned or unpruned) C4.5 decision tree.
J48graft() - Constructor for class weka.classifiers.trees.J48graft
 
Javadoc - Class in weka.core
Abstract superclass for classes that generate Javadoc comments and replace the content between certain comment tags.
Javadoc() - Constructor for class weka.core.Javadoc
 
JComponentWriter - Class in weka.gui.visualize
This class takes any JComponent and outputs it to a file.
JComponentWriter() - Constructor for class weka.gui.visualize.JComponentWriter
initializes the object
JComponentWriter(JComponent) - Constructor for class weka.gui.visualize.JComponentWriter
initializes the object with the given Component
JComponentWriter(JComponent, File) - Constructor for class weka.gui.visualize.JComponentWriter
initializes the object with the given Component and filename
JComponentWriterFileFilter(String, String, JComponentWriter) - Constructor for class weka.gui.visualize.PrintableComponent.JComponentWriterFileFilter
Creates the ExtensionFileFilter.
JListHelper - Class in weka.gui
A helper class for JList GUI elements with DefaultListModel or derived models.
JListHelper() - Constructor for class weka.gui.JListHelper
 
joinOptions(String[]) - Static method in class weka.core.Utils
Joins all the options in an option array into a single string, as might be used on the command line.
joinSubsets(ScatterSearchV1.Subset, ScatterSearchV1.Subset) - Method in class weka.attributeSelection.ScatterSearchV1
Join two subsets
jp - Variable in class weka.gui.visualize.MatrixPanel
Split pane for splitting the matrix and the buttons and bars
JPEGWriter - Class in weka.gui.visualize
This class takes any JComponent and outputs it to a JPEG-file.
JPEGWriter() - Constructor for class weka.gui.visualize.JPEGWriter
initializes the object.
JPEGWriter(JComponent) - Constructor for class weka.gui.visualize.JPEGWriter
initializes the object with the given Component.
JPEGWriter(JComponent, File) - Constructor for class weka.gui.visualize.JPEGWriter
initializes the object with the given Component and filename.
JRip - Class in weka.classifiers.rules
This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was proposed by William W.
JRip() - Constructor for class weka.classifiers.rules.JRip
 
JRip.Antd - Class in weka.classifiers.rules
The single antecedent in the rule, which is composed of an attribute and the corresponding value.
JRip.NominalAntd - Class in weka.classifiers.rules
The antecedent with nominal attribute
JRip.NumericAntd - Class in weka.classifiers.rules
The antecedent with numeric attribute
JRip.RipperRule - Class in weka.classifiers.rules
This class implements a single rule that predicts specified class.
JTableHelper - Class in weka.gui
A helper class for JTable, e.g.
JTableHelper(JTable) - Constructor for class weka.gui.JTableHelper
initializes the object
JTreePopupMenu(JTree) - Constructor for class weka.gui.GenericObjectEditor.JTreePopupMenu
Constructs a new popup menu.
Jython - Class in weka.core
A helper class for Jython.
Jython() - Constructor for class weka.core.Jython
default constructor, tries to instantiate a Python Interpreter
JythonObject - Interface in weka.core
An indicator interface for Jython objects.
JythonSerializableObject - Interface in weka.core
An indicator interface for serializable Jython objects.

K

K2 - Class in weka.classifiers.bayes.net.search.global
This Bayes Network learning algorithm uses a hill climbing algorithm restricted by an order on the variables.

For more information see:

G.F.
K2() - Constructor for class weka.classifiers.bayes.net.search.global.K2
 
K2 - Class in weka.classifiers.bayes.net.search.local
This Bayes Network learning algorithm uses a hill climbing algorithm restricted by an order on the variables.

For more information see:

G.F.
K2() - Constructor for class weka.classifiers.bayes.net.search.local.K2
 
k_MarginResolution - Static variable in class weka.classifiers.Evaluation
Resolution of the margin histogram
k_nextNeighbourQuery(int, double, DataObject) - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database
Emits the k next-neighbours and performs an epsilon-range-query at the parallel.
k_nextNeighbourQuery(int, double, DataObject) - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase
Emits the k next-neighbours and performs an epsilon-range-query at the parallel.
kappa() - Method in class weka.classifiers.Evaluation
Returns value of kappa statistic if class is nominal.
KBInformation() - Method in class weka.classifiers.Evaluation
Return the total Kononenko & Bratko Information score in bits
KBMeanInformation() - Method in class weka.classifiers.Evaluation
Return the Kononenko & Bratko Information score in bits per instance.
KBRelativeInformation() - Method in class weka.classifiers.Evaluation
Return the Kononenko & Bratko Relative Information score
KDConditionalEstimator - Class in weka.estimators
Conditional probability estimator for a numeric domain conditional upon a discrete domain (utilises separate kernel estimators for each discrete conditioning value).
KDConditionalEstimator(int, double) - Constructor for class weka.estimators.KDConditionalEstimator
Constructor
KDDataGenerator - Class in weka.gui.boundaryvisualizer
KDDataGenerator.
KDDataGenerator() - Constructor for class weka.gui.boundaryvisualizer.KDDataGenerator
 
KDTree - Class in weka.core.neighboursearch
Class implementing the KDTree search algorithm for nearest neighbour search.
The connection to dataset is only a reference.
KDTree() - Constructor for class weka.core.neighboursearch.KDTree
Creates a new instance of KDTree.
KDTree(Instances) - Constructor for class weka.core.neighboursearch.KDTree
Creates a new instance of KDTree.
KDTreeNode - Class in weka.core.neighboursearch.kdtrees
A class representing a KDTree node.
KDTreeNode() - Constructor for class weka.core.neighboursearch.kdtrees.KDTreeNode
Constructor.
KDTreeNode(int, int, int, double[][]) - Constructor for class weka.core.neighboursearch.kdtrees.KDTreeNode
Constructor.
KDTreeNode(int, int, int, double[][], double[][]) - Constructor for class weka.core.neighboursearch.kdtrees.KDTreeNode
 
KDTreeNodeSplitter - Class in weka.core.neighboursearch.kdtrees
Class that splits up a KDTreeNode.
KDTreeNodeSplitter() - Constructor for class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
default constructor.
KDTreeNodeSplitter(int[], Instances, EuclideanDistance) - Constructor for class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
Creates a new instance of KDTreeNodeSplitter.
KDTreeTipText() - Method in class weka.clusterers.XMeans
Returns the tip text for this property.
keepLastModel() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
decide to keep the last model in the committee
Kernel - Class in weka.classifiers.functions.supportVector
Abstract kernel.
Kernel() - Constructor for class weka.classifiers.functions.supportVector.Kernel
 
kernel(int, char[], int, char[], int) - Method in class weka.classifiers.functions.supportVector.StringKernel
the kernel function (Kn).
KernelEstimator - Class in weka.estimators
Simple kernel density estimator.
KernelEstimator(double) - Constructor for class weka.estimators.KernelEstimator
Constructor that takes a precision argument.
KernelEvaluation - Class in weka.classifiers.functions.supportVector
Class for evaluating Kernels.
KernelEvaluation() - Constructor for class weka.classifiers.functions.supportVector.KernelEvaluation
default constructor
kernelFactorExpressionTipText() - Method in class weka.filters.unsupervised.attribute.KernelFilter
Returns the tip text for this property
KernelFilter - Class in weka.filters.unsupervised.attribute
Converts the given set of predictor variables into a kernel matrix.
KernelFilter() - Constructor for class weka.filters.unsupervised.attribute.KernelFilter
 
kernelHelper(int, char[], int, char[], int) - Method in class weka.classifiers.functions.supportVector.StringKernel
The kernel helper function, called K' in [1] and [2].
kernelHelper2(int, char[], int, char[], int) - Method in class weka.classifiers.functions.supportVector.StringKernel
helper function for the evaluation of the kernel K'' see section 'Efficient Computation of SSK' in [1]
kernelHelper2LP(int, char[], int, char[], int, int) - Method in class weka.classifiers.functions.supportVector.StringKernel
helper function for the evaluation of the kernel (K''n) using lambda pruning
kernelHelperLP(int, char[], int, char[], int, int) - Method in class weka.classifiers.functions.supportVector.StringKernel
helper function for the evaluation of the kernel (K'n) using lambda pruning
kernelLP(int, char[], int, char[], int, int) - Method in class weka.classifiers.functions.supportVector.StringKernel
the kernel function K explained in [1] using lambda pruning, explained in [2].
kernelMatrixFileTipText() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
Returns the tip text for this property
kernelTipText() - Method in class weka.classifiers.functions.GaussianProcesses
Returns the tip text for this property
kernelTipText() - Method in class weka.classifiers.functions.SMO
Returns the tip text for this property
kernelTipText() - Method in class weka.classifiers.functions.SMOreg
Returns the tip text for this property
kernelTipText() - Method in class weka.classifiers.mi.MISMO
Returns the tip text for this property
kernelTipText() - Method in class weka.classifiers.mi.MISVM
Returns the tip text for this property
kernelTipText() - Method in class weka.filters.unsupervised.attribute.KernelFilter
Returns the tip text for this property
KERNELTYPE_LINEAR - Static variable in class weka.classifiers.functions.LibSVM
kernel type linear: u'*v
KERNELTYPE_POLYNOMIAL - Static variable in class weka.classifiers.functions.LibSVM
kernel type polynomial: (gamma*u'*v + coef0)^degree
KERNELTYPE_RBF - Static variable in class weka.classifiers.functions.LibSVM
kernel type radial basis function: exp(-gamma*|u-v|^2)
KERNELTYPE_SIGMOID - Static variable in class weka.classifiers.functions.LibSVM
kernel type sigmoid: tanh(gamma*u'*v + coef0)
kernelTypeTipText() - Method in class weka.classifiers.functions.LibSVM
Returns the tip text for this property
key - Variable in class weka.classifiers.lazy.kstar.KStarCache.TableEntry
attribute value
keyFieldNameTipText() - Method in class weka.experiment.AveragingResultProducer
Returns the tip text for this property
keyIterator() - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database
Returns an iterator over all the keys
keyIterator() - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase
Returns an iterator over all the keys
keys() - Method in class weka.core.xml.MethodHandler
returns an enumeration over all currently stored custom methods, i.e.
keysTipText() - Method in class weka.core.converters.DatabaseLoader
the tip text for this property
kFoldCV(BayesNet, int) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
kFoldCV uses k-fold cross validation to measure the accuracy of a Bayes network classifier.
KKConditionalEstimator - Class in weka.estimators
Conditional probability estimator for a numeric domain conditional upon a numeric domain.
KKConditionalEstimator(double) - Constructor for class weka.estimators.KKConditionalEstimator
Constructor
KMeansInpiredMethod - Class in weka.core.neighboursearch.kdtrees
The class that splits a node into two such that the overall sum of squared distances of points to their centres on both sides of the (axis-parallel) splitting plane is minimum.

For more information see also:

Ashraf Masood Kibriya (2007).
KMeansInpiredMethod() - Constructor for class weka.core.neighboursearch.kdtrees.KMeansInpiredMethod
 
kNearestNeighbours(Instance, int) - Method in class weka.core.neighboursearch.BallTree
Returns k nearest instances in the current neighbourhood to the supplied instance.
kNearestNeighbours(Instance, int) - Method in class weka.core.neighboursearch.CoverTree
Returns k-NNs of a given target instance, from among the previously supplied training instances (supplied through setInstances method) P.S.: May return more than k-NNs if more one instances have the same distance to the target as the kth NN.
kNearestNeighbours(Instance, int) - Method in class weka.core.neighboursearch.KDTree
Returns the k nearest neighbours of the supplied instance.
kNearestNeighbours(Instance, int) - Method in class weka.core.neighboursearch.LinearNNSearch
Returns k nearest instances in the current neighbourhood to the supplied instance.
kNearestNeighbours(Instance, int) - Method in class weka.core.neighboursearch.NearestNeighbourSearch
Returns k nearest instances in the current neighbourhood to the supplied instance.
KNNTipText() - Method in class weka.classifiers.lazy.IBk
Returns the tip text for this property.
KNNTipText() - Method in class weka.classifiers.lazy.LWL
Returns the tip text for this property.
KnowledgeFlow - Class in weka.gui.beans
Startup class for the KnowledgeFlow.
KnowledgeFlow() - Constructor for class weka.gui.beans.KnowledgeFlow
 
KnowledgeFlowApp - Class in weka.gui.beans
Main GUI class for the KnowledgeFlow.
KnowledgeFlowApp(boolean) - Constructor for class weka.gui.beans.KnowledgeFlowApp
Creates a new KnowledgeFlowApp instance.
KnowledgeFlowApp.BeanLayout - Class in weka.gui.beans
Used for displaying the bean components and their visible connections
KOML - Class in weka.core.xml
This class is a helper class for XML serialization using KOML .
KOML() - Constructor for class weka.core.xml.KOML
 
komlToBinary(String, String) - Static method in class weka.core.xml.SerialUIDChanger
converts a KOML file into a binary one
KOMLV - Static variable in class weka.gui.beans.SerializedModelSaver
 
KStar - Class in weka.classifiers.lazy
K* is an instance-based classifier, that is the class of a test instance is based upon the class of those training instances similar to it, as determined by some similarity function.
KStar() - Constructor for class weka.classifiers.lazy.KStar
 
KStarCache - Class in weka.classifiers.lazy.kstar
A class representing the caching system used to keep track of each attribute value and its corresponding scale factor or stop parameter.
KStarCache() - Constructor for class weka.classifiers.lazy.kstar.KStarCache
 
KStarCache.CacheTable - Class in weka.classifiers.lazy.kstar
A custom hashtable class to support the caching system.
KStarCache.TableEntry - Class in weka.classifiers.lazy.kstar
Hashtable collision list.
KStarConstants - Interface in weka.classifiers.lazy.kstar
 
KStarNominalAttribute - Class in weka.classifiers.lazy.kstar
A custom class which provides the environment for computing the transformation probability of a specified test instance nominal attribute to a specified train instance nominal attribute.
KStarNominalAttribute(Instance, Instance, int, Instances, int[][], KStarCache) - Constructor for class weka.classifiers.lazy.kstar.KStarNominalAttribute
Constructor
KStarNumericAttribute - Class in weka.classifiers.lazy.kstar
A custom class which provides the environment for computing the transformation probability of a specified test instance numeric attribute to a specified train instance numeric attribute.
KStarNumericAttribute(Instance, Instance, int, Instances, int[][], KStarCache) - Constructor for class weka.classifiers.lazy.kstar.KStarNumericAttribute
Constructor
KStarWrapper - Class in weka.classifiers.lazy.kstar
 
KStarWrapper() - Constructor for class weka.classifiers.lazy.kstar.KStarWrapper
 
kthSmallestValue(Attribute, int) - Method in class weka.core.Instances
Returns the kth-smallest attribute value of a numeric attribute.
kthSmallestValue(int, int) - Method in class weka.core.Instances
Returns the kth-smallest attribute value of a numeric attribute.
kthSmallestValue(int[], int) - Static method in class weka.core.Utils
Returns the kth-smallest value in the array.
kthSmallestValue(double[], int) - Static method in class weka.core.Utils
Returns the kth-smallest value in the array
kullback(double[], double[], double[], double[], int) - Method in class weka.classifiers.mi.MINND
This function calculates the Kullback Leibler distance between two normal distributions.
KValueTipText() - Method in class weka.classifiers.trees.RandomTree
Returns the tip text for this property

L

labelData(Instances) - Method in class weka.classifiers.meta.Decorate
Labels the artificially generated data.
LabeledItemSet - Class in weka.associations
Class for storing a set of items together with a class label.
LabeledItemSet(int, int) - Constructor for class weka.associations.LabeledItemSet
Constructor
labelsTipText() - Method in class weka.filters.unsupervised.attribute.AddValues
Returns the tip text for this property
LADInstance(Instance) - Constructor for class weka.classifiers.trees.LADTree.LADInstance
 
LADTree - Class in weka.classifiers.trees
Class for generating a multi-class alternating decision tree using the LogitBoost strategy.
LADTree() - Constructor for class weka.classifiers.trees.LADTree
 
LADTree.LADInstance - Class in weka.classifiers.trees
helper classes
LADTree.PredictionNode - Class in weka.classifiers.trees
 
LADTree.Splitter - Class in weka.classifiers.trees
splitter classes
LADTree.TwoWayNominalSplit - Class in weka.classifiers.trees
 
LADTree.TwoWayNumericSplit - Class in weka.classifiers.trees
 
LAGDHillClimber - Class in weka.classifiers.bayes.net.search.local
This Bayes Network learning algorithm uses a Look Ahead Hill Climbing algorithm called LAGD Hill Climbing.
LAGDHillClimber() - Constructor for class weka.classifiers.bayes.net.search.local.LAGDHillClimber
 
lambdaTipText() - Method in class weka.classifiers.functions.SPegasos
Returns the tip text for this property
lambdaTipText() - Method in class weka.classifiers.functions.supportVector.StringKernel
Returns the tip text for this property
LaplaceEstimate(double, double, double) - Method in class weka.classifiers.bayes.AODEsr
Returns the probability estimate, using laplace correction
laplaceForSubsetOfInterest() - Method in class weka.classifiers.trees.j48.GraftSplit
 
LaplacePriorImpl - Class in weka.classifiers.bayes.blr
Implementation of the Gaussian Prior update function based on modified CLG Algorithm (CLG-Lasso) with a certain Trust Region Update based on Laplace Priors.
LaplacePriorImpl() - Constructor for class weka.classifiers.bayes.blr.LaplacePriorImpl
 
laplaceProb(int) - Method in class weka.classifiers.trees.j48.Distribution
Returns relative frequency of class over all bags with Laplace correction.
laplaceProb(int, int) - Method in class weka.classifiers.trees.j48.Distribution
Returns relative frequency of class for given bag.
laplaceUpdate(int, double) - Method in class weka.classifiers.bayes.blr.LaplacePriorImpl
This is the CLG-lasso update function described in the
LAPLACIAN - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
 
last() - Method in class weka.core.neighboursearch.covertrees.Stack
Returns the last element in the stack.
LAST - Static variable in class weka.filters.unsupervised.attribute.ClassAssigner
use the last attribute as class.
lastActionMsg() - Method in class weka.classifiers.bayes.net.EditableBayesNet
get message representing the last action performed on the network
lastElement() - Method in class weka.core.FastVector
Returns the last element of the vector.
lastElement() - Method in class weka.gui.CheckBoxList.CheckBoxListModel
Returns the last component of the list.
lastIndexOf(Object) - Method in class weka.gui.CheckBoxList.CheckBoxListModel
Returns the index of the last occurrence of elem.
lastIndexOf(Object, int) - Method in class weka.gui.CheckBoxList.CheckBoxListModel
Searches backwards for elem, starting from the specified index, and returns an index to it.
lastInstance() - Method in class weka.core.Instances
Returns the last instance in the set.
LatentSemanticAnalysis - Class in weka.attributeSelection
Performs latent semantic analysis and transformation of the data.
LatentSemanticAnalysis() - Constructor for class weka.attributeSelection.LatentSemanticAnalysis
 
launchNext(int, int) - Method in class weka.experiment.RemoteExperiment
Launch a sub experiment on a remote host
launchNext(int, int) - Method in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
 
launchThread(Startable, int) - Method in class weka.gui.beans.FlowRunner
 
layoutCompleted(LayoutCompleteEvent) - Method in class weka.classifiers.bayes.net.GUI
This method is an implementation for LayoutCompleteEventListener class.
layoutCompleted(LayoutCompleteEvent) - Method in class weka.gui.graphvisualizer.GraphVisualizer
This method is an implementation for LayoutCompleteEventListener class.
layoutCompleted(LayoutCompleteEvent) - Method in interface weka.gui.graphvisualizer.LayoutCompleteEventListener
 
LayoutCompleteEvent - Class in weka.gui.graphvisualizer
This is an event which is fired by a LayoutEngine once a LayoutEngine finishes laying out the graph, so that the Visualizer can repaint the screen to show the changes.
LayoutCompleteEvent(Object) - Constructor for class weka.gui.graphvisualizer.LayoutCompleteEvent
 
LayoutCompleteEventListener - Interface in weka.gui.graphvisualizer
This interface should be implemented by any class which needs to receive LayoutCompleteEvents from the LayoutEngine.
layoutCompleteListeners - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
FastVector containing listeners for layoutCompleteEvent generated by this LayoutEngine
LayoutEngine - Interface in weka.gui.graphvisualizer
This interface class has been added to facilitate the addition of other layout engines to this package.
layoutGraph(FastVector, FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet
set positions of all nodes
layoutGraph() - Method in class weka.gui.graphvisualizer.GraphVisualizer
This method lays out the graph by calling the LayoutEngine's layoutGraph() method.
layoutGraph() - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
This method does a complete layout of the graph which includes removing cycles, assigning levels to nodes, reducing edge crossings and laying out the vertices horizontally for better visibility.
layoutGraph() - Method in interface weka.gui.graphvisualizer.LayoutEngine
This method lays out the graph for better visualization
lBCenter(int, int, int[]) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
 
LBR - Class in weka.classifiers.lazy
Lazy Bayesian Rules Classifier.
LBR() - Constructor for class weka.classifiers.lazy.LBR
 
LBR.Indexes - Class in weka.classifiers.lazy
Class for handling instances and the associated attributes.
lConnectivity(int, int) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
 
LE - Static variable in interface weka.core.mathematicalexpression.sym
 
LE - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
leafString() - Method in class weka.classifiers.trees.RandomTree
Outputs a leaf.
leafString(REPTree.Tree) - Method in class weka.classifiers.trees.REPTree.Tree
Outputs description of a leaf node.
LearningRateResultProducer - Class in weka.experiment
Tells a sub-ResultProducer to reproduce the current run for varying sized subsamples of the dataset.
LearningRateResultProducer() - Constructor for class weka.experiment.LearningRateResultProducer
 
learningRateTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
leastExplainingColumn(PaceMatrix, IntVector, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Returns the index of the column that has the smallest (squared) response, when the column is moved to become the (ks-1)-th column.
LeastMedSq - Class in weka.classifiers.functions
Implements a least median sqaured linear regression utilising the existing weka LinearRegression class to form predictions.
LeastMedSq() - Constructor for class weka.classifiers.functions.LeastMedSq
 
leaveOneOut(LBR.Indexes, int[][][], int[], boolean[]) - Method in class weka.classifiers.lazy.LBR
Leave-one-out strategy.
leaveOneOutCV(BayesNet) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
LeaveOneOutCV returns the accuracy calculated using Leave One Out cross validation.
LED24 - Class in weka.datagenerators.classifiers.classification
This generator produces data for a display with 7 LEDs.
LED24() - Constructor for class weka.datagenerators.classifiers.classification.LED24
initializes the generator with default values
LEFT - Static variable in class weka.classifiers.trees.m5.Rule
 
left - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
LEFT_PARENTHESES - Variable in class weka.experiment.ResultMatrix
the left parentheses for enumerating cols/rows
leftHand - Variable in class weka.classifiers.lazy.LBR
best attribute's index list.
leftNode() - Method in class weka.classifiers.trees.m5.RuleNode
Get the left child of this node
leftSide(Instances) - Method in class weka.classifiers.trees.j48.BinC45Split
Prints left side of condition.
leftSide(Instances) - Method in class weka.classifiers.trees.j48.C45Split
Prints left side of condition..
leftSide(Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Prints left side of condition satisfied by instances.
leftSide(Instances) - Method in class weka.classifiers.trees.j48.GraftSplit
Prints left side of condition satisfied by instances.
leftSide(Instances) - Method in class weka.classifiers.trees.j48.NBTreeNoSplit
Does nothing because no condition has to be satisfied.
leftSide(Instances) - Method in class weka.classifiers.trees.j48.NBTreeSplit
Prints left side of condition..
leftSide(Instances) - Method in class weka.classifiers.trees.j48.NoSplit
Does nothing because no condition has to be satisfied.
leftSide(Instances) - Method in class weka.classifiers.trees.lmt.ResidualSplit
Returns name of splitting attribute (left side of condition).
legend() - Method in class weka.classifiers.trees.ADTree
Returns the legend of the tree, describing how results are to be interpreted.
legend() - Method in class weka.classifiers.trees.LADTree
Returns the legend of the tree, describing how results are to be interpreted.
LegendEntry(PlotData2D, int) - Constructor for class weka.gui.visualize.LegendPanel.LegendEntry
 
LegendPanel - Class in weka.gui.visualize
This panel displays legends for a list of plots.
LegendPanel() - Constructor for class weka.gui.visualize.LegendPanel
Constructor
LegendPanel.LegendEntry - Class in weka.gui.visualize
Inner class for handling legend entries
length() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList
Returns the size of the list.
length - Variable in class weka.core.neighboursearch.covertrees.Stack
The number of elements in the stack.
LEVERAGE - Static variable in class weka.associations.Apriori
Metric type: Leverage
leverageForRule(AprioriItemSet, AprioriItemSet, int, int) - Method in class weka.associations.AprioriItemSet
Outputs the leverage for a rule.
LFSMethods - Class in weka.attributeSelection
 
LFSMethods() - Constructor for class weka.attributeSelection.LFSMethods
empty constructor methods are not static because of access to inner class Link2 and LinkedList2
LFSMethods.Link2 - Class in weka.attributeSelection
Class for a node in a linked list.
LFSMethods.LinkedList2 - Class in weka.attributeSelection
Class for handling a linked list.
LibLINEAR - Class in weka.classifiers.functions
A wrapper class for the liblinear tools (the liblinear classes, typically the jar file, need to be in the classpath to use this classifier).
Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin (2008).
LibLINEAR() - Constructor for class weka.classifiers.functions.LibLINEAR
 
LibSVM - Class in weka.classifiers.functions
A wrapper class for the libsvm tools (the libsvm classes, typically the jar file, need to be in the classpath to use this classifier).
LibSVM runs faster than SMO since it uses LibSVM to build the SVM classifier.
LibSVM allows users to experiment with One-class SVM, Regressing SVM, and nu-SVM supported by LibSVM tool.
LibSVM() - Constructor for class weka.classifiers.functions.LibSVM
 
LibSVMLoader - Class in weka.core.converters
Reads a source that is in libsvm format.

For more information about libsvm see:

http://www.csie.ntu.edu.tw/~cjlin/libsvm/

LibSVMLoader() - Constructor for class weka.core.converters.LibSVMLoader
 
LibSVMSaver - Class in weka.core.converters
Writes to a destination that is in libsvm format.

For more information about libsvm see:

http://www.csie.ntu.edu.tw/~cjlin/libsvm/

Valid options are:

LibSVMSaver() - Constructor for class weka.core.converters.LibSVMSaver
Constructor
libsvmToArray(String) - Method in class weka.core.converters.LibSVMLoader
turns a libsvm row into a double array with the class as the last entry.
LIFT - Static variable in class weka.associations.Apriori
Metric type: Lift
LIFT_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
attribute name: Lift
liftForRule(AprioriItemSet, AprioriItemSet, int) - Method in class weka.associations.AprioriItemSet
Outputs the lift for a rule.
likelihoodThresholdTipText() - Method in class weka.classifiers.meta.LogitBoost
Returns the tip text for this property
LINE - Static variable in class weka.gui.visualize.VisualizePanelEvent
 
LINEAR - Static variable in class weka.classifiers.lazy.LWL
The available kernel weighting methods.
LinearForwardSelection - Class in weka.attributeSelection
LinearForwardSelection:

Extension of BestFirst.
LinearForwardSelection() - Constructor for class weka.attributeSelection.LinearForwardSelection
Constructor
LinearNNSearch - Class in weka.core.neighboursearch
Class implementing the brute force search algorithm for nearest neighbour search.
LinearNNSearch() - Constructor for class weka.core.neighboursearch.LinearNNSearch
Constructor.
LinearNNSearch(Instances) - Constructor for class weka.core.neighboursearch.LinearNNSearch
Constructor that uses the supplied set of instances.
LinearRegression - Class in weka.classifiers.functions
Class for using linear regression for prediction.
LinearRegression() - Constructor for class weka.classifiers.functions.LinearRegression
 
LinearRegression - Class in weka.core.matrix
Class for performing (ridged) linear regression.
LinearRegression(Matrix, Matrix, double) - Constructor for class weka.core.matrix.LinearRegression
Performs a (ridged) linear regression.
LinearRegression(Matrix, Matrix, double[], double) - Constructor for class weka.core.matrix.LinearRegression
Performs a weighted (ridged) linear regression.
LinearUnit - Class in weka.classifiers.functions.neural
This can be used by the neuralnode to perform all it's computations (as a Linear unit).
LinearUnit() - Constructor for class weka.classifiers.functions.neural.LinearUnit
 
Link2(Object[], double) - Constructor for class weka.attributeSelection.BestFirst.Link2
Constructor
Link2(Object[], double) - Constructor for class weka.attributeSelection.LFSMethods.Link2
 
LinkedList2(int) - Constructor for class weka.attributeSelection.BestFirst.LinkedList2
 
LinkedList2(int) - Constructor for class weka.attributeSelection.LFSMethods.LinkedList2
 
LinkedListInverseIterator() - Constructor for class weka.associations.tertius.SimpleLinkedList.LinkedListInverseIterator
 
LinkedListIterator() - Constructor for class weka.associations.tertius.SimpleLinkedList.LinkedListIterator
 
linkTypeTipText() - Method in class weka.clusterers.HierarchicalClusterer
 
LINUX_BROWSERS - Static variable in class weka.gui.BrowserHelper
Linux/Unix binaries to look for
listCapabilities(Capabilities) - Method in class weka.gui.PropertySheetPanel.CapabilitiesHelpDialog
returns a comma-separated list of all the capabilities.
listener - Variable in class weka.gui.visualize.VisualizePanel
An optional listener that we will inform when ComboBox selections change
ListNode(int, double) - Constructor for class weka.core.neighboursearch.balltrees.MiddleOutConstructor.ListNode
Constructor.
listOptions() - Method in class weka.associations.Apriori
Returns an enumeration describing the available options.
listOptions() - Method in class weka.associations.CheckAssociator
Returns an enumeration describing the available options.
listOptions() - Method in class weka.associations.FilteredAssociator
Returns an enumeration describing the available options.
listOptions() - Method in class weka.associations.FPGrowth
Returns an enumeration describing the available options.
listOptions() - Method in class weka.associations.GeneralizedSequentialPatterns
Returns an enumeration of the available options.
listOptions() - Method in class weka.associations.PredictiveApriori
Returns an enumeration describing the available options.
listOptions() - Method in class weka.associations.SingleAssociatorEnhancer
Returns an enumeration describing the available options.
listOptions() - Method in class weka.associations.Tertius
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.BestFirst
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.CfsSubsetEval
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.CheckAttributeSelection
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
Returns an enumeration describing the available options
listOptions() - Method in class weka.attributeSelection.ClassifierSubsetEval
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.ExhaustiveSearch
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.FilteredAttributeEval
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.FilteredSubsetEval
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.GainRatioAttributeEval
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.GeneticSearch
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.GreedyStepwise
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.InfoGainAttributeEval
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.LatentSemanticAnalysis
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.LinearForwardSelection
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.OneRAttributeEval
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.PrincipalComponents
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.RaceSearch
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.RandomSearch
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.Ranker
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.RankSearch
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.ReliefFAttributeEval
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.ScatterSearchV1
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.SVMAttributeEval
Returns an enumeration describing all the available options
listOptions() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.WrapperSubsetEval
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.AODE
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.bayes.AODEsr
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.BayesNet
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.bayes.ComplementNaiveBayes
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.DMNBtext
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.NaiveBayes
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.BayesNetGenerator
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.fixed.FromFile
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.bayes.net.search.global.HillClimber
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.global.K2
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.global.TAN
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.local.HillClimber
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.local.K2
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.local.TAN
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.WAODE
Gets an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.BVDecompose
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.BVDecomposeSegCVSub
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.CheckClassifier
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.CheckSource
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.Classifier
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.GaussianProcesses
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.LeastMedSq
Returns an enumeration of all the available options..
listOptions() - Method in class weka.classifiers.functions.LibLINEAR
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.LibSVM
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.LinearRegression
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.Logistic
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.functions.MultilayerPerceptron
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.PaceRegression
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.PLSClassifier
Gets an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.RBFNetwork
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.functions.SimpleLogistic
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.SMO
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.SMOreg
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.SPegasos
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.supportVector.CachedKernel
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.supportVector.CheckKernel
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.supportVector.Kernel
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.supportVector.PolyKernel
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.supportVector.Puk
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.supportVector.RBFKernel
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.supportVector.RegOptimizer
Gets an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.supportVector.RegSMO
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.functions.supportVector.StringKernel
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.VotedPerceptron
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.Winnow
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.IteratedSingleClassifierEnhancer
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.lazy.IBk
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.lazy.KStar
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.lazy.LWL
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.AdaBoostM1
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.AdditiveRegression
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.Bagging
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.ClassificationViaClustering
Gets an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.CostSensitiveClassifier
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.CVParameterSelection
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.Dagging
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.Decorate
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.meta.FilteredClassifier
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.GridSearch
Gets an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.LogitBoost
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.MetaCost
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.MultiBoostAB
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.meta.MultiClassClassifier
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.meta.MultiScheme
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.OrdinalClassClassifier
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.meta.RandomSubSpace
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.RegressionByDiscretization
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.RotationForest
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.Stacking
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.ThresholdSelector
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.Vote
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.mi.CitationKNN
Returns an enumeration of all the available options..
listOptions() - Method in class weka.classifiers.mi.MDD
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.mi.MIBoost
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.mi.MIDD
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.mi.MIEMDD
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.mi.MILR
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.mi.MINND
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.mi.MIOptimalBall
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.mi.MISMO
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.mi.MISVM
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.mi.MIWrapper
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.mi.SimpleMI
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.misc.SerializedClassifier
Gets an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.misc.VFI
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.MultipleClassifiersCombiner
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.RandomizableClassifier
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.RandomizableMultipleClassifiersCombiner
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.RandomizableSingleClassifierEnhancer
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.rules.ConjunctiveRule
Returns an enumeration describing the available options Valid options are:
listOptions() - Method in class weka.classifiers.rules.DecisionTable
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.rules.DTNB
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.rules.JRip
Returns an enumeration describing the available options Valid options are:
listOptions() - Method in class weka.classifiers.rules.NNge
Returns an enumeration of all the available options..
listOptions() - Method in class weka.classifiers.rules.OneR
Returns an enumeration describing the available options..
listOptions() - Method in class weka.classifiers.rules.PART
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.rules.Ridor
Returns an enumeration describing the available options Valid options are:
listOptions() - Method in class weka.classifiers.SingleClassifierEnhancer
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.trees.ADTree
Returns an enumeration describing the available options..
listOptions() - Method in class weka.classifiers.trees.BFTree
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.trees.FT
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.trees.J48
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.trees.J48graft
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.trees.LADTree
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.trees.LMT
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.trees.m5.M5Base
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.trees.M5P
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.trees.RandomForest
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.trees.RandomTree
Lists the command-line options for this classifier.
listOptions() - Method in class weka.classifiers.trees.REPTree
Lists the command-line options for this classifier.
listOptions() - Method in class weka.classifiers.trees.SimpleCart
Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.CheckClusterer
Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.CLOPE
 
listOptions() - Method in class weka.clusterers.Cobweb
Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.DBScan
Returns an enumeration of all the available options..
listOptions() - Method in class weka.clusterers.EM
Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.FarthestFirst
Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.FilteredClusterer
Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.HierarchicalClusterer
Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.MakeDensityBasedClusterer
Returns an enumeration describing the available options..
listOptions() - Method in class weka.clusterers.OPTICS
Returns an enumeration of all the available options.
listOptions() - Method in class weka.clusterers.RandomizableClusterer
Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.RandomizableDensityBasedClusterer
Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.RandomizableSingleClustererEnhancer
Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.sIB
Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.SimpleKMeans
Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.SingleClustererEnhancer
Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.XMeans
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.Check
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.CheckGOE
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.CheckOptionHandler
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.CheckScheme
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.converters.AbstractFileSaver
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.converters.ArffSaver
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.converters.C45Saver
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.converters.CSVLoader
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.converters.DatabaseLoader
Lists the available options
listOptions() - Method in class weka.core.converters.DatabaseSaver
Lists the available options.
listOptions() - Method in class weka.core.converters.LibSVMSaver
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.converters.SVMLightSaver
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.converters.TextDirectoryLoader
Lists the available options
listOptions() - Method in class weka.core.converters.XRFFSaver
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.FindWithCapabilities
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.Javadoc
Returns an enumeration describing the available options.
ListOptions - Class in weka.core
Lists the options of an OptionHandler
ListOptions() - Constructor for class weka.core.ListOptions
 
listOptions() - Method in class weka.core.ListOptions
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.BallTree
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.balltrees.BallSplitter
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.CoverTree
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.KDTree
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.LinearNNSearch
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.NearestNeighbourSearch
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.NormalizableDistance
Returns an enumeration describing the available options.
listOptions() - Method in interface weka.core.OptionHandler
Returns an enumeration of all the available options..
listOptions() - Method in class weka.core.OptionHandlerJavadoc
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.stemmers.SnowballStemmer
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.TechnicalInformationHandlerJavadoc
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.TestInstances
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.tokenizers.CharacterDelimitedTokenizer
Returns an enumeration of all the available options..
listOptions() - Method in class weka.core.tokenizers.NGramTokenizer
Returns an enumeration of all the available options..
listOptions() - Method in class weka.core.tokenizers.Tokenizer
Returns an enumeration of all the available options..
listOptions() - Method in class weka.datagenerators.ClassificationGenerator
Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.classifiers.classification.Agrawal
Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.classifiers.classification.BayesNet
Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.classifiers.classification.LED24
Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.classifiers.classification.RDG1
Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.classifiers.regression.Expression
Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.ClusterDefinition
Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.clusterers.SubspaceCluster
Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.ClusterGenerator
Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.DataGenerator
Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.RegressionGenerator
Returns an enumeration describing the available options.
listOptions() - Method in class weka.estimators.CheckEstimator
Returns an enumeration describing the available options.
listOptions() - Method in class weka.estimators.Estimator
Returns an enumeration describing the available options.
listOptions() - Method in class weka.experiment.AveragingResultProducer
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.ClassifierSplitEvaluator
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.CrossValidationResultProducer
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.CSVResultListener
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.DatabaseResultProducer
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
Returns an enumeration describing the available options.
listOptions() - Method in class weka.experiment.Experiment
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.InstanceQuery
Returns an enumeration describing the available options
listOptions() - Method in class weka.experiment.LearningRateResultProducer
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.PairedTTester
Lists options understood by this object.
listOptions() - Method in class weka.experiment.RandomSplitResultProducer
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.RegressionSplitEvaluator
Returns an enumeration describing the available options..
listOptions() - Method in class weka.filters.CheckSource
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.MultiFilter
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.SimpleFilter
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.attribute.AddClassification
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.attribute.AttributeSelection
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.attribute.ClassOrder
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.attribute.Discretize
Gets an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.attribute.NominalToBinary
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.attribute.PLSFilter
Gets an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.instance.Resample
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.instance.SMOTE
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.instance.SpreadSubsample
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Gets an enumeration describing the available options..
listOptions() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.Add
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.AddCluster
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.AddExpression
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.AddID
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.AddNoise
Returns an enumeration describing the available options
listOptions() - Method in class weka.filters.unsupervised.attribute.AddValues
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
Returns an enumeration describing the available options
listOptions() - Method in class weka.filters.unsupervised.attribute.ClassAssigner
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.ClusterMembership
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.Copy
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.Discretize
Gets an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.FirstOrder
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.KernelFilter
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.MathExpression
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
Returns an enumeration describing the available options
listOptions() - Method in class weka.filters.unsupervised.attribute.NominalToBinary
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.NominalToString
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.Normalize
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.NumericToNominal
Gets an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.NumericTransform
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Gets an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.PotentialClassIgnorer
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
Returns an enumeration describing the available options
listOptions() - Method in class weka.filters.unsupervised.attribute.RandomProjection
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.RandomSubset
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.RELAGGS
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.Remove
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.RemoveType
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.RemoveUseless
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.Reorder
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.StringToNominal
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.SwapValues
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.Wavelet
Gets an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.Normalize
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.Randomize
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.RemoveFolds
Gets an enumeration describing the available options..
listOptions() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.RemovePercentage
Gets an enumeration describing the available options..
listOptions() - Method in class weka.filters.unsupervised.instance.RemoveRange
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.Resample
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.ReservoirSample
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.SubsetByExpression
Returns an enumeration describing the available options.
listOptions() - Method in class weka.gui.Main
Gets an enumeration describing the available options.
ListSelectorDialog - Class in weka.gui
A dialog to present the user with a list of items, that the user can make a selection from, or cancel the selection.
ListSelectorDialog(Frame, JList) - Constructor for class weka.gui.ListSelectorDialog
Create the list selection dialog.
listStemmers() - Static method in class weka.core.stemmers.SnowballStemmer
returns an enumeration over all currently stored stemmer names.
listToArray(String) - Static method in class weka.core.CheckScheme
turns the comma-separated list into an array
listToArray(String) - Static method in class weka.core.TestInstances
turns the comma-separated list into an array
Literal - Class in weka.associations.tertius
 
Literal(Predicate, int, int) - Constructor for class weka.associations.tertius.Literal
 
LiteralSet - Class in weka.associations.tertius
Class representing a set of literals, being either the body or the head of a rule.
LiteralSet() - Constructor for class weka.associations.tertius.LiteralSet
Constructor for a set that does not store its counter-instances.
LiteralSet(Instances) - Constructor for class weka.associations.tertius.LiteralSet
Constructor initializing the set of counter-instances to all the instances.
LMT - Class in weka.classifiers.trees
Classifier for building 'logistic model trees', which are classification trees with logistic regression functions at the leaves.
LMT() - Constructor for class weka.classifiers.trees.LMT
Creates an instance of LMT with standard options
LMTNode - Class in weka.classifiers.trees.lmt
Class for logistic model tree structure.
LMTNode(ModelSelection, int, boolean, boolean, int, double, boolean) - Constructor for class weka.classifiers.trees.lmt.LMTNode
Constructor for logistic model tree node.
lnFactorial(int) - Method in class weka.classifiers.bayes.NaiveBayesMultinomial
Fast computation of ln(n!) for non-negative ints negative ints are passed on to the general gamma-function based version in weka.core.SpecialFunctions if the current n value is higher than any previous one, the cache is extended and filled to cover it the common case is reduced to a simple array lookup
lnFactorial(double) - Static method in class weka.core.SpecialFunctions
Returns natural logarithm of factorial using gamma function.
lnGamma(double) - Static method in class weka.core.Statistics
Returns natural logarithm of gamma function.
LNormTipText() - Method in class weka.filters.unsupervised.instance.Normalize
Returns the tip text for this property
lnsrch(double[], double[], double[], double, boolean[], double[][], Optimization.DynamicIntArray) - Method in class weka.core.Optimization
Find a new point x in the direction p from a point xold at which the value of the function has decreased sufficiently, the positive definiteness of B matrix (approximation of the inverse of the Hessian) is preserved and no bound constraints are violated.
load(InputStream) - Method in class weka.core.ProtectedProperties
Overrides a method to prevent the properties from being modified.
load(String) - Method in class weka.gui.beans.FlowRunner
Load a serialized KnowledgeFlow (either binary or xml)
loadBinary(String) - Method in class weka.gui.beans.FlowRunner
Load a binary serialized KnowledgeFlow
loadCache(ResultProducer, Object[]) - Method in class weka.experiment.DatabaseResultListener
Executes a database query to fill the key cache
loadClassifier() - Method in class weka.gui.explorer.ClassifierPanel
Loads a classifier
loadClusterer() - Method in class weka.gui.explorer.ClustererPanel
Loads a clusterer
Loader - Interface in weka.core.converters
Interface to something that can load Instances from an input source in some format.
Loader - Class in weka.gui.beans
Loads data sets using weka.core.converter classes
Loader() - Constructor for class weka.gui.beans.Loader
 
Loader - Class in weka.gui
This class is for loading resources from a JAR archive.
Loader(String) - Constructor for class weka.gui.Loader
initializes the object
LOADER_DIALOG - Static variable in class weka.gui.ConverterFileChooser
the loader dialog
LoaderBeanInfo - Class in weka.gui.beans
Bean info class for the loader bean
LoaderBeanInfo() - Constructor for class weka.gui.beans.LoaderBeanInfo
 
LoaderCustomizer - Class in weka.gui.beans
GUI Customizer for the loader bean
LoaderCustomizer() - Constructor for class weka.gui.beans.LoaderCustomizer
 
loadFile(String) - Method in class weka.gui.arffviewer.ArffViewerMainPanel
loads the specified file
loadFile() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
loads the specified file into the table
loadFromFile(String) - Static method in class weka.core.Debug
deserializes the content of the file and returns it, null if an error occurred.
loadHistory() - Method in class weka.gui.SimpleCLIPanel
loads the command history from the user's properties file.
loadHistory(boolean) - Method in class weka.gui.sql.SqlViewer
loads the history properties of the SqlViewer in the user's home directory.
loadIcons(String, String) - Method in class weka.gui.beans.BeanVisual
Loads static and animated versions of a beans icons.
loadInitialLayout(String) - Method in class weka.gui.beans.KnowledgeFlowApp
Loads the specified file at input Added by Zerbetto
loadInputProperties() - Method in class weka.gui.GenericPropertiesCreator
loads the property file containing the layout and the packages of the output-property-file.
loadModel() - Method in class weka.gui.beans.Classifier
 
loadModel() - Method in class weka.gui.beans.Clusterer
 
loadProperties() - Static method in class weka.gui.beans.KnowledgeFlowApp
Loads KnowledgeFlow properties and any plugins (adds jars to the classpath)
loadXML(String) - Method in class weka.gui.beans.FlowRunner
Load an XML serialized KnowledgeFlow
localDistributionForInstance(Instance, LBR.Indexes) - Method in class weka.classifiers.lazy.LBR
Calculates the class membership probabilities.
locallyPredictiveTipText() - Method in class weka.attributeSelection.CfsSubsetEval
Returns the tip text for this property
localModel() - Method in class weka.classifiers.rules.part.ClassifierDecList
Method just exists to make program easier to read.
localNaiveBayes(LBR.Indexes) - Method in class weka.classifiers.lazy.LBR
Class for building and using a simple Naive Bayes classifier.
LocalScoreSearchAlgorithm - Class in weka.classifiers.bayes.net.search.local
The ScoreBasedSearchAlgorithm class supports Bayes net structure search algorithms that are based on maximizing scores (as opposed to for example conditional independence based search algorithms).
LocalScoreSearchAlgorithm() - Constructor for class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
default constructor
LocalScoreSearchAlgorithm(BayesNet, Instances) - Constructor for class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
constructor
locate() - Method in class weka.core.AttributeLocator
sets up the structure
locateIndex(int) - Method in class weka.core.SparseInstance
Locates the greatest index that is not greater than the given index.
locateNode(int, int[]) - Method in class weka.classifiers.meta.nestedDichotomies.ND.NDTree
Locates the node with the given index (depth-first traversal).
log(String) - Method in class weka.classifiers.meta.GridSearch
prints the specified message to stdout if debug is on and can also dump the message to a log file
log(String, boolean) - Method in class weka.classifiers.meta.GridSearch
prints the specified message to stdout if debug is on and can also dump the message to a log file
log(String) - Method in class weka.core.Debug
prints the given message with level INFO
log(Level, String) - Method in class weka.core.Debug
prints the given message with the specified level and an empty sourceclass
log(Level, String, String) - Method in class weka.core.Debug
prints the given message with the specified level
log(Level, String, String, String) - Method in class weka.core.Debug
prints the given message with the specified level
Log() - Constructor for class weka.core.Debug.Log
default constructor, uses only stdout
Log(String) - Constructor for class weka.core.Debug.Log
creates a logger that logs into the specified file, if null then only stdout is used.
Log(String, int, int) - Constructor for class weka.core.Debug.Log
creates a logger that logs into the specified file, if null then only stdout is used.
log(Level, String) - Method in class weka.core.Debug.Log
logs the given message
log(Level, String, String) - Method in class weka.core.Debug.Log
prints the given message with the specified level
log(Level, String, String, String) - Method in class weka.core.Debug.Log
prints the given message with the specified level
log(String) - Method in class weka.core.Debug.SimpleLog
logs the given message to the file
log(Logger.Level, String) - Static method in class weka.core.logging.Logger
Logs the given message under the given level.
log(Logger.Level, Throwable) - Static method in class weka.core.logging.Logger
Logs the given message under the given level.
LOG - Static variable in interface weka.core.mathematicalexpression.sym
 
LOG - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
LOG2 - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
 
log2 - Static variable in class weka.classifiers.trees.j48.EntropyBasedSplitCrit
The log of 2.
log2 - Static variable in class weka.core.Utils
The natural logarithm of 2.
log2(double) - Static method in class weka.core.Utils
Returns the logarithm of a for base 2.
log2Binomial(double, double) - Static method in class weka.core.SpecialFunctions
Returns base 2 logarithm of binomial coefficient using gamma function.
log2Multinomial(double, double[]) - Static method in class weka.core.SpecialFunctions
Returns base 2 logarithm of multinomial using gamma function.
log2MultipleHypergeometric(double[][]) - Static method in class weka.core.ContingencyTables
Returns negative base 2 logarithm of multiple hypergeometric probability for a contingency table.
log_likelihood - Variable in class weka.classifiers.bayes.blr.Prior
 
log_posterior - Variable in class weka.classifiers.bayes.blr.Prior
 
logbinomialCoefficient(int, int) - Static method in class weka.associations.PriorEstimation
Method that calculates the base 2 logarithm of a binomial coefficient
logDensityForInstance(Instance) - Method in class weka.clusterers.AbstractDensityBasedClusterer
Computes the density for a given instance.
logDensityForInstance(Instance) - Method in interface weka.clusterers.DensityBasedClusterer
Computes the density for a given instance.
logDensityPerClusterForInstance(Instance) - Method in class weka.clusterers.AbstractDensityBasedClusterer
Computes the log of the conditional density (per cluster) for a given instance.
logDensityPerClusterForInstance(Instance) - Method in interface weka.clusterers.DensityBasedClusterer
Computes the log of the conditional density (per cluster) for a given instance.
logDensityPerClusterForInstance(Instance) - Method in class weka.clusterers.EM
Computes the log of the conditional density (per cluster) for a given instance.
logDensityPerClusterForInstance(Instance) - Method in class weka.clusterers.MakeDensityBasedClusterer
Computes the log of the conditional density (per cluster) for a given instance.
logFileTipText() - Method in class weka.classifiers.meta.GridSearch
Returns the tip text for this property
logFunc(double) - Method in class weka.classifiers.trees.j48.EntropyBasedSplitCrit
Help method for computing entropy.
Logger - Class in weka.core.logging
Abstract superclass for all loggers.
Logger() - Constructor for class weka.core.logging.Logger
Initializes the logger.
Logger - Interface in weka.gui
Interface for objects that display log (permanent historical) and status (transient) messages.
Logger.Level - Enum in weka.core.logging
The logging level.
Logistic - Class in weka.classifiers.functions
Class for building and using a multinomial logistic regression model with a ridge estimator.

There are some modifications, however, compared to the paper of leCessie and van Houwelingen(1992):

If there are k classes for n instances with m attributes, the parameter matrix B to be calculated will be an m*(k-1) matrix.

The probability for class j with the exception of the last class is

Pj(Xi) = exp(XiBj)/((sum[j=1..(k-1)]exp(Xi*Bj))+1)

The last class has probability

1-(sum[j=1..(k-1)]Pj(Xi))
= 1/((sum[j=1..(k-1)]exp(Xi*Bj))+1)

The (negative) multinomial log-likelihood is thus:

L = -sum[i=1..n]{
sum[j=1..(k-1)](Yij * ln(Pj(Xi)))
+(1 - (sum[j=1..(k-1)]Yij))
* ln(1 - sum[j=1..(k-1)]Pj(Xi))
} + ridge * (B^2)

In order to find the matrix B for which L is minimised, a Quasi-Newton Method is used to search for the optimized values of the m*(k-1) variables.
Logistic() - Constructor for class weka.classifiers.functions.Logistic
 
LogisticBase - Class in weka.classifiers.trees.lmt
Base/helper class for building logistic regression models with the LogitBoost algorithm.
LogisticBase() - Constructor for class weka.classifiers.trees.lmt.LogisticBase
Constructor that creates LogisticBase object with standard options.
LogisticBase(int, boolean, boolean) - Constructor for class weka.classifiers.trees.lmt.LogisticBase
Constructor to create LogisticBase object.
logisticLinkFunction(double) - Static method in class weka.classifiers.bayes.BayesianLogisticRegression
This method computes the values for the logistic link function.
LogitBoost - Class in weka.classifiers.meta
Class for performing additive logistic regression.
LogitBoost() - Constructor for class weka.classifiers.meta.LogitBoost
Constructor.
logJointDensitiesForInstance(Instance) - Method in class weka.clusterers.AbstractDensityBasedClusterer
Returns the logs of the joint densities for a given instance.
logJointDensitiesForInstance(Instance) - Method in interface weka.clusterers.DensityBasedClusterer
Returns the logs of the joint densities for a given instance.
LogLikelihood - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
Log-likelihood values to be used to choose the best hyperparameter.
logLikelihood() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
calculate the log likelihood on the validation data
logLikelihoodAfter() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
calculate the log likelihood on the validation data after adding the last model
logLikelihoodEstimate(int, Instance, double, int) - Method in class weka.clusterers.XMeans
Calculates the log-likelihood of the data for the given model, taken at the maximum likelihood point.
LOGLOSS - Static variable in class weka.classifiers.functions.SPegasos
 
logMessage(String) - Method in class weka.gui.beans.FlowRunner.SimpleLogger
 
logMessage(String) - Method in class weka.gui.beans.LogPanel
Sends the supplied message to the log area.
logMessage(String) - Method in class weka.gui.experiment.RunPanel
Sends the supplied message to the log panel log area.
logMessage(String) - Method in interface weka.gui.Logger
Sends the supplied message to the log area.
logMessage(String) - Method in class weka.gui.LogPanel
Sends the supplied message to the log area.
logMessage(String) - Method in class weka.gui.SysErrLog
Sends the supplied message to the log area.
LogPanel - Class in weka.gui.beans
Class for displaying a status area (made up of a variable number of lines) and a log area.
LogPanel() - Constructor for class weka.gui.beans.LogPanel
 
LogPanel - Class in weka.gui
This panel allows log and status messages to be posted.
LogPanel() - Constructor for class weka.gui.LogPanel
Creates the log panel with no task monitor and the log always visible.
LogPanel(WekaTaskMonitor) - Constructor for class weka.gui.LogPanel
Creates the log panel with a task monitor, where the log is hidden.
LogPanel(WekaTaskMonitor, boolean) - Constructor for class weka.gui.LogPanel
Creates the log panel, possibly with task monitor, where the log is optionally hidden.
LogPanel(WekaTaskMonitor, boolean, boolean, boolean) - Constructor for class weka.gui.LogPanel
Creates the log panel, possibly with task monitor, where the either the log is optionally hidden or the status (having both hidden is not allowed).
logPerformances(GridSearch.Grid, Vector<GridSearch.Performance>, Tag) - Method in class weka.classifiers.meta.GridSearch
generates a table string for all the performances in the grid and returns that.
logPerformances(GridSearch.Grid, Vector) - Method in class weka.classifiers.meta.GridSearch
aligns all performances in the grid and prints those tables to the log file.
LOGPI - Static variable in class weka.core.Statistics
 
logPSI - Static variable in class weka.core.matrix.Maths
The constant - log( sqrt(2 pi) )
logs2densities(int, Instance) - Method in class weka.filters.unsupervised.attribute.ClusterMembership
Converts logs back to density values.
logs2probs(double[]) - Static method in class weka.core.Utils
Converts an array containing the natural logarithms of probabilities stored in a vector back into probabilities.
logScore(int, int) - Method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes
Gets the log score contribution of this distribution
logScore(int) - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
logScore returns the log of the quality of a network (e.g.
logScore(int, int) - Method in interface weka.classifiers.bayes.net.search.local.Scoreable
Returns log-score
logSystemInfo() - Method in class weka.core.Debug.Log
a convenience method for dumping the current system info in the log file
logSystemInfo() - Method in class weka.core.Debug.SimpleLog
a convenience method for dumping the current system info in the log file
LogWindow - Class in weka.gui
Frame that shows the output from stdout and stderr.
LogWindow() - Constructor for class weka.gui.LogWindow
creates the frame
LogWindow.LogWindowPrintStream - Class in weka.gui
inner class for printing to the window, is used instead of standard System.out and System.err
LogWindowPrintStream(LogWindow, PrintStream, String) - Constructor for class weka.gui.LogWindow.LogWindowPrintStream
the constructor
LogWriter - Interface in weka.gui.beans
Interface to be implemented by classes that should be able to write their own output to the Weka logger.
LONG - Static variable in class weka.experiment.DatabaseUtils
Type mapping for LONG used for reading experiment results.
lookAheadInGoodDirectionsSearch(BayesNet, Instances, int, int) - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
lookAheadInGoodDirectionsSearch determines the network structure/graph of the network with best score according to LAGD Hill Climbing
LookAndFeel - Class in weka.gui
A little helper class for setting the Look and Feel of the user interface.
LookAndFeel() - Constructor for class weka.gui.LookAndFeel
 
LOOKANDFEEL_PROPERTIES - Static variable in class weka.gui.LookAndFeel
Contains the look and feel properties
lookupCacheSizeTipText() - Method in class weka.attributeSelection.BestFirst
Returns the tip text for this property
lookupCacheSizeTipText() - Method in class weka.attributeSelection.LinearForwardSelection
Returns the tip text for this property
lookupCacheSizeTipText() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Returns the tip text for this property
LOSS_STRING - Variable in class weka.experiment.ResultMatrix
loss string
lossFunctionTipText() - Method in class weka.classifiers.functions.SPegasos
Returns the tip text for this property
lossTipText() - Method in class weka.classifiers.functions.LibSVM
Returns the tip text for this property
LovinsStemmer - Class in weka.core.stemmers
A stemmer based on the Lovins stemmer, described here:

Julie Beth Lovins (1968).
LovinsStemmer() - Constructor for class weka.core.stemmers.LovinsStemmer
 
lowerBoundMinSupportTipText() - Method in class weka.associations.Apriori
Returns the tip text for this property
lowerBoundMinSupportTipText() - Method in class weka.associations.FPGrowth
Returns the tip text for this property
lowerCaseTokensTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Returns the tip text for this property.
lowerNumericBoundIsOpen() - Method in class weka.core.Attribute
Returns whether the lower numeric bound of the attribute is open.
lowerSizeTipText() - Method in class weka.experiment.LearningRateResultProducer
Returns the tip text for this property
LPAREN - Static variable in interface weka.core.mathematicalexpression.sym
 
LPAREN - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
lsqr(PaceMatrix, IntVector, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
QR transformation for a least squares problem
A x = b
implicitly both A and b are transformed.
lsqrSelection(PaceMatrix, IntVector, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
QR transformation for a least squares problem
A x = b
implicitly both A and b are transformed.
LT - Static variable in interface weka.core.mathematicalexpression.sym
 
LT - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
lu() - Method in class weka.core.matrix.Matrix
LU Decomposition
LUDecomposition - Class in weka.core.matrix
LU Decomposition.
LUDecomposition() - Method in class weka.core.Matrix
Deprecated.
Performs a LUDecomposition on the matrix.
LUDecomposition(Matrix) - Constructor for class weka.core.matrix.LUDecomposition
LU Decomposition
LWL - Class in weka.classifiers.lazy
Locally weighted learning.
LWL() - Constructor for class weka.classifiers.lazy.LWL
Constructor.

M

m - Variable in class weka.core.matrix.Matrix
Row and column dimensions.
M5Base - Class in weka.classifiers.trees.m5
M5Base.
M5Base() - Constructor for class weka.classifiers.trees.m5.M5Base
Constructor
M5P - Class in weka.classifiers.trees
M5Base.
M5P() - Constructor for class weka.classifiers.trees.M5P
Creates a new M5P instance.
M5Rules - Class in weka.classifiers.rules
Generates a decision list for regression problems using separate-and-conquer.
M5Rules() - Constructor for class weka.classifiers.rules.M5Rules
Constructor
m_ACC - Variable in class weka.classifiers.meta.GridSearch.Performance
the Accuracy
m_accuracy - Variable in class weka.associations.RuleItem
The expected predictive accuracy of a rule.
m_activationFunction - Variable in class weka.classifiers.pmml.consumer.NeuralNetwork
The activation function to use
m_ActiveIndices - Variable in class weka.core.NormalizableDistance
The boolean flags, whether an attribute will be used or not.
m_ActualClassifier - Variable in class weka.filters.supervised.attribute.AddClassification
The actual classifier used to do the classification.
m_ActualClusterer - Variable in class weka.classifiers.meta.ClassificationViaClustering
the actual cluster algorithm being used
m_ActualCount - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The number of train instances with no missing attribute values
m_ActualFilter - Variable in class weka.classifiers.functions.PLSClassifier
the actual filter to use
m_ActualFilter - Variable in class weka.filters.unsupervised.attribute.KernelFilter
for centering/standardizing the data (the actual filter to use)
m_ActualKernel - Variable in class weka.filters.unsupervised.attribute.KernelFilter
the Kernel which is actually used for computation
m_acuity - Variable in class weka.clusterers.Cobweb
Acuity (minimum standard deviation).
m_add - Variable in class weka.attributeSelection.RankSearch
add this many attributes in each iteration from the ranking
m_AddBut - Variable in class weka.gui.experiment.AlgorithmListPanel
Click to add an algorithm
m_AddBut - Variable in class weka.gui.experiment.DatasetListPanel
Click to add a dataset.
m_Additional - Variable in class weka.core.TechnicalInformation
additional technical informations
m_AdditionalMeasures - Variable in class weka.experiment.AveragingResultProducer
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.ClassifierSplitEvaluator
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.CrossValidationResultProducer
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.DatabaseResultProducer
The names of any additional measures to look for in SplitEvaluators
m_additionalMeasures - Variable in class weka.experiment.DensityBasedClustererSplitEvaluator
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.Experiment
Method names of additional measures of objects contained in the custom property iterator.
m_AdditionalMeasures - Variable in class weka.experiment.LearningRateResultProducer
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.RandomSplitResultProducer
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.RegressionSplitEvaluator
The names of any additional measures to look for in SplitEvaluators
m_addPointsButton - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_addRemovePointsButtonGroup - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_addRemovePointsPanel - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_ADNodes - Variable in class weka.classifiers.bayes.net.VaryNode
list of ADNode children
m_AdvanceDataSetFirst - Variable in class weka.experiment.Experiment
If true an experiment will advance the current data set befor any custom itererator
m_advanceDataSetFirst - Variable in class weka.gui.experiment.SetupPanel
Click to advacne data set before custom generator
m_advancedPanel - Variable in class weka.gui.experiment.SetupModePanel
The advanced setup panel
m_AdvancedSetupRBut - Variable in class weka.gui.experiment.SetupModePanel
The button for choosing advanced setup mode
m_advanceIteratorFirst - Variable in class weka.gui.experiment.SetupPanel
Click to advance custom generator before data set
m_AEEPanel - Variable in class weka.gui.explorer.AttributeSelectionPanel
The panel showing the current attribute evaluation method
m_ALF - Variable in class weka.core.Optimization
 
m_Algorithm - Variable in class weka.filters.supervised.attribute.PLSFilter
the type of algorithm
m_Algorithm - Variable in class weka.filters.unsupervised.attribute.Wavelet
the type of algorithm
m_AlgorithmListModel - Variable in class weka.gui.experiment.AlgorithmListPanel
The list model used
m_AlgorithmListPanel - Variable in class weka.gui.experiment.SimpleSetupPanel
The panel for configuring selected algorithms
m_algorithmName - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
 
m_algorithmName - Variable in class weka.classifiers.pmml.consumer.Regression
Description of the algorithm
m_AlgorithmStart - Variable in class weka.associations.GeneralizedSequentialPatterns
String indicating the starting time of the algorithm.
m_AlgorithmType - Variable in class weka.classifiers.mi.MILR
the type of processing
m_Alin - Variable in class weka.classifiers.functions.GaussianProcesses
The parameters of the linear transforamtion realized by the filter on the class attribute
m_Allowed - Variable in class weka.core.xml.PropertyHandler
lists for a class the properties allowed to use for setting and getting.
m_AllowedIndices - Variable in class weka.core.AttributeLocator
the attribute indices that may be inspected
m_AllowUnclassifiedInstances - Variable in class weka.classifiers.trees.RandomTree
Whether unclassified instances are allowed
m_AllSequentialPatterns - Variable in class weka.associations.GeneralizedSequentialPatterns
all generated frequent sequences, i.e.
m_allTheRules - Variable in class weka.associations.Apriori
The list of all generated rules.
m_allTheRules - Variable in class weka.associations.PredictiveApriori
The list of all generated rules.
m_alpha - Variable in class weka.classifiers.functions.SMO.BinarySMO
The Lagrange multipliers.
m_alpha - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
alpha and alpha* arrays containing weights for solving dual problem
m_Alpha - Variable in class weka.classifiers.functions.Winnow
The promotion coefficient
m_alpha - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
The Lagrange multipliers.
m_alpha - Variable in class weka.classifiers.trees.lmt.LMTNode
Alpha-value (for pruning) at the node
m_Alpha - Variable in class weka.classifiers.trees.SimpleCart
Alpha-value (for pruning) at the node.
m_alpha1 - Variable in class weka.classifiers.functions.supportVector.RegSMO
alpha value for first candidate
m_alpha1Star - Variable in class weka.classifiers.functions.supportVector.RegSMO
alpha* value for first candidate
m_alpha2 - Variable in class weka.classifiers.functions.supportVector.RegSMO
alpha value for second candidate
m_alpha2Star - Variable in class weka.classifiers.functions.supportVector.RegSMO
alpha* value for second candidate
m_alphaStar - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
 
m_altitude - Variable in class weka.classifiers.pmml.consumer.NeuralNetwork
Altitude for radial basis
m_alwaysDisplayPointsOfThisSize - Variable in class weka.gui.visualize.PlotData2D
If the shape size of a point equals this size then always plot it (i.e.
m_Amplitude - Variable in class weka.datagenerators.classifiers.regression.MexicanHat
the amplitude of y
m_AnalysisResults - Variable in class weka.estimators.CheckEstimator
The results of the analysis as a string
m_animatedIcon - Variable in class weka.gui.beans.BeanVisual
 
m_animatedIconPath - Variable in class weka.gui.beans.BeanVisual
Holds name (including path) of the animated icon
m_Antds - Variable in class weka.classifiers.rules.ConjunctiveRule
The vector of antecedents of this rule
m_Antds - Variable in class weka.classifiers.rules.JRip.RipperRule
The vector of antecedents of this rule
m_appendProbabilities - Variable in class weka.gui.beans.PredictionAppender
Append classifier's predicted probabilities (if the class is discrete and the classifier is a distribution classifier)
m_ApplyFilterBut - Variable in class weka.gui.explorer.PreprocessPanel
Click to apply filters and save the results
m_arffFileFilter - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_arffFileFilter - Variable in class weka.gui.experiment.ResultsPanel
ARFF file filter.
m_arffFileFilter - Variable in class weka.gui.experiment.SimpleSetupPanel
FIlter for choosing ARFF files
m_ArffFilter - Variable in class weka.gui.visualize.VisualizePanel
Filter to ensure only arff files are selected
m_ArffPanel - Variable in class weka.gui.ViewerDialog
the panel to display the Instances-object
m_ArffReader - Variable in class weka.core.converters.ArffLoader
The parser for the ARFF file
m_ArffViewers - Variable in class weka.gui.GUIChooser
keeps track of the opened ArffViewer instancs
m_ArrayEditor - Variable in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Allows editing of the custom property values
m_ArtSize - Variable in class weka.classifiers.meta.Decorate
Amount of artificial/random instances to use - specified as a fraction of the training data size.
m_as - Variable in class weka.gui.AttributeVisualizationPanel
This holds the attribute stats of the current attribute on display.
m_asCache - Variable in class weka.gui.AttributeVisualizationPanel
Cache of attribute stats info for the current data set
m_ASEPanel - Variable in class weka.gui.explorer.AttributeSelectionPanel
The panel showing the current search method
m_ASEval - Variable in class weka.attributeSelection.GreedyStepwise
 
m_AspectRatio - Variable in class weka.gui.visualize.PrintableComponent
the aspect ratio.
m_AspectRatioCheckBox - Static variable in class weka.gui.visualize.PrintableComponent
the checkbox for keeping the aspect ration.
m_Assignments - Variable in class weka.clusterers.SimpleKMeans
Assignments obtained
m_associatedConnections - Variable in class weka.gui.beans.MetaBean
 
m_Associator - Variable in class weka.associations.CheckAssociator
The associator to be examined
m_Associator - Variable in class weka.associations.SingleAssociatorEnhancer
The base associator to use
m_AssociatorEditor - Variable in class weka.gui.explorer.AssociationsPanel
Lets the user configure the associator
m_attIndex - Variable in class weka.classifiers.trees.lmt.ResidualSplit
The index of the attribute selected for the split
m_AttIndex - Variable in class weka.filters.unsupervised.attribute.AddValues
The attribute's index setting.
m_AttIndexes - Variable in class weka.classifiers.lazy.LBR.Indexes
the array attribute indexes
m_AttPanel - Variable in class weka.gui.explorer.PreprocessPanel
Panel to let the user toggle attributes
m_attrFilter - Variable in class weka.classifiers.meta.StackingC
Filter to transform metaData - Remove
m_attrib - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
The attribute itself.
m_attrib - Variable in class weka.gui.visualize.VisualizePanel
The panel that displays the attributes , using color to represent another attribute.
m_attribIndex - Variable in class weka.gui.AttributeVisualizationPanel
This holds the index of the current attribute on display and should be set through setAttribute(int idx).
m_attribIndex - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
The index for this attribute.
m_attribList - Variable in class weka.gui.visualize.MatrixPanel
The list for selecting the attributes to display the plot matrix
m_attribute - Variable in class weka.associations.FPGrowth.BinaryItem
The attribute that the item corresponds to
m_Attribute - Variable in class weka.classifiers.trees.BFTree
Attribute used for splitting.
m_attribute - Variable in class weka.classifiers.trees.lmt.ResidualSplit
The attribute selected for the split
m_Attribute - Variable in class weka.classifiers.trees.RandomTree
The attribute to split on.
m_Attribute - Variable in class weka.classifiers.trees.REPTree.Tree
The attribute to split on.
m_Attribute - Variable in class weka.classifiers.trees.SimpleCart
Attribute used to split data.
m_AttributeEvaluatorEditor - Variable in class weka.gui.explorer.AttributeSelectionPanel
Lets the user configure the attribute evaluator
m_AttributeFilter - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
Filter for removing class attribute, nominal attributes with 0 or 1 value.
m_attributeFilter - Variable in class weka.filters.unsupervised.attribute.RemoveType
The attribute filter used to do the filtering
m_AttributeIndices - Variable in class weka.core.NormalizableDistance
The range of attributes to use for calculating the distance.
m_AttributeIndices - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
the generated indices (only for performance reasons)
m_AttributeNameLab - Variable in class weka.gui.AttributeSummaryPanel
Displays the name of the relation
m_Attributes - Variable in class weka.classifiers.mi.CitationKNN
attribute name structure of the relational attribute
m_Attributes - Variable in class weka.classifiers.mi.MDD
All attribute names
m_Attributes - Variable in class weka.classifiers.mi.MIBoost
attributes name for the new dataset used to build the model
m_Attributes - Variable in class weka.classifiers.mi.MIDD
All attribute names
m_Attributes - Variable in class weka.classifiers.mi.MIEMDD
All attribute names
m_Attributes - Variable in class weka.classifiers.mi.MILR
All attribute names
m_Attributes - Variable in class weka.classifiers.mi.MINND
header info of the data
m_Attributes - Variable in class weka.core.AttributeLocator
contains the attribute locations, either true or false Boolean objects
m_Attributes - Variable in class weka.core.Instances
The attribute information.
m_attributes - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
attributes of this cluster
m_Attributes - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
the attribute range to work on
m_AttributeSearchEditor - Variable in class weka.gui.explorer.AttributeSelectionPanel
Lets the user configure the search method
m_AttributeSelection - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The attribute selection object
m_AttributeStats - Variable in class weka.classifiers.meta.Decorate
Attribute statistics - used for generating artificial examples.
m_AttributeStats - Variable in class weka.gui.AttributeSummaryPanel
Cached stats on the attributes we've summarized so far
m_AttributeTest - Variable in class weka.core.Capabilities
whether to perform attribute based tests
m_AttributeType - Variable in class weka.filters.unsupervised.attribute.Add
Record the type of attribute to insert.
m_AttributeTypeLab - Variable in class weka.gui.AttributeSummaryPanel
Displays the type of attribute
m_AttributeTypes - Variable in class weka.experiment.InstancesResultListener
Stores the attribute types for each column
m_AttributeTypes - Variable in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
attribute - attribute-type (constants from weka.core.Attribute) relation.
m_AttrIndex - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The index of the nominal attribute in the test and train instances
m_AttrIndex - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The index of the attribute in the test and train instances
m_AttrIndexRange - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
range of atttributes
m_attrIndices - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
global indices of the attributes of the cluster
m_AttStats - Variable in class weka.filters.unsupervised.attribute.RELAGGS
stores the attribute statistics att_index-att_index_in_rel_att <-> AttributeStats
m_AttSummaryPanel - Variable in class weka.gui.explorer.PreprocessPanel
Displays summary stats on the selected attribute
m_attTypeToDelete - Variable in class weka.filters.unsupervised.attribute.RemoveType
The type of attribute to delete
m_AttValues - Variable in class weka.core.Instance
The instance's attribute values.
m_AttVisualizePanel - Variable in class weka.gui.explorer.PreprocessPanel
The visualization of the attribute values
m_auxLocalModel - Variable in class weka.classifiers.trees.ft.FTtree
Auxiliary copy ClassifierSplitModel (for splitting)
M_AVERAGE - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
 
m_AverageProb - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
Average probability of test attribute transforming into train attribute
m_AverageProb - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
Average probability of test attribute transforming into train attribute
m_avg_target - Variable in class weka.classifiers.functions.GaussianProcesses
The training data.
m_axisChanged - Variable in class weka.gui.visualize.Plot2D
if the user changes attribute assigned to an axis
m_axisColour - Variable in class weka.gui.visualize.Plot2D
Default colour for the axis
m_axisPad - Variable in class weka.gui.visualize.Plot2D
Axis padding
m_b - Variable in class weka.classifiers.functions.SMO.BinarySMO
The thresholds.
m_b - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
offset
m_b - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
The thresholds.
m_Background - Variable in class weka.gui.Main.BackgroundDesktopPane
the actual background image.
m_Background - Variable in class weka.gui.visualize.BMPWriter
the background color
m_Background - Variable in class weka.gui.visualize.JPEGWriter
the background color.
m_Background - Variable in class weka.gui.visualize.PNGWriter
the background color
m_BackgroundColor - Variable in class weka.gui.beans.StripChart
the background color.
m_BackgroundColor - Variable in class weka.gui.MemoryUsagePanel
the background color.
m_BackgroundColor - Variable in class weka.gui.treevisualizer.TreeVisualizer
the background color.
m_backgroundColor - Variable in class weka.gui.visualize.AttributePanel
If set, it allows this panel to avoid setting a color in the color list that is equal to the background color
m_backgroundColor - Variable in class weka.gui.visualize.ClassPanel
if set, it allows this panel to steer away from setting up a color in the color list that is equal to the background color
m_backgroundColour - Variable in class weka.gui.visualize.Plot2D
Default colour for the plot background
m_backup - Variable in class weka.gui.beans.ClassifierCustomizer
Copy of the current classifier in case cancel is selected
m_Backup - Variable in class weka.gui.GenericObjectEditor
Holds a copy of the current object that can be reverted to if the user decides to cancel.
m_backward - Variable in class weka.attributeSelection.GreedyStepwise
Use a backwards search instead of a forwards one
m_backwardWithDelete - Variable in class weka.classifiers.rules.DTNB
 
m_bagger - Variable in class weka.classifiers.trees.RandomForest
The bagger.
m_BagRelAtts - Variable in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
Indices of relational attributes in the bag
m_BagRelAtts - Variable in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
Indices of relational attributes in the bag
m_BagSizePercent - Variable in class weka.classifiers.meta.Bagging
The size of each bag sample, as a percentage of the training size
m_BagSizePercent - Variable in class weka.classifiers.meta.MetaCost
The size of each bag sample, as a percentage of the training size
m_BagStringAtts - Variable in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
Indices of string attributes in the bag
m_BagStringAtts - Variable in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
Indices of string attributes in the bag
m_BalanceClass - Variable in class weka.datagenerators.classifiers.classification.Agrawal
whether to balance the class
m_Balanced - Variable in class weka.classifiers.functions.Winnow
Use the balanced variant?
m_barColour - Variable in class weka.gui.visualize.AttributePanel
The default colour to use for the background of the bars if a colour is not defined in Visualize.props
m_barRange - Variable in class weka.gui.AttributeVisualizationPanel
Contains the range of each bar in a histogram.
m_Base - Variable in class weka.core.neighboursearch.CoverTree
The base of our expansion constant.
m_BaseFormat - Variable in class weka.classifiers.meta.Stacking
Format for base data
m_BatchBuffer - Variable in class weka.core.converters.ConverterUtils.DataSource
the batch buffer.
m_BatchCounter - Variable in class weka.core.converters.ConverterUtils.DataSource
the instance counter for the batch case.
m_BayesNetGUIFrame - Variable in class weka.gui.GUIChooser
The frame containing the Bayes net GUI
m_bcSupport - Variable in class weka.gui.beans.AbstractDataSource
BeanContextChild support
m_bcSupport - Variable in class weka.gui.beans.CostBenefitAnalysis
BeanContextChild support
m_bcSupport - Variable in class weka.gui.beans.DataVisualizer
BeanContextChild support
m_bcSupport - Variable in class weka.gui.beans.GraphViewer
BeanContextChild support
m_bcSupport - Variable in class weka.gui.beans.KnowledgeFlowApp
 
m_bcSupport - Variable in class weka.gui.beans.ModelPerformanceChart
BeanContextChild support
m_bcSupport - Variable in class weka.gui.beans.TextViewer
BeanContextChild support
m_bDebug - Variable in class weka.clusterers.HierarchicalClusterer
Whether the classifier is run in debug mode.
m_bDistanceIsBranchLength - Variable in class weka.clusterers.HierarchicalClusterer
Whether the distance represent node height (if false) or branch length (if true).
m_BeanConnectionRelation - Variable in class weka.gui.beans.xml.XMLBeans
the relation between Bean and connection, MetaBean BeanConnections are stored under the reference of the MetaBean, regular connections are stored under REGULAR_CONNECTION.
m_beanContext - Variable in class weka.gui.beans.AbstractDataSource
BeanContex that this bean might be contained within
m_beanContext - Variable in class weka.gui.beans.CostBenefitAnalysis
BeanContex that this bean might be contained within
m_beanContext - Variable in class weka.gui.beans.DataVisualizer
BeanContex that this bean might be contained within
m_beanContext - Variable in class weka.gui.beans.GraphViewer
BeanContex that this bean might be contained within
m_beanContext - Variable in class weka.gui.beans.ModelPerformanceChart
BeanContex that this bean might be contained within
m_beanContext - Variable in class weka.gui.beans.TextViewer
BeanContex that this bean might be contained within
m_BeanContextSupport - Variable in class weka.gui.beans.xml.XMLBeans
the beancontext to use for loading from XML and the beancontext is null in the bean
m_BeanInstances - Variable in class weka.gui.beans.xml.XMLBeans
keeps track of the BeanInstances read so far, used for the BeanConnections
m_BeanInstancesID - Variable in class weka.gui.beans.xml.XMLBeans
keeps track of the BeanInstances read so far, used for the BeanConnections
m_BeanLayout - Variable in class weka.gui.beans.xml.XMLBeans
the component that manages the layout of the beans
m_beans - Variable in class weka.gui.beans.FlowRunner
The potential flow(s) to execute
m_benefitR - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_best - Variable in class weka.associations.PredictiveApriori
The n best rules.
m_best - Variable in class weka.associations.RuleGeneration
The list of the actual n best rules.
m_best_group - Variable in class weka.attributeSelection.GreedyStepwise
the best subset found
m_bestChanged - Variable in class weka.associations.PredictiveApriori
Flag keeping track if the list of the n best rules has changed.
m_BestClassifier - Variable in class weka.classifiers.meta.GridSearch
the Classifier with the best setup
m_BestClassifierOptions - Variable in class weka.classifiers.meta.CVParameterSelection
The set of all classifier options as determined by cross-validation
m_bestCommittee - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The current best committee
m_BestFilter - Variable in class weka.classifiers.meta.GridSearch
the Filter with the best setup
m_bestMerit - Variable in class weka.attributeSelection.BestFirst
holds the merit of the best subset found
m_bestMerit - Variable in class weka.attributeSelection.GreedyStepwise
the merit of the best subset found
m_bestMerit - Variable in class weka.attributeSelection.LinearForwardSelection
holds the merit of the best subset found
m_bestMerit - Variable in class weka.attributeSelection.ScatterSearchV1
holds the merit of the best subset found
m_bestMerit - Variable in class weka.attributeSelection.SubsetSizeForwardSelection
holds the merit of the best subset found
m_BestPerformance - Variable in class weka.classifiers.meta.CVParameterSelection
The cross-validated performance of the best options
m_BestThreshold - Variable in class weka.classifiers.meta.ThresholdSelector
The threshold that lead to the best performance
m_BestValue - Variable in class weka.classifiers.meta.ThresholdSelector
The best value that has been observed
m_Beta - Variable in class weka.classifiers.functions.Winnow
The demotion coefficient
m_Beta - Variable in class weka.classifiers.mi.MIBoost
Voting weights of models
m_BETA - Variable in class weka.core.Optimization
 
m_Betas - Variable in class weka.classifiers.meta.AdaBoostM1
Array for storing the weights for the votes.
m_Bias - Variable in class weka.classifiers.BVDecompose
The calculated bias (squared)
m_Bias - Variable in class weka.classifiers.functions.LibLINEAR
bias term value
m_bias - Variable in class weka.classifiers.misc.VFI
Bias towards more confident intervals
m_BiasToUniformClass - Variable in class weka.filters.supervised.instance.Resample
The degree of bias towards uniform (nominal) class distribution.
m_Bic - Variable in class weka.clusterers.XMeans
BIC-Score of the current model.
m_binaryFilter - Variable in class weka.gui.beans.Classifier
 
m_bInitAsNaiveBayes - Variable in class weka.classifiers.bayes.net.search.SearchAlgorithm
determines whether initial structure is an empty graph or a Naive Bayes network
m_bins - Variable in class weka.core.pmml.Discretize
The bins for this discretization
m_BinValue - Variable in class weka.clusterers.XMeans
Distance value between true and false of binary attributes and "same" and "different" of nominal attributes (default = 1.0).
m_BlendFactor - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
default sphere of influence blend setting
m_BlendFactor - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
default sphere of influence blend setting
m_BlendMethod - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
B_SPHERE = use specified blend, B_ENTROPY = entropic blend setting
m_BlendMethod - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
0 = use specified blend, 1 = entropic blend setting
m_BlendMethod - Variable in class weka.classifiers.lazy.KStar
0 = use specified blend, 1 = entropic blend setting
m_Blin - Variable in class weka.classifiers.functions.GaussianProcesses
 
m_block - Variable in class weka.gui.beans.Classifier
true if we should block any further training data sets.
m_bLow - Variable in class weka.classifiers.functions.SMO.BinarySMO
The thresholds.
m_bLow - Variable in class weka.classifiers.functions.supportVector.RegSMOImproved
b.up and b.low boundaries used to determine stopping criterion
m_bLow - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
The thresholds.
m_bMarkovBlanketClassifier - Variable in class weka.classifiers.bayes.net.search.SearchAlgorithm
Determines whether after structure is found a MarkovBlanketClassifier correction should be applied If this is true, m_bInitAsNaiveBayes is overridden and interpreted as false.
m_bModelBuilt - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
flag to indicate whether the model is built yet
m_booleanCols - Variable in class weka.datagenerators.ClusterGenerator
Stores which columns are boolean (default numeric)
m_boostedModel - Variable in class weka.classifiers.functions.SimpleLogistic
The actual logistic regression model
m_boostingIterations - Variable in class weka.classifiers.trees.ADTree
Option - the number of boosting iterations o perform
m_boostingIterations - Variable in class weka.classifiers.trees.LADTree
 
m_boundaryPanel - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
the plotting panel
m_BoundaryVisualizerFrame - Variable in class weka.gui.GUIChooser
The frame containing the boundary visualizer
m_BrowseDestinationButton - Variable in class weka.gui.experiment.SimpleSetupPanel
Button for browsing destination files
m_Buffer - Variable in class weka.core.converters.LibSVMLoader
the buffer of the rows read so far.
m_Buffer - Variable in class weka.core.converters.SVMLightLoader
the buffer of the rows read so far.
m_Builder - Variable in class weka.core.xml.XMLDocument
the instance of a DocumentBuilder.
m_bUp - Variable in class weka.classifiers.functions.SMO.BinarySMO
The thresholds.
m_bUp - Variable in class weka.classifiers.functions.supportVector.RegSMOImproved
b.up and b.low boundaries used to determine stopping criterion
m_bUp - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
The thresholds.
m_bUseK2Prior - Variable in class weka.classifiers.bayes.net.estimate.BMAEstimator
whether to use K2 prior
m_bUseK2Prior - Variable in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
whether to use K2 prior
m_Button - Variable in class weka.gui.arffviewer.ArffTable.RelationalCellEditor
the button for opening the dialog
m_ButtonCancel - Variable in class weka.gui.sql.SqlViewerDialog
the Cancel button
m_ButtonClear - Variable in class weka.gui.LogWindow
the clear button
m_ButtonClear - Variable in class weka.gui.sql.InfoPanel
the button to clear the area
m_ButtonClear - Variable in class weka.gui.sql.QueryPanel
the clear button.
m_ButtonClose - Variable in class weka.gui.LogWindow
the close button
m_ButtonClose - Variable in class weka.gui.sql.ResultPanel
the close button
m_ButtonCloseAll - Variable in class weka.gui.sql.ResultPanel
the close all button
m_ButtonConnect - Variable in class weka.gui.sql.ConnectionPanel
the button for connecting to the database.
m_ButtonCopy - Variable in class weka.gui.sql.InfoPanel
the button to copy the selected message
m_ButtonCopyQuery - Variable in class weka.gui.sql.ResultPanel
the button that copies the query into the QueryPanel
m_ButtonDatabase - Variable in class weka.gui.sql.ConnectionPanel
the button for the DB-Dialog.
m_ButtonExecute - Variable in class weka.gui.sql.QueryPanel
the execute button.
m_ButtonGC - Variable in class weka.gui.MemoryUsagePanel
the button for running the garbage collector.
m_ButtonHistory - Variable in class weka.gui.sql.ConnectionPanel
the button for the history.
m_ButtonHistory - Variable in class weka.gui.sql.QueryPanel
the history button.
m_ButtonOK - Variable in class weka.gui.sql.SqlViewerDialog
the OK button
m_ButtonOptWidth - Variable in class weka.gui.sql.ResultPanel
the button for the optimal column width of the current table
m_C - Variable in class weka.classifiers.functions.GaussianProcesses
The covariance matrix.
m_C - Variable in class weka.classifiers.functions.SMO
The complexity parameter.
m_C - Variable in class weka.classifiers.functions.SMOreg
capacity parameter
m_C - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
capacity parameter, copied from SMOreg
m_C - Variable in class weka.classifiers.mi.MISMO
The complexity parameter.
m_C - Variable in class weka.classifiers.mi.MISVM
The complexity parameter.
m_Cache - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
A cache for storing attribute values and their corresponding stop parameters
m_Cache - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
A cache for storing attribute values and their corresponding scale parameters
m_Cache - Variable in class weka.classifiers.lazy.KStar
A custom data structure for caching distinct attribute values and their scale factor or stop parameter.
m_Cache - Variable in class weka.classifiers.meta.GridSearch
the cache for points in the grid that got calculated
m_Cache - Variable in class weka.classifiers.meta.GridSearch.PerformanceCache
the cache for points in the grid that got calculated
m_Cache - Static variable in class weka.core.ClassDiscovery
for caching queries (classname+packagename <-> Vector with classnames).
m_Cache - Variable in class weka.experiment.DatabaseResultListener
Stores the cached values
m_cached - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
The x position of each point.
m_cacheHits - Variable in class weka.classifiers.functions.supportVector.CachedKernel
Counts the number of kernel cache hits.
m_CacheKey - Variable in class weka.experiment.DatabaseResultListener
Stores the key for which the cache is valid
m_CacheKeyIndex - Variable in class weka.experiment.DatabaseResultListener
Stores the index of the key column holding the cache key data
m_CacheKeyName - Variable in class weka.experiment.DatabaseResultListener
Holds the name of the key field to cache upon, or null if no caching
m_cacheSize - Variable in class weka.attributeSelection.BestFirst
holds the maximum size of the lookup cache for evaluated subsets
m_cacheSize - Variable in class weka.attributeSelection.LinearForwardSelection
holds the maximum size of the lookup cache for evaluated subsets
m_cacheSize - Variable in class weka.attributeSelection.SubsetSizeForwardSelection
holds the maximum size of the lookup cache for evaluated subsets
m_CacheSize - Variable in class weka.classifiers.functions.LibSVM
in MB
m_cacheSize - Variable in class weka.classifiers.functions.supportVector.CachedKernel
The size of the cache (a prime number)
m_cacheSlots - Variable in class weka.classifiers.functions.supportVector.CachedKernel
number of cache slots in an entry
m_CalcOutOfBag - Variable in class weka.classifiers.meta.Bagging
Whether to calculate the out of bag error
m_calculatedNumToSelect - Variable in class weka.attributeSelection.GreedyStepwise
 
m_CalculateStdDevs - Variable in class weka.experiment.AveragingResultProducer
True if standard deviation fields should be produced
m_cancel - Variable in class weka.gui.visualize.VisualizePanel
Button for the user to remove all splits.
m_cancelBut - Variable in class weka.gui.GenericObjectEditor.GOEPanel
cancel button.
m_CancelBut - Variable in class weka.gui.ListSelectorDialog
Click to cancel the property selection
m_CancelBut - Variable in class weka.gui.PropertySelectorDialog
Click to cancel the property selection
m_CancelButton - Variable in class weka.gui.experiment.OutputFormatDialog
Click to cancel the dialog.
m_CancelButton - Variable in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
the Cancel button.
m_CancelButton - Variable in class weka.gui.ViewerDialog
Click to cancel the dialog
m_canChangeClassInDialog - Variable in class weka.gui.GenericObjectEditor
whether the class can be changed.
m_CanMeasureCpuTime - Variable in class weka.core.Debug.Clock
whether the system can measure the CPU time
m_Capabilities - Variable in class weka.core.Capabilities
the hashset for storing the active capabilities
m_Capabilities - Variable in class weka.core.FindWithCapabilities
the capabilities to look for.
m_Capabilities - Variable in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
the capabilities used for initializing the dialog.
m_Capabilities - Variable in class weka.gui.GenericObjectEditor.GOETreeNode
the Capabilities object to use for filtering.
m_CapabilitiesFilter - Variable in class weka.gui.ConverterFileChooser
the Capabilities filter for the savers
m_CapabilitiesFilter - Variable in class weka.gui.GenericObjectEditor
for filtering the tree based on the Capabilities of the leaves.
m_CapabilitiesFilterChangeListeners - Variable in class weka.gui.explorer.Explorer
the listeners that listen to filter changes
m_car - Variable in class weka.associations.Apriori
Flag indicating whether class association rules are mined.
m_car - Variable in class weka.associations.PredictiveApriori
Flag indicating whether class association rules are mined.
m_CARs - Variable in class weka.associations.PriorEstimation
Flag indicating whether standard association rules or class association rules are mined.
m_castInteger - Variable in class weka.core.pmml.TargetMetaInfo
cast integers (default no casting)
m_categoricalConst - Variable in class weka.core.pmml.Constant
 
m_CC - Variable in class weka.classifiers.meta.GridSearch.Performance
the Correlation coefficient
m_Center - Variable in class weka.classifiers.mi.MIOptimalBall
center of the optimal ball
m_centerFilter - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
Filter for centering the data
m_CenterInput - Variable in class weka.clusterers.XMeans
input file for the cluster centers.
m_CenterOutput - Variable in class weka.clusterers.XMeans
output file for the cluster centers.
m_centroidClasses - Variable in class weka.datagenerators.classifiers.classification.RandomRBF
the classes of the centroids
m_centroids - Variable in class weka.datagenerators.classifiers.classification.RandomRBF
the centroids
m_centroidStdDevs - Variable in class weka.datagenerators.classifiers.classification.RandomRBF
the stddevs of the centroids
m_centroidWeights - Variable in class weka.datagenerators.classifiers.classification.RandomRBF
the weights of the centroids
m_CEPanel - Variable in class weka.gui.explorer.AssociationsPanel
The panel showing the current associator selection
m_CEPanel - Variable in class weka.gui.explorer.ClassifierPanel
The panel showing the current classifier selection
m_CF - Variable in class weka.classifiers.trees.ft.FTtree
Confidence level
m_change - Variable in class weka.associations.RuleGeneration
Flag indicating whether the list fo the best rules has changed.
m_charSet - Variable in class weka.core.converters.TextDirectoryLoader
The charset to use when loading text files (default is to just use the default charset).
m_CheckBoxWordwrap - Variable in class weka.gui.LogWindow
whether to allow wordwrap or not
m_checkForLowerCaseNames - Variable in class weka.experiment.DatabaseUtils
For databases where Tables and Columns are created in lower case.
m_checkForUpperCaseNames - Variable in class weka.experiment.DatabaseUtils
For databases where Tables and Columns are created in upper case.
m_checksTurnedOff - Variable in class weka.classifiers.functions.GaussianProcesses
Turn off all checks and conversions? Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a numeric class.
m_checksTurnedOff - Variable in class weka.classifiers.functions.SMO
Turn off all checks and conversions? Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a nominal class.
m_ChecksTurnedOff - Variable in class weka.classifiers.functions.supportVector.Kernel
Turns off all checks
m_checksTurnedOff - Variable in class weka.classifiers.mi.MISMO
Turn off all checks and conversions? Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a nominal class.
m_checksTurnedOff - Variable in class weka.filters.unsupervised.attribute.KernelFilter
Turn off all checks and conversions? Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a nominal class.
m_ChildFrames - Variable in class weka.gui.GUIChooser
contains the child frames (title <-> object).
m_ChildFrames - Variable in class weka.gui.Main
contains the child frames (title <-> object).
m_ChildPropertySheet - Variable in class weka.gui.GenericObjectEditor.GOEPanel
The component that performs classifier customization.
m_children - Variable in class weka.associations.FPGrowth.FPTreeNode
the children of this node
m_children - Variable in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode
 
m_Children - Variable in class weka.core.Trie.TrieNode
for fast access to the children
m_chunkSize - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_cIndex - Variable in class weka.gui.visualize.AttributePanel
 
m_cIndex - Variable in class weka.gui.visualize.Plot2D
 
m_cIndex - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
 
m_Citers - Variable in class weka.classifiers.mi.CitationKNN
C nearest citers
m_CitersDebug - Variable in class weka.classifiers.mi.CitationKNN
 
m_class - Variable in class weka.classifiers.functions.SMO.BinarySMO
The transformed class values.
m_Class - Variable in class weka.classifiers.mi.MINND
The class label of each exemplar
m_class - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
The transformed class values.
m_classAttBox - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_classAttrib - Variable in class weka.gui.visualize.MatrixPanel
The combo box to allow user to select the colouring attribute
m_classAttribute - Variable in class weka.classifiers.functions.SMO
The class attribute
m_ClassAttribute - Variable in class weka.classifiers.meta.LogitBoost
The actual class attribute (for getting class names)
m_ClassAttribute - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The actual class attribute (for getting class names)
m_classAttribute - Variable in class weka.classifiers.mi.MISMO
The class attribute
m_ClassAttribute - Variable in class weka.classifiers.trees.BFTree
Class attribute of a dataset.
m_ClassAttribute - Variable in class weka.classifiers.trees.SimpleCart
Class attriubte of data.
m_classAttribute - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
The class attribute from the data that was used to generate the threshold curve
m_ClassCombo - Variable in class weka.gui.explorer.AttributeSelectionPanel
Lets the user select the class column
m_ClassCombo - Variable in class weka.gui.explorer.ClassifierPanel
Lets the user select the class column
m_ClassCombo - Variable in class weka.gui.explorer.ClustererPanel
Lets the user select the class column for classes to clusters based evaluation
m_ClassDistribution - Variable in class weka.classifiers.bayes.NaiveBayes
The class estimator.
m_ClassDistribution - Variable in class weka.classifiers.trees.RandomTree
Class probabilities from the training data.
m_Classes - Variable in class weka.classifiers.mi.CitationKNN
Class labels for each bag
m_Classes - Variable in class weka.classifiers.mi.MDD
Class labels for each bag
m_Classes - Variable in class weka.classifiers.mi.MIBoost
Class labels for each bag
m_Classes - Variable in class weka.classifiers.mi.MIDD
Class labels for each bag
m_Classes - Variable in class weka.classifiers.mi.MIEMDD
Class labels for each bag
m_Classes - Variable in class weka.classifiers.mi.MILR
Class labels for each bag
m_ClassesToClustersBut - Variable in class weka.gui.explorer.ClustererPanel
Click to set test mode to classes to clusters based evaluation
m_ClassFirst - Variable in class weka.experiment.Experiment
True if the class attribute is the first attribute for all datasets involved in this experiment.
m_ClassFirst - Variable in class weka.gui.experiment.Experimenter
True if the class attribute is the first attribute for all datasets involved in this experiment.
m_ClassFlag - Variable in class weka.datagenerators.ClusterGenerator
class flag
m_classificationAccV - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
Classification accuracy
m_Classifier - Variable in class weka.classifiers.BVDecompose
An instantiated base classifier used for getting and testing options.
m_Classifier - Variable in class weka.classifiers.BVDecomposeSegCVSub
An instantiated base classifier used for getting and testing options.
m_Classifier - Variable in class weka.classifiers.CheckClassifier
The classifier to be examined
m_Classifier - Variable in class weka.classifiers.CheckSource
the classifier used for generating the source code
m_Classifier - Variable in class weka.classifiers.meta.MultiScheme
The classifier that had the best performance on training data.
m_Classifier - Variable in class weka.classifiers.SingleClassifierEnhancer
The base classifier to use
m_Classifier - Variable in class weka.experiment.ClassifierSplitEvaluator
The classifier used for evaluation
m_Classifier - Variable in class weka.experiment.RegressionSplitEvaluator
The classifier used for evaluation
m_Classifier - Variable in class weka.filters.supervised.attribute.AddClassification
The classifier template used to do the classification.
m_classifier - Variable in class weka.gui.beans.BatchClassifierEvent
The classifier
m_classifier - Variable in class weka.gui.beans.IncrementalClassifierEvent
 
m_classifier - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
distribution classifier to use
m_classifierEditor - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_ClassifierEditor - Variable in class weka.gui.experiment.AlgorithmListPanel
Lets the user configure the classifier
m_ClassifierEditor - Variable in class weka.gui.explorer.ClassifierPanel
Lets the user configure the classifier
m_ClassifierIndex - Variable in class weka.classifiers.meta.MultiScheme
The index into the vector for the selected scheme
m_ClassifierOptions - Variable in class weka.classifiers.BVDecompose
The options to be passed to the base classifier.
m_ClassifierOptions - Variable in class weka.classifiers.BVDecomposeSegCVSub
The options to be passed to the base classifier.
m_ClassifierOptions - Variable in class weka.classifiers.meta.CVParameterSelection
The base classifier options (not including those being set by cross-validation)
m_ClassifierOptions - Variable in class weka.experiment.ClassifierSplitEvaluator
The classifier options (if any)
m_ClassifierOptions - Variable in class weka.experiment.RegressionSplitEvaluator
The classifier options (if any)
m_ClassifierPanel - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_classifiers - Variable in class weka.classifiers.functions.SMO
The binary classifier(s)
m_Classifiers - Variable in class weka.classifiers.IteratedSingleClassifierEnhancer
Array for storing the generated base classifiers.
m_Classifiers - Variable in class weka.classifiers.meta.LogitBoost
Array for storing the generated base classifiers.
m_classifiers - Variable in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
The hashtable for this node.
m_classifiers - Variable in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
The hashtable for this node.
m_classifiers - Variable in class weka.classifiers.meta.nestedDichotomies.ND
The hashtable containing all the classifiers
m_classifiers - Variable in class weka.classifiers.mi.MISMO
The binary classifier(s)
m_Classifiers - Variable in class weka.classifiers.MultipleClassifiersCombiner
Array for storing the generated base classifiers.
m_ClassifierVersion - Variable in class weka.experiment.ClassifierSplitEvaluator
The classifier version
m_ClassifierVersion - Variable in class weka.experiment.RegressionSplitEvaluator
The classifier version
m_ClassifyIterations - Variable in class weka.classifiers.BVDecomposeSegCVSub
The number of times an instance is classified
m_classIndex - Variable in class weka.associations.Apriori
The class index.
m_ClassIndex - Variable in class weka.associations.FilteredAssociator
The class index.
m_classIndex - Variable in class weka.associations.PredictiveApriori
The class index.
m_classIndex - Variable in class weka.attributeSelection.BestFirst
holds the class index
m_classIndex - Variable in class weka.attributeSelection.GreedyStepwise
holds the class index
m_classIndex - Variable in class weka.attributeSelection.LinearForwardSelection
holds the class index
m_ClassIndex - Variable in class weka.classifiers.BVDecompose
The index of the class attribute
m_ClassIndex - Variable in class weka.classifiers.BVDecomposeSegCVSub
The index of the class attribute
m_ClassIndex - Variable in class weka.classifiers.CheckSource
the class index
m_classIndex - Variable in class weka.classifiers.functions.GaussianProcesses
The class index from the training data
m_ClassIndex - Variable in class weka.classifiers.functions.Logistic
The index of the class attribute
m_classIndex - Variable in class weka.classifiers.functions.SMO
The class index from the training data
m_classIndex - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
index of class variable in data set
m_ClassIndex - Variable in class weka.classifiers.lazy.LBR.Indexes
the Class Index for the data set
m_ClassIndex - Variable in class weka.classifiers.mi.CitationKNN
The index of the class attribute
m_ClassIndex - Variable in class weka.classifiers.mi.MDD
The index of the class attribute
m_ClassIndex - Variable in class weka.classifiers.mi.MIDD
The index of the class attribute
m_ClassIndex - Variable in class weka.classifiers.mi.MIEMDD
The index of the class attribute
m_classIndex - Variable in class weka.classifiers.mi.MISMO
The class index from the training data
m_ClassIndex - Variable in class weka.classifiers.misc.HyperPipes
The index of the class attribute
m_ClassIndex - Variable in class weka.classifiers.misc.VFI
The index of the class attribute
m_ClassIndex - Variable in class weka.core.converters.LibSVMSaver
the class index
m_ClassIndex - Variable in class weka.core.converters.SVMLightSaver
the class index.
m_ClassIndex - Variable in class weka.core.converters.XRFFSaver
the class index
m_ClassIndex - Variable in class weka.core.FindWithCapabilities
the class index, in case the capabilities are based on a file.
m_ClassIndex - Variable in class weka.core.Instances
The class attribute's index
m_ClassIndex - Variable in class weka.core.TestInstances
the class index (-1 is LAST, -2 means no class)
m_ClassIndex - Variable in class weka.filters.CheckSource
the class index
m_ClassIndex - Variable in class weka.filters.unsupervised.attribute.ClassAssigner
the class index.
m_ClassIndex - Variable in class weka.filters.unsupervised.attribute.PotentialClassIgnorer
Storing the class index
m_ClassIndex - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
Class index.
m_classIndex - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
The attribute to treat as the class for purposes of cleansing.
m_classIndex - Variable in class weka.gui.AttributeVisualizationPanel
Contains the current class index.
m_ClassIsNominal - Variable in class weka.classifiers.Evaluation
Is the class nominal or numeric?
m_classIsNominal - Variable in class weka.classifiers.rules.DecisionTable
Class is nominal
m_classLabel - Variable in class weka.associations.LabeledItemSet
The class label.
m_ClassMean - Variable in class weka.filters.supervised.attribute.PLSFilter
the mean of the class
m_ClassMeans - Variable in class weka.classifiers.meta.RegressionByDiscretization
The mean values for each Discretized class interval.
m_ClassMode - Variable in class weka.classifiers.meta.ThresholdSelector
Method to determine which class to optimize for
m_Classname - Variable in class weka.core.Javadoc
the classname
m_Classname - Variable in class weka.core.ListOptions
the classname
m_ClassNameLabel - Variable in class weka.gui.GenericObjectEditor.GOEPanel
The name of the current class.
m_ClassnameOverride - Variable in class weka.core.xml.XMLSerialization
for overriding class names (Class <-> Classname (String))
m_ClassNames - Variable in class weka.classifiers.evaluation.ConfusionMatrix
Stores the names of the classes
m_ClassNames - Variable in class weka.classifiers.Evaluation
The names of the classes.
m_classPanel - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_classPanel - Variable in class weka.gui.visualize.VisualizePanel
The panel that displays the legend for the colouring attribute
m_ClasspathProblems - Variable in class weka.core.CheckScheme
whether classpath problems occurred
m_ClasspathProblems - Variable in class weka.estimators.CheckEstimator
whether classpath problems occurred
m_classPriorCounts - Variable in class weka.classifiers.rules.DecisionTable
The class priors to use when there is no match in the table
m_ClassPriors - Variable in class weka.classifiers.Evaluation
The prior probabilities of the classes
m_classPriors - Variable in class weka.classifiers.rules.DecisionTable
 
m_ClassPriorsSum - Variable in class weka.classifiers.Evaluation
The sum of counts for priors
m_ClassProbs - Variable in class weka.classifiers.trees.BFTree
Class probabilities.
m_ClassProbs - Variable in class weka.classifiers.trees.REPTree.Tree
Class probabilities from the training data in the nominal case.
m_ClassProbs - Variable in class weka.classifiers.trees.SimpleCart
Class probabilities.
m_ClassStdDev - Variable in class weka.filters.supervised.attribute.PLSFilter
the standard deviation of the class
m_classSurround - Variable in class weka.gui.visualize.VisualizePanel
Panel that surrounds the class panel with a titled border
m_ClassType - Variable in class weka.classifiers.lazy.IBk
The class attribute type.
m_ClassType - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The class attribute type
m_ClassType - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The class attribute type
m_ClassType - Variable in class weka.classifiers.lazy.KStar
The class attribute type
m_ClassType - Variable in class weka.core.TestInstances
the class type
m_ClassType - Variable in class weka.gui.GenericObjectEditor
The Class of objects being edited.
m_ClassValue - Variable in class weka.classifiers.trees.BFTree
Class value for a node.
m_ClassValue - Variable in class weka.classifiers.trees.SimpleCart
Class value if the node is leaf.
m_classValueIndex - Variable in class weka.estimators.Estimator
The class value index is > -1 if subset is taken with specific class value only
m_ClassValueIndex - Variable in class weka.filters.supervised.instance.SMOTE
the index of the class value.
m_classValueSelector - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_cleansingClassifier - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
The classifier used to do the cleansing
m_Clock - Variable in class weka.core.Debug
for clocking
m_CloseBut - Variable in class weka.gui.SetInstancesPanel
Click to close the dialog
m_CloseButPanel - Variable in class weka.gui.SetInstancesPanel
the panel the Close-Button is located in
m_CloseTo - Variable in class weka.filters.unsupervised.attribute.NumericCleaner
the number the values are checked for closeness to
m_CloseToDefault - Variable in class weka.filters.unsupervised.attribute.NumericCleaner
the default replacement value for numbers "close-to"
m_CloseToTolerance - Variable in class weka.filters.unsupervised.attribute.NumericCleaner
the tolerance distance, below which numbers are considered being "close-to"
m_closure - Variable in class weka.core.pmml.FieldMetaInfo.Interval
 
m_CLPanel - Variable in class weka.gui.explorer.ClustererPanel
The panel showing the current clusterer selection
m_clusterAssignments - Variable in class weka.clusterers.CLOPE
 
m_ClusterAssignments - Variable in class weka.clusterers.XMeans
temporary variable holding cluster assignments while iterating.
m_ClusterCenters - Variable in class weka.clusterers.XMeans
cluster centers.
m_ClusterCentroids - Variable in class weka.clusterers.FarthestFirst
holds the cluster centroids
m_Clusterer - Variable in class weka.classifiers.meta.ClassificationViaClustering
the cluster algorithm used (template)
m_Clusterer - Variable in class weka.clusterers.CheckClusterer
The clusterer to be examined
m_Clusterer - Variable in class weka.clusterers.SingleClustererEnhancer
the clusterer
m_clusterer - Variable in class weka.experiment.DensityBasedClustererSplitEvaluator
The clusterer used for evaluation
m_Clusterer - Variable in class weka.filters.unsupervised.attribute.AddCluster
The clusterer used to do the cleansing
m_clusterer - Variable in class weka.filters.unsupervised.attribute.ClusterMembership
The clusterer
m_clusterer - Variable in class weka.gui.beans.BatchClustererEvent
The clusterer
m_ClustererEditor - Variable in class weka.gui.explorer.ClustererPanel
Lets the user configure the clusterer
m_clustererOptions - Variable in class weka.experiment.DensityBasedClustererSplitEvaluator
The clusterer options (if any)
m_clusterers - Variable in class weka.filters.unsupervised.attribute.ClusterMembership
Array for storing the clusterers
m_clustererVersion - Variable in class weka.experiment.DensityBasedClustererSplitEvaluator
The clusterer version
m_ClusteringHeader - Variable in class weka.classifiers.meta.ClassificationViaClustering
the modified training data header
m_Clusters - Variable in class weka.datagenerators.clusterers.SubspaceCluster
cluster list
m_ClustersToClasses - Variable in class weka.classifiers.meta.ClassificationViaClustering
the mapping between clusters and classes
m_clustersubtype - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
cluster subtypes
m_clustertype - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
cluster type
m_CNN - Variable in class weka.classifiers.mi.CitationKNN
C nearest neighbors considering all the bags
m_CNNDebug - Variable in class weka.classifiers.mi.CitationKNN
Different debugging output
m_Cnsqt - Variable in class weka.classifiers.rules.ConjunctiveRule
The consequent of this rule
m_cobwebTree - Variable in class weka.clusterers.Cobweb
Holds the root of the Cobweb tree.
m_Coef0 - Variable in class weka.classifiers.functions.LibSVM
for poly/sigmoid
m_Coefficients - Variable in class weka.core.matrix.LinearRegression
the coefficients
m_col - Variable in class weka.gui.treevisualizer.NamedColor
The actual color object
m_ColHidden - Variable in class weka.experiment.ResultMatrix
whether a column is hidden
m_ColNames - Variable in class weka.experiment.ResultMatrix
the column names
m_ColNameWidth - Variable in class weka.experiment.ResultMatrix
the size of the names of the columns
m_colorAttrib - Variable in class weka.gui.AttributeVisualizationPanel
This stores and lets the user select a class attribute.
m_ColOrder - Variable in class weka.experiment.PairedTTester
The sorting of the columns (test base is always first)
m_ColOrder - Variable in class weka.experiment.ResultMatrix
the ordering of the columns
m_coloringIndex - Variable in class weka.gui.beans.AttributeSummarizer
Index on which to color the plots.
m_colorList - Variable in class weka.gui.beans.StripChart
default colours for colouring lines
m_colorList - Variable in class weka.gui.visualize.AttributePanel
The colour map to use for colouring points
m_colorList - Variable in class weka.gui.visualize.Plot2D
The list of the colors used
m_colorList - Variable in class weka.gui.visualize.VisualizePanel
The list of the colors used
m_Colors - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_Colors - Variable in class weka.gui.MemoryUsagePanel
the corresponding colors for the thresholds.
m_ColourCombo - Variable in class weka.gui.visualize.VisualizePanel
Lets the user select the attribute to use for colouring
m_Cols - Variable in class weka.filters.unsupervised.attribute.NumericCleaner
Stores which columns to cleanse
m_Cols - Variable in class weka.filters.unsupervised.attribute.NumericToNominal
Stores which columns to turn into nominals
m_cols - Variable in class weka.gui.treevisualizer.Colors
The array with all the colors input
m_ColumnClasses - Variable in class weka.gui.sql.ResultSetHelper
the class for each column.
m_ColumnCount - Variable in class weka.gui.sql.ResultSetHelper
the number of columns.
m_ColumnIndex - Variable in class weka.gui.arffviewer.ArffTable.RelationalCellEditor
the column index this editor is for
m_ColumnNames - Variable in class weka.gui.sql.ResultSetHelper
the column names.
m_Columns - Variable in class weka.filters.unsupervised.attribute.NominalToBinary
Stores which columns to act on
m_CombinationRule - Variable in class weka.classifiers.meta.Vote
Combination Rule variable
m_CommandHistory - Variable in class weka.gui.SimpleCLIPanel
The history of commands entered interactively.
m_Comment - Variable in enum weka.core.TechnicalInformation.Field
the comment about this type
m_Comment - Variable in enum weka.core.TechnicalInformation.Type
the comment about this type
m_Committee - Variable in class weka.classifiers.meta.Decorate
Vector of classifiers that make up the committee/ensemble.
m_committees - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The committees
m_CompareCombo - Variable in class weka.gui.experiment.ResultsPanel
Lets the user select which performance measure to analyze.
m_CompareModel - Variable in class weka.gui.experiment.ResultsPanel
The model embedded in m_CompareCombo.
m_completedSets - Variable in class weka.gui.beans.Classifier
Stores which sets from which runs have been completed.
m_completeReLayout - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
This tells the the LayoutGraph method if a completeReLayout should be performed when it is called.
m_Completion - Variable in class weka.gui.SimpleCLIPanel
The commandline completion.
m_Component - Variable in class weka.gui.visualize.PrintableComponent
the parent component of this print dialog.
m_CompressOutput - Variable in class weka.core.converters.ArffSaver
whether to compress the output
m_CompressOutput - Variable in class weka.core.converters.XRFFSaver
whether to compress the output
m_ComputeRandomCols - Variable in class weka.classifiers.lazy.KStar
Flag turning on and off the computation of random class colomns
m_conf_aa - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_conf_ab - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_conf_actualA - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_conf_actualB - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_conf_ba - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_conf_bb - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_conf_predictedA - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_conf_predictedB - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_ConfigureBut - Variable in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Click to select the property to iterate over
m_ConfigureButton - Variable in class weka.gui.ConverterFileChooser
the configure button
m_configureHostNames - Variable in class weka.gui.experiment.DistributeExperimentPanel
Popup the HostListPanel
m_ConfusionMatrix - Variable in class weka.classifiers.Evaluation
Array for storing the confusion matrix.
m_Connected - Variable in class weka.gui.sql.QueryPanel
whether we have a connection to a database or not.
m_Connection - Variable in class weka.experiment.DatabaseUtils
The database connection.
m_ConnectionListeners - Variable in class weka.gui.sql.ConnectionPanel
the connection listeners.
m_ConnectionPanel - Variable in class weka.gui.sql.SqlViewer
the connection panel.
m_connectPoints - Variable in class weka.gui.visualize.PlotData2D
Additional optional information to control the drawing of lines between consecutive points.
m_consequence - Variable in class weka.associations.FPGrowth.AssociationRule
The consequence of the rule
m_consequence - Variable in class weka.associations.RuleItem
The consequence of a rule.
m_consequenceSupport - Variable in class weka.associations.FPGrowth.AssociationRule
The support for the consequence
m_conservativeSelection - Variable in class weka.attributeSelection.GreedyStepwise
If set then attributes will continue to be added during a forward search as long as the merit does not degrade
m_constError - Variable in class weka.classifiers.trees.ft.FTtree
Constructor error
m_Contents - Variable in class weka.core.Queue.QueueNode
The nodes contents
m_continuousConst - Variable in class weka.core.pmml.Constant
 
m_controlPanel - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_controlsPanel - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
The panel containing extra options, specific to this LayoutEngine, for greater control over layout of the graph
m_convertNominal - Variable in class weka.classifiers.trees.FT
convert nominal attributes to binary ?
m_convertNominal - Variable in class weka.classifiers.trees.LMT
convert nominal attributes to binary ?
m_ConvertToMI - Variable in class weka.classifiers.mi.MIOptimalBall
filter used to convert the single-instance dataset into MI dataset
m_ConvertToProp - Variable in class weka.classifiers.mi.MISVM
filter used to convert the MI dataset into single-instance dataset
m_ConvertToProp - Variable in class weka.classifiers.mi.MIWrapper
Filter used to convert MI dataset into single-instance dataset
m_ConvertToSI - Variable in class weka.classifiers.mi.MIBoost
filter used to convert the MI dataset into single-instance dataset
m_ConvertToSI - Variable in class weka.classifiers.mi.MIOptimalBall
filter used to convert the MI dataset into single-instance dataset
m_CoordCount - Variable in class weka.core.neighboursearch.PerformanceStats
The number of coordinates looked at for the current/last query.
m_CopyCols - Variable in class weka.filters.unsupervised.attribute.Copy
Stores which columns to copy
m_CoreConvertersOnly - Variable in class weka.gui.ConverterFileChooser
whether to display only core converters (hardcoded in ConverterUtils).
m_Correct - Variable in class weka.classifiers.Evaluation
The weight of all correctly classified instances.
m_Correlation - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
Correlation matrix for the original data.
m_Cost - Variable in class weka.classifiers.functions.LibLINEAR
cost Parameter C
m_Cost - Variable in class weka.classifiers.functions.LibSVM
cost, for C_SVC, EPSILON_SVR and NU_SVR
m_cost_aa - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_cost_ab - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_cost_actualA - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_cost_actualB - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_cost_ba - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_cost_bb - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_cost_predictedA - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_cost_predictedB - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_costBenefit - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
Data for the cost/benefit curve
m_costBenefitL - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_costBenefitPanel - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
Displays the cost/benefit (profit/loss) graph
m_costBenefitV - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_CostFile - Variable in class weka.attributeSelection.CostSensitiveASEvaluation
The name of the cost file, for command line options
m_CostFile - Variable in class weka.classifiers.meta.CostSensitiveClassifier
The name of the cost file, for command line options
m_CostFile - Variable in class weka.classifiers.meta.MetaCost
The name of the cost file, for command line options
m_CostMatrix - Variable in class weka.attributeSelection.CostSensitiveASEvaluation
The cost matrix
m_CostMatrix - Variable in class weka.classifiers.Evaluation
The cost matrix (if given).
m_CostMatrix - Variable in class weka.classifiers.meta.CostSensitiveClassifier
The cost matrix
m_CostMatrix - Variable in class weka.classifiers.meta.MetaCost
The cost matrix
m_CostMatrixEditor - Variable in class weka.gui.explorer.ClassifierPanel
The cost matrix editor for evaluation costs
m_costR - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_count - Variable in class weka.associations.PredictiveApriori
Counter for the time of generation for an association rule.
m_count - Variable in class weka.associations.RuleGeneration
Integer indicating the generation time of a rule.
m_counter - Variable in class weka.associations.ItemSet
Counter for how many transactions contain this item set.
m_counter - Variable in class weka.associations.RuleGeneration
Counter for how many transactions contain this item set.
m_Counter - Variable in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
A classifier counter.
m_Counter - Variable in class weka.filters.unsupervised.attribute.AddID
the counter for the ID
m_CountFieldName - Variable in class weka.experiment.AveragingResultProducer
The name of the field that will contain the number of results averaged over.
m_Counts - Variable in class weka.classifiers.bayes.NaiveBayesSimple
All the counts for nominal attributes.
m_Counts - Variable in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes
Hold the counts
m_Counts - Variable in class weka.classifiers.lazy.LBR
All the counts for nominal attributes.
m_counts - Variable in class weka.classifiers.misc.VFI
The class counts for each interval of each attribute
m_Counts - Variable in class weka.experiment.ResultMatrix
the counts for the different datasets
m_CountWidth - Variable in class weka.experiment.ResultMatrix
the size of the counts
m_covariateList - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
 
m_CoverVariance - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
the amount of varaince to cover in the original data when retaining the best n PC's.
m_cp - Variable in class weka.gui.visualize.MatrixPanel
The panel that displays the legend of the colouring attribute
m_createIndex - Variable in class weka.experiment.DatabaseUtils
create index on the database?
m_CreatingRelationName - Variable in class weka.datagenerators.DataGenerator
flag, that indicates whether the relationname is currently assembled
m_creatorApplication - Variable in class weka.classifiers.pmml.consumer.PMMLClassifier
Creator application
m_CrossValidate - Variable in class weka.classifiers.lazy.IBk
Whether to select k by cross validation.
m_csvFileFilter - Variable in class weka.gui.experiment.ResultsPanel
CSV file filter.
m_csvFileFilter - Variable in class weka.gui.experiment.SimpleSetupPanel
Filter for choosing CSV files
m_cumulativeInstances - Variable in class weka.core.converters.CSVLoader
Holds instances accumulated so far.
m_cumulativeLinkFunction - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
 
m_cumulativeStructure - Variable in class weka.core.converters.CSVLoader
A list of hash tables for accumulating nominal values during parsing.
m_CurrDebugFlag - Variable in class weka.clusterers.XMeans
Flag: I'm debugging.
m_currentBatchIdentifier - Variable in class weka.gui.beans.Classifier
Identifier for the current batch.
m_CurrentConverter - Variable in class weka.gui.ConverterFileChooser
the converter that was chosen by the user
m_CurrentID - Static variable in class weka.core.Debug.Random
for keeping track of unique IDs
m_currentInst - Variable in class weka.filters.unsupervised.instance.ReservoirSample
The current instance being processed
m_CurrentInst - Variable in class weka.gui.arffviewer.ArffTable.RelationalCellEditor
the current instances
m_currentInstance - Variable in class weka.gui.beans.IncrementalClassifierEvent
 
m_CurrentInstances - Variable in class weka.experiment.Experiment
The dataset currently being used
m_CurrentLeaf - Variable in class weka.core.Trie.TrieIterator
the current leaf node
m_CurrentMetaBean - Variable in class weka.gui.beans.xml.XMLBeans
the current MetaBean (for the BeanConnections)
m_CurrentNode - Variable in class weka.core.xml.XMLSerialization
the node that is currently processed, in case of writing the parent node (something might go wrong writing the new child) and in case of reading the actual node that is tried to process
m_CurrentPos - Variable in class weka.core.tokenizers.AlphabeticTokenizer
the current position
m_CurrentPosition - Variable in class weka.core.tokenizers.NGramTokenizer
the current position for returning elements
m_CurrentProperty - Variable in class weka.experiment.Experiment
The custom property value that has actually been set
m_currentSet - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The instances currently in memory for training
m_CurrentSize - Variable in class weka.experiment.LearningRateResultProducer
The current dataset size during stepping
m_CurrentVis - Variable in class weka.gui.explorer.ClassifierPanel
The current visualization object
m_CurrentVis - Variable in class weka.gui.explorer.ClustererPanel
The current visualization object
m_customColour - Variable in class weka.gui.visualize.PlotData2D
 
m_CustomDimensionsCheckBox - Static variable in class weka.gui.visualize.PrintableComponent
the checkbox for the custom dimensions.
m_CustomHeight - Variable in class weka.gui.visualize.JComponentWriter
the custom height
m_CustomHeightText - Static variable in class weka.gui.visualize.PrintableComponent
the edit field for the custom height.
m_CustomMethods - Variable in class weka.core.xml.XMLSerialization
for handling custom read/write methods
m_CustomWidth - Variable in class weka.gui.visualize.JComponentWriter
the custom width
m_CustomWidthText - Static variable in class weka.gui.visualize.PrintableComponent
the edit field for the custom width.
m_cutoff - Variable in class weka.clusterers.Cobweb
Cutoff (minimum category utility).
m_CutOffFactor - Variable in class weka.clusterers.XMeans
cutoff factor - percentage of splits done in Improve-Structure part only relevant, if all children lost.
m_CutPoints - Variable in class weka.filters.supervised.attribute.Discretize
Store the current cutpoints
m_CutPoints - Variable in class weka.filters.unsupervised.attribute.Discretize
Store the current cutpoints
m_CVBut - Variable in class weka.gui.explorer.AttributeSelectionPanel
Click to set evaluation mode to cross-validation
m_CVBut - Variable in class weka.gui.explorer.ClassifierPanel
Click to set test mode to cross-validation
m_CVLab - Variable in class weka.gui.explorer.AttributeSelectionPanel
Label by where the cv folds are entered
m_CVLab - Variable in class weka.gui.explorer.ClassifierPanel
Label by where the cv folds are entered
m_CVParams - Variable in class weka.classifiers.meta.CVParameterSelection
The set of parameters to cross-validate over
m_CVText - Variable in class weka.gui.explorer.AttributeSelectionPanel
The field where the cv folds are entered
m_CVText - Variable in class weka.gui.explorer.ClassifierPanel
The field where the cv folds are entered
m_CycleEnd - Variable in class weka.associations.GeneralizedSequentialPatterns
String indicating the ending time of an cycle.
m_cycles - Variable in class weka.associations.Apriori
Number of cycles used before required number of rules was one.
m_Cycles - Variable in class weka.associations.GeneralizedSequentialPatterns
number of cycles performed until termination
m_CycleStart - Variable in class weka.associations.GeneralizedSequentialPatterns
String indicating the starting time of an cycle.
m_Data - Variable in class weka.classifiers.functions.Logistic
The data saved as a matrix
m_data - Variable in class weka.classifiers.functions.SMO.BinarySMO
The training data.
m_data - Variable in class weka.classifiers.functions.SPegasos
Holds the header of the training data
m_data - Variable in class weka.classifiers.functions.supportVector.Kernel
The dataset
m_data - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
points to data set
m_Data - Variable in class weka.classifiers.meta.GridSearch
the training data
m_Data - Variable in class weka.classifiers.mi.MDD
MI data
m_Data - Variable in class weka.classifiers.mi.MIDD
MI data
m_Data - Variable in class weka.classifiers.mi.MIEMDD
MI data
m_Data - Variable in class weka.classifiers.mi.MILR
MI data
m_data - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
The training data.
m_data - Variable in class weka.classifiers.trees.lmt.ResidualSplit
The set of instances
m_Data - Variable in class weka.core.AttributeLocator
the referenced data
m_Data - Variable in class weka.core.converters.ArffLoader.ArffReader
the actual data
m_Data - Variable in class weka.core.NormalizableDistance
the instances used internally.
m_Data - Variable in class weka.core.TestInstances
the generated data
m_data - Variable in class weka.gui.AttributeVisualizationPanel
This holds the current set of instances
m_Data - Variable in class weka.gui.sql.ResultSetTableModel
the data.
m_data - Variable in class weka.gui.visualize.MatrixPanel
The dataset for which this panel will display the plot matrix for
m_DatabaseURL - Variable in class weka.experiment.DatabaseUtils
Database URL.
m_dataDictionary - Variable in class weka.classifiers.pmml.consumer.PMMLClassifier
The data dictionary
m_DataFileName - Variable in class weka.classifiers.BVDecompose
The name of the data file used for the decomposition
m_DataFileName - Variable in class weka.classifiers.BVDecomposeSegCVSub
The name of the data file used for the decomposition
m_dataGenerator - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
data generator to use
m_DataGenerator - Variable in class weka.gui.explorer.PreprocessPanel
The last generator that was selected
m_DataSeqID - Variable in class weka.associations.GeneralizedSequentialPatterns
number indicating the attribute holding the data sequence ID
m_Dataset - Variable in class weka.classifiers.CheckSource
the dataset to use for testing
m_Dataset - Variable in class weka.core.converters.SerializedInstancesLoader
Holds the structure (header) of the data set.
m_Dataset - Variable in class weka.core.Instance
The dataset the instance has access to.
m_Dataset - Variable in class weka.filters.CheckSource
the dataset to use for testing
m_DatasetFormat - Variable in class weka.datagenerators.DataGenerator
The format for the generated dataset
m_DatasetKeyBut - Variable in class weka.gui.experiment.ResultsPanel
Click to edit the columns used to determine the scheme.
m_DatasetKeyColumns - Variable in class weka.experiment.PairedTTester
An array containing the indexes of just the selected columns
m_DatasetKeyColumnsRange - Variable in class weka.experiment.PairedTTester
The range of columns that specify a unique "dataset" (eg: scheme plus configuration)
m_DatasetKeyLabel - Variable in class weka.gui.experiment.ResultsPanel
Displays the currently selected column names for the scheme & options.
m_DatasetKeyList - Variable in class weka.gui.experiment.ResultsPanel
Displays the list of selected columns determining the scheme.
m_DatasetKeyModel - Variable in class weka.gui.experiment.ResultsPanel
Stores the list of attributes for selecting the scheme columns.
m_DatasetListPanel - Variable in class weka.gui.experiment.SetupPanel
The panel for configuring selected datasets
m_DatasetListPanel - Variable in class weka.gui.experiment.SimpleSetupPanel
The panel for configuring selected datasets
m_DatasetModel - Variable in class weka.gui.experiment.ResultsPanel
The model embedded in m_DatasetCombo.
m_DatasetNumber - Variable in class weka.experiment.Experiment
The current dataset number when the experiment is running
m_Datasets - Variable in class weka.experiment.Experiment
An array of dataset files
m_DatasetSpecifiers - Variable in class weka.experiment.PairedTTester
The list of dataset specifiers
m_dataSourceListeners - Variable in class weka.gui.beans.PredictionAppender
Objects listenening for dataset events
m_DataType - Variable in class weka.gui.beans.xml.XMLBeans
the type of data that is be read/written
m_dataWs - Variable in class weka.classifiers.trees.lmt.ResidualSplit
The LogitBoost-weights for the set of instances
m_dataZs - Variable in class weka.classifiers.trees.lmt.ResidualSplit
The Z-values (LogitBoost response) for the set of instances
m_dateAttributes - Variable in class weka.core.converters.CSVLoader
The range of attributes to force to type date
m_dateFormat - Variable in class weka.core.converters.CSVLoader
The formatting string to use to parse dates
m_DateFormat - Static variable in class weka.core.logging.Logger
for formatting the dates.
m_DateFormat - Variable in class weka.filters.unsupervised.attribute.Add
The date format.
m_DbaseURLLab - Variable in class weka.gui.DatabaseConnectionDialog
 
m_DbaseURLText - Variable in class weka.gui.DatabaseConnectionDialog
 
m_DbDialog - Variable in class weka.gui.sql.ConnectionPanel
the databae connection dialog.
m_DbUtils - Variable in class weka.gui.sql.ConnectionPanel
for connecting to the database.
m_DbUtils - Variable in class weka.gui.sql.event.ConnectionEvent
the databaseutils instance reponsible for the connection
m_DbUtils - Variable in class weka.gui.sql.event.QueryExecuteEvent
the Db utils instance for the current DB connection
m_DbUtils - Variable in class weka.gui.sql.QueryPanel
for working on the database.
m_Debug - Variable in class weka.associations.GeneralizedSequentialPatterns
Whether the classifier is run in debug mode.
m_debug - Variable in class weka.attributeSelection.BestFirst
for debugging
m_Debug - Variable in class weka.classifiers.BVDecompose
Debugging mode, gives extra output if true
m_Debug - Variable in class weka.classifiers.BVDecomposeSegCVSub
Debugging mode, gives extra output if true.
m_Debug - Variable in class weka.classifiers.Classifier
Whether the classifier is run in debug mode.
m_Debug - Variable in class weka.classifiers.functions.Logistic
Debugging output
m_Debug - Variable in class weka.classifiers.functions.supportVector.Kernel
enables debugging output
m_Debug - Variable in class weka.classifiers.mi.CitationKNN
Debugging output
m_Debug - Variable in class weka.core.Check
Debugging mode, gives extra output if true
m_Debug - Variable in class weka.core.converters.TextDirectoryLoader
whether to print some debug information
m_Debug - Variable in class weka.core.Debug.Random
whether to output debug information
m_Debug - Static variable in class weka.core.Optimization
 
m_Debug - Variable in class weka.datagenerators.DataGenerator
Debugging mode
m_Debug - Variable in class weka.estimators.CheckEstimator
Debugging mode, gives extra output if true
m_Debug - Variable in class weka.experiment.DatabaseResultListener
True if debugging output should be printed
m_Debug - Variable in class weka.experiment.DatabaseUtils
True if debugging output should be printed.
m_Debug - Variable in class weka.filters.SimpleFilter
Whether debugging is on
m_Debug - Variable in class weka.gui.SimpleCLIPanel.CommandlineCompletion
debug mode on/off.
m_DebugCheckBox - Variable in class weka.gui.DatabaseConnectionDialog
 
m_DebugLab - Variable in class weka.gui.DatabaseConnectionDialog
 
m_DebugLevel - Variable in class weka.clusterers.XMeans
level of debug output, 0 is no output.
m_debugOutput - Variable in class weka.experiment.CrossValidationResultProducer
Save raw output of split evaluators --- for debugging purposes
m_debugOutput - Variable in class weka.experiment.RandomSplitResultProducer
Save raw output of split evaluators --- for debugging purposes
m_DebugVectors - Variable in class weka.clusterers.XMeans
all the debug vectors.
m_DebugVectorsFile - Variable in class weka.clusterers.XMeans
file name of the input file for the random vectors.
m_DebugVectorsIndex - Variable in class weka.clusterers.XMeans
the index for the current debug vector.
m_DebugVectorsInput - Variable in class weka.clusterers.XMeans
input file for the random vectors --> USED FOR DEBUGGING.
m_Decimals - Variable in class weka.filters.unsupervised.attribute.NumericCleaner
the number of decimals to round to (-1 means no rounding)
m_decisionFeatures - Variable in class weka.classifiers.rules.DecisionTable
Holds the final feature set
m_Default - Variable in class weka.core.Tee
the default printstream.
m_DefaultColor - Variable in class weka.gui.MemoryUsagePanel
the default color.
m_DefaultColors - Variable in class weka.gui.visualize.AttributePanel
default colours for colouring discrete class
m_DefaultColors - Variable in class weka.gui.visualize.ClassPanel
default colours for colouring discrete class
m_DefaultColors - Variable in class weka.gui.visualize.Plot2D
default colours for colouring discrete class
m_DefaultColors - Variable in class weka.gui.visualize.VisualizePanel
default colours for colouring discrete class
m_DefaultCols - Variable in class weka.filters.unsupervised.attribute.Discretize
The default columns to discretize
m_DefaultCols - Variable in class weka.filters.unsupervised.attribute.NumericToNominal
The default columns to turn into nominals
m_defaultExpression - Static variable in class weka.filters.unsupervised.attribute.MathExpression
The default modification expression
m_defaultModel - Variable in class weka.classifiers.lazy.IBk
Default ZeroR model to use when there are no training instances
m_DefaultOutput - Variable in class weka.datagenerators.DataGenerator
default output (is printed to stdout after generation)
m_defaultValue - Variable in class weka.core.pmml.Discretize
The default value (if defined)
m_defaultValueDefined - Variable in class weka.core.pmml.Discretize
True if a default value has been specified
m_defaultValueOrPriorProbs - Variable in class weka.core.pmml.TargetMetaInfo
default value (numeric) or prior distribution (categorical)
m_defaultWeight - Variable in class weka.classifiers.functions.Winnow
Starting weights for the prediction vector(s)
m_DefDstr - Variable in class weka.classifiers.rules.ConjunctiveRule
The default rule distribution of the data not covered
m_Degree - Variable in class weka.classifiers.functions.LibSVM
for poly - in older versions of libsvm declared as a double.
m_degreesOfFreedom - Variable in class weka.experiment.PairedStats
The degrees of freedom (if set programmatically)
m_Del - Static variable in class weka.classifiers.functions.SMO
Precision constant for updating sets
m_Del - Static variable in class weka.classifiers.functions.supportVector.RegSMO
Precision constant for updating sets
m_Del - Static variable in class weka.classifiers.mi.MISMO
Precision constant for updating sets
M_DELETE - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
Missing value handling mode
m_DeleteBut - Variable in class weka.gui.experiment.AlgorithmListPanel
Click to remove the selected dataset from the list
m_DeleteBut - Variable in class weka.gui.experiment.DatasetListPanel
Click to remove the selected dataset from the list.
m_DeleteBut - Variable in class weka.gui.experiment.HostListPanel
Click to remove the selected host from the list
m_DeleteEmptyBins - Variable in class weka.classifiers.meta.RegressionByDiscretization
Whether to delete empty intervals.
m_Delimiters - Variable in class weka.core.tokenizers.CharacterDelimitedTokenizer
Delimiters used in tokenization
m_delta - Variable in class weka.associations.Apriori
Delta by which m_minSupport is decreased in each iteration.
m_delta - Variable in class weka.associations.FPGrowth
The amount by which to decrease the support in each iteration
m_delta - Variable in class weka.classifiers.functions.GaussianProcesses
Gaussian Noise Value.
m_DeltaCols - Variable in class weka.filters.unsupervised.attribute.FirstOrder
Stores which columns to take differences between
m_delTransform - Variable in class weka.classifiers.rules.DecisionTable
Filter used to remove columns discarded by feature selection
m_Dependencies - Variable in class weka.core.Capabilities
the hashset for storing dependent capabilities, eg for meta-classifiers
m_derivedMeta - Variable in class weka.core.pmml.MiningSchema
Meta information about derived fields (those defined in the TransformationDictionary followed by those defined in LocalTransformations)
m_Description - Variable in class weka.gui.ExtensionFileFilter
The text description of the types of files accepted
m_Descriptor - Variable in class weka.core.PropertyPath.PropertyContainer
the descriptor
m_design - Variable in class weka.gui.beans.AbstractDataSource
True if this bean's appearance is the design mode appearance
m_design - Variable in class weka.gui.beans.CostBenefitAnalysis
True if this bean's appearance is the design mode appearance
m_design - Variable in class weka.gui.beans.DataVisualizer
True if this bean's appearance is the design mode appearance
m_design - Variable in class weka.gui.beans.GraphViewer
True if this bean's appearance is the design mode appearance
m_design - Variable in class weka.gui.beans.ModelPerformanceChart
True if this bean's appearance is the design mode appearance
m_design - Variable in class weka.gui.beans.TextViewer
True if this bean's appearance is the design mode appearance
m_DesignatedClass - Variable in class weka.classifiers.meta.ThresholdSelector
Designated class value, determined during building
m_DesiredSize - Variable in class weka.classifiers.meta.Decorate
The desired ensemble size.
m_DesiredWeightOfInstancesPerInterval - Variable in class weka.filters.unsupervised.attribute.Discretize
The desired weight of instances per bin
m_DestFileChooser - Variable in class weka.gui.experiment.SimpleSetupPanel
The file chooser for selecting result destinations
m_destinationDatabaseURL - Variable in class weka.gui.experiment.SimpleSetupPanel
The database destination URL to store results into
m_destinationFilename - Variable in class weka.gui.experiment.SimpleSetupPanel
The filename to store results into
m_DetectionPerAttribute - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
whether to generate Outlier/ExtremeValue attributes for each attribute instead of a general one
m_DetectMinorityClass - Variable in class weka.filters.supervised.instance.SMOTE
whether to detect the minority class automatically.
m_Devs - Variable in class weka.classifiers.bayes.NaiveBayesSimple
The standard deviations for numeric attributes.
m_DialogType - Variable in class weka.gui.ConverterFileChooser
the type of dialog to display
m_Dimension - Variable in class weka.classifiers.mi.MINND
The dimension of each exemplar, i.e.
m_Dir - Variable in class weka.core.Javadoc
the directory above the class to update
m_Disc - Variable in class weka.classifiers.bayes.NaiveBayes
The discretization filter.
m_DiscretizeBin - Variable in class weka.classifiers.mi.MIBoost
the number of discretization bins
m_DiscretizeCols - Variable in class weka.filters.supervised.attribute.Discretize
Stores which columns to Discretize
m_DiscretizeCols - Variable in class weka.filters.unsupervised.attribute.Discretize
Stores which columns to Discretize
m_DiscretizedHeader - Variable in class weka.classifiers.meta.RegressionByDiscretization
Header of discretized data.
m_DiscretizeFilter - Variable in class weka.classifiers.bayes.BayesNet
filter used to quantize continuous variables, if any
m_Discretizer - Variable in class weka.classifiers.meta.RegressionByDiscretization
The discretization filter.
m_Display - Variable in enum weka.core.TechnicalInformation.Field
the string used in toString()
m_Display - Variable in enum weka.core.TechnicalInformation.Type
the string used in toString()
m_displayAllPoints - Variable in class weka.gui.visualize.PlotData2D
Display all points (ie.
m_DisplayedButton - Variable in class weka.gui.experiment.ResultsPanel
Lets the user select which schemes are compared to base.
m_DisplayedList - Variable in class weka.gui.experiment.ResultsPanel
Holds the list of schemes to display.
m_DisplayedModel - Variable in class weka.gui.experiment.ResultsPanel
The model embedded in m_DisplayedList.
m_DisplayedResultsets - Variable in class weka.experiment.PairedTTester
An array containing the indexes of the datasets to display
m_displayModelInOldFormat - Variable in class weka.classifiers.bayes.NaiveBayes
 
m_displayName - Variable in class weka.core.pmml.DerivedFieldMetaInfo
display name
m_displayRules - Variable in class weka.classifiers.rules.DecisionTable
Display Rules
m_displayValue - Variable in class weka.core.pmml.FieldMetaInfo.Value
The display value (might hold a human readable value - e.g.
m_displayValues - Variable in class weka.core.pmml.TargetMetaInfo
corresponding display values
m_Distance - Variable in class weka.classifiers.mi.MIOptimalBall
the distances from each instance in a positive bag to each bag
m_Distance - Variable in class weka.core.neighboursearch.NearestNeighbourSearch.NeighborNode
The distance from the current instance to this neighbor.
m_DistanceF - Variable in class weka.clusterers.XMeans
the distance function used.
m_DistanceFunction - Variable in class weka.clusterers.HierarchicalClusterer
distance function used for comparing members of a cluster
m_DistanceFunction - Variable in class weka.clusterers.SimpleKMeans
the distance function used.
m_DistanceFunction - Variable in class weka.core.neighboursearch.balltrees.BallSplitter
The distance function (metric) from which the tree is (OR is to be) built.
m_DistanceFunction - Variable in class weka.core.neighboursearch.balltrees.BallTreeConstructor
The distance function to use to build the tree.
m_DistanceFunction - Variable in class weka.core.neighboursearch.NearestNeighbourSearch
the distance function used.
m_DistanceList - Variable in class weka.core.neighboursearch.CoverTree
Array holding the distances of the nearest neighbours.
m_DistanceList - Variable in class weka.core.neighboursearch.KDTree
Array holding the distances of the nearest neighbours.
m_Distances - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The set of disctances from the test attribute to the set of train attributes
m_Distances - Variable in class weka.core.neighboursearch.BallTree
Array holding the distances of the nearest neighbours.
m_Distances - Variable in class weka.core.neighboursearch.LinearNNSearch
Array holding the distances of the nearest neighbours.
m_DistanceWeighting - Variable in class weka.classifiers.lazy.IBk
Whether the neighbours should be distance-weighted.
m_DistinctLab - Variable in class weka.gui.AttributeSummaryPanel
Displays the number of distinct values
m_distParameter - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
 
m_disTransform - Variable in class weka.classifiers.rules.DecisionTable
Discretization filter
m_DistributeExperimentPanel - Variable in class weka.gui.experiment.SetupPanel
The panel for enabling a distributed experiment
m_distribution - Variable in class weka.associations.PriorEstimation
Hashtable to store the confidence values of randomly generated rules.
m_Distribution - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
Distribution of the attribute value in the train dataset
m_distribution - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
 
m_Distribution - Variable in class weka.classifiers.trees.BFTree
Class distributions.
m_distribution - Variable in class weka.classifiers.trees.j48.ClassifierSplitModel
Distribution of class values.
m_Distribution - Variable in class weka.classifiers.trees.REPTree.Tree
The (unnormalized) class distribution in the nominal case.
m_Distribution - Variable in class weka.classifiers.trees.SimpleCart
Distributions of leaf node (or temporary leaf node in minimal cost-complexity pruning)
m_distribution - Variable in class weka.filters.unsupervised.attribute.RandomProjection
Stores the distribution to use for calculating the random matrix
m_Distributions - Variable in class weka.classifiers.bayes.BayesNet
The attribute estimators containing CPTs.
m_Distributions - Variable in class weka.classifiers.bayes.NaiveBayes
The attribute estimators.
m_Dists - Variable in class weka.classifiers.trees.BFTree
Distributions of each attribute for two successor nodes.
m_DocType - Variable in class weka.core.xml.XMLDocument
the DOCTYPE node as String.
m_Document - Variable in class weka.core.xml.XMLDocument
the DOM document.
m_Document - Variable in class weka.core.xml.XMLSerialization
the XMLDocument that performs the transformation to and fro XML
m_doesProduce - Variable in class weka.experiment.ClassifierSplitEvaluator
Array of booleans corresponding to the measures in m_AdditionalMeasures indicating which of the AdditionalMeasures the current classifier can produce
m_doesProduce - Variable in class weka.experiment.DensityBasedClustererSplitEvaluator
Array of booleans corresponding to the measures in m_AdditionalMeasures indicating which of the AdditionalMeasures the current clusterer can produce
m_doesProduce - Variable in class weka.experiment.RegressionSplitEvaluator
Array of booleans corresponding to the measures in m_AdditionalMeasures indicating which of the AdditionalMeasures the current classifier can produce
m_doneRanking - Variable in class weka.attributeSelection.GreedyStepwise
used to indicate whether or not ranking has been performed
m_dontFilterAfterFirstBatch - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
Whether to filter instances after the first batch has been processed
m_dontNormalize - Variable in class weka.classifiers.functions.SPegasos
Turn off normalization of the input data.
m_DontNormalize - Variable in class weka.core.NormalizableDistance
True if normalization is turned off (default false).
m_dontReplaceMissing - Variable in class weka.classifiers.functions.SPegasos
Turn off global replacement of missing values.
m_doRank - Variable in class weka.attributeSelection.GreedyStepwise
go from one side of the search space to the other in order to generate a ranking
m_doubleType - Variable in class weka.experiment.DatabaseUtils
double type for the create table statement.
m_DownBut - Variable in class weka.gui.experiment.AlgorithmListPanel
Click to move the selected algorithm(s) one down
m_DownBut - Variable in class weka.gui.experiment.DatasetListPanel
Click to move the selected dataset(s) one down.
m_drawnPoints - Variable in class weka.gui.visualize.Plot2D
An array used to show if a point is hidden or not.
m_dtInstances - Variable in class weka.classifiers.rules.DecisionTable
Holds the final feature selected set of instances
m_edges - Variable in class weka.gui.graphvisualizer.BIFParser
These holds the nodes and edges of the graph
m_edges - Variable in class weka.gui.graphvisualizer.DotParser
These holds the nodes and edges of the graph
m_edges - Variable in class weka.gui.graphvisualizer.GraphVisualizer
Vector containing edges
m_edges - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
FastVector containing nodes and edges
m_EditBut - Variable in class weka.gui.experiment.AlgorithmListPanel
Click to edit the selected algorithm
m_EditBut - Variable in class weka.gui.experiment.DatasetListPanel
Click to edit the selected algorithm.
m_EditBut - Variable in class weka.gui.explorer.PreprocessPanel
Click to open the current instances in a viewer
m_Editing - Variable in class weka.gui.experiment.AlgorithmListPanel
Whether an algorithm is added or only edited
m_EditorComponent - Variable in class weka.gui.GenericObjectEditor
The GUI component for editing values, created when needed.
m_EditorsRegistered - Static variable in class weka.gui.GenericObjectEditor
whether the Weka Editors were already registered.
m_Eigenvalues - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
Eigenvalues for the corresponding eigenvectors.
m_Eigenvectors - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
Will hold the unordered linear transformations of the (normalized) original data.
m_Elements - Variable in class weka.associations.gsp.Sequence
ordered list of the comprised elements/itemsets
m_Elements - Variable in class weka.core.AlgVector
The values of the matrix
m_Elements - Variable in class weka.core.PropertyPath.Path
the structure
m_emData - Variable in class weka.classifiers.mi.MIEMDD
MI data
m_Enabled - Variable in class weka.core.Debug
whether logging is enabled
m_Enabled - Static variable in class weka.core.Memory
whether memory management is enabled
m_Enabled - Variable in class weka.gui.GenericObjectEditor
True if the GUI component is needed.
m_enableDistributedExperiment - Variable in class weka.gui.experiment.DistributeExperimentPanel
Distribute the current experiment to remote hosts
m_Enclosures - Variable in class weka.core.converters.CSVLoader
enclosure character(s) to use for strings
m_End - Variable in class weka.core.neighboursearch.balltrees.BallNode
The end index of the portion of the master index array, which stores indices of the instances/points the node contains.
m_End - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode
The end index of the portion of the master index array, which stores indices of the instances/points the node contains.
m_EndTag - Variable in class weka.core.Javadoc
the end tag
m_EnsembleLibraryFrame - Variable in class weka.gui.GUIChooser
The frame containing the ensemble library interface
m_entries - Variable in class weka.classifiers.rules.DecisionTable
The hashtable used to hold training instances
m_EnumerateColNames - Variable in class weka.experiment.ResultMatrix
whether a "(x)" is printed before each column name with "x" as the index
m_EnumerateRowNames - Variable in class weka.experiment.ResultMatrix
whether a "(x)" is printed before each row name with "x" as the index
m_env - Variable in class weka.core.converters.AbstractFileLoader
Environment variables
m_env - Variable in class weka.core.converters.AbstractFileSaver
Environment variables
m_env - Variable in class weka.gui.beans.FlowRunner
 
m_env - Variable in class weka.gui.beans.Loader
The environment variables.
m_env - Variable in class weka.gui.beans.Saver
The environment variables.
m_env - Variable in class weka.gui.beans.SerializedModelSaver
The environment variables.
m_epochs - Variable in class weka.classifiers.functions.SPegasos
The number of epochs to perform (batch learning).
m_eps - Variable in class weka.classifiers.functions.LibLINEAR
stopping criteria
m_eps - Variable in class weka.classifiers.functions.LibSVM
stopping criteria
m_eps - Variable in class weka.classifiers.functions.SMO
Epsilon for rounding.
m_eps - Variable in class weka.classifiers.functions.supportVector.RegSMO
tolerance parameter, smaller changes on alpha in inner loop will be ignored
m_eps - Variable in class weka.classifiers.mi.MISMO
Epsilon for rounding.
m_epsilon - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
epsilon of epsilon-insensitive cost function
m_Epsilon - Static variable in class weka.core.Optimization
Compute machine precision
m_Error - Variable in class weka.classifiers.BVDecompose
The error rate
m_Error - Variable in class weka.classifiers.BVDecomposeSegCVSub
The error rate
m_ErrorEstimator - Variable in class weka.classifiers.Evaluation
Numeric class error estimator for scheme
m_ErrorFlags - Variable in class weka.classifiers.lazy.LBR
leave-one-out error flags on the training dataaet.
m_errorOnProbabilities - Variable in class weka.classifiers.functions.SimpleLogistic
If true, use minimize error on probabilities instead of misclassification error
m_errorOnProbabilities - Variable in class weka.classifiers.trees.FT
use error on probabilties instead of misclassification for stopping criterion of LogitBoost?
m_errorOnProbabilities - Variable in class weka.classifiers.trees.lmt.LogisticBase
Use error on probabilities for stopping criterion of LogitBoost?
m_errorOnProbabilities - Variable in class weka.classifiers.trees.LMT
use error on probabilties instead of misclassification for stopping criterion of LogitBoost?
m_errors - Variable in class weka.classifiers.functions.SMO.BinarySMO
The current set of errors for all non-bound examples.
m_Errors - Variable in class weka.classifiers.lazy.LBR
leave-one-out errors on the training dataset.
m_errors - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
The current set of errors for all non-bound examples.
m_ErrRedirector - Variable in class weka.gui.SimpleCLIPanel
The thread that sends output from m_POE to the output box.
m_Estimator - Variable in class weka.estimators.CheckEstimator
The estimator to be examined
m_EstimatorOptions - Variable in class weka.estimators.CheckEstimator
The options to be passed to the base estimator.
m_EuclideanDistance - Variable in class weka.core.neighboursearch.CoverTree
The euclidean distance function to use.
m_EuclideanDistance - Variable in class weka.core.neighboursearch.KDTree
The euclidean distance function to use.
m_EuclideanDistance - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
The distance function used for building the tree.
m_EvalMode - Variable in class weka.classifiers.meta.ThresholdSelector
The evaluation mode
m_Evaluation - Variable in class weka.classifiers.meta.GridSearch
the type of evaluation
m_Evaluation - Variable in class weka.classifiers.meta.GridSearch.PerformanceComparator
the performance measure to use for comparison
m_evaluation - Variable in class weka.classifiers.rules.DecisionTable
The evaluation object used to evaluate subsets
m_evaluationMeasure - Variable in class weka.classifiers.rules.DecisionTable
 
m_Evaluations - Variable in class weka.classifiers.functions.supportVector.KernelEvaluation
the kernel evaluation results
m_Evaluator - Variable in class weka.attributeSelection.CheckAttributeSelection
The evaluator to be examined
m_evaluator - Variable in class weka.attributeSelection.CostSensitiveASEvaluation
The base evaluator to use
m_evaluator - Variable in class weka.attributeSelection.FilteredAttributeEval
Base evaluator
m_evaluator - Variable in class weka.attributeSelection.FilteredSubsetEval
Base evaluator
m_Evaluator - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The attribute evaluator to use
m_evaluator - Variable in class weka.classifiers.rules.DecisionTable
Our own internal evaluator
m_EvalWRTCostsBut - Variable in class weka.gui.explorer.ClassifierPanel
Check to evaluate w.r.t a cost matrix
m_Events - Variable in class weka.associations.gsp.Element
events/items stored as an array of ints
m_examplesCounted - Variable in class weka.classifiers.trees.ADTree
Statistics - the number of instances processed during search
m_examplesCounted - Variable in class weka.classifiers.trees.LADTree
 
m_Exception - Variable in class weka.gui.sql.event.ConnectionEvent
a possible exception that occurred if not successful
m_Exception - Variable in class weka.gui.sql.event.QueryExecuteEvent
a possible exception, if the query failed
m_Excludes - Variable in class weka.gui.GenericPropertiesCreator
the hashtable that stores the excludes: key -> Hashtable(prefix -> Vector of classnames)
m_executionSlots - Variable in class weka.gui.beans.Classifier
Number of threads to use to train models with
m_executorPool - Variable in class weka.gui.beans.Classifier
Pool of threads to train models on incoming data
m_ExitIfNoWindowsOpen - Static variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
whether the exit if there are no more windows open
m_Exp - Variable in class weka.gui.experiment.AlgorithmListPanel
The experiment to set the algorithm list of
m_Exp - Variable in class weka.gui.experiment.DatasetListPanel
The experiment to set the dataset list of.
m_Exp - Variable in class weka.gui.experiment.GeneratorPropertyIteratorPanel
The experiment this all applies to
m_Exp - Variable in class weka.gui.experiment.HostListPanel
The remote experiment to set the host list of
m_Exp - Variable in class weka.gui.experiment.ResultsPanel
An experiment (used for identifying a result source) -- optional.
m_Exp - Variable in class weka.gui.experiment.RunNumberPanel
The experiment being configured
m_Exp - Variable in class weka.gui.experiment.RunPanel
The experiment to run
m_Exp - Variable in class weka.gui.experiment.SetupPanel
The experiment being configured
m_Exp - Variable in class weka.gui.experiment.SimpleSetupPanel
The experiment being configured
m_Expansion - Static variable in class weka.classifiers.trees.BFTree
Number of expansions.
m_ExpClassificationRBut - Variable in class weka.gui.experiment.SimpleSetupPanel
Radio button for choosing classification experiment
m_expectation - Variable in class weka.associations.PredictiveApriori
The expected predictive accuracy a rule needs to be a candidate for the output.
m_expectation - Variable in class weka.associations.RuleGeneration
The minimum expected predictive accuracy that is needed to be a candidate for the list of the best rules.
m_ExpectedResultsPerAverage - Variable in class weka.experiment.AveragingResultProducer
The number of results expected to average over for each run
m_ExperimenterBut - Variable in class weka.gui.GUIChooser
Click to open the Explorer
m_ExperimenterFrame - Variable in class weka.gui.GUIChooser
The frame containing the experiment interface
m_experimentFinished - Variable in class weka.experiment.RemoteExperimentEvent
True if a remote experiment has finished
m_ExperimentParameterLabel - Variable in class weka.gui.experiment.SimpleSetupPanel
Label for parameter field
m_ExperimentParameterTField - Variable in class weka.gui.experiment.SimpleSetupPanel
Input field for experiment parameter
m_ExperimentTypeCBox - Variable in class weka.gui.experiment.SimpleSetupPanel
Combo box for choosing experiment type
m_ExpFilter - Variable in class weka.gui.experiment.SetupPanel
A filter to ensure only experiment files get shown in the chooser
m_ExpFilter - Variable in class weka.gui.experiment.SimpleSetupPanel
A filter to ensure only experiment files get shown in the chooser
m_ExplicitPropsFile - Variable in class weka.gui.GenericPropertiesCreator
whether an explicit input file was given - if false, the Utils class is used to locate the props-file
m_Explorer - Variable in class weka.gui.explorer.AssociationsPanel
the parent frame
m_Explorer - Variable in class weka.gui.explorer.AttributeSelectionPanel
the parent frame
m_Explorer - Variable in class weka.gui.explorer.ClassifierPanel
the parent frame
m_Explorer - Variable in class weka.gui.explorer.ClustererPanel
the parent frame
m_Explorer - Variable in class weka.gui.explorer.PreprocessPanel
the parent frame
m_Explorer - Variable in class weka.gui.explorer.VisualizePanel
the parent frame
m_ExplorerBut - Variable in class weka.gui.GUIChooser
Click to open the Explorer
m_ExplorerFrame - Variable in class weka.gui.GUIChooser
The frame containing the explorer interface
m_exponent - Variable in class weka.classifiers.functions.supportVector.PolyKernel
The exponent for the polynomial kernel.
m_ExpRegressionRBut - Variable in class weka.gui.experiment.SimpleSetupPanel
Radio button for choosing regression experiment
m_expression - Variable in class weka.core.pmml.DefineFunction
The Expression for this function to use
m_expression - Variable in class weka.core.pmml.DerivedFieldMetaInfo
the single expression that defines the value of this field
m_Expression - Variable in class weka.datagenerators.classifiers.regression.Expression
the expression for computing y
m_Expression - Variable in class weka.filters.unsupervised.instance.SubsetByExpression
the expresion to use for filtering.
m_Extension - Variable in class weka.gui.ExtensionFileFilter
The filename extensions of accepted files
m_extent - Variable in class weka.gui.visualize.PostscriptGraphics
The bounding box of the output
m_ExtremeValuesAsOutliers - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
whether extreme values are also tagged as outliers
m_ExtremeValuesFactor - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
the factor for detecting extreme values, by default 2*m_OutlierFactor
m_f - Variable in class weka.core.Optimization
function value
m_factor - Variable in class weka.classifiers.functions.supportVector.Puk
Cached factor for the Puk kernel.
m_factorList - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
 
m_Factory - Variable in class weka.core.xml.XMLDocument
the factory for DocumentBuilder.
m_FailReason - Variable in class weka.core.Capabilities
the reason why the test failed, used to throw an exception
m_fAlpha - Variable in class weka.classifiers.bayes.net.estimate.BayesNetEstimator
Holds prior on count
m_fastRegression - Variable in class weka.classifiers.trees.lmt.LMTNode
Use heuristic that determines the number of LogitBoost iterations only once in the beginning?
m_fastRegression - Variable in class weka.classifiers.trees.LMT
use heuristic that determines the number of LogitBoost iterations only once in the beginning?
m_field - Variable in class weka.core.pmml.NormDiscrete
The actual attribute itself
m_fieldDefs - Variable in class weka.core.pmml.Expression
The field defs
m_fieldIndex - Variable in class weka.core.pmml.Discretize
The index of the field
m_fieldIndex - Variable in class weka.core.pmml.NormContinuous
The index of the field
m_fieldIndex - Variable in class weka.core.pmml.NormDiscrete
The index of the attribute
m_fieldInstancesStructure - Variable in class weka.core.pmml.MiningSchema
The structure of all the fields (both mining schema and derived) as Instances
m_fieldName - Variable in class weka.core.pmml.Discretize
The name of the field to be discretized
m_fieldName - Variable in class weka.core.pmml.FieldMetaInfo
the name of the field
m_fieldName - Variable in class weka.core.pmml.FieldRef
The name of the field to reference
m_fieldName - Variable in class weka.core.pmml.NormContinuous
The name of the field to use
m_fieldName - Variable in class weka.core.pmml.NormDiscrete
The name of the field to lookup our value in
m_fieldsMap - Variable in class weka.classifiers.pmml.consumer.PMMLClassifier
The mapping between mining schema fields and incoming instance attributes
m_fieldValue - Variable in class weka.core.pmml.NormDiscrete
The actual value (as a String) that will correspond to an output of 1
m_fieldValueIndex - Variable in class weka.core.pmml.NormDiscrete
If we are referring to a nominal (rather than String) attribute then this holds the index of the value in question.
m_File - Variable in class weka.core.converters.AbstractFileLoader
the file
m_File - Variable in class weka.core.converters.ConverterUtils.DataSource
the file to load.
m_FileChooser - Variable in class weka.gui.beans.KnowledgeFlowApp
The file chooser for selecting layout files
m_FileChooser - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
The file chooser for selecting arff files
m_FileChooser - Variable in class weka.gui.experiment.AlgorithmListPanel
The file chooser for selecting experiments
m_FileChooser - Variable in class weka.gui.experiment.DatasetListPanel
The file chooser component.
m_FileChooser - Variable in class weka.gui.experiment.ResultsPanel
The file chooser for selecting result files.
m_FileChooser - Variable in class weka.gui.experiment.SetupPanel
The file chooser for selecting experiments
m_FileChooser - Variable in class weka.gui.experiment.SimpleSetupPanel
The file chooser for selecting experiments
m_FileChooser - Variable in class weka.gui.explorer.ClassifierPanel
The file chooser for selecting model files
m_FileChooser - Variable in class weka.gui.explorer.ClustererPanel
The file chooser for selecting model files
m_FileChooser - Variable in class weka.gui.explorer.PreprocessPanel
The file chooser for selecting data files
m_FileChooser - Variable in class weka.gui.FileEditor
The file chooser used for selecting files
m_FileChooser - Variable in class weka.gui.GenericObjectEditor.GOEPanel
The filechooser for opening and saving object files.
m_FileChooser - Variable in class weka.gui.SetInstancesPanel
The file chooser for selecting arff files
m_FileChooser - Variable in class weka.gui.visualize.VisualizePanel
file chooser for saving instances
m_FileChooserGraphVisualizer - Variable in class weka.gui.GUIChooser
filechooser for the GraphVisualizer
m_FileChooserGraphVisualizer - Variable in class weka.gui.Main
filechooser for the GraphVisualizer.
m_FileChooserPanel - Static variable in class weka.gui.visualize.PrintableComponent
the filechooser for saving the panel.
m_FileChooserPlot - Variable in class weka.gui.GUIChooser
filechooser for Plots
m_FileChooserPlot - Variable in class weka.gui.Main
filechooser for Plots.
m_FileChooserROC - Variable in class weka.gui.GUIChooser
filechooser for ROC curves
m_FileChooserROC - Variable in class weka.gui.Main
filechooser for ROC curves.
m_FileChooserTreeVisualizer - Variable in class weka.gui.GUIChooser
filechooser for the TreeVisualizer
m_FileChooserTreeVisualizer - Variable in class weka.gui.Main
filechooser for the TreeVisualizer.
m_FileLoaders - Static variable in class weka.core.converters.ConverterUtils
all available loaders (extension <-> classname).
m_FileMustExist - Variable in class weka.gui.ConverterFileChooser
whether the file to be opened must exist (only open dialog)
m_Filename - Variable in class weka.core.Debug.Log
the filename, if any
m_Filename - Variable in class weka.core.Debug.SimpleLog
the file to write to (if null then only stdout is used)
m_Filename - Variable in class weka.core.FindWithCapabilities
a file the capabilities can be based on.
m_FileSavers - Static variable in class weka.core.converters.ConverterUtils
all available savers (extension <-> classname).
m_FillWithMissing - Variable in class weka.filters.unsupervised.attribute.AbstractTimeSeries
True if missing values should be used rather than removing instances where the translated value is not known (due to border effects).
m_Filter - Variable in class weka.associations.FilteredAssociator
The filter
m_filter - Variable in class weka.attributeSelection.FilteredAttributeEval
Filter
m_filter - Variable in class weka.attributeSelection.FilteredSubsetEval
Filter
m_Filter - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Filter interface used to point to weka.filters.unsupervised.attribute.Normalize object
m_Filter - Variable in class weka.classifiers.functions.GaussianProcesses
The filter used to standardize/normalize all values.
m_Filter - Variable in class weka.classifiers.functions.LibLINEAR
for normalizing the data
m_Filter - Variable in class weka.classifiers.functions.LibSVM
for normalizing the data
m_Filter - Variable in class weka.classifiers.functions.PLSClassifier
the PLS filter
m_Filter - Variable in class weka.classifiers.functions.SMO
The filter used to standardize/normalize all values.
m_Filter - Variable in class weka.classifiers.functions.SMOreg
The filter used to standardize/normalize all values.
m_Filter - Variable in class weka.classifiers.meta.FilteredClassifier
The filter
m_Filter - Variable in class weka.classifiers.meta.GridSearch
the Filter
m_Filter - Variable in class weka.classifiers.mi.MDD
The filter used to standardize/normalize all values.
m_Filter - Variable in class weka.classifiers.mi.MIBoost
filter used for discretization
m_Filter - Variable in class weka.classifiers.mi.MIDD
The filter used to standardize/normalize all values.
m_Filter - Variable in class weka.classifiers.mi.MIEMDD
The filter used to standardize/normalize all values.
m_Filter - Variable in class weka.classifiers.mi.MIOptimalBall
The filter used to standardize/normalize all values.
m_Filter - Variable in class weka.classifiers.mi.MISMO
The filter used to standardize/normalize all values.
m_Filter - Variable in class weka.classifiers.mi.MISVM
The filter used to standardize/normalize all values.
m_Filter - Variable in class weka.clusterers.FilteredClusterer
The filter.
m_Filter - Variable in class weka.datagenerators.classifiers.regression.Expression
the filter for generating y out of x
m_Filter - Variable in class weka.filters.CheckSource
the classifier used for generating the source code
m_Filter - Variable in class weka.filters.supervised.attribute.PLSFilter
for centering the data
m_Filter - Variable in class weka.filters.unsupervised.attribute.KernelFilter
for centering/standardizing the data
m_Filter - Variable in class weka.filters.unsupervised.attribute.Wavelet
an optional filter for preprocessing of the data
m_Filter - Variable in class weka.gui.explorer.Explorer.CapabilitiesFilterChangeEvent
the capabilities filter
m_filterAfterFirstBatch - Variable in class weka.filters.unsupervised.instance.SubsetByExpression
Whether to filter instances after the first batch has been processed
m_FilterAttributes - Variable in class weka.associations.GeneralizedSequentialPatterns
String containing the attribute numbers that are used for result filtering; -1 means no filtering
m_FilterAttrVector - Variable in class weka.associations.GeneralizedSequentialPatterns
Vector containing the attribute numbers that are used for result filtering; -1 means no filtering
m_FilteredClassifier - Variable in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
The filtered classifier in which the base classifier is wrapped.
m_FilteredClassifier - Variable in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
The filtered classifier in which the base classifier is wrapped.
m_FilteredInstances - Variable in class weka.associations.FilteredAssociator
The instance structure of the filtered instances
m_filteredInstances - Variable in class weka.attributeSelection.FilteredAttributeEval
Filtered instances structure
m_filteredInstances - Variable in class weka.attributeSelection.FilteredSubsetEval
Filtered instances structure
m_FilteredInstances - Variable in class weka.classifiers.meta.FilteredClassifier
The instance structure of the filtered instances
m_FilteredInstances - Variable in class weka.clusterers.FilteredClusterer
The instance structure of the filtered instances.
m_FilterEditor - Variable in class weka.gui.explorer.PreprocessPanel
Lets the user configure the filter
m_FilterPanel - Variable in class weka.gui.explorer.PreprocessPanel
Filter configuration
m_Filters - Variable in class weka.filters.MultiFilter
The filters
m_Filters - Variable in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
The filters.
m_filterThread - Variable in class weka.gui.beans.Filter
 
m_filterType - Variable in class weka.classifiers.functions.GaussianProcesses
Whether to normalize/standardize/neither
m_filterType - Variable in class weka.classifiers.functions.SMO
Whether to normalize/standardize/neither
m_filterType - Variable in class weka.classifiers.functions.SMOreg
Whether to normalize/standardize/neither
m_filterType - Variable in class weka.classifiers.mi.MDD
Whether to normalize/standardize/neither, default:standardize
m_filterType - Variable in class weka.classifiers.mi.MIDD
Whether to normalize/standardize/neither, default:standardize
m_filterType - Variable in class weka.classifiers.mi.MIEMDD
Whether to normalize/standardize/neither, default:standardize
m_filterType - Variable in class weka.classifiers.mi.MIOptimalBall
Whether to normalize/standardize/neither
m_filterType - Variable in class weka.classifiers.mi.MISMO
Whether to normalize/standardize/neither
m_filterType - Variable in class weka.classifiers.mi.MISVM
Whether to normalize/standardize/neither
m_filterType - Variable in class weka.filters.unsupervised.attribute.StringToWordVector
The normalization to apply.
m_findAllRulesForSupportLevel - Variable in class weka.associations.FPGrowth
If true, just all rules meeting the lower bound on the minimum support will be found.
m_FindNumBins - Variable in class weka.filters.unsupervised.attribute.Discretize
Find the number of bins using cross-validated entropy.
m_Finished - Variable in class weka.experiment.Experiment
True if the experiment has finished running
m_First - Variable in class weka.core.neighboursearch.NearestNeighbourSearch.NeighborList
The first node in the list.
m_First - Variable in class weka.gui.LogPanel
An indicator for whether text has been output yet
m_FirstBatchDone - Variable in class weka.filters.Filter
True if the first batch has been done
m_firstBatchFinished - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
Have we processed the first batch (i.e.
m_FirstCheck - Variable in class weka.core.converters.CSVLoader
whether the first row has been read.
m_FirstSuccessor - Variable in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
The first successor
m_FirstSuccessor - Variable in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
The first successor
m_fitLogisticModels - Variable in class weka.classifiers.functions.SMO
Whether logistic models are to be fit
m_fitLogisticModels - Variable in class weka.classifiers.mi.MISMO
Whether logistic models are to be fit
m_FixedExpansion - Variable in class weka.classifiers.trees.BFTree
Fixed number of expansions (if no pruning method is used, its value is -1.
m_fixedNumIterations - Variable in class weka.classifiers.trees.lmt.LogisticBase
Use fixed number of iterations for LogitBoost? (if negative, cross-validate number of iterations)
m_flowEnvironment - Variable in class weka.gui.beans.KnowledgeFlowApp
Environment variables for the current flow
m_FlowHeight - Variable in class weka.gui.beans.KnowledgeFlowApp
the flow layout height
m_FlowWidth - Variable in class weka.gui.beans.KnowledgeFlowApp
the flow layout width
m_fMarginP - Variable in class weka.classifiers.bayes.net.EditableBayesNet
marginal distributions *
m_fnPrevious - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_FoldColumn - Variable in class weka.experiment.PairedTTester
The option setting for the fold number column (-1 means none)
m_FontColor - Variable in class weka.gui.treevisualizer.TreeVisualizer
the font color.
m_Format - Variable in class weka.core.Debug.Timestamp
the format of the timestamp
m_format - Variable in class weka.gui.beans.PredictionAppender
Format of instances to be produced.
m_formatter - Variable in class weka.core.converters.CSVLoader
The formatter to use on dates
m_Formatter - Variable in class weka.core.Debug.Timestamp
handles the format of the output
m_forwardSearchMethod - Variable in class weka.attributeSelection.LinearForwardSelection
0 == forward selection, 1 == floating forward search
m_fpPrevious - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_fPrior - Variable in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes
Holds the prior probability
m_FramedOutput - Variable in class weka.gui.ResultHistoryPanel
A Hashtable mapping names to output text components
m_FrameLocation - Variable in class weka.gui.MemoryUsagePanel
the position for the dialog.
m_framePoppedUp - Variable in class weka.gui.beans.CostBenefitAnalysis
 
m_framePoppedUp - Variable in class weka.gui.beans.DataVisualizer
 
m_framePoppedUp - Variable in class weka.gui.beans.ModelPerformanceChart
 
m_frequency - Variable in class weka.associations.FPGrowth.BinaryItem
The frequency of the item
m_FromDBaseBut - Variable in class weka.gui.experiment.ResultsPanel
Click to load results from a database.
m_FromExpBut - Variable in class weka.gui.experiment.ResultsPanel
Click to get results from the destination given in the experiment.
m_FromFileBut - Variable in class weka.gui.experiment.ResultsPanel
Click to load results from a file.
m_FromLab - Variable in class weka.gui.experiment.ResultsPanel
Displays a message about the current result set.
m_FullyContainChildBalls - Variable in class weka.core.neighboursearch.balltrees.BallTreeConstructor
Should a parent ball completely enclose the balls of its two children, or only the points inside its children.
m_func - Variable in class weka.core.pmml.BuiltInMath
The function to apply
m_func - Variable in class weka.core.pmml.BuiltInString
The function to apply
m_Function - Variable in class weka.datagenerators.classifiers.classification.Agrawal
the function to use for generating the data
m_functionName - Variable in class weka.core.pmml.Function
The name of this function
m_functionType - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
 
m_functionType - Variable in class weka.classifiers.pmml.consumer.NeuralNetwork
The mining function
m_gainV - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_Gamma - Variable in class weka.classifiers.functions.LibSVM
for poly/rbf/sigmoid
m_gamma - Variable in class weka.classifiers.functions.supportVector.RBFKernel
Gamma for the RBF kernel.
m_GammaActual - Variable in class weka.classifiers.functions.LibSVM
for poly/rbf/sigmoid (the actual gamma)
m_GenerateBut - Variable in class weka.gui.explorer.PreprocessPanel
Click to generate artificial data
m_Generator - Variable in class weka.datagenerators.classifiers.classification.BayesNet
the bayesian net generator, that produces the actual data
m_GeneratorEditor - Variable in class weka.gui.explorer.DataGeneratorPanel
the GOE for the generators
m_GeneratorPropertyPanel - Variable in class weka.gui.experiment.SetupPanel
The panel that configures iteration on custom resultproducer property
m_GenericPropertiesCreator - Variable in class weka.core.FindWithCapabilities
whether to use the GenericPropertiesCreator with the superclass.
m_genTime - Variable in class weka.associations.RuleItem
The generation time of a rule.
m_GetCurrentMethod - Variable in class weka.core.stemmers.SnowballStemmer
the getCurrent method.
m_GlobalBlend - Variable in class weka.classifiers.lazy.KStar
default sphere of influence blend setting
m_globalCounts - Variable in class weka.classifiers.misc.VFI
The global class counts
m_globalInfo - Variable in class weka.gui.beans.Associator
Global info for the wrapped associator (if it exists).
m_globalInfo - Variable in class weka.gui.beans.Classifier
Global info for the wrapped classifier (if it exists).
m_globalInfo - Variable in class weka.gui.beans.Clusterer
Global info for the wrapped classifier (if it exists).
m_globalInfo - Variable in class weka.gui.beans.Filter
Global info for the wrapped filter (if it exists).
m_globalInfo - Variable in class weka.gui.beans.Loader
Global info for the wrapped loader (if it exists).
m_globalInfo - Variable in class weka.gui.beans.Saver
Global info for the wrapped loader (if it exists).
m_globalMaxValue - Variable in class weka.datagenerators.clusterers.SubspaceCluster
store global max values
m_globalMinValue - Variable in class weka.datagenerators.clusterers.SubspaceCluster
store global min values
m_gp - Variable in class weka.gui.graphvisualizer.GraphVisualizer
Panel actually displaying the graph
m_graphName - Variable in class weka.gui.graphvisualizer.DotParser
This holds the name of the graph if there is any otherwise it is null
m_GraphPanel - Variable in class weka.classifiers.bayes.net.GUI
Panel actually displaying the graph
m_graphString - Variable in class weka.gui.beans.GraphEvent
 
m_graphTitle - Variable in class weka.gui.beans.GraphEvent
 
m_graphType - Variable in class weka.gui.beans.GraphEvent
 
m_GraphVisualizers - Variable in class weka.gui.GUIChooser
keeps track of the opened graph visualizer instancs
m_Grid - Variable in class weka.classifiers.meta.GridSearch
the value-pairs grid
m_Grid - Variable in class weka.classifiers.meta.GridSearch.PerformanceTable
the corresponding grid
m_GridExtensionsPerformed - Variable in class weka.classifiers.meta.GridSearch
the number of extensions performed
m_GridIsExtendable - Variable in class weka.classifiers.meta.GridSearch
whether the grid can be extended
m_gridWidth - Variable in class weka.gui.beans.AttributeSummarizer
The number of plots horizontally in the display
m_groupIdentifier - Variable in class weka.gui.beans.BatchClassifierEvent
An identifier that can be used to group all related runs/sets together.
m_Groups - Variable in class weka.classifiers.meta.RotationForest
The attributes of each group
m_GUIType - Variable in class weka.gui.Main
the type of GUI to display.
m_Handler - Variable in class weka.core.FindWithCapabilities
a capabilities handler to retrieve the capabilities from.
m_Handler - Variable in class weka.core.TestInstances
the CapabilitiesHandler to get the Capabilities from
m_HandleRightClicks - Variable in class weka.gui.ResultHistoryPanel
Let the result history list handle right clicks in the default manner---ie, pop up a window displaying the buffer
m_hasClass - Variable in class weka.attributeSelection.BestFirst
does the data have a class
m_hasClass - Variable in class weka.attributeSelection.GreedyStepwise
does the data have a class
m_hasClass - Variable in class weka.attributeSelection.LinearForwardSelection
does the data have a class
m_HasClass - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
Data has a class set.
m_hasConstr - Variable in class weka.classifiers.trees.ft.FTtree
True if node has or splits on constructor
m_HashCode - Variable in class weka.core.Trie
the hash code
m_hashtable - Variable in class weka.classifiers.meta.END
The hashtable containing the classifiers for the END.
m_hashtablegiven - Variable in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
Is Hashtable given from END?
m_hashtablegiven - Variable in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
Is Hashtable given from END?
m_hashtablegiven - Variable in class weka.classifiers.meta.nestedDichotomies.ND
Is Hashtable given from END?
m_hashtables - Variable in class weka.associations.Apriori
The same information stored in hash tables.
m_hashtables - Variable in class weka.associations.PredictiveApriori
The same information stored in hash tables.
m_HDistanceDebug - Variable in class weka.classifiers.mi.CitationKNN
 
m_HDRank - Variable in class weka.classifiers.mi.CitationKNN
Rank associated to the Hausdorff distance
m_Head - Variable in class weka.core.Queue
Store a reference to the head of the queue
m_headerInfo - Variable in class weka.classifiers.bayes.DMNBtext
 
m_headerInfo - Variable in class weka.classifiers.bayes.NaiveBayesMultinomial
copy of header information for use in toString method
m_HeaderKeys - Variable in class weka.experiment.ResultMatrix
contains the keys for the header
m_Headers - Variable in class weka.classifiers.meta.RotationForest
Headers of the transformed dataset
m_HeaderValues - Variable in class weka.experiment.ResultMatrix
contains the values for the header
m_Height - Variable in class weka.classifiers.meta.GridSearch.Grid
the number of points on the Y axis
m_heights - Variable in class weka.gui.visualize.AttributePanel
Holds the random height for each instance.
m_Helper - Variable in class weka.gui.sql.ResultSetTableModel
for retrieving the data etc.
m_Heuristic - Variable in class weka.classifiers.trees.BFTree
If use huristic search for binary split (default true).
m_Heuristic - Variable in class weka.classifiers.trees.SimpleCart
If use huristic search for nominal attributes in multi-class problems (default true).
m_heuristicStop - Variable in class weka.classifiers.functions.SimpleLogistic
Parameter for the heuristic for early stopping of LogitBoost
m_heuristicStop - Variable in class weka.classifiers.trees.lmt.LogisticBase
Use heuristic to stop performing LogitBoost iterations earlier? If enabled, LogitBoost is stopped if the current (local) minimum of the error on a test set as a function of the number of iterations has not changed for m_heuristicStop iterations.
m_higherRegressions - Variable in class weka.classifiers.trees.ft.FTtree
Simple regression functions fit by LogitBoost at higher levels in the tree
m_higherRegressions - Variable in class weka.classifiers.trees.lmt.LMTNode
Simple regression functions fit by LogitBoost at higher levels in the tree
m_HighThreshold - Variable in class weka.classifiers.meta.ThresholdSelector
The upper threshold used as the basis of correction
m_highValue - Variable in class weka.core.pmml.MiningFieldMetaInfo
outlier high value
m_histBarCounts - Variable in class weka.gui.AttributeVisualizationPanel
This array holds the count (or height) for the each of the bars in a barplot or a histogram.
m_History - Variable in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Stores the historical instances to copy values between
m_history - Variable in class weka.gui.beans.GraphViewer
 
m_history - Variable in class weka.gui.beans.TextViewer
List of text revieved so far
m_History - Variable in class weka.gui.experiment.ResultsPanel
A panel controlling results viewing.
m_History - Variable in class weka.gui.explorer.AssociationsPanel
A panel controlling results viewing
m_History - Variable in class weka.gui.explorer.AttributeSelectionPanel
A panel controlling results viewing
m_History - Variable in class weka.gui.explorer.ClassifierPanel
A panel controlling results viewing
m_History - Variable in class weka.gui.explorer.ClustererPanel
A panel controlling results viewing
m_History - Variable in class weka.gui.MemoryUsagePanel
the memory usage over time.
m_History - Variable in class weka.gui.sql.ConnectionPanel
the history of connections.
m_History - Variable in class weka.gui.sql.event.HistoryChangedEvent
the history model
m_History - Variable in class weka.gui.sql.QueryPanel
the query history.
m_History - Variable in class weka.gui.sql.SqlViewer
stores the history.
m_HistoryChangedListeners - Variable in class weka.gui.sql.ConnectionPanel
the history listeners.
m_HistoryChangedListeners - Variable in class weka.gui.sql.QueryPanel
the history listeners.
m_HistoryName - Variable in class weka.gui.sql.event.HistoryChangedEvent
the name of the history
m_HistoryPos - Variable in class weka.gui.SimpleCLIPanel
The current position in the command history.
m_HoldOutDist - Variable in class weka.classifiers.trees.REPTree.Tree
Class distribution of hold-out set at node in the nominal case.
m_HoldOutError - Variable in class weka.classifiers.trees.REPTree.Tree
The hold-out error of the node.
m_HostField - Variable in class weka.gui.experiment.HostListPanel
The field with which to enter host names
m_hostList - Variable in class weka.gui.experiment.DistributeExperimentPanel
The host list panel
m_HyperPipes - Variable in class weka.classifiers.misc.HyperPipes
Stores the HyperPipe for each class
m_I0 - Variable in class weka.classifiers.functions.SMO.BinarySMO
{i: 0 < m_alpha[i] < C}
m_I0 - Variable in class weka.classifiers.functions.supportVector.RegSMOImproved
The different sets used by the algorithm.
m_I0 - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
{i: 0 < m_alpha[i] < C}
m_I1 - Variable in class weka.classifiers.functions.SMO.BinarySMO
{i: m_class[i] = 1, m_alpha[i] = 0}
m_I1 - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
{i: m_class[i] = 1, m_alpha[i] = 0}
m_I2 - Variable in class weka.classifiers.functions.SMO.BinarySMO
{i: m_class[i] = -1, m_alpha[i] =C}
m_I2 - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
{i: m_class[i] = -1, m_alpha[i] = C}
m_I3 - Variable in class weka.classifiers.functions.SMO.BinarySMO
{i: m_class[i] = 1, m_alpha[i] = C}
m_I3 - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
{i: m_class[i] = 1, m_alpha[i] = C}
m_I4 - Variable in class weka.classifiers.functions.SMO.BinarySMO
{i: m_class[i] = -1, m_alpha[i] = 0}
m_I4 - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
{i: m_class[i] = -1, m_alpha[i] = 0}
m_ibk - Variable in class weka.classifiers.rules.DecisionTable
IB1 used to classify non matching instances rather than majority class
m_icon - Variable in class weka.gui.beans.BeanVisual
ImageIcons for the icons.
m_Icon - Variable in class weka.gui.GUIChooser
the icon for the frames
m_iconPath - Variable in class weka.gui.beans.BeanVisual
Holds name (including path) of the static icon
m_ID - Variable in class weka.associations.FPGrowth.FPTreeNode
ID (for graphing the tree)
m_id - Variable in class weka.classifiers.functions.neural.NeuralConnection
The string that uniquely (provided naming is done properly) identifies this unit.
m_id - Variable in class weka.classifiers.trees.ft.FTtree
Node id
m_id - Variable in class weka.classifiers.trees.j48.ClassifierTree
The id for the node.
m_id - Variable in class weka.classifiers.trees.lmt.LMTNode
Node id
m_ID - Variable in class weka.core.Debug.Random
the unique ID for this number generator
m_ID - Variable in class weka.core.Tag
The ID
m_ID - Variable in class weka.core.TechnicalInformation
the unique identifier of this information, will be generated automatically if left empty
m_IdIndex - Variable in class weka.classifiers.mi.CitationKNN
 
m_IDStr - Variable in class weka.core.Tag
The unique string for this tag, doesn't have to be numeric
m_IgnoreAttributesRange - Variable in class weka.filters.unsupervised.attribute.AddCluster
Range of attributes to ignore
m_ignoreAttributesRange - Variable in class weka.filters.unsupervised.attribute.ClusterMembership
Range of attributes to ignore
m_IgnoreBeanConnections - Variable in class weka.gui.beans.xml.XMLBeans
whether to ignore the BeanConnection
m_ignoreBut - Variable in class weka.gui.explorer.ClustererPanel
The button used to popup a list for choosing attributes to ignore while clustering
m_IgnoreChange - Variable in class weka.gui.visualize.PrintableComponent
whether to ignore the update of the text field (in case of "keep ratio").
m_IgnoreClass - Variable in class weka.filters.unsupervised.attribute.PotentialClassIgnorer
True if the class is to be unset
m_Ignored - Variable in class weka.core.xml.PropertyHandler
contains display names of properties to ignore in the serialization process
m_IgnoredProperties - Variable in class weka.core.CheckGOE
properties that are skipped in the checkToolTips method
m_ignoreKeyList - Variable in class weka.gui.explorer.ClustererPanel
 
m_ignoreKeyModel - Variable in class weka.gui.explorer.ClustererPanel
 
m_iLow - Variable in class weka.classifiers.functions.SMO.BinarySMO
The indices for m_bLow and m_bUp
m_iLow - Variable in class weka.classifiers.functions.supportVector.RegSMOImproved
index of the instance that gave us b.up and b.low
m_iLow - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
The indices for m_bLow and m_bUp
m_importance - Variable in class weka.core.pmml.MiningFieldMetaInfo
importance (if defined)
m_IncludeAll - Variable in class weka.gui.AttributeSelectionPanel
Press to select all attributes
m_IncludeClass - Variable in class weka.core.InstanceComparator
whether to include the class in the comparison
m_IncludeClass - Variable in class weka.filters.unsupervised.attribute.NumericCleaner
whether to include the class attribute
m_Incorrect - Variable in class weka.classifiers.Evaluation
The weight of all incorrectly classified instances.
m_Incremental - Variable in class weka.core.converters.ConverterUtils.DataSource
whether the loader is incremental.
m_IncrementalBuffer - Variable in class weka.core.converters.ConverterUtils.DataSource
the last internally read instance.
m_incrementalCounter - Variable in class weka.core.converters.AbstractFileSaver
Counter.
m_IncrementalIndex - Variable in class weka.core.converters.SerializedInstancesLoader
The current index position for incremental reading
m_index - Variable in class weka.core.pmml.MiningFieldMetaInfo
the index of the field in the mining schema Instances
m_Index - Variable in class weka.core.PropertyPath.PathElement
the index of the array (-1 for none)
m_Index - Variable in class weka.filters.unsupervised.attribute.AddID
the index of the attribute
m_Index - Variable in class weka.gui.SortedTableModel.SortContainer
the index of the value.
m_IndexString - Variable in class weka.core.SingleIndex
Record the string representation of the number
m_indexVal - Variable in class weka.gui.visualize.AttributePanelEvent
The index for the new attribute
m_indices - Variable in class weka.classifiers.meta.nestedDichotomies.ND.NDTree
The indices associated with this node
m_Indices - Variable in class weka.core.AttributeLocator
the indices
m_Indices - Variable in class weka.core.SparseInstance
The index of the attribute associated with each stored value.
m_Indices - Variable in class weka.filters.unsupervised.attribute.RandomSubset
The indices of the attributes that got selected.
m_IndicesBuffer - Variable in class weka.core.converters.ArffLoader.ArffReader
Buffer of indices for sparse instance
m_IndicesUnused - Variable in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
the indices of the unused attributes.
m_Info - Variable in class weka.classifiers.trees.RandomTree
The header information.
m_Info - Variable in class weka.classifiers.trees.REPTree.Tree
The header information (for printing the tree).
m_Info - Variable in class weka.gui.sql.InfoPanel
the list the contains the messages
m_InfoLabel - Variable in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
the label, listing the name of the superclass.
m_InfoPanel - Variable in class weka.gui.sql.SqlViewer
the info panel.
m_InitFile - Variable in class weka.filters.unsupervised.attribute.KernelFilter
The dataset to initialize the filter with
m_InitFileClassIndex - Variable in class weka.filters.unsupervised.attribute.KernelFilter
the class index for the file to initialized with
m_InitFlag - Variable in class weka.classifiers.lazy.KStar
Flag turning on and off the initialisation of config variables
m_Initial - Static variable in class weka.core.Memory
the initial size of the JVM
m_initialized - Variable in class weka.classifiers.pmml.consumer.PMMLClassifier
Has the classifier been initialized (i.e.
m_Initialized - Variable in class weka.filters.unsupervised.attribute.KernelFilter
whether the filter was initialized
m_Initialized - Variable in class weka.gui.sql.ResultSetHelper
whether we initialized.
m_InitOptions - Variable in class weka.classifiers.meta.CVParameterSelection
The set of all options at initialization time.
m_iNode - Variable in class weka.classifiers.bayes.net.VaryNode
index of the node varied
m_input - Variable in class weka.gui.graphvisualizer.DotParser
This is the input containing DOT stream to be parsed
m_Input - Variable in class weka.gui.SimpleCLIPanel
The command input area.
m_InputCenterFile - Variable in class weka.clusterers.XMeans
file name of the output file for the cluster centers.
m_InputFilename - Variable in class weka.gui.GenericPropertiesCreator
the input file with the packages
m_InputFormat - Variable in class weka.gui.streams.InstanceJoiner
The input format for instances
m_inputList - Variable in class weka.classifiers.functions.neural.NeuralConnection
The list of inputs to this unit.
m_inputMap - Variable in class weka.classifiers.pmml.consumer.NeuralNetwork
A map for storing network input values (computed from an incoming instance)
m_inputNums - Variable in class weka.classifiers.functions.neural.NeuralConnection
The numbering for the connections at the other end of the input lines.
m_InputProperties - Variable in class weka.gui.GenericPropertiesCreator
the "template" properties file with the layout and the packages
m_InputRelAtts - Variable in class weka.filters.Filter
Indices of relational attributes in the input format
m_inputs - Variable in class weka.classifiers.pmml.consumer.NeuralNetwork
The inputs to the network
m_inputs - Variable in class weka.gui.beans.MetaBean
 
m_InputStringAtts - Variable in class weka.filters.Filter
Indices of string attributes in the input format
m_InputStringIndex - Variable in class weka.filters.unsupervised.attribute.Reorder
Contains an index of string attributes in the input format that survive the filtering process -- some entries may be duplicated
m_Instance - Variable in class weka.core.neighboursearch.NearestNeighbourSearch.NeighborNode
The neighbor instance.
m_instanceEvent - Variable in class weka.gui.beans.PredictionAppender
 
m_InstanceInfo - Variable in class weka.gui.visualize.Plot2D
For popping up text info on data points
m_InstanceInfoText - Variable in class weka.gui.visualize.Plot2D
 
m_instanceListeners - Variable in class weka.gui.beans.PredictionAppender
Objects listening for instances events
m_InstanceQuery - Variable in class weka.gui.experiment.ResultsPanel
Does any database querying for us.
m_InstanceRange - Variable in class weka.filters.unsupervised.attribute.AbstractTimeSeries
The number of instances forward to translate values between.
m_instances - Variable in class weka.associations.Apriori
The instances (transactions) to be used for generating the association rules.
m_instances - Variable in class weka.associations.PredictiveApriori
The instances (transactions) to be used for generating the association rules.
m_instances - Variable in class weka.associations.PriorEstimation
The instances for which association rules are mined.
m_instances - Variable in class weka.associations.RuleGeneration
The instances.
m_Instances - Variable in class weka.attributeSelection.GreedyStepwise
 
m_Instances - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Dataset provided to do Training/Test set.
m_Instances - Variable in class weka.classifiers.bayes.BayesNet
The dataset header for the purposes of printing out a semi-intelligible model
m_Instances - Variable in class weka.classifiers.bayes.blr.Prior
 
m_Instances - Variable in class weka.classifiers.bayes.NaiveBayes
The dataset header for the purposes of printing out a semi-intelligible model
m_Instances - Variable in class weka.classifiers.bayes.NaiveBayesSimple
The instances used for training.
m_Instances - Variable in class weka.classifiers.bayes.net.ADNode
list of Instance children (either m_Instances or m_VaryNodes is instantiated)
m_Instances - Variable in class weka.classifiers.lazy.LBR
The set of instances used for current training.
m_Instances - Variable in class weka.classifiers.misc.HyperPipes
The structure of the training data
m_Instances - Variable in class weka.classifiers.misc.VFI
The training data
m_instances - Variable in class weka.clusterers.FarthestFirst
training instances, not necessary to keep, could be replaced by m_ClusterCentroids where needed for header info
m_Instances - Variable in class weka.clusterers.XMeans
training instances.
m_Instances - Variable in class weka.core.Instances
The instances.
m_Instances - Variable in class weka.core.neighboursearch.balltrees.BallSplitter
The instance on which the tree is built.
m_Instances - Variable in class weka.core.neighboursearch.balltrees.BallTreeConstructor
The instances on which to build the tree.
m_Instances - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
The instances that'll be used for tree construction.
m_Instances - Variable in class weka.core.neighboursearch.NearestNeighbourSearch
The neighbourhood of instances to find neighbours in.
m_Instances - Variable in class weka.core.xml.XMLInstances
the underlying Instances
m_Instances - Variable in class weka.experiment.AveragingResultProducer
The dataset of interest
m_Instances - Variable in class weka.experiment.CrossValidationResultProducer
The dataset of interest
m_Instances - Variable in class weka.experiment.DatabaseResultProducer
The dataset of interest
m_Instances - Variable in class weka.experiment.InstancesResultListener
Stores the instances created so far, before assigning to a header
m_Instances - Variable in class weka.experiment.LearningRateResultProducer
The dataset of interest
m_Instances - Variable in class weka.experiment.PairedTTester
The set of instances we will analyse
m_Instances - Variable in class weka.experiment.RandomSplitResultProducer
The dataset of interest
m_Instances - Variable in class weka.gui.AttributeSummaryPanel
The instances we're playing with
m_Instances - Variable in class weka.gui.experiment.ResultsPanel
The instances we're extracting results from.
m_Instances - Variable in class weka.gui.explorer.AssociationsPanel
The main set of instances we're playing with
m_Instances - Variable in class weka.gui.explorer.AttributeSelectionPanel
The main set of instances we're playing with
m_Instances - Variable in class weka.gui.explorer.ClassifierPanel
The main set of instances we're playing with
m_Instances - Variable in class weka.gui.explorer.ClustererPanel
The main set of instances we're playing with
m_Instances - Variable in class weka.gui.explorer.DataGeneratorPanel
the generated Instances
m_Instances - Variable in class weka.gui.explorer.PreprocessPanel
The working instances
m_Instances - Variable in class weka.gui.InstancesSummaryPanel
The instances we're playing with
m_Instances - Variable in class weka.gui.SetInstancesPanel
The current set of instances loaded
m_instancesConsumed - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
number eaten from m_currentSet
m_InstancesTest - Variable in class weka.core.Capabilities
whether to perform data based tests
m_InstIndexes - Variable in class weka.classifiers.lazy.LBR.Indexes
the array instance indexes
m_InstList - Variable in class weka.core.neighboursearch.BallTree
The instances indices sorted inorder of appearence in the tree from left most leaf node to the right most leaf node.
m_Instlist - Variable in class weka.core.neighboursearch.balltrees.BallSplitter
The master index array that'll be reshuffled as nodes are split (and the tree is constructed).
m_InstList - Variable in class weka.core.neighboursearch.balltrees.BallTreeConstructor
The master index array.
m_InstList - Variable in class weka.core.neighboursearch.KDTree
Indexlist of the instances of this kdtree.
m_InstList - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
The master index array that'll be reshuffled as nodes are split and the tree is constructed.
m_InstPerClass - Variable in class weka.classifiers.meta.Grading
InstPerClass
m_InstSummaryPanel - Variable in class weka.gui.explorer.PreprocessPanel
Displays simple stats on the working instances
m_Interpreter - Variable in class weka.core.Jython
the interpreter
m_Interval - Variable in class weka.gui.MemoryUsagePanel.MemoryMonitor
the refresh interval in msecs.
m_intervalBounds - Variable in class weka.classifiers.misc.VFI
The lower bounds for each attribute
m_IntNodeCount - Variable in class weka.core.neighboursearch.TreePerformanceStats
The number of internal nodes looked at for the current/last query.
m_intType - Variable in class weka.experiment.DatabaseUtils
integer type for the create table statement.
m_invert - Variable in class weka.filters.unsupervised.attribute.RemoveType
Whether to invert selection
m_Invert - Variable in class weka.filters.unsupervised.instance.RemoveFrequentValues
whether to invert the matching sense.
m_Invert - Variable in class weka.gui.AttributeSelectionPanel
Press to invert the current selection
m_invertMatching - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
Whether to invert the match so the correctly classified instances are discarded
m_InvertSelection - Variable in class weka.filters.supervised.instance.Resample
Whether to invert the selection (only if instances are drawn WITHOUT replacement).
m_InvertSelection - Variable in class weka.filters.unsupervised.instance.Resample
Whether to invert the selection (only if instances are drawn WITHOUT replacement)
m_IOThread - Variable in class weka.gui.explorer.PreprocessPanel
A thread for loading/saving instances from a file or URL
m_IOThread - Variable in class weka.gui.SetInstancesPanel
The thread we do loading in
m_IQR - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
the interquartile range
m_isEmpty - Variable in class weka.classifiers.rules.part.ClassifierDecList
True if node is empty.
m_isEmpty - Variable in class weka.classifiers.trees.j48.ClassifierTree
True if node is empty.
m_iSet - Variable in class weka.classifiers.functions.supportVector.RegSMOImproved
Index set {i: 0 < m_alpha[i] < C || 0 < m_alphaStar[i] < C}}
m_isLeaf - Variable in class weka.classifiers.rules.part.ClassifierDecList
True if node is leaf.
m_isLeaf - Variable in class weka.classifiers.trees.BFTree
If the ndoe is leaf node.
m_isLeaf - Variable in class weka.classifiers.trees.ft.FTtree
True if node is leaf
m_isLeaf - Variable in class weka.classifiers.trees.j48.ClassifierTree
True if node is leaf.
m_isLeaf - Variable in class weka.classifiers.trees.lmt.LMTNode
True if node is leaf
m_isLeaf - Variable in class weka.classifiers.trees.SimpleCart
Indicate if the node is a leaf node.
m_item - Variable in class weka.associations.FPGrowth.FPTreeNode
item at this node
m_items - Variable in class weka.associations.FPGrowth.FrequentBinaryItemSet
The list of items in the item set
m_items - Variable in class weka.associations.ItemSet
The items stored as an array of of ints.
m_items - Variable in class weka.associations.RuleGeneration
The items stored as an array of of integer.
m_IterationCount - Variable in class weka.clusterers.XMeans
counts iterations done in main loop.
m_iUp - Variable in class weka.classifiers.functions.SMO.BinarySMO
The indices for m_bLow and m_bUp
m_iUp - Variable in class weka.classifiers.functions.supportVector.RegSMOImproved
index of the instance that gave us b.up and b.low
m_iUp - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
The indices for m_bLow and m_bUp
m_Javadocs - Static variable in class weka.core.AllJavadoc
contains all the
m_jBtSave - Variable in class weka.gui.graphvisualizer.GraphVisualizer
Save button to save the current graph in DOT or XMLBIF format.
m_jCbEdgeConcentration - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
controls edge concentration by concentrating multilple singular dummy child nodes into one plural dummy child node
m_jitter - Variable in class weka.gui.visualize.MatrixPanel
The slider to add jitter to the plots
m_Jitter - Variable in class weka.gui.visualize.VisualizePanel
The jitter slider
m_JitterLab - Variable in class weka.gui.visualize.VisualizePanel
Label for the jitter slider
m_JitterVal - Variable in class weka.gui.visualize.Plot2D
the level of jitter
m_JRand - Variable in class weka.gui.visualize.Plot2D
random values for perterbing the data points
m_jRbBottomup - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
 
m_jRbNaiveLayout - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
 
m_jRbPriorityLayout - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
 
m_jRbTopdown - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
 
m_js - Variable in class weka.gui.visualize.MatrixPanel
The scroll pane to scrolling the matrix
m_k - Variable in class weka.filters.unsupervised.attribute.RandomProjection
Stores the number of dimensions to reduce the data to
m_Kappa - Variable in class weka.classifiers.meta.GridSearch.Performance
the kappa value
m_KDTree - Variable in class weka.clusterers.XMeans
KDTrees class if KDTrees are used.
m_kernel - Variable in class weka.classifiers.functions.GaussianProcesses
Kernel to use
m_kernel - Variable in class weka.classifiers.functions.SMO.BinarySMO
Kernel to use
m_kernel - Variable in class weka.classifiers.functions.SMO
the kernel to use
m_kernel - Variable in class weka.classifiers.functions.SMOreg
the configured kernel
m_Kernel - Variable in class weka.classifiers.functions.supportVector.CheckKernel
The kernel to be examined
m_kernel - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
the kernel
m_kernel - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
Kernel to use
m_kernel - Variable in class weka.classifiers.mi.MISMO
Kernel to use
m_kernel - Variable in class weka.classifiers.mi.MISVM
the kernel to use
m_Kernel - Variable in class weka.filters.unsupervised.attribute.KernelFilter
Kernel to use
m_kernelEvals - Variable in class weka.classifiers.functions.supportVector.CachedKernel
Counts the number of kernel evaluations.
m_KernelFactor - Variable in class weka.filters.unsupervised.attribute.KernelFilter
the calculated kernel factor
m_KernelFactorExpression - Variable in class weka.filters.unsupervised.attribute.KernelFilter
optimizes the kernel with this formula (A = # of attributes, N = # of instances)
m_KernelIsLinear - Variable in class weka.classifiers.functions.GaussianProcesses
whether the kernel is a linear one
m_KernelIsLinear - Variable in class weka.classifiers.functions.SMO
whether the kernel is a linear one
m_kernelMatrix - Variable in class weka.classifiers.functions.supportVector.CachedKernel
The kernel matrix if full cache is used (i.e.
m_KernelMatrix - Variable in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
The kernel matrix.
m_KernelMatrixFile - Variable in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
The file holding the kernel matrix.
m_kernelPrecalc - Variable in class weka.classifiers.functions.supportVector.Puk
The precalculated dotproducts of <inst_i,inst_i>
m_kernelPrecalc - Variable in class weka.classifiers.functions.supportVector.RBFKernel
The precalculated dotproducts of <inst_i,inst_i>
m_kernelPrecalc - Variable in class weka.classifiers.mi.supportVector.MIRBFKernel
The precalculated dotproducts of <inst_i,inst_i>
m_KernelType - Variable in class weka.classifiers.functions.LibSVM
the kernel type
m_KeyFieldName - Variable in class weka.experiment.AveragingResultProducer
The name of the key field to average over
m_KeyIndex - Variable in class weka.experiment.AveragingResultProducer
The index of the field to average over in the resultproducers key
m_keys - Variable in class weka.classifiers.functions.supportVector.CachedKernel
 
m_Keys - Variable in class weka.core.converters.DatabaseLoader
the keys for unique ordering
m_Keys - Variable in class weka.experiment.AveragingResultProducer
Collects the keys from a single run
m_Keywords - Variable in class weka.experiment.DatabaseUtils
the keywords for the current database type.
m_KeywordsMaskChar - Variable in class weka.experiment.DatabaseUtils
the character to mask SQL keywords (by appending this character).
m_KfFilter - Variable in class weka.gui.beans.KnowledgeFlowApp
A filter to ensure only KnowledgeFlow files in binary format get shown in the chooser
m_KMeansStopped - Variable in class weka.clusterers.XMeans
counter to say how often kMeans was stopped by loop counter.
m_kNN - Variable in class weka.classifiers.lazy.IBk
The number of neighbours to use for classification (currently).
m_kNN - Variable in class weka.classifiers.lazy.LWL
The number of neighbours used to select the kernel bandwidth.
m_kNN - Variable in class weka.core.neighboursearch.NearestNeighbourSearch
The number of neighbours to find.
m_kNNUpper - Variable in class weka.classifiers.lazy.IBk
The value of kNN provided by the user.
m_kNNValid - Variable in class weka.classifiers.lazy.IBk
Whether the value of k selected by cross validation has been invalidated by a change in the training instances.
m_KnowledgeFlowBut - Variable in class weka.gui.GUIChooser
Click to open the KnowledgeFlow
m_KnowledgeFlowFrame - Variable in class weka.gui.GUIChooser
The frame containing the knowledge flow interface
m_KOMLFilter - Variable in class weka.gui.beans.Classifier
 
m_KOMLFilter - Variable in class weka.gui.beans.KnowledgeFlowApp
A filter to ensure only KnowledgeFlow files in KOML format get shown in the chooser
m_KOMLFilter - Variable in class weka.gui.experiment.SetupPanel
A filter to ensure only experiment (in KOML format) files get shown in the chooser
m_KOMLFilter - Variable in class weka.gui.experiment.SimpleSetupPanel
A filter to ensure only experiment (in KOML format) files get shown in the chooser
m_KValue - Variable in class weka.classifiers.trees.RandomForest
Final number of features that were considered in last build.
m_KValue - Variable in class weka.classifiers.trees.RandomTree
The number of attributes considered for a split.
m_KWBias - Variable in class weka.classifiers.BVDecomposeSegCVSub
The calculated Kohavi & Wolpert bias (squared)
m_KWSigma - Variable in class weka.classifiers.BVDecomposeSegCVSub
The calculated Kohavi & Wolpert sigma
m_KWVariance - Variable in class weka.classifiers.BVDecomposeSegCVSub
The calculated Kohavi & Wolpert variance
m_LabelCurrentSize - Variable in class weka.gui.LogWindow
the current size
m_labelFont - Variable in class weka.gui.visualize.Plot2D
Font for labels
m_labelMetrics - Variable in class weka.gui.visualize.Plot2D
 
m_LabelQuery - Variable in class weka.gui.sql.SqlViewerDialog
displays the current query
m_Labels - Variable in class weka.filters.unsupervised.attribute.Add
The list of labels for nominal attribute.
m_Labels - Variable in class weka.filters.unsupervised.attribute.AddValues
The values to add.
m_LabelURL - Variable in class weka.gui.sql.ConnectionPanel
the label for the URL.
m_LabelX - Variable in class weka.classifiers.meta.GridSearch.Grid
the label for the X axis
m_LabelY - Variable in class weka.classifiers.meta.GridSearch.Grid
the label for the Y axis
m_lambda - Variable in class weka.classifiers.functions.SPegasos
The regularization parameter
m_lambda - Variable in class weka.classifiers.functions.supportVector.StringKernel
the decay factor that penalizes non-continuous substring matches.
m_largeItemSets - Variable in class weka.associations.FPGrowth
Holds the large item sets found
m_Last - Variable in class weka.core.neighboursearch.NearestNeighbourSearch.NeighborList
The last node in the list.
m_lastAddedSplitNum - Variable in class weka.classifiers.trees.ADTree
The number of the last splitter added to the tree
m_lastAddedSplitNum - Variable in class weka.classifiers.trees.LADTree
 
m_LastFilter - Variable in class weka.gui.ConverterFileChooser
the last filter that was used for opening/saving
m_lastLabel - Variable in class weka.datagenerators.classifiers.classification.Agrawal
the last class label that was generated
m_LastLeaf - Variable in class weka.core.Trie.TrieIterator
the last leaf for this root node
m_lastLogLikelihood - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_LastURL - Variable in class weka.gui.explorer.PreprocessPanel
Stores the last URL that instances were loaded from
m_LastURL - Variable in class weka.gui.SetInstancesPanel
Stores the last URL that instances were loaded from
m_lastValidationError - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_layers - Variable in class weka.classifiers.pmml.consumer.NeuralNetwork
The hidden layers in the network
m_layoutEngine - Variable in class weka.classifiers.bayes.net.GUI
The current LayoutEngine
m_le - Variable in class weka.gui.graphvisualizer.GraphVisualizer
The current LayoutEngine
m_leafclass - Variable in class weka.classifiers.trees.ft.FTtree
Stores leaf class value
m_LeafCount - Variable in class weka.core.neighboursearch.TreePerformanceStats
The number of leaf nodes looked at for the current/last query.
m_leafModelNum - Variable in class weka.classifiers.trees.ft.FTtree
ID of logistic model at leaf
m_leafModelNum - Variable in class weka.classifiers.trees.lmt.LMTNode
ID of logistic model at leaf
m_LeastValues - Variable in class weka.filters.unsupervised.instance.RemoveFrequentValues
whether to retain values with least instances instead of most.
m_left - Variable in class weka.classifiers.meta.nestedDichotomies.ND.NDTree
The left successor
m_left - Variable in class weka.classifiers.trees.m5.RuleNode
left child node
m_Left - Variable in class weka.core.neighboursearch.balltrees.BallNode
The left child of the node.
m_Left - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode
left subtree; contains instances with smaller or equal to split value.
m_leftMargin - Variable in class weka.core.pmml.FieldMetaInfo.Interval
The left boundary value
m_legendPanel - Variable in class weka.gui.visualize.VisualizePanel
The panel that displays legend info if there is more than one plot
m_LegendPanelBorderColor - Variable in class weka.gui.beans.StripChart
the color of the legend panel's border.
m_Length - Variable in class weka.core.neighboursearch.NearestNeighbourSearch.NeighborList
The number of nodes to attempt to maintain in the list.
m_levelSibling - Variable in class weka.associations.FPGrowth.FPTreeNode
link to another sibling at this level in the tree
m_linearNormNorm - Variable in class weka.core.pmml.NormContinuous
norm values for the LinearNorm entries
m_linearNormOrig - Variable in class weka.core.pmml.NormContinuous
original values for the LinearNorm entries
m_linearSelectionType - Variable in class weka.attributeSelection.LinearForwardSelection
0 == fixed-set, 1 == fixed-width
m_linearSelectionType - Variable in class weka.attributeSelection.SubsetSizeForwardSelection
0 == fixed-set, 1 == fixed-width
m_LineColor - Variable in class weka.gui.treevisualizer.TreeVisualizer
the line color.
m_LineFeed - Variable in class weka.core.logging.FileLogger
the line feed.
m_LineFeed - Variable in class weka.core.logging.OutputLogger.OutputPrintStream
the line feed.
m_Lines - Variable in class weka.core.converters.ArffLoader.ArffReader
the number of lines read so far
m_Lines - Variable in class weka.core.Instances
The lines read so far in case of incremental loading.
m_linkFunction - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
 
m_linkParameter - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
 
m_List - Variable in class weka.gui.experiment.AlgorithmListPanel
The component displaying the algorithm list
m_List - Variable in class weka.gui.experiment.DatasetListPanel
The component displaying the dataset list.
m_List - Variable in class weka.gui.experiment.HostListPanel
The component displaying the host list
m_List - Variable in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
the list with all the capabilities.
m_List - Variable in class weka.gui.ListSelectorDialog
The list component
m_List - Variable in class weka.gui.ResultHistoryPanel
The list component
m_listenee - Variable in class weka.gui.beans.AbstractDataSink
Non null if this object is a target for any events.
m_listenee - Variable in class weka.gui.beans.AbstractEvaluator
 
m_listenee - Variable in class weka.gui.beans.AbstractTestSetProducer
non null if this object is a target for any events.
m_listenee - Variable in class weka.gui.beans.AbstractTrainAndTestSetProducer
non null if this object is a target for any events.
m_listenee - Variable in class weka.gui.beans.AbstractTrainingSetProducer
non null if this object is a target for any events.
m_listenee - Variable in class weka.gui.beans.CostBenefitAnalysis
The object sending us data (we allow only one connection at any one time)
m_listenee - Variable in class weka.gui.beans.PredictionAppender
Non null if this object is a target for any events.
m_listenee - Variable in class weka.gui.beans.SerializedModelSaver
Non null if this object is a target for any events.
m_Listener - Variable in class weka.gui.ConverterFileChooser
the propertychangelistener
m_listeners - Variable in class weka.gui.beans.AbstractDataSource
Objects listening for events from data sources
m_listeners - Variable in class weka.gui.beans.AbstractTestSetProducer
Objects listening to us
m_listeners - Variable in class weka.gui.beans.AbstractTrainingSetProducer
Objects listening for training set events
m_listeners - Variable in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
a list of RemoteExperimentListeners
m_Listeners - Variable in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Listeners who want to be notified about editing status of this panel
m_Listeners - Variable in class weka.gui.sql.ResultPanel
the result change listeners
m_Listeners - Variable in class weka.gui.sql.ResultSetTableModel
the listeners.
m_Listeners - Variable in class weka.gui.visualize.AttributePanel
The list of things listening to this panel
m_LL - Variable in class weka.classifiers.functions.Logistic
Log-likelihood of the searched model
m_lnFactorialCache - Variable in class weka.classifiers.bayes.NaiveBayesMultinomial
cache lnFactorial computations
m_LNorm - Variable in class weka.filters.unsupervised.instance.Normalize
The L-norm to use
m_Loader - Variable in class weka.core.converters.ConverterUtils.DataSource
the loader.
m_Loader - Variable in class weka.gui.SetInstancesPanel
The current loader used to obtain the current instances
m_LoaderFileFilters - Static variable in class weka.gui.ConverterFileChooser
the file filters for the loaders
m_LoadOptionsBut - Variable in class weka.gui.experiment.AlgorithmListPanel
Click to edit the load the options for athe selected algorithm
m_LoadThread - Variable in class weka.gui.experiment.ResultsPanel
A thread to load results instances from a file or database.
m_localGraphicsState - Variable in class weka.gui.visualize.PostscriptGraphics
The current local graphics state for this PostscriptGraphics object
m_localModel - Variable in class weka.classifiers.rules.part.ClassifierDecList
Local model at node.
m_localModel - Variable in class weka.classifiers.trees.ft.FTtree
The ClassifierSplitModel (for splitting)
m_localModel - Variable in class weka.classifiers.trees.j48.ClassifierTree
Local model at node.
m_localModel - Variable in class weka.classifiers.trees.lmt.LMTNode
The ClassifierSplitModel (for splitting)
m_LocatorIndices - Variable in class weka.core.AttributeLocator
the indices of locator objects
m_Locators - Variable in class weka.core.AttributeLocator
contains the locator locations, either null or a AttributeLocator reference
m_log - Variable in class weka.classifiers.pmml.consumer.PMMLClassifier
Logger
m_Log - Variable in class weka.core.Debug
for logging
m_Log - Variable in class weka.core.Debug.Random
the log to use for outputting the data, otherwise just stdout
m_log - Variable in class weka.gui.beans.FlowRunner
 
m_log - Variable in class weka.gui.beans.Loader
Logging
m_Log - Variable in class weka.gui.experiment.RunPanel
 
m_Log - Variable in class weka.gui.explorer.AssociationsPanel
The destination for log/status messages
m_Log - Variable in class weka.gui.explorer.AttributeSelectionPanel
The destination for log/status messages
m_Log - Variable in class weka.gui.explorer.ClassifierPanel
The destination for log/status messages
m_Log - Variable in class weka.gui.explorer.ClustererPanel
The destination for log/status messages
m_Log - Variable in class weka.gui.explorer.DataGeneratorPanel
The destination for log/status messages
m_Log - Variable in class weka.gui.explorer.PreprocessPanel
The message logger
m_Log - Variable in class weka.gui.visualize.VisualizePanel
the logger
m_logButton - Variable in class weka.gui.LogPanel
The button for viewing the log
m_LogFile - Variable in class weka.classifiers.meta.GridSearch
the log file to use
m_LogFile - Variable in class weka.core.logging.FileLogger
the log file.
m_Logger - Variable in class weka.core.Debug.Log
the actual logger, if null only stdout is used
m_logger - Variable in class weka.gui.beans.AbstractDataSink
 
m_logger - Variable in class weka.gui.beans.AbstractEvaluator
 
m_logger - Variable in class weka.gui.beans.AbstractTestSetProducer
Logger
m_logger - Variable in class weka.gui.beans.AbstractTrainAndTestSetProducer
 
m_logger - Variable in class weka.gui.beans.AbstractTrainingSetProducer
 
m_logger - Variable in class weka.gui.beans.ClassAssigner
 
m_logger - Variable in class weka.gui.beans.ClassValuePicker
 
m_logger - Variable in class weka.gui.beans.PredictionAppender
 
m_logger - Variable in class weka.gui.beans.SerializedModelSaver
The log for this bean
m_LoggerInitFailed - Variable in class weka.core.Debug.Log
whether the initialization of the logger failed
m_logistic - Variable in class weka.classifiers.functions.SMO.BinarySMO
Stores logistic regression model for probability estimate
m_logistic - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
Stores logistic regression model for probability estimate
m_logMessage - Variable in class weka.experiment.RemoteExperimentEvent
A log type message
m_logPanel - Variable in class weka.gui.beans.KnowledgeFlowApp
 
m_LogPanel - Variable in class weka.gui.explorer.Explorer
The panel for log and status messages
m_LogText - Variable in class weka.gui.LogPanel
Displays the log messages
m_LogWindow - Static variable in class weka.gui.GUIChooser
The frame of the LogWindow
m_LogWindow - Static variable in class weka.gui.Main
The frame of the LogWindow.
m_Loss - Variable in class weka.classifiers.functions.LibSVM
loss, for EPSILON_SVR
m_loss - Variable in class weka.classifiers.functions.SPegasos
The current loss function to minimize
m_lowerBoundMinSupport - Variable in class weka.associations.Apriori
The lower bound for the minimum support.
m_lowerBoundMinSupport - Variable in class weka.associations.FPGrowth
The lower bound on minimum support
m_LowerExtremeValue - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
the lower extreme value threshold (= Q1 - EVF*IQR)
m_lowerOrder - Variable in class weka.classifiers.functions.supportVector.PolyKernel
Use lower-order terms?
m_LowerOutlier - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
the lower outlier threshold (= Q1 - OF*IQR)
m_LowerSize - Variable in class weka.experiment.LearningRateResultProducer
The minimum number of instances to use.
m_LowerText - Variable in class weka.gui.experiment.RunNumberPanel
Configures the lower run number
m_LowThreshold - Variable in class weka.classifiers.meta.ThresholdSelector
The lower threshold used as the basis of correction
m_lowValue - Variable in class weka.core.pmml.MiningFieldMetaInfo
outlier low value
m_Ls - Variable in class weka.associations.Apriori
The set of all sets of itemsets L.
m_Ls - Variable in class weka.associations.PredictiveApriori
The set of all sets of itemsets.
m_MAE - Variable in class weka.classifiers.meta.GridSearch.Performance
the Mean absolute error
m_MainCommandline - Static variable in class weka.gui.Main
variable for the Main class which would be set to null by the memory monitoring thread to free up some memory if we running out of memory.
m_MainSingleton - Static variable in class weka.gui.Main
singleton instance of the GUI.
m_majority - Variable in class weka.classifiers.rules.DecisionTable
Holds the majority class
m_MakeBinary - Variable in class weka.filters.supervised.attribute.Discretize
Output binary attributes for discretized attributes.
m_MakeBinary - Variable in class weka.filters.unsupervised.attribute.Discretize
Output binary attributes for discretized attributes.
m_makeIndicatorFilter - Variable in class weka.classifiers.meta.StackingC
Filter to transform metaData - MakeIndicator
m_manualThreshold - Variable in class weka.classifiers.meta.ThresholdSelector
True if a manually set threshold is being used
m_manualThresholdValue - Variable in class weka.classifiers.meta.ThresholdSelector
-1 = not used by default
m_mapMissingDefined - Variable in class weka.core.pmml.Discretize
True if a replacement for missing values has been specified
m_mapMissingDefined - Variable in class weka.core.pmml.NormContinuous
True if a replacement for missing values has been specified
m_mapMissingDefined - Variable in class weka.core.pmml.NormDiscrete
True if a replacement for missing values has been specified
m_mapMissingTo - Variable in class weka.core.pmml.Discretize
The value of the missing value replacement (if defined)
m_mapMissingTo - Variable in class weka.core.pmml.NormContinuous
The value of the missing value replacement (if defined)
m_mapMissingTo - Variable in class weka.core.pmml.NormDiscrete
The value of the missing value replacement (if defined)
m_MarginCounts - Variable in class weka.classifiers.Evaluation
Cumulative margin distribution
m_masterName - Variable in class weka.gui.visualize.Plot2D
The name of the master plot
m_masterPlot - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
Data for the threshold curve
m_masterPlot - Variable in class weka.gui.beans.ModelPerformanceChart
 
m_masterPlot - Variable in class weka.gui.visualize.Plot2D
The master plot
m_Matches - Variable in class weka.core.FindWithCapabilities
the classes that matched.
m_MatchMissingValues - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
True if missing values should count as a match
m_matrix - Variable in class weka.classifiers.CostMatrix
[rows][columns]
m_Matrix - Variable in class weka.core.Matrix
Deprecated.
The actual matrix
m_matrixPanel - Variable in class weka.gui.beans.ScatterPlotMatrix
 
m_MatrixSource - Variable in class weka.attributeSelection.CostSensitiveASEvaluation
Indicates the current cost matrix source
m_MatrixSource - Variable in class weka.classifiers.meta.CostSensitiveClassifier
Indicates the current cost matrix source
m_MatrixSource - Variable in class weka.classifiers.meta.MetaCost
Indicates the current cost matrix source
m_Max - Variable in class weka.classifiers.meta.GridSearch.PerformanceTable
the maximum performance
m_Max - Variable in class weka.core.Memory
the maximum amount of memory that can be used
m_max - Variable in class weka.core.pmml.TargetMetaInfo
 
m_Max - Variable in class weka.gui.boundaryvisualizer.KDDataGenerator
The maximum values for numeric attributes.
m_MaxArray - Variable in class weka.filters.unsupervised.attribute.Normalize
The maximum values for numeric attributes.
m_MaxAttributes - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
maximum number of attributes in the transformed data (-1 for all).
m_MaxAttrsInName - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
maximum number of attributes in the transformed attribute name.
m_maxBatchSizeRequired - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The maximum number of instances required for processing
m_maxBoostingIterations - Variable in class weka.classifiers.functions.SimpleLogistic
Maximum number of iterations for LogitBoost
m_MaxC - Variable in class weka.core.neighboursearch.PerformanceStats
The min and max coordinates(attributes) looked at per query.
m_maxC - Variable in class weka.gui.visualize.AttributePanel
Holds the min and max values of the colouring attributes
m_maxC - Variable in class weka.gui.visualize.Plot2D
 
m_maxC - Variable in class weka.gui.visualize.PlotData2D
 
m_MaxCardinality - Variable in class weka.filters.unsupervised.attribute.RELAGGS
the max.
m_maxChunkSize - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The maimum chunk size used for training
m_MaxDefault - Variable in class weka.filters.unsupervised.attribute.NumericCleaner
the maximum default replacement value
m_MaxDepth - Variable in class weka.classifiers.trees.RandomForest
The maximum depth of the trees (0 = unlimited)
m_MaxDepth - Variable in class weka.classifiers.trees.RandomTree
The maximum depth of the tree (0 = unlimited)
m_MaxDepth - Variable in class weka.classifiers.trees.REPTree
Upper bound on the tree depth
m_MaxDepth - Variable in class weka.core.neighboursearch.balltrees.BallTreeConstructor
The depth of the built tree.
m_MaxDepth - Variable in class weka.core.neighboursearch.CoverTree
Number of nodes in the tree.
m_MaxDepth - Variable in class weka.core.neighboursearch.KDTree
Tree stats.
M_MAXDIFF - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
 
m_maxEntrop - Variable in class weka.classifiers.misc.VFI
The maximum entropy for the class
m_MaxGridExtensions - Variable in class weka.classifiers.meta.GridSearch
maximum number of grid extensions (-1 means unlimited)
m_MaxGroup - Variable in class weka.classifiers.meta.RotationForest
The maximum size of a group
m_maximizeCB - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_MaxInstancesInLeaf - Variable in class weka.core.neighboursearch.BallTree
The maximum number of instances in a leaf.
m_MaxInstancesInLeaf - Variable in class weka.core.neighboursearch.balltrees.BallTreeConstructor
The maximum number of instances allowed in a leaf.
m_MaxInstInLeaf - Variable in class weka.core.neighboursearch.KDTree
maximal number of instances in a leaf.
m_MaxInstNum - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
maximal number of instances for this cluster
m_MaxIntNodes - Variable in class weka.core.neighboursearch.TreePerformanceStats
The min and max number internal nodes looked for a query by the tree based NNS algorithm.
m_maxItems - Variable in class weka.associations.FPGrowth
 
m_MaxIterations - Variable in class weka.classifiers.mi.MIBoost
the maximum number of boost iterations
m_MaxIterations - Variable in class weka.classifiers.mi.MISVM
the maximum number of iterations to perform
m_maxIterations - Variable in class weka.classifiers.trees.lmt.LogisticBase
The maximum number of LogitBoost iterations
m_MaxIterations - Variable in class weka.clusterers.XMeans
maximum overall iterations.
m_MAXITS - Variable in class weka.core.Optimization
 
m_MaxKMeans - Variable in class weka.clusterers.XMeans
maximum iterations to perform Kmeans part if negative, iterations are not checked.
m_MaxKMeansForChildren - Variable in class weka.clusterers.XMeans
see above, but for kMeans of splitted clusters.
m_MaxLeaves - Variable in class weka.core.neighboursearch.TreePerformanceStats
The min and max number leaf nodes looked for a query by the tree based NNS algorithm.
m_MaxNumClusters - Variable in class weka.clusterers.XMeans
max number of clusters to generate.
m_MaxP - Variable in class weka.core.neighboursearch.PerformanceStats
The min and max data points looked for a query by the NNS algorithm.
m_maxPlots - Variable in class weka.gui.beans.AttributeSummarizer
The maximum number of plots to show
m_MaxPosition - Variable in class weka.core.tokenizers.NGramTokenizer
the number of strings available
m_MaxRange - Variable in class weka.datagenerators.classifiers.regression.MexicanHat
the upper boundary of the range, x is drawn from
m_MaxRelLeafRadius - Variable in class weka.core.neighboursearch.balltrees.BallTreeConstructor
The maximum relative radius of a leaf node (relative to the smallest ball enclosing all the data (training) points).
m_MaxRows - Variable in class weka.gui.sql.event.QueryExecuteEvent
the maximum number of rows to retrieve
m_MaxRows - Variable in class weka.gui.sql.ResultSetHelper
the maximum number of rows to retrieve.
m_maxRunNumber - Variable in class weka.gui.beans.BatchClassifierEvent
The maximum number of runs
m_maxRunNumber - Variable in class weka.gui.beans.TestSetEvent
Maximum number of runs.
m_maxRunNumber - Variable in class weka.gui.beans.TrainingSetEvent
Maximum number of runs.
m_maxSetNumber - Variable in class weka.gui.beans.BatchClassifierEvent
The last set number for this series
m_maxSetNumber - Variable in class weka.gui.beans.BatchClustererEvent
The last set number for this series
m_maxSetNumber - Variable in class weka.gui.beans.TestSetEvent
Maximum number of sets (ie 10 in a 10 fold)
m_maxSetNumber - Variable in class weka.gui.beans.TrainingSetEvent
Maximum number of sets (ie 10 in a 10 fold)
m_maxStale - Variable in class weka.attributeSelection.BestFirst
maximum number of stale nodes before terminating search
m_maxStale - Variable in class weka.attributeSelection.LinearForwardSelection
maximum number of stale nodes before terminating search
m_MaxThreshold - Variable in class weka.filters.unsupervised.attribute.NumericCleaner
the maximum threshold
m_maxVal - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
The min and max values for this attribute.
m_maxValue - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
ranges of each attribute (max); not used if gaussian
m_maxValue - Variable in class weka.gui.AttributeVisualizationPanel
This holds the max value of the current attribute.
m_maxVariancePercentage - Variable in class weka.filters.unsupervised.attribute.RemoveUseless
The type of attribute to delete
m_MaxX - Variable in class weka.classifiers.meta.GridSearch.Grid
the maximum on the X axis
m_maxX - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_maxX - Variable in class weka.gui.visualize.Plot2D
Holds the min and max values of the x, y and colouring attributes over all plots
m_maxX - Variable in class weka.gui.visualize.PlotData2D
Holds the min and max values of the x, y and colouring attributes for this plot
m_MaxY - Variable in class weka.classifiers.meta.GridSearch.Grid
the maximum on the Y axis
m_maxY - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_maxY - Variable in class weka.gui.visualize.Plot2D
 
m_maxY - Variable in class weka.gui.visualize.PlotData2D
 
m_Mean - Variable in class weka.classifiers.mi.MINND
The mean for each attribute of each exemplar
m_Mean - Variable in class weka.experiment.ResultMatrix
the values
m_MeanPrec - Variable in class weka.experiment.ResultMatrix
the standard mean precision
m_MeanPrec - Variable in class weka.gui.experiment.OutputFormatDialog
the number of digits after the period (= precision) for printing the mean.
m_MeanPrecSpinner - Variable in class weka.gui.experiment.OutputFormatDialog
the spinner to choose the precision for the mean from.
m_Means - Variable in class weka.classifiers.bayes.NaiveBayesSimple
The means for numeric attributes.
m_MeanSquared - Variable in class weka.classifiers.lazy.IBk
Whether to minimise mean squared error rather than mean absolute error when cross-validating on numeric prediction tasks.
m_meanValue - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
mean ; only used if gaussian
m_MeanWidth - Variable in class weka.experiment.ResultMatrix
the size of the mean columns
m_MeasurePerformance - Variable in class weka.core.neighboursearch.NearestNeighbourSearch
Should we measure Performance.
m_Median - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
the median
m_Memory - Static variable in class weka.gui.Main
for monitoring the Memory consumption.
m_Memory - Variable in class weka.gui.MemoryUsagePanel
for monitoring the memory usage.
m_MemoryUsageFrame - Variable in class weka.gui.GUIChooser
The frame containing the memory usage
m_messageString - Variable in class weka.experiment.RemoteExperimentEvent
The message
m_MetaClassifier - Variable in class weka.classifiers.meta.Stacking
The meta classifier
m_MetaClassifiers - Variable in class weka.classifiers.meta.Grading
The meta classifiers, one for each base classifier.
m_MetaClassifiers - Variable in class weka.classifiers.meta.StackingC
The meta classifiers (one for each class, like in ClassificationViaRegression)
m_MetaFormat - Variable in class weka.classifiers.meta.Stacking
Format for meta data
m_Method - Variable in class weka.classifiers.mi.MIWrapper
the test method
m_Methods - Variable in class weka.core.xml.MethodHandler
stores the properties/class - Method relationship
m_metric - Variable in class weka.associations.FPGrowth
 
m_metricThreshold - Variable in class weka.associations.FPGrowth
 
m_metricType - Variable in class weka.associations.Apriori
The selected metric type.
m_metricType - Variable in class weka.associations.FPGrowth.AssociationRule
The metric type for this rule
m_midPoints - Variable in class weka.associations.PredictiveApriori
The mid points of the intervals used for the prior estimation.
m_midPoints - Variable in class weka.associations.PriorEstimation
The mid points of the discrete intervals in which the interval [0,1] is divided.
m_midPoints - Variable in class weka.associations.RuleGeneration
Sorted array of the mied points of the intervals used for prior estimation.
m_Min - Variable in class weka.classifiers.meta.GridSearch.PerformanceTable
the minimum performance
m_min - Variable in class weka.core.pmml.TargetMetaInfo
min and max
m_Min - Variable in class weka.gui.boundaryvisualizer.KDDataGenerator
The minimum values for numeric attributes.
m_MinArray - Variable in class weka.filters.unsupervised.attribute.Normalize
The minimum values for numeric attributes.
m_MinBoxRelWidth - Variable in class weka.core.neighboursearch.KDTree
minimal relative width of a KDTree rectangle.
m_MinC - Variable in class weka.core.neighboursearch.PerformanceStats
The min and max coordinates(attributes) looked at per query.
m_minC - Variable in class weka.gui.visualize.AttributePanel
 
m_minC - Variable in class weka.gui.visualize.Plot2D
 
m_minC - Variable in class weka.gui.visualize.PlotData2D
 
m_minChunkSize - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The minimum chunk size used for training
m_MinDefault - Variable in class weka.filters.unsupervised.attribute.NumericCleaner
the minimum default replacement value
m_MinGroup - Variable in class weka.classifiers.meta.RotationForest
The minimum size of a group
m_minimax - Variable in class weka.classifiers.mi.MISMO
Use MIMinimax feature space?
m_minimizeCB - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_MinimizeExpectedCost - Variable in class weka.classifiers.meta.CostSensitiveClassifier
True if the costs should be used by selecting the minimum expected cost (false means weight training data by the costs)
m_MinimumNumberInstances - Variable in class weka.core.Capabilities
the minimum number of instances in a dataset
m_MinimumNumberInstancesTest - Variable in class weka.core.Capabilities
whether to test for minimum number of instances
m_minInfoGain - Variable in class weka.classifiers.trees.lmt.ResidualModelSelection
Minimum information gain for split
m_miningMeta - Variable in class weka.core.pmml.MiningSchema
Meta information about the mining schema fields
m_miningSchema - Variable in class weka.classifiers.pmml.consumer.PMMLClassifier
The fields and meta data used by the model
m_miningSchemaInstancesStructure - Variable in class weka.core.pmml.MiningSchema
Just the mining schema fields as Instances
m_MinInstNum - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
minimal number of instances for this cluster
m_MinIntNodes - Variable in class weka.core.neighboursearch.TreePerformanceStats
The min and max number internal nodes looked for a query by the tree based NNS algorithm.
m_MinLeaves - Variable in class weka.core.neighboursearch.TreePerformanceStats
The min and max number leaf nodes looked for a query by the tree based NNS algorithm.
m_MinLevel - Variable in class weka.core.logging.Logger
the minimum level of log events to have in order to end up in the log.
m_minMetric - Variable in class weka.associations.Apriori
The minimum metric score.
m_MinNum - Variable in class weka.classifiers.trees.RandomTree
Minimum number of instances for leaf.
m_MinNum - Variable in class weka.classifiers.trees.REPTree
The minimum number of instances per leaf.
m_MinNumClusters - Variable in class weka.clusterers.XMeans
min number of clusters to generate.
m_minNumInstances - Variable in class weka.classifiers.trees.ft.FTtree
minimum number of instances at which a node is considered for splitting
m_minNumInstances - Variable in class weka.classifiers.trees.FT
minimum number of instances at which a node is considered for splitting
m_minNumInstances - Variable in class weka.classifiers.trees.lmt.LMTNode
minimum number of instances at which a node is considered for splitting
m_minNumInstances - Variable in class weka.classifiers.trees.LMT
minimum number of instances at which a node is considered for splitting
m_minNumInstances - Variable in class weka.classifiers.trees.lmt.ResidualModelSelection
Minimum number of instances for leaves
m_minNumInstances - Variable in class weka.classifiers.trees.m5.M5Base
The minimum number of instances to allow at a leaf node
m_minNumObj - Variable in class weka.classifiers.rules.part.ClassifierDecList
Minimum number of objects
m_minNumObj - Variable in class weka.classifiers.trees.BFTree
Minimum number of instances at leaf nodes.
m_minNumObj - Variable in class weka.classifiers.trees.SimpleCart
Minimum number of instances in at the terminal nodes.
m_MinP - Variable in class weka.core.neighboursearch.PerformanceStats
The min and max data points looked for a query by the NNS algorithm.
m_MinRange - Variable in class weka.datagenerators.classifiers.regression.MexicanHat
the lower boundary of the range, x is drawn from
m_minRuleCount - Variable in class weka.associations.RuleGeneration
The minimum support a rule needs to be a candidate for the list of the best rules.
m_minSupport - Variable in class weka.associations.Apriori
The minimum support.
m_MinSupport - Variable in class weka.associations.GeneralizedSequentialPatterns
the minimum support threshold
m_MinThreshold - Variable in class weka.filters.unsupervised.attribute.NumericCleaner
the minimum threshold
m_minVal - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
 
m_minValue - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
ranges of each attribute (min); not used if gaussian
m_MinVarianceProp - Variable in class weka.classifiers.trees.REPTree
The minimum proportion of the total variance (over all the data) required for split.
m_MinX - Variable in class weka.classifiers.meta.GridSearch.Grid
the minimum on the X axis
m_minX - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_minX - Variable in class weka.gui.visualize.Plot2D
 
m_minX - Variable in class weka.gui.visualize.PlotData2D
 
m_MinY - Variable in class weka.classifiers.meta.GridSearch.Grid
the minimum on the Y axis
m_minY - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_minY - Variable in class weka.gui.visualize.Plot2D
 
m_minY - Variable in class weka.gui.visualize.PlotData2D
 
m_Misses - Variable in class weka.core.FindWithCapabilities
the class that didn't match.
m_missing - Variable in class weka.associations.tertius.Literal
 
m_Missing - Variable in class weka.classifiers.functions.GaussianProcesses
The filter used to get rid of missing values.
m_Missing - Variable in class weka.classifiers.functions.SMO
The filter used to get rid of missing values.
m_Missing - Variable in class weka.classifiers.functions.SMOreg
The filter used to get rid of missing values.
m_Missing - Variable in class weka.classifiers.mi.MDD
The filter used to get rid of missing values.
m_Missing - Variable in class weka.classifiers.mi.MIDD
The filter used to get rid of missing values.
m_Missing - Variable in class weka.classifiers.mi.MIEMDD
The filter used to get rid of missing values.
m_Missing - Variable in class weka.classifiers.mi.MISMO
The filter used to get rid of missing values.
m_Missing - Variable in class weka.filters.supervised.attribute.PLSFilter
for replacing missing values
m_Missing - Variable in class weka.filters.unsupervised.attribute.KernelFilter
The filter used to get rid of missing values.
m_MissingClass - Variable in class weka.classifiers.Evaluation
The weight of all instances that had no class assigned to them.
m_MissingClassValuesTest - Variable in class weka.core.Capabilities
whether to test for missing class values
m_MissingLab - Variable in class weka.gui.AttributeSummaryPanel
Displays the number of missing values
m_MissingMode - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
missing value treatment
m_MissingMode - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
missing value treatment
m_MissingMode - Variable in class weka.classifiers.lazy.KStar
missing value treatment
m_MissingProb - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
Probability of test attribute transforming into train attribute with missing value
m_MissingProb - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
Probability of test attribute transforming into train attribute with missing value
m_MissingValue - Variable in class weka.core.converters.CSVLoader
The placeholder for missing values.
m_missingValueReplacementNominal - Variable in class weka.core.pmml.MiningFieldMetaInfo
actual missing value replacements (if specified)
m_missingValueReplacementNumeric - Variable in class weka.core.pmml.MiningFieldMetaInfo
 
m_MissingValuesFilter - Variable in class weka.classifiers.bayes.BayesNet
filter used to fill in missing values, if any
m_MissingValuesTest - Variable in class weka.core.Capabilities
whether to test for missing values
m_missingValueTreatmentMethod - Variable in class weka.core.pmml.MiningFieldMetaInfo
missing values treatment method
m_Mistakes - Variable in class weka.classifiers.functions.Winnow
Accumulated mistake count (for statistics)
m_Mle - Variable in class weka.clusterers.XMeans
Distortion.
m_Model - Variable in class weka.classifiers.functions.LibLINEAR
LibLINEAR Model
m_Model - Variable in class weka.classifiers.functions.LibSVM
LibSVM Model
m_Model - Variable in class weka.classifiers.misc.SerializedClassifier
the serialized classifier model used for making predictions
m_Model - Variable in class weka.clusterers.XMeans
model information, should increase readability.
m_Model - Variable in class weka.gui.AttributeListPanel
The table model containing attribute names
m_Model - Variable in class weka.gui.AttributeSelectionPanel
The table model containing attribute names and selection status
m_Model - Variable in class weka.gui.ResultHistoryPanel
The list model
m_Model - Variable in class weka.gui.sql.InfoPanel
the model for the list
m_ModelFile - Variable in class weka.classifiers.misc.SerializedClassifier
the file where the serialized model is stored
m_ModelFilter - Variable in class weka.gui.explorer.ClassifierPanel
Filter to ensure only model files are selected
m_ModelFilter - Variable in class weka.gui.explorer.ClustererPanel
Filter to ensure only model files are selected
m_modelHasChanged - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_modelHasChangedLL - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_modelName - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
 
m_models - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_Models - Variable in class weka.classifiers.mi.MIBoost
the models for the iterations
m_modelSelection - Variable in class weka.classifiers.trees.ft.FTtree
ModelSelection object (for splitting)
m_modelSelection - Variable in class weka.classifiers.trees.lmt.LMTNode
ModelSelection object (for splitting)
m_modelType - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
 
m_modelType - Variable in class weka.classifiers.trees.FT
Model Type, value: 0 is FT, 1 is FTLeaves, 2 is FTInner
m_modePanel - Variable in class weka.gui.experiment.SimpleSetupPanel
The panel which switched between simple and advanced setup modes
m_ModifyHeader - Variable in class weka.filters.unsupervised.instance.RemoveFrequentValues
Modify header for nominal attributes?
m_ModifyHeader - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
Modify header for nominal attributes?
m_Monitor - Variable in class weka.gui.MemoryUsagePanel
the thread for monitoring the memory usage.
m_Monitoring - Variable in class weka.gui.MemoryUsagePanel.MemoryMonitor
whether the thread is still running.
m_MultiInstance - Variable in class weka.core.TestInstances
whether to generate Multi-Instance data or not
m_MultinomialWord - Variable in class weka.classifiers.bayes.DMNBtext
 
m_mustContainOR - Variable in class weka.associations.FPGrowth
Use OR rather than AND when considering must contain lists
m_MWeight - Variable in class weka.classifiers.bayes.AODEsr
m value for m-estimation
m_N - Variable in class weka.core.tokenizers.NGramTokenizer
the current length of the N-grams
m_Name - Variable in class weka.core.PropertyPath.PathElement
the property
m_Name - Variable in class weka.filters.unsupervised.attribute.Add
The name for the new attribute.
m_Name - Variable in class weka.filters.unsupervised.attribute.AddID
the name of the attribute
m_name - Variable in class weka.gui.treevisualizer.NamedColor
The name of the color
m_NameCounter - Variable in class weka.gui.sql.ResultPanel
the counter for the tab names
m_NB - Variable in class weka.classifiers.rules.DTNB
The naive Bayes half of the hybrid
m_nCacheHits - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
number of kernel cache hits, used for printing statistics only
m_nCount - Variable in class weka.classifiers.bayes.net.ADNode
count
m_NCV - Variable in class weka.classifiers.lazy.LBR
for printing in n-fold cross validation
m_ndtree - Variable in class weka.classifiers.meta.nestedDichotomies.ND
The tree of classes
m_NearestNeighbors - Variable in class weka.filters.supervised.instance.SMOTE
the number of neighbors to use.
m_negTrainInstances - Variable in class weka.classifiers.trees.ADTree
The training instances with negative class - referencing the training dataset
m_NeighborListDebug - Variable in class weka.classifiers.mi.CitationKNN
 
m_Neighbour - Variable in class weka.classifiers.mi.MINND
The number of nearest neighbour for prediction
m_nEvals - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
number of kernel evaluations, used for printing statistics only
m_nEvidence - Variable in class weka.classifiers.bayes.net.EditableBayesNet
evidence values, used for evidence propagation *
m_NewBatch - Variable in class weka.filters.Filter
Record whether the filter is at the start of a batch
m_NewBut - Variable in class weka.gui.experiment.SetupPanel
Click to create a new experiment with default settings
m_NewBut - Variable in class weka.gui.experiment.SimpleSetupPanel
Click to create a new experiment with default settings
m_NewOrderCols - Variable in class weka.filters.unsupervised.attribute.Reorder
Stores which columns to reorder
m_newValidationFs - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_Next - Variable in class weka.core.neighboursearch.NearestNeighbourSearch.NeighborNode
A link to the next neighbor instance.
m_Next - Variable in class weka.core.Queue.QueueNode
The next node in the queue
m_nextClassShouldBeZero - Variable in class weka.datagenerators.classifiers.classification.Agrawal
used for balancing the class
m_nInstances - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
number of instances in data set
m_NMax - Variable in class weka.core.tokenizers.NGramTokenizer
the maximum number of N
m_nMaxNrOfParents - Variable in class weka.classifiers.bayes.net.search.SearchAlgorithm
Holds upper bound on number of parents
m_nMCV - Variable in class weka.classifiers.bayes.net.VaryNode
most common value
m_NMin - Variable in class weka.core.tokenizers.NGramTokenizer
the minimum number of N
m_nNodes - Variable in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode
nodes of the Bayes net in this junction node
m_NNSearch - Variable in class weka.classifiers.lazy.IBk
for nearest-neighbor search.
m_NNSearch - Variable in class weka.classifiers.lazy.LWL
The nearest neighbour search algorithm to use.
m_noClass - Variable in class weka.estimators.Estimator
set if class is not important
m_NodeColor - Variable in class weka.gui.treevisualizer.TreeVisualizer
the node color.
m_nodeHeight - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
The nodeWidth and nodeHeight
m_NodeNumber - Variable in class weka.core.neighboursearch.balltrees.BallNode
The node number/id.
m_NodeNumber - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode
node number (only for debug).
m_NodeRanges - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode
lowest and highest value and width (= high - low) for each dimension.
m_nodes - Variable in class weka.gui.graphvisualizer.BIFParser
These holds the nodes and edges of the graph
m_nodes - Variable in class weka.gui.graphvisualizer.DotParser
These holds the nodes and edges of the graph
m_nodes - Variable in class weka.gui.graphvisualizer.GraphVisualizer
Vector containing nodes
m_nodes - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
FastVector containing nodes and edges
m_nodesExpanded - Variable in class weka.classifiers.trees.ADTree
Statistics - the number of prediction nodes investigated during search
m_nodesExpanded - Variable in class weka.classifiers.trees.LADTree
 
m_NodesRectBounds - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode
The lo and high bounds of the hyper rectangle described by the node.
m_nodeWidth - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
The nodeWidth and nodeHeight
m_NoisePercent - Variable in class weka.datagenerators.classifiers.classification.LED24
the noise rate
m_NoiseRandom - Variable in class weka.datagenerators.classifiers.regression.MexicanHat
the random number generator for the noise
m_NoiseRate - Variable in class weka.datagenerators.classifiers.regression.MexicanHat
the rate of the gaussian noise
m_NoiseRate - Variable in class weka.datagenerators.clusterers.SubspaceCluster
noise rate in percent (option P, between 0 and 30)
m_NoiseVariance - Variable in class weka.datagenerators.classifiers.regression.MexicanHat
the variance of the gaussian noise
m_nominalAttIndices - Variable in class weka.classifiers.trees.ADTree
An array containing the inidices to the nominal attributes in the data
m_NominalAttributes - Variable in class weka.core.converters.CSVLoader
The range of attributes to force to type nominal.
m_nominalCols - Variable in class weka.datagenerators.ClusterGenerator
Stores which columns are nominal (default numeric)
m_NominalIndexes - Variable in class weka.experiment.InstancesResultListener
For lookup of indices given a string value for each nominal attribute
m_NominalMapping - Variable in class weka.filters.unsupervised.instance.RemoveFrequentValues
If m_ModifyHeader, stores a mapping from old to new indexes
m_NominalMapping - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
If m_ModifyHeader, stores a mapping from old to new indexes
m_NominalStrings - Variable in class weka.experiment.InstancesResultListener
Contains strings seen so far for each nominal attribute
m_NominalToBinary - Variable in class weka.classifiers.functions.GaussianProcesses
The filter used to make attributes numeric.
m_NominalToBinary - Variable in class weka.classifiers.functions.LibLINEAR
The filter used to make attributes numeric.
m_NominalToBinary - Variable in class weka.classifiers.functions.SimpleLogistic
Filter for converting nominal attributes to binary ones
m_NominalToBinary - Variable in class weka.classifiers.functions.SMO
The filter used to make attributes numeric.
m_NominalToBinary - Variable in class weka.classifiers.functions.SMOreg
The filter used to make attributes numeric.
m_nominalToBinary - Variable in class weka.classifiers.functions.SPegasos
Convert nominal attributes to numerically coded binary ones
m_NominalToBinary - Variable in class weka.classifiers.mi.MISMO
The filter used to make attributes numeric.
m_nominalToBinary - Variable in class weka.classifiers.trees.ft.FTtree
Filter to convert nominal attributes to binary
m_nominalToBinary - Variable in class weka.classifiers.trees.FT
Filter to replace nominal attributes
m_nominalToBinary - Variable in class weka.classifiers.trees.lmt.LMTNode
Filter to convert nominal attributes to binary
m_nominalToBinary - Variable in class weka.classifiers.trees.LMT
Filter to replace nominal attributes
m_NominalToBinary - Variable in class weka.filters.unsupervised.attribute.KernelFilter
The filter used to make attributes numeric.
m_NominalToBinaryFilter - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
Filter for turning nominal values into numeric ones.
m_NonSigWins - Variable in class weka.experiment.ResultMatrix
the non-significant wins
m_NoPriors - Variable in class weka.classifiers.Evaluation
enables/disables the use of priors, e.g., if no training set is present in case of de-serialized schemes
m_NoPruning - Variable in class weka.classifiers.trees.REPTree
Don't prune
m_NoReplacement - Variable in class weka.filters.supervised.instance.Resample
Whether to perform sampling with replacement or without.
m_NoReplacement - Variable in class weka.filters.unsupervised.instance.Resample
Whether to perform sampling with replacement or without
m_Norm - Variable in class weka.filters.unsupervised.instance.Normalize
The norm that each instance must have at the end
M_NORMAL - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
 
m_normal - Static variable in class weka.clusterers.Cobweb
Normal constant.
m_normalizationMethod - Variable in class weka.classifiers.pmml.consumer.NeuralNetwork
The normalization method
m_normalizationMethod - Variable in class weka.classifiers.pmml.consumer.Regression
The normalization to use
m_Normalize - Variable in class weka.classifiers.functions.LibLINEAR
normalize input data
m_Normalize - Variable in class weka.classifiers.functions.LibSVM
normalize input data
m_normalize - Variable in class weka.classifiers.functions.SPegasos
Normalize the training data
m_Normalize - Variable in class weka.classifiers.meta.RotationForest
Filter that normalized the attributes
m_NormalizeDimWidths - Variable in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
Should we normalize the widths(ranges) of the dimensions (attributes) before selecting the widest one.
m_NormalizeNodeWidth - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
Stores whether if the width of a KDTree node is normalized or not.
m_NotCapabilities - Variable in class weka.core.FindWithCapabilities
the capabilities to look for to "not have".
m_Notes - Variable in class weka.experiment.Experiment
User notes about the experiment
m_NotesButton - Variable in class weka.gui.experiment.SetupPanel
A button for bringing up the notes
m_NotesButton - Variable in class weka.gui.experiment.SimpleSetupPanel
A button for bringing up the notes
m_NotesFrame - Variable in class weka.gui.experiment.SetupPanel
Frame for the notes
m_NotesFrame - Variable in class weka.gui.experiment.SimpleSetupPanel
Frame for the notes
m_NotesText - Variable in class weka.gui.experiment.SetupPanel
Area for user notes Default of 10 rows
m_NotesText - Variable in class weka.gui.experiment.SimpleSetupPanel
Area for user notes Default of 10 rows
m_nPositionX - Variable in class weka.classifiers.bayes.net.BIFReader
 
m_nPositionX - Variable in class weka.classifiers.bayes.net.EditableBayesNet
location of nodes, used for graph drawing *
m_nPositionY - Variable in class weka.classifiers.bayes.net.BIFReader
 
m_nPositionY - Variable in class weka.classifiers.bayes.net.EditableBayesNet
 
m_nSeed - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
seed for initializing random number generator
m_nStartNode - Variable in class weka.classifiers.bayes.net.ADNode
first node in VaryNode array
m_nSymbols - Variable in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes
Holds number of symbols in distribution
m_ntob - Variable in class weka.filters.unsupervised.attribute.RandomProjection
The NominalToBinary filter applied to the data before this filter
m_nu - Variable in class weka.classifiers.functions.LibSVM
for NU_SVC, ONE_CLASS, and NU_SVR
m_numAttribs - Variable in class weka.attributeSelection.BestFirst
number of attributes in the data
m_numAttribs - Variable in class weka.attributeSelection.GreedyStepwise
number of attributes in the data
m_numAttribs - Variable in class weka.attributeSelection.LinearForwardSelection
number of attributes in the data
m_numAttribs - Variable in class weka.attributeSelection.SubsetSizeForwardSelection
number of attributes in the data
m_NumAttribs - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
Number of attributes.
m_numAttributes - Variable in class weka.classifiers.bayes.NaiveBayesMultinomial
number of unique words
m_NumAttributes - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The number of attributes
m_NumAttributes - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The number of attributes
m_NumAttributes - Variable in class weka.classifiers.lazy.KStar
The number of attributes
m_NumAttributes - Variable in class weka.classifiers.meta.CVParameterSelection
The number of attributes in the data
m_numAttributes - Variable in class weka.classifiers.rules.DecisionTable
The number of attributes in the dataset
m_NumAttributes - Variable in class weka.core.SparseInstance
The maximum number of values that can be stored.
m_NumAttributes - Variable in class weka.datagenerators.classifiers.classification.RandomRBF
Number of attribute the dataset should have
m_NumAttributes - Variable in class weka.datagenerators.classifiers.classification.RDG1
Number of attribute the dataset should have
m_NumAttributes - Variable in class weka.datagenerators.ClusterGenerator
Number of attribute the dataset should have
m_NumAttributes - Variable in class weka.filters.unsupervised.attribute.RandomSubset
The number of attributes to randomly choose (>= 1 absolute number of attributes, < 1 percentage).
m_NumAttributesLab - Variable in class weka.gui.InstancesSummaryPanel
Displays the number of attributes
m_numAttributesSelected - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The number of attributes selected by the attribute selection phase
m_NumAttributesUsed - Variable in class weka.classifiers.lazy.IBk
The number of attributes the contribute to a prediction.
m_numAtts - Variable in class weka.classifiers.lazy.LBR
number of attributes for the dataset
m_NumAttsSet - Variable in class weka.classifiers.lazy.LBR.Indexes
the number of attributes "in use" or set to a the original value (true or false)
m_NumBags - Variable in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
the total number of bags
m_Number - Variable in class weka.classifiers.lazy.LBR
the number of instance to be processed
m_numberAdditionalMeasures - Variable in class weka.experiment.ClassifierSplitEvaluator
The number of additional measures that need to be filled in after taking into account column constraints imposed by the final destination for results
m_numberAdditionalMeasures - Variable in class weka.experiment.DensityBasedClustererSplitEvaluator
The number of additional measures that need to be filled in after taking into account column constraints imposed by the final destination for results
m_numberMerges - Variable in class weka.clusterers.Cobweb
the number of merges that happened
m_numberOfClusters - Variable in class weka.clusterers.CLOPE
Number of clusters
m_numberOfClusters - Variable in class weka.clusterers.Cobweb
Number of clusters (nodes in the tree).
m_numberOfClustersDetermined - Variable in class weka.clusterers.CLOPE
whether the number of clusters was already determined
m_numberOfClustersDetermined - Variable in class weka.clusterers.Cobweb
whether the number of clusters was already determined
m_NumberOfGroups - Variable in class weka.classifiers.meta.RotationForest
Whether minGroup and maxGroup refer to the number of groups or their size
m_numberOfInputs - Variable in class weka.classifiers.pmml.consumer.NeuralNetwork
The number of inputs to the network
m_NumberOfInstances - Variable in class weka.classifiers.lazy.LBR
the Number of Instances to be used in building a classifiers
m_numberOfInstances - Variable in class weka.clusterers.CLOPE
Number of instances
m_numberOfLayers - Variable in class weka.classifiers.pmml.consumer.NeuralNetwork
Number of hidden layers in the network
m_NumberOfRepetitionsTField - Variable in class weka.gui.experiment.SimpleSetupPanel
Input field for number of repetitions
m_numberOfTransactions - Variable in class weka.associations.FPGrowth.FrequentItemSets
The total number of transactions in the data
m_numberSplits - Variable in class weka.clusterers.Cobweb
the number of splits that happened
m_NumBins - Variable in class weka.classifiers.meta.RegressionByDiscretization
The number of discretization intervals.
m_NumBins - Variable in class weka.filters.unsupervised.attribute.Discretize
The number of bins to divide the attribute into
m_numBoostingIterations - Variable in class weka.classifiers.functions.SimpleLogistic
If non-negative, use this as fixed number of LogitBoost iterations
m_numBoostingIterations - Variable in class weka.classifiers.trees.FT
if non-zero, use fixed number of iterations for LogitBoost
m_numBoostingIterations - Variable in class weka.classifiers.trees.LMT
if non-zero, use fixed number of iterations for LogitBoost
m_NumCacheHits - Variable in class weka.classifiers.functions.supportVector.KernelEvaluation
the number of cache hits
m_NumCentroids - Variable in class weka.datagenerators.classifiers.classification.RandomRBF
the number of centroids to use for generation
m_NumCiters - Variable in class weka.classifiers.mi.CitationKNN
Number of citers
m_NumClasses - Variable in class weka.classifiers.bayes.BayesNet
The number of classes
m_NumClasses - Variable in class weka.classifiers.bayes.NaiveBayes
The number of classes (or 1 for numeric class)
m_numClasses - Variable in class weka.classifiers.bayes.NaiveBayesMultinomial
number of class values
m_NumClasses - Variable in class weka.classifiers.Evaluation
The number of classes.
m_NumClasses - Variable in class weka.classifiers.functions.Logistic
The number of the class labels
m_NumClasses - Variable in class weka.classifiers.lazy.IBk
The number of class values (or 1 if predicting numeric).
m_NumClasses - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The number of class values
m_NumClasses - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The number of class values
m_NumClasses - Variable in class weka.classifiers.lazy.KStar
The number of class values
m_numClasses - Variable in class weka.classifiers.lazy.LBR
number of classes for dataset
m_NumClasses - Variable in class weka.classifiers.meta.AdaBoostM1
The number of classes
m_numClasses - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The number of class vals in the training data (1 if class is numeric)
m_NumClasses - Variable in class weka.classifiers.meta.LogitBoost
The number of classes
m_NumClasses - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The number of classes
m_NumClasses - Variable in class weka.classifiers.mi.CitationKNN
The number of the class labels
m_NumClasses - Variable in class weka.classifiers.mi.MDD
The number of the class labels
m_NumClasses - Variable in class weka.classifiers.mi.MIBoost
The number of the class labels
m_NumClasses - Variable in class weka.classifiers.mi.MIDD
The number of the class labels
m_NumClasses - Variable in class weka.classifiers.mi.MIEMDD
The number of the class labels
m_NumClasses - Variable in class weka.classifiers.mi.MILR
The number of the class labels
m_NumClasses - Variable in class weka.classifiers.mi.MINND
The number of class labels in the data
m_NumClasses - Variable in class weka.classifiers.mi.MIWrapper
The number of the class labels
m_NumClasses - Variable in class weka.classifiers.misc.VFI
The number of classes
m_numClasses - Variable in class weka.classifiers.trees.lmt.LogisticBase
The number of different classes
m_numClasses - Variable in class weka.classifiers.trees.lmt.ResidualSplit
Number of classed
m_NumClasses - Variable in class weka.core.TestInstances
the number of classes (in case of NOMINAL class)
m_NumClasses - Variable in class weka.datagenerators.classifiers.classification.RandomRBF
Number of Classes the dataset should have
m_NumClasses - Variable in class weka.datagenerators.classifiers.classification.RDG1
Number of Classes the dataset should have
m_numClusterAttributes - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
number of attributes the cluster is defined for
m_NumClusters - Variable in class weka.clusterers.FarthestFirst
number of clusters to generate
m_NumClusters - Variable in class weka.clusterers.XMeans
The actual number of clusters.
m_NumClusters - Variable in class weka.datagenerators.clusterers.BIRCHCluster
Number of Clusters the dataset should have
m_NumComponents - Variable in class weka.filters.supervised.attribute.PLSFilter
the maximum number of components to generate
m_NumDate - Variable in class weka.core.CheckScheme
the number of date attributes
m_NumDate - Variable in class weka.core.TestInstances
the number of date attributes
m_numericAttIndices - Variable in class weka.classifiers.trees.ADTree
An array containing the inidices to the numeric attributes in the data
m_numericAttIndices - Variable in class weka.classifiers.trees.LADTree
 
m_NumericClassData - Variable in class weka.classifiers.meta.LogitBoost
Dummy dataset with a numeric class
m_NumericClassData - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
Dummy dataset with a numeric class
m_numericClassifyThreshold - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
The threshold for deciding when a numeric value is correctly classified
m_NumericColumns - Variable in class weka.gui.sql.ResultSetHelper
whether a column is numeric.
m_numericData - Variable in class weka.classifiers.trees.lmt.LogisticBase
Numeric version of the training data.
m_numericDataHeader - Variable in class weka.classifiers.trees.lmt.LogisticBase
Header-only version of the numeric version of the training data
m_NumEvals - Variable in class weka.classifiers.functions.supportVector.KernelEvaluation
the number of performed evaluations
m_NumExamples - Variable in class weka.datagenerators.ClassificationGenerator
Number of instances
m_NumExamples - Variable in class weka.datagenerators.RegressionGenerator
Number of instances
m_NumExamplesAct - Variable in class weka.datagenerators.DataGenerator
Number of instances that should be produced into the dataset this number is by default m_NumExamples, but can be reset by the generator
m_numFeatures - Variable in class weka.classifiers.trees.RandomForest
Number of features to consider in random feature selection.
m_NumFiles - Variable in class weka.core.Debug.Log
the number of files for rotating the logs
m_numFolds - Variable in class weka.attributeSelection.SubsetSizeForwardSelection
Number of cross validation folds for subset size determination (default = 5).
m_NumFolds - Variable in class weka.classifiers.Evaluation
The number of folds for a cross-validation.
m_numFolds - Variable in class weka.classifiers.functions.SMO
The number of folds for the internal cross-validation
m_NumFolds - Variable in class weka.classifiers.meta.CVParameterSelection
The number of folds used in cross-validation
m_NumFolds - Variable in class weka.classifiers.meta.Dagging
the number of folds to use to split the training data
m_NumFolds - Variable in class weka.classifiers.meta.LogitBoost
The number of folds for the internal cross-validation.
m_NumFolds - Variable in class weka.classifiers.meta.Stacking
Set the number of folds for the cross-validation
m_numFolds - Variable in class weka.classifiers.mi.MISMO
The number of folds for the internal cross-validation
m_NumFolds - Variable in class weka.classifiers.trees.RandomTree
Determines how much data is used for backfitting
m_NumFolds - Variable in class weka.classifiers.trees.REPTree
Number of folds for reduced error pruning.
m_NumFolds - Variable in class weka.experiment.CrossValidationResultProducer
The number of folds in the cross-validation
m_numFolds - Variable in class weka.gui.experiment.SimpleSetupPanel
The number of folds for a cross-validation experiment
m_numFoldsBoosting - Static variable in class weka.classifiers.trees.lmt.LogisticBase
Number of folds for cross-validating number of LogitBoost iterations
m_numFoldsPruning - Variable in class weka.classifiers.trees.BFTree
Number of folds for the pruning.
m_numFoldsPruning - Static variable in class weka.classifiers.trees.lmt.LMTNode
Number of folds for CART pruning
m_numFoldsPruning - Variable in class weka.classifiers.trees.SimpleCart
Number of folds for minimal cost-complexity pruning.
m_NumGenerated - Variable in class weka.classifiers.meta.LogitBoost
The number of successfully generated base classifiers.
m_numHigherRegressions - Variable in class weka.classifiers.trees.ft.FTtree
Number of simple regression functions fit by LogitBoost at higher levels in the tree
m_numHigherRegressions - Variable in class weka.classifiers.trees.lmt.LMTNode
Number of simple regression functions fit by LogitBoost at higher levels in the tree
m_numIncorrectModel - Variable in class weka.classifiers.trees.lmt.LMTNode
Weighted number of training examples currently misclassified by the logistic model at the node
m_numIncorrectModel - Variable in class weka.classifiers.trees.SimpleCart
Number of training examples misclassified by the model (subtree rooted).
m_numIncorrectTree - Variable in class weka.classifiers.trees.lmt.LMTNode
Weighted number of training examples currently misclassified by the subtree rooted at the node
m_numIncorrectTree - Variable in class weka.classifiers.trees.SimpleCart
Number of training examples misclassified by the model (subtree not rooted).
m_numInputs - Variable in class weka.classifiers.functions.neural.NeuralConnection
The number of inputs.
m_NumInstances - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The number of instances in the dataset
m_NumInstances - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The number of instances in the dataset
m_NumInstances - Variable in class weka.classifiers.lazy.KStar
The number of instances in the dataset
m_numInstances - Variable in class weka.classifiers.trees.ft.FTtree
Number of instances at the node
m_numInstances - Variable in class weka.classifiers.trees.lmt.LMTNode
Number of instances at the node
m_numInstances - Variable in class weka.classifiers.trees.lmt.ResidualSplit
Number of instances in the set
m_numInstances - Variable in class weka.classifiers.trees.m5.RuleNode
the number of instances reaching this node
m_NumInstances - Variable in class weka.core.CheckScheme
The number of instances in the datasets
m_NumInstances - Variable in class weka.core.neighboursearch.balltrees.BallNode
The number of instances/points in the node.
m_NumInstances - Variable in class weka.core.TestInstances
the number of instances
m_numInstances - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
number of instances for this cluster
m_NumInstances - Variable in class weka.estimators.CheckEstimator
The number of instances in the datasets
m_NumInstances - Variable in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
the total number of the propositional instance in the dataset
m_NumInstances - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
Number of instances.
m_numInstancesConsumed - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The number of instances consumed
m_NumInstancesLab - Variable in class weka.gui.InstancesSummaryPanel
Displays the number of instances
m_NumInstancesRelational - Variable in class weka.core.CheckScheme
the number of instances in relational attributes (applies also for bags in multi-instance)
m_NumInstancesRelational - Variable in class weka.core.TestInstances
the number of instances in relational attributes (applies also for bags in multi-instance)
m_numInsts - Variable in class weka.classifiers.functions.supportVector.CachedKernel
The number of instance in the dataset
m_numInsts - Variable in class weka.classifiers.lazy.LBR
number of instances in dataset
m_NumInstsSet - Variable in class weka.classifiers.lazy.LBR.Indexes
the number of instances "in use" or set to a the original value (true or false)
m_numIntervals - Static variable in class weka.associations.PredictiveApriori
The number of intervals used for the prior estimation.
m_numIntervals - Variable in class weka.associations.PriorEstimation
The number of intervals.
m_numIrrelevantAttributes - Variable in class weka.datagenerators.classifiers.classification.LED24
used for generating the output, i.e., the additional noise attributes
m_NumIterations - Variable in class weka.classifiers.bayes.DMNBtext
The number of iterations.
m_numIterations - Variable in class weka.classifiers.functions.Winnow
The number of iterations
m_NumIterations - Variable in class weka.classifiers.IteratedSingleClassifierEnhancer
The number of iterations.
m_NumIterations - Variable in class weka.classifiers.meta.MetaCost
The number of iterations.
m_NumIterationsPerformed - Variable in class weka.classifiers.meta.AdaBoostM1
The number of successfully generated base classifiers.
m_NumIterationsPerformed - Variable in class weka.classifiers.meta.AdditiveRegression
The number of successfully generated base classifiers.
m_NumLeaves - Variable in class weka.core.neighboursearch.balltrees.BallTreeConstructor
The number of leaf nodes in the built tree.
m_NumLeaves - Variable in class weka.core.neighboursearch.CoverTree
Number of nodes in the tree.
m_NumLeaves - Variable in class weka.core.neighboursearch.KDTree
Tree stats.
m_NumNodes - Variable in class weka.core.neighboursearch.balltrees.BallTreeConstructor
The number of internal and leaf nodes in the built tree.
m_NumNodes - Variable in class weka.core.neighboursearch.CoverTree
Number of nodes in the tree.
m_NumNodes - Variable in class weka.core.neighboursearch.KDTree
Tree stats.
m_NumNominal - Variable in class weka.core.CheckScheme
the number of nominal attributes
m_NumNominal - Variable in class weka.core.TestInstances
the number of nominal attributes
m_NumNominalValues - Variable in class weka.core.TestInstances
the number of values for nominal attributes
m_NumNumeric - Variable in class weka.core.CheckScheme
the number of numeric attributes
m_NumNumeric - Variable in class weka.core.TestInstances
the number of numeric attributes
m_numOfClasses - Variable in class weka.classifiers.trees.LADTree
 
m_numOfCleansingIterations - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
The maximum number of cleansing iterations to perform (<1 = until fully cleansed)
m_numOfCrossValidationFolds - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
The number of cross validation folds to perform (<2 = no cross validation)
m_numOfSamplesPerGenerator - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_numOfSamplesPerRegion - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_numOutputs - Variable in class weka.classifiers.functions.neural.NeuralConnection
The number of outputs.
m_numParameters - Variable in class weka.classifiers.trees.lmt.LogisticBase
Effective number of parameters used for AIC / BIC automatic stopping
m_numParameters - Variable in class weka.classifiers.trees.m5.RuleNode
the number of paramters in the chosen model for this node---either the subtree model or the linear model.
m_NumPredictors - Variable in class weka.classifiers.functions.Logistic
The number of attributes in the model
m_NumQueries - Variable in class weka.core.neighboursearch.PerformanceStats
The total number of queries looked at.
m_numRandRules - Static variable in class weka.associations.PredictiveApriori
The number of rules created for the prior estimation.
m_numRandRules - Variable in class weka.associations.PriorEstimation
The number of rnadom rules.
m_NumReferences - Variable in class weka.classifiers.mi.CitationKNN
Number of references
m_numRegressions - Variable in class weka.classifiers.trees.lmt.LogisticBase
The number of LogitBoost iterations performed.
m_NumRelational - Variable in class weka.core.CheckScheme
the number of relational attributes
m_NumRelational - Variable in class weka.core.TestInstances
the number of relational attributes
m_NumRelationalDate - Variable in class weka.core.TestInstances
the number of date attributes in a relational attribute
m_NumRelationalNominal - Variable in class weka.core.TestInstances
the number of nominal attributes in a relational attribute
m_NumRelationalNominalValues - Variable in class weka.core.TestInstances
the number of values for nominal attributes in relational attributes
m_NumRelationalNumeric - Variable in class weka.core.TestInstances
the number of numeric attributes in a relational attribute
m_NumRelationalString - Variable in class weka.core.TestInstances
the number of string attributes in a relational attribute
m_numRepetitions - Variable in class weka.gui.experiment.SimpleSetupPanel
The number of times to repeat the sub-experiment
m_numRules - Variable in class weka.associations.Apriori
The maximum number of rules that are output.
m_numRules - Variable in class weka.associations.PredictiveApriori
The maximum number of rules that are output.
m_numRulesToFind - Variable in class weka.associations.FPGrowth
The number of rules to find
m_NumRuns - Variable in class weka.classifiers.meta.LogitBoost
The number of runs for the internal cross-validation.
m_NumSeqAttsSet - Variable in class weka.classifiers.lazy.LBR.Indexes
the number of sequential attributes "in use" or set to a the original value (true or false)
m_NumSeqInstsSet - Variable in class weka.classifiers.lazy.LBR.Indexes
the number of sequential instances "in use" or set to a the original value (true or false)
m_NumSplits - Variable in class weka.clusterers.XMeans
Number of splits prepared.
m_NumSplitsDone - Variable in class weka.clusterers.XMeans
Number of splits accepted (including cutoff factor decisions).
m_NumSplitsStillDone - Variable in class weka.clusterers.XMeans
Number of splits accepted just because of cutoff factor.
m_NumString - Variable in class weka.core.CheckScheme
the number of string attributes
m_NumString - Variable in class weka.core.TestInstances
the number of string attributes
m_NumSubCmtys - Variable in class weka.classifiers.meta.MultiBoostAB
The number of sub-committees to use
m_numSubsets - Variable in class weka.classifiers.trees.j48.ClassifierSplitModel
Number of created subsets.
m_numToSelect - Variable in class weka.attributeSelection.GreedyStepwise
The number of attributes to select.
m_NumTrain - Variable in class weka.classifiers.functions.GaussianProcesses
The number of training instances
m_NumTrainClassVals - Variable in class weka.classifiers.Evaluation
Number of non-missing class training instances seen
m_NumTrainInstances - Variable in class weka.filters.unsupervised.attribute.KernelFilter
The number of instances in the training data.
m_numTrees - Variable in class weka.classifiers.trees.RandomForest
Number of trees in forest.
m_numUsedAttributes - Variable in class weka.attributeSelection.LinearForwardSelection
number of top-ranked attributes that are taken into account for the search
m_numUsedAttributes - Variable in class weka.attributeSelection.SubsetSizeForwardSelection
number of top-ranked attributes that are taken into account for the search
m_numValues - Variable in class weka.datagenerators.clusterers.SubspaceCluster
if nominal, store number of values
m_NumValues - Variable in class weka.filters.unsupervised.instance.RemoveFrequentValues
the number of values to retain.
m_NumXValFolds - Variable in class weka.classifiers.meta.MultiScheme
Number of folds to use for cross validation (0 means use training error for selection)
m_NumXValFolds - Variable in class weka.classifiers.meta.ThresholdSelector
The number of folds used in cross-validation
m_Object - Variable in class weka.core.CheckGOE
the object to test
m_Object - Variable in class weka.core.PropertyPath.PropertyContainer
the associated object
m_Object - Variable in class weka.gui.GenericObjectEditor
The object being configured.
m_ObjectNames - Variable in class weka.gui.GenericObjectEditor
The model containing the list of names to select from.
m_ObjectPropertyPanel - Variable in class weka.gui.GenericObjectEditor
The property panel created for the objects.
m_objectstream - Variable in class weka.core.converters.SerializedInstancesSaver
the output stream.
m_Objs - Variable in class weka.gui.ResultHistoryPanel
A hashtable mapping names to arbitrary objects
m_Offset - Variable in class weka.classifiers.meta.LogitBoost
The value by which the actual target value for the true class is offset.
m_offsetValue - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
 
m_offsetVariable - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
 
m_okBut - Variable in class weka.gui.GenericObjectEditor.GOEPanel
ok button.
m_OkButton - Variable in class weka.gui.experiment.OutputFormatDialog
Click to activate the current set parameters.
m_OkButton - Variable in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
the OK button.
m_OkButton - Variable in class weka.gui.ViewerDialog
Click to activate the current set parameters
m_oldText - Variable in class weka.gui.beans.Classifier
Holds original icon label text
m_oldWidth - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
Used to determine if the positions need to be recalculated.
m_omega - Variable in class weka.classifiers.functions.supportVector.Puk
Omega for the Puk kernel.
m_OnDemandDirectory - Variable in class weka.attributeSelection.CostSensitiveASEvaluation
The directory used when loading cost files on demand, null indicates current directory
m_OnDemandDirectory - Variable in class weka.classifiers.meta.CostSensitiveClassifier
The directory used when loading cost files on demand, null indicates current directory
m_OnDemandDirectory - Variable in class weka.classifiers.meta.MetaCost
The directory used when loading cost files on demand, null indicates current directory
m_OnDemandDirectory - Variable in class weka.experiment.CostSensitiveClassifierSplitEvaluator
The directory used when loading cost files on demand, null indicates current directory
m_onlyClass - Variable in class weka.associations.Apriori
Only the class attribute of all Instances.
m_onlyNumeric - Variable in class weka.classifiers.functions.SMOreg
Only numeric attributes in the dataset? If so, less need to filter
m_OpenBut - Variable in class weka.gui.experiment.SetupPanel
Click to load an experiment
m_OpenBut - Variable in class weka.gui.experiment.SimpleSetupPanel
Click to load an experiment
m_OpenBut - Variable in class weka.gui.GenericObjectEditor.GOEPanel
Open object from disk.
m_openBut - Variable in class weka.gui.visualize.VisualizePanel
Button for the user to open the visualized set of instances
m_OpenDBBut - Variable in class weka.gui.explorer.PreprocessPanel
Click to load base instances from a Database
m_OpenFileBut - Variable in class weka.gui.explorer.PreprocessPanel
Click to load base instances from a file
m_OpenFileBut - Variable in class weka.gui.SetInstancesPanel
Click to open instances from a file
m_OpenURLBut - Variable in class weka.gui.explorer.PreprocessPanel
Click to load base instances from a URL
m_OpenURLBut - Variable in class weka.gui.SetInstancesPanel
Click to open instances from a URL
m_operator - Variable in class weka.core.pmml.BuiltInArithmetic
The operator for this function
m_optimizer - Variable in class weka.classifiers.functions.SMOreg
contains the algorithm used for learning
m_OptionBlacklist - Static variable in class weka.datagenerators.DataGenerator
a black list for options not to be listed (for derived generators) in the makeOptionString method
m_OptionHandler - Variable in class weka.core.CheckOptionHandler
the optionhandler to test
m_Options - Variable in class weka.classifiers.functions.supportVector.KernelEvaluation
user-supplied options
m_opType - Variable in class weka.core.pmml.Expression
The optype of this Expression
m_optype - Variable in class weka.core.pmml.FieldMetaInfo
The optype for the target
m_optypeOverride - Variable in class weka.core.pmml.MiningFieldMetaInfo
optype overrides (override data dictionary type - NOT SUPPORTED AT PRESENT)
m_OrderAlgorithmsFirstRBut - Variable in class weka.gui.experiment.SimpleSetupPanel
Radio button for choosing algorithms first in order of execution
m_OrderDatasetsFirstRBut - Variable in class weka.gui.experiment.SimpleSetupPanel
Radio button for choosing datasets first in order of execution
m_OriginalDataSet - Variable in class weka.associations.GeneralizedSequentialPatterns
original sequential data set to be used for sequential patterns extraction
m_OriginalHeader - Variable in class weka.classifiers.meta.ClassificationViaClustering
the original training data header
m_originalInstances - Static variable in class weka.datagenerators.classifiers.classification.LED24
the 7-bit LEDs
m_originalPlot - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
The master plot
m_originalPopSize - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_osi - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
used for offscreen drawing
m_otherBayesNet - Variable in class weka.classifiers.bayes.BayesNet
Bayes network to compare the structure with.
m_Out - Variable in class weka.experiment.CSVResultListener
The destination for results (typically connected to the output file)
m_OutlierAttributePosition - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
the position of the outlier attribute
m_OutlierFactor - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
the factor for detecting outliers
m_outlierTreatmentMethod - Variable in class weka.core.pmml.MiningFieldMetaInfo
outlier treatmemnt method
m_outlierTreatmentMethod - Variable in class weka.core.pmml.NormContinuous
Outlier treatment method (default = asIs)
m_OutOfBagError - Variable in class weka.classifiers.meta.Bagging
The out of bag error that has been calculated
m_Output - Variable in class weka.datagenerators.DataGenerator
PrintWriter for outputting the generated data
m_Output - Variable in class weka.gui.explorer.DataGeneratorPanel
the generated output (as text)
m_Output - Variable in class weka.gui.LogWindow
the output
m_OutputAdditionalAttributesLab - Variable in class weka.gui.explorer.ClassifierPanel
Label for the text field with additional attributes in the output
m_OutputAdditionalAttributesRange - Variable in class weka.gui.explorer.ClassifierPanel
the range of attributes to output
m_OutputAdditionalAttributesText - Variable in class weka.gui.explorer.ClassifierPanel
Lists indices for additional attributes to output
m_OutputArea - Variable in class weka.gui.SimpleCLIPanel
The output area canvas added to the frame.
m_OutputCenterFile - Variable in class weka.clusterers.XMeans
file name of the output file for the cluster centers.
m_OutputClassification - Variable in class weka.filters.supervised.attribute.AddClassification
whether to output the classification.
m_OutputConfusionBut - Variable in class weka.gui.explorer.ClassifierPanel
Check to output a confusion matrix
m_outputDef - Variable in class weka.core.pmml.BuiltInString
The output structure produced by this function
m_outputDef - Variable in class weka.core.pmml.Discretize
The output structure of this discretization
m_OutputDistribution - Variable in class weka.filters.supervised.attribute.AddClassification
whether to output the class distribution.
m_OutputEntropyBut - Variable in class weka.gui.explorer.ClassifierPanel
Check to output entropy statistics
m_OutputErrorFlag - Variable in class weka.filters.supervised.attribute.AddClassification
whether to output the error flag.
m_OutputFile - Variable in class weka.experiment.CrossValidationResultProducer
The destination output file/directory for raw output
m_OutputFile - Variable in class weka.experiment.CSVResultListener
The destination output file, null sends to System.out
m_OutputFile - Variable in class weka.experiment.RandomSplitResultProducer
The destination output file/directory for raw output
m_OutputFilename - Variable in class weka.core.converters.TextDirectoryLoader
whether to include the filename as an extra attribute
m_OutputFileName - Variable in class weka.experiment.CSVResultListener
The name of the output file.
m_OutputFilename - Variable in class weka.gui.GenericPropertiesCreator
the output props file for the GenericObjectEditor
m_OutputFormat - Variable in class weka.core.Debug.Clock
the format of the output
m_OutputFormatButton - Variable in class weka.gui.experiment.ResultsPanel
lets the user choose the format for the output.
m_OutputFormatClasses - Static variable in class weka.gui.experiment.OutputFormatDialog
the different classes for outputting the comparison tables.
m_OutputFormatComboBox - Variable in class weka.gui.experiment.OutputFormatDialog
lets the user choose the format for the output.
m_OutputFormatDefined - Variable in class weka.filters.unsupervised.attribute.RandomProjection
Keeps track of output format if it is defined or not
m_OutputFormatNames - Static variable in class weka.gui.experiment.OutputFormatDialog
the different names of matrices for outputting the comparison tables.
m_outputItemSets - Variable in class weka.associations.Apriori
Output itemsets found?
m_outputList - Variable in class weka.classifiers.functions.neural.NeuralConnection
The list of outputs from this unit.
m_OutputModelBut - Variable in class weka.gui.explorer.ClassifierPanel
Check to output the model built from the training data
m_OutputNumAtts - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
The number of attributes in the pc transformed data.
m_outputNums - Variable in class weka.classifiers.functions.neural.NeuralConnection
The numbering for the connections at the other end of the out lines.
m_OutputOffsetMultiplier - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
whether to add another attribute called "Offset", that lists the 'multiplier' by which the outlier/extreme value is away from the median, i.e., value = median + 'multiplier' * IQR
automatically enables m_DetectionPerAttribute!
m_OutputPerClassBut - Variable in class weka.gui.explorer.ClassifierPanel
Check to output true/false positives, precision/recall for each class
m_OutputPredictionsTextBut - Variable in class weka.gui.explorer.ClassifierPanel
Check to output text predictions
m_OutputProperties - Variable in class weka.gui.GenericPropertiesCreator
the output properties file with the filled in classes
m_outputQueues - Variable in class weka.gui.beans.Classifier
Stores completed models and associated data sets.
m_OutputRelAtts - Variable in class weka.filters.Filter
Indices of relational attributes in the output format
m_outputs - Variable in class weka.classifiers.pmml.consumer.NeuralNetwork
The outputs of the network
m_outputs - Variable in class weka.gui.beans.MetaBean
 
m_OutputSourceCode - Variable in class weka.gui.explorer.ClassifierPanel
Whether to output the source code (only for classifiers importing Sourcable)
m_OutputStringAtts - Variable in class weka.filters.Filter
Indices of string attributes in the output format
m_outputTypes - Variable in class weka.core.Debug.DBO
range of outputtyp
m_OutRedirector - Variable in class weka.gui.SimpleCLIPanel
The thread that sends output from m_POO to the output box.
m_OutText - Variable in class weka.gui.experiment.ResultsPanel
Displays the output of tests.
m_OutText - Variable in class weka.gui.explorer.AssociationsPanel
The output area for associations
m_OutText - Variable in class weka.gui.explorer.AttributeSelectionPanel
The output area for attribute selection results
m_OutText - Variable in class weka.gui.explorer.ClassifierPanel
The output area for classification results
m_OutText - Variable in class weka.gui.explorer.ClustererPanel
The output area for classification results
m_OverwriteWarning - Variable in class weka.gui.ConverterFileChooser
whether to popup a dialog in case the file already exists (only save dialog)
m_Owner - Variable in class weka.core.Capabilities
the object that owns this capabilities instance
m_Owner - Variable in class weka.core.logging.OutputLogger.OutputPrintStream
the owning logger.
m_P - Variable in class weka.classifiers.BVDecomposeSegCVSub
Proportion of instances common between any two training sets.
m_Packages - Variable in class weka.core.FindWithCapabilities
the packages to search in.
m_Packages - Static variable in class weka.gui.SimpleCLIPanel.CommandlineCompletion
all the available packages.
m_Padding - Variable in class weka.filters.unsupervised.attribute.Wavelet
the type of padding
m_PanelApplications - Variable in class weka.gui.GUIChooser
the panel for the application buttons
m_PanelButtons - Variable in class weka.gui.sql.SqlViewerDialog
the panel for the buttons
m_panelHeight - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_Panels - Variable in class weka.gui.explorer.Explorer
Contains all the additional panels apart from the pre-processing panel
m_panelWidth - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_Par - Variable in class weka.classifiers.functions.Logistic
The coefficients (optimized parameters) of the model
m_Par - Variable in class weka.classifiers.mi.MDD
 
m_Par - Variable in class weka.classifiers.mi.MIDD
 
m_Par - Variable in class weka.classifiers.mi.MIEMDD
 
m_Par - Variable in class weka.classifiers.mi.MILR
 
m_parameterDefs - Variable in class weka.core.pmml.Function
The structure of the parameters to this function
m_parameterList - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
 
m_parameters - Variable in class weka.core.pmml.DefineFunction
The list of parameters expected by this function.
m_paramMatrix - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
 
m_parent - Variable in class weka.associations.FPGrowth.FPTreeNode
link to the parent node
m_parent - Variable in class weka.classifiers.meta.nestedDichotomies.ND.NDTree
The parent
m_Parent - Variable in class weka.datagenerators.ClusterDefinition
the parent of the cluster
m_Parent - Variable in class weka.gui.GUIChooser.ChildFrameSDI
the parent frame.
m_Parent - Variable in class weka.gui.LogWindow.LogWindowPrintStream
the parent
m_Parent - Variable in class weka.gui.Main.ChildFrameMDI
the parent frame.
m_Parent - Variable in class weka.gui.Main.ChildFrameSDI
the parent frame.
m_Parent - Variable in class weka.gui.sql.ConnectionPanel
the parent frame.
m_Parent - Variable in class weka.gui.sql.InfoPanel
the parent of this panel
m_Parent - Variable in class weka.gui.sql.QueryPanel
the parent of this panel.
m_Parent - Variable in class weka.gui.sql.ResultPanel
the parent of this panel
m_Parent - Variable in class weka.gui.sql.SqlViewer
the parent of this panel.
m_Parent - Variable in class weka.gui.sql.SqlViewerDialog
the parent frame
m_parentFrame - Variable in class weka.gui.beans.AssociatorCustomizer
 
m_ParentFrame - Variable in class weka.gui.SetInstancesPanel
the parent frame.
m_ParentSets - Variable in class weka.classifiers.bayes.BayesNet
The parent sets.
m_Password - Variable in class weka.core.converters.DatabaseLoader
the database password to use
m_password - Variable in class weka.experiment.DatabaseUtils
Database Password.
m_Password - Variable in class weka.gui.sql.ConnectionPanel
the password to use for connecting to the DB.
m_Password - Variable in class weka.gui.sql.event.ResultChangedEvent
the password that was used to connect to the DB
m_Password - Variable in class weka.gui.sql.ResultSetTable
the password that was used to connect to the DB
m_Password - Variable in class weka.gui.sql.SqlViewer
the password that was used to connect to the DB.
m_Password - Variable in class weka.gui.sql.SqlViewerDialog
the password that was used to connect to the DB
m_PasswordLab - Variable in class weka.gui.DatabaseConnectionDialog
 
m_PasswordText - Variable in class weka.gui.DatabaseConnectionDialog
 
m_Pattern - Variable in class weka.gui.AttributeSelectionPanel
Press to enter a perl regular expression for selection
m_PatternBut - Variable in class weka.gui.ListSelectorDialog
Click to enter a regex pattern for selection
m_PatternRegEx - Variable in class weka.gui.AttributeSelectionPanel
The current regular expression.
m_PatternRegEx - Variable in class weka.gui.ListSelectorDialog
The current regular expression.
m_PD - Variable in class weka.gui.experiment.AlgorithmListPanel
The currently displayed property dialog, if any
m_pendingKnowledgeFlowLoad - Variable in class weka.gui.GUIChooser
Pending file to load on startup of the KnowledgeFlow
m_percent - Variable in class weka.filters.unsupervised.attribute.RandomProjection
Stores the dimensionality the data should be reduced to as percentage of the original dimension
m_Percentage - Variable in class weka.filters.supervised.instance.SMOTE
the percentage of SMOTE instances to create.
m_percentage - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell
 
m_Percentages - Variable in class weka.gui.MemoryUsagePanel
the threshold percentages to change color.
m_PercentBut - Variable in class weka.gui.explorer.ClassifierPanel
Click to set test mode to generate a % split
m_PercentBut - Variable in class weka.gui.explorer.ClustererPanel
Click to set test mode to generate a % split
m_PercentLab - Variable in class weka.gui.explorer.ClassifierPanel
Label by where the % split is entered
m_PercentLab - Variable in class weka.gui.explorer.ClustererPanel
Label by where the % split is entered
m_PercentText - Variable in class weka.gui.explorer.ClassifierPanel
The field where the % split is entered
m_PercentText - Variable in class weka.gui.explorer.ClustererPanel
The field where the % split is entered
m_percOfTarget - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_percOfTargetLab - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_percPop - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_percPopLab - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_performancePanel - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
Displays the performance graphs(s)
m_Performances - Variable in class weka.classifiers.meta.GridSearch.PerformanceTable
the performances
m_PerformBut - Variable in class weka.gui.experiment.ResultsPanel
Click to start the test.
m_PerformPrediction - Variable in class weka.filters.supervised.attribute.PLSFilter
whether to include the prediction, i.e., modifying the class attribute
m_performRanking - Variable in class weka.attributeSelection.LinearForwardSelection
perform initial ranking to select top-ranked attributes
m_performRanking - Variable in class weka.attributeSelection.SubsetSizeForwardSelection
perform initial ranking to select top-ranked attributes
m_PerturbationFraction - Variable in class weka.datagenerators.classifiers.classification.Agrawal
the perturabation fraction
m_Pivot - Variable in class weka.core.neighboursearch.balltrees.BallNode
The pivot/centre of the ball.
m_pixHeight - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_pixWidth - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_plot - Variable in class weka.gui.visualize.VisualizePanel
The panel that displays the plot
m_plot2D - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
The actual generic plotting panel
m_plotAreaHeight - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_plotAreaWidth - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_plotCompanion - Variable in class weka.gui.visualize.Plot2D
An optional "compainion" of the panel.
m_plotInstances - Variable in class weka.gui.visualize.AttributePanel
The instances to be plotted
m_plotInstances - Variable in class weka.gui.visualize.Plot2D
The instances to be plotted
m_plotInstances - Variable in class weka.gui.visualize.PlotData2D
The instances
m_plotInstances - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
The instances from the master plot
m_plotName - Variable in class weka.gui.visualize.PlotData2D
The name of this plot
m_plotName - Variable in class weka.gui.visualize.VisualizePanel
The name of the plot (not currently displayed, but can be used in the containing Frame or Panel)
m_plotNameHTML - Variable in class weka.gui.visualize.PlotData2D
The name of this plot (possibly in html) suitable for using in a tool tip text.
m_plotResize - Variable in class weka.gui.visualize.Plot2D
if the user resizes the window, or the attributes selected for the attributes change, then the lookup table for points needs to be recalculated
m_Plots - Variable in class weka.gui.GUIChooser
keeps track of the opened plots
m_plots - Variable in class weka.gui.visualize.LegendPanel
the list of plot elements
m_plots - Variable in class weka.gui.visualize.Plot2D
The plots to display
m_plotSize - Variable in class weka.gui.visualize.MatrixPanel
The slider to adjust the size of the cells in the matrix
m_plotSurround - Variable in class weka.gui.visualize.VisualizePanel
Panel that surrounds the plot panel with a titled border
m_plotTrainingData - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
plot the training data
m_plotTrainingData - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_PLS1_b_hat - Variable in class weka.filters.supervised.attribute.PLSFilter
the b-hat vector for PLS1
m_PLS1_P - Variable in class weka.filters.supervised.attribute.PLSFilter
the P matrix for PLS1
m_PLS1_RegVector - Variable in class weka.filters.supervised.attribute.PLSFilter
the regression vector "r-hat" for PLS1
m_PLS1_W - Variable in class weka.filters.supervised.attribute.PLSFilter
the W matrix for PLS1
m_PMMLModelFilter - Variable in class weka.gui.explorer.ClassifierPanel
 
m_pmmlVersion - Variable in class weka.classifiers.pmml.consumer.PMMLClassifier
PMML version
m_POE - Variable in class weka.gui.SimpleCLIPanel
The new output stream for System.err.
m_PointCount - Variable in class weka.core.neighboursearch.PerformanceStats
The number of data points looked at for the current/last query.
m_pointDrawn - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
A temporary array used to strike any instances that would be drawn redundantly.
m_pointLookup - Variable in class weka.gui.visualize.Plot2D
lookup table for plotted points
m_pointLookup - Variable in class weka.gui.visualize.PlotData2D
Panel coordinate cache for data points
m_pointSize - Variable in class weka.gui.visualize.MatrixPanel
The slider to adjust the size of the datapoints
m_POO - Variable in class weka.gui.SimpleCLIPanel
The new output stream for System.out.
m_Popup - Variable in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
the popup to display again.
m_popupFrame - Variable in class weka.gui.beans.CostBenefitAnalysis
 
m_popupFrame - Variable in class weka.gui.beans.DataVisualizer
 
m_popupFrame - Variable in class weka.gui.beans.ModelPerformanceChart
 
m_positiveIndex - Variable in class weka.associations.FPGrowth
The index (1 based) of binary attributes to treat as the positive value
m_PostProcessor - Variable in class weka.core.CheckScheme
for post-processing the data even further
m_PostProcessor - Variable in class weka.estimators.CheckEstimator
for post-processing the data even further
m_posTrainInstances - Variable in class weka.classifiers.trees.ADTree
The training instances with positive class - referencing the training dataset
m_powersOflambda - Variable in class weka.classifiers.functions.supportVector.StringKernel
the precalculated powers of lambda
m_ppMatrix - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
 
m_Precision - Variable in class weka.classifiers.meta.LogitBoost
The threshold on the improvement of the likelihood
m_Precision - Variable in class weka.core.xml.XMLInstances
the precision for numbers
m_preferredColourDimension - Variable in class weka.gui.visualize.VisualizePanel
 
m_PreferredExtension - Variable in class weka.gui.beans.KnowledgeFlowApp
the preferred file extension
m_preferredXDimension - Variable in class weka.gui.visualize.VisualizePanel
These hold the names of preferred columns to visualize on---if the user has defined them in the Visualize.props file
m_preferredYDimension - Variable in class weka.gui.visualize.VisualizePanel
 
m_Prefixes - Variable in class weka.core.Tee
whether to add a prefix or not.
m_premise - Variable in class weka.associations.FPGrowth.AssociationRule
The premise of the rule
m_premise - Variable in class weka.associations.RuleItem
The premise of a rule.
m_premiseCount - Variable in class weka.associations.PredictiveApriori
The minimum support.
m_premiseSupport - Variable in class weka.associations.FPGrowth.AssociationRule
The support for the premise
m_PreparedStatement - Variable in class weka.experiment.DatabaseUtils
The prepared statement used for database queries.
m_Preprocessing - Variable in class weka.filters.supervised.attribute.PLSFilter
the type of preprocessing
m_PreprocessPanel - Variable in class weka.gui.explorer.Explorer
The panel for preprocessing instances
m_Present - Static variable in class weka.classifiers.functions.LibLINEAR
whether the liblinear classes are in the Classpath
m_Present - Static variable in class weka.classifiers.functions.LibSVM
whether the libsvm classes are in the Classpath
m_Present - Static variable in class weka.core.Jython
whether the Jython classes are in the Classpath
m_Present - Static variable in class weka.core.stemmers.SnowballStemmer
whether the snowball stemmers are in the Classpath.
m_Present - Static variable in class weka.core.xml.KOML
indicates whether KOML (Koala Object Markup Language) is present
m_Present - Static variable in class weka.core.xml.XStream
indicates whether XStream is present
m_PreserveOrderBut - Variable in class weka.gui.explorer.ClassifierPanel
Whether randomization is turned off to preserve order
m_previousShapeIndex - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
The index of the previous plotted point that was highlighted
m_PrintColNames - Variable in class weka.experiment.ResultMatrix
whether the names or numbers are output as column declarations
m_Printer - Variable in class weka.gui.beans.StripChart
the class responsible for printing
m_Printer - Variable in class weka.gui.ResultHistoryPanel
for printing the output to files
m_Printer - Variable in class weka.gui.visualize.PrintablePanel
the class responsible for printing
m_PrintRowNames - Variable in class weka.experiment.ResultMatrix
whether the names or numbers are output as row declarations
m_printstream - Variable in class weka.gui.visualize.PostscriptGraphics
The output file
m_PriorErrorEstimator - Variable in class weka.classifiers.Evaluation
Numeric class error estimator for prior
m_priorEstimator - Variable in class weka.associations.PredictiveApriori
The prior estimator.
m_priors - Variable in class weka.associations.PredictiveApriori
The hashtable containing the prior probabilities.
m_priors - Variable in class weka.associations.PriorEstimation
Hashtable containing the estimated prior probabilities.
m_priors - Variable in class weka.associations.RuleGeneration
Hashtable conatining the estimated prior probabilities.
m_Priors - Variable in class weka.classifiers.bayes.NaiveBayesSimple
The prior probabilities of the classes.
m_Priors - Variable in class weka.classifiers.lazy.LBR
The prior probabilities of the classes.
m_priors - Variable in class weka.filters.unsupervised.attribute.ClusterMembership
The prior probability for each class
m_PriorUpdate - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Prior class object interface
m_probabilityCache - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
cache of probabilities for fast replotting
m_ProbabilityEstimates - Variable in class weka.classifiers.functions.LibLINEAR
whether to generate probability estimates instead of +1/-1 in case of classification problems
m_ProbabilityEstimates - Variable in class weka.classifiers.functions.LibSVM
whether to generate probability estimates instead of +1/-1 in case of classification problems
m_probOfClass - Variable in class weka.classifiers.bayes.NaiveBayesMultinomial
the probability of a class (i.e.
m_probOfWordGivenClass - Variable in class weka.classifiers.bayes.NaiveBayesMultinomial
probability that a word (w) exists in a class (H) (i.e.
m_processed_InstanceID - Variable in class weka.clusterers.CLOPE
Counter for the processed instances
m_progress - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
The progress bar to show the progress of the layout process
m_projectedCounts - Variable in class weka.associations.FPGrowth.FPTreeNode
counts associated with projected versions of this node
m_ProjectionFilter - Variable in class weka.classifiers.meta.RotationForest
The type of projection filter
m_ProjectionFilters - Variable in class weka.classifiers.meta.RotationForest
The projection filters
m_Prolog - Variable in class weka.core.OptionHandlerJavadoc
whether to include the "Valid options..." prolog in the Javadoc
m_Prolog - Variable in class weka.core.TechnicalInformationHandlerJavadoc
whether to include the "Valid options..." prolog in the Javadoc
m_Prop - Variable in class weka.classifiers.trees.RandomTree
The proportions of training instances going down each branch.
m_Prop - Variable in class weka.classifiers.trees.REPTree.Tree
The proportions of training instances going down each branch.
m_Properties - Static variable in class weka.core.logging.Logger
the properties file.
m_Properties - Variable in class weka.core.xml.XMLSerialization
for handling properties (ignored/allowed)
m_property - Variable in class weka.core.pmml.FieldMetaInfo.Value
 
m_PropertyArray - Variable in class weka.experiment.Experiment
The array of values to set the property to
m_PropertyNumber - Variable in class weka.experiment.Experiment
The current custom property value index when the experiment is running
m_PropertyPath - Variable in class weka.experiment.Experiment
The path to the iterator property
m_Props - Variable in class weka.classifiers.trees.BFTree
Branch proportions.
m_Props - Variable in class weka.classifiers.trees.SimpleCart
Proportion for each branch.
m_Prune - Variable in class weka.classifiers.trees.SimpleCart
If use minimal cost-compexity pruning.
m_PruningMethod - Variable in class weka.classifiers.functions.supportVector.StringKernel
the pruning method
m_PruningStrategy - Variable in class weka.classifiers.trees.BFTree
the pruning strategy
m_PruningType - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The pruning type used
m_PSFontReplacement - Static variable in class weka.gui.visualize.PostscriptGraphics
the font replacement
m_psGraphicsState - Variable in class weka.gui.visualize.PostscriptGraphics
The current global PostScript graphics state for all cloned objects
m_Quality - Variable in class weka.gui.visualize.JPEGWriter
the quality of the image.
m_Query - Variable in class weka.gui.sql.event.QueryExecuteEvent
the query that was executed
m_Query - Variable in class weka.gui.sql.event.ResultChangedEvent
the query that is associated with the active result table
m_Query - Variable in class weka.gui.sql.ResultSetTable
the query the table model is based on
m_Query - Variable in class weka.gui.sql.SqlViewer
the currently selected query.
m_Query - Variable in class weka.gui.sql.SqlViewerDialog
the currently selected query
m_QueryExecuteListeners - Variable in class weka.gui.sql.QueryPanel
the connection listeners.
m_QueryPanel - Variable in class weka.gui.sql.ResultPanel
the panel where to output the queries
m_QueryPanel - Variable in class weka.gui.sql.SqlViewer
the query panel.
m_Radius - Variable in class weka.classifiers.mi.MIOptimalBall
radius of the optimal ball
m_Radius - Variable in class weka.core.neighboursearch.balltrees.BallNode
The radius of this ball (hyper sphere).
m_RAE - Variable in class weka.classifiers.meta.GridSearch.Performance
the Relative absolute error
m_Rand - Variable in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
Random number generator for selecting an abitrary (random) point.
m_RandClassCols - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
Set of colomns: each colomn representing a randomised version of the train dataset class colomn
m_RandClassCols - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
Set of colomns: each colomn representing a randomised version of the train dataset class colomn
m_RandClassCols - Variable in class weka.classifiers.lazy.KStar
Table of random class value colomns
m_randNum - Variable in class weka.associations.PriorEstimation
The random number generator.
m_random - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
random number generator
m_Random - Variable in class weka.classifiers.meta.Decorate
The random number generator.
m_Random - Variable in class weka.classifiers.meta.MultiBoostAB
Random number generator
m_Random - Variable in class weka.classifiers.meta.Vote
the random number generator used for breaking ties in majority voting
m_random - Variable in class weka.classifiers.trees.ADTree
The random number generator - used for the random search heuristic
m_Random - Variable in class weka.core.TestInstances
the random number generator
m_Random - Variable in class weka.datagenerators.DataGenerator
random number generator
m_random - Variable in class weka.filters.unsupervised.attribute.RandomProjection
The random number generator used for generating the random matrix
m_Random - Variable in class weka.filters.unsupervised.instance.Randomize
The current random number generator
m_random - Variable in class weka.filters.unsupervised.instance.ReservoirSample
The random number generator
m_RandomInitialAnchor - Variable in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
True if the initial anchor is chosen randomly.
m_RandomInstance - Variable in class weka.classifiers.meta.LogitBoost
The random number generator used
m_RandomInstance - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The random number generator used
m_randomize - Variable in class weka.experiment.RandomSplitResultProducer
Whether dataset is to be randomized
m_Randomize - Variable in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
whether to randomize the output data
m_RandomLab - Variable in class weka.gui.explorer.ClassifierPanel
the label for the random seed textfield
m_randomSeed - Variable in class weka.classifiers.functions.SMO
The random number seed
m_randomSeed - Variable in class weka.classifiers.mi.MISMO
The random number seed
m_randomSeed - Variable in class weka.classifiers.trees.ADTree
Option - the seed to use for a random search
m_randomSeed - Variable in class weka.classifiers.trees.RandomForest
The random seed.
m_randomSeed - Variable in class weka.classifiers.trees.RandomTree
The random seed to use.
m_RandomSeed - Variable in class weka.filters.supervised.instance.Resample
The random number generator seed.
m_RandomSeed - Variable in class weka.filters.supervised.instance.SMOTE
the random seed to use.
m_RandomSeed - Variable in class weka.filters.unsupervised.instance.Resample
The random number generator seed
m_RandomSeed - Variable in class weka.filters.unsupervised.instance.ReservoirSample
The random number generator seed
m_RandomSeedText - Variable in class weka.gui.explorer.ClassifierPanel
User specified random seed for cross validation or % split
m_randomV - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_RandSeed - Variable in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
Seed for random number generator.
m_Range - Variable in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
The classes that are grouped together at the current node
m_Range - Variable in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
The classes that are grouped together at the current node
m_RangeMode - Variable in class weka.classifiers.meta.ThresholdSelector
The range correction mode
m_Ranges - Variable in class weka.core.NormalizableDistance
The range of the attributes.
m_Ranges - Variable in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
The attribute ranges.
m_rankedAtts - Variable in class weka.attributeSelection.GreedyStepwise
a ranked list of attribute indexes
m_rankedSoFar - Variable in class weka.attributeSelection.GreedyStepwise
 
m_RankingDiff - Variable in class weka.experiment.ResultMatrix
the difference between wins and losses
m_RankingLosses - Variable in class weka.experiment.ResultMatrix
the losses in ranking
m_rankingRequested - Variable in class weka.attributeSelection.GreedyStepwise
true if the user has requested a ranked list of attributes
m_RankingWins - Variable in class weka.experiment.ResultMatrix
the wins in ranking
m_Rate - Variable in class weka.classifiers.mi.MINND
The learning rate in the gradient descent
m_RawData - Variable in class weka.datagenerators.classifiers.regression.Expression
the input data structure for the filter
m_Readable - Variable in class weka.core.Tag
The descriptive text
m_readIncrementally - Variable in class weka.gui.SetInstancesPanel
 
m_ReadMethods - Variable in class weka.core.xml.XMLSerializationMethodHandler
for storing read methods
m_RecalcHashCode - Variable in class weka.core.Trie
whether the structure got modified and the hash code needs to be re-calculated
m_receivedStopNotification - Variable in class weka.gui.beans.TestSetMaker
 
m_receivedStopNotification - Variable in class weka.gui.beans.TrainingSetMaker
 
m_ReducedHeader - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The header of the dimensionally reduced data
m_ReducedHeaders - Variable in class weka.classifiers.meta.RotationForest
Headers of the reduced datasets
m_References - Variable in class weka.classifiers.mi.CitationKNN
R nearest references
m_ReferencesDebug - Variable in class weka.classifiers.mi.CitationKNN
 
m_regressions - Variable in class weka.classifiers.trees.lmt.LogisticBase
Array holding the simple regression functions fit by LogitBoost
m_regressionTables - Variable in class weka.classifiers.pmml.consumer.Regression
The regression tables for this regression
m_regressionTree - Variable in class weka.classifiers.trees.m5.M5Base
Make a regression tree/rule instead of a model tree/rule
m_Relation - Variable in class weka.core.TestInstances
the name of the relation
m_RelationalClassFormat - Variable in class weka.core.TestInstances
the format of the multi-instance data of the class
m_RelationalFormat - Variable in class weka.core.TestInstances
the format of the multi-instance data
m_RelationName - Variable in class weka.core.Instances
The dataset's name.
m_RelationName - Variable in class weka.datagenerators.DataGenerator
Relation name the dataset should have
m_RelationNameLab - Variable in class weka.gui.InstancesSummaryPanel
Displays the name of the relation
m_relativeCheck - Variable in class weka.gui.experiment.DatasetListPanel
Make file paths relative to the user (start) directory.
m_RemainderErrors - Variable in class weka.classifiers.lazy.LBR
the number of instances to be classified incorrectly besides the subset.
m_remoteHosts - Variable in class weka.experiment.RemoteExperiment
Holds the names of machines with remoteEngine servers running
m_remoteHosts - Variable in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
Holds the names of machines with remoteEngine servers running
m_RemoveAll - Variable in class weka.gui.AttributeSelectionPanel
Press to deselect all attributes
m_removeAttributes - Variable in class weka.filters.unsupervised.attribute.AddCluster
Filter for removing attributes
m_removeAttributes - Variable in class weka.filters.unsupervised.attribute.ClusterMembership
Filter for removing attributes
m_RemoveButton - Variable in class weka.gui.explorer.PreprocessPanel
Button for removing attributes
m_removeClassColumn - Variable in class weka.experiment.DensityBasedClustererSplitEvaluator
Remove the class column (if set) from the data
m_RemovedPercentage - Variable in class weka.classifiers.meta.RotationForest
The percentage of instances to be removed
m_removeFilter - Variable in class weka.filters.unsupervised.attribute.RemoveUseless
The filter used to remove attributes
m_RemoveFilterName - Variable in class weka.experiment.ResultMatrix
whether to remove the filter name from the dataaset name
m_RemoveFilterName - Variable in class weka.gui.experiment.OutputFormatDialog
whether to remove the filter names from the names.
m_RemoveFilterNameCheckBox - Variable in class weka.gui.experiment.OutputFormatDialog
the checkbox for the removing of filter classnames.
m_removeMissingCols - Variable in class weka.associations.Apriori
Remove columns with all missing values
m_RemoveOldClass - Variable in class weka.filters.supervised.attribute.AddClassification
whether to remove the old class attribute.
m_removePointsButton - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_RemoveUnused - Variable in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
Whether unused attributes are left out of the output.
m_RemoveUseless - Variable in class weka.classifiers.meta.RotationForest
Filter that remove useless attributes
m_Repainters - Variable in class weka.gui.visualize.LegendPanel
a list of components that need to be repainted when a colour is changed
m_replaceMissing - Variable in class weka.classifiers.functions.SPegasos
Replace missing values
m_replaceMissing - Variable in class weka.classifiers.trees.FT
Filter to replace missing values
m_replaceMissing - Variable in class weka.classifiers.trees.LMT
Filter to replace missing values
m_ReplaceMissing - Variable in class weka.filters.supervised.attribute.PLSFilter
whether to replace missing values
m_replaceMissing - Variable in class weka.filters.unsupervised.attribute.RandomProjection
The ReplaceMissingValues filter
m_ReplaceMissingFilter - Variable in class weka.clusterers.FarthestFirst
replace missing values in training instances
m_ReplaceMissingFilter - Variable in class weka.clusterers.XMeans
replace missing values in training instances.
m_ReplaceMissingFilter - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
Filters for replacing missing values.
m_ReplaceMissingValues - Variable in class weka.classifiers.functions.LibLINEAR
The filter used to get rid of missing values.
m_ReplaceMissingValues - Variable in class weka.classifiers.functions.LibSVM
The filter used to get rid of missing values.
m_ReplaceMissingValues - Variable in class weka.classifiers.functions.SimpleLogistic
Filter for replacing missing values
m_Repulsion - Variable in class weka.clusterers.CLOPE
Specifies the repulsion
m_RepulsionDefault - Variable in class weka.clusterers.CLOPE
Specifies the repulsion default
m_resampleBt - Variable in class weka.gui.visualize.MatrixPanel
The label for resample percentage
m_resamplePercent - Variable in class weka.gui.visualize.MatrixPanel
The text area for percentage to resample data
m_rescaleConstant - Variable in class weka.core.pmml.TargetMetaInfo
re-scaling of target value (if defined)
m_rescaleFactor - Variable in class weka.core.pmml.TargetMetaInfo
 
m_Result - Variable in class weka.associations.AssociatorEvaluation
the result string
m_Result - Variable in class weka.classifiers.functions.supportVector.KernelEvaluation
the result string
m_Result - Variable in class weka.core.mathematicalexpression.Parser
for storing the result of the expresion.
m_result - Variable in class weka.experiment.ClassifierSplitEvaluator
Holds the statistics for the most recent application of the classifier
m_result - Variable in class weka.experiment.DensityBasedClustererSplitEvaluator
Holds the statistics for the most recent application of the clusterer
m_result - Variable in class weka.experiment.RegressionSplitEvaluator
Holds the statistics for the most recent application of the classifier
m_Result - Variable in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
for storing the result of the expression.
m_Result - Variable in class weka.gui.experiment.OutputFormatDialog
the result of the user's action, either OK or CANCEL.
m_Result - Variable in class weka.gui.ListSelectorDialog
Whether the selection was made or cancelled
m_Result - Variable in class weka.gui.PropertySelectorDialog
Whether the selection was made or cancelled
m_Result - Variable in class weka.gui.ViewerDialog
the result of the user's action, either OK or CANCEL
m_ResultKeyBut - Variable in class weka.gui.experiment.ResultsPanel
Click to edit the columns used to determine the scheme.
m_ResultKeyLabel - Variable in class weka.gui.experiment.ResultsPanel
Displays the currently selected column names for the scheme & options.
m_ResultKeyList - Variable in class weka.gui.experiment.ResultsPanel
Displays the list of selected columns determining the scheme.
m_ResultKeyModel - Variable in class weka.gui.experiment.ResultsPanel
Stores the list of attributes for selecting the scheme columns.
m_ResultListener - Variable in class weka.experiment.AveragingResultProducer
The ResultListener to send results to
m_ResultListener - Variable in class weka.experiment.CrossValidationResultProducer
The ResultListener to send results to
m_ResultListener - Variable in class weka.experiment.DatabaseResultProducer
The ResultListener to send results to
m_ResultListener - Variable in class weka.experiment.Experiment
Where results will be sent
m_ResultListener - Variable in class weka.experiment.LearningRateResultProducer
The ResultListener to send results to
m_ResultListener - Variable in class weka.experiment.RandomSplitResultProducer
The ResultListener to send results to
m_ResultMatrix - Variable in class weka.experiment.PairedTTester
the instance of the class to produce the output.
m_ResultMatrix - Variable in class weka.gui.experiment.OutputFormatDialog
the output format specific matrix.
m_ResultMatrix - Variable in class weka.gui.experiment.ResultsPanel
the initial result matrix.
m_ResultPanel - Variable in class weka.gui.sql.SqlViewer
the result panel.
m_ResultPath - Variable in class weka.gui.PropertySelectorDialog
Stores the path to the selected property
m_ResultProducer - Variable in class weka.experiment.AveragingResultProducer
The ResultProducer used to generate results
m_ResultProducer - Variable in class weka.experiment.DatabaseResultListener
The ResultProducer to listen to
m_ResultProducer - Variable in class weka.experiment.DatabaseResultProducer
The ResultProducer used to generate results
m_ResultProducer - Variable in class weka.experiment.Experiment
The result producer
m_ResultProducer - Variable in class weka.experiment.LearningRateResultProducer
The ResultProducer used to generate results
m_Results - Variable in class weka.experiment.AveragingResultProducer
Collects the results from a single run
m_Results - Variable in class weka.gui.ResultHistoryPanel
A Hashtable mapping names to result buffers
m_ResultsDestinationCBox - Variable in class weka.gui.experiment.SimpleSetupPanel
Combo box for choosing experiment destination type
m_ResultsDestinationPathLabel - Variable in class weka.gui.experiment.SimpleSetupPanel
Label for destination field
m_ResultsDestinationPathTField - Variable in class weka.gui.experiment.SimpleSetupPanel
Input field for result destination path
m_ResultSet - Variable in class weka.gui.sql.event.QueryExecuteEvent
the produced ResultSet, if any
m_ResultSet - Variable in class weka.gui.sql.ResultSetHelper
the resultset to work on.
m_ResultsetKeyColumns - Variable in class weka.experiment.PairedTTester
An array containing the indexes of just the selected columns
m_ResultsetKeyColumnsRange - Variable in class weka.experiment.PairedTTester
The range of columns that specify a unique result set (eg: scheme plus configuration)
m_Resultsets - Variable in class weka.experiment.PairedTTester
Stores a vector for each resultset holding all instances in each set
m_ResultsetsValid - Variable in class weka.experiment.PairedTTester
Indicates whether the instances have been partitioned
m_ResultsPanel - Variable in class weka.gui.experiment.Experimenter
The panel for analysing experimental results
m_ResultsPanel - Variable in class weka.gui.experiment.RunPanel
A pointer to the results panel
m_ResultsTableName - Variable in class weka.experiment.DatabaseResultListener
The name of the current results table
m_retrieval - Variable in class weka.core.converters.AbstractLoader
The current retrieval mode
m_retrieval - Variable in class weka.core.converters.AbstractSaver
The current retrieval mode
m_returnValue - Variable in class weka.gui.DatabaseConnectionDialog
 
m_ReturnValue - Variable in class weka.gui.sql.SqlViewerDialog
the return value
m_Ridge - Variable in class weka.classifiers.functions.Logistic
The ridge parameter.
m_ridge - Variable in class weka.classifiers.functions.RBFNetwork
The ridge parameter for the logistic regression.
m_Ridge - Variable in class weka.classifiers.mi.MILR
The ridge parameter.
m_right - Variable in class weka.classifiers.meta.nestedDichotomies.ND.NDTree
The right successor
m_right - Variable in class weka.classifiers.trees.m5.RuleNode
right child node
m_Right - Variable in class weka.core.neighboursearch.balltrees.BallNode
The right child of the node.
m_Right - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode
right subtree; contains instances with larger than split value.
m_rightMargin - Variable in class weka.core.pmml.FieldMetaInfo.Interval
The right boundary value
m_RLEditor - Variable in class weka.gui.experiment.SetupPanel
The ResultListener editor
m_RLEditorPanel - Variable in class weka.gui.experiment.SetupPanel
The panel to contain the ResultListener editor
m_rmatrix - Variable in class weka.filters.unsupervised.attribute.RandomProjection
The random matrix
m_RMSE - Variable in class weka.classifiers.meta.GridSearch.Performance
the Root mean squared error
m_rndmSeed - Variable in class weka.filters.unsupervised.attribute.RandomProjection
Stores the random seed used to generate the random matrix
m_ROCs - Variable in class weka.gui.GUIChooser
keeps track of the opened ROCs
m_root - Variable in class weka.classifiers.bayes.net.MarginCalculator
 
m_root - Variable in class weka.classifiers.trees.ADTree
The root of the tree
m_root - Variable in class weka.classifiers.trees.LADTree
 
m_Root - Variable in class weka.core.neighboursearch.BallTree
The root node of the BallTree.
m_Root - Variable in class weka.core.neighboursearch.CoverTree
The root node.
m_Root - Variable in class weka.core.neighboursearch.KDTree
The root node of the tree.
m_Root - Variable in class weka.core.Trie
the root node
m_Root - Variable in class weka.core.Trie.TrieIterator
the node to use as root
m_Root - Variable in class weka.gui.PropertySelectorDialog
The root of the property tree
m_RootNode - Variable in class weka.core.xml.XMLDocument
the root node as String.
m_RootObject - Variable in class weka.gui.PropertySelectorDialog
The object at the root of the tree
m_RowCount - Variable in class weka.gui.sql.ResultSetHelper
the number of rows.
m_RowHidden - Variable in class weka.experiment.ResultMatrix
whether a row is hidden
m_RowIndex - Variable in class weka.gui.arffviewer.ArffTable.RelationalCellEditor
the row index this editor is for
m_RowNames - Variable in class weka.experiment.ResultMatrix
the row names
m_RowNameWidth - Variable in class weka.experiment.ResultMatrix
the size of the names of the rows
m_RowOrder - Variable in class weka.experiment.ResultMatrix
the ordering of the rows
m_RP - Variable in class weka.experiment.CSVResultListener
The ResultProducer sending us results
m_RPEditor - Variable in class weka.gui.experiment.SetupPanel
The ResultProducer editor
m_RPEditorPanel - Variable in class weka.gui.experiment.SetupPanel
The panel to contain the ResultProducer editor
m_RRSE - Variable in class weka.classifiers.meta.GridSearch.Performance
the Root relative squared error
m_RSeed - Variable in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Seed form random number generator.
m_rseed - Variable in class weka.gui.visualize.MatrixPanel
Random seed for random subsample
m_rules - Variable in class weka.associations.FPGrowth
Holds the rules
m_ruleSet - Variable in class weka.classifiers.trees.m5.M5Base
the rule set
m_rulesMustContain - Variable in class weka.associations.FPGrowth
If set, then only output rules containing these itmes
m_ruleSupCounter - Variable in class weka.associations.LabeledItemSet
The support of the rule.
m_RunColumn - Variable in class weka.experiment.PairedTTester
The index of the column containing the run number
m_RunColumnSet - Variable in class weka.experiment.PairedTTester
The option setting for the run number column (-1 means last)
m_RunLower - Variable in class weka.experiment.Experiment
Lower run number
m_Running - Variable in class weka.core.Debug.Clock
whether the time is still clocked
m_runningCount - Variable in class weka.gui.beans.FlowRunner
 
m_RunNumber - Variable in class weka.experiment.Experiment
The current run number when the experiment is running
m_runNumber - Variable in class weka.gui.beans.BatchClassifierEvent
The run number that this classifier was generated for
m_runNumber - Variable in class weka.gui.beans.TestSetEvent
What run number is this training set from.
m_runNumber - Variable in class weka.gui.beans.TrainingSetEvent
What run number is this training set from.
m_RunNumberPanel - Variable in class weka.gui.experiment.SetupPanel
The panel for configuring run numbers
m_RunPanel - Variable in class weka.gui.experiment.Experimenter
The panel for running the experiment
m_RunThread - Variable in class weka.gui.experiment.RunPanel
The thread running the experiment
m_RunThread - Variable in class weka.gui.explorer.AssociationsPanel
A thread that associator runs in
m_RunThread - Variable in class weka.gui.explorer.AttributeSelectionPanel
A thread that attribute selection runs in
m_RunThread - Variable in class weka.gui.explorer.ClassifierPanel
A thread that classification runs in
m_RunThread - Variable in class weka.gui.explorer.ClustererPanel
A thread that clustering runs in
m_RunThread - Variable in class weka.gui.SimpleCLIPanel
The thread currently running a class main method.
m_Runtime - Variable in class weka.core.Memory
the current runtime variable
m_RunUpper - Variable in class weka.experiment.Experiment
Upper run number
m_samplesBase - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_SampleSize - Variable in class weka.classifiers.meta.GridSearch
the sample size to search the initial grid with
m_SampleSize - Variable in class weka.filters.unsupervised.instance.ReservoirSample
The subsample size, number of instances%
m_SampleSizePercent - Variable in class weka.filters.supervised.instance.Resample
The subsample size, percent of original set, default 100%.
m_SampleSizePercent - Variable in class weka.filters.unsupervised.instance.Resample
The subsample size, percent of original set, default 100%
m_SaveBut - Variable in class weka.gui.experiment.SetupPanel
Click to save an experiment
m_SaveBut - Variable in class weka.gui.experiment.SimpleSetupPanel
Click to save an experiment
m_SaveBut - Variable in class weka.gui.explorer.PreprocessPanel
Click to apply filters and save the results
m_SaveBut - Variable in class weka.gui.GenericObjectEditor.GOEPanel
Save object to disk.
m_saveBut - Variable in class weka.gui.visualize.VisualizePanel
Button for the user to save the visualized set of instances
m_SaveDialogTitle - Variable in class weka.gui.visualize.PrintableComponent
the title of the save dialog.
m_saveInstanceData - Variable in class weka.classifiers.trees.ADTree
Option - whether the tree should remember the instance data
m_saveInstances - Variable in class weka.classifiers.trees.m5.M5Base
Save instances at each node in an M5 tree for visualization purposes.
m_saveInstances - Variable in class weka.clusterers.Cobweb
Output instances in graph representation of Cobweb tree (Allows instances at nodes in the tree to be visualized in the Explorer).
m_saveMemory - Variable in class weka.classifiers.rules.DecisionTable
 
m_SaveOptionsBut - Variable in class weka.gui.experiment.AlgorithmListPanel
Click to edit the save the options from selected algorithm
m_SaveOut - Variable in class weka.gui.explorer.AssociationsPanel
The buffer saving object for saving output
m_SaveOutBut - Variable in class weka.gui.experiment.ResultsPanel
Click to save test output to a file.
m_Saver - Variable in class weka.core.converters.ConverterUtils.DataSink
the saver to use for storing the data.
m_SaverFileFilters - Static variable in class weka.gui.ConverterFileChooser
the file filters for the savers
m_Scale - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The scale parameter
m_Scale - Variable in class weka.filters.unsupervised.attribute.Normalize
The scaling factor of the output range.
m_ScalingEnabled - Variable in class weka.gui.visualize.JComponentWriter
whether scaling is enabled
m_ScrollBarIncrementComponents - Variable in class weka.gui.beans.KnowledgeFlowApp
the scrollbar increment of the components scrollpane
m_ScrollBarIncrementLayout - Variable in class weka.gui.beans.KnowledgeFlowApp
the scrollbar increment of the layout scrollpane
m_Search - Variable in class weka.attributeSelection.CheckAttributeSelection
The search method to be used
m_Search - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The search method to use
m_search - Variable in class weka.classifiers.rules.DecisionTable
The search method to use
m_search_bestInsertionNode - Variable in class weka.classifiers.trees.ADTree
The best node to insert under, as found so far by the latest search
m_search_bestInsertionNode - Variable in class weka.classifiers.trees.LADTree
 
m_search_bestPathInstances - Variable in class weka.classifiers.trees.LADTree
 
m_search_bestPathNegInstances - Variable in class weka.classifiers.trees.ADTree
The negative instances that apply to the best path found so far
m_search_bestPathPosInstances - Variable in class weka.classifiers.trees.ADTree
The positive instances that apply to the best path found so far
m_search_bestSplitter - Variable in class weka.classifiers.trees.ADTree
The best splitter to insert, as found so far by the latest search
m_search_bestSplitter - Variable in class weka.classifiers.trees.LADTree
 
m_search_smallestLeastSquares - Variable in class weka.classifiers.trees.LADTree
 
m_search_smallestZ - Variable in class weka.classifiers.trees.ADTree
The smallest Z value found so far by the latest search
m_searchDirection - Variable in class weka.attributeSelection.BestFirst
0 == backward search, 1 == forward search, 2 == bidirectional
m_searchPath - Variable in class weka.classifiers.trees.ADTree
Option - the search mode
m_SecondSuccessor - Variable in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
The second successor
m_SecondSuccessor - Variable in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
The second successor
m_seed - Variable in class weka.attributeSelection.CostSensitiveASEvaluation
random number seed
m_seed - Variable in class weka.attributeSelection.SubsetSizeForwardSelection
Seed for cross validation subset size determination.
m_seed - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
seed for randomizing the instances before CV
m_Seed - Variable in class weka.classifiers.BVDecompose
The random number seed
m_Seed - Variable in class weka.classifiers.BVDecomposeSegCVSub
The random number seed
m_Seed - Variable in class weka.classifiers.functions.Winnow
Random seed used for shuffling the dataset, -1 == disable
m_Seed - Variable in class weka.classifiers.RandomizableClassifier
The random number seed.
m_Seed - Variable in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
The random number seed.
m_Seed - Variable in class weka.classifiers.RandomizableMultipleClassifiersCombiner
The random number seed.
m_Seed - Variable in class weka.classifiers.RandomizableSingleClassifierEnhancer
The random number seed.
m_Seed - Variable in class weka.classifiers.trees.REPTree
Seed for random data shuffling.
m_Seed - Variable in class weka.clusterers.RandomizableClusterer
The random number seed.
m_Seed - Variable in class weka.clusterers.RandomizableDensityBasedClusterer
The random number seed.
m_Seed - Variable in class weka.clusterers.RandomizableSingleClustererEnhancer
The random number seed.
m_Seed - Variable in class weka.core.TestInstances
the seed value
m_Seed - Variable in class weka.datagenerators.DataGenerator
random number generator seed
m_Seed - Variable in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
the seed for randomizing, default is 1
m_Seed - Variable in class weka.filters.unsupervised.attribute.RandomSubset
The seed value.
m_Seed - Variable in class weka.filters.unsupervised.instance.Randomize
The random number seed
m_SeedDefault - Variable in class weka.clusterers.RandomizableClusterer
the default seed value
m_SeedDefault - Variable in class weka.clusterers.RandomizableDensityBasedClusterer
the default seed value
m_SeedDefault - Variable in class weka.clusterers.RandomizableSingleClustererEnhancer
the default seed value
m_SeedLab - Variable in class weka.gui.explorer.AttributeSelectionPanel
Label by where cv random seed is entered
m_SeedText - Variable in class weka.gui.explorer.AttributeSelectionPanel
The field where the seed value is entered
m_selAttrib - Variable in class weka.gui.visualize.MatrixPanel
The button to display a window to select attributes
m_SelectBut - Variable in class weka.gui.ListSelectorDialog
Click to choose the currently selected property
m_SelectBut - Variable in class weka.gui.PropertySelectorDialog
Click to choose the currently selected property
m_SelectCols - Variable in class weka.filters.unsupervised.attribute.MathExpression
Stores which columns to select as a funky range
m_SelectCols - Variable in class weka.filters.unsupervised.attribute.Remove
Stores which columns to select as a funky range
m_Selected - Variable in class weka.core.SelectedTag
The index of the selected tag
m_SelectedAttributes - Variable in class weka.filters.unsupervised.attribute.Copy
Stores the indexes of the selected attributes in order, once the dataset is seen
m_SelectedAttributes - Variable in class weka.filters.unsupervised.attribute.Remove
Stores the indexes of the selected attributes in order, once the dataset is seen
m_SelectedAttributes - Variable in class weka.filters.unsupervised.attribute.Reorder
Stores the indexes of the selected attributes in order, once the dataset is seen
m_SelectedCols - Variable in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Stores which columns to copy
m_SelectedIndex - Variable in class weka.core.SingleIndex
The selected index
m_SelectedRange - Variable in class weka.filters.unsupervised.attribute.RELAGGS
the range of attributes to process (only relational ones will be processed)
m_SelectedRange - Variable in class weka.filters.unsupervised.attribute.StringToWordVector
Range of columns to convert to word vectors.
m_selectionTime - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The time taken to select attributes in milliseconds
m_Self - Variable in class weka.gui.ConverterFileChooser
the file chooser itself
m_Self - Variable in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
the dialog itself.
m_Self - Variable in class weka.gui.GUIChooser
the GUIChooser itself
m_Self - Variable in class weka.gui.Main
the frame itself.
m_SequentialAttIndexes - Variable in class weka.classifiers.lazy.LBR.Indexes
an array of attribute indexes that are set to either true or false
m_SequentialInstIndexes - Variable in class weka.classifiers.lazy.LBR.Indexes
the array of instance indexes that are set to a either true or false
m_SerializedClassifierFile - Variable in class weka.filters.supervised.attribute.AddClassification
The file from which to load a serialized classifier.
m_SerializedHeader - Variable in class weka.filters.supervised.attribute.AddClassification
the header of the file the serialized classifier was trained with.
m_setAutoCommit - Variable in class weka.experiment.DatabaseUtils
setAutoCommit on the database?
m_SetCostsBut - Variable in class weka.gui.explorer.ClassifierPanel
for the cost matrix
m_SetCostsFrame - Variable in class weka.gui.explorer.ClassifierPanel
The frame used to show the cost matrix editing panel
m_SetCurrentMethod - Variable in class weka.core.stemmers.SnowballStemmer
the setCurrent method.
m_setNumber - Variable in class weka.gui.beans.BatchClassifierEvent
The set number for the test set
m_setNumber - Variable in class weka.gui.beans.BatchClustererEvent
The set number for the test set
m_setNumber - Variable in class weka.gui.beans.TestSetEvent
what number is this test set (ie fold 2 of 10 folds)
m_setNumber - Variable in class weka.gui.beans.TrainingSetEvent
what number is this training set (ie fold 2 of 10 folds)
m_sets - Variable in class weka.associations.FPGrowth.FrequentItemSets
The list of frequent item sets
m_SetTestBut - Variable in class weka.gui.explorer.ClassifierPanel
The button used to open a separate test dataset
m_SetTestBut - Variable in class weka.gui.explorer.ClustererPanel
The button used to open a separate test dataset
m_SetTestFrame - Variable in class weka.gui.explorer.ClassifierPanel
The frame used to show the test set selection panel
m_SetTestFrame - Variable in class weka.gui.explorer.ClustererPanel
The frame used to show the test set selection panel
m_SetupPanel - Variable in class weka.gui.experiment.Experimenter
The panel for configuring the experiment
m_sFileName - Variable in class weka.classifiers.bayes.net.GUI
String containing file name storing current network
m_ShapeCombo - Variable in class weka.gui.visualize.VisualizePanel
Lets the user select the shape they want to create for instance selection.
m_shapeSize - Variable in class weka.gui.visualize.PlotData2D
Additional optional information to control the size of points.
m_shapeSizes - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
The size of the points being plotted
m_shapeType - Variable in class weka.gui.visualize.PlotData2D
Additional optional information to control the point shape for this data.
m_showAttBars - Variable in class weka.gui.visualize.VisualizePanel
Show the attribute bar panel
m_ShowAverage - Variable in class weka.experiment.ResultMatrix
whether the average for each column should be printed
m_ShowAverage - Variable in class weka.gui.experiment.OutputFormatDialog
whether to show the average too.
m_ShowAverageCheckBox - Variable in class weka.gui.experiment.OutputFormatDialog
the checkbox for outputting the average.
m_ShowBorder - Variable in class weka.gui.treevisualizer.TreeVisualizer
whether to show the border or not.
m_showClassPanel - Variable in class weka.gui.visualize.VisualizePanel
Show the class panel
m_ShowStdDev - Variable in class weka.experiment.ResultMatrix
whether std.
m_ShowStdDevs - Variable in class weka.experiment.PairedTTester
Indicates whether standard deviations should be displayed
m_ShowStdDevs - Variable in class weka.gui.experiment.ResultsPanel
Lets the user select whether standard deviations are to be output or not.
m_ShowToolTip - Static variable in class weka.gui.visualize.PrintableComponent
whether to display the tooltip or not.
m_shrinkage - Variable in class weka.classifiers.meta.AdditiveRegression
Shrinkage (Learning rate).
m_Shrinkage - Variable in class weka.classifiers.meta.LogitBoost
The value of the shrinkage parameter
m_Shrinking - Variable in class weka.classifiers.functions.LibSVM
use the shrinking heuristics
m_Sigma - Variable in class weka.classifiers.BVDecompose
The calculated sigma (squared)
m_sigma - Variable in class weka.classifiers.functions.supportVector.Puk
Sigma for the Puk kernel.
m_Significance - Variable in class weka.experiment.ResultMatrix
the significance
m_significanceLevel - Variable in class weka.associations.Apriori
Significance level for optional significance test.
m_SignificanceLevel - Variable in class weka.experiment.PairedTTester
The significance level for comparisons
m_SignificanceWidth - Variable in class weka.experiment.ResultMatrix
the size of the significance columns
m_SigTex - Variable in class weka.gui.experiment.ResultsPanel
Lets the user edit the test significance.
m_Silent - Variable in class weka.core.Check
Silent mode, for no output at all to stdout
m_Silent - Variable in class weka.core.Javadoc
whether to suppress error messages (no printout in the console)
m_Silent - Variable in class weka.estimators.CheckEstimator
Silent mode, for no output at all to stdout
m_SimpleBut - Variable in class weka.gui.GUIChooser
Click to open the simplecli
m_SimpleCLI - Variable in class weka.gui.GUIChooser
The SimpleCLI
m_simplePanel - Variable in class weka.gui.experiment.SetupModePanel
The simple setup panel
m_SimpleSetupRBut - Variable in class weka.gui.experiment.SetupModePanel
The button for choosing simple setup mode
m_SIMPLS_B - Variable in class weka.filters.supervised.attribute.PLSFilter
the B matrix for SIMPLS (used for prediction)
m_SIMPLS_W - Variable in class weka.filters.supervised.attribute.PLSFilter
the W matrix for SIMPLS
m_sIndex - Variable in class weka.gui.visualize.Plot2D
 
m_sIndex - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
 
m_SingleName - Variable in class weka.gui.ResultHistoryPanel
The named result being viewed in the single-click display
m_SingleText - Variable in class weka.gui.ResultHistoryPanel
An optional component for single-click display
m_Singleton - Static variable in class weka.core.logging.Logger
the singleton instance of the logger.
m_Size - Variable in class weka.core.Debug.Log
the size of the file (in bytes)
m_Size - Variable in class weka.core.Queue
Store the c m_Tail.m_Nexturrent number of elements in the queue
m_SizePer - Variable in class weka.classifiers.trees.BFTree
The training data size (0-1).
m_SizePer - Variable in class weka.classifiers.trees.SimpleCart
Training data size.
m_SkipIdentical - Variable in class weka.core.neighboursearch.LinearNNSearch
Whether to skip instances from the neighbours that are identical to the query instance.
m_SmallestProb - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
Smallest probability of test attribute transforming into train attribute
m_SmallestProb - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
Smallest probability of test attribute transforming into train attribute
m_sons - Variable in class weka.classifiers.rules.part.ClassifierDecList
References to sons.
m_sons - Variable in class weka.classifiers.trees.ft.FTtree
Array of children of the node
m_sons - Variable in class weka.classifiers.trees.j48.ClassifierTree
References to sons.
m_sons - Variable in class weka.classifiers.trees.lmt.LMTNode
Array of children of the node
m_Sort - Variable in class weka.filters.unsupervised.attribute.AddValues
Whether to sort the values.
m_SortColumn - Variable in class weka.experiment.PairedTTester
The column to sort on (-1 means default sorting)
m_SortCombo - Variable in class weka.gui.experiment.ResultsPanel
Lets the user select which column to use for sorting.
m_SortedEigens - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
Sorted eigenvalues.
m_SortedIndices - Variable in class weka.classifiers.trees.BFTree
Sorted indices.
m_SortedIndices - Variable in class weka.filters.unsupervised.attribute.AddValues
the array with the sorted label indices
m_SortModel - Variable in class weka.gui.experiment.ResultsPanel
The model embedded in m_SortCombo.
m_SortOrder - Variable in class weka.experiment.PairedTTester
The sorting of the datasets (according to the sort column)
m_SourceCode - Variable in class weka.classifiers.CheckSource
the generated source code
m_SourceCode - Variable in class weka.filters.CheckSource
the generated source code
m_SourceCodeClass - Variable in class weka.gui.explorer.ClassifierPanel
The name of the generated class (only applicable to Sourcable schemes)
m_sourceFile - Variable in class weka.core.converters.AbstractFileLoader
Holds the source of the data set.
m_sourceFile - Variable in class weka.core.converters.TextDirectoryLoader
Holds the source of the data set.
m_sourceReader - Variable in class weka.core.converters.ArffLoader
The reader for the source file.
m_sourceReader - Variable in class weka.core.converters.CSVLoader
The reader for the data.
m_sourceReader - Variable in class weka.core.converters.LibSVMLoader
The reader for the source file.
m_sourceReader - Variable in class weka.core.converters.SVMLightLoader
The reader for the source file.
m_sourceReader - Variable in class weka.core.converters.XRFFLoader
The reader for the source file.
m_span - Variable in class weka.gui.visualize.AttributePanel
The container window for the attribute bars, and also where the X,Y or B get printed.
m_span - Variable in class weka.gui.visualize.LegendPanel
the panel that contains the legend entries
m_SparseFilter - Variable in class weka.classifiers.mi.MISVM
The filter used to transform the sparse datasets to nonsparse
m_sparseIndices - Variable in class weka.classifiers.functions.SMO.BinarySMO
 
m_sparseIndices - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
 
m_sparseIndices - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
 
m_sparseWeights - Variable in class weka.classifiers.functions.SMO.BinarySMO
Variables to hold weight vector in sparse form.
m_sparseWeights - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
Variables to hold weight vector in sparse form.
m_sparseWeights - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
Variables to hold weight vector in sparse form.
m_SpinnerMaxRows - Variable in class weka.gui.sql.QueryPanel
the spinner for the maximum number of rows.
m_SpinnerMaxSize - Variable in class weka.gui.LogWindow
the spinner for the max number of chars
m_SplitAttrib - Variable in class weka.core.neighboursearch.balltrees.BallNode
The attribute that splits this node (not always used).
m_splitByDataSet - Variable in class weka.experiment.RemoteExperiment
If true, then sub experiments are created on the basis of data sets rather than run number.
m_splitByDataSet - Variable in class weka.gui.experiment.DistributeExperimentPanel
Split experiment up by data set.
m_splitByRun - Variable in class weka.gui.experiment.DistributeExperimentPanel
Split experiment up by run number.
m_splitCrit - Static variable in class weka.classifiers.rules.part.ClassifierDecList
To compute the entropy.
m_SplitDim - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode
attribute to split on.
m_SplitEvaluator - Variable in class weka.experiment.CrossValidationResultProducer
The SplitEvaluator used to generate results
m_SplitEvaluator - Variable in class weka.experiment.RandomSplitResultProducer
The SplitEvaluator used to generate results
m_splitListener - Variable in class weka.gui.visualize.VisualizePanel
An optional listener that we will inform when the user creates a split to seperate instances.
m_splitOnResiduals - Variable in class weka.classifiers.trees.LMT
split on residuals?
m_splitPoint - Variable in class weka.classifiers.trees.lmt.ResidualSplit
The split point (for numeric attributes)
m_SplitPoint - Variable in class weka.classifiers.trees.RandomTree
The split point.
m_SplitPoint - Variable in class weka.classifiers.trees.REPTree.Tree
The split point.
m_SplitString - Variable in class weka.classifiers.trees.BFTree
Split subset (for nominal attributes).
m_SplitString - Variable in class weka.classifiers.trees.SimpleCart
Split subset used to split data for nominal attributes.
m_SplitString - Variable in class weka.core.tokenizers.NGramTokenizer
all the available grams
m_Splitter - Variable in class weka.core.neighboursearch.balltrees.TopDownConstructor
The BallSplitter algorithm used by the TopDown BallTree constructor, if it is selected.
m_Splitter - Variable in class weka.core.neighboursearch.KDTree
The node splitter.
m_SplitVal - Variable in class weka.core.neighboursearch.balltrees.BallNode
The value of m_SpiltAttrib that splits this node (not always used).
m_SplitValue - Variable in class weka.classifiers.trees.BFTree
Split point (for numeric attributes).
m_SplitValue - Variable in class weka.classifiers.trees.SimpleCart
Split point for a numeric attribute.
m_SplitValue - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode
value to split on.
m_SQLQ - Variable in class weka.gui.explorer.PreprocessPanel
Stores the last sql query executed
m_SqlViewerFrame - Variable in class weka.gui.GUIChooser
The frame containing the SqlViewer
m_st - Variable in class weka.core.converters.CSVLoader
Tokenizer for the data.
m_Stamp - Variable in class weka.core.Debug.Timestamp
the actual date
m_standardizeFilter - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
Filter for standardizing the data
m_Start - Variable in class weka.core.Debug.Clock
the start time
m_Start - Variable in class weka.core.neighboursearch.balltrees.BallNode
The start index of the portion of the master index array, which stores the indices of the instances/points the node contains.
m_Start - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode
The start index of the portion of the master index array, which stores the indices of the instances/points the node contains.
m_startBut - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_StartBut - Variable in class weka.gui.experiment.RunPanel
Click to start running the experiment
m_StartBut - Variable in class weka.gui.explorer.AssociationsPanel
Click to start running the associator
m_StartBut - Variable in class weka.gui.explorer.AttributeSelectionPanel
Click to start running the attribute selector
m_StartBut - Variable in class weka.gui.explorer.ClassifierPanel
Click to start running the classifier
m_StartBut - Variable in class weka.gui.explorer.ClustererPanel
Click to start running the clusterer
m_starting - Variable in class weka.attributeSelection.BestFirst
holds an array of starting attributes
m_starting - Variable in class weka.attributeSelection.GreedyStepwise
holds an array of starting attributes
m_starting - Variable in class weka.attributeSelection.LinearForwardSelection
holds an array of starting attributes
m_startPoint - Variable in class weka.attributeSelection.RankSearch
start from this point in the ranking
m_startRange - Variable in class weka.attributeSelection.BestFirst
holds the start set for the search as a Range
m_startRange - Variable in class weka.attributeSelection.GreedyStepwise
holds the start set for the search as a Range
m_startRange - Variable in class weka.attributeSelection.LinearForwardSelection
holds the start set for the search as a Range
m_startSequentially - Variable in class weka.gui.beans.FlowRunner
run each Startable bean sequentially? (default in parallel)
m_StartTag - Variable in class weka.core.Javadoc
the start tag
m_StartupListeners - Static variable in class weka.gui.Main
list of things to be notified when the startup process of the KnowledgeFlow is complete.
m_staticPotentialSplitters2way - Variable in class weka.classifiers.trees.LADTree
 
m_Stats - Variable in class weka.core.neighboursearch.NearestNeighbourSearch
Performance statistics.
m_StatsTable - Variable in class weka.gui.AttributeSummaryPanel
Displays other stats in a table
m_StatusBox - Variable in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Controls whether the custom iterator is used or not
m_StatusLab - Variable in class weka.gui.LogPanel
Displays the current status
m_statusMessage - Variable in class weka.experiment.RemoteExperimentEvent
A status type message
m_StdDev - Variable in class weka.experiment.ResultMatrix
the standard deviation
m_StdDevPrec - Variable in class weka.experiment.ResultMatrix
the standard std.
m_StdDevPrec - Variable in class weka.gui.experiment.OutputFormatDialog
the number of digits after the period (= precision) for printing the std.
m_StdDevPrecSpinner - Variable in class weka.gui.experiment.OutputFormatDialog
the spinner to choose the precision for the std.
m_stddevValue - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
standarddev; only used if gaussian
m_StdDevWidth - Variable in class weka.experiment.ResultMatrix
the size of the std dev columns
m_StdErr - Variable in class weka.core.logging.OutputLogger
the Tee instance to redirect stderr.
m_StdOut - Variable in class weka.core.logging.OutputLogger
the Tee instance to redirect stdout.
m_Stemmer - Variable in class weka.core.stemmers.SnowballStemmer
the current stemmer.
m_Stemmers - Static variable in class weka.core.stemmers.SnowballStemmer
contains the all the found stemmers (language names).
m_StemMethod - Variable in class weka.core.stemmers.SnowballStemmer
the stem method.
m_StepSize - Variable in class weka.experiment.LearningRateResultProducer
The number of instances to add at each step
m_StepX - Variable in class weka.classifiers.meta.GridSearch.Grid
the step size for the X axis
m_StepY - Variable in class weka.classifiers.meta.GridSearch.Grid
the step size for the Y axis
m_Stop - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The stop parameter
m_Stop - Variable in class weka.core.Debug.Clock
the end time
m_StopBut - Variable in class weka.gui.experiment.RunPanel
Click to signal the running experiment to halt
m_StopBut - Variable in class weka.gui.explorer.AssociationsPanel
Click to stop a running associator
m_StopBut - Variable in class weka.gui.explorer.AttributeSelectionPanel
Click to stop a running classifier
m_StopBut - Variable in class weka.gui.explorer.ClassifierPanel
Click to stop a running classifier
m_StopBut - Variable in class weka.gui.explorer.ClustererPanel
Click to stop a running clusterer
m_stopped - Variable in class weka.gui.beans.Loader
Asked to stop?
m_stopPlotting - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
Stop the plotting thread
m_stopReplotting - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
Stop any replotting threads
m_Stopwords - Static variable in class weka.core.Stopwords
The default stopwords object (stoplist based on Rainbow)
m_storage - Variable in class weka.classifiers.functions.supportVector.CachedKernel
Kernel cache
m_StorePredictionsBut - Variable in class weka.gui.explorer.ClassifierPanel
Check to save the predictions in the results list for visualizing later on
m_StorePredictionsBut - Variable in class weka.gui.explorer.ClustererPanel
Check to save the predictions in the results list for visualizing later on
m_STPMX - Variable in class weka.core.Optimization
 
m_Str - Variable in class weka.core.tokenizers.AlphabeticTokenizer
the characters of the string
m_Stream - Variable in class weka.core.converters.ConverterUtils.DataSink
the stream to store the data in (always in ARFF format).
m_Streamable - Variable in class weka.filters.MultiFilter
caches the streamable state
m_StreamableChecked - Variable in class weka.filters.MultiFilter
whether we already checked the streamable state
m_StreamErr - Variable in class weka.core.logging.OutputLogger
the stream object used for logging stderr.
m_StreamOut - Variable in class weka.core.logging.OutputLogger
the stream object used for logging stdout.
m_Streams - Variable in class weka.core.Tee
the different PrintStreams.
m_String - Variable in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner
 
m_StringAttributes - Variable in class weka.core.converters.CSVLoader
The range of attributes to force to type string.
m_stringType - Variable in class weka.experiment.DatabaseUtils
string type for the create table statement.
m_structure - Variable in class weka.core.converters.AbstractFileLoader
Holds the determined structure (header) of the data set.
m_structure - Variable in class weka.core.converters.DatabaseLoader
The header information that is retrieved in the beginning of incremental loading
m_structure - Variable in class weka.core.converters.TextDirectoryLoader
Holds the determined structure (header) of the data set.
m_Style - Variable in class weka.gui.LogWindow.LogWindowPrintStream
the style of the printstream
m_subExpComplete - Variable in class weka.experiment.RemoteExperiment
The status of each of the sub-experiments
m_subExperiments - Variable in class weka.experiment.RemoteExperiment
The sub experiments
m_subFlow - Variable in class weka.gui.beans.MetaBean
 
m_subFlowPreview - Variable in class weka.gui.beans.MetaBean
 
m_subInstances - Variable in class weka.classifiers.lazy.LBR
index of instances and attributes for the given dataset
m_submit - Variable in class weka.gui.visualize.VisualizePanel
Button for the user to enter the splits.
m_subOldErrorFlags - Variable in class weka.classifiers.lazy.LBR
following is defined by wangzh, the number of instances to be classified incorrectly on the subset.
m_subSample - Variable in class weka.filters.unsupervised.instance.ReservoirSample
Holds the sub-sample (reservoir)
m_SubSpaceSize - Variable in class weka.classifiers.meta.RandomSubSpace
The size of each bag sample, as a percentage of the training size
m_Success - Variable in class weka.core.CheckGOE
whether the tests were successful
m_Success - Variable in class weka.core.CheckOptionHandler
whether the tests were successful
m_Successors - Variable in class weka.classifiers.trees.BFTree
Successor nodes.
m_Successors - Variable in class weka.classifiers.trees.RandomTree
The subtrees appended to this tree.
m_Successors - Variable in class weka.classifiers.trees.REPTree.Tree
The subtrees of this tree.
m_Successors - Variable in class weka.classifiers.trees.SimpleCart
Successor nodes.
m_SuitableData - Variable in class weka.classifiers.meta.AdditiveRegression
whether we have suitable data or nor (if not, ZeroR model is used)
m_sum - Variable in class weka.associations.PriorEstimation
Sums up the confidences of all rules with a certain length.
m_SumAbsErr - Variable in class weka.classifiers.Evaluation
Sum of absolute errors.
m_SumC - Variable in class weka.core.neighboursearch.PerformanceStats
The sum of coordinates/attributes looked at for all the queries.
m_SumClass - Variable in class weka.classifiers.Evaluation
Sum of class values.
m_SumClassPredicted - Variable in class weka.classifiers.Evaluation
Sum of predicted * class values.
m_SumErr - Variable in class weka.classifiers.Evaluation
Sum of errors.
m_SumIntNodes - Variable in class weka.core.neighboursearch.TreePerformanceStats
The sum of internal nodes looked at for all the queries.
m_SumKBInfo - Variable in class weka.classifiers.Evaluation
Total Kononenko & Bratko Information
m_SumLeaves - Variable in class weka.core.neighboursearch.TreePerformanceStats
The sum of leaf nodes looked at for all the queries.
m_Summary - Variable in class weka.gui.explorer.ClustererPanel
The instances summary panel displayed by m_SetTestFrame
m_Summary - Variable in class weka.gui.SetInstancesPanel
The instance summary component
m_SumOfCounts - Variable in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes
Hold the sum of counts
m_SumOfEigenValues - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
sum of the eigenvalues.
m_sumOfWeights - Variable in class weka.classifiers.functions.SMO.BinarySMO
Stores the weight of the training instances
m_sumOfWeights - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
Stores the weight of the training instances
m_SumP - Variable in class weka.core.neighboursearch.PerformanceStats
The sum of data points looked at for all the queries.
m_SumPredicted - Variable in class weka.classifiers.Evaluation
Sum of predicted values.
m_SumPriorAbsErr - Variable in class weka.classifiers.Evaluation
Sum of absolute errors of the prior
m_SumPriorEntropy - Variable in class weka.classifiers.Evaluation
Total entropy of prior predictions
m_SumPriorSqrErr - Variable in class weka.classifiers.Evaluation
Sum of absolute errors of the prior
m_SumSchemeEntropy - Variable in class weka.classifiers.Evaluation
Total entropy of scheme predictions
m_SumSqC - Variable in class weka.core.neighboursearch.PerformanceStats
The squared sum of coordinates/attributes looked at for all the queries.
m_SumSqIntNodes - Variable in class weka.core.neighboursearch.TreePerformanceStats
The squared sum of internal nodes looked at for all the queries.
m_SumSqLeaves - Variable in class weka.core.neighboursearch.TreePerformanceStats
The squared sum of leaf nodes looked at for all the queries.
m_SumSqP - Variable in class weka.core.neighboursearch.PerformanceStats
The squared sum of data points looked at for all the queries.
m_SumSqrClass - Variable in class weka.classifiers.Evaluation
Sum of squared class values.
m_SumSqrErr - Variable in class weka.classifiers.Evaluation
Sum of squared errors.
m_SumSqrPredicted - Variable in class weka.classifiers.Evaluation
Sum of squared predicted values.
m_Superclass - Variable in class weka.core.FindWithCapabilities
the superclass from the GenericPropertiesCreator to retrieve the packages from.
m_support - Variable in class weka.associations.FPGrowth.FrequentBinaryItemSet
the support of this item set
m_Support - Variable in class weka.gui.experiment.SetupPanel
Manages sending notifications to people when we change the experiment, at this stage, only the resultlistener so the resultpanel can update.
m_Support - Variable in class weka.gui.experiment.SimpleSetupPanel
Manages sending notifications to people when we change the experiment, at this stage, only the resultlistener so the resultpanel can update.
m_Support - Variable in class weka.gui.explorer.PreprocessPanel
Manages sending notifications to people when we change the set of working instances.
m_Support - Variable in class weka.gui.GenericObjectEditor
Handles property change notification.
m_Support - Variable in class weka.gui.SetInstancesPanel
Manages sending notifications to people when we change the set of working instances.
m_SupportCount - Variable in class weka.associations.gsp.Sequence
the support count of the Sequence
m_supportVectors - Variable in class weka.classifiers.functions.SMO.BinarySMO
The set of support vectors
m_supportVectors - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
set of support vectors, that is, vectors with alpha(*)!=0
m_supportVectors - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
The set of support vectors {i: 0 < m_alpha[i]}
m_SVM - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
parent SMOreg class
m_SVM - Variable in class weka.classifiers.mi.MISVM
The SMO classifier used to compute SVM soluton w,b for the dataset
m_SVMType - Variable in class weka.classifiers.functions.LibLINEAR
the SVM solver type
m_SVMType - Variable in class weka.classifiers.functions.LibSVM
the SVM type
m_Symbols - Variable in class weka.core.mathematicalexpression.Parser
variable - value relation.
m_Symbols - Variable in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
variable - value relation.
m_SymFactory - Variable in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner
 
m_SystemInfoFrame - Variable in class weka.gui.GUIChooser
The frame containing the system info
m_t - Variable in class weka.classifiers.functions.GaussianProcesses
The vector of target values.
m_t - Variable in class weka.classifiers.functions.SPegasos
Holds the current iteration number
m_TabbedPane - Variable in class weka.gui.experiment.Experimenter
The tabbed pane that controls which sub-pane we are working with
m_TabbedPane - Variable in class weka.gui.explorer.Explorer
The tabbed pane that controls which sub-pane we are working with
m_TabbedPane - Variable in class weka.gui.sql.ResultPanel
the tabbed pane for the results
m_Table - Variable in class weka.classifiers.meta.GridSearch.PerformanceTable
the table with the values
m_Table - Variable in class weka.gui.AttributeListPanel
The table displaying attribute names
m_Table - Variable in class weka.gui.AttributeSelectionPanel
The table displaying attribute names and selection status
m_Tags - Variable in class weka.core.SelectedTag
The set of tags to choose from
m_Tail - Variable in class weka.core.Queue
Store a reference to the tail of the queue
m_target - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
class values/desired output vector
m_targetMetaInfo - Variable in class weka.core.pmml.MiningSchema
target meta info (may be null if not defined)
m_TaskMonitor - Variable in class weka.gui.LogPanel
The panel for monitoring the number of running tasks (if supplied)
m_tCounts - Variable in class weka.classifiers.lazy.LBR
All the counts for nominal attributes.
m_TeeErr - Static variable in class weka.gui.LogWindow
for redirecting stderr
m_TeeOut - Static variable in class weka.gui.LogWindow
for redirecting stdout
m_Template - Variable in class weka.experiment.ClassifierSplitEvaluator
The template classifier
m_Template - Variable in class weka.experiment.RegressionSplitEvaluator
The template classifier
m_tempUndoFiles - Variable in class weka.gui.explorer.PreprocessPanel
Keeps track of undo points
m_tempUndoIndex - Variable in class weka.gui.explorer.PreprocessPanel
The next available slot for an undo point
m_Test - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The test instance
m_Test - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The test instance
m_test - Variable in class weka.classifiers.rules.part.ClassifierDecList
The pruning instances.
m_test - Variable in class weka.classifiers.trees.j48.ClassifierTree
The pruning instances.
m_Test - Variable in class weka.core.Capabilities
whether to perform any tests at all
m_TesterClasses - Variable in class weka.gui.experiment.ResultsPanel
Lists all the available classes implementing the Tester-Interface.
m_TesterClassesLabel - Variable in class weka.gui.experiment.ResultsPanel
Displays the currently selected Tester-Class.
m_TesterClassesModel - Static variable in class weka.gui.experiment.ResultsPanel
Contains all the available classes implementing the Tester-Interface (the display names).
m_Testers - Static variable in class weka.gui.experiment.ResultsPanel
Contains all the available classes implementing the Tester-Interface (the actual Classes).
m_TestEvaluator - Variable in class weka.attributeSelection.CheckAttributeSelection
whether to test the evaluator (default) or the search method
m_TestInstances - Variable in class weka.gui.explorer.AssociationsPanel
The user-supplied test set (if any)
m_TestInstances - Variable in class weka.gui.explorer.ClustererPanel
The user-supplied test set (if any)
m_testListeners - Variable in class weka.gui.beans.AbstractTrainAndTestSetProducer
Objects listening for test set events
m_TestLoader - Variable in class weka.gui.explorer.ClassifierPanel
The loader used to load the user-supplied test set (if any)
m_testOrTrain - Variable in class weka.gui.beans.BatchClustererEvent
Indicates if m_testSet is a training or a test set.
m_TestsButton - Variable in class weka.gui.experiment.ResultsPanel
Lets the user select which scheme to base comparisons against.
m_testSet - Variable in class weka.gui.beans.BatchClassifierEvent
Instances that can be used for testing the classifier
m_testSet - Variable in class weka.gui.beans.BatchClustererEvent
Training or Test Instances
m_testSet - Variable in class weka.gui.beans.TestSetEvent
The test set instances
m_testSetListeners - Variable in class weka.gui.beans.PredictionAppender
Objects listening for test set events
m_TestsList - Variable in class weka.gui.experiment.ResultsPanel
Holds the list of schemes to base the test against.
m_TestsModel - Variable in class weka.gui.experiment.ResultsPanel
The model embedded in m_TestsList.
m_TestSplitBut - Variable in class weka.gui.explorer.ClassifierPanel
Click to set test mode to a user-specified test set
m_TestSplitBut - Variable in class weka.gui.explorer.ClustererPanel
Click to set test mode to a user-specified test set
m_testTrainRatio - Variable in class weka.experiment.PairedStatsCorrected
The ratio used to correct the significane test
m_text - Variable in class weka.gui.beans.TextEvent
The text
m_TextQuery - Variable in class weka.gui.sql.QueryPanel
the textarea for the query.
m_textTitle - Variable in class weka.gui.beans.TextEvent
The title for the text.
m_TextURL - Variable in class weka.gui.sql.ConnectionPanel
the textfield for the URL.
m_theInstances - Variable in class weka.classifiers.rules.DecisionTable
Holds the original training instances
m_ThreadID - Variable in class weka.core.Debug.Clock
the thread ID
m_ThreadMonitor - Variable in class weka.core.Debug.Clock
the thread monitor, if the system can measure the CPU time
m_threshold - Variable in class weka.attributeSelection.GreedyStepwise
A threshold by which to discard attributes---used by the AttributeSelection module
m_Threshold - Variable in class weka.classifiers.functions.Winnow
Prediction threshold, <0 == numAttributes
m_threshold - Variable in class weka.classifiers.pmml.consumer.NeuralNetwork
Threshold activation
m_threshold - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_thresholdLab - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_thresholdSlider - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
The slider for adjusting the threshold
m_tickSize - Variable in class weka.gui.visualize.Plot2D
Tick size
m_Timestamps - Variable in class weka.core.Tee
whether to add timestamps or not.
m_tnPrevious - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_Tokenizer - Variable in class weka.core.converters.ArffLoader.ArffReader
the tokenizer for reading the stream
m_Tokenizer - Variable in class weka.core.tokenizers.WordTokenizer
the actual tokenizer
m_tol - Variable in class weka.classifiers.functions.SMO
Tolerance for accuracy of result.
m_tol - Variable in class weka.classifiers.mi.MISMO
Tolerance for accuracy of result.
m_TOLX - Variable in class weka.core.Optimization
 
m_ToolTipUserAsked - Static variable in class weka.gui.visualize.PrintableComponent
whether the user was already asked about the tooltip behavior.
m_topOfTree - Variable in class weka.classifiers.trees.m5.Rule
the top of the m5 tree for this rule
m_toSelectModel - Variable in class weka.classifiers.rules.part.ClassifierDecList
The model selection method.
m_toSelectModel - Variable in class weka.classifiers.trees.j48.ClassifierTree
The model selection method.
m_Total - Variable in class weka.core.Memory
the total memory that is used
m_TotalCost - Variable in class weka.classifiers.Evaluation
The total cost of predictions (includes instance weights)
m_TotalCount - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
Number of trai instances with no missing attribute values
m_totalEvals - Variable in class weka.attributeSelection.BestFirst
total number of subsets evaluated during a search
m_totalEvals - Variable in class weka.attributeSelection.LinearForwardSelection
total number of subsets evaluated during a search
m_totalEvals - Variable in class weka.attributeSelection.SubsetSizeForwardSelection
total number of subsets evaluated during a search
m_totalInstanceWeight - Variable in class weka.classifiers.trees.ft.FTtree
Total number of training instances.
m_totalInstanceWeight - Variable in class weka.classifiers.trees.lmt.LMTNode
Total number of training instances.
m_totalPopField - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
Population text field
m_totalPopPrevious - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_totalSupport - Variable in class weka.associations.FPGrowth.AssociationRule
The total support for the item set (premise + consequence)
m_totalTime - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The time taken to select attributes AND build the classifier
m_totalTrainInstances - Variable in class weka.classifiers.trees.SimpleCart
Total number of instances used to build the classifier.
m_totalTransactions - Variable in class weka.associations.FPGrowth.AssociationRule
The total number of transactions in the data
m_totalTransactions - Variable in class weka.associations.ItemSet
The total number of transactions
m_totalTransactions - Variable in class weka.associations.RuleGeneration
The total number of transactions
m_TotalWeight - Variable in class weka.classifiers.trees.BFTree
Total weights.
m_tpPrevious - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
 
m_tPriors - Variable in class weka.classifiers.lazy.LBR
The prior probabilities of the classes.
m_Train - Variable in class weka.classifiers.lazy.IBk
The training instances used for classification.
m_Train - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The train instance
m_Train - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The train instance
m_Train - Variable in class weka.classifiers.lazy.KStar
The training instances used for classification.
m_Train - Variable in class weka.classifiers.lazy.LWL
The training instances used for classification.
m_train - Variable in class weka.classifiers.rules.part.ClassifierDecList
The training instances.
m_train - Variable in class weka.classifiers.trees.j48.ClassifierTree
The training instances.
m_train - Variable in class weka.classifiers.trees.lmt.LogisticBase
Training data
m_train - Variable in class weka.classifiers.trees.SimpleCart
Training data.
m_TrainBags - Variable in class weka.classifiers.mi.CitationKNN
Training bags
m_TrainBut - Variable in class weka.gui.explorer.AttributeSelectionPanel
Click to set test mode to test on training data
m_TrainBut - Variable in class weka.gui.explorer.ClassifierPanel
Click to set test mode to test on training data
m_TrainBut - Variable in class weka.gui.explorer.ClustererPanel
Click to set test mode to test on training data
m_TrainClassVals - Variable in class weka.classifiers.Evaluation
Array containing all numeric training class values seen
m_TrainClassWeights - Variable in class weka.classifiers.Evaluation
Array containing all numeric training class weights
m_TrainCopy - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
Keep a copy for the class attribute (if set).
m_TrainFoldSize - Variable in class weka.classifiers.meta.CVParameterSelection
The number of instances in a training fold
m_trainingData - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
training data
m_trainingListeners - Variable in class weka.gui.beans.AbstractTrainAndTestSetProducer
Objects listening for trainin set events
m_trainingSet - Variable in class weka.gui.beans.TrainingSetEvent
The training instances
m_trainingSetListeners - Variable in class weka.gui.beans.PredictionAppender
Objects listening for training set events
m_trainInstances - Variable in class weka.classifiers.trees.ADTree
The instances used to train the tree
m_trainInstances - Variable in class weka.classifiers.trees.LADTree
 
m_TrainInstances - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
The data to transform analyse/transform.
m_TrainIterations - Variable in class weka.classifiers.BVDecompose
The number of train iterations
m_TrainPercent - Variable in class weka.experiment.RandomSplitResultProducer
The percentage of instances to use for training
m_trainPercent - Variable in class weka.gui.experiment.SimpleSetupPanel
The training percentage for a train/test split experiment
m_TrainPoolSize - Variable in class weka.classifiers.BVDecompose
The number of instances used in the training pool
m_TrainSet - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The training instances used for classification.
m_TrainSet - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The training instances used for classification.
m_trainSet - Variable in class weka.gui.beans.BatchClassifierEvent
Instances that were used to train the classifier (may be null if not available)
m_TrainSize - Variable in class weka.classifiers.BVDecomposeSegCVSub
The training set size
m_trainTotalWeight - Variable in class weka.classifiers.trees.ADTree
The total weight of the instances - used to speed Z calculations
m_transactionsMustContain - Variable in class weka.associations.FPGrowth
If set, limit the transactions (instances) input to the algorithm to those that contain these items
m_transformationDictionary - Variable in class weka.core.pmml.MiningSchema
The transformation dictionary (if defined)
m_TransformedFormat - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
The header for the transformed data format.
m_TransformMethod - Variable in class weka.classifiers.mi.SimpleMI
the method used in transformation
m_Translation - Variable in class weka.filters.unsupervised.attribute.Normalize
The translation of the output range.
m_Traversal - Variable in class weka.classifiers.meta.GridSearch
the traversal
m_tree - Variable in class weka.classifiers.trees.FT
root of the logistic model tree
m_tree - Variable in class weka.classifiers.trees.LMT
root of the logistic model tree
m_Tree - Variable in class weka.classifiers.trees.REPTree
The Tree object
m_Tree - Variable in class weka.gui.PropertySelectorDialog
The component displaying the property tree
m_TreeConstructor - Variable in class weka.core.neighboursearch.BallTree
The constructor method to use to build the tree.
m_treeNodeOfCurrentObject - Variable in class weka.gui.GenericObjectEditor
The tree node of the current object so we can re-select it for the user.
m_TreeStats - Variable in class weka.core.neighboursearch.BallTree
Tree Stats variables.
m_TreeStats - Variable in class weka.core.neighboursearch.CoverTree
Tree Stats variables.
m_TreeStats - Variable in class weka.core.neighboursearch.KDTree
Tree Stats variables.
m_TreeVisualizers - Variable in class weka.gui.GUIChooser
keeps track of the opened tree visualizer instancs
m_trialsValue - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
 
m_trialsVariable - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
 
m_Trie - Static variable in class weka.gui.SimpleCLIPanel.CommandlineCompletion
a trie for storing the packages.
m_TTester - Variable in class weka.gui.experiment.ResultsPanel
The PairedTTester object.
m_type - Variable in class weka.classifiers.functions.neural.NeuralConnection
The type of unit this is.
m_Type - Variable in class weka.classifiers.meta.GridSearch.PerformanceTable
the type of performance the table was generated for
m_Type - Variable in class weka.core.AttributeLocator
the type of the attribute
m_Type - Variable in class weka.core.TechnicalInformation
the type of this technical information
m_Type - Variable in class weka.gui.sql.event.ConnectionEvent
the type of event, CONNECT or DISCONNECT
m_Unclassified - Variable in class weka.classifiers.Evaluation
The weight of all unclassified instances.
m_UndoBut - Variable in class weka.gui.explorer.PreprocessPanel
Click to revert back to the last saved point
m_UndoButton - Variable in class weka.gui.ViewerDialog
Click to undo the last action
m_UniformPerformance - Variable in class weka.classifiers.meta.GridSearch
whether all performances in the grid are the same
m_UniqueLab - Variable in class weka.gui.AttributeSummaryPanel
Displays the number of unique values
m_unitError - Variable in class weka.classifiers.functions.neural.NeuralConnection
The error value for this unit, NaN if not calculated.
m_unitValue - Variable in class weka.classifiers.functions.neural.NeuralConnection
The output value for this unit, NaN if not calculated.
m_UpBut - Variable in class weka.gui.experiment.AlgorithmListPanel
Click to move the selected algorithm(s) one up
m_UpBut - Variable in class weka.gui.experiment.DatasetListPanel
Click to move the selected dataset(s) one up.
m_updateBt - Variable in class weka.gui.visualize.MatrixPanel
The button that updates the display to reflect the changes made by the user.
m_Upper - Variable in class weka.core.SingleIndex
Store the maximum value permitted.
m_upperBoundMinSupport - Variable in class weka.associations.Apriori
The upper bound on the support
m_upperBoundMinSupport - Variable in class weka.associations.FPGrowth
The upper bound on the minimum support
m_UpperExtremeValue - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
the upper extreme value threshold (= Q3 + EVF*IQR)
m_UpperOutlier - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
the upper outlier threshold (= Q3 + OF*IQR)
m_UpperSize - Variable in class weka.experiment.LearningRateResultProducer
The maximum number of instances to use.
m_UpperText - Variable in class weka.gui.experiment.RunNumberPanel
Configures the upper run number
m_URL - Variable in class weka.core.converters.ArffLoader
the url
m_URL - Variable in class weka.core.converters.ConverterUtils.DataSource
the URL to load.
m_URL - Variable in class weka.core.converters.DatabaseLoader
the JDBC URL to use
m_URL - Variable in class weka.core.converters.LibSVMLoader
the url.
m_URL - Variable in class weka.core.converters.SVMLightLoader
the url.
m_URL - Variable in class weka.core.converters.XRFFLoader
the url
m_URL - Variable in class weka.gui.sql.ConnectionPanel
the URL to use.
m_URL - Variable in class weka.gui.sql.event.ResultChangedEvent
the connect string with which the query was run
m_URL - Variable in class weka.gui.sql.ResultSetTable
the connect string with which the query was run
m_URL - Variable in class weka.gui.sql.SqlViewer
the connect string with which the query was run.
m_URL - Variable in class weka.gui.sql.SqlViewerDialog
the connect string with which the query was run
m_URLFileLoaders - Static variable in class weka.core.converters.ConverterUtils
all available URL loaders (extension <-> classname).
m_useAIC - Variable in class weka.classifiers.trees.FT
If true, the AIC is used to choose the best LogitBoost iteration
m_UseAllK - Variable in class weka.classifiers.lazy.LWL
True if m_kNN should be set to all instances.
m_UseBetterEncoding - Variable in class weka.filters.supervised.attribute.Discretize
Use better encoding of split point for MDL.
m_UseCpuTime - Variable in class weka.core.Debug.Clock
whether to use the CPU time (by default TRUE)
m_useCrossValidation - Variable in class weka.classifiers.functions.SimpleLogistic
If true, cross-validate number of LogitBoost iterations
m_useCrossValidation - Variable in class weka.classifiers.trees.lmt.LogisticBase
Use cross-validation to determine best number of LogitBoost iterations ?
m_useCustomColour - Variable in class weka.gui.visualize.PlotData2D
Custom colour for this plot
m_UseCustomDimensions - Variable in class weka.gui.visualize.JComponentWriter
whether to use custom dimensions
m_UseDiscretization - Variable in class weka.classifiers.bayes.NaiveBayes
Whether to use discretization than normal distribution for numeric attributes
m_UseEqualFrequency - Variable in class weka.classifiers.meta.RegressionByDiscretization
Use equal-frequency binning
m_UseEqualFrequency - Variable in class weka.filters.unsupervised.attribute.Discretize
Use equal-frequency binning if unsupervised discretization turned on
m_UseErrorRate - Variable in class weka.classifiers.trees.BFTree
If use error rate in internal cross-validation to fix the number of expansions - default (if not, root mean squared error is used).
m_useGaussian - Variable in class weka.filters.unsupervised.attribute.RandomProjection
Is the random matrix will be computed using Gaussian distribution or not
m_UseGini - Variable in class weka.classifiers.trees.BFTree
If use Gini index as the splitting criterion - default (if not, information is used).
m_UseGUI - Variable in class weka.core.Memory
whether a GUI is present
m_useIBk - Variable in class weka.classifiers.rules.DecisionTable
Use the IBk classifier rather than majority class
m_UseKDTree - Variable in class weka.clusterers.XMeans
whether to use the KDTree (the KDTree is only initialized to be configurable from the GUI).
m_UseKernelEstimator - Variable in class weka.classifiers.bayes.NaiveBayes
Whether to use kernel density estimator rather than normal distribution for numeric attributes
m_UseKononenko - Variable in class weka.filters.supervised.attribute.Discretize
Use Kononenko's MDL criterion instead of Fayyad et al.'s
m_UseOneSE - Variable in class weka.classifiers.trees.BFTree
If use the 1SE rule to make the decision.
m_UseOneSE - Variable in class weka.classifiers.trees.SimpleCart
If use the 1SE rule to make final decision tree.
m_UsePropertyIterator - Variable in class weka.experiment.Experiment
True if the exp should also iterate over a property of the RP
m_User - Variable in class weka.core.converters.DatabaseLoader
the database user to use
m_User - Variable in class weka.gui.sql.ConnectionPanel
the user to use for connecting to the DB.
m_User - Variable in class weka.gui.sql.event.ResultChangedEvent
the user that was used to connect to the DB
m_User - Variable in class weka.gui.sql.ResultSetTable
the user that was used to connect to the DB
m_User - Variable in class weka.gui.sql.SqlViewer
the user that was used to connect to the DB.
m_User - Variable in class weka.gui.sql.SqlViewerDialog
the user that was used to connect to the DB
m_UserComponentsInXML - Variable in class weka.gui.beans.KnowledgeFlowApp
whether to store the user components in XML or in binary format
m_useRelativePath - Variable in class weka.core.converters.AbstractFileLoader
use relative file paths
m_useRelativePath - Variable in class weka.core.converters.AbstractFileSaver
use relative file paths
m_useReplaceMissing - Variable in class weka.filters.unsupervised.attribute.RandomProjection
Should the missing values be replaced using unsupervised.ReplaceMissingValues filter
m_UseResampling - Variable in class weka.classifiers.meta.AdaBoostM1
Use boosting with reweighting?
m_UseResampling - Variable in class weka.classifiers.meta.LogitBoost
Use boosting with reweighting?
m_UseResampling - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
Whether to use resampling
m_userHasBeenAskedAboutConversion - Variable in class weka.gui.experiment.SimpleSetupPanel
Whether or not the user has consented for the experiment to be simplified
m_userName - Variable in class weka.experiment.DatabaseUtils
Database username.
m_UserNameLab - Variable in class weka.gui.DatabaseConnectionDialog
 
m_UserNameText - Variable in class weka.gui.DatabaseConnectionDialog
 
m_UserOptions - Variable in class weka.core.CheckOptionHandler
the user-supplied options
m_UseStars - Variable in class weka.core.Javadoc
whether to include the stars in the Javadoc
m_useUnpruned - Variable in class weka.classifiers.trees.m5.M5Base
Do not prune tree/rules
m_UseWordwrap - Variable in class weka.gui.LogWindow
whether the JTextPane has wordwrap or not
m_Validated - Variable in class weka.core.NormalizableDistance
Whether all the necessary preparations have been done.
m_Validating - Variable in class weka.core.xml.XMLDocument
whether to use a validating parser or not.
m_validationChunkSize - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The size of the validation set
m_validationFs - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_validationSet - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The instances used for validation
m_validationSetChanged - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
Whether the validation set has recently been changed
m_value - Variable in class weka.core.pmml.FieldMetaInfo.Value
The value
m_Value - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
Stores which value of a numeric attribute is to be used for filtering.
m_Value - Variable in class weka.gui.SortedTableModel.SortContainer
the value to sort.
m_ValueBuffer - Variable in class weka.core.converters.ArffLoader.ArffReader
Buffer of values for sparse instance
m_valueIndex - Variable in class weka.associations.FPGrowth.BinaryItem
The index of the value considered to be positive
m_Values - Variable in class weka.classifiers.meta.GridSearch
the best values
m_Values - Variable in class weka.classifiers.meta.GridSearch.Performance
the value pair the classifier was built with
m_values - Variable in class weka.core.pmml.DerivedFieldMetaInfo
the list of values (if the field is ordinal) - may be of size zero if none are specified.
m_values - Variable in class weka.core.pmml.TargetMetaInfo
for categorical values.
m_Values - Variable in class weka.core.TechnicalInformation
stores all the values associated with the fields (FIELD - String)
m_Values - Variable in class weka.filters.unsupervised.instance.RemoveFrequentValues
contains the values to retain
m_Values - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
Stores which values of nominal attribute are to be used for filtering.
m_Variance - Variable in class weka.classifiers.BVDecompose
The calculated variance
m_Variance - Variable in class weka.classifiers.mi.MINND
The variance for each attribute of each exemplar
m_VaryNodes - Variable in class weka.classifiers.bayes.net.ADNode
list of VaryNode children
m_verbose - Variable in class weka.associations.Apriori
Report progress iteratively
m_verbose - Variable in class weka.attributeSelection.LinearForwardSelection
for debugging
m_verbose - Variable in class weka.attributeSelection.SubsetSizeForwardSelection
for debugging
m_Verbose - Variable in class weka.classifiers.meta.Dagging
whether to output some progress information during building
m_verboseOn - Variable in class weka.core.Debug.DBO
enables/disables output of debug information
m_Viewer - Variable in class weka.gui.sql.SqlViewerDialog
the SQL panel
m_visual - Variable in class weka.gui.beans.AbstractDataSink
Default visual for data sources
m_visual - Variable in class weka.gui.beans.AbstractDataSource
Default visual for data sources
m_visual - Variable in class weka.gui.beans.AbstractEvaluator
Default visual for evaluators
m_visual - Variable in class weka.gui.beans.AbstractTestSetProducer
 
m_visual - Variable in class weka.gui.beans.AbstractTrainAndTestSetProducer
 
m_visual - Variable in class weka.gui.beans.AbstractTrainingSetProducer
 
m_visual - Variable in class weka.gui.beans.Associator
 
m_visual - Variable in class weka.gui.beans.ClassAssigner
 
m_visual - Variable in class weka.gui.beans.Classifier
 
m_visual - Variable in class weka.gui.beans.ClassValuePicker
 
m_visual - Variable in class weka.gui.beans.Clusterer
 
m_visual - Variable in class weka.gui.beans.CostBenefitAnalysis
 
m_visual - Variable in class weka.gui.beans.DataVisualizer
 
m_visual - Variable in class weka.gui.beans.Filter
 
m_visual - Variable in class weka.gui.beans.GraphViewer
 
m_visual - Variable in class weka.gui.beans.InstanceStreamToBatchMaker
 
m_visual - Variable in class weka.gui.beans.MetaBean
 
m_visual - Variable in class weka.gui.beans.ModelPerformanceChart
 
m_visual - Variable in class weka.gui.beans.PredictionAppender
 
m_visual - Variable in class weka.gui.beans.SerializedModelSaver
Default visual for data sources
m_visual - Variable in class weka.gui.beans.StripChart
 
m_visual - Variable in class weka.gui.beans.TextViewer
 
m_visualizeDataSet - Variable in class weka.gui.beans.DataVisualizer
 
m_visualLabel - Variable in class weka.gui.beans.BeanVisual
 
m_visualName - Variable in class weka.gui.beans.BeanVisual
Name for the bean
m_Vote - Variable in class weka.classifiers.meta.Dagging
the classifier used for voting
m_WBias - Variable in class weka.classifiers.BVDecomposeSegCVSub
The calculated Webb bias
m_Weight - Variable in class weka.classifiers.functions.LibLINEAR
 
m_Weight - Variable in class weka.classifiers.functions.LibSVM
for C_SVC
m_Weight - Variable in class weka.core.Instance
The instance's weight.
m_weightByConfidence - Variable in class weka.classifiers.misc.VFI
Exponentially bias more confident intervals
m_WeightKernel - Variable in class weka.classifiers.lazy.LWL
The weighting kernel method currently selected.
m_WeightLabel - Variable in class weka.classifiers.functions.LibLINEAR
 
m_WeightLabel - Variable in class weka.classifiers.functions.LibSVM
for C_SVC
m_WeightMethod - Variable in class weka.classifiers.mi.MIWrapper
the single-instance weight setting method
m_WeightMethod - Variable in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
the propositional instance weight setting method
m_weights - Variable in class weka.classifiers.functions.SMO.BinarySMO
Weight vector for linear machine.
m_weights - Variable in class weka.classifiers.functions.SPegasos
Stores the weights (+ bias in the last element)
m_weights - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
weights for linear kernel
m_Weights - Variable in class weka.classifiers.mi.MINND
The weight of each exemplar
m_weights - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
Weight vector for linear machine.
m_Weights - Variable in class weka.classifiers.trees.BFTree
Sorted weights.
m_weightsUpdated - Variable in class weka.classifiers.functions.neural.NeuralConnection
True if the weights have already been updated.
m_WeightThreshold - Variable in class weka.classifiers.meta.AdaBoostM1
Weight Threshold.
m_WeightThreshold - Variable in class weka.classifiers.meta.LogitBoost
Weight thresholding.
m_weightTrimBeta - Variable in class weka.classifiers.functions.SimpleLogistic
Threshold for trimming weights.
m_weightTrimBeta - Variable in class weka.classifiers.trees.FT
Threshold for trimming weights.
m_weightTrimBeta - Variable in class weka.classifiers.trees.lmt.LogisticBase
Threshold for trimming weights.
m_weightTrimBeta - Variable in class weka.classifiers.trees.LMT
Threshold for trimming weights.
m_Width - Variable in class weka.classifiers.meta.GridSearch.Grid
the number of points on the X axis
m_width - Variable in class weka.classifiers.pmml.consumer.NeuralNetwork
Width for radial basis
m_WindowCount - Static variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
the number of visualizer windows we have open.
m_WindowSize - Variable in class weka.classifiers.lazy.IBk
The maximum number of training instances allowed.
m_Wins - Variable in class weka.experiment.ResultMatrix
the significant wins
m_WithClass - Variable in class weka.classifiers.Evaluation
The weight of all instances that had a class assigned to them.
m_Words - Variable in class weka.core.CheckScheme
for generating String attributes/classes
m_Words - Variable in class weka.core.Stopwords
The hash set containing the list of stopwords
m_Words - Variable in class weka.core.TestInstances
for generating String attributes/classes
m_WordSeparators - Variable in class weka.core.CheckScheme
for generating String attributes/classes
m_WordSeparators - Variable in class weka.core.TestInstances
for generating String attributes/classes
m_wordsPerClass - Variable in class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable
the word count per class
m_WriteMethods - Variable in class weka.core.xml.XMLSerializationMethodHandler
for storing write methods
m_WVariance - Variable in class weka.classifiers.BVDecomposeSegCVSub
The calculated Webb variance
m_x - Variable in class weka.classifiers.functions.neural.NeuralConnection
The x coord of this unit purely for displaying purposes.
m_x0 - Variable in class weka.classifiers.functions.SMOreg
 
m_x1 - Variable in class weka.classifiers.functions.SMOreg
coefficients used by normalization filter for doing its linear transformation so that result = svmoutput * m_x1 + m_x0
m_X_Base - Variable in class weka.classifiers.meta.GridSearch
the base for
m_X_Expression - Variable in class weka.classifiers.meta.GridSearch
The expression for the X property.
m_X_Max - Variable in class weka.classifiers.meta.GridSearch
the maximum of X
m_X_Min - Variable in class weka.classifiers.meta.GridSearch
the minimum of X
m_X_Property - Variable in class weka.classifiers.meta.GridSearch
the X option to work on (without leading dash, preceding 'classifier.' means to set the option for the classifier 'filter.' for the filter)
m_X_Step - Variable in class weka.classifiers.meta.GridSearch
the step size of
m_xAttBox - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_xAttribute - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_XaxisEnd - Variable in class weka.gui.visualize.Plot2D
 
m_XaxisStart - Variable in class weka.gui.visualize.Plot2D
the offsets of the axes once label metrics are calculated
m_xChange - Variable in class weka.gui.visualize.AttributePanelEvent
True if the x selection changed
m_XCombo - Variable in class weka.gui.visualize.VisualizePanel
Lets the user select the attribute for the x axis
m_xIndex - Variable in class weka.gui.visualize.AttributePanel
 
m_xIndex - Variable in class weka.gui.visualize.Plot2D
Indexes of the attributes to go on the x and y axis and the attribute to use for colouring and the current shape for drawing
m_xIndex - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
Indexes of the attributes to go on the x and y axis and the attribute to use for colouring and the current shape for drawing
m_XMLDocument - Variable in class weka.core.xml.XMLOptions
the XML document.
m_XMLFilter - Variable in class weka.gui.beans.KnowledgeFlowApp
A filter to ensure only KnowledgeFlow layout files in XML format get shown in the chooser
m_XMLFilter - Variable in class weka.gui.experiment.AlgorithmListPanel
A filter to ensure only experiment (in XML format) files get shown in the chooser
m_XMLFilter - Variable in class weka.gui.experiment.SetupPanel
A filter to ensure only experiment (in XML format) files get shown in the chooser
m_XMLFilter - Variable in class weka.gui.experiment.SimpleSetupPanel
A filter to ensure only experiment (in XML format) files get shown in the chooser
m_XMLInstances - Variable in class weka.core.converters.XRFFLoader
the loaded XML document
m_XMLInstances - Variable in class weka.core.converters.XRFFSaver
the generated XML document
m_XPath - Variable in class weka.core.xml.XMLDocument
for XPath queries.
m_xScale - Variable in class weka.gui.visualize.JComponentWriter
the x scale factor
m_xScale - Variable in class weka.gui.visualize.PrintableComponent
the x scale factor.
m_XStreamFilter - Variable in class weka.gui.beans.Classifier
 
m_XStreamFilter - Variable in class weka.gui.beans.KnowledgeFlowApp
A filter to ensure only KnowledgeFlow files in XStream format get shown in the chooser
m_y - Variable in class weka.classifiers.functions.neural.NeuralConnection
The y coord of this unit purely for displaying purposes.
m_Y_Base - Variable in class weka.classifiers.meta.GridSearch
the base for Y
m_Y_Expression - Variable in class weka.classifiers.meta.GridSearch
The expression for the Y property.
m_Y_Max - Variable in class weka.classifiers.meta.GridSearch
the maximum of Y
m_Y_Min - Variable in class weka.classifiers.meta.GridSearch
the minimum of Y
m_Y_Property - Variable in class weka.classifiers.meta.GridSearch
the Y option to work on (without leading dash, preceding 'classifier.' means to set the option for the classifier 'filter.' for the filter)
m_Y_Step - Variable in class weka.classifiers.meta.GridSearch
the step size of Y
m_yAttBox - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_yAttribute - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_YaxisEnd - Variable in class weka.gui.visualize.Plot2D
 
m_YaxisStart - Variable in class weka.gui.visualize.Plot2D
 
m_yChange - Variable in class weka.gui.visualize.AttributePanelEvent
True if the y selection changed
m_YCombo - Variable in class weka.gui.visualize.VisualizePanel
Lets the user select the attribute for the y axis
m_yIndex - Variable in class weka.gui.visualize.AttributePanel
 
m_yIndex - Variable in class weka.gui.visualize.Plot2D
 
m_yIndex - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
 
m_yScale - Variable in class weka.gui.visualize.JComponentWriter
the y scale factor
m_yScale - Variable in class weka.gui.visualize.PrintableComponent
the y scale factor.
m_Zero - Static variable in class weka.core.Optimization
Compute machine precision
m_ZeroR - Variable in class weka.classifiers.lazy.LWL
a ZeroR model in case no model can be built from the data.
m_ZeroR - Variable in class weka.classifiers.meta.AdaBoostM1
a ZeroR model in case no model can be built from the data
m_zeroR - Variable in class weka.classifiers.meta.AdditiveRegression
The model for the mean
m_ZeroR - Variable in class weka.classifiers.meta.ClassificationViaClustering
the default model
m_ZeroR - Variable in class weka.classifiers.meta.LogitBoost
a ZeroR model in case no model can be built from the data
m_zeroR - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The default scheme used when committees aren't ready
m_ZeroR - Variable in class weka.classifiers.meta.RandomSubSpace
a ZeroR model in case no model can be built from the data
m_ZeroR - Variable in class weka.classifiers.misc.HyperPipes
a ZeroR model in case no model can be built from the data
m_ZeroR - Variable in class weka.classifiers.trees.RandomTree
a ZeroR model in case no model can be built from the data
m_zeroR - Variable in class weka.classifiers.trees.REPTree
ZeroR model that is used if no attributes are present.
m_ZipDest - Variable in class weka.experiment.CrossValidationResultProducer
The output zipper to use for saving raw splitEvaluator output
m_ZipDest - Variable in class weka.experiment.RandomSplitResultProducer
The output zipper to use for saving raw splitEvaluator output
m_ZoomBoxColor - Variable in class weka.gui.treevisualizer.TreeVisualizer
the color of the zoombox.
m_ZoomBoxXORColor - Variable in class weka.gui.treevisualizer.TreeVisualizer
the XOR color of the zoombox.
MACHEP - Static variable in class weka.core.Statistics
Some constants
MahalanobisEstimator - Class in weka.estimators
Simple probability estimator that places a single normal distribution over the observed values.
MahalanobisEstimator(Matrix, double, double) - Constructor for class weka.estimators.MahalanobisEstimator
Constructor
main(String[]) - Static method in class weka.associations.Apriori
Main method.
main(String[]) - Static method in class weka.associations.AssociatorEvaluation
A test method for this class.
main(String[]) - Static method in class weka.associations.CheckAssociator
Test method for this class
main(String[]) - Static method in class weka.associations.FilteredAssociator
Main method for running this class.
main(String[]) - Static method in class weka.associations.FPGrowth
Main method.
main(String[]) - Static method in class weka.associations.GeneralizedSequentialPatterns
Main method.
main(String[]) - Static method in class weka.associations.PredictiveApriori
Main method.
main(String[]) - Static method in class weka.associations.Tertius
Main method.
main(String[]) - Static method in class weka.attributeSelection.AttributeSelection
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.CfsSubsetEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.CheckAttributeSelection
Test method for this class
main(String[]) - Static method in class weka.attributeSelection.ChiSquaredAttributeEval
Main method.
main(String[]) - Static method in class weka.attributeSelection.ClassifierSubsetEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.ConsistencySubsetEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.CostSensitiveAttributeEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.CostSensitiveSubsetEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.FilteredAttributeEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.FilteredSubsetEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.GainRatioAttributeEval
Main method.
main(String[]) - Static method in class weka.attributeSelection.InfoGainAttributeEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.LatentSemanticAnalysis
Main method for testing this class
main(String[]) - Static method in class weka.attributeSelection.OneRAttributeEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.PrincipalComponents
Main method for testing this class
main(String[]) - Static method in class weka.attributeSelection.ReliefFAttributeEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.SVMAttributeEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.WrapperSubsetEval
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.AODE
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.AODEsr
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.BayesianLogisticRegression
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.BayesNet
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.ComplementNaiveBayes
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.DMNBtext
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.HNB
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.NaiveBayes
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.NaiveBayesMultinomial
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.NaiveBayesSimple
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.NaiveBayesUpdateable
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.net.ADNode
for testing only
main(String[]) - Static method in class weka.classifiers.bayes.net.BayesNetGenerator
Main method
main(String[]) - Static method in class weka.classifiers.bayes.net.BIFReader
Loads the file specified as first parameter and prints it to stdout.
main(String[]) - Static method in class weka.classifiers.bayes.net.EditableBayesNet
 
main(String[]) - Static method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorFullBayes
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.net.GUI
Main method.
main(String[]) - Static method in class weka.classifiers.bayes.net.MarginCalculator
 
main(String[]) - Static method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm
for testing the class
main(String[]) - Static method in class weka.classifiers.bayes.WAODE
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.BVDecompose
Test method for this class
main(String[]) - Static method in class weka.classifiers.BVDecomposeSegCVSub
Test method for this class
main(String[]) - Static method in class weka.classifiers.CheckClassifier
Test method for this class
main(String[]) - Static method in class weka.classifiers.CheckSource
Executes the tests, use "-h" to list the commandline options.
main(String[]) - Static method in class weka.classifiers.evaluation.CostCurve
Tests the CostCurve generation from the command line.
main(String[]) - Static method in class weka.classifiers.Evaluation
A test method for this class.
main(String[]) - Static method in class weka.classifiers.evaluation.MarginCurve
Tests the MarginCurve generation from the command line.
main(String[]) - Static method in class weka.classifiers.evaluation.ThresholdCurve
Tests the ThresholdCurve generation from the command line.
main(String[]) - Static method in class weka.classifiers.functions.GaussianProcesses
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.IsotonicRegression
Main method for testing this class
main(String[]) - Static method in class weka.classifiers.functions.LeastMedSq
generate a Linear regression predictor for testing
main(String[]) - Static method in class weka.classifiers.functions.LibLINEAR
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.LibSVM
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.LinearRegression
Generates a linear regression function predictor.
main(String[]) - Static method in class weka.classifiers.functions.Logistic
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.MultilayerPerceptron
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.pace.ChisqMixture
Method to test this class
main(String[]) - Static method in class weka.classifiers.functions.pace.DiscreteFunction
 
main(String[]) - Static method in class weka.classifiers.functions.pace.NormalMixture
Method to test this class
main(String[]) - Static method in class weka.classifiers.functions.pace.PaceMatrix
for testing only
main(String[]) - Static method in class weka.classifiers.functions.PaceRegression
Generates a linear regression function predictor.
main(String[]) - Static method in class weka.classifiers.functions.PLSClassifier
Main method for running this classifier from commandline.
main(String[]) - Static method in class weka.classifiers.functions.RBFNetwork
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.SimpleLinearRegression
Main method for testing this class
main(String[]) - Static method in class weka.classifiers.functions.SimpleLogistic
Main method for testing this class
main(String[]) - Static method in class weka.classifiers.functions.SMO
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.SMOreg
Main method for running this classifier.
main(String[]) - Static method in class weka.classifiers.functions.SPegasos
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.supportVector.CheckKernel
Test method for this class
main(String[]) - Static method in class weka.classifiers.functions.supportVector.KernelEvaluation
A test method for this class.
main(String[]) - Static method in class weka.classifiers.functions.VotedPerceptron
Main method.
main(String[]) - Static method in class weka.classifiers.functions.Winnow
Main method.
main(String[]) - Static method in class weka.classifiers.lazy.IB1
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.lazy.IBk
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.lazy.KStar
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.lazy.LBR
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.lazy.LWL
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.AdaBoostM1
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.AdditiveRegression
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.AttributeSelectedClassifier
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.Bagging
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.ClassificationViaClustering
Runs the classifier with the given options
main(String[]) - Static method in class weka.classifiers.meta.ClassificationViaRegression
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.CostSensitiveClassifier
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.CVParameterSelection
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.Dagging
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.Decorate
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.END
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.FilteredClassifier
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.Grading
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.GridSearch
Main method for running this classifier from commandline.
main(String[]) - Static method in class weka.classifiers.meta.LogitBoost
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.MetaCost
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.MultiBoostAB
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.MultiClassClassifier
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.MultiScheme
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.nestedDichotomies.ND
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.OrdinalClassClassifier
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Main method for this class.
main(String[]) - Static method in class weka.classifiers.meta.RandomCommittee
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.RandomSubSpace
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.RegressionByDiscretization
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.RotationForest
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.Stacking
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.StackingC
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.ThresholdSelector
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.Vote
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.mi.CitationKNN
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.mi.MDD
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.mi.MIBoost
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.mi.MIDD
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.mi.MIEMDD
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.mi.MILR
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.mi.MINND
Main method for testing.
main(String[]) - Static method in class weka.classifiers.mi.MIOptimalBall
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.mi.MISMO
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.mi.MISVM
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.mi.MIWrapper
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.mi.SimpleMI
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.misc.HyperPipes
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.misc.SerializedClassifier
Runs the classifier with the given options
main(String[]) - Static method in class weka.classifiers.misc.VFI
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.rules.ConjunctiveRule
Main method.
main(String[]) - Static method in class weka.classifiers.rules.DecisionTable
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.rules.DTNB
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.rules.JRip
Main method.
main(String[]) - Static method in class weka.classifiers.rules.M5Rules
Main method by which this class can be tested
main(String[]) - Static method in class weka.classifiers.rules.NNge
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.rules.OneR
Main method for testing this class
main(String[]) - Static method in class weka.classifiers.rules.PART
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.rules.Prism
Main method for testing this class
main(String[]) - Static method in class weka.classifiers.rules.Ridor
Main method.
main(String[]) - Static method in class weka.classifiers.rules.ZeroR
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.trees.ADTree
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.trees.BFTree
Main method.
main(String[]) - Static method in class weka.classifiers.trees.DecisionStump
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.trees.FT
Main method for testing this class
main(String[]) - Static method in class weka.classifiers.trees.Id3
Main method.
main(String[]) - Static method in class weka.classifiers.trees.J48
Main method for testing this class
main(String[]) - Static method in class weka.classifiers.trees.J48graft
Main method for testing this class
main(String[]) - Static method in class weka.classifiers.trees.LADTree
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.trees.LMT
Main method for testing this class
main(String[]) - Static method in class weka.classifiers.trees.M5P
Main method by which this class can be tested
main(String[]) - Static method in class weka.classifiers.trees.NBTree
Main method for testing this class
main(String[]) - Static method in class weka.classifiers.trees.RandomForest
Main method for this class.
main(String[]) - Static method in class weka.classifiers.trees.RandomTree
Main method for this class.
main(String[]) - Static method in class weka.classifiers.trees.REPTree
Main method for this class.
main(String[]) - Static method in class weka.classifiers.trees.SimpleCart
Main method.
main(String[]) - Static method in class weka.classifiers.trees.UserClassifier
Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.CheckClusterer
Test method for this class
main(String[]) - Static method in class weka.clusterers.CLOPE
Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.ClusterEvaluation
Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.Cobweb
Main method.
main(String[]) - Static method in class weka.clusterers.DBScan
Main Method for testing DBScan
main(String[]) - Static method in class weka.clusterers.EM
Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.FarthestFirst
Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.FilteredClusterer
Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.OPTICS_Visualizer
Displays the GUI.
main(String[]) - Static method in class weka.clusterers.HierarchicalClusterer
 
main(String[]) - Static method in class weka.clusterers.MakeDensityBasedClusterer
Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.OPTICS
Main Method for testing OPTICS
main(String[]) - Static method in class weka.clusterers.sIB
 
main(String[]) - Static method in class weka.clusterers.SimpleKMeans
Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.XMeans
Main method for testing this class.
main(String[]) - Static method in class weka.core.AlgVector
Main method for testing this class, can take an ARFF file as first argument.
main(String[]) - Static method in class weka.core.AllJavadoc
Parses the given commandline parameters and generates the Javadoc.
main(String[]) - Static method in class weka.core.Attribute
Simple main method for testing this class.
main(String[]) - Static method in class weka.core.BinarySparseInstance
Main method for testing this class.
main(String[]) - Static method in class weka.core.Capabilities
loads the given dataset and prints the Capabilities necessary to process it.
main(String[]) - Static method in class weka.core.CheckGOE
Main method for using the CheckGOE.
main(String[]) - Static method in class weka.core.CheckOptionHandler
Main method for using the CheckOptionHandler.
main(String[]) - Static method in class weka.core.ClassDiscovery
Possible calls: weka.core.ClassDiscovery <packages>
Prints all the packages in the current classpath weka.core.ClassDiscovery <classname> <packagename(s)>
Prints the classes it found.
main(String[]) - Static method in class weka.core.ContingencyTables
Main method for testing this class.
main(String[]) - Static method in class weka.core.converters.ArffLoader
Main method.
main(String[]) - Static method in class weka.core.converters.ArffSaver
Main method.
main(String[]) - Static method in class weka.core.converters.C45Loader
Main method for testing this class.
main(String[]) - Static method in class weka.core.converters.C45Saver
Main method.
main(String[]) - Static method in class weka.core.converters.ConverterUtils.DataSink
for testing only - takes a data file as input and a data file for the output.
main(String[]) - Static method in class weka.core.converters.ConverterUtils.DataSource
for testing only - takes a data file as input.
main(String[]) - Static method in class weka.core.converters.CSVLoader
Main method.
main(String[]) - Static method in class weka.core.converters.CSVSaver
Main method.
main(String[]) - Static method in class weka.core.converters.DatabaseLoader
Main method.
main(String[]) - Static method in class weka.core.converters.DatabaseSaver
Main method.
main(String[]) - Static method in class weka.core.converters.LibSVMLoader
Main method.
main(String[]) - Static method in class weka.core.converters.LibSVMSaver
Main method.
main(String[]) - Static method in class weka.core.converters.SerializedInstancesLoader
Main method.
main(String[]) - Static method in class weka.core.converters.SerializedInstancesSaver
Main method.
main(String[]) - Static method in class weka.core.converters.SVMLightLoader
Main method.
main(String[]) - Static method in class weka.core.converters.SVMLightSaver
Main method.
main(String[]) - Static method in class weka.core.converters.TextDirectoryLoader
Main method.
main(String[]) - Static method in class weka.core.converters.XRFFLoader
Main method.
main(String[]) - Static method in class weka.core.converters.XRFFSaver
Main method.
main(String[]) - Static method in class weka.core.Copyright
Only for testing
main(String[]) - Static method in class weka.core.Environment
Main method for testing this class.
main(String[]) - Static method in class weka.core.FindWithCapabilities
Executes the location of classes with parameters from the commandline.
main(String[]) - Static method in class weka.core.GlobalInfoJavadoc
Parses the given commandline parameters and generates the Javadoc.
main(String[]) - Static method in class weka.core.Instance
Main method for testing this class.
main(String[]) - Static method in class weka.core.InstanceComparator
for testing only.
main(String[]) - Static method in class weka.core.Instances
Main method for this class.
main(String[]) - Static method in class weka.core.Jython
If no arguments are given, it just prints the presence of the Jython classes, otherwise it expects a Jython filename to execute.
main(String[]) - Static method in class weka.core.ListOptions
runs the javadoc producer with the given commandline options
main(String[]) - Static method in class weka.core.mathematicalexpression.Parser
Runs the parser from commandline.
main(String[]) - Static method in class weka.core.matrix.DoubleVector
 
main(String[]) - Static method in class weka.core.matrix.IntVector
Tests the IntVector class
main(String[]) - Static method in class weka.core.Matrix
Deprecated.
Main method for testing this class.
main(String[]) - Static method in class weka.core.matrix.Matrix
Main method for testing this class.
main(String[]) - Static method in class weka.core.Memory
prints only some statistics
main(String[]) - Static method in class weka.core.neighboursearch.CoverTree
Method for testing the class from command line.
main(String[]) - Static method in class weka.core.OptionHandlerJavadoc
Parses the given commandline parameters and generates the Javadoc.
main(String[]) - Static method in class weka.core.pmml.Constant
 
main(String[]) - Static method in class weka.core.pmml.PMMLFactory
 
main(String[]) - Static method in class weka.core.PropertyPath
for testing only
main(String[]) - Static method in class weka.core.Queue
Main method for testing this class.
main(String[]) - Static method in class weka.core.RandomVariates
Main method for testing this class.
main(String[]) - Static method in class weka.core.Range
Main method for testing this class.
main(String[]) - Static method in class weka.core.RevisionUtils
For testing only.
main(String[]) - Static method in class weka.core.SerializationHelper
Outputs information about a class on the commandline, takes class name as arguments.
main(String[]) - Static method in class weka.core.SingleIndex
Main method for testing this class.
main(String[]) - Static method in class weka.core.SparseInstance
Main method for testing this class.
main(String[]) - Static method in class weka.core.SpecialFunctions
Main method for testing this class.
main(String[]) - Static method in class weka.core.Statistics
Main method for testing this class.
main(String[]) - Static method in class weka.core.stemmers.IteratedLovinsStemmer
Runs the stemmer with the given options
main(String[]) - Static method in class weka.core.stemmers.LovinsStemmer
Runs the stemmer with the given options
main(String[]) - Static method in class weka.core.stemmers.NullStemmer
Runs the stemmer with the given options
main(String[]) - Static method in class weka.core.stemmers.SnowballStemmer
Runs the stemmer with the given options.
main(String[]) - Static method in class weka.core.Stopwords
Accepts the following parameter:

-i file
loads the stopwords from the given file

-o file
saves the stopwords to the given file

-p
outputs the current stopwords on stdout

Any additional parameters are interpreted as words to test as stopwords.

main(String[]) - Static method in class weka.core.SystemInfo
for printing the system info to stdout.
main(String[]) - Static method in class weka.core.TechnicalInformation
Prints some examples of technical informations if there are no commandline options given.
main(String[]) - Static method in class weka.core.TechnicalInformationHandlerJavadoc
Parses the given commandline parameters and generates the Javadoc.
main(String[]) - Static method in class weka.core.TestInstances
for running the class from commandline, prints the generated data to stdout
main(String[]) - Static method in class weka.core.tokenizers.AlphabeticTokenizer
Runs the tokenizer with the given options and strings to tokenize.
main(String[]) - Static method in class weka.core.tokenizers.NGramTokenizer
Runs the tokenizer with the given options and strings to tokenize.
main(String[]) - Static method in class weka.core.tokenizers.WordTokenizer
Runs the tokenizer with the given options and strings to tokenize.
main(String[]) - Static method in class weka.core.Trie
Only for testing (prints the built Trie).
main(String[]) - Static method in class weka.core.Utils
Main method for testing this class.
main(String[]) - Static method in class weka.core.Version
only for testing
main(String[]) - Static method in class weka.core.xml.SerialUIDChanger
exchanges an old UID for a new one.
main(String[]) - Static method in class weka.core.xml.XMLDocument
for testing only.
main(String[]) - Static method in class weka.core.xml.XMLInstances
takes an XML document as first argument and then outputs the Instances statistics
main(String[]) - Static method in class weka.core.xml.XMLOptions
for testing only.
main(String[]) - Static method in class weka.core.xml.XMLSerialization
for testing only.
main(String[]) - Static method in class weka.datagenerators.classifiers.classification.Agrawal
Main method for executing this class.
main(String[]) - Static method in class weka.datagenerators.classifiers.classification.BayesNet
Main method for executing this class.
main(String[]) - Static method in class weka.datagenerators.classifiers.classification.LED24
Main method for executing this class.
main(String[]) - Static method in class weka.datagenerators.classifiers.classification.RandomRBF
Main method for executing this class.
main(String[]) - Static method in class weka.datagenerators.classifiers.classification.RDG1
Main method for testing this class.
main(String[]) - Static method in class weka.datagenerators.classifiers.regression.Expression
Main method for testing this class.
main(String[]) - Static method in class weka.datagenerators.classifiers.regression.MexicanHat
Main method for testing this class.
main(String[]) - Static method in class weka.datagenerators.clusterers.BIRCHCluster
Main method for testing this class.
main(String[]) - Static method in class weka.datagenerators.clusterers.SubspaceCluster
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.CheckEstimator
Test method for this class
main(String[]) - Static method in class weka.estimators.DDConditionalEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.DiscreteEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.DKConditionalEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.DNConditionalEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.KDConditionalEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.KernelEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.KKConditionalEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.MahalanobisEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.NDConditionalEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.NNConditionalEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.NormalEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.PoissonEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.experiment.CrossValidationResultProducer
Quick test of timestamp
main(String[]) - Static method in class weka.experiment.Experiment
Configures/Runs the Experiment from the command line.
main(String[]) - Static method in class weka.experiment.InstanceQuery
Test the class from the command line.
main(String[]) - Static method in class weka.experiment.OutputZipper
Main method for testing this class
main(String[]) - Static method in class weka.experiment.PairedCorrectedTTester
Test the class from the command line.
main(String[]) - Static method in class weka.experiment.PairedStats
Tests the paired stats object from the command line.
main(String[]) - Static method in class weka.experiment.PairedTTester
Test the class from the command line.
main(String[]) - Static method in class weka.experiment.RemoteEngine
Main method.
main(String[]) - Static method in class weka.experiment.RemoteExperiment
Configures/Runs the Experiment from the command line.
main(String[]) - Static method in class weka.experiment.ResultMatrixCSV
for testing only
main(String[]) - Static method in class weka.experiment.ResultMatrixGnuPlot
for testing only
main(String[]) - Static method in class weka.experiment.ResultMatrixHTML
for testing only
main(String[]) - Static method in class weka.experiment.ResultMatrixLatex
for testing only
main(String[]) - Static method in class weka.experiment.ResultMatrixPlainText
for testing only
main(String[]) - Static method in class weka.experiment.ResultMatrixSignificance
for testing only
main(String[]) - Static method in class weka.experiment.Stats
Tests the paired stats object from the command line.
main(String[]) - Static method in class weka.experiment.xml.XMLExperiment
for testing only.
main(String[]) - Static method in class weka.filters.AllFilter
Main method for testing this class.
main(String[]) - Static method in class weka.filters.CheckSource
Executes the tests, use "-h" to list the commandline options.
main(String[]) - Static method in class weka.filters.Filter
Main method for testing this class.
main(String[]) - Static method in class weka.filters.MultiFilter
Main method for executing this class.
main(String[]) - Static method in class weka.filters.supervised.attribute.AddClassification
runs the filter with the given arguments.
main(String[]) - Static method in class weka.filters.supervised.attribute.AttributeSelection
Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.attribute.ClassOrder
Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.attribute.Discretize
Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.attribute.NominalToBinary
Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.attribute.PLSFilter
runs the filter with the given arguments.
main(String[]) - Static method in class weka.filters.supervised.instance.Resample
Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.instance.SMOTE
Main method for running this filter.
main(String[]) - Static method in class weka.filters.supervised.instance.SpreadSubsample
Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Add
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.AddCluster
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.AddExpression
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.AddID
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.AddNoise
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.AddValues
Main method for testing and running this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Center
Main method for running this filter.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.ChangeDateFormat
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.ClassAssigner
Main method for executing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.ClusterMembership
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Copy
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Discretize
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.FirstOrder
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.InterquartileRange
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.KernelFilter
runs the filter with the given arguments
main(String[]) - Static method in class weka.filters.unsupervised.attribute.MakeIndicator
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.MathExpression
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.MergeTwoValues
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
Main method for running this filter.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.NominalToBinary
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.NominalToString
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Normalize
Main method for running this filter.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.NumericCleaner
Runs the filter from commandline, use "-h" to see all options.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.NumericToBinary
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.NumericToNominal
Runs the filter with the given parameters.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.NumericTransform
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Obfuscate
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
Main method for executing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.PKIDiscretize
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.PrincipalComponents
Main method for running this filter.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
Main method for running this filter.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.RandomProjection
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.RandomSubset
Runs the filter with the given parameters.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.RELAGGS
runs the filter with the given arguments
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Remove
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.RemoveType
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.RemoveUseless
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Reorder
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.ReplaceMissingValues
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Standardize
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.StringToNominal
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.StringToWordVector
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.SwapValues
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.TimeSeriesDelta
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Wavelet
runs the filter with the given arguments
main(String[]) - Static method in class weka.filters.unsupervised.instance.NonSparseToSparse
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.Normalize
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.Randomize
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveFolds
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveMisclassified
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemovePercentage
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveRange
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveWithValues
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.Resample
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.ReservoirSample
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.SparseToNonSparse
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.SubsetByExpression
Main method for running this filter.
main(String[]) - Static method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
Runs the parser from commandline.
main(String[]) - Static method in class weka.gui.arffviewer.ArffViewer
shows the frame and it tries to load all the arff files that were provided as arguments.
main(String[]) - Static method in class weka.gui.AttributeListPanel
Tests the attribute list panel from the command line.
main(String[]) - Static method in class weka.gui.AttributeSelectionPanel
Tests the attribute selection panel from the command line.
main(String[]) - Static method in class weka.gui.AttributeSummaryPanel
Tests out the attribute summary panel from the command line.
main(String[]) - Static method in class weka.gui.AttributeVisualizationPanel
Main method to test this class from command line
main(String[]) - Static method in class weka.gui.beans.AttributeSummarizer
 
main(String[]) - Static method in class weka.gui.beans.CostBenefitAnalysis
 
main(String[]) - Static method in class weka.gui.beans.DataVisualizer
 
main(String[]) - Static method in class weka.gui.beans.FlowRunner
Main method for testing this class.
main(String[]) - Static method in class weka.gui.beans.KnowledgeFlow
Shows the splash screen, launches the application and then disposes the splash screen.
main(String[]) - Static method in class weka.gui.beans.KnowledgeFlowApp
Main method.
main(String[]) - Static method in class weka.gui.beans.Loader
 
main(String[]) - Static method in class weka.gui.beans.LogPanel
Main method to test this class.
main(String[]) - Static method in class weka.gui.beans.ModelPerformanceChart
 
main(String[]) - Static method in class weka.gui.beans.Saver
The main method for testing
main(String[]) - Static method in class weka.gui.beans.ScatterPlotMatrix
 
main(String[]) - Static method in class weka.gui.beans.StripChart
Tests out the StripChart from the command line
main(String[]) - Static method in class weka.gui.beans.TextViewer
 
main(String[]) - Static method in class weka.gui.boundaryvisualizer.BoundaryPanel
Main method for testing this class
main(String[]) - Static method in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
Main method for testing this class
main(String[]) - Static method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
Main method for testing this class
main(String[]) - Static method in class weka.gui.ConverterFileChooser
For testing the file chooser
main(String[]) - Static method in class weka.gui.DatabaseConnectionDialog
for testing only
main(String[]) - Static method in class weka.gui.experiment.AlgorithmListPanel
Tests out the algorithm list panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.DatasetListPanel
Tests out the dataset list panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.DistributeExperimentPanel
Tests out the panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.Experimenter
Tests out the experiment environment.
main(String[]) - Static method in class weka.gui.experiment.ExperimenterDefaults
only for testing - prints the content of the props file
main(String[]) - Static method in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Tests out the panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.HostListPanel
Tests out the host list panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.OutputFormatDialog
for testing only.
main(String[]) - Static method in class weka.gui.experiment.ResultsPanel
Tests out the results panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.RunNumberPanel
Tests out the panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.RunPanel
Tests out the run panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.SetupPanel
Tests out the experiment setup from the command line.
main(String[]) - Static method in class weka.gui.explorer.AssociationsPanel
Tests out the Associator panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.AttributeSelectionPanel
Tests out the attribute selection panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.ClassifierPanel
Tests out the classifier panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.ClustererPanel
Tests out the clusterer panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.Explorer
Tests out the explorer environment.
main(String[]) - Static method in class weka.gui.explorer.ExplorerDefaults
only for testing - prints the content of the props file.
main(String[]) - Static method in class weka.gui.explorer.PreprocessPanel
Tests out the instance-preprocessing panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.VisualizePanel
Tests out the visualize panel from the command line.
main(String[]) - Static method in class weka.gui.GenericArrayEditor
Tests out the array editor from the command line.
main(String[]) - Static method in class weka.gui.GenericObjectEditor
Tests out the Object editor from the command line.
main(String[]) - Static method in class weka.gui.GenericPropertiesCreator
for generating props file: no parameter: see default constructor 1 parameter (i.e., filename): see default constructor + setOutputFilename(String) 2 parameters (i.e, filenames): see constructor with String argument + setOutputFilename(String)
main(String[]) - Static method in class weka.gui.graphvisualizer.GraphVisualizer
Main method to load a text file with the description of a graph from the command line
main(String[]) - Static method in class weka.gui.GUIChooser
Tests out the GUIChooser environment.
main(String[]) - Static method in class weka.gui.HierarchyPropertyParser
Tests out the parser.
main(String[]) - Static method in class weka.gui.hierarchyvisualizer.HierarchyVisualizer
Main method for testing this class.
main(String[]) - Static method in class weka.gui.InstancesSummaryPanel
Tests out the instance summary panel from the command line.
main(String[]) - Static method in class weka.gui.ListSelectorDialog
Tests out the list selector from the command line.
main(String[]) - Static method in class weka.gui.LogPanel
Tests out the log panel from the command line.
main(String[]) - Static method in class weka.gui.LogWindow
for testing only
main(String[]) - Static method in class weka.gui.LookAndFeel
prints all the available LnFs to stdout
Main - Class in weka.gui
Menu-based GUI for Weka, replacement for the GUIChooser.
Main() - Constructor for class weka.gui.Main
default constructor.
main(String[]) - Static method in class weka.gui.Main
starts the application.
main(String[]) - Static method in class weka.gui.PropertySelectorDialog
Tests out the property selector from the command line.
main(String[]) - Static method in class weka.gui.ResultHistoryPanel
Tests out the result history from the command line.
main(String[]) - Static method in class weka.gui.SaveBuffer
Main method for testing this class
main(String[]) - Static method in class weka.gui.SelectedTagEditor
Tests out the selectedtag editor from the command line.
main(String[]) - Static method in class weka.gui.SimpleCLI
Method to start up the simple cli
main(String[]) - Static method in class weka.gui.SimpleCLIPanel
Main method for testing this class.
main(String[]) - Static method in class weka.gui.sql.SqlViewer
starts the SQL-Viewer interface.
main(String[]) - Static method in class weka.gui.sql.SqlViewerDialog
for testing only
main(String[]) - Static method in class weka.gui.treevisualizer.TreeVisualizer
Main method for testing this class.
main(String[]) - Static method in class weka.gui.visualize.AttributePanel
Main method for testing this class.
main(String[]) - Static method in class weka.gui.visualize.BMPWriter
for testing only
main(String[]) - Static method in class weka.gui.visualize.ClassPanel
Main method for testing this class.
main(String[]) - Static method in class weka.gui.visualize.JPEGWriter
for testing only.
main(String[]) - Static method in class weka.gui.visualize.LegendPanel
Main method for testing this class
main(String[]) - Static method in class weka.gui.visualize.MatrixPanel
Main method for testing this class
main(String[]) - Static method in class weka.gui.visualize.Plot2D
Main method for testing this class
main(String[]) - Static method in class weka.gui.visualize.PNGWriter
for testing only
main(String[]) - Static method in class weka.gui.visualize.PostscriptWriter
for testing only
main(String[]) - Static method in class weka.gui.visualize.ThresholdVisualizePanel
Starts the ThresholdVisualizationPanel with parameters from the command line.
main(String[]) - Static method in class weka.gui.visualize.VisualizePanel
Main method for testing this class
main(String[]) - Static method in class weka.gui.WekaTaskMonitor
Main method for testing this class
Main.BackgroundDesktopPane - Class in weka.gui
DesktopPane with background image.
Main.ChildFrameMDI - Class in weka.gui
Specialized JInternalFrame class.
Main.ChildFrameSDI - Class in weka.gui
Specialized JFrame class.
MainMenuExtension - Interface in weka.gui
Classes implementing this interface will be displayed in the "Extensions" menu in the main GUI of Weka.
MAJOR - Static variable in class weka.core.Version
the major version
MAJORITY_VOTING_RULE - Static variable in class weka.classifiers.meta.Vote
combination rule: Majority Voting (only nominal classes)
majorityClassTipText() - Method in class weka.classifiers.rules.Ridor
Returns the tip text for this property
makeADTree(int, FastVector, Instances) - Static method in class weka.classifiers.bayes.net.ADNode
create sub tree
makeADTree(Instances) - Static method in class weka.classifiers.bayes.net.ADNode
create AD tree from set of instances
makeBallTree(BottomUpConstructor.TempNode, int, int, int[], int, double) - Method in class weka.core.neighboursearch.balltrees.BottomUpConstructor
Makes ball tree nodes of temp nodes that were used in the merging process.
makeBallTreeNodes(MiddleOutConstructor.TempNode, int, int, int) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Makes BallTreeNodes out of TempNodes.
makeBinaryTipText() - Method in class weka.filters.supervised.attribute.Discretize
Returns the tip text for this property
makeBinaryTipText() - Method in class weka.filters.unsupervised.attribute.Discretize
Returns the tip text for this property
makeCentersRandomly(Random, Instances, int) - Method in class weka.clusterers.XMeans
Generates new centers randomly.
makeCopies(Associator, int) - Static method in class weka.associations.AbstractAssociator
Creates copies of the current associator.
makeCopies(ASEvaluation, int) - Static method in class weka.attributeSelection.ASEvaluation
Creates copies of the current evaluator.
makeCopies(ASSearch, int) - Static method in class weka.attributeSelection.ASSearch
Creates copies of the current search scheme.
makeCopies(Object, int) - Method in class weka.attributeSelection.CheckAttributeSelection
returns deep copies of the given object
makeCopies(Classifier, int) - Static method in class weka.classifiers.Classifier
Creates a given number of deep copies of the given classifier using serialization.
makeCopies(Kernel, int) - Static method in class weka.classifiers.functions.supportVector.Kernel
Creates a given number of deep copies of the given kernel using serialization.
makeCopies(Clusterer, int) - Static method in class weka.clusterers.AbstractClusterer
Creates copies of the current clusterer.
makeCopies(DensityBasedClusterer, int) - Static method in class weka.clusterers.AbstractDensityBasedClusterer
Creates copies of the current clusterer.
makeCopies(Estimator, int) - Static method in class weka.estimators.Estimator
Creates a given number of deep copies of the given estimator using serialization.
makeCopies(Filter, int) - Static method in class weka.filters.Filter
Creates a given number of deep copies of the given filter using serialization.
makeCopy(Associator) - Static method in class weka.associations.AbstractAssociator
Creates a deep copy of the given associator using serialization.
makeCopy(Classifier) - Static method in class weka.classifiers.Classifier
Creates a deep copy of the given classifier using serialization.
makeCopy(Kernel) - Static method in class weka.classifiers.functions.supportVector.Kernel
Creates a deep copy of the given kernel using serialization.
makeCopy(Clusterer) - Static method in class weka.clusterers.AbstractClusterer
Creates a deep copy of the given clusterer using serialization.
makeCopy(Estimator) - Static method in class weka.estimators.Estimator
Creates a deep copy of the given estimator using serialization.
makeCopy(Filter) - Static method in class weka.filters.Filter
Creates a deep copy of the given filter using serialization.
makeCopy(Object) - Static method in class weka.gui.GenericArrayEditor
Makes a copy of an object using serialization.
makeCopy(Object) - Static method in class weka.gui.GenericObjectEditor
Makes a copy of an object using serialization.
makeData(DataGenerator, String[]) - Static method in class weka.datagenerators.DataGenerator
Calls the data generator.
MakeDecList - Class in weka.classifiers.rules.part
Class for handling a decision list.
MakeDecList(ModelSelection, int) - Constructor for class weka.classifiers.rules.part.MakeDecList
Constructor for unpruned dec list.
MakeDecList(ModelSelection, double, int) - Constructor for class weka.classifiers.rules.part.MakeDecList
Constructor for dec list pruned using C4.5 pruning.
MakeDecList(ModelSelection, int, int, int) - Constructor for class weka.classifiers.rules.part.MakeDecList
Constructor for dec list pruned using hold-out pruning.
MakeDensityBasedClusterer - Class in weka.clusterers
Class for wrapping a Clusterer to make it return a distribution and density.
MakeDensityBasedClusterer() - Constructor for class weka.clusterers.MakeDensityBasedClusterer
Default constructor.
MakeDensityBasedClusterer(Clusterer) - Constructor for class weka.clusterers.MakeDensityBasedClusterer
Contructs a MakeDensityBasedClusterer wrapping a given Clusterer.
makeDistribution(double) - Method in class weka.classifiers.Evaluation
Convert a single prediction into a probability distribution with all zero probabilities except the predicted value which has probability 1.0;
makeDistribution(double, int) - Static method in class weka.classifiers.evaluation.NominalPrediction
Convert a single prediction into a probability distribution with all zero probabilities except the predicted value which has probability 1.0.
makeDistribution(Instances, double[]) - Method in class weka.classifiers.lazy.IBk
Turn the list of nearest neighbors into a probability distribution.
makeDistribution() - Method in class weka.classifiers.mi.CitationKNN
Turn the references and citers list into a probability distribution
makeGUIPanel(boolean) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
This methods makes the gui extra controls panel "m_controlsPanel"
MakeIndicator - Class in weka.filters.unsupervised.attribute
A filter that creates a new dataset with a boolean attribute replacing a nominal attribute.
MakeIndicator() - Constructor for class weka.filters.unsupervised.attribute.MakeIndicator
Constructor
makeLeaf(Instances) - Method in class weka.classifiers.trees.BFTree
Make the node leaf node.
makeLeaf(Instances) - Method in class weka.classifiers.trees.SimpleCart
Make the node leaf node.
makeOptionsString(Stemmer) - Static method in class weka.core.stemmers.Stemming
lists all the options on the command line
makeOptionStr(AbstractFileLoader) - Static method in class weka.core.converters.AbstractFileLoader
generates a string suitable for output on the command line displaying all available options (currently only a simple usage).
makeOptionStr(AbstractFileSaver) - Static method in class weka.core.converters.AbstractFileSaver
generates a string suitable for output on the command line displaying all available options.
makeOptionString(Associator) - Static method in class weka.associations.AssociatorEvaluation
Generates an option string to output on the commandline.
makeOptionString(Classifier, boolean) - Static method in class weka.classifiers.Evaluation
Make up the help string giving all the command line options
makeOptionString(Kernel) - Static method in class weka.classifiers.functions.supportVector.KernelEvaluation
Generates an option string to output on the commandline.
makeOptionString(DataGenerator) - Static method in class weka.datagenerators.DataGenerator
returns all the options in a string
makeProperHierarchy() - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
 
makeSuccessors(FastVector, Instances, int[][][], double[][][], double[][][], Attribute, boolean, boolean) - Method in class weka.classifiers.trees.BFTree
Generate successor nodes for a node and put them into BestFirstElements according to gini gain or information gain in a descending order.
makeTestDataset(int, int, int, int, int, int, int, int, int, boolean) - Method in class weka.associations.CheckAssociator
Make a simple set of instances, which can later be modified for use in specific tests.
makeTestDataset(int, int, int, int, int, int, int, int, int, int, boolean) - Method in class weka.associations.CheckAssociator
Make a simple set of instances with variable position of the class attribute, which can later be modified for use in specific tests.
makeTestDataset(int, int, int, int, int, int, int, int, int, boolean) - Method in class weka.attributeSelection.CheckAttributeSelection
Make a simple set of instances, which can later be modified for use in specific tests.
makeTestDataset(int, int, int, int, int, int, int, int, int, int, boolean) - Method in class weka.attributeSelection.CheckAttributeSelection
Make a simple set of instances with variable position of the class attribute, which can later be modified for use in specific tests.
makeTestDataset(int, int, int, int, int, int, int, int, int, boolean) - Method in class weka.classifiers.CheckClassifier
Make a simple set of instances, which can later be modified for use in specific tests.
makeTestDataset(int, int, int, int, int, int, int, int, int, int, boolean) - Method in class weka.classifiers.CheckClassifier
Make a simple set of instances with variable position of the class attribute, which can later be modified for use in specific tests.
makeTestDataset(int, int, int, int, int, int, int, int, int, boolean) - Method in class weka.classifiers.functions.supportVector.CheckKernel
Make a simple set of instances, which can later be modified for use in specific tests.
makeTestDataset(int, int, int, int, int, int, int, int, int, int, boolean) - Method in class weka.classifiers.functions.supportVector.CheckKernel
Make a simple set of instances with variable position of the class attribute, which can later be modified for use in specific tests.
makeTestDataset(int, int, int, int, int, int, int, boolean) - Method in class weka.clusterers.CheckClusterer
Make a simple set of instances with variable position of the class attribute, which can later be modified for use in specific tests.
makeTestDataset(int, int, int, CheckEstimator.AttrTypes, int, int) - Method in class weka.estimators.CheckEstimator
Make a simple set of instances, which can later be modified for use in specific tests.
makeTestDataset(int, int, int, CheckEstimator.AttrTypes, int, int, int) - Method in class weka.estimators.CheckEstimator
Make a simple set of instances with variable position of the class attribute, which can later be modified for use in specific tests.
makeTestValueList(int, int, Instances, int, int) - Method in class weka.estimators.CheckEstimator
Make a simple set of values.
makeTestValueList(int, int, double, double, int) - Method in class weka.estimators.CheckEstimator
Make a simple set of values.
makeTree(FastVector, Instances, int[][], double[][], double[][][], double[], double, double[], int, boolean, boolean, int) - Method in class weka.classifiers.trees.BFTree
Recursively build a best-first decision tree.
makeTree(FastVector, BFTree, Instances, int[][], double[][], double[][][], double[], double, double[], int, boolean, boolean) - Method in class weka.classifiers.trees.BFTree
This method is to find the number of expansions based on internal cross-validation for just pre-pruning.
makeTree(FastVector, BFTree, Instances, Instances, FastVector, int[][], double[][], double[][][], double[], double, double[], int, boolean, boolean, boolean) - Method in class weka.classifiers.trees.BFTree
This method is to find the number of expansions based on internal cross-validation for just post-pruning.
makeTree(Instances, int, int[][], double[][], double[], double, double, boolean) - Method in class weka.classifiers.trees.SimpleCart
Make binary decision tree recursively.
makeUniformDistribution(int) - Static method in class weka.classifiers.evaluation.NominalPrediction
Creates a uniform probability distribution -- where each of the possible classes is assigned equal probability.
makeVaryNode(int, FastVector, Instances) - Static method in class weka.classifiers.bayes.net.ADNode
create sub tree
makeWeighted(CostMatrix) - Method in class weka.classifiers.evaluation.ConfusionMatrix
Makes a copy of this ConfusionMatrix after applying the supplied CostMatrix to the cells.
ManhattanDataObject - Class in weka.clusterers.forOPTICSAndDBScan.DataObjects
ManhattanDataObject.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 19, 2004
Time: 5:50:22 PM
$ Revision 1.4 $
ManhattanDataObject(Instance, String, Database) - Constructor for class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
Constructs a new DataObject.
ManhattanDistance - Class in weka.core
Implements the Manhattan distance (or Taxicab geometry).
ManhattanDistance() - Constructor for class weka.core.ManhattanDistance
Constructs an Manhattan Distance object, Instances must be still set.
ManhattanDistance(Instances) - Constructor for class weka.core.ManhattanDistance
Constructs an Manhattan Distance object and automatically initializes the ranges.
manualThresholdValueTipText() - Method in class weka.classifiers.meta.ThresholdSelector
 
map(String, String) - Method in class weka.core.matrix.DoubleVector
Applies a method to the vector
mapClasses(int, int, int[][], int[], double[], double[], int) - Static method in class weka.clusterers.ClusterEvaluation
Finds the minimum error mapping of classes to clusters.
MappingInfo - Class in weka.core.pmml
Class that maintains the mapping between incoming data set structure and that of the mining schema.
MappingInfo(Instances, MiningSchema, Logger) - Constructor for class weka.core.pmml.MappingInfo
 
mapToMiningSchema(Instances) - Method in class weka.classifiers.pmml.consumer.PMMLClassifier
Map mining schema to incoming instances.
margin() - Method in class weka.classifiers.evaluation.NominalPrediction
Calculates the prediction margin.
MarginCalculator - Class in weka.classifiers.bayes.net
 
MarginCalculator() - Constructor for class weka.classifiers.bayes.net.MarginCalculator
 
MarginCalculator.JunctionTreeNode - Class in weka.classifiers.bayes.net
 
MarginCalculator.JunctionTreeSeparator - Class in weka.classifiers.bayes.net
 
MarginCurve - Class in weka.classifiers.evaluation
Generates points illustrating the prediction margin.
MarginCurve() - Constructor for class weka.classifiers.evaluation.MarginCurve
 
markovBlanketClassifierTipText() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
 
markovBlanketClassifierTipText() - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
 
markovBlanketClassifierTipText() - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
 
mAscending - Variable in class weka.gui.SortedTableModel
whether sorting is ascending or descending
maskKeyword(String) - Method in class weka.experiment.DatabaseUtils
If the given string is a keyword, then the mask character will be appended and returned.
Matchable - Interface in weka.core
Interface to something that can be matched with tree matching algorithms.
matchesTemplate(Object[], Object[]) - Method in class weka.experiment.AveragingResultProducer
Compares a key to a template to see whether they match.
matchesTemplate(Instance) - Method in class weka.experiment.PairedTTester.Dataset
Returns true if the two instances match on those attributes that have been designated key columns (eg: scheme name and scheme options)
matchesTemplate(Instance) - Method in class weka.experiment.PairedTTester.Resultset
Returns true if the two instances match on those attributes that have been designated key columns (eg: scheme name and scheme options)
matchMissingValuesTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Returns the tip text for this property
MathematicalExpression - Class in weka.core
Class for evaluating a string adhering the following grammar:
MathematicalExpression() - Constructor for class weka.core.MathematicalExpression
 
MathExpression - Class in weka.filters.unsupervised.attribute
Modify numeric attributes according to a given expression

Valid options are:

MathExpression() - Constructor for class weka.filters.unsupervised.attribute.MathExpression
Constructor
Maths - Class in weka.core.matrix
Utility class.
Maths() - Constructor for class weka.core.matrix.Maths
 
matrix() - Method in class weka.classifiers.trees.j48.Distribution
Returns matrix with distribution of class values.
Matrix - Class in weka.core
Deprecated.
Use weka.core.matrix.Matrix instead - only for backwards compatibility.
Matrix(int, int) - Constructor for class weka.core.Matrix
Deprecated.
Constructs a matrix and initializes it with default values.
Matrix(double[][]) - Constructor for class weka.core.Matrix
Deprecated.
Constructs a matrix using a given array.
Matrix(Reader) - Constructor for class weka.core.Matrix
Deprecated.
Reads a matrix from a reader.
Matrix - Class in weka.core.matrix
Jama = Java Matrix class.
Matrix(int, int) - Constructor for class weka.core.matrix.Matrix
Construct an m-by-n matrix of zeros.
Matrix(int, int, double) - Constructor for class weka.core.matrix.Matrix
Construct an m-by-n constant matrix.
Matrix(double[][]) - Constructor for class weka.core.matrix.Matrix
Construct a matrix from a 2-D array.
Matrix(double[][], int, int) - Constructor for class weka.core.matrix.Matrix
Construct a matrix quickly without checking arguments.
Matrix(double[], int) - Constructor for class weka.core.matrix.Matrix
Construct a matrix from a one-dimensional packed array
Matrix(Reader) - Constructor for class weka.core.matrix.Matrix
Reads a matrix from a reader.
MATRIX_ON_DEMAND - Static variable in class weka.attributeSelection.CostSensitiveASEvaluation
load cost matrix on demand
MATRIX_ON_DEMAND - Static variable in class weka.classifiers.meta.CostSensitiveClassifier
load cost matrix on demand
MATRIX_ON_DEMAND - Static variable in class weka.classifiers.meta.MetaCost
load cost matrix on demand
MATRIX_SUPPLIED - Static variable in class weka.attributeSelection.CostSensitiveASEvaluation
use explicit cost matrix
MATRIX_SUPPLIED - Static variable in class weka.classifiers.meta.CostSensitiveClassifier
use explicit cost matrix
MATRIX_SUPPLIED - Static variable in class weka.classifiers.meta.MetaCost
use explicit matrix
MatrixPanel - Class in weka.gui.visualize
This panel displays a plot matrix of the user selected attributes of a given data set.
MatrixPanel() - Constructor for class weka.gui.visualize.MatrixPanel
Constructor
max() - Method in class weka.core.matrix.DoubleVector
Returns the maximum value of all elements
MAX - Static variable in class weka.core.neighboursearch.KDTree
The index of MAX value in attributes' range array.
MAX - Static variable in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
Index of max value in an array of attributes' range.
max - Variable in class weka.experiment.Stats
The maximum value seen, or Double.NaN if no values seen
MAX_DECIMALS - Static variable in class weka.filters.unsupervised.attribute.NumericToNominal
the maximum number of decimals to use
MAX_DIGITS - Static variable in class weka.core.converters.SVMLightSaver
the number of digits after the decimal point.
MAX_FAILURES - Static variable in class weka.experiment.RemoteExperiment
allow at most 3 failures on a host before it is removed from the list of usable hosts
MAX_FAILURES - Static variable in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
 
MAX_N - Static variable in class weka.associations.PriorEstimation
The maximum number of attributes for which a prior can be estimated.
MAX_N - Static variable in class weka.associations.RuleGeneration
Threshold.
MAX_POWER_OF_LAMBDA - Static variable in class weka.classifiers.functions.supportVector.StringKernel
powers of lambda are prepared prior to kernel evaluations.
MAX_PRECISION - Static variable in class weka.gui.visualize.VisualizeUtils
Default maximum precision for the display of numeric values
MAX_ROWS - Static variable in class weka.gui.sql.QueryPanel
the name for the max rows in the history.
MAX_RULE - Static variable in class weka.classifiers.meta.Vote
combination rule: Maximum Probability
max_set(Stack<CoverTree.DistanceNode>) - Method in class weka.core.neighboursearch.CoverTree
Returns the max distance of the reference point p in current node to it's children nodes.
MAX_SHAPES - Static variable in class weka.gui.visualize.Plot2D
 
maxAbs() - Method in class weka.classifiers.functions.pace.PaceMatrix
Returns the maximum absolute value of all elements
maxAbs(int, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Returns the maximum absolute value of some elements of a column, that is, the elements of A[i0:i1][j].
maxBag() - Method in class weka.classifiers.trees.j48.Distribution
Returns index of bag containing maximum number of instances.
maxBoostingIterationsTipText() - Method in class weka.classifiers.functions.SimpleLogistic
Returns the tip text for this property
maxCardinalityTipText() - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm
 
maxCardinalityTipText() - Method in class weka.filters.unsupervised.attribute.RELAGGS
Returns the tip text for this property
maxChunkSizeTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
 
maxClass() - Method in class weka.classifiers.trees.j48.Distribution
Returns class with highest frequency over all bags.
maxClass(int) - Method in class weka.classifiers.trees.j48.Distribution
Returns class with highest frequency for given bag.
maxClassForSubsetOfInterest() - Method in class weka.classifiers.trees.j48.GraftSplit
 
maxCountTipText() - Method in class weka.filters.supervised.instance.SpreadSubsample
Returns the tip text for this property
maxDefaultTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Returns the tip text for this property
maxDepthTipText() - Method in class weka.classifiers.trees.RandomForest
Returns the tip text for this property
maxDepthTipText() - Method in class weka.classifiers.trees.RandomTree
Returns the tip text for this property
maxDepthTipText() - Method in class weka.classifiers.trees.REPTree
Returns the tip text for this property
MAXGAM - Static variable in class weka.core.Statistics
 
maxGenerationsTipText() - Method in class weka.attributeSelection.GeneticSearch
Returns the tip text for this property
maxGridExtensionsTipText() - Method in class weka.classifiers.meta.GridSearch
Returns the tip text for this property
maxGroupTipText() - Method in class weka.classifiers.meta.RotationForest
Returns the tip text for this property
maxImpurity() - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo
Returns the impurity of this split
maxImpurity() - Method in interface weka.classifiers.trees.m5.SplitEvaluate
Returns the impurity of this split
maxImpurity() - Method in class weka.classifiers.trees.m5.YongSplitInfo
Returns the impurity of this split
maximumAttributeNamesTipText() - Method in class weka.attributeSelection.LatentSemanticAnalysis
Returns the tip text for this property
maximumAttributeNamesTipText() - Method in class weka.attributeSelection.PrincipalComponents
Returns the tip text for this property
maximumAttributeNamesTipText() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
Returns the tip text for this property.
maximumAttributesTipText() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
Returns the tip text for this property.
maximumVariancePercentageAllowedTipText() - Method in class weka.filters.unsupervised.attribute.RemoveUseless
Returns the tip text for this property
maxIndex(double[]) - Static method in class weka.core.Utils
Returns index of maximum element in a given array of doubles.
maxIndex(int[]) - Static method in class weka.core.Utils
Returns index of maximum element in a given array of integers.
maxInfoGain - Variable in class weka.classifiers.rules.JRip.Antd
The maximum infoGain achieved by this antecedent test in the growing data
maxInstancesInLeafTipText() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
Returns the tip text for this property.
maxInstInLeafTipText() - Method in class weka.core.neighboursearch.KDTree
Tip text for this property.
maxInstNumTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Returns the tip text for this property
maxInstNumTipText() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Returns the tip text for this property
maxIterations - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Maximum number of iterations
maxIterationsTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns the tip text for this property
maxIterationsTipText() - Method in class weka.classifiers.mi.MIBoost
Returns the tip text for this property
maxIterationsTipText() - Method in class weka.classifiers.mi.MISVM
Returns the tip text for this property
maxIterationsTipText() - Method in class weka.clusterers.EM
Returns the tip text for this property
maxIterationsTipText() - Method in class weka.clusterers.sIB
Returns the tip text for this property.
maxIterationsTipText() - Method in class weka.clusterers.SimpleKMeans
Returns the tip text for this property
maxIterationsTipText() - Method in class weka.clusterers.XMeans
Returns the tip text for this property.
maxIterationsTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Returns the tip text for this property
maxItsTipText() - Method in class weka.classifiers.functions.Logistic
Returns the tip text for this property
maxItsTipText() - Method in class weka.classifiers.functions.RBFNetwork
Returns the tip text for this property
maxKMeansForChildrenTipText() - Method in class weka.clusterers.XMeans
Returns the tip text for this property.
maxKMeansTipText() - Method in class weka.clusterers.XMeans
Returns the tip text for this property.
maxKTipText() - Method in class weka.classifiers.functions.VotedPerceptron
Returns the tip text for this property
MAXLOG - Static variable in class weka.core.Statistics
 
maxNrOfParentsTipText() - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
 
maxNumberOfItemsTipText() - Method in class weka.associations.FPGrowth
Tip text for this property suitable for displaying in the GUI.
maxNumClustersTipText() - Method in class weka.clusterers.XMeans
Returns the tip text for this property.
maxNumSupportPoints - Variable in class weka.classifiers.functions.pace.ChisqMixture
 
maxParentSetSize(int) - Method in class weka.classifiers.bayes.net.ParentSet
reserve memory for parent set
maxRadiusTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Returns the tip text for this property
maxRangeTipText() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Returns the tip text for this property
maxRelativeLeafRadiusTipText() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
Returns the tip text for this property.
maxRuleSizeTipText() - Method in class weka.datagenerators.classifiers.classification.RDG1
Returns the tip text for this property
maxSubsequenceLengthTipText() - Method in class weka.classifiers.functions.supportVector.StringKernel
Returns the tip text for this property
maxThresholdTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Returns the tip text for this property
MAYBE_SUPPORT - Static variable in class weka.gui.GenericObjectEditor.GOETreeNode
color for "maybe support".
MDD - Class in weka.classifiers.mi
Modified Diverse Density algorithm, with collective assumption.

More information about DD:

Oded Maron (1998).
MDD() - Constructor for class weka.classifiers.mi.MDD
 
MDL - Static variable in interface weka.classifiers.bayes.net.search.local.Scoreable
 
mean(double[]) - Static method in class weka.core.Utils
Computes the mean for an array of doubles.
mean - Variable in class weka.experiment.Stats
The mean of values at the last calculateDerived() call
meanAbsoluteError() - Method in class weka.classifiers.Evaluation
Returns the mean absolute error.
meanOrMode(Instances, int[], int) - Method in class weka.clusterers.XMeans
Computes Mean Or Mode of one attribute on a subset of m_Instances.
meanOrMode(int) - Method in class weka.core.Instances
Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
meanOrMode(Attribute) - Method in class weka.core.Instances
Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
meanPriorAbsoluteError() - Method in class weka.classifiers.Evaluation
Returns the mean absolute error of the prior.
meanSquaredTipText() - Method in class weka.classifiers.lazy.IBk
Returns the tip text for this property.
meanStddevTipText() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Returns the tip text for this property
measureAICScore() - Method in class weka.classifiers.bayes.BayesNet
 
measureAttributesUsed() - Method in class weka.classifiers.functions.SimpleLogistic
Returns the fraction of all attributes in the data that are used in the logistic model (in percent).
measureBayesScore() - Method in class weka.classifiers.bayes.BayesNet
 
measureBDeuScore() - Method in class weka.classifiers.bayes.BayesNet
 
measureCacheHits() - Method in class weka.classifiers.functions.SMOreg
number of kernel cache hits used during learing
measureDivergence() - Method in class weka.classifiers.bayes.BayesNet
 
measureEntropyScore() - Method in class weka.classifiers.bayes.BayesNet
 
measureExamplesCounted() - Method in class weka.classifiers.trees.LADTree
Returns the number of examples "counted".
measureExamplesProcessed() - Method in class weka.classifiers.trees.ADTree
Returns the number of examples "counted".
measureExtraArcs() - Method in class weka.classifiers.bayes.BayesNet
 
measureKernelEvaluations() - Method in class weka.classifiers.functions.SMOreg
number of kernel evaluations used in learing
measureMaxDepth() - Method in class weka.core.neighboursearch.BallTree
Returns the depth of the tree.
measureMaxDepth() - Method in class weka.core.neighboursearch.CoverTree
Returns the depth of the tree.
measureMaxDepth() - Method in class weka.core.neighboursearch.KDTree
Returns the depth of the tree.
measureMDLScore() - Method in class weka.classifiers.bayes.BayesNet
 
measureMissingArcs() - Method in class weka.classifiers.bayes.BayesNet
 
measureNodesExpanded() - Method in class weka.classifiers.trees.ADTree
Returns the number of nodes expanded.
measureNodesExpanded() - Method in class weka.classifiers.trees.LADTree
Returns the number of nodes expanded.
measureNumAttributesSelected() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Additional measure --- number of attributes selected
measureNumIterations() - Method in class weka.classifiers.meta.AdditiveRegression
return the number of iterations (base classifiers) completed
measureNumLeaves() - Method in class weka.classifiers.trees.ADTree
Calls measure function for leaf size - the number of prediction nodes.
measureNumLeaves() - Method in class weka.classifiers.trees.FT
Returns the number of leaves in the tree
measureNumLeaves() - Method in class weka.classifiers.trees.J48
Returns the number of leaves
measureNumLeaves() - Method in class weka.classifiers.trees.J48graft
Returns the number of leaves
measureNumLeaves() - Method in class weka.classifiers.trees.LADTree
Calls measure function for leaf size.
measureNumLeaves() - Method in class weka.classifiers.trees.LMT
Returns the number of leaves in the tree
measureNumLeaves() - Method in class weka.classifiers.trees.NBTree
Returns the number of leaves
measureNumLeaves() - Method in class weka.core.neighboursearch.BallTree
Returns the number of leaves.
measureNumLeaves() - Method in class weka.core.neighboursearch.CoverTree
Returns the number of leaves.
measureNumLeaves() - Method in class weka.core.neighboursearch.KDTree
Returns the number of leaves.
measureNumPredictionLeaves() - Method in class weka.classifiers.trees.ADTree
Calls measure function for prediction leaf size - the number of prediction nodes without children.
measureNumPredictionLeaves() - Method in class weka.classifiers.trees.LADTree
Calls measure function for leaf size.
measureNumRules() - Method in class weka.classifiers.rules.DecisionTable
Returns the number of rules
measureNumRules() - Method in class weka.classifiers.rules.PART
Return the number of rules.
measureNumRules() - Method in class weka.classifiers.trees.J48
Returns the number of rules (same as number of leaves)
measureNumRules() - Method in class weka.classifiers.trees.J48graft
Returns the number of rules (same as number of leaves)
measureNumRules() - Method in class weka.classifiers.trees.m5.M5Base
return the number of rules
measureNumRules() - Method in class weka.classifiers.trees.NBTree
Returns the number of rules (same as number of leaves)
measureOutOfBagError() - Method in class weka.classifiers.meta.Bagging
Gets the out of bag error that was calculated as the classifier was built.
measureOutOfBagError() - Method in class weka.classifiers.trees.RandomForest
Gets the out of bag error that was calculated as the classifier was built.
measurePercentAttsUsedByDT() - Method in class weka.classifiers.rules.DTNB
Returns the number of rules
measurePerformanceTipText() - Method in class weka.core.neighboursearch.NearestNeighbourSearch
Returns the tip text for this property.
measureReversedArcs() - Method in class weka.classifiers.bayes.BayesNet
 
measureSelectionTime() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Additional measure --- time taken (milliseconds) to select the attributes
measureTime() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Additional measure --- time taken (milliseconds) to select attributes and build the classifier
measureTipText() - Method in class weka.classifiers.meta.ThresholdSelector
Tooltip for this property.
measureTreeSize() - Method in class weka.classifiers.trees.ADTree
Calls measure function for tree size - the total number of nodes.
measureTreeSize() - Method in class weka.classifiers.trees.BFTree
Return number of tree size.
measureTreeSize() - Method in class weka.classifiers.trees.FT
Returns the size of the tree
measureTreeSize() - Method in class weka.classifiers.trees.J48
Returns the size of the tree
measureTreeSize() - Method in class weka.classifiers.trees.J48graft
Returns the size of the tree
measureTreeSize() - Method in class weka.classifiers.trees.LADTree
Calls measure function for tree size.
measureTreeSize() - Method in class weka.classifiers.trees.LMT
Returns the size of the tree
measureTreeSize() - Method in class weka.classifiers.trees.NBTree
Returns the size of the tree
measureTreeSize() - Method in class weka.classifiers.trees.SimpleCart
Return number of tree size.
measureTreeSize() - Method in class weka.core.neighboursearch.BallTree
Returns the size of the tree.
measureTreeSize() - Method in class weka.core.neighboursearch.CoverTree
Returns the size of the tree.
measureTreeSize() - Method in class weka.core.neighboursearch.KDTree
Returns the size of the tree.
MEDIAN_RULE - Static variable in class weka.classifiers.meta.Vote
combination rule: Median Probability (only numeric class)
MedianDistanceFromArbitraryPoint - Class in weka.core.neighboursearch.balltrees
Class that splits a BallNode of a ball tree using Uhlmann's described method.

For information see:

Jeffrey K.
MedianDistanceFromArbitraryPoint() - Constructor for class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
Constructor.
MedianDistanceFromArbitraryPoint(int[], Instances, EuclideanDistance) - Constructor for class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
Constructor.
MedianOfWidestDimension - Class in weka.core.neighboursearch.balltrees
Class that splits a BallNode of a ball tree based on the median value of the widest dimension of the points in the ball.
MedianOfWidestDimension() - Constructor for class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
Constructor.
MedianOfWidestDimension(int[], Instances, EuclideanDistance) - Constructor for class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
Constructor.
MedianOfWidestDimension - Class in weka.core.neighboursearch.kdtrees
The class that splits a KDTree node based on the median value of a dimension in which the node's points have the widest spread.

For more information see also:

Jerome H.
MedianOfWidestDimension() - Constructor for class weka.core.neighboursearch.kdtrees.MedianOfWidestDimension
 
Memory - Class in weka.core
A little helper class for Memory management.
Memory() - Constructor for class weka.core.Memory
initializes the memory management without GUI support
Memory(boolean) - Constructor for class weka.core.Memory
initializes the memory management
MemoryMonitor() - Constructor for class weka.gui.MemoryUsagePanel.MemoryMonitor
default constructor.
MemoryUsagePanel - Class in weka.gui
A panel for displaying the memory usage.
MemoryUsagePanel() - Constructor for class weka.gui.MemoryUsagePanel
default constructor.
MemoryUsagePanel.MemoryMonitor - Class in weka.gui
Specialized thread for monitoring the memory usage.
menuBar - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
menuEdit - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
menuEditAttributeAsClass - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
menuEditClearSearch - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
menuEditCopy - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
menuEditDeleteAttribute - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
menuEditDeleteAttributes - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
menuEditDeleteInstance - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
menuEditDeleteInstances - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
menuEditRenameAttribute - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
menuEditSearch - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
menuEditSortInstances - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
menuEditUndo - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
menuFile - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
menuFileClose - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
menuFileCloseAll - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
menuFileExit - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
menuFileOpen - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
menuFileProperties - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
menuFileSave - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
menuFileSaveAs - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
menuView - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
menuViewAttributes - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
menuViewOptimalColWidths - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
menuViewValues - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
merge(Element, Element) - Static method in class weka.associations.gsp.Element
Merges two Elements into one.
merge(Sequence, Sequence, boolean, boolean) - Static method in class weka.associations.gsp.Sequence
Merges two Sequences in the course of candidate generation.
merge(SimpleLinkedList, Comparator) - Method in class weka.associations.tertius.SimpleLinkedList
 
merge(ADTree) - Method in class weka.classifiers.trees.ADTree
Merges two trees together.
merge(PredictionNode, ADTree) - Method in class weka.classifiers.trees.adtree.PredictionNode
Merges this node with another.
merge(LADTree) - Method in class weka.classifiers.trees.LADTree
Merges two trees together.
merge(LADTree.PredictionNode) - Method in class weka.classifiers.trees.LADTree.PredictionNode
 
mergeAllItemSets(FastVector, int, int) - Static method in class weka.associations.AprioriItemSet
Merges all item sets in the set of (k-1)-item sets to create the (k)-item sets and updates the counters.
mergeAllItemSets(FastVector, int, int) - Static method in class weka.associations.ItemSet
Merges all item sets in the set of (k-1)-item sets to create the (k)-item sets and updates the counters.
mergeAllItemSets(FastVector, int, int) - Static method in class weka.associations.LabeledItemSet
Merges all item sets in the set of (k-1)-item sets to create the (k)-item sets and updates the counters.
mergeArrays(SimpleLinearRegression[][], SimpleLinearRegression[][]) - Method in class weka.classifiers.trees.ft.FTtree
Merges two arrays of regression functions into one
mergeArrays(SimpleLinearRegression[][], SimpleLinearRegression[][]) - Method in class weka.classifiers.trees.lmt.LMTNode
Merges two arrays of regression functions into one
mergeInstance(Instance) - Method in class weka.core.BinarySparseInstance
Merges this instance with the given instance and returns the result.
mergeInstance(Instance) - Method in class weka.core.Instance
Merges this instance with the given instance and returns the result.
mergeInstance(Instance) - Method in class weka.core.SparseInstance
Merges this instance with the given instance and returns the result.
mergeInstances(Instances, Instances) - Static method in class weka.core.Instances
Merges two sets of Instances together.
mergeInstances(Instance, Instance) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Creates a new instance the same as one instance (the "destination") but with some attribute values copied from another instance (the "source")
mergeInstances(Instance, Instance) - Method in class weka.filters.unsupervised.attribute.TimeSeriesDelta
Creates a new instance the same as one instance (the "destination") but with some attribute values copied from another instance (the "source")
mergeInstances(Instance, Instance) - Method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate
Creates a new instance the same as one instance (the "destination") but with some attribute values copied from another instance (the "source")
mergeNodes(FastVector, int, int, int[]) - Method in class weka.core.neighboursearch.balltrees.BottomUpConstructor
Merges nodes into one top node.
mergeNodes(Vector, int, int) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Merges nodes created by createAnchorsHierarchy() into one top node.
MergeTwoValues - Class in weka.filters.unsupervised.attribute
Merges two values of a nominal attribute into one value.
MergeTwoValues() - Constructor for class weka.filters.unsupervised.attribute.MergeTwoValues
 
Messages - Class in weka.associations.gsp
Messages.
Messages() - Constructor for class weka.associations.gsp.Messages
 
Messages - Class in weka.associations
Messages.
Messages() - Constructor for class weka.associations.Messages
 
Messages - Class in weka.gui.arffviewer
Messages.
Messages() - Constructor for class weka.gui.arffviewer.Messages
 
Messages - Class in weka.gui.beans
Messages.
Messages() - Constructor for class weka.gui.beans.Messages
 
Messages - Class in weka.gui.beans.xml
Messages.
Messages() - Constructor for class weka.gui.beans.xml.Messages
 
Messages - Class in weka.gui.boundaryvisualizer
Messages.
Messages() - Constructor for class weka.gui.boundaryvisualizer.Messages
 
Messages - Class in weka.gui.experiment
Messages.
Messages() - Constructor for class weka.gui.experiment.Messages
 
Messages - Class in weka.gui.explorer
Messages.
Messages() - Constructor for class weka.gui.explorer.Messages
 
Messages - Class in weka.gui.graphvisualizer
Messages.
Messages() - Constructor for class weka.gui.graphvisualizer.Messages
 
Messages - Class in weka.gui.hierarchyvisualizer
Messages.
Messages() - Constructor for class weka.gui.hierarchyvisualizer.Messages
 
Messages - Class in weka.gui
Messages.
Messages() - Constructor for class weka.gui.Messages
 
Messages - Class in weka.gui.sql.event
Messages.
Messages() - Constructor for class weka.gui.sql.event.Messages
 
Messages - Class in weka.gui.sql
Messages.
Messages() - Constructor for class weka.gui.sql.Messages
 
Messages - Class in weka.gui.streams
Messages.
Messages() - Constructor for class weka.gui.streams.Messages
 
Messages - Class in weka.gui.treevisualizer
Messages.
Messages() - Constructor for class weka.gui.treevisualizer.Messages
 
Messages - Class in weka.gui.visualize
Messages.
Messages() - Constructor for class weka.gui.visualize.Messages
 
MEstimate(double, double, double) - Method in class weka.classifiers.bayes.AODEsr
Returns the probability estimate, using m-estimate
mestWeightTipText() - Method in class weka.classifiers.bayes.AODEsr
Returns the tip text for this property
MetaBean - Class in weka.gui.beans
A meta bean that encapsulates several other regular beans, useful for grouping large KnowledgeFlows.
MetaBean() - Constructor for class weka.gui.beans.MetaBean
 
metaClassifierTipText() - Method in class weka.classifiers.meta.Stacking
Returns the tip text for this property
MetaCost - Class in weka.classifiers.meta
This metaclassifier makes its base classifier cost-sensitive using the method specified in

Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive.
MetaCost() - Constructor for class weka.classifiers.meta.MetaCost
 
metaFormat(Instances) - Method in class weka.classifiers.meta.Grading
Makes the format for the level-1 data.
metaFormat(Instances) - Method in class weka.classifiers.meta.Stacking
Makes the format for the level-1 data.
metaInstance(Instance, int) - Method in class weka.classifiers.meta.Grading
Makes a level-1 instance from the given instance.
metaInstance(Instance) - Method in class weka.classifiers.meta.Stacking
Makes a level-1 instance from the given instance.
metaOption() - Method in class weka.classifiers.meta.Stacking
String describing option for setting meta classifier
metaOption() - Method in class weka.classifiers.meta.StackingC
String describing option for setting meta classifier
METHOD_1_AGAINST_1 - Static variable in class weka.classifiers.meta.MultiClassClassifier
1-against-1
METHOD_1_AGAINST_ALL - Static variable in class weka.classifiers.meta.MultiClassClassifier
1-against-all
METHOD_ERROR_EXHAUSTIVE - Static variable in class weka.classifiers.meta.MultiClassClassifier
exhaustive correction code
METHOD_ERROR_RANDOM - Static variable in class weka.classifiers.meta.MultiClassClassifier
random correction code
MethodHandler - Class in weka.core.xml
This class handles relationships between display names of properties (or classes) and Methods that are associated with them.
MethodHandler() - Constructor for class weka.core.xml.MethodHandler
initializes the handler
methodNameTipText() - Method in class weka.filters.unsupervised.attribute.NumericTransform
Returns the tip text for this property
methodTipText() - Method in class weka.classifiers.meta.MultiClassClassifier
 
methodTipText() - Method in class weka.classifiers.mi.MIWrapper
Returns the tip text for this property
metricString() - Method in class weka.associations.Apriori
Returns the metric string for the chosen metric type
metricString() - Method in interface weka.associations.CARuleMiner
Gets name of the scoring metric used for car mining
metricString() - Method in class weka.associations.PredictiveApriori
Returns the metric string for the chosen metric type.
metricTypeTipText() - Method in class weka.associations.Apriori
Returns the tip text for this property
metricTypeTipText() - Method in class weka.associations.FPGrowth
Tip text for this property suitable for displaying in the GUI.
MexicanHat - Class in weka.datagenerators.classifiers.regression
A data generator for the simple 'Mexian Hat' function:
y = sin|x| / |x|
In addition to this simple function, the amplitude can be changed and gaussian noise can be added.
MexicanHat() - Constructor for class weka.datagenerators.classifiers.regression.MexicanHat
initializes the generator
MIBoost - Class in weka.classifiers.mi
MI AdaBoost method, considers the geometric mean of posterior of instances inside a bag (arithmatic mean of log-posterior) and the expectation for a bag is taken inside the loss function.

For more information about Adaboost, see:

Yoav Freund, Robert E.
MIBoost() - Constructor for class weka.classifiers.mi.MIBoost
 
MIDD - Class in weka.classifiers.mi
Re-implement the Diverse Density algorithm, changes the testing procedure.

Oded Maron (1998).
MIDD() - Constructor for class weka.classifiers.mi.MIDD
 
MiddleOutConstructor - Class in weka.core.neighboursearch.balltrees
The class that builds a BallTree middle out.

For more information see also:

Andrew W.
MiddleOutConstructor() - Constructor for class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Creates a new instance of MiddleOutConstructor.
MiddleOutConstructor.ListNode - Class in weka.core.neighboursearch.balltrees
An element of MyIdxList.
MiddleOutConstructor.MyIdxList - Class in weka.core.neighboursearch.balltrees
Class implementing a list.
MiddleOutConstructor.TempNode - Class in weka.core.neighboursearch.balltrees
Temp class to represent either a leaf node or an internal node.
midPoint(double, int) - Method in class weka.associations.PriorEstimation
calculates the mid point of an interval
MidPointOfWidestDimension - Class in weka.core.neighboursearch.kdtrees
The class that splits a KDTree node based on the midpoint value of a dimension in which the node's points have the widest spread.

For more information see also:

Andrew Moore (1991).
MidPointOfWidestDimension() - Constructor for class weka.core.neighboursearch.kdtrees.MidPointOfWidestDimension
 
midPoints() - Method in class weka.associations.PriorEstimation
split the interval [0,1] into a predefined number of intervals and calculates their mid points
MIEMDD - Class in weka.classifiers.mi
EMDD model builds heavily upon Dietterich's Diverse Density (DD) algorithm.
It is a general framework for MI learning of converting the MI problem to a single-instance setting using EM.
MIEMDD() - Constructor for class weka.classifiers.mi.MIEMDD
 
MILR - Class in weka.classifiers.mi
Uses either standard or collective multi-instance assumption, but within linear regression.
MILR() - Constructor for class weka.classifiers.mi.MILR
 
MIN - Static variable in class weka.core.neighboursearch.KDTree
The index of MIN value in attributes' range array.
MIN - Static variable in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
Index of min value in an array of attributes' range.
min - Variable in class weka.experiment.Stats
The minimum value seen, or Double.NaN if no values seen
MIN_RULE - Static variable in class weka.classifiers.meta.Vote
combination rule: Minimum Probability
MIN_SF_PROB - Static variable in class weka.classifiers.Evaluation
The minimum probablility accepted from an estimator to avoid taking log(0) in Sf calculations.
MIN_VALUE - Static variable in class weka.classifiers.meta.ThresholdSelector
The minimum value for the criterion.
minAbs(int, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Returns the minimum absolute value of some elements of a column, that is, the elements of A[i0:i1][j].
minBagDistance(Instance, Instance) - Method in class weka.classifiers.mi.MIOptimalBall
Calculate the distance from one data point to a bag
minBoxRelWidthTipText() - Method in class weka.core.neighboursearch.KDTree
Tip text for this property.
minBucketSizeTipText() - Method in class weka.classifiers.rules.OneR
Returns the tip text for this property
minChangeTipText() - Method in class weka.clusterers.sIB
Returns the tip text for this property.
minChunkSizeTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
 
minDataDLIfDeleted(int, double, boolean) - Method in class weka.classifiers.rules.RuleStats
Compute the minimal data description length of the ruleset if the rule in the given position is deleted.
The min_data_DL_if_deleted = data_DL_if_deleted - potential
minDataDLIfExists(int, double, boolean) - Method in class weka.classifiers.rules.RuleStats
Compute the minimal data description length of the ruleset if the rule in the given position is NOT deleted.
The min_data_DL_if_n_deleted = data_DL_if_n_deleted - potential
minDefaultTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Returns the tip text for this property
mIndices - Variable in class weka.gui.SortedTableModel
the mapping between displayed and actual index
mineCARs(Instances) - Method in class weka.associations.Apriori
Method that mines all class association rules with minimum support and with a minimum confidence.
mineCARs(Instances) - Method in interface weka.associations.CARuleMiner
Method for mining class association rules.
mineCARs(Instances) - Method in class weka.associations.PredictiveApriori
Method that mines the n best class association rules.
mineTree(FPGrowth.FPTreeRoot, FPGrowth.FrequentItemSets, int, FPGrowth.FrequentBinaryItemSet, int) - Method in class weka.associations.FPGrowth
Find large item sets in the FP-tree.
minGroupTipText() - Method in class weka.classifiers.meta.RotationForest
Returns the tip text for this property
minimax(Instances, int) - Static method in class weka.classifiers.mi.SimpleMI
Get the minimal and maximal value of a certain attribute in a certain data
minimaxTipText() - Method in class weka.classifiers.mi.MISMO
Returns the tip text for this property
minimizeExpectedCostTipText() - Method in class weka.classifiers.meta.CostSensitiveClassifier
 
minimizeWindows() - Method in class weka.gui.Main
minimizes all windows.
minimumBucketSizeTipText() - Method in class weka.attributeSelection.OneRAttributeEval
Returns a string for this option suitable for display in the gui as a tip text
minIndex(int[]) - Static method in class weka.core.Utils
Returns index of minimum element in a given array of integers.
minIndex(double[]) - Static method in class weka.core.Utils
Returns index of minimum element in a given array of doubles.
MiningFieldMetaInfo - Class in weka.core.pmml
Class encapsulating information about a MiningField.
MiningFieldMetaInfo(Element) - Constructor for class weka.core.pmml.MiningFieldMetaInfo
Constructs a new MiningFieldMetaInfo object.
MiningSchema - Class in weka.core.pmml
This class encapsulates the mining schema from a PMML xml file.
MiningSchema(Element, Instances, TransformationDictionary) - Constructor for class weka.core.pmml.MiningSchema
Constructor for MiningSchema.
minInstNumTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Returns the tip text for this property
minInstNumTipText() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Returns the tip text for this property
MINLOG - Static variable in class weka.core.Statistics
 
minMetricTipText() - Method in class weka.associations.Apriori
Returns the tip text for this property
minMetricTipText() - Method in class weka.associations.FPGrowth
Returns the tip text for this property
MINND - Class in weka.classifiers.mi
Multiple-Instance Nearest Neighbour with Distribution learner.

It uses gradient descent to find the weight for each dimension of each exeamplar from the starting point of 1.0.
MINND() - Constructor for class weka.classifiers.mi.MINND
 
minNoTipText() - Method in class weka.classifiers.rules.ConjunctiveRule
Returns the tip text for this property
minNoTipText() - Method in class weka.classifiers.rules.JRip
Returns the tip text for this property
minNoTipText() - Method in class weka.classifiers.rules.Ridor
Returns the tip text for this property
minNumClustersTipText() - Method in class weka.clusterers.XMeans
Returns the tip text for this property.
minNumInstancesTipText() - Method in class weka.classifiers.trees.FT
Returns the tip text for this property
minNumInstancesTipText() - Method in class weka.classifiers.trees.LMT
Returns the tip text for this property
minNumInstancesTipText() - Method in class weka.classifiers.trees.m5.M5Base
Returns the tip text for this property
minNumObjTipText() - Method in class weka.classifiers.rules.PART
Returns the tip text for this property
minNumObjTipText() - Method in class weka.classifiers.trees.BFTree
Returns the tip text for this property
minNumObjTipText() - Method in class weka.classifiers.trees.J48
Returns the tip text for this property
minNumObjTipText() - Method in class weka.classifiers.trees.J48graft
Returns the tip text for this property
minNumObjTipText() - Method in class weka.classifiers.trees.SimpleCart
Returns the tip text for this property
minNumTipText() - Method in class weka.classifiers.trees.RandomTree
Returns the tip text for this property
minNumTipText() - Method in class weka.classifiers.trees.REPTree
Returns the tip text for this property
MINOR - Static variable in class weka.core.Version
the minor version
minPointsTipText() - Method in class weka.clusterers.DBScan
Returns the tip text for this property
minPointsTipText() - Method in class weka.clusterers.OPTICS
Returns the tip text for this property
minProb - Variable in class weka.classifiers.lazy.kstar.KStarWrapper
used/reused to hold the smallest transformation probability
minRadiusTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Returns the tip text for this property
minRangeTipText() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Returns the tip text for this property
minRuleSizeTipText() - Method in class weka.datagenerators.classifiers.classification.RDG1
Returns the tip text for this property
minsAndMaxs(Instances, double[][], int) - Method in class weka.classifiers.trees.j48.C45Split
Returns the minsAndMaxs of the index.th subset.
minStdDevTipText() - Method in class weka.classifiers.functions.RBFNetwork
Returns the tip text for this property
minStdDevTipText() - Method in class weka.clusterers.EM
Returns the tip text for this property
minStdDevTipText() - Method in class weka.clusterers.MakeDensityBasedClusterer
Returns the tip text for this property
minSupportTipText() - Method in class weka.associations.GeneralizedSequentialPatterns
Returns the minimum support option tip text for the Weka GUI.
minTermFreqTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Returns the tip text for this property.
minThresholdTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Returns the tip text for this property
MINUS - Static variable in interface weka.core.mathematicalexpression.sym
 
minus(double) - Method in class weka.core.matrix.DoubleVector
Subtracts a value
minus(DoubleVector) - Method in class weka.core.matrix.DoubleVector
Subtracts another DoubleVector element by element
minus(Matrix) - Method in class weka.core.matrix.Matrix
C = A - B
MINUS - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
minusEquals(double) - Method in class weka.core.matrix.DoubleVector
Subtracts a value in place
minusEquals(DoubleVector) - Method in class weka.core.matrix.DoubleVector
Subtracts another DoubleVector element by element in place
minusEquals(Matrix) - Method in class weka.core.matrix.Matrix
A = A - B
minVariancePropTipText() - Method in class weka.classifiers.trees.REPTree
Returns the tip text for this property
MIOptimalBall - Class in weka.classifiers.mi
This classifier tries to find a suitable ball in the multiple-instance space, with a certain data point in the instance space as a ball center.
MIOptimalBall() - Constructor for class weka.classifiers.mi.MIOptimalBall
 
MIPolyKernel - Class in weka.classifiers.mi.supportVector
The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1)^p

Valid options are:

MIPolyKernel() - Constructor for class weka.classifiers.mi.supportVector.MIPolyKernel
default constructor - does nothing.
MIPolyKernel(Instances, int, double, boolean) - Constructor for class weka.classifiers.mi.supportVector.MIPolyKernel
Creates a new MIPolyKernel instance.
MIRBFKernel - Class in weka.classifiers.mi.supportVector
The RBF kernel.
MIRBFKernel() - Constructor for class weka.classifiers.mi.supportVector.MIRBFKernel
default constructor - does nothing.
MIRBFKernel(Instances, int, double) - Constructor for class weka.classifiers.mi.supportVector.MIRBFKernel
Constructor.
MISMO - Class in weka.classifiers.mi
Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.

This implementation globally replaces all missing values and transforms nominal attributes into binary ones.
MISMO() - Constructor for class weka.classifiers.mi.MISMO
 
MISMO.BinaryMISMO - Class in weka.classifiers.mi
Class for building a binary support vector machine.
MISSING_ID - Static variable in class weka.core.TechnicalInformation
will be returned if no ID can be generated
MISSING_SHAPE - Static variable in class weka.gui.visualize.Plot2D
 
MISSING_VALUE - Static variable in interface weka.classifiers.evaluation.Prediction
Constant representing a missing value.
MISSING_VALUE - Static variable in class weka.core.Instance
Constant representing a missing value.
missingArcs(BayesNet) - Method in class weka.classifiers.bayes.net.BIFReader
Count nr of arcs missing from other network compared to current network Note that an arc is not 'missing' if it is reversed.
missingCount - Variable in class weka.core.AttributeStats
The number of missing values
missingMergeTipText() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
Returns the tip text for this property
missingMergeTipText() - Method in class weka.attributeSelection.GainRatioAttributeEval
Returns the tip text for this property
missingMergeTipText() - Method in class weka.attributeSelection.InfoGainAttributeEval
Returns the tip text for this property
missingMergeTipText() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Returns the tip text for this property
missingModeTipText() - Method in class weka.classifiers.lazy.KStar
Returns the tip text for this property
missingSeparateTipText() - Method in class weka.attributeSelection.CfsSubsetEval
Returns the tip text for this property
missingValue() - Static method in class weka.core.Instance
Returns the double that codes "missing".
missingValuesTipText() - Method in class weka.associations.Tertius
Returns the tip text for this property.
missingValueTipText() - Method in class weka.core.converters.CSVLoader
Returns the tip text for this property.
MISVM - Class in weka.classifiers.mi
Implements Stuart Andrews' mi_SVM (Maximum pattern Margin Formulation of MIL).
MISVM() - Constructor for class weka.classifiers.mi.MISVM
 
MIWrapper - Class in weka.classifiers.mi
A simple Wrapper method for applying standard propositional learners to multi-instance data.

For more information see:

E.
MIWrapper() - Constructor for class weka.classifiers.mi.MIWrapper
 
mixingDistribution - Variable in class weka.classifiers.functions.pace.MixtureDistribution
 
MixtureDistribution - Class in weka.classifiers.functions.pace
Abtract class for manipulating mixture distributions.
MixtureDistribution() - Constructor for class weka.classifiers.functions.pace.MixtureDistribution
 
mModel - Variable in class weka.gui.SortedTableModel
the actual table model
MODEL_FILE_EXTENSION - Static variable in class weka.gui.explorer.ClassifierPanel
The filename extension that should be used for model files
MODEL_FILE_EXTENSION - Static variable in class weka.gui.explorer.ClustererPanel
The filename extension that should be used for model files
MODEL_FT - Static variable in class weka.classifiers.trees.FT
model types
MODEL_FTInner - Static variable in class weka.classifiers.trees.FT
 
MODEL_FTLeaves - Static variable in class weka.classifiers.trees.FT
 
modelBuilt() - Method in class weka.classifiers.functions.supportVector.RegOptimizer
flag to indicate whether the model was built yet
modelDistributionForInstance(Instance) - Method in class weka.classifiers.trees.ft.FTtree
Returns the class probabilities for an instance according to the logistic model at the node.
modelDistributionForInstance(Instance) - Method in class weka.classifiers.trees.lmt.LMTNode
Returns the class probabilities for an instance according to the logistic model at the node.
modelErrors() - Method in class weka.classifiers.trees.lmt.LMTNode
Updates the numIncorrectModel field for all nodes.
modelErrors() - Method in class weka.classifiers.trees.SimpleCart
Updates the numIncorrectModel field for all nodes when subtree (to be pruned) is rooted.
modelFileTipText() - Method in class weka.classifiers.misc.SerializedClassifier
Returns the tip text for this property
ModelPerformanceChart - Class in weka.gui.beans
Bean that can be used for displaying threshold curves (e.g.
ModelPerformanceChart() - Constructor for class weka.gui.beans.ModelPerformanceChart
 
ModelPerformanceChartBeanInfo - Class in weka.gui.beans
Bean info class for the model performance chart
ModelPerformanceChartBeanInfo() - Constructor for class weka.gui.beans.ModelPerformanceChartBeanInfo
 
ModelSelection - Class in weka.classifiers.trees.j48
Abstract class for model selection criteria.
ModelSelection() - Constructor for class weka.classifiers.trees.j48.ModelSelection
 
modelsToString() - Method in class weka.classifiers.trees.ft.FTtree
Returns a string describing the logistic regression function at the node.
modelsToString() - Method in class weka.classifiers.trees.lmt.LMTNode
Returns a string describing the logistic regression function at the node.
modelToString(DefaultListModel) - Method in class weka.gui.sql.SqlViewer
converts the given model into a comma-separated string.
modelTypeTipText() - Method in class weka.classifiers.trees.FT
Returns the tip text for this property
modifyHeader(Instances) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
modifies the header of the Instances and returns the format w/o any instances
modifyHeaderTipText() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Returns the tip text for this property
modifyHeaderTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Returns the tip text for this property
momentumTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
moralize(BayesNet) - Method in class weka.classifiers.bayes.net.MarginCalculator
moralize DAG and calculate adjacency matrix representation for a Bayes Network, effecively converting the directed acyclic graph to an undirected graph.
mostExplainingColumn(PaceMatrix, IntVector, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Returns the index of the column that has the largest (squared) response, when each of columns pvt[ks:] is moved to become the ks-th column.
mouseClicked(MouseEvent) - Method in class weka.gui.arffviewer.ArffPanel
Invoked when a mouse button has been pressed and released on a component
mouseClicked(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
Does nothing.
mouseDragged(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
Performs intermediate updates to what the user wishes to do.
mouseEntered(MouseEvent) - Method in class weka.gui.arffviewer.ArffPanel
Invoked when the mouse enters a component.
mouseEntered(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
Does nothing.
mouseExited(MouseEvent) - Method in class weka.gui.arffviewer.ArffPanel
Invoked when the mouse exits a component
mouseExited(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
Does nothing.
mouseMoved(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
Does nothing.
mousePressed(MouseEvent) - Method in class weka.gui.arffviewer.ArffPanel
Invoked when a mouse button has been pressed on a component
mousePressed(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
Determines what action the user wants to perform.
mouseReleased(MouseEvent) - Method in class weka.gui.arffviewer.ArffPanel
Invoked when a mouse button has been released on a component.
mouseReleased(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
Performs the final stages of what the user wants to perform.
MOVE_DOWN - Static variable in class weka.gui.JListHelper
moves items down
MOVE_UP - Static variable in class weka.gui.JListHelper
moves items up
moveBottom(JList) - Static method in class weka.gui.JListHelper
moves the selected items to the end
moveCentroid(int, Instances, boolean) - Method in class weka.clusterers.SimpleKMeans
Move the centroid to it's new coordinates.
moveDown(JList) - Static method in class weka.gui.JListHelper
moves the selected item down by 1
MoveInstanceToBestCluster(Instance) - Method in class weka.clusterers.CLOPE
Move instance to best cluster
moveItems(JList, int, int) - Static method in class weka.gui.JListHelper
moves the selected items by a certain amount of items in a given direction
moveTop(JList) - Static method in class weka.gui.JListHelper
moves the selected items to the top
moveUp(JList) - Static method in class weka.gui.JListHelper
moves the selected items up by 1
MOVING - Static variable in class weka.gui.beans.KnowledgeFlowApp
 
mSortColumn - Variable in class weka.gui.SortedTableModel
the sort column
MultiBoostAB - Class in weka.classifiers.meta
Class for boosting a classifier using the MultiBoosting method.

MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees.
MultiBoostAB() - Constructor for class weka.classifiers.meta.MultiBoostAB
 
MultiClassClassifier - Class in weka.classifiers.meta
A metaclassifier for handling multi-class datasets with 2-class classifiers.
MultiClassClassifier() - Constructor for class weka.classifiers.meta.MultiClassClassifier
Constructor.
MultiFilter - Class in weka.filters
Applies several filters successively.
MultiFilter() - Constructor for class weka.filters.MultiFilter
 
MultiInstanceCapabilitiesHandler - Interface in weka.core
Multi-Instance classifiers can specify an additional Capabilities object for the data in the relational attribute, since the format of multi-instance data is fixed to "bag/NOMINAL,data/RELATIONAL,class".
multiInstanceHandler() - Method in class weka.associations.CheckAssociator
Checks whether the scheme handles multi-instance data.
multiInstanceHandler() - Method in class weka.attributeSelection.CheckAttributeSelection
Checks whether the scheme handles multi-instance data.
multiInstanceHandler() - Method in class weka.classifiers.CheckClassifier
Checks whether the scheme handles multi-instance data.
multiInstanceHandler() - Method in class weka.classifiers.functions.supportVector.CheckKernel
Checks whether the scheme handles multi-instance data.
multiInstanceHandler() - Method in class weka.clusterers.CheckClusterer
Checks whether the scheme handles multi-instance data.
MultiInstanceToPropositional - Class in weka.filters.unsupervised.attribute
Converts the multi-instance dataset into single instance dataset so that the Nominalize, Standardize and other type of filters or transformation can be applied to these data for the further preprocessing.
Note: the first attribute of the converted dataset is a nominal attribute and refers to the bagId.
MultiInstanceToPropositional() - Constructor for class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
 
MultilayerPerceptron - Class in weka.classifiers.functions
A Classifier that uses backpropagation to classify instances.
This network can be built by hand, created by an algorithm or both.
MultilayerPerceptron() - Constructor for class weka.classifiers.functions.MultilayerPerceptron
The constructor.
MultilayerPerceptron.NeuralEnd - Class in weka.classifiers.functions
This inner class is used to connect the nodes in the network up to the data that they are classifying, Note that objects of this class are only suitable to go on the attribute side or class side of the network and not both.
MultiNomialBMAEstimator - Class in weka.classifiers.bayes.net.estimate
Multinomial BMA Estimator.
MultiNomialBMAEstimator() - Constructor for class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
 
multinomialWordTipText() - Method in class weka.classifiers.bayes.DMNBtext
Returns the tip text for this property
MultipleClassifiersCombiner - Class in weka.classifiers
Abstract utility class for handling settings common to meta classifiers that build an ensemble from multiple classifiers.
MultipleClassifiersCombiner() - Constructor for class weka.classifiers.MultipleClassifiersCombiner
 
multiply(Matrix) - Method in class weka.core.Matrix
Deprecated.
Returns the multiplication of two matrices
multiResultsetFull(int, int) - Method in class weka.experiment.PairedTTester
Creates a comparison table where a base resultset is compared to the other resultsets.
multiResultsetFull(int, int) - Method in interface weka.experiment.Tester
Creates a comparison table where a base resultset is compared to the other resultsets.
multiResultsetRanking(int) - Method in class weka.experiment.PairedTTester
returns a ranking of the resultsets
multiResultsetRanking(int) - Method in interface weka.experiment.Tester
 
multiResultsetSummary(int) - Method in class weka.experiment.PairedTTester
Carries out a comparison between all resultsets, counting the number of datsets where one resultset outperforms the other.
multiResultsetSummary(int) - Method in interface weka.experiment.Tester
Carries out a comparison between all resultsets, counting the number of datsets where one resultset outperforms the other.
multiResultsetWins(int, int[][]) - Method in class weka.experiment.PairedTTester
Carries out a comparison between all resultsets, counting the number of datsets where one resultset outperforms the other.
multiResultsetWins(int, int[][]) - Method in interface weka.experiment.Tester
Carries out a comparison between all resultsets, counting the number of datsets where one resultset outperforms the other.
MultiScheme - Class in weka.classifiers.meta
Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
MultiScheme() - Constructor for class weka.classifiers.meta.MultiScheme
 
mutationProbTipText() - Method in class weka.attributeSelection.GeneticSearch
Returns the tip text for this property
MyHeap(int) - Constructor for class weka.core.neighboursearch.CoverTree.MyHeap
constructor.
MyHeap(int) - Constructor for class weka.core.neighboursearch.NearestNeighbourSearch.MyHeap
constructor.
MyHeapElement(double) - Constructor for class weka.core.neighboursearch.CoverTree.MyHeapElement
constructor.
MyHeapElement(int, double) - Constructor for class weka.core.neighboursearch.NearestNeighbourSearch.MyHeapElement
constructor.
MyIdxList() - Constructor for class weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList
Constructor.
MyIdxList(int) - Constructor for class weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList
Constructor.

N

n - Variable in class weka.core.matrix.Matrix
Row and column dimensions.
NaiveBayes - Class in weka.classifiers.bayes
Class for a Naive Bayes classifier using estimator classes.
NaiveBayes() - Constructor for class weka.classifiers.bayes.NaiveBayes
 
NaiveBayes - Class in weka.classifiers.bayes.net.search.fixed
The NaiveBayes class generates a fixed Bayes network structure with arrows from the class variable to each of the attribute variables.
NaiveBayes() - Constructor for class weka.classifiers.bayes.net.search.fixed.NaiveBayes
 
NaiveBayesMultinomial - Class in weka.classifiers.bayes
Class for building and using a multinomial Naive Bayes classifier.
NaiveBayesMultinomial() - Constructor for class weka.classifiers.bayes.NaiveBayesMultinomial
 
NaiveBayesMultinomialUpdateable - Class in weka.classifiers.bayes
Class for building and using a multinomial Naive Bayes classifier.
NaiveBayesMultinomialUpdateable() - Constructor for class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable
 
NaiveBayesSimple - Class in weka.classifiers.bayes
Class for building and using a simple Naive Bayes classifier.Numeric attributes are modelled by a normal distribution.

For more information, see

Richard Duda, Peter Hart (1973).
NaiveBayesSimple() - Constructor for class weka.classifiers.bayes.NaiveBayesSimple
 
NaiveBayesUpdateable - Class in weka.classifiers.bayes
Class for a Naive Bayes classifier using estimator classes.
NaiveBayesUpdateable() - Constructor for class weka.classifiers.bayes.NaiveBayesUpdateable
 
naiveLayout() - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
This method lays out the vertices horizontally, in each level.
name() - Method in class weka.core.Attribute
Returns the attribute's name.
name() - Method in class weka.core.Option
Returns the option's name.
NAME_CLASSFIRST - Static variable in class weka.experiment.xml.XMLExperiment
the name of the classFirst property
NAME_PROPERTYNODE_PARENTCLASS - Static variable in class weka.experiment.xml.XMLExperiment
PropertyNode member
NAME_PROPERTYNODE_PROPERTY - Static variable in class weka.experiment.xml.XMLExperiment
PropertyNode member
NAME_PROPERTYNODE_VALUE - Static variable in class weka.experiment.xml.XMLExperiment
PropertyNode member
NamedColor - Class in weka.gui.treevisualizer
This class contains a color name and the rgb values of that color
NamedColor(String, int, int, int) - Constructor for class weka.gui.treevisualizer.NamedColor
 
nameTipText() - Method in class weka.filters.unsupervised.attribute.AddExpression
Returns the tip text for this property
NBconditionalProb(Instance, int) - Method in class weka.classifiers.bayes.AODE
Calculates the probability of the specified class for the given test instance, using naive Bayes.
NBconditionalProb(Instance, int) - Method in class weka.classifiers.bayes.AODEsr
Calculates the probability of the specified class for the given test instance, using naive Bayes.
NBTree - Class in weka.classifiers.trees
Class for generating a decision tree with naive Bayes classifiers at the leaves.

For more information, see

Ron Kohavi: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid.
NBTree() - Constructor for class weka.classifiers.trees.NBTree
 
NBTreeClassifierTree - Class in weka.classifiers.trees.j48
Class for handling a naive bayes tree structure used for classification.
NBTreeClassifierTree(ModelSelection) - Constructor for class weka.classifiers.trees.j48.NBTreeClassifierTree
 
NBTreeModelSelection - Class in weka.classifiers.trees.j48
Class for selecting a NB tree split.
NBTreeModelSelection(int, Instances) - Constructor for class weka.classifiers.trees.j48.NBTreeModelSelection
Initializes the split selection method with the given parameters.
NBTreeNoSplit - Class in weka.classifiers.trees.j48
Class implementing a "no-split"-split (leaf node) for naive bayes trees.
NBTreeNoSplit() - Constructor for class weka.classifiers.trees.j48.NBTreeNoSplit
 
NBTreeSplit - Class in weka.classifiers.trees.j48
Class implementing a NBTree split on an attribute.
NBTreeSplit(int, int, double) - Constructor for class weka.classifiers.trees.j48.NBTreeSplit
Initializes the split model.
ND - Class in weka.classifiers.meta.nestedDichotomies
A meta classifier for handling multi-class datasets with 2-class classifiers by building a random tree structure.

For more info, check

Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.
ND() - Constructor for class weka.classifiers.meta.nestedDichotomies.ND
Constructor.
ND.NDTree - Class in weka.classifiers.meta.nestedDichotomies
a node class
NDConditionalEstimator - Class in weka.estimators
Conditional probability estimator for a numeric domain conditional upon a discrete domain (utilises separate normal estimators for each discrete conditioning value).
NDConditionalEstimator(int, double) - Constructor for class weka.estimators.NDConditionalEstimator
Constructor
NDTree() - Constructor for class weka.classifiers.meta.nestedDichotomies.ND.NDTree
Constructor.
nearestNeighborsTipText() - Method in class weka.filters.supervised.instance.SMOTE
Returns the tip text for this property.
nearestNeighbour(Instance) - Method in class weka.core.neighboursearch.BallTree
Returns the nearest instance in the current neighbourhood to the supplied instance.
nearestNeighbour(Instance) - Method in class weka.core.neighboursearch.CoverTree
Returns the NN instance of a given target instance, from among the previously supplied training instances.
nearestNeighbour(Instance) - Method in class weka.core.neighboursearch.KDTree
Returns the nearest neighbour of the supplied target instance.
nearestNeighbour(Instance) - Method in class weka.core.neighboursearch.LinearNNSearch
Returns the nearest instance in the current neighbourhood to the supplied instance.
nearestNeighbour(Instance) - Method in class weka.core.neighboursearch.NearestNeighbourSearch
Returns the nearest instance in the current neighbourhood to the supplied instance.
nearestNeighbours(NearestNeighbourSearch.MyHeap, BallNode, Instance, int) - Method in class weka.core.neighboursearch.BallTree
Does NN search according to Moore's method.
NearestNeighbourSearch - Class in weka.core.neighboursearch
Abstract class for nearest neighbour search.
NearestNeighbourSearch() - Constructor for class weka.core.neighboursearch.NearestNeighbourSearch
Constructor.
NearestNeighbourSearch(Instances) - Constructor for class weka.core.neighboursearch.NearestNeighbourSearch
Constructor.
NearestNeighbourSearch.MyHeap - Class in weka.core.neighboursearch
A class for a heap to store the nearest k neighbours to an instance.
NearestNeighbourSearch.MyHeapElement - Class in weka.core.neighboursearch
A class for storing data about a neighboring instance.
NearestNeighbourSearch.NeighborList - Class in weka.core.neighboursearch
A class for a linked list to store the nearest k neighbours to an instance.
NearestNeighbourSearch.NeighborNode - Class in weka.core.neighboursearch
A class for storing data about a neighboring instance.
nearestNeighbourSearchAlgorithmTipText() - Method in class weka.classifiers.lazy.IBk
Returns the tip text for this property.
nearestNeighbourSearchAlgorithmTipText() - Method in class weka.classifiers.lazy.LWL
Returns the tip text for this property.
needExponentialFormat(double) - Method in class weka.core.matrix.FlexibleDecimalFormat
 
needsUID(String) - Static method in class weka.core.SerializationHelper
checks whether a class needs to declare a serialVersionUID, i.e., it implements the java.io.Serializable interface but doesn't declare a serialVersionUID.
needsUID(Class) - Static method in class weka.core.SerializationHelper
checks whether a class needs to declare a serialVersionUID, i.e., it implements the java.io.Serializable interface but doesn't declare a serialVersionUID.
NEG - Static variable in class weka.associations.tertius.Literal
 
negationIncludedIn(LiteralSet) - Method in class weka.associations.tertius.LiteralSet
Test if the negation of this LiteralSet is included in another LiteralSet.
negationSatisfies(Instance) - Method in class weka.associations.tertius.AttributeValueLiteral
 
negationSatisfies(Instance) - Method in class weka.associations.tertius.Literal
 
negationTipText() - Method in class weka.associations.Tertius
Returns the tip text for this property.
negative() - Method in class weka.associations.tertius.Literal
 
negativeLogLikelihood(double[][], double[][]) - Method in class weka.classifiers.trees.lmt.LogisticBase
Returns the negative loglikelihood of the Y-values (actual class probabilities) given the p-values (current probability estimates).
NeighborList(int) - Constructor for class weka.core.neighboursearch.NearestNeighbourSearch.NeighborList
Creates the neighborlist with a desired length.
NeighborNode(double, Instance, NearestNeighbourSearch.NeighborNode) - Constructor for class weka.core.neighboursearch.NearestNeighbourSearch.NeighborNode
Create a new neighbor node.
NeighborNode(double, Instance) - Constructor for class weka.core.neighboursearch.NearestNeighbourSearch.NeighborNode
Create a new neighbor node that doesn't link to any other nodes.
nestedEstimate(DoubleVector) - Method in class weka.classifiers.functions.pace.NormalMixture
Returns the optimal nested model estimate of a vector.
NeuralConnection - Class in weka.classifiers.functions.neural
Abstract unit in a NeuralNetwork.
NeuralConnection(String) - Constructor for class weka.classifiers.functions.neural.NeuralConnection
Constructs The unit with the basic connection information prepared for use.
NeuralEnd(String) - Constructor for class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd
Constructor
NeuralMethod - Interface in weka.classifiers.functions.neural
This is an interface used to create classes that can be used by the neuralnode to perform all it's computations.
NeuralNetwork - Class in weka.classifiers.pmml.consumer
Class implementing import of PMML Neural Network model.
NeuralNetwork(Element, Instances, MiningSchema) - Constructor for class weka.classifiers.pmml.consumer.NeuralNetwork
 
NeuralNode - Class in weka.classifiers.functions.neural
This class is used to represent a node in the neuralnet.
NeuralNode(String, Random, NeuralMethod) - Constructor for class weka.classifiers.functions.neural.NeuralNode
 
NEW_BATCH - Static variable in class weka.gui.beans.IncrementalClassifierEvent
 
new_leaf(Integer) - Method in class weka.core.neighboursearch.CoverTree
Creates a new leaf node for a given Instance/point p.
new_node(Integer) - Method in class weka.core.neighboursearch.CoverTree
Creates a new internal node for a given Instance/point p.
newArray(Object, String, Class, int) - Method in class weka.classifiers.functions.LibLINEAR
sets a new array for the field
newArray(Object, String, Class, int[]) - Method in class weka.classifiers.functions.LibLINEAR
sets a new array for the field
newArray(Object, String, Class, int) - Method in class weka.classifiers.functions.LibSVM
sets a new array for the field
newArray(Object, String, Class, int[]) - Method in class weka.classifiers.functions.LibSVM
sets a new array for the field
newCentersAfterSplit(double[], double[], double, Instances) - Method in class weka.clusterers.XMeans
Returns new center list.
newCentersAfterSplit(boolean[], Instances) - Method in class weka.clusterers.XMeans
Returns new centers.
newClock() - Static method in class weka.core.Debug
returns a new instance of a clock
newColorAttribute(int, Instances) - Method in class weka.gui.visualize.VisualizePanel
Sets the Colors in use for a different attrib if it is not a nominal attrib and or does not have more possible values then this will do nothing.
newDataFormat(DataSetEvent) - Method in class weka.gui.beans.ClassAssignerCustomizer
 
newDataFormat(DataSetEvent) - Method in interface weka.gui.beans.DataFormatListener
Recieve a DataSetEvent that encapsulates a new data format.
newDocument(String, String) - Method in class weka.core.xml.XMLDocument
creates a new Document with the given information.
newEnt(Distribution) - Method in class weka.classifiers.trees.j48.EntropyBasedSplitCrit
Computes entropy of distribution after splitting.
newFileSelected() - Method in class weka.gui.beans.Loader
 
Newick - Static variable in interface weka.core.Drawable
 
newInstance(File, Class) - Static method in class weka.core.Jython
loads the module and returns a new instance of it as instance of the provided Java class template.
newInstance(File, Class, File[]) - Static method in class weka.core.Jython
loads the module and returns a new instance of it as instance of the provided Java class template.
newInterpreter() - Static method in class weka.core.Jython
initializes and returns a Python Interpreter
newLog(String, int, int) - Static method in class weka.core.Debug
returns a new Log instance
newNominalRule(Attribute, Instances, int[]) - Method in class weka.classifiers.rules.OneR
Create a rule branching on this nominal attribute.
newNumericRule(Attribute, Instances, int[]) - Method in class weka.classifiers.rules.OneR
Create a rule branching on this numeric attribute
newRandom() - Static method in class weka.core.Debug
returns a default debug random object, with no particular seed and debugging enabled.
newRandom(int) - Static method in class weka.core.Debug
returns a debug random object with the specified seed and debugging enabled.
newRule(Attribute, Instances) - Method in class weka.classifiers.rules.OneR
Create a rule branching on this attribute.
newStructure() - Method in class weka.gui.beans.Loader
 
newTimestamp() - Static method in class weka.core.Debug
returns a default timestamp for the current date/time
next() - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListIterator
 
next(int) - Method in interface weka.classifiers.IterativeClassifier
Performs one iteration.
next - Variable in class weka.classifiers.lazy.kstar.KStarCache.TableEntry
next table entry (separate chaining)
next(int) - Method in class weka.classifiers.trees.ADTree
Performs one iteration.
next(int) - Method in class weka.classifiers.trees.LADTree
 
next() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueue
Returns the element with the highest priority
next() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueue
Returns the element with the lowest priority
next(Queue.QueueNode) - Method in class weka.core.Queue.QueueNode
Sets the next node in the queue, and returns it.
next() - Method in class weka.core.Queue.QueueNode
Gets the next node in the queue.
next(int) - Method in class weka.core.RandomVariates
Simply use the method of the super class
next() - Method in class weka.core.Trie.TrieIterator
Returns the next element in the iteration.
next_token() - Method in class weka.core.mathematicalexpression.Scanner
Resumes scanning until the next regular expression is matched, the end of input is encountered or an I/O-Error occurs.
next_token() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner
Resumes scanning until the next regular expression is matched, the end of input is encountered or an I/O-Error occurs.
nextAssignedOne(int, int, int[]) - Method in class weka.clusterers.XMeans
Searches along the assignment array for the next entry of the center in question.
nextBoolean() - Method in class weka.core.Debug.Random
Returns the next pseudorandom, uniformly distributed boolean value from this random number generator's sequence.
nextBytes(byte[]) - Method in class weka.core.Debug.Random
Generates random bytes and places them into a user-supplied byte array.
nextDouble() - Method in class weka.core.Debug.Random
Returns the next pseudorandom, uniformly distributed double value between 0.0 and 1.0 from this random number generator's sequence.
nextElement(Instances) - Method in class weka.core.converters.ConverterUtils.DataSource
returns the next element and sets the specified dataset, null if none available.
nextElement() - Method in class weka.core.FastVector.FastVectorEnumeration
Returns the next element.
nextElement() - Method in class weka.core.tokenizers.AlphabeticTokenizer
returns the next element
nextElement() - Method in class weka.core.tokenizers.NGramTokenizer
Returns N-grams and also (N-1)-grams and ....
nextElement() - Method in class weka.core.tokenizers.Tokenizer
Returns the next element of this enumeration if this enumeration object has at least one more element to provide.
nextElement() - Method in class weka.core.tokenizers.WordTokenizer
Returns the next element of this enumeration if this enumeration object has at least one more element to provide.
nextErlang(int) - Method in class weka.core.RandomVariates
Generate a value of a variate following standard Erlang distribution.
nextExponential() - Method in class weka.core.RandomVariates
Generate a value of a variate following standard exponential distribution using simple inverse method.
nextFloat() - Method in class weka.core.Debug.Random
Returns the next pseudorandom, uniformly distributed float value between 0.0 and 1.0 from this random number generator's sequence.
nextGamma(double) - Method in class weka.core.RandomVariates
Generate a value of a variate following standard Gamma distribution with shape parameter a.
nextGaussian() - Method in class weka.core.Debug.Random
Returns the next pseudorandom, Gaussian ("normally") distributed double value with mean 0.0 and standard deviation 1.0 from this random number generator's sequence.
nextID() - Static method in class weka.classifiers.trees.j48.ClassifierTree
Gets the next unique node ID.
nextID() - Static method in class weka.classifiers.trees.REPTree
Gets the next unique node ID.
nextID() - Static method in class weka.core.Debug.Random
returns the next unique ID for a number generator
nextInt() - Method in class weka.core.Debug.Random
Returns the next pseudorandom, uniformly distributed int value from this random number generator's sequence.
nextInt(int) - Method in class weka.core.Debug.Random
Returns a pseudorandom, uniformly distributed int value between 0 (inclusive) and the specified value (exclusive), drawn from this random number generator's sequence.
nextIteration() - Method in class weka.experiment.Experiment
Carries out the next iteration of the experiment.
nextIteration() - Method in class weka.experiment.RemoteExperiment
Overides the one in Experiment
nextLong() - Method in class weka.core.Debug.Random
Returns the next pseudorandom, uniformly distributed long value from this random number generator's sequence.
nextPowerOf2(int) - Static method in class weka.filters.unsupervised.attribute.Wavelet
returns the next bigger number that's a power of 2.
nextSplitAddedOrder() - Method in class weka.classifiers.trees.ADTree
Returns the next number in the order that splitter nodes have been added to the tree, and records that a new splitter has been added.
nf - Variable in class weka.core.matrix.ExponentialFormat
 
nf - Variable in class weka.core.matrix.FloatingPointFormat
 
NGramMaxSizeTipText() - Method in class weka.core.tokenizers.NGramTokenizer
Returns the tip text for this property.
NGramMinSizeTipText() - Method in class weka.core.tokenizers.NGramTokenizer
Returns the tip text for this property.
NGramTokenizer - Class in weka.core.tokenizers
Splits a string into an n-gram with min and max grams.
NGramTokenizer() - Constructor for class weka.core.tokenizers.NGramTokenizer
 
NNConditionalEstimator - Class in weka.estimators
Conditional probability estimator for a numeric domain conditional upon a numeric domain (using Mahalanobis distance).
NNConditionalEstimator() - Constructor for class weka.estimators.NNConditionalEstimator
 
NNge - Class in weka.classifiers.rules
Nearest-neighbor-like algorithm using non-nested generalized exemplars (which are hyperrectangles that can be viewed as if-then rules).
NNge() - Constructor for class weka.classifiers.rules.NNge
 
nnls(PaceMatrix, IntVector) - Method in class weka.classifiers.functions.pace.PaceMatrix
Solves the nonnegative linear squares problem.
nnlse(PaceMatrix, PaceMatrix, PaceMatrix, IntVector) - Method in class weka.classifiers.functions.pace.PaceMatrix
Solves the nonnegative least squares problem with equality constraint.
nnlse1(PaceMatrix, IntVector) - Method in class weka.classifiers.functions.pace.PaceMatrix
Solves the nonnegative least squares problem with equality constraint.
NNMMethod - Static variable in class weka.classifiers.functions.pace.MixtureDistribution
The nonnegative-measure-based method
NO_CLASS - Static variable in class weka.associations.CheckAssociator
a "dummy" class type
NO_CLASS - Static variable in class weka.core.TestInstances
can be used to avoid generating a class attribute
NO_COMMAND - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
 
NO_SOURCE - Static variable in class weka.gui.AttributeSummaryPanel
Message shown when no instances have been loaded and no attribute set
NO_SOURCE - Static variable in class weka.gui.experiment.ResultsPanel
Message shown when no experimental results have been loaded.
NO_SOURCE - Static variable in class weka.gui.InstancesSummaryPanel
Message shown when no instances have been loaded
NO_SUPPORT - Static variable in class weka.gui.GenericObjectEditor.GOETreeNode
color for "no support".
Node - Class in weka.gui.treevisualizer
This class records all the data about a particular node for displaying.
Node(String, String, int, int, Color, String) - Constructor for class weka.gui.treevisualizer.Node
This will setup all the values of the node except for its top and center.
nodeID(StreamTokenizer) - Method in class weka.gui.graphvisualizer.DotParser
 
nodeLevels - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
Array containing the indices of nodes in each level.
NodePlace - Interface in weka.gui.treevisualizer
This is an interface for classes that wish to take a node structure and arrange them
nodeSplitterTipText() - Method in class weka.core.neighboursearch.KDTree
Returns the tip text for this property.
nodeStmt(StreamTokenizer, int) - Method in class weka.gui.graphvisualizer.DotParser
 
nodeToPrune(Vector) - Method in class weka.classifiers.trees.SimpleCart
Find the node with minimal alpha value.
nodeToString() - Method in class weka.classifiers.trees.m5.RuleNode
Returns a description of this node (debugging purposes)
nodeType - Variable in class weka.gui.graphvisualizer.GraphNode
Type of node.
NOISE - Static variable in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
 
noisePercentTipText() - Method in class weka.datagenerators.classifiers.classification.LED24
Returns the tip text for this property
noiseRateTipText() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Returns the tip text for this property
noiseRateTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Returns the tip text for this property
noiseRateTipText() - Method in class weka.datagenerators.clusterers.SubspaceCluster
Returns the tip text for this property
noiseThresholdTipText() - Method in class weka.associations.Tertius
Returns the tip text for this property.
noiseTipText() - Method in class weka.classifiers.functions.GaussianProcesses
Returns the tip text for this property
noiseVarianceTipText() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Returns the tip text for this property
NOMINAL - Static variable in class weka.core.Attribute
Constant set for nominal attributes.
NominalAntd(Attribute) - Constructor for class weka.classifiers.rules.JRip.NominalAntd
Constructor
nominalAttributesTipText() - Method in class weka.core.converters.CSVLoader
Returns the tip text for this property.
nominalColsTipText() - Method in class weka.datagenerators.ClusterGenerator
Returns the tip text for this property
nominalCounts - Variable in class weka.core.AttributeStats
Counts of each nominal value
nominalDistribution(double[][], double[][][], Attribute, int[], double[], double[][], double[], Instances, boolean, boolean) - Method in class weka.classifiers.trees.BFTree
Compute distributions, proportions and total weights of two successor nodes for a given nominal attribute.
nominalDistribution(double[][], double[][][], Attribute, int[], double[], double[][], double[], Instances, boolean) - Method in class weka.classifiers.trees.SimpleCart
Compute distributions, proportions and total weights of two successor nodes for a given nominal attribute.
nominalIndicesTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Returns the tip text for this property
nominalLabelsTipText() - Method in class weka.filters.unsupervised.attribute.Add
Returns the tip text for this property.
NominalPrediction - Class in weka.classifiers.evaluation
Encapsulates an evaluatable nominal prediction: the predicted probability distribution plus the actual class value.
NominalPrediction(double, double[]) - Constructor for class weka.classifiers.evaluation.NominalPrediction
Creates the NominalPrediction object with a default weight of 1.0.
NominalPrediction(double, double[], double) - Constructor for class weka.classifiers.evaluation.NominalPrediction
Creates the NominalPrediction object.
NominalToBinary - Class in weka.filters.supervised.attribute
Converts all nominal attributes into binary numeric attributes.
NominalToBinary() - Constructor for class weka.filters.supervised.attribute.NominalToBinary
 
NominalToBinary - Class in weka.filters.unsupervised.attribute
Converts all nominal attributes into binary numeric attributes.
NominalToBinary() - Constructor for class weka.filters.unsupervised.attribute.NominalToBinary
Constructor - initialises the filter
nominalToBinaryFilterTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
NominalToString - Class in weka.filters.unsupervised.attribute
Converts a nominal attribute (i.e.
NominalToString() - Constructor for class weka.filters.unsupervised.attribute.NominalToString
 
NON_NUMERIC - Static variable in class weka.filters.unsupervised.attribute.InterquartileRange
indicator for non-numeric attributes
NONE - Static variable in interface weka.core.converters.Loader
The retrieval modes
NONE - Static variable in interface weka.core.converters.Saver
The retrieval modes
NONE - Static variable in class weka.gui.beans.KnowledgeFlowApp
 
NONE - Static variable in class weka.gui.visualize.VisualizePanelEvent
No longer used
NonSparseToSparse - Class in weka.filters.unsupervised.instance
An instance filter that converts all incoming instances into sparse format.
NonSparseToSparse() - Constructor for class weka.filters.unsupervised.instance.NonSparseToSparse
 
noOfKthNearest() - Method in class weka.core.neighboursearch.CoverTree.MyHeap
returns the number of k nearest.
noOfKthNearest() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.MyHeap
returns the number of k nearest.
noPruningTipText() - Method in class weka.classifiers.trees.REPTree
Returns the tip text for this property
noReplacementTipText() - Method in class weka.filters.supervised.instance.Resample
Returns the tip text for this property.
noReplacementTipText() - Method in class weka.filters.unsupervised.instance.Resample
Returns the tip text for this property
norm(double, int) - Method in class weka.clusterers.FarthestFirst
Normalizes a given value of a numeric attribute.
norm() - Method in class weka.core.AlgVector
Returns the norm of the vector
norm(double, int) - Method in class weka.core.NormalizableDistance
Normalizes a given value of a numeric attribute.
norm1() - Method in class weka.core.matrix.DoubleVector
Returns the L1-norm of the vector
norm1() - Method in class weka.core.matrix.Matrix
One norm
norm2() - Method in class weka.core.matrix.DoubleVector
Returns the L2-norm of the vector
norm2() - Method in class weka.core.matrix.Matrix
Two norm
norm2() - Method in class weka.core.matrix.SingularValueDecomposition
Two norm
NORM_BASED - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
Methods for selecting the hyperparameter value
NORM_CONST - Static variable in class weka.classifiers.bayes.NaiveBayesSimple
Constant for normal distribution.
NORM_EXPECTED_COST_NAME - Static variable in class weka.classifiers.evaluation.CostCurve
attribute name: Normalized Expected Cost
NORMAL - Static variable in interface weka.gui.graphvisualizer.GraphConstants
NORMAL node - node actually contained in graphs description
normalDens(double, double, double) - Method in class weka.classifiers.bayes.NaiveBayesSimple
Density function of normal distribution.
normalDistribution - Static variable in class weka.core.matrix.Maths
Distribution type: noraml
NormalEstimator - Class in weka.estimators
Simple probability estimator that places a single normal distribution over the observed values.
NormalEstimator(double) - Constructor for class weka.estimators.NormalEstimator
Constructor that takes a precision argument.
normalInverse(double) - Static method in class weka.core.Statistics
Returns the value, x, for which the area under the Normal (Gaussian) probability density function (integrated from minus infinity to x) is equal to the argument y (assumes mean is zero, variance is one).
NormalizableDistance - Class in weka.core
Represents the abstract ancestor for normalizable distance functions, like Euclidean or Manhattan distance.
NormalizableDistance() - Constructor for class weka.core.NormalizableDistance
Invalidates the distance function, Instances must be still set.
NormalizableDistance(Instances) - Constructor for class weka.core.NormalizableDistance
Initializes the distance function and automatically initializes the ranges.
normalize() - Method in class weka.classifiers.CostMatrix
Normalizes the matrix so that the diagonal contains zeros.
normalize() - Method in class weka.classifiers.functions.pace.DiscreteFunction
Normalizes the function values with L1-norm.
normalize(double[]) - Static method in class weka.core.Utils
Normalizes the doubles in the array by their sum.
normalize(double[], double) - Static method in class weka.core.Utils
Normalizes the doubles in the array using the given value.
Normalize - Class in weka.filters.unsupervised.attribute
Normalizes all numeric values in the given dataset (apart from the class attribute, if set).
Normalize() - Constructor for class weka.filters.unsupervised.attribute.Normalize
 
Normalize - Class in weka.filters.unsupervised.instance
An instance filter that normalize instances considering only numeric attributes and ignoring class index.
Normalize() - Constructor for class weka.filters.unsupervised.instance.Normalize
 
normalizeAttributesTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
NormalizeData - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Choose whether to normalize data or not
normalizeDataSet(Instances) - Method in class weka.classifiers.bayes.BayesNet
ensure that all variables are nominal and that there are no missing values
normalizeDataTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns the tip text for this property
normalizeDimWidthsTipText() - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
Returns the tip text for this property.
normalizedKernel(char[], char[]) - Method in class weka.classifiers.functions.supportVector.StringKernel
evaluates the normalized kernel between s and t.
normalizeDocLengthTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Returns the tip text for this property.
NormalizedPolyKernel - Class in weka.classifiers.functions.supportVector
The normalized polynomial kernel.
K(x,y) = <x,y>/sqrt(<x,x><y,y>) where <x,y> = PolyKernel(x,y)

Valid options are:

NormalizedPolyKernel() - Constructor for class weka.classifiers.functions.supportVector.NormalizedPolyKernel
default constructor - does nothing
NormalizedPolyKernel(Instances, int, double, boolean) - Constructor for class weka.classifiers.functions.supportVector.NormalizedPolyKernel
Creates a new NormalizedPolyKernel instance.
normalizeInstance(Instance) - Method in class weka.classifiers.bayes.BayesNet
ensure that all variables are nominal and that there are no missing values
normalizeNodeWidthTipText() - Method in class weka.core.neighboursearch.KDTree
Tip text for this property.
normalizeNumericClassTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
normalizeTipText() - Method in class weka.attributeSelection.LatentSemanticAnalysis
Returns the tip text for this property
normalizeTipText() - Method in class weka.classifiers.functions.LibLINEAR
Returns the tip text for this property
normalizeTipText() - Method in class weka.classifiers.functions.LibSVM
Returns the tip text for this property
normalizeVector(Matrix) - Method in class weka.filters.supervised.attribute.PLSFilter
normalizes the given vector (inplace)
normalizeWordWeightsTipText() - Method in class weka.classifiers.bayes.ComplementNaiveBayes
Returns the tip text for this property
NormalMixture - Class in weka.classifiers.functions.pace
Class for manipulating normal mixture distributions.
NormalMixture() - Constructor for class weka.classifiers.functions.pace.NormalMixture
Contructs an empty NormalMixture
normalProbability(double) - Static method in class weka.core.Statistics
Returns the area under the Normal (Gaussian) probability density function, integrated from minus infinity to x (assumes mean is zero, variance is one).
normBasedHyperParameter() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
This function computes the norm-based hyperparameters and stores them in the m_Hyperparameters.
NormContinuous - Class in weka.core.pmml
Class encapsulating a NormContinuous Expression.
NormContinuous(Element, FieldMetaInfo.Optype, ArrayList<Attribute>) - Constructor for class weka.core.pmml.NormContinuous
 
NormDiscrete - Class in weka.core.pmml
Class encapsulating a NormDiscrete Expression.
NormDiscrete(Element, FieldMetaInfo.Optype, ArrayList<Attribute>) - Constructor for class weka.core.pmml.NormDiscrete
Constructor.
normF() - Method in class weka.core.matrix.Matrix
Frobenius norm
normInf() - Method in class weka.core.matrix.Matrix
Infinity norm
normTipText() - Method in class weka.filters.unsupervised.instance.Normalize
Returns the tip text for this property
normVector() - Method in class weka.core.AlgVector
Norms this vector to length 1.0
NORTH_CONNECTOR - Static variable in class weka.gui.beans.BeanVisual
 
NoSplit - Class in weka.classifiers.trees.j48
Class implementing a "no-split"-split.
NoSplit(Distribution) - Constructor for class weka.classifiers.trees.j48.NoSplit
Creates "no-split"-split for given distribution.
NoSupportForMissingValuesException - Exception in weka.core
Exception that is raised by an object that is unable to process data with missing values.
NoSupportForMissingValuesException() - Constructor for exception weka.core.NoSupportForMissingValuesException
Creates a new NoSupportForMissingValuesException with no message.
NoSupportForMissingValuesException(String) - Constructor for exception weka.core.NoSupportForMissingValuesException
Creates a new NoSupportForMissingValuesException.
NOT - Static variable in interface weka.core.mathematicalexpression.sym
 
NOT - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
NOT_DRAWABLE - Static variable in interface weka.core.Drawable
 
NOT_RUNNING - Static variable in class weka.gui.experiment.RunPanel
The message displayed when no experiment is running
notCoveredInstances() - Method in class weka.classifiers.trees.m5.Rule
Get the instances not covered by this rule
notifyCapabilitiesFilterListener(Capabilities) - Method in class weka.gui.explorer.Explorer
notifies all the listeners of a change
notifyConnectionListeners(int) - Method in class weka.gui.sql.ConnectionPanel
notifies the connection listeners of the event.
notifyConnectionListeners(int, Exception) - Method in class weka.gui.sql.ConnectionPanel
notifies the connection listeners of the event.
notifyDataFormatListeners() - Method in class weka.gui.beans.ClassAssigner
 
notifyDataFormatListeners() - Method in class weka.gui.beans.ClassValuePicker
 
notifyDataListeners(DataSetEvent) - Method in class weka.gui.beans.ClassAssigner
 
notifyDataListeners(DataSetEvent) - Method in class weka.gui.beans.ClassValuePicker
 
notifyDataListeners(DataSetEvent) - Method in class weka.gui.beans.InstanceStreamToBatchMaker
Notify all data source listeners.
notifyDataSetAvailable(DataSetEvent) - Method in class weka.gui.beans.PredictionAppender
Notify all Data source listeners that a data set is available
notifyDataSetLoaded(DataSetEvent) - Method in class weka.gui.beans.Loader
Notify all Data source listeners that a data set has been loaded
notifyHistoryChangedListeners() - Method in class weka.gui.sql.ConnectionPanel
notifies the history listeners of the event.
notifyHistoryChangedListeners() - Method in class weka.gui.sql.QueryPanel
notifies the history listeners of the event.
notifyInstanceAvailable(InstanceEvent) - Method in class weka.gui.beans.PredictionAppender
Notify all instance listeners that an instance is available
notifyInstanceListeners(InstanceEvent) - Method in class weka.gui.beans.ClassAssigner
 
notifyInstanceListeners(InstanceEvent) - Method in class weka.gui.beans.Filter
 
notifyInstanceLoaded(InstanceEvent) - Method in class weka.gui.beans.Loader
Notify all instance listeners that a new instance is available
notifyInstanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceJoiner
 
notifyInstanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceLoader
 
notifyListener() - Method in class weka.gui.arffviewer.ArffPanel
notfies all listener of the change
notifyListener(TableModelEvent) - Method in class weka.gui.arffviewer.ArffSortedTableModel
notfies all listener of the change of the model
notifyListener(TableModelEvent) - Method in class weka.gui.arffviewer.ArffTableModel
notfies all listener of the change of the model
notifyListeners(String, String, String, String) - Method in class weka.gui.sql.ResultPanel
notifies the listeners of the event
notifyQueryExecuteListeners(ResultSet, Exception) - Method in class weka.gui.sql.QueryPanel
notifies the listeners of the event.
notifyStructureAvailable(Instances) - Method in class weka.gui.beans.Loader
Notify all listeners that the structure of a data set is available.
notifyTestListeners(TestSetEvent) - Method in class weka.gui.beans.ClassAssigner
 
notifyTestSetAvailable(TestSetEvent) - Method in class weka.gui.beans.PredictionAppender
Notify all test set listeners that a test set is available
notifyTestSetProduced(TestSetEvent) - Method in class weka.gui.beans.TestSetMaker
Tells all listeners that a test set is available
notifyTestSetProduced(TestSetEvent) - Method in class weka.gui.beans.TrainTestSplitMaker
Notify test set listeners that a test set is available
notifyTrainingListeners(TrainingSetEvent) - Method in class weka.gui.beans.ClassAssigner
 
notifyTrainingSetAvailable(TrainingSetEvent) - Method in class weka.gui.beans.PredictionAppender
Notify all test set listeners that a test set is available
notifyTrainingSetProduced(TrainingSetEvent) - Method in class weka.gui.beans.CrossValidationFoldMaker
Notify all listeners of a TrainingSet event
notifyTrainingSetProduced(TrainingSetEvent) - Method in class weka.gui.beans.TrainingSetMaker
Inform training set listeners that a training set is available
notifyTrainingSetProduced(TrainingSetEvent) - Method in class weka.gui.beans.TrainTestSplitMaker
Notify training set listeners that a training set is available
notUnifyNormTipText() - Method in class weka.clusterers.sIB
Returns the tip text for this property.
nrOfGoodOperationsTipText() - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
 
nrOfLookAheadStepsTipText() - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
 
NullStemmer - Class in weka.core.stemmers
A dummy stemmer that performs no stemming at all.
NullStemmer() - Constructor for class weka.core.stemmers.NullStemmer
 
num2ShortID(int, char[], int) - Method in class weka.classifiers.Evaluation
Method for generating indices for the confusion matrix.
NUM_RAND_COLS - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
 
numAllConditions(Instances) - Static method in class weka.classifiers.rules.RuleStats
Compute the number of all possible conditions that could appear in a rule of a given data.
numAntdsTipText() - Method in class weka.classifiers.rules.ConjunctiveRule
Returns the tip text for this property
numArcsTipText() - Method in class weka.datagenerators.classifiers.classification.BayesNet
Returns the tip text for this property
numArguments() - Method in class weka.core.Option
Returns the option's number of arguments.
numAttemptsOfGeneOptionTipText() - Method in class weka.classifiers.rules.NNge
Returns the tip text for this property
numAttributes() - Method in class weka.core.Instance
Returns the number of attributes.
numAttributes() - Method in class weka.core.Instances
Returns the number of attributes.
numAttributes() - Method in class weka.core.SparseInstance
Returns the number of attributes.
numAttributesTipText() - Method in class weka.datagenerators.classifiers.classification.BayesNet
Returns the tip text for this property
numAttributesTipText() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
Returns the tip text for this property
numAttributesTipText() - Method in class weka.datagenerators.classifiers.classification.RDG1
Returns the tip text for this property
numAttributesTipText() - Method in class weka.datagenerators.clusterers.SubspaceCluster
Returns the tip text for this property
numAttributesTipText() - Method in class weka.datagenerators.ClusterGenerator
Returns the tip text for this property
numAttributesTipText() - Method in class weka.filters.unsupervised.attribute.RandomSubset
Returns the tip text for this property.
numBags() - Method in class weka.classifiers.trees.j48.Distribution
Returns number of bags.
NUMBER - Static variable in interface weka.core.mathematicalexpression.sym
 
NUMBER - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
numberAttributesSelected() - Method in class weka.attributeSelection.AttributeSelection
Return the number of attributes selected from the most recent run of attribute selection
numberLiteralsTipText() - Method in class weka.associations.Tertius
Returns the tip text for this property.
numberOfAttributes(int, double) - Method in class weka.classifiers.meta.RandomSubSpace
calculates the number of attributes
numberOfAttributesTipText() - Method in class weka.filters.unsupervised.attribute.RandomProjection
Returns the tip text for this property
numberOfClusters() - Method in class weka.clusterers.AbstractClusterer
Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.CLOPE
 
numberOfClusters() - Method in interface weka.clusterers.Clusterer
Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.Cobweb
Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.DBScan
Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.EM
Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.FarthestFirst
Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.HierarchicalClusterer
 
numberOfClusters() - Method in class weka.clusterers.MakeDensityBasedClusterer
Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.OPTICS
Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.sIB
Get the number of clusters
numberOfClusters() - Method in class weka.clusterers.SimpleKMeans
Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.SingleClustererEnhancer
Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.XMeans
Returns the number of clusters.
NumberOfClustersRequestable - Interface in weka.clusterers
Interface to a clusterer that can generate a requested number of clusters
numberOfGroupsTipText() - Method in class weka.classifiers.meta.RotationForest
Returns the tip text for this property
numberOfItems() - Method in class weka.associations.FPGrowth.FrequentBinaryItemSet
Get the number of items in this item set.
numberOfLinearModels() - Method in class weka.classifiers.trees.m5.RuleNode
Get the number of linear models in the tree
numBinsTipText() - Method in class weka.classifiers.meta.RegressionByDiscretization
Returns the tip text for this property
numBoostingIterationsTipText() - Method in class weka.classifiers.functions.SimpleLogistic
Returns the tip text for this property
numBoostingIterationsTipText() - Method in class weka.classifiers.trees.FT
Returns the tip text for this property
numBoostingIterationsTipText() - Method in class weka.classifiers.trees.LMT
Returns the tip text for this property
numCacheHits() - Method in class weka.classifiers.functions.supportVector.CachedKernel
Returns the number of cache hits on dot products.
numCacheHits() - Method in class weka.classifiers.functions.supportVector.Kernel
Returns the number of dot product cache hits.
numCacheHits() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
Returns the number of dot product cache hits.
numCacheHits() - Method in class weka.classifiers.functions.supportVector.StringKernel
Returns the number of dot product cache hits.
numCentroidsTipText() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
Returns the tip text for this property
numChildren() - Method in class weka.gui.HierarchyPropertyParser
The number of the children nodes.
numCitersTipText() - Method in class weka.classifiers.mi.CitationKNN
Returns the tip text for this property
numClassAttributeValues() - Method in class weka.classifiers.functions.SMO
 
numClassAttributeValues() - Method in class weka.classifiers.mi.MISMO
Returns the number of values of the class attribute.
numClasses() - Method in class weka.classifiers.trees.j48.Distribution
Returns number of classes.
numClasses() - Method in class weka.core.Instance
Returns the number of class labels.
numClasses() - Method in class weka.core.Instances
Returns the number of class labels.
numClassesTipText() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
Returns the tip text for this property
numClassesTipText() - Method in class weka.datagenerators.classifiers.classification.RDG1
Returns the tip text for this property
numClustersTipText() - Method in class weka.classifiers.functions.RBFNetwork
Returns the tip text for this property
numClustersTipText() - Method in class weka.clusterers.EM
Returns the tip text for this property
numClustersTipText() - Method in class weka.clusterers.FarthestFirst
Returns the tip text for this property
numClustersTipText() - Method in class weka.clusterers.HierarchicalClusterer
 
numClustersTipText() - Method in class weka.clusterers.sIB
Returns the tip text for this property.
numClustersTipText() - Method in class weka.clusterers.SimpleKMeans
Returns the tip text for this property
numClustersTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Returns the tip text for this property
numColumns() - Method in class weka.classifiers.CostMatrix
Same as size
numColumns() - Method in class weka.core.Matrix
Deprecated.
Returns the number of columns in the matrix.
numComponentsTipText() - Method in class weka.filters.supervised.attribute.PLSFilter
Returns the tip text for this property
numCorrect() - Method in class weka.classifiers.trees.j48.Distribution
Returns perClass(maxClass()).
numCorrect(int) - Method in class weka.classifiers.trees.j48.Distribution
Returns perClassPerBag(index,maxClass(index)).
numCyclesTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Returns the tip text for this property
numDistinctValues(int) - Method in class weka.core.Instances
Returns the number of distinct values of a given attribute.
numDistinctValues(Attribute) - Method in class weka.core.Instances
Returns the number of distinct values of a given attribute.
numElements() - Method in class weka.classifiers.functions.supportVector.SMOset
Returns the number of elements in the set.
numElements() - Method in class weka.core.AlgVector
Returns the number of elements in the vector.
NUMERIC - Static variable in class weka.core.Attribute
Constant set for numeric attributes.
NumericAntd(Attribute) - Constructor for class weka.classifiers.rules.JRip.NumericAntd
Constructor
NumericCleaner - Class in weka.filters.unsupervised.attribute
A filter that 'cleanses' the numeric data from values that are too small, too big or very close to a certain value (e.g., 0) and sets these values to a pre-defined default.
NumericCleaner() - Constructor for class weka.filters.unsupervised.attribute.NumericCleaner
 
numericDistribution(double[][], double[][][], Attribute, int[], double[], double[][], double[], Instances, boolean) - Method in class weka.classifiers.trees.BFTree
Compute distributions, proportions and total weights of two successor nodes for a given numeric attribute.
numericDistribution(double[][], double[][][], int, int[], double[], double[][], Instances, double[]) - Method in class weka.classifiers.trees.REPTree.Tree
Computes class distribution for an attribute.
numericDistribution(double[][], double[][][], Attribute, int[], double[], double[][], double[], Instances) - Method in class weka.classifiers.trees.SimpleCart
Compute distributions, proportions and total weights of two successor nodes for a given numeric attribute.
NumericPrediction - Class in weka.classifiers.evaluation
Encapsulates an evaluatable numeric prediction: the predicted class value plus the actual class value.
NumericPrediction(double, double) - Constructor for class weka.classifiers.evaluation.NumericPrediction
Creates the NumericPrediction object with a default weight of 1.0.
NumericPrediction(double, double, double) - Constructor for class weka.classifiers.evaluation.NumericPrediction
Creates the NumericPrediction object.
numericStats - Variable in class weka.core.AttributeStats
Stats on numeric value distributions
numericTipText() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
 
NumericToBinary - Class in weka.filters.unsupervised.attribute
Converts all numeric attributes into binary attributes (apart from the class attribute, if set): if the value of the numeric attribute is exactly zero, the value of the new attribute will be zero.
NumericToBinary() - Constructor for class weka.filters.unsupervised.attribute.NumericToBinary
 
NumericToNominal - Class in weka.filters.unsupervised.attribute
A filter for turning numeric attributes into nominal ones.
NumericToNominal() - Constructor for class weka.filters.unsupervised.attribute.NumericToNominal
 
NumericTransform - Class in weka.filters.unsupervised.attribute
Transforms numeric attributes using a given transformation method.
NumericTransform() - Constructor for class weka.filters.unsupervised.attribute.NumericTransform
Default constructor -- sets the default transform method to java.lang.Math.abs().
numEvals() - Method in class weka.classifiers.functions.supportVector.CachedKernel
Returns the number of time Eval has been called.
numEvals() - Method in class weka.classifiers.functions.supportVector.Kernel
Returns the number of kernel evaluation performed.
numEvals() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
Returns the number of kernel evaluation performed.
numEvals() - Method in class weka.classifiers.functions.supportVector.StringKernel
Returns the number of kernel evaluation performed.
numExamplesActTipText() - Method in class weka.datagenerators.DataGenerator
Returns the tip text for this property
numExamplesTipText() - Method in class weka.datagenerators.ClassificationGenerator
Returns the tip text for this property
numExamplesTipText() - Method in class weka.datagenerators.RegressionGenerator
Returns the tip text for this property
numFalseNegatives(int) - Method in class weka.classifiers.Evaluation
Calculate number of false negatives with respect to a particular class.
numFalsePositives(int) - Method in class weka.classifiers.Evaluation
Calculate number of false positives with respect to a particular class.
numFeaturesTipText() - Method in class weka.classifiers.trees.RandomForest
Returns the tip text for this property
numFoldersMIOptionTipText() - Method in class weka.classifiers.rules.NNge
Returns the tip text for this property
NumFolds - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
NumFolds for CV based Hyperparameters selection
numFoldsPruningTipText() - Method in class weka.classifiers.trees.BFTree
Returns the tip text for this property
numFoldsPruningTipText() - Method in class weka.classifiers.trees.SimpleCart
Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.functions.SMO
Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.meta.CVParameterSelection
Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.meta.Dagging
Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.meta.LogitBoost
Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.meta.MultiScheme
Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.meta.Stacking
Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.mi.MISMO
Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.rules.PART
Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.trees.J48
Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.trees.RandomTree
Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.trees.REPTree
Returns the tip text for this property
numFoldsTipText() - Method in class weka.experiment.CrossValidationResultProducer
Returns the tip text for this property
numFoldsTipText() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Returns the tip text for this property
numFoldsTipText() - Method in class weka.filters.unsupervised.instance.RemoveFolds
Returns the tip text for this property
numFoldsTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Returns the tip text for this property
numIncorrect() - Method in class weka.classifiers.trees.j48.Distribution
Returns total-numCorrect().
numIncorrect(int) - Method in class weka.classifiers.trees.j48.Distribution
Returns perBag(index)-numCorrect(index).
numInnerNodes() - Method in class weka.classifiers.trees.SimpleCart
Method to count the number of inner nodes in the tree.
numInstances() - Method in class weka.classifiers.Evaluation
Gets the number of test instances that had a known class value (actually the sum of the weights of test instances with known class value).
numInstances() - Method in class weka.core.Instances
Returns the number of instances in the dataset.
numInstances() - Method in class weka.core.neighboursearch.balltrees.BallNode
Returns the number of instances in the hyper-spherical region of this node.
numInstances() - Method in class weka.core.neighboursearch.kdtrees.KDTreeNode
Returns the number of Instances in the rectangular region defined by this node.
numIrrelevantTipText() - Method in class weka.datagenerators.classifiers.classification.RDG1
Returns the tip text for this property
numIterationsTipText() - Method in class weka.classifiers.bayes.DMNBtext
Returns the tip text for this property
numIterationsTipText() - Method in class weka.classifiers.functions.VotedPerceptron
Returns the tip text for this property
numIterationsTipText() - Method in class weka.classifiers.functions.Winnow
Returns the tip text for this property
numIterationsTipText() - Method in class weka.classifiers.IteratedSingleClassifierEnhancer
Returns the tip text for this property
numIterationsTipText() - Method in class weka.classifiers.meta.Decorate
Returns the tip text for this property
numIterationsTipText() - Method in class weka.classifiers.meta.MetaCost
Returns the tip text for this property
numLeaves() - Method in class weka.classifiers.trees.BFTree
Compute number of leaf nodes.
numLeaves() - Method in class weka.classifiers.trees.ft.FTtree
Returns the number of leaves (normal count).
numLeaves() - Method in class weka.classifiers.trees.j48.ClassifierTree
Returns number of leaves in tree structure.
numLeaves() - Method in class weka.classifiers.trees.lmt.LMTNode
Returns the number of leaves (normal count).
numLeaves(int) - Method in class weka.classifiers.trees.m5.RuleNode
Sets the leaves' numbers
numLeaves() - Method in class weka.classifiers.trees.SimpleCart
Compute number of leaf nodes.
numLiterals() - Method in class weka.associations.tertius.LiteralSet
Give the number of literals in this set.
numLiterals() - Method in class weka.associations.tertius.Predicate
 
numLiterals() - Method in class weka.associations.tertius.Rule
Give the number of literals in this rule.
numNeighboursTipText() - Method in class weka.attributeSelection.ReliefFAttributeEval
Returns the tip text for this property
numNeighboursTipText() - Method in class weka.classifiers.mi.MINND
Returns the tip text for this property
numNodes() - Method in class weka.classifiers.trees.BFTree
Compute size of the tree.
numNodes() - Method in class weka.classifiers.trees.ft.FTtree
Returns the number of nodes.
numNodes() - Method in class weka.classifiers.trees.j48.ClassifierTree
Returns number of nodes in tree structure.
numNodes() - Method in class weka.classifiers.trees.lmt.LMTNode
Returns the number of nodes.
numNodes() - Method in class weka.classifiers.trees.RandomTree
Computes size of the tree.
numNodes() - Method in class weka.classifiers.trees.REPTree
Computes size of the tree.
numNodes() - Method in class weka.classifiers.trees.REPTree.Tree
Computes size of the tree.
numNodes() - Method in class weka.classifiers.trees.SimpleCart
Compute size of the tree.
numNumericTipText() - Method in class weka.datagenerators.classifiers.classification.RDG1
Returns the tip text for this property
numOfAllNodes(PredictionNode) - Method in class weka.classifiers.trees.ADTree
Returns the total number of nodes in a tree.
numOfAllNodes(LADTree.PredictionNode) - Method in class weka.classifiers.trees.LADTree
Returns the total number of nodes in a tree.
numOfBoostingIterationsTipText() - Method in class weka.classifiers.trees.ADTree
 
numOfBoostingIterationsTipText() - Method in class weka.classifiers.trees.LADTree
 
numOfLeafNodes(LADTree.PredictionNode) - Method in class weka.classifiers.trees.LADTree
Returns the number of leaf nodes in a tree.
numOfPredictionLeafNodes(PredictionNode) - Method in class weka.classifiers.trees.ADTree
Returns the number of leaf nodes in a tree - prediction nodes without children.
numOfPredictionNodes(PredictionNode) - Method in class weka.classifiers.trees.ADTree
Returns the number of prediction nodes in a tree.
numOfPredictionNodes(LADTree.PredictionNode) - Method in class weka.classifiers.trees.LADTree
Returns the number of prediction nodes in a tree.
numParameters() - Method in class weka.classifiers.functions.LinearRegression
Get the number of coefficients used in the model
numParameters() - Method in class weka.classifiers.functions.PaceRegression
Get the number of coefficients used in the model
numParameters() - Method in class weka.classifiers.trees.m5.PreConstructedLinearModel
Return the number of parameters (coefficients) in the linear model
numPendingOutput() - Method in class weka.filters.Filter
Returns the number of instances pending output
numPendingOutput() - Method in class weka.filters.unsupervised.attribute.RemoveType
Returns the number of instances pending output
numReferencesTipText() - Method in class weka.classifiers.mi.CitationKNN
Returns the tip text for this property
numRestartsTipText() - Method in class weka.clusterers.sIB
Returns the tip text for this property.
numRows() - Method in class weka.classifiers.CostMatrix
Same as size
numRows() - Method in class weka.core.Matrix
Deprecated.
Returns the number of rows in the matrix.
numRules() - Method in class weka.classifiers.rules.part.MakeDecList
Outputs the number of rules in the classifier.
numRulesTipText() - Method in class weka.associations.Apriori
Returns the tip text for this property
numRulesTipText() - Method in class weka.associations.PredictiveApriori
Returns the tip text for this property
numRulesToFindTipText() - Method in class weka.associations.FPGrowth
Tip text for this property suitable for displaying in the GUI.
numRunsTipText() - Method in class weka.classifiers.meta.LogitBoost
Returns the tip text for this property
numSpecifiers() - Method in class weka.experiment.PairedTTester.DatasetSpecifiers
Gets the number of specifiers.
numSubCmtysTipText() - Method in class weka.classifiers.meta.MultiBoostAB
Returns the tip text for this property
numSubsets() - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Returns the number of created subsets for the split.
numSubsetSizeCVFoldsTipText() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Returns the tip text for this property
numTestingNoisesTipText() - Method in class weka.classifiers.mi.MINND
Returns the tip text for this property
numToSelectTipText() - Method in class weka.attributeSelection.GreedyStepwise
Returns the tip text for this property
numToSelectTipText() - Method in class weka.attributeSelection.RaceSearch
Returns the tip text for this property
numToSelectTipText() - Method in class weka.attributeSelection.Ranker
Returns the tip text for this property
numTrainingNoisesTipText() - Method in class weka.classifiers.mi.MINND
Returns the tip text for this property
numTreesTipText() - Method in class weka.classifiers.trees.RandomForest
Returns the tip text for this property
numTrueNegatives(int) - Method in class weka.classifiers.Evaluation
Calculate the number of true negatives with respect to a particular class.
numTruePositives(int) - Method in class weka.classifiers.Evaluation
Calculate the number of true positives with respect to a particular class.
numUsedAttributesTipText() - Method in class weka.attributeSelection.LinearForwardSelection
Returns the tip text for this property
numUsedAttributesTipText() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Returns the tip text for this property
numValues() - Method in class weka.core.Attribute
Returns the number of attribute values.
numValues() - Method in class weka.core.Instance
Returns the number of values present.
numValues() - Method in class weka.core.SparseInstance
Returns the number of values in the sparse vector.
numValuesTipText() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Returns the tip text for this property
numXValFoldsTipText() - Method in class weka.classifiers.meta.ThresholdSelector
 
nuTipText() - Method in class weka.classifiers.functions.LibSVM
Returns the tip text for this property

O

Obfuscate - Class in weka.filters.unsupervised.attribute
A simple instance filter that renames the relation, all attribute names and all nominal (and string) attribute values.
Obfuscate() - Constructor for class weka.filters.unsupervised.attribute.Obfuscate
 
ObjectCellRenderer() - Constructor for class weka.gui.experiment.AlgorithmListPanel.ObjectCellRenderer
 
objectiveFunction(double[]) - Method in class weka.core.Optimization
Subclass should implement this procedure to evaluate objective function to be minimized
observedComparator - Static variable in class weka.associations.tertius.Rule
Comparator used to compare two rules according to their observed number of counter-instances.
obtainVotes(Instance) - Method in class weka.classifiers.functions.SMO
Returns an array of votes for the given instance.
OFF - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
 
OFF - Static variable in class weka.core.Debug
the log level Off - i.e., no logging
oldEnt(Distribution) - Method in class weka.classifiers.trees.j48.EntropyBasedSplitCrit
Computes entropy of distribution before splitting.
omegaTipText() - Method in class weka.classifiers.functions.supportVector.Puk
Returns the tip text for this property
ON - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
Some usefull constants
onDemandDirectoryTipText() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
 
onDemandDirectoryTipText() - Method in class weka.classifiers.meta.CostSensitiveClassifier
 
onDemandDirectoryTipText() - Method in class weka.classifiers.meta.MetaCost
Returns the tip text for this property
onDemandDirectoryTipText() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
Returns the tip text for this property
oneElementsToSequences(FastVector) - Static method in class weka.associations.gsp.Sequence
Converts a set of 1-Elements into a set of 1-Sequences.
OneR - Class in weka.classifiers.rules
Class for building and using a 1R classifier; in other words, uses the minimum-error attribute for prediction, discretizing numeric attributes.
OneR() - Constructor for class weka.classifiers.rules.OneR
 
OneRAttributeEval - Class in weka.attributeSelection
OneRAttributeEval :

Evaluates the worth of an attribute by using the OneR classifier.

Valid options are:

OneRAttributeEval() - Constructor for class weka.attributeSelection.OneRAttributeEval
Constructor
onUnit(Graphics, int, int, int, int) - Method in class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd
Call this function to determine if the point at x,y is on the unit.
onUnit(Graphics, int, int, int, int) - Method in class weka.classifiers.functions.neural.NeuralConnection
Call this function to determine if the point at x,y is on the unit.
openCapabilitiesHelpDialog() - Method in class weka.gui.PropertySheetPanel
opens the help dialog for the capabilities.
openFrame(String) - Method in class weka.gui.ResultHistoryPanel
Opens the named result in a separate frame.
openHelpFrame() - Method in class weka.gui.PropertySheetPanel
opens the help dialog.
openObject() - Method in class weka.gui.GenericObjectEditor.GOEPanel
Opens an object from a file selected by the user.
openURL(String) - Static method in class weka.gui.BrowserHelper
opens the URL in a browser.
openURL(Component, String) - Static method in class weka.gui.BrowserHelper
opens the URL in a browser.
openURL(Component, String, boolean) - Static method in class weka.gui.BrowserHelper
opens the URL in a browser.
openVisibleInstances(Instances) - Method in class weka.gui.visualize.ThresholdVisualizePanel
displays the previously saved instances
openVisibleInstances(Instances) - Method in class weka.gui.visualize.VisualizePanel
displays the previously saved instances
openVisibleInstances() - Method in class weka.gui.visualize.VisualizePanel
Loads previously saved instances from a file
OPTICS - Class in weka.clusterers
Mihael Ankerst, Markus M.
OPTICS() - Constructor for class weka.clusterers.OPTICS
 
OPTICS_Visualizer - Class in weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI
Start the OPTICS Visualizer from command-line:
java weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.OPTICS_Visualizer [file.ser]
OPTICS_Visualizer(SERObject, String) - Constructor for class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.OPTICS_Visualizer
 
optimisticComparator - Static variable in class weka.associations.tertius.Rule
Comparator used to compare two rules according to their optimistic estimate.
optimisticThenObservedComparator - Static variable in class weka.associations.tertius.Rule
Comparator used to compare two rules according to their optimistic estimate and then their observed number of counter-instances.
Optimization - Class in weka.core
Implementation of Active-sets method with BFGS update to solve optimization problem with only bounds constraints in multi-dimensions.
Optimization() - Constructor for class weka.core.Optimization
 
optimizationsTipText() - Method in class weka.classifiers.rules.JRip
Returns the tip text for this property
optimize() - Method in class weka.classifiers.functions.supportVector.RegSMO
finds alpha and alpha* parameters that optimize the SVM target function
optimize1() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
use variant 1 of Shevade's et al.s paper
optimize2() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
use variant 2 of Shevade's et al.s paper
OPTIMIZE_0 - Static variable in class weka.classifiers.meta.ThresholdSelector
first class value
OPTIMIZE_1 - Static variable in class weka.classifiers.meta.ThresholdSelector
second class value
OPTIMIZE_LFREQ - Static variable in class weka.classifiers.meta.ThresholdSelector
least frequent class value
OPTIMIZE_MFREQ - Static variable in class weka.classifiers.meta.ThresholdSelector
most frequent class value
OPTIMIZE_POS_NAME - Static variable in class weka.classifiers.meta.ThresholdSelector
class value name, either 'yes' or 'pos(itive)'
Option - Class in weka.core
Class to store information about an option.
Option(String, String, int, String) - Constructor for class weka.core.Option
Creates new option with the given parameters.
OptionHandler - Interface in weka.core
Interface to something that understands options.
OptionHandlerJavadoc - Class in weka.core
Generates Javadoc comments from the OptionHandler's options.
OptionHandlerJavadoc() - Constructor for class weka.core.OptionHandlerJavadoc
default constructor
OPTIONS_ENDTAG - Static variable in class weka.core.OptionHandlerJavadoc
the end comment tag for inserting the generated Javadoc
OPTIONS_STARTTAG - Static variable in class weka.core.OptionHandlerJavadoc
the start comment tag for inserting the generated Javadoc
optionsPanel - Variable in class weka.gui.visualize.MatrixPanel
The panel that contains all the buttons and tools, i.e.
or(Capabilities) - Method in class weka.core.Capabilities
performs an OR conjunction with the capabilities of the given Capabilities object and updates itself
OR - Static variable in interface weka.core.mathematicalexpression.sym
 
OR - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
orderAdded - Variable in class weka.classifiers.trees.adtree.Splitter
The number this node was in the order of nodes added to the tree
orderAdded - Variable in class weka.classifiers.trees.LADTree.Splitter
 
ORDERED - Static variable in class weka.datagenerators.clusterers.BIRCHCluster
Constant set for input order (option O)
ordering() - Method in class weka.core.Attribute
Returns the ordering of the attribute.
ORDERING_MODULO - Static variable in class weka.core.Attribute
Constant set for modulo-ordered attributes.
ORDERING_ORDERED - Static variable in class weka.core.Attribute
Constant set for ordered attributes.
ORDERING_SYMBOLIC - Static variable in class weka.core.Attribute
Constant set for symbolic attributes.
OrdinalClassClassifier - Class in weka.classifiers.meta
Meta classifier that allows standard classification algorithms to be applied to ordinal class problems.

For more information see:

Eibe Frank, Mark Hall: A Simple Approach to Ordinal Classification.
OrdinalClassClassifier() - Constructor for class weka.classifiers.meta.OrdinalClassClassifier
Default constructor.
originalValue(double) - Method in class weka.filters.supervised.attribute.ClassOrder
Return the original internal class value given the randomized class value, i.e.
outlierFactorTipText() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Returns the tip text for this property
OUTPUT - Static variable in class weka.classifiers.functions.neural.NeuralConnection
This unit is an output unit.
output() - Method in class weka.filters.Filter
Output an instance after filtering and remove from the output queue.
output() - Method in class weka.filters.unsupervised.attribute.RemoveType
Output an instance after filtering and remove from the output queue.
outputCenterFileTipText() - Method in class weka.clusterers.XMeans
Returns the tip text for this property.
outputClassificationTipText() - Method in class weka.filters.supervised.attribute.AddClassification
Returns the tip text for this property.
outputDistributionTipText() - Method in class weka.filters.supervised.attribute.AddClassification
Returns the tip text for this property.
outputErrorFlagTipText() - Method in class weka.filters.supervised.attribute.AddClassification
Returns the tip text for this property.
outputFileName() - Method in class weka.experiment.CSVResultListener
Get the value of OutputFileName.
outputFilenameTipText() - Method in class weka.core.converters.TextDirectoryLoader
the tip text for this property
outputFileTipText() - Method in class weka.experiment.CrossValidationResultProducer
Returns the tip text for this property
outputFileTipText() - Method in class weka.experiment.CSVResultListener
Returns the tip text for this property
outputFileTipText() - Method in class weka.experiment.RandomSplitResultProducer
Returns the tip text for this property
outputFormat() - Method in class weka.gui.streams.InstanceJoiner
Gets the format of the output instances.
outputFormat() - Method in class weka.gui.streams.InstanceLoader
 
outputFormat() - Method in interface weka.gui.streams.InstanceProducer
 
OutputFormatDialog - Class in weka.gui.experiment
A dialog for setting various output format parameters.
OutputFormatDialog(Frame) - Constructor for class weka.gui.experiment.OutputFormatDialog
initializes the dialog with the given parent frame.
outputFormatPeek() - Method in class weka.filters.Filter
Returns a reference to the current output format without copying it.
outputItemSetsTipText() - Method in class weka.associations.Apriori
Returns the tip text for this property
OutputLogger - Class in weka.core.logging
A logger that logs all output on stdout and stderr to a file.
OutputLogger() - Constructor for class weka.core.logging.OutputLogger
 
OutputLogger.OutputPrintStream - Class in weka.core.logging
A print stream class to capture all data from stdout and stderr.
outputOffsetMultiplierTipText() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Returns the tip text for this property
outputPeek() - Method in class weka.filters.Filter
Output an instance after filtering but do not remove from the output queue.
outputPeek() - Method in class weka.filters.unsupervised.attribute.RemoveType
Output an instance after filtering but do not remove from the output queue.
outputPeek() - Method in class weka.gui.streams.InstanceJoiner
Output an instance after filtering but do not remove from the output queue.
outputPeek() - Method in class weka.gui.streams.InstanceLoader
 
outputPeek() - Method in interface weka.gui.streams.InstanceProducer
 
outputPerClassInfoRetrievalStatsTipText() - Method in class weka.gui.beans.IncrementalClassifierEvaluator
Return a tip text string for this property
OutputPrintStream(OutputLogger, PrintStream) - Constructor for class weka.core.logging.OutputLogger.OutputPrintStream
Default constructor.
outputs(Vector) - Static method in class weka.gui.beans.BeanConnection
Returns a vector of BeanInstances that can be considered as outputs (or the right-hand side of a sub-flow)
outputsContains(BeanInstance) - Method in class weka.gui.beans.MetaBean
 
outputTipText() - Method in class weka.datagenerators.DataGenerator
Returns the tip text for this property
outputTypeSet(int) - Method in class weka.core.Debug.DBO
Return true if the outputtype is set
outputValue(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd
Call this to get the output value of this unit.
outputValue(NeuralNode) - Method in class weka.classifiers.functions.neural.LinearUnit
This function calculates what the output value should be.
outputValue(boolean) - Method in class weka.classifiers.functions.neural.NeuralConnection
Call this to get the output value of this unit.
outputValue(NeuralNode) - Method in interface weka.classifiers.functions.neural.NeuralMethod
This function calculates what the output value should be.
outputValue(boolean) - Method in class weka.classifiers.functions.neural.NeuralNode
Call this to get the output value of this unit.
outputValue(NeuralNode) - Method in class weka.classifiers.functions.neural.SigmoidUnit
This function calculates what the output value should be.
outputWordCountsTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Returns the tip text for this property.
OutputZipper - Class in weka.experiment
OutputZipper writes output to either gzipped files or to a multi entry zip file.
OutputZipper(File) - Constructor for class weka.experiment.OutputZipper
Constructor.
OVAL - Static variable in class weka.gui.visualize.VisualizePanelEvent
 
overFrequencyThreshold(double) - Method in class weka.associations.tertius.LiteralSet
Test if this LiteralSet has more counter-instances than the threshold.
overFrequencyThreshold(double) - Method in class weka.associations.tertius.Rule
Test if this rule is over the frequency threshold.
overrideClassname(Object) - Method in class weka.core.xml.XMLSerialization
if the class of the given object (or one of its ancestors) is stored in the classname override hashtable, then the override name is returned otherwise the classname of the given object.
overrideClassname(String) - Method in class weka.core.xml.XMLSerialization
if the given classname is stored in the classname override hashtable, then the override name is returned otherwise the given classname.
owner - Variable in class weka.core.xml.XMLSerializationMethodHandler
the object to retrieve the methods from

P

p(String) - Static method in class weka.core.Debug.DBO
prints out text.
p() - Method in class weka.core.neighboursearch.CoverTree.CoverTreeNode
Returns the instance represented by the node.
P0 - Static variable in class weka.core.Statistics
COEFFICIENTS FOR METHOD normalInverse() *
P1 - Static variable in class weka.core.Statistics
 
p1evl(double, double[], int) - Static method in class weka.core.Statistics
Evaluates the given polynomial of degree N at x.
P2 - Static variable in class weka.core.Statistics
 
pace2(DoubleVector) - Method in class weka.classifiers.functions.pace.ChisqMixture
Returns the pace2 estimate of a vector.
pace4(DoubleVector) - Method in class weka.classifiers.functions.pace.ChisqMixture
Returns the pace4 estimate of a vector.
pace6(double) - Method in class weka.classifiers.functions.pace.ChisqMixture
Returns the pace6 estimate of a single value.
pace6(DoubleVector) - Method in class weka.classifiers.functions.pace.ChisqMixture
Returns the pace6 estimate of a vector.
PaceMatrix - Class in weka.classifiers.functions.pace
Class for matrix manipulation used for pace regression.
PaceMatrix(int, int) - Constructor for class weka.classifiers.functions.pace.PaceMatrix
Construct an m-by-n PACE matrix of zeros.
PaceMatrix(int, int, double) - Constructor for class weka.classifiers.functions.pace.PaceMatrix
Construct an m-by-n constant PACE matrix.
PaceMatrix(double[][]) - Constructor for class weka.classifiers.functions.pace.PaceMatrix
Construct a PACE matrix from a 2-D array.
PaceMatrix(double[][], int, int) - Constructor for class weka.classifiers.functions.pace.PaceMatrix
Construct a PACE matrix quickly without checking arguments.
PaceMatrix(double[], int) - Constructor for class weka.classifiers.functions.pace.PaceMatrix
Construct a PaceMatrix from a one-dimensional packed array
PaceMatrix(DoubleVector) - Constructor for class weka.classifiers.functions.pace.PaceMatrix
Construct a PaceMatrix with a single column from a DoubleVector
PaceMatrix(Matrix) - Constructor for class weka.classifiers.functions.pace.PaceMatrix
Construct a PaceMatrix from a Matrix
PaceRegression - Class in weka.classifiers.functions
Class for building pace regression linear models and using them for prediction.
PaceRegression() - Constructor for class weka.classifiers.functions.PaceRegression
 
PACKAGE - Static variable in class weka.core.stemmers.SnowballStemmer
the package name for snowball.
PACKAGE_EXT - Static variable in class weka.core.stemmers.SnowballStemmer
the package name where the stemmers are located.
pad(String, String, int, boolean) - Static method in class weka.core.pmml.PMMLUtils
Utility method to left or right pad strings with arbitrary characters.
pad(Instances) - Method in class weka.filters.unsupervised.attribute.Wavelet
pads the data to conform to the necessary number of attributes
PADDING_ZERO - Static variable in class weka.filters.unsupervised.attribute.Wavelet
the type of padding: Zero padding
paddingTipText() - Method in class weka.filters.unsupervised.attribute.Wavelet
Returns the tip text for this property
padLeft(String, int) - Static method in class weka.core.Utils
Pads a string to a specified length, inserting spaces on the left as required.
padRight(String, int) - Static method in class weka.core.Utils
Pads a string to a specified length, inserting spaces on the right as required.
padString(String, int) - Method in class weka.experiment.ResultMatrix
pads the given string on the right until it reaches the given length, if longer cuts it down.
padString(String, int, boolean) - Method in class weka.experiment.ResultMatrix
pads the given string until it reaches the given length, if longer cuts it down.
paint(Graphics) - Method in class weka.gui.SplashWindow
Paints the image on the window.
paintComponent(Graphics) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel
Draws the OPTICS Plot
paintComponent(Graphics) - Method in class weka.gui.AttributeVisualizationPanel
Paints this component
paintComponent(Graphics) - Method in class weka.gui.beans.BeanVisual
 
paintComponent(Graphics) - Method in class weka.gui.beans.KnowledgeFlowApp.BeanLayout
 
paintComponent(Graphics) - Method in class weka.gui.hierarchyvisualizer.HierarchyVisualizer
Updates the screen contents.
paintComponent(Graphics) - Method in class weka.gui.Main.BackgroundDesktopPane
draws the background image.
paintComponent(Graphics) - Method in class weka.gui.MemoryUsagePanel
draws the background image.
paintComponent(Graphics) - Method in class weka.gui.PropertyPanel
Paints the component, using the property editor's paint method.
paintComponent(Graphics) - Method in class weka.gui.treevisualizer.TreeVisualizer
Updates the screen contents.
paintComponent(Graphics) - Method in class weka.gui.visualize.AttributePanel.AttributeSpacing
paints all the visible instances to the panel , and recalculates their position if need be.
paintComponent(Graphics) - Method in class weka.gui.visualize.ClassPanel
Renders this component
paintComponent(Graphics) - Method in class weka.gui.visualize.Plot2D
Renders this component
paintConnections(Graphics) - Static method in class weka.gui.beans.BeanConnection
Renders the connections and their names on the supplied graphics context
paintLabels(Graphics) - Static method in class weka.gui.beans.BeanInstance
Renders the textual labels for the beans.
paintNominal(Graphics) - Method in class weka.gui.visualize.ClassPanel
Renders the legend for a nominal colouring attribute
paintNumeric(Graphics) - Method in class weka.gui.visualize.ClassPanel
Renders the legend for a numeric colouring attribute
paintValue(Graphics, Rectangle) - Method in class weka.gui.CostMatrixEditor
Paints a graphical representation of the object.
paintValue(Graphics, Rectangle) - Method in class weka.gui.FileEditor
Paints a representation of the current Object.
paintValue(Graphics, Rectangle) - Method in class weka.gui.GenericArrayEditor
Paints a representation of the current classifier.
paintValue(Graphics, Rectangle) - Method in class weka.gui.GenericObjectEditor
Paints a representation of the current Object.
paintValue(Graphics, Rectangle) - Method in class weka.gui.SimpleDateFormatEditor
Paints a graphical representation of the object.
PairedCorrectedTTester - Class in weka.experiment
Behaves the same as PairedTTester, only it uses the corrected resampled t-test statistic.

For more information see:

Claude Nadeau, Yoshua Bengio (2001).

PairedCorrectedTTester() - Constructor for class weka.experiment.PairedCorrectedTTester
 
PairedStats - Class in weka.experiment
A class for storing stats on a paired comparison (t-test and correlation)
PairedStats(double) - Constructor for class weka.experiment.PairedStats
Creates a new PairedStats object with the supplied significance level.
PairedStatsCorrected - Class in weka.experiment
A class for storing stats on a paired comparison.
PairedStatsCorrected(double, double) - Constructor for class weka.experiment.PairedStatsCorrected
Creates a new PairedStatsCorrected object with the supplied significance level and train/test ratio.
PairedTTester - Class in weka.experiment
Calculates T-Test statistics on data stored in a set of instances.
PairedTTester() - Constructor for class weka.experiment.PairedTTester
 
PairedTTester.Dataset - Class in weka.experiment
Utility class to store the instances pertaining to a dataset
PairedTTester.DatasetSpecifiers - Class in weka.experiment
A list of unique "dataset" specifiers that have been observed
PairedTTester.Resultset - Class in weka.experiment
Utility class to store the instances in a resultset
pairwiseCoupling(double[][], double[][]) - Static method in class weka.classifiers.meta.MultiClassClassifier
Implements pairwise coupling.
pairwiseCoupling(double[][], double[][]) - Method in class weka.classifiers.mi.MISMO
Implements pairwise coupling.
ParameterField(Element) - Constructor for class weka.core.pmml.DefineFunction.ParameterField
 
parent - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
parentClass - Variable in class weka.experiment.PropertyNode
The class of the object with this property
parentNode() - Method in class weka.classifiers.trees.m5.RuleNode
Get the parent of this node
ParentSet - Class in weka.classifiers.bayes.net
Helper class for Bayes Network classifiers.
ParentSet() - Constructor for class weka.classifiers.bayes.net.ParentSet
default constructor
ParentSet(int) - Constructor for class weka.classifiers.bayes.net.ParentSet
constructor
ParentSet(ParentSet) - Constructor for class weka.classifiers.bayes.net.ParentSet
copy constructor
parentTipText() - Method in class weka.datagenerators.ClusterDefinition
Returns the tip text for this property
parentValue() - Method in class weka.gui.HierarchyPropertyParser
The value in the parent node.
parse() - Method in class weka.gui.graphvisualizer.BIFParser
This method parses the string or the InputStream that we passed in through the constructor and builds up the m_nodes and m_edges vectors
parse() - Method in class weka.gui.graphvisualizer.DotParser
This method parses the string or the InputStream that we passed in through the constructor and builds up the m_nodes and m_edges vectors
parseAttribute(FastVector) - Method in class weka.core.converters.ArffLoader.ArffReader
Parses the attribute declaration.
parseColor(String, Color) - Method in class weka.gui.MemoryUsagePanel
parses the color and returns the corresponding Color object.
parseDate(String) - Method in class weka.core.Attribute
Parses the given String as Date, according to the current format and returns the corresponding amount of milliseconds.
parseMatlab(String) - Static method in class weka.classifiers.CostMatrix
creates a matrix from the given Matlab string.
parseMatlab(String) - Static method in class weka.core.matrix.Matrix
creates a matrix from the given Matlab string.
parseMatlab(String) - Static method in class weka.core.Matrix
Deprecated.
creates a matrix from the given Matlab string.
parsePath(String) - Static method in class weka.core.PropertyPath.Path
returns a path object based on the given path string
Parser - Class in weka.core.mathematicalexpression
CUP v0.11a beta 20060608 generated parser.
Parser() - Constructor for class weka.core.mathematicalexpression.Parser
Default constructor.
Parser(Scanner) - Constructor for class weka.core.mathematicalexpression.Parser
Constructor which sets the default scanner.
Parser(Scanner, SymbolFactory) - Constructor for class weka.core.mathematicalexpression.Parser
Constructor which sets the default scanner.
Parser - Class in weka.filters.unsupervised.instance.subsetbyexpression
CUP v0.11a beta 20060608 generated parser.
Parser() - Constructor for class weka.filters.unsupervised.instance.subsetbyexpression.Parser
Default constructor.
Parser(Scanner) - Constructor for class weka.filters.unsupervised.instance.subsetbyexpression.Parser
Constructor which sets the default scanner.
Parser(Scanner, SymbolFactory) - Constructor for class weka.filters.unsupervised.instance.subsetbyexpression.Parser
Constructor which sets the default scanner.
PART - Class in weka.classifiers.rules
Class for generating a PART decision list.
PART() - Constructor for class weka.classifiers.rules.PART
 
PART_PROPERTY - Static variable in class weka.associations.tertius.IndividualLiteral
 
partFileTipText() - Method in class weka.associations.Tertius
Returns the tip text for this property.
partition(Instances, int) - Static method in class weka.classifiers.rules.RuleStats
Patition the data into 2, first of which has (numFolds-1)/numFolds of the data and the second has 1/numFolds of the data
partition(int, int, int) - Method in class weka.core.Instances
Partitions the instances around a pivot.
partition(double[], int[], int, int, int) - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
Partitions the instances around a pivot.
partition(int, int[], int, int) - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
Partitions the instances around a pivot.
partition(Instances, int[], int, int, int) - Static method in class weka.core.neighboursearch.kdtrees.KMeansInpiredMethod
Partitions the instances around a pivot.
partition(int, int[], int, int) - Method in class weka.core.neighboursearch.kdtrees.MedianOfWidestDimension
Partitions the instances around a pivot.
partition(double[], double[], int, int) - Static method in class weka.core.neighboursearch.NearestNeighbourSearch
Partitions the instances around a pivot.
PartitionedMultiFilter - Class in weka.filters.unsupervised.attribute
A filter that applies filters on subsets of attributes and assembles the output into a new dataset.
PartitionedMultiFilter() - Constructor for class weka.filters.unsupervised.attribute.PartitionedMultiFilter
 
partitionOptions(String[]) - Static method in class weka.classifiers.bayes.BayesNet
Returns the secondary set of options (if any) contained in the supplied options array.
partitionOptions(String[]) - Static method in class weka.core.Utils
Returns the secondary set of options (if any) contained in the supplied options array.
passesTest(Instance) - Method in class weka.datagenerators.Test
Determines whether an instance passes the test.
passwordTipText() - Method in class weka.core.converters.DatabaseLoader
the tip text for this property
passwordTipText() - Method in class weka.core.converters.DatabaseSaver
Returns the tip text for this property.
passwordTipText() - Method in class weka.experiment.DatabaseUtils
Returns the tip text for this property.
paste(String) - Method in class weka.classifiers.bayes.net.EditableBayesNet
Apply paste operation with XMLBIF fragment.
Path() - Constructor for class weka.core.PropertyPath.Path
default constructor, only used internally
Path(String) - Constructor for class weka.core.PropertyPath.Path
uses the given dot-path
Path(Vector) - Constructor for class weka.core.PropertyPath.Path
uses the vector with PathElement objects to initialize with
Path(String[]) - Constructor for class weka.core.PropertyPath.Path
uses the given array as elements for the path
PathElement(String) - Constructor for class weka.core.PropertyPath.PathElement
initializes the path element with the given property
pattern(int, int) - Static method in class weka.core.matrix.FloatingPointFormat
 
patternTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Returns the tip text for this property
pchisq(double) - Static method in class weka.core.matrix.Maths
Returns the cumulative probability of the Chi-squared distribution
pchisq(double, double) - Static method in class weka.core.matrix.Maths
Returns the cumulative probability of the noncentral Chi-squared distribution.
pchisq(double, DoubleVector) - Static method in class weka.core.matrix.Maths
Returns the cumulative probability of a set of noncentral Chi-squared distributions.
pctCorrect() - Method in class weka.classifiers.Evaluation
Gets the percentage of instances correctly classified (that is, for which a correct prediction was made).
pctIncorrect() - Method in class weka.classifiers.Evaluation
Gets the percentage of instances incorrectly classified (that is, for which an incorrect prediction was made).
pctUnclassified() - Method in class weka.classifiers.Evaluation
Gets the percentage of instances not classified (that is, for which no prediction was made by the classifier).
peek() - Method in class weka.core.neighboursearch.CoverTree.MyHeap
peeks at the first element.
peek() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.MyHeap
peeks at the first element.
peek() - Method in class weka.core.Queue
Gets object from the front of the queue.
penalty - Variable in class weka.classifiers.bayes.blr.Prior
 
perBag(int) - Method in class weka.classifiers.trees.j48.Distribution
Returns number of (possibly fractional) instances in given bag.
percentageTipText() - Method in class weka.filters.supervised.instance.SMOTE
Returns the tip text for this property.
percentageTipText() - Method in class weka.filters.unsupervised.instance.RemovePercentage
Returns the tip text for this property
percentAttributesUsed() - Method in class weka.classifiers.trees.lmt.LogisticBase
Returns the fraction of all attributes in the data that are used in the logistic model (in percent).
percentThresholdTipText() - Method in class weka.attributeSelection.SVMAttributeEval
Returns a tip text for this property suitable for display in the GUI
percentTipText() - Method in class weka.filters.unsupervised.attribute.AddNoise
Returns the tip text for this property
percentTipText() - Method in class weka.filters.unsupervised.attribute.RandomProjection
Returns the tip text for this property
percentToEliminatePerIterationTipText() - Method in class weka.attributeSelection.SVMAttributeEval
Returns a tip text for this property suitable for display in the GUI
perClass(int) - Method in class weka.classifiers.trees.j48.Distribution
Returns number of (possibly fractional) instances of given class.
perClassPerBag(int, int) - Method in class weka.classifiers.trees.j48.Distribution
Returns number of (possibly fractional) instances of given class in given bag.
Performance(GridSearch.PointDouble, Evaluation) - Constructor for class weka.classifiers.meta.GridSearch.Performance
initializes the performance container
PerformanceCache() - Constructor for class weka.classifiers.meta.GridSearch.PerformanceCache
 
PerformanceComparator(int) - Constructor for class weka.classifiers.meta.GridSearch.PerformanceComparator
initializes the comparator with the given performance measure
PerformanceStats - Class in weka.core.neighboursearch
The class that measures the performance of a nearest neighbour search (NNS) algorithm.
PerformanceStats() - Constructor for class weka.core.neighboursearch.PerformanceStats
default constructor.
PerformanceTable(GridSearch.Grid, Vector<GridSearch.Performance>, int) - Constructor for class weka.classifiers.meta.GridSearch.PerformanceTable
initializes the table
performBoosting(Instances, Instances, double[], int) - Method in class weka.classifiers.trees.lmt.LogisticBase
Runs LogitBoost on a training set and monitors the error on a test set.
performBoosting(int) - Method in class weka.classifiers.trees.lmt.LogisticBase
Runs LogitBoost with a fixed number of iterations.
performBoosting() - Method in class weka.classifiers.trees.lmt.LogisticBase
Runs LogitBoost using the stopping criterion on the training set.
performBoostingCV() - Method in class weka.classifiers.trees.lmt.LogisticBase
Runs LogitBoost, determining the best number of iterations by cross-validation.
performBoostingInfCriterion() - Method in class weka.classifiers.trees.lmt.LogisticBase
Runs LogitBoost, determining the best number of iterations by an information criterion (currently AIC).
performIteration(int, double[][], double[][], double[][], Instances) - Method in class weka.classifiers.trees.lmt.LogisticBase
Performs a single iteration of LogitBoost, and updates the model accordingly.
performPredictionTipText() - Method in class weka.filters.supervised.attribute.PLSFilter
Returns the tip text for this property
performRankingTipText() - Method in class weka.attributeSelection.LinearForwardSelection
Returns the tip text for this property
performRankingTipText() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Returns the tip text for this property
performRequest(String) - Method in class weka.gui.beans.Associator
Perform a particular request
performRequest(String) - Method in class weka.gui.beans.AttributeSummarizer
Perform a named user request
performRequest(String) - Method in class weka.gui.beans.Classifier
Perform a particular request
performRequest(String) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator
Perform the named request
performRequest(String) - Method in class weka.gui.beans.Clusterer
Perform a particular request
performRequest(String) - Method in class weka.gui.beans.ClustererPerformanceEvaluator
Perform the named request
performRequest(String) - Method in class weka.gui.beans.CostBenefitAnalysis
 
performRequest(String) - Method in class weka.gui.beans.CrossValidationFoldMaker
Perform the named request
performRequest(String) - Method in class weka.gui.beans.DataVisualizer
Describe performRequest method here.
performRequest(String) - Method in class weka.gui.beans.Filter
Perform the named request
performRequest(String) - Method in class weka.gui.beans.GraphViewer
Perform the named request
performRequest(String) - Method in class weka.gui.beans.MetaBean
Perform a particular request
performRequest(String) - Method in class weka.gui.beans.ModelPerformanceChart
Describe performRequest method here.
performRequest(String) - Method in class weka.gui.beans.ScatterPlotMatrix
Perform a named user request
performRequest(String) - Method in class weka.gui.beans.StripChart
Describe performRequest method here.
performRequest(String) - Method in class weka.gui.beans.TextViewer
Perform the named request
performRequest(String) - Method in class weka.gui.beans.TrainTestSplitMaker
Perform the named request
performRequest(String) - Method in interface weka.gui.beans.UserRequestAcceptor
Perform the named request
performTest() - Method in class weka.gui.experiment.ResultsPanel
Carries out a t-test using the current configuration.
periodicPruningTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Returns the tip text for this property.
permute(int[], Random) - Method in class weka.classifiers.meta.RotationForest
permutes the elements of a given array.
perturbationFractionTipText() - Method in class weka.datagenerators.classifiers.classification.Agrawal
Returns the tip text for this property
perturbValue(double, double, double) - Method in class weka.datagenerators.classifiers.classification.Agrawal
perturbs the given value
perturbValue(double, double, double, double) - Method in class weka.datagenerators.classifiers.classification.Agrawal
perturbs the given value
PFD(int, String) - Method in class weka.clusterers.XMeans
Does debug printouts.
PFD_CURR(String) - Method in class weka.clusterers.XMeans
Does debug printouts.
phaseID(int, int[][]) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
See Sugiyama et al.
phaseIID(int, int[][]) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
See Sugiyama et al.
phaseIIU(int, int[][]) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
See Sugiyama et al.
phaseIU(int, int[][]) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
See Sugiyama et al.
PI - Static variable in class weka.core.xml.XMLDocument
the parsing instructions "<?xml version=\"1.0\" encoding=\"utf-8\"?>" (may not show up in Javadoc due to tags!).
PKIDiscretize - Class in weka.filters.unsupervised.attribute
Discretizes numeric attributes using equal frequency binning, where the number of bins is equal to the square root of the number of non-missing values.

For more information, see:

Ying Yang, Geoffrey I.
PKIDiscretize() - Constructor for class weka.filters.unsupervised.attribute.PKIDiscretize
 
place(Node) - Method in interface weka.gui.treevisualizer.NodePlace
The function to call to postion the tree that starts at Node r
place(Node) - Method in class weka.gui.treevisualizer.PlaceNode1
Call this function to have each node in the tree starting at 'r' placed in a visual (not logical, they already are) tree position.
place(Node) - Method in class weka.gui.treevisualizer.PlaceNode2
The Funtion to call to have the nodes arranged.
PlaceNode1 - Class in weka.gui.treevisualizer
This class will place the Nodes of a tree.
PlaceNode1() - Constructor for class weka.gui.treevisualizer.PlaceNode1
 
PlaceNode2 - Class in weka.gui.treevisualizer
This class will place the Nodes of a tree.
PlaceNode2() - Constructor for class weka.gui.treevisualizer.PlaceNode2
 
PLAINTEXT_ENDTAG - Static variable in class weka.core.TechnicalInformationHandlerJavadoc
the end comment tag for inserting the generated BibTex
PLAINTEXT_STARTTAG - Static variable in class weka.core.TechnicalInformationHandlerJavadoc
the start comment tag for inserting the generated BibTex
pln(String) - Static method in class weka.core.Debug.DBO
prints out text + endofline.
Plot2D - Class in weka.gui.visualize
This class plots datasets in two dimensions.
Plot2D() - Constructor for class weka.gui.visualize.Plot2D
Constructor
Plot2DCompanion - Interface in weka.gui.visualize
Interface for classes that need to draw to the Plot2D panel *before* Plot2D renders anything (eg.
PlotData2D - Class in weka.gui.visualize
This class is a container for plottable data.
PlotData2D(Instances) - Constructor for class weka.gui.visualize.PlotData2D
Construct a new PlotData2D using the supplied instances
PlotPanel() - Constructor for class weka.gui.visualize.VisualizePanel.PlotPanel
Constructor
plotPoint(int, int, double[], boolean) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Plot a point in our visualization on-screen.
PlotThread() - Constructor for class weka.gui.boundaryvisualizer.BoundaryPanel.PlotThread
 
plotTrainingData() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Render the training points on-screen.
plotTrainingData() - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
Plots the training data on-screen.
PLSClassifier - Class in weka.classifiers.functions
A wrapper classifier for the PLSFilter, utilizing the PLSFilter's ability to perform predictions.
PLSClassifier() - Constructor for class weka.classifiers.functions.PLSClassifier
 
PLSFilter - Class in weka.filters.supervised.attribute
Runs Partial Least Square Regression over the given instances and computes the resulting beta matrix for prediction.
By default it replaces missing values and centers the data.

For more information see:

Tormod Naes, Tomas Isaksson, Tom Fearn, Tony Davies (2002).
PLSFilter() - Constructor for class weka.filters.supervised.attribute.PLSFilter
default constructor
PLURAL_DUMMY - Static variable in interface weka.gui.graphvisualizer.GraphConstants
PLURAL_DUMMY node - node with more than one outgoing edge i.e.
plus(DiscreteFunction) - Method in class weka.classifiers.functions.pace.DiscreteFunction
Returns the combined of two discrete functions
PLUS - Static variable in interface weka.core.mathematicalexpression.sym
 
plus(double) - Method in class weka.core.matrix.DoubleVector
Adds a value to all the elements
plus(DoubleVector) - Method in class weka.core.matrix.DoubleVector
Adds another vector element by element
plus(Matrix) - Method in class weka.core.matrix.Matrix
C = A + B
PLUS - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
PLUS_SHAPE - Static variable in class weka.gui.visualize.Plot2D
 
plusEquals(DiscreteFunction) - Method in class weka.classifiers.functions.pace.DiscreteFunction
Returns the combined of two discrete functions.
plusEquals(double) - Method in class weka.core.matrix.DoubleVector
Adds a value to all the elements in place
plusEquals(DoubleVector) - Method in class weka.core.matrix.DoubleVector
Adds another vector in place element by element
plusEquals(Matrix) - Method in class weka.core.matrix.Matrix
A = A + B
pmiss - Variable in class weka.classifiers.lazy.kstar.KStarCache.TableEntry
transformation probability to missing value
PMMethod - Static variable in class weka.classifiers.functions.pace.MixtureDistribution
The probability-measure-based method
PMML_FILE_EXTENSION - Static variable in class weka.gui.explorer.ClassifierPanel
The filename extension that should be used for PMML xml files
PMMLClassifier - Class in weka.classifiers.pmml.consumer
Abstract base class for all PMML classifiers.
PMMLFactory - Class in weka.core.pmml
This class is a factory class for reading/writing PMML models
PMMLFactory() - Constructor for class weka.core.pmml.PMMLFactory
 
PMMLFactory.ModelType - Enum in weka.core.pmml
for serialization
PMMLModel - Interface in weka.core.pmml
Interface for all PMML models
PMMLUtils - Class in weka.core.pmml
Utility routines.
PMMLUtils() - Constructor for class weka.core.pmml.PMMLUtils
 
PNGWriter - Class in weka.gui.visualize
This class takes any JComponent and outputs it to a PNG-file.
PNGWriter() - Constructor for class weka.gui.visualize.PNGWriter
initializes the object
PNGWriter(JComponent) - Constructor for class weka.gui.visualize.PNGWriter
initializes the object with the given Component
PNGWriter(JComponent, File) - Constructor for class weka.gui.visualize.PNGWriter
initializes the object with the given Component and filename
pnorm(double) - Static method in class weka.core.matrix.Maths
Returns the cumulative probability of the standard normal.
pnorm(double, double, double) - Static method in class weka.core.matrix.Maths
Returns the cumulative probability of a normal distribution.
pnorm(double, DoubleVector, double) - Static method in class weka.core.matrix.Maths
Returns the cumulative probability of a set of normal distributions with different means.
PointDouble(double, double) - Constructor for class weka.classifiers.meta.GridSearch.PointDouble
the default constructor
PointInt(int, int) - Constructor for class weka.classifiers.meta.GridSearch.PointInt
the default constructor
points - Variable in class weka.classifiers.functions.pace.DiscreteFunction
 
PointsClosestToFurthestChildren - Class in weka.core.neighboursearch.balltrees
Implements the Moore's method to split a node of a ball tree.

For more information please see section 2 of the 1st and 3.2.3 of the 2nd:

Andrew W.
PointsClosestToFurthestChildren() - Constructor for class weka.core.neighboursearch.balltrees.PointsClosestToFurthestChildren
Constructor.
PointsClosestToFurthestChildren(int[], Instances, EuclideanDistance) - Constructor for class weka.core.neighboursearch.balltrees.PointsClosestToFurthestChildren
Constructor.
PoissonEstimator - Class in weka.estimators
Simple probability estimator that places a single Poisson distribution over the observed values.
PoissonEstimator() - Constructor for class weka.estimators.PoissonEstimator
 
POLYGON - Static variable in class weka.gui.visualize.VisualizePanelEvent
 
PolyKernel - Class in weka.classifiers.functions.supportVector
The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1)^p

Valid options are:

PolyKernel() - Constructor for class weka.classifiers.functions.supportVector.PolyKernel
default constructor - does nothing.
PolyKernel(Instances, int, double, boolean) - Constructor for class weka.classifiers.functions.supportVector.PolyKernel
Creates a new PolyKernel instance.
pop() - Method in class weka.core.neighboursearch.covertrees.Stack
Pops (removes) the first (last added) element in the stack.
pop() - Method in class weka.core.Queue
Pops an object from the front of the queue.
populationSizeTipText() - Method in class weka.attributeSelection.GeneticSearch
Returns the tip text for this property
populationSizeTipText() - Method in class weka.attributeSelection.ScatterSearchV1
Returns the tip text for this property
populationSizeTipText() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
 
populationSizeTipText() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
 
POS - Static variable in class weka.associations.tertius.Literal
 
position() - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo
Returns the position of the split in the sorted values.
position() - Method in interface weka.classifiers.trees.m5.SplitEvaluate
Returns the position of the split in the sorted values.
position() - Method in class weka.classifiers.trees.m5.YongSplitInfo
Returns the position of the split in the sorted values.
positive() - Method in class weka.associations.tertius.Literal
 
positiveDiagonal(PaceMatrix, IntVector) - Method in class weka.classifiers.functions.pace.PaceMatrix
Sets all diagonal elements to be positive (or nonnegative) without changing the least squares solution
positiveIndexTipText() - Method in class weka.associations.FPGrowth
Tip text for this property suitable for displaying in the GUI.
positives(int) - Method in class weka.classifiers.trees.j48.GraftSplit
 
positivesForSubsetOfInterest() - Method in class weka.classifiers.trees.j48.GraftSplit
 
posteriorsArray - Variable in class weka.classifiers.lazy.LBR
probability values array
postProcess(int[]) - Method in class weka.attributeSelection.ASEvaluation
Provides a chance for a attribute evaluator to do any special post processing of the selected attribute set.
postProcess(int[]) - Method in class weka.attributeSelection.CfsSubsetEval
Calls locallyPredictive in order to include locally predictive attributes (if requested).
postProcess(int[]) - Method in class weka.attributeSelection.CostSensitiveASEvaluation
Provides a chance for a attribute evaluator to do any special post processing of the selected attribute set.
postProcess(ResultProducer) - Method in class weka.experiment.AveragingResultProducer
When this method is called, it indicates that no more results will be sent that need to be grouped together in any way.
postProcess() - Method in class weka.experiment.AveragingResultProducer
When this method is called, it indicates that no more requests to generate results for the current experiment will be sent.
postProcess() - Method in class weka.experiment.CrossValidationResultProducer
Perform any postprocessing.
postProcess(ResultProducer) - Method in class weka.experiment.CSVResultListener
Perform any postprocessing.
postProcess(ResultProducer) - Method in class weka.experiment.DatabaseResultListener
Perform any postprocessing.
postProcess(ResultProducer) - Method in class weka.experiment.DatabaseResultProducer
When this method is called, it indicates that no more results will be sent that need to be grouped together in any way.
postProcess() - Method in class weka.experiment.DatabaseResultProducer
When this method is called, it indicates that no more requests to generate results for the current experiment will be sent.
postProcess() - Method in class weka.experiment.Experiment
Signals that the experiment is finished running, so that cleanup can be done.
postProcess(ResultProducer) - Method in class weka.experiment.InstancesResultListener
Perform any postprocessing.
postProcess(ResultProducer) - Method in class weka.experiment.LearningRateResultProducer
When this method is called, it indicates that no more results will be sent that need to be grouped together in any way.
postProcess() - Method in class weka.experiment.LearningRateResultProducer
When this method is called, it indicates that no more requests to generate results for the current experiment will be sent.
postProcess() - Method in class weka.experiment.RandomSplitResultProducer
Perform any postprocessing.
postProcess() - Method in class weka.experiment.RemoteExperiment
overides the one in Experiment
postProcess(ResultProducer) - Method in interface weka.experiment.ResultListener
Perform any postprocessing.
postProcess() - Method in interface weka.experiment.ResultProducer
Perform any postprocessing.
postProcessDistances(double[]) - Method in interface weka.core.DistanceFunction
Does post processing of the distances (if necessary) returned by distance(distance(Instance first, Instance second, double cutOffValue).
postProcessDistances(double[]) - Method in class weka.core.EuclideanDistance
Does post processing of the distances (if necessary) returned by distance(distance(Instance first, Instance second, double cutOffValue).
postProcessDistances(double[]) - Method in class weka.core.NormalizableDistance
Does nothing, derived classes may override it though.
PostProcessor() - Constructor for class weka.core.CheckScheme.PostProcessor
 
PostProcessor() - Constructor for class weka.estimators.CheckEstimator.PostProcessor
 
PostscriptGraphics - Class in weka.gui.visualize
The PostscriptGraphics class extends the Graphics2D class to produce an encapsulated postscript file rather than on-screen display.
PostscriptGraphics(int, int, OutputStream) - Constructor for class weka.gui.visualize.PostscriptGraphics
Constructor Creates a new PostscriptGraphics object, given dimensions and output file.
PostscriptWriter - Class in weka.gui.visualize
This class takes any Component and outputs it to a Postscript file.
PostscriptWriter() - Constructor for class weka.gui.visualize.PostscriptWriter
initializes the object
PostscriptWriter(JComponent) - Constructor for class weka.gui.visualize.PostscriptWriter
initializes the object with the given Component
PostscriptWriter(JComponent, File) - Constructor for class weka.gui.visualize.PostscriptWriter
initializes the object with the given Component and filename
potential(int, double, double[], double[], boolean) - Method in class weka.classifiers.rules.RuleStats
Calculate the potential to decrease DL of the ruleset, i.e.
PotentialClassIgnorer - Class in weka.filters.unsupervised.attribute
This filter should be extended by other unsupervised attribute filters to allow processing of the class attribute if that's required.
PotentialClassIgnorer() - Constructor for class weka.filters.unsupervised.attribute.PotentialClassIgnorer
 
POW - Static variable in interface weka.core.mathematicalexpression.sym
 
POW - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
PrCentersFD(int) - Method in class weka.clusterers.XMeans
Print centers for debug.
precision(int) - Method in class weka.classifiers.Evaluation
Calculate the precision with respect to a particular class.
PRECISION - Static variable in class weka.classifiers.meta.ThresholdSelector
precision
PRECISION_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
attribute name: Precision
PrecomputedKernelMatrixKernel - Class in weka.classifiers.functions.supportVector
This kernel is based on a static kernel matrix that is read from a file.
PrecomputedKernelMatrixKernel() - Constructor for class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
 
PreConstructedLinearModel - Class in weka.classifiers.trees.m5
This class encapsulates a linear regression function.
PreConstructedLinearModel(double[], double) - Constructor for class weka.classifiers.trees.m5.PreConstructedLinearModel
Constructor
Predicate - Class in weka.associations.tertius
 
Predicate(String, int, boolean) - Constructor for class weka.associations.tertius.Predicate
 
predicted() - Method in class weka.classifiers.evaluation.NominalPrediction
Gets the predicted class value.
predicted() - Method in class weka.classifiers.evaluation.NumericPrediction
Gets the predicted class value.
predicted() - Method in interface weka.classifiers.evaluation.Prediction
Gets the predicted class value.
predictInterval(Instance, double) - Method in class weka.classifiers.functions.GaussianProcesses
Predicts a confidence interval for the given instance and confidence level.
predictInterval(Instance, double) - Method in interface weka.classifiers.IntervalEstimator
Returns an N*2 array, where N is the number of possible classes, that estimate the boundaries for the confidence interval with a confidence level specified by the second parameter.
Prediction - Interface in weka.classifiers.evaluation
Encapsulates a single evaluatable prediction: the predicted value plus the actual class value.
PredictionAppender - Class in weka.gui.beans
Bean that can can accept batch or incremental classifier events and produce dataset or instance events which contain instances with predictions appended.
PredictionAppender() - Constructor for class weka.gui.beans.PredictionAppender
Creates a new PredictionAppender instance.
PredictionAppenderBeanInfo - Class in weka.gui.beans
Bean info class for PredictionAppender.
PredictionAppenderBeanInfo() - Constructor for class weka.gui.beans.PredictionAppenderBeanInfo
 
PredictionAppenderCustomizer - Class in weka.gui.beans
GUI Customizer for the prediction appender bean
PredictionAppenderCustomizer() - Constructor for class weka.gui.beans.PredictionAppenderCustomizer
 
PredictionNode - Class in weka.classifiers.trees.adtree
Class representing a prediction node in an alternating tree.
PredictionNode(double) - Constructor for class weka.classifiers.trees.adtree.PredictionNode
Creates a new prediction node.
PredictionNode(double[]) - Constructor for class weka.classifiers.trees.LADTree.PredictionNode
 
predictions() - Method in class weka.classifiers.Evaluation
Returns the predictions that have been collected.
predictionText(Classifier, Instance, int, Range, boolean) - Static method in class weka.classifiers.Evaluation
store the prediction made by the classifier as a string
predictionText(Classifier, Instance, int) - Method in class weka.gui.explorer.ClassifierPanel
generates a prediction row for an instance
predictionValueForInstance(Instance, PredictionNode, double) - Method in class weka.classifiers.trees.ADTree
Returns the class prediction value (vote) for an instance.
PredictiveApriori - Class in weka.associations
Class implementing the predictive apriori algorithm to mine association rules.
It searches with an increasing support threshold for the best 'n' rules concerning a support-based corrected confidence value.

For more information see:

Tobias Scheffer: Finding Association Rules That Trade Support Optimally against Confidence.
PredictiveApriori() - Constructor for class weka.associations.PredictiveApriori
Constructor that allows to sets default values for the minimum confidence and the maximum number of rules the minimum confidence.
predictiveError(Instances) - Method in class weka.classifiers.trees.LADTree
 
prefix() - Method in class weka.classifiers.trees.j48.ClassifierTree
Returns tree in prefix order.
prefix() - Method in class weka.classifiers.trees.J48
Returns tree in prefix order.
prefix() - Method in class weka.classifiers.trees.J48graft
Returns tree in prefix order.
prefix() - Method in interface weka.core.Matchable
Returns a string that describes a tree representing the object in prefix order.
PREFIX_CLASSIFIER - Static variable in class weka.classifiers.meta.GridSearch
the prefix to indicate that the option is for the classifier
PREFIX_FILTER - Static variable in class weka.classifiers.meta.GridSearch
the prefix to indicate that the option is for the filter
premise() - Method in class weka.associations.RuleItem
Gets the premise of a rule
prepareData() - Method in class weka.experiment.PairedTTester
Separates the instances into resultsets and by dataset/run.
prePlot(Graphics) - Method in interface weka.gui.visualize.Plot2DCompanion
Something to be drawn before the plot itself
prePlot(Graphics) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
Renders the polygons if necessary
preprocess(Instances, int) - Method in class weka.classifiers.mi.MINND
Pre-process the given exemplar according to the other exemplars in the given exemplars.
preProcess(ResultProducer) - Method in class weka.experiment.AveragingResultProducer
Prepare for the results to be received.
preProcess() - Method in class weka.experiment.AveragingResultProducer
Prepare to generate results.
preProcess() - Method in class weka.experiment.CrossValidationResultProducer
Prepare to generate results.
preProcess(ResultProducer) - Method in class weka.experiment.CSVResultListener
Prepare for the results to be received.
preProcess(ResultProducer) - Method in class weka.experiment.DatabaseResultListener
Prepare for the results to be received.
preProcess(ResultProducer) - Method in class weka.experiment.DatabaseResultProducer
Prepare for the results to be received.
preProcess() - Method in class weka.experiment.DatabaseResultProducer
Prepare to generate results.
preProcess(ResultProducer) - Method in class weka.experiment.InstancesResultListener
Prepare for the results to be received.
preProcess(ResultProducer) - Method in class weka.experiment.LearningRateResultProducer
Prepare for the results to be received.
preProcess() - Method in class weka.experiment.LearningRateResultProducer
Prepare to generate results.
preProcess() - Method in class weka.experiment.RandomSplitResultProducer
Prepare to generate results.
preProcess(ResultProducer) - Method in interface weka.experiment.ResultListener
Prepare for the results to be received.
preProcess() - Method in interface weka.experiment.ResultProducer
Prepare to generate results.
preprocess(Instances) - Method in class weka.filters.SimpleStreamFilter
In case the output format cannot be returned immediately, this method is called before the actual processing of the instances.
preprocessData() - Method in class weka.classifiers.mi.CitationKNN
Calculates the normalization of each attribute.
PREPROCESSING_CENTER - Static variable in class weka.filters.supervised.attribute.PLSFilter
the type of preprocessing: Center
PREPROCESSING_NONE - Static variable in class weka.filters.supervised.attribute.PLSFilter
the type of preprocessing: None
PREPROCESSING_STANDARDIZE - Static variable in class weka.filters.supervised.attribute.PLSFilter
the type of preprocessing: Standardize
preprocessingTipText() - Method in class weka.filters.supervised.attribute.PLSFilter
Returns the tip text for this property
preprocessingTipText() - Method in class weka.filters.unsupervised.attribute.KernelFilter
Returns the tip text for this property
PreprocessPanel - Class in weka.gui.explorer
This panel controls simple preprocessing of instances.
PreprocessPanel() - Constructor for class weka.gui.explorer.PreprocessPanel
Creates the instances panel with no initial instances.
preserveInstancesOrderTipText() - Method in class weka.clusterers.SimpleKMeans
Returns the tip text for this property
previous() - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListInverseIterator
 
PrincipalComponents - Class in weka.attributeSelection
Performs a principal components analysis and transformation of the data.
PrincipalComponents() - Constructor for class weka.attributeSelection.PrincipalComponents
 
PrincipalComponents - Class in weka.filters.unsupervised.attribute
Performs a principal components analysis and transformation of the data.
Dimensionality reduction is accomplished by choosing enough eigenvectors to account for some percentage of the variance in the original data -- default 0.95 (95%).
Based on code of the attribute selection scheme 'PrincipalComponents' by Mark Hall and Gabi Schmidberger.
PrincipalComponents() - Constructor for class weka.filters.unsupervised.attribute.PrincipalComponents
 
print() - Method in class weka.classifiers.bayes.net.ADNode
print is used for debugging only and shows the ADTree in ASCII graphics
print(String) - Method in class weka.classifiers.bayes.net.VaryNode
print is used for debugging only, called from ADNode
print(Object) - Method in class weka.core.Check
prints the given message to stdout, if not silent mode
print(int) - Method in class weka.core.logging.OutputLogger.OutputPrintStream
prints the given int to the streams.
print(boolean) - Method in class weka.core.logging.OutputLogger.OutputPrintStream
prints the given boolean to the streams.
print(String) - Method in class weka.core.logging.OutputLogger.OutputPrintStream
prints the given string to the streams.
print(Object) - Method in class weka.core.logging.OutputLogger.OutputPrintStream
prints the given object to the streams.
print(int, int) - Method in class weka.core.matrix.Matrix
Print the matrix to stdout.
print(PrintWriter, int, int) - Method in class weka.core.matrix.Matrix
Print the matrix to the output stream.
print(NumberFormat, int) - Method in class weka.core.matrix.Matrix
Print the matrix to stdout.
print(PrintWriter, NumberFormat, int) - Method in class weka.core.matrix.Matrix
Print the matrix to the output stream.
print(String) - Static method in class weka.core.neighboursearch.CoverTree
Prints a string to stdout.
print(Object) - Static method in class weka.core.neighboursearch.CoverTree
Prints an object to stdout.
print(int, CoverTree.CoverTreeNode) - Static method in class weka.core.neighboursearch.CoverTree
Prints a cover tree starting from the given node.
print(int) - Method in class weka.core.Tee
prints the given int to the streams.
print(long) - Method in class weka.core.Tee
prints the given long to the streams.
print(float) - Method in class weka.core.Tee
prints the given float to the streams.
print(double) - Method in class weka.core.Tee
prints the given double to the streams.
print(boolean) - Method in class weka.core.Tee
prints the given boolean to the streams.
print(char) - Method in class weka.core.Tee
prints the given char to the streams.
print(char[]) - Method in class weka.core.Tee
prints the given char array to the streams.
print(String) - Method in class weka.core.Tee
prints the given string to the streams.
print(Object) - Method in class weka.core.Tee
prints the given object to the streams.
print() - Method in class weka.core.xml.XMLDocument
prints the current DOM document to standard out.
print(Object) - Method in class weka.estimators.CheckEstimator
prints the given message to stdout, if not silent mode
print(int) - Method in class weka.gui.LogWindow.LogWindowPrintStream
prints the given int
print(boolean) - Method in class weka.gui.LogWindow.LogWindowPrintStream
prints the given boolean
print(String) - Method in class weka.gui.LogWindow.LogWindowPrintStream
prints the given string
print(Object) - Method in class weka.gui.LogWindow.LogWindowPrintStream
prints the given object
print_hash_code() - Method in class weka.attributeSelection.ConsistencySubsetEval.hashKey
Prints the hash code
print_hash_code() - Method in class weka.classifiers.rules.DecisionTableHashKey
Prints the hash code
print_space(int) - Static method in class weka.core.neighboursearch.CoverTree
Prints the specified number of spaces.
PrintableComponent - Class in weka.gui.visualize
This class extends the component which is handed over in the constructor by a print dialog.
PrintableComponent(JComponent) - Constructor for class weka.gui.visualize.PrintableComponent
initializes the panel.
PrintableComponent.JComponentWriterFileFilter - Class in weka.gui.visualize
a specialized filter that also contains the associated filter class.
PrintableHandler - Interface in weka.gui.visualize
This interface is for all JComponent classes that provide the ability to print itself to a file.
PrintablePanel - Class in weka.gui.visualize
This Panel enables the user to print the panel to various file formats.
PrintablePanel() - Constructor for class weka.gui.visualize.PrintablePanel
initializes the panel
printAllModels() - Method in class weka.classifiers.trees.m5.RuleNode
Print all the linear models at the learf (debugging purposes)
printAttributeSummary(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.associations.CheckAssociator
Print out a short summary string for the dataset characteristics
printAttributeSummary(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.attributeSelection.CheckAttributeSelection
Print out a short summary string for the dataset characteristics
printAttributeSummary(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.classifiers.CheckClassifier
Print out a short summary string for the dataset characteristics
printAttributeSummary(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.classifiers.functions.supportVector.CheckKernel
Print out a short summary string for the dataset characteristics
printAttributeSummary(boolean, boolean, boolean, boolean, boolean, boolean) - Method in class weka.clusterers.CheckClusterer
Print out a short summary string for the dataset characteristics
printAttributeSummary(CheckEstimator.AttrTypes, int) - Method in class weka.estimators.CheckEstimator
Print out a short summary string for the dataset characteristics
printAttributeSummary(int, int) - Method in class weka.estimators.CheckEstimator
Print out a short summary string for the dataset characteristics
printClassifications(Classifier, Instances, ConverterUtils.DataSource, int, Range, StringBuffer) - Static method in class weka.classifiers.Evaluation
Prints the predictions for the given dataset into a String variable.
printClassifications(Classifier, Instances, ConverterUtils.DataSource, int, Range, boolean, StringBuffer) - Static method in class weka.classifiers.Evaluation
Prints the predictions for the given dataset into a supplied StringBuffer
printClassificationsHeader(Instances, Range, boolean, StringBuffer) - Static method in class weka.classifiers.Evaluation
Prints the header for the predictions output into a supplied StringBuffer
printElements() - Method in class weka.classifiers.functions.supportVector.SMOset
Prints all the current elements in the set.
printFeatures() - Method in class weka.classifiers.rules.DecisionTable
Returns a string description of the features selected
printGroup(BitSet, int) - Method in class weka.attributeSelection.BestFirst
 
printGroup(BitSet, int) - Static method in class weka.attributeSelection.LFSMethods
Debug-out
printGroups() - Method in class weka.classifiers.meta.RotationForest
prints the groups.
printInsts(int, int) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
For printing indices in some given portion of the master index array.
printLeafModels() - Method in class weka.classifiers.trees.j48.NBTreeClassifierTree
Print the models at the leaves
printLeafModels() - Method in class weka.classifiers.trees.m5.RuleNode
print all leaf models
printList(MiddleOutConstructor.MyIdxList) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
For printing indices in a given point list.
printList() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.NeighborList
Prints out the contents of the neighborlist.
println(Object) - Method in class weka.core.Check
prints the given message (+ LF) to stdout, if not silent mode
println() - Method in class weka.core.Check
prints a LF to stdout, if not silent mode
println(String) - Method in class weka.core.Debug.Random
prints the given message only if m_Debug is TRUE
println(Object) - Method in class weka.core.Javadoc
prints the given object to System.err
println() - Method in class weka.core.logging.OutputLogger.OutputPrintStream
prints a new line to the streams.
println(int) - Method in class weka.core.logging.OutputLogger.OutputPrintStream
prints the given int to the streams.
println(boolean) - Method in class weka.core.logging.OutputLogger.OutputPrintStream
prints the given boolean to the streams.
println(String) - Method in class weka.core.logging.OutputLogger.OutputPrintStream
prints the given string to the streams.
println(Object) - Method in class weka.core.logging.OutputLogger.OutputPrintStream
prints the given object to the streams (for Throwables we print the stack trace).
println(String) - Static method in class weka.core.neighboursearch.CoverTree
Prints a string to stdout followed by newline.
println(Object) - Static method in class weka.core.neighboursearch.CoverTree
Prints an object to stdout followed by newline.
println() - Method in class weka.core.Tee
prints a new line to the streams.
println(int) - Method in class weka.core.Tee
prints the given int to the streams.
println(long) - Method in class weka.core.Tee
prints the given long to the streams.
println(float) - Method in class weka.core.Tee
prints the given float to the streams.
println(double) - Method in class weka.core.Tee
prints the given double to the streams.
println(boolean) - Method in class weka.core.Tee
prints the given boolean to the streams.
println(char) - Method in class weka.core.Tee
prints the given char to the streams.
println(char[]) - Method in class weka.core.Tee
prints the given char array to the streams.
println(String) - Method in class weka.core.Tee
prints the given string to the streams.
println(Object) - Method in class weka.core.Tee
prints the given object to the streams (for Throwables we print the stack trace).
println(Object) - Method in class weka.estimators.CheckEstimator
prints the given message (+ LF) to stdout, if not silent mode
println() - Method in class weka.estimators.CheckEstimator
prints a LF to stdout, if not silent mode
println() - Method in class weka.gui.LogWindow.LogWindowPrintStream
prints a new line
println(int) - Method in class weka.gui.LogWindow.LogWindowPrintStream
prints the given int
println(boolean) - Method in class weka.gui.LogWindow.LogWindowPrintStream
prints the given boolean
println(String) - Method in class weka.gui.LogWindow.LogWindowPrintStream
prints the given string
println(Object) - Method in class weka.gui.LogWindow.LogWindowPrintStream
prints the given object (for Throwables we print the stack trace)
printMatrices(int[][]) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
Prints out the interconnection matrix at each level.
printNewickTipText() - Method in class weka.clusterers.HierarchicalClusterer
 
printNodeLinearModel() - Method in class weka.classifiers.trees.m5.RuleNode
print the linear model at this node
printOptions(OptionHandler) - Static method in class weka.classifiers.bayes.net.BayesNetGenerator
prints all the options to stdout
printOptions(String[]) - Method in class weka.core.CheckOptionHandler
Prints the given options to a string.
printParameterMatrix(StringBuffer) - Method in class weka.classifiers.pmml.consumer.GeneralRegression
Format and print the parameter matrix to the supplied StringBuffer.
printPPMatrix(StringBuffer) - Method in class weka.classifiers.pmml.consumer.GeneralRegression
Format and print the PPMatrix to the supplied StringBuffer.
printPredictionsHeader(StringBuffer, Instances, String) - Method in class weka.gui.explorer.ClassifierPanel
outputs the header for the predictions on the data
printSetOfSequences(FastVector) - Static method in class weka.associations.gsp.Sequence
Prints a set of Sequences as String output.
printStackTrace() - Method in class weka.core.Debug.Random
prints the current stacktrace
printSubset(ScatterSearchV1.Subset) - Method in class weka.attributeSelection.ScatterSearchV1
 
Prior - Class in weka.classifiers.bayes.blr
This is an interface to plug various priors into the Bayesian Logistic Regression Model.
Prior() - Constructor for class weka.classifiers.bayes.blr.Prior
 
PriorClass - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Distribution Prior class
priorClassTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns the tip text for this property
priorEntropy() - Method in class weka.classifiers.Evaluation
Calculate the entropy of the prior distribution
PriorEstimation - Class in weka.associations
Class implementing the prior estimattion of the predictive apriori algorithm for mining association rules.
PriorEstimation(Instances, int, int, boolean) - Constructor for class weka.associations.PriorEstimation
Constructor
priorityLayout1() - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
This method lays out the vertices horizontally, in each level.
PriorityQueue - Class in weka.clusterers.forOPTICSAndDBScan.Utils
PriorityQueue.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 27, 2004
Time: 5:36:35 PM
$ Revision 1.4 $
PriorityQueue() - Constructor for class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueue
Creates a new PriorityQueue backed on a binary heap.
PriorityQueueElement - Class in weka.clusterers.forOPTICSAndDBScan.Utils
PriorityQueueElement.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 31, 2004
Time: 6:43:18 PM
$ Revision 1.4 $
PriorityQueueElement(double, Object) - Constructor for class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueueElement
 
priorVal(double[][]) - Method in class weka.classifiers.trees.RandomTree
Computes value of splitting criterion before split.
priorVal(double[][]) - Method in class weka.classifiers.trees.REPTree.Tree
Computes value of splitting criterion before split.
Prism - Class in weka.classifiers.rules
Class for building and using a PRISM rule set for classification.
Prism() - Constructor for class weka.classifiers.rules.Prism
 
prob(int) - Method in class weka.classifiers.trees.j48.Distribution
Returns relative frequency of class over all bags.
prob(int, int) - Method in class weka.classifiers.trees.j48.Distribution
Returns relative frequency of class for given bag.
PROB_COST_FUNC_NAME - Static variable in class weka.classifiers.evaluation.CostCurve
attribute name: Probability Cost Function
probabilityEstimatesTipText() - Method in class weka.classifiers.functions.LibLINEAR
Returns the tip text for this property
probabilityEstimatesTipText() - Method in class weka.classifiers.functions.LibSVM
Returns the tip text for this property
probabilityMatrix(DoubleVector, PaceMatrix) - Method in class weka.classifiers.functions.pace.ChisqMixture
Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
probabilityMatrix(DoubleVector, PaceMatrix) - Method in class weka.classifiers.functions.pace.MixtureDistribution
Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
probabilityMatrix(DoubleVector, PaceMatrix) - Method in class weka.classifiers.functions.pace.NormalMixture
Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
probRound(double, Random) - Static method in class weka.core.Utils
Rounds a double to the next nearest integer value in a probabilistic fashion (e.g.
probs(double[]) - Method in class weka.classifiers.trees.lmt.LogisticBase
Computes the p-values (probabilities for the classes) from the F-values of the logistic model.
probToLogOdds(double) - Static method in class weka.core.Utils
Returns the log-odds for a given probabilitiy.
process(boolean[][], BayesNet) - Method in class weka.classifiers.bayes.net.MarginCalculator
 
process(Instances) - Method in class weka.core.CheckScheme.PostProcessor
Provides a hook for derived classes to further modify the data.
process(Instances) - Method in class weka.core.CheckScheme
Provides a hook for derived classes to further modify the data.
process(Instances) - Method in class weka.estimators.CheckEstimator.PostProcessor
Provides a hook for derived classes to further modify the data.
process(Instances) - Method in class weka.estimators.CheckEstimator
Provides a hook for derived classes to further modify the data.
process(Instance) - Method in class weka.filters.MultiFilter
processes the given instance (may change the provided instance) and returns the modified version.
process(Instances) - Method in class weka.filters.MultiFilter
Processes the given data (may change the provided dataset) and returns the modified version.
process(Instances) - Method in class weka.filters.SimpleFilter
Processes the given data (may change the provided dataset) and returns the modified version.
process(Instance) - Method in class weka.filters.SimpleStreamFilter
processes the given instance (may change the provided instance) and returns the modified version.
process(Instances) - Method in class weka.filters.SimpleStreamFilter
Processes the given data (may change the provided dataset) and returns the modified version.
process(Instances) - Method in class weka.filters.supervised.attribute.AddClassification
Processes the given data (may change the provided dataset) and returns the modified version.
process(Instances) - Method in class weka.filters.supervised.attribute.PLSFilter
Processes the given data (may change the provided dataset) and returns the modified version.
process(Instance) - Method in class weka.filters.unsupervised.attribute.ClassAssigner
processes the given instance (may change the provided instance) and returns the modified version.
process(Instances) - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Processes the given data (may change the provided dataset) and returns the modified version.
process(Instances) - Method in class weka.filters.unsupervised.attribute.KernelFilter
Processes the given data (may change the provided dataset) and returns the modified version.
process(Instance) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
processes the given instance (may change the provided instance) and returns the modified version.
process(Instances) - Method in class weka.filters.unsupervised.attribute.NumericToNominal
Processes the given data (may change the provided dataset) and returns the modified version.
process(Instances) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
Processes the given data (may change the provided dataset) and returns the modified version.
process(Instance) - Method in class weka.filters.unsupervised.attribute.RandomSubset
processes the given instance (may change the provided instance) and returns the modified version.
process(Instances) - Method in class weka.filters.unsupervised.attribute.RELAGGS
Processes the given data (may change the provided dataset) and returns the modified version.
process(Instances) - Method in class weka.filters.unsupervised.attribute.Wavelet
Processes the given data (may change the provided dataset) and returns the modified version.
process(Instances) - Method in class weka.filters.unsupervised.instance.SubsetByExpression
Processes the given data (may change the provided dataset) and returns the modified version.
processClassifierPrediction(Instance, Classifier, Evaluation, Instances, FastVector, FastVector) - Static method in class weka.gui.explorer.ClassifierPanel
Process a classifier's prediction for an instance and update a set of plotting instances and additional plotting info.
processColour(String, Color) - Static method in class weka.gui.visualize.VisualizeUtils
Parses a string containing either a named colour or r,g,b values.
processFile(String) - Method in class weka.classifiers.bayes.net.BIFReader
processFile reads a BIFXML file and initializes a Bayes Net
processFilename(String) - Method in class weka.gui.Loader
returns the processed filename, i.e.
processGraph() - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
This method makes the "graphMatrix" interconnection matrix for the graph given by m_nodes and m_edges vectors.
processHAAR(Instances) - Method in class weka.filters.unsupervised.attribute.Wavelet
processes the instances using the HAAR algorithm
PROCESSING - Static variable in class weka.experiment.TaskStatusInfo
 
processInstance(Instance) - Method in class weka.filters.unsupervised.instance.ReservoirSample
Decides whether the current instance gets retained in the reservoir.
processKeyString(String) - Static method in class weka.experiment.DatabaseUtils
processes the string in such a way that it can be stored in the database, i.e., it changes backslashes into slashes and doubles single quotes.
processMetaOptions(String[]) - Method in class weka.classifiers.meta.Stacking
Process options setting meta classifier.
processMetaOptions(String[]) - Method in class weka.classifiers.meta.StackingC
Process options setting meta classifier.
processNodesAfterAddInstance(BallNode) - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor
Post process method to correct the start and end indices of BallNodes on the right of the node where the instance was added.
processPLS1(Instances) - Method in class weka.filters.supervised.attribute.PLSFilter
processes the instances using the PLS1 algorithm
processSIMPLS(Instances) - Method in class weka.filters.supervised.attribute.PLSFilter
processes the instances using the SIMPLS algorithm
processString(String) - Method in class weka.classifiers.bayes.net.BIFReader
 
PRODUCT_RULE - Static variable in class weka.classifiers.meta.Vote
combination rule: Product of Probabilities (only nominal classes)
production_table() - Method in class weka.core.mathematicalexpression.Parser
Access to production table.
production_table() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
Access to production table.
projectionFilterTipText() - Method in class weka.classifiers.meta.RotationForest
Returns the tip text for this property
propagateClassIndex(int) - Method in class weka.classifiers.meta.nestedDichotomies.ND.NDTree
Propagates class index to the root.
PROPERTIES - Static variable in class weka.core.Capabilities
the actual properties
PROPERTIES - Static variable in class weka.core.converters.DatabaseLoader
Properties associated with the database connection
PROPERTIES - Static variable in class weka.core.converters.DatabaseSaver
Properties associated with the database connection.
PROPERTIES - Static variable in class weka.core.Copyright
Contains the properties
PROPERTIES - Variable in class weka.experiment.DatabaseUtils
Properties associated with the database connection.
PROPERTIES - Static variable in class weka.gui.experiment.ExperimenterDefaults
Properties associated with the experimenter options
PROPERTIES - Static variable in class weka.gui.explorer.ExplorerDefaults
Properties associated with the explorer options.
PROPERTIES - Static variable in class weka.gui.MemoryUsagePanel
Contains the properties.
PROPERTIES - Static variable in class weka.gui.SimpleCLIPanel
Contains the SimpleCLI properties.
PROPERTIES_FILE - Static variable in class weka.core.Capabilities
the properties file for managing the tests
PROPERTIES_FILE - Static variable in class weka.core.logging.Logger
the properties file.
PROPERTIES_FILE - Static variable in class weka.gui.treevisualizer.TreeVisualizer
the props file.
property - Variable in class weka.experiment.PropertyNode
Other info about the property
PROPERTY_FILE - Static variable in class weka.core.converters.DatabaseLoader
The property file for the database connection
PROPERTY_FILE - Static variable in class weka.core.converters.DatabaseSaver
The property file for the database connection.
PROPERTY_FILE - Static variable in class weka.core.Copyright
the copyright file
PROPERTY_FILE - Static variable in class weka.experiment.DatabaseUtils
The name of the properties file.
PROPERTY_FILE - Static variable in class weka.gui.beans.KnowledgeFlowApp
Location of the property file for the KnowledgeFlowApp
PROPERTY_FILE - Static variable in class weka.gui.experiment.ExperimenterDefaults
The name of the properties file
PROPERTY_FILE - Static variable in class weka.gui.explorer.ExplorerDefaults
The name of the properties file.
PROPERTY_FILE - Static variable in class weka.gui.GenericObjectEditor
The name of the properties file.
PROPERTY_FILE - Static variable in class weka.gui.GenericPropertiesCreator
The name of the properties file for the static GenericObjectEditor (USE_DYNAMIC = false)
PROPERTY_FILE - Static variable in class weka.gui.LookAndFeel
The name of the properties file
PROPERTY_FILE - Static variable in class weka.gui.MemoryUsagePanel
The name of the properties file.
PROPERTY_FILE - Static variable in class weka.gui.SimpleCLIPanel
The default location of the properties file.
PROPERTY_FILE - Static variable in class weka.gui.visualize.VisualizeUtils
The name of the properties file
PROPERTY_SHOW - Static variable in class weka.gui.visualize.PrintableComponent
the property name for showing the tooltip.
PROPERTY_USERASKED - Static variable in class weka.gui.visualize.PrintableComponent
the property name whether the user was already asked.
propertyChange(PropertyChangeEvent) - Method in class weka.gui.beans.KnowledgeFlowApp
Accept property change events
propertyChange(PropertyChangeEvent) - Method in class weka.gui.PropertySheetPanel.CapabilitiesHelpDialog
This method gets called when a bound property is changed.
propertyChange(PropertyChangeEvent) - Method in class weka.gui.PropertySheetPanel
Updates the property sheet panel with a changed property and also passed the event along.
PropertyContainer(PropertyDescriptor, Object) - Constructor for class weka.core.PropertyPath.PropertyContainer
initializes the container
PropertyDialog - Class in weka.gui
Support for PropertyEditors with custom editors: puts the editor into a separate frame.
PropertyDialog(PropertyEditor, int, int) - Constructor for class weka.gui.PropertyDialog
Deprecated.
instead of this constructor, one should use the constructors with an explicit owner (either derived from java.awt.Dialog or from java.awt.Frame) or, if none available, using (Frame) null as owner.
PropertyDialog(Dialog, PropertyEditor) - Constructor for class weka.gui.PropertyDialog
Creates the (screen-centered) editor dialog.
PropertyDialog(Dialog, PropertyEditor, int, int) - Constructor for class weka.gui.PropertyDialog
Creates the editor dialog at the given position.
PropertyDialog(Frame, PropertyEditor) - Constructor for class weka.gui.PropertyDialog
Creates the (screen-centered) editor dialog.
PropertyDialog(Frame, PropertyEditor, int, int) - Constructor for class weka.gui.PropertyDialog
Creates the editor dialog at the given position.
PropertyHandler - Class in weka.core.xml
This class stores information about properties to ignore or properties that are allowed for a certain class.
PropertyHandler() - Constructor for class weka.core.xml.PropertyHandler
initializes the handling
PropertyNode - Class in weka.experiment
Stores information on a property of an object: the class of the object with the property; the property descriptor, and the current value.
PropertyNode(Object) - Constructor for class weka.experiment.PropertyNode
Creates a mostly empty property.
PropertyNode(Object, PropertyDescriptor, Class) - Constructor for class weka.experiment.PropertyNode
Creates a fully specified property node.
PropertyPanel - Class in weka.gui
Support for drawing a property value in a component.
PropertyPanel(PropertyEditor) - Constructor for class weka.gui.PropertyPanel
Create the panel with the supplied property editor.
PropertyPanel(PropertyEditor, boolean) - Constructor for class weka.gui.PropertyPanel
Create the panel with the supplied property editor, optionally ignoring any custom panel the editor can provide.
PropertyPath - Class in weka.core
A helper class for accessing properties in nested objects, e.g., accessing the "getRidge" method of a LinearRegression classifier part of MultipleClassifierCombiner, e.g., Vote.
PropertyPath() - Constructor for class weka.core.PropertyPath
 
PropertyPath.Path - Class in weka.core
Contains a (property) path structure
PropertyPath.PathElement - Class in weka.core
Represents a single element of a property path
PropertyPath.PropertyContainer - Class in weka.core
A helper class that stores Object and PropertyDescriptor together.
PropertySelectorDialog - Class in weka.gui
Allows the user to select any (supported) property of an object, including properties that any of it's property values may have.
PropertySelectorDialog(Frame, Object) - Constructor for class weka.gui.PropertySelectorDialog
Create the property selection dialog.
PropertySheetPanel - Class in weka.gui
Displays a property sheet where (supported) properties of the target object may be edited.
PropertySheetPanel() - Constructor for class weka.gui.PropertySheetPanel
Creates the property sheet panel.
PropertySheetPanel.CapabilitiesHelpDialog - Class in weka.gui
A specialized dialog for displaying the capabilities.
PropositionalToMultiInstance - Class in weka.filters.unsupervised.attribute
Converts the propositional instance dataset into multi-instance dataset (with relational attribute).
PropositionalToMultiInstance() - Constructor for class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
 
ProtectedProperties - Class in weka.core
Simple class that extends the Properties class so that the properties are unable to be modified.
ProtectedProperties(Properties) - Constructor for class weka.core.ProtectedProperties
Creates a set of protected properties from a set of normal ones.
prune(Instances, boolean) - Method in class weka.classifiers.rules.JRip.RipperRule
Prune all the possible final sequences of the rule using the pruning data.
prune() - Method in class weka.classifiers.trees.ft.FTInnerNode
Prunes a tree using C4.5 pruning procedure.
prune() - Method in class weka.classifiers.trees.ft.FTLeavesNode
Prunes a tree using C4.5 pruning procedure.
prune() - Method in class weka.classifiers.trees.ft.FTNode
Method for prunning a tree using C4.5 pruning procedure.
prune() - Method in class weka.classifiers.trees.ft.FTtree
Abstract Method that prunes a tree using C4.5 pruning procedure.
prune() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTree
Prunes a tree using C4.5's pruning procedure.
prune() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG
Prunes a tree using C4.5's pruning procedure.
prune() - Method in class weka.classifiers.trees.j48.PruneableClassifierTree
Prunes a tree.
prune(double) - Method in class weka.classifiers.trees.lmt.LMTNode
Prunes a logistic model tree using the CART pruning scheme, given a cost-complexity parameter alpha.
prune(double[], double[], Instances) - Method in class weka.classifiers.trees.lmt.LMTNode
Method for performing one fold in the cross-validation of the cost-complexity parameter.
prune() - Method in class weka.classifiers.trees.m5.RuleNode
Recursively prune the tree
prune(double) - Method in class weka.classifiers.trees.SimpleCart
Prunes the original tree using the CART pruning scheme, given a cost-complexity parameter alpha.
prune(double[], double[], Instances) - Method in class weka.classifiers.trees.SimpleCart
Method for performing one fold in the cross-validation of minimal cost-complexity pruning.
PruneableClassifierTree - Class in weka.classifiers.trees.j48
Class for handling a tree structure that can be pruned using a pruning set.
PruneableClassifierTree(ModelSelection, boolean, int, boolean, int) - Constructor for class weka.classifiers.trees.j48.PruneableClassifierTree
Constructor for pruneable tree structure.
PruneableDecList - Class in weka.classifiers.rules.part
Class for handling a partial tree structure that can be pruned using a pruning set.
PruneableDecList(ModelSelection, int) - Constructor for class weka.classifiers.rules.part.PruneableDecList
Constructor for pruneable partial tree structure.
pruneCadidates(FastVector, FastVector) - Static method in class weka.associations.gsp.Sequence
Prunes a k-Sequence of a given candidate set if one of its (k-1)-Sequences is infrequent.
pruneEnd() - Method in class weka.classifiers.rules.part.C45PruneableDecList
Prunes the end of the rule.
pruneEnd() - Method in class weka.classifiers.rules.part.PruneableDecList
Prunes the end of the rule.
pruneItemSets(FastVector, Hashtable) - Static method in class weka.associations.ItemSet
Prunes a set of (k)-item sets using the given (k-1)-item sets.
pruneItemSets(FastVector, Hashtable) - Static method in class weka.associations.LabeledItemSet
Prunes a set of (k)-item sets using the given (k-1)-item sets.
pruneLastModel() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
remove the last model from the committee
pruneRules(List<FPGrowth.AssociationRule>, ArrayList<Attribute>, boolean) - Static method in class weka.associations.FPGrowth.AssociationRule
 
pruneRules(FastVector[], double) - Static method in class weka.associations.ItemSet
Prunes a set of rules.
pruneToK(Instances, double[], int) - Method in class weka.classifiers.lazy.IBk
Prunes the list to contain the k nearest neighbors.
pruneToK(int) - Method in class weka.core.neighboursearch.NearestNeighbourSearch.NeighborList
Prunes the list to contain the k nearest neighbors.
PRUNETYPE_LOGLIKELIHOOD - Static variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
log likelihood pruning
PRUNETYPE_NONE - Static variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
no pruning
PRUNING_LAMBDA - Static variable in class weka.classifiers.functions.supportVector.StringKernel
Pruning method: Lambda See [2] for details.
PRUNING_NONE - Static variable in class weka.classifiers.functions.supportVector.StringKernel
Pruning method: No Pruning
PRUNING_POSTPRUNING - Static variable in class weka.classifiers.trees.BFTree
pruning strategy: post-pruning
PRUNING_PREPRUNING - Static variable in class weka.classifiers.trees.BFTree
pruning strategy: pre-pruning
PRUNING_UNPRUNED - Static variable in class weka.classifiers.trees.BFTree
pruning strategy: un-pruned
pruningMethodTipText() - Method in class weka.classifiers.functions.supportVector.StringKernel
Returns the tip text for this property
pruningStrategyTipText() - Method in class weka.classifiers.trees.BFTree
Returns the tip text for this property
pruningTypeTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
 
PSI - Static variable in class weka.core.matrix.Maths
The constant 1 / sqrt(2 pi)
Puk - Class in weka.classifiers.functions.supportVector
The Pearson VII function-based universal kernel.

For more information see:

B.
Puk() - Constructor for class weka.classifiers.functions.supportVector.Puk
default constructor - does nothing.
Puk(Instances, int, double, double) - Constructor for class weka.classifiers.functions.supportVector.Puk
Constructor.
PURE_INPUT - Static variable in class weka.classifiers.functions.neural.NeuralConnection
This unit is a pure input unit.
PURE_OUTPUT - Static variable in class weka.classifiers.functions.neural.NeuralConnection
This unit is a pure output unit.
push(T) - Method in class weka.core.neighboursearch.covertrees.Stack
Pushes the given element to the stack.
push(Stack<T>, T) - Method in class weka.core.neighboursearch.covertrees.Stack
Pushes the given element onto the given stack.
push(Object) - Method in class weka.core.Queue
Appends an object to the back of the queue.
push(Instance) - Method in class weka.filters.Filter
Adds an output instance to the queue.
push(Instance) - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
Adds an output instance to the queue.
put(double) - Method in class weka.core.neighboursearch.CoverTree.MyHeap
adds the distance value to the heap.
put(int, double) - Method in class weka.core.neighboursearch.NearestNeighbourSearch.MyHeap
adds the value to the heap.
put(Object, Object) - Method in class weka.core.ProtectedProperties
Overrides a method to prevent the properties from being modified.
putAll(Map) - Method in class weka.core.ProtectedProperties
Overrides a method to prevent the properties from being modified.
putBySubstitute(double) - Method in class weka.core.neighboursearch.CoverTree.MyHeap
Puts an element by substituting it in place of the top most element.
putBySubstitute(int, double) - Method in class weka.core.neighboursearch.NearestNeighbourSearch.MyHeap
Puts an element by substituting it in place of the top most element.
putKthNearest(double) - Method in class weka.core.neighboursearch.CoverTree.MyHeap
Stores kth nearest elements (if there are more than one).
putKthNearest(int, double) - Method in class weka.core.neighboursearch.NearestNeighbourSearch.MyHeap
Stores kth nearest elements (if there are more than one).
putResultInTable(String, ResultProducer, Object[], Object[]) - Method in class weka.experiment.DatabaseUtils
Executes a database query to insert a result for the supplied key into the database.
pVector - Variable in class weka.classifiers.trees.LADTree.LADInstance
 

Q

Q0 - Static variable in class weka.core.Statistics
 
Q1 - Static variable in class weka.core.Statistics
 
Q2 - Static variable in class weka.core.Statistics
 
qr() - Method in class weka.core.matrix.Matrix
QR Decomposition
QRDecomposition - Class in weka.core.matrix
QR Decomposition.
QRDecomposition(Matrix) - Constructor for class weka.core.matrix.QRDecomposition
QR Decomposition, computed by Householder reflections.
queryExecuted(QueryExecuteEvent) - Method in interface weka.gui.sql.event.QueryExecuteListener
This method gets called when a query has been executed.
queryExecuted(QueryExecuteEvent) - Method in class weka.gui.sql.ResultPanel
This method gets called when a query has been executed.
queryExecuted(QueryExecuteEvent) - Method in class weka.gui.sql.SqlViewer
This method gets called when a query has been executed.
QueryExecuteEvent - Class in weka.gui.sql.event
An event that is generated when a query is executed.
QueryExecuteEvent(Object, DbUtils, String, int, ResultSet, Exception) - Constructor for class weka.gui.sql.event.QueryExecuteEvent
constructs the event
QueryExecuteListener - Interface in weka.gui.sql.event
A listener for executing queries.
QueryPanel - Class in weka.gui.sql
Represents a panel for entering an SQL query.
QueryPanel(JFrame) - Constructor for class weka.gui.sql.QueryPanel
initializes the panel.
queryTipText() - Method in class weka.core.converters.DatabaseLoader
the tip text for this property
queryTipText() - Method in class weka.experiment.InstanceQuery
Returns the tip text for this property
Queue - Class in weka.core
Class representing a FIFO queue.
Queue() - Constructor for class weka.core.Queue
 
Queue.QueueNode - Class in weka.core
Represents one node in the queue.
QueueNode(Object) - Constructor for class weka.core.Queue.QueueNode
Creates a queue node with the given contents
quickSort(int, int, int) - Method in class weka.core.Instances
Implements quicksort according to Manber's "Introduction to Algorithms".
quickSort(Instances, int[], int, int, int) - Static method in class weka.core.neighboursearch.kdtrees.KMeansInpiredMethod
Sorts the instances according to the given attribute/dimension.
quickSort(double[], double[], int, int) - Static method in class weka.core.neighboursearch.NearestNeighbourSearch
performs quicksort.
quote(String) - Static method in class weka.core.Utils
Quotes a string if it contains special characters.

R

R - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
R(i)= BetaVector X x(i) X y(i).
R - Variable in class weka.classifiers.bayes.blr.Prior
 
R_HIGH - Static variable in class weka.clusterers.XMeans
Index in ranges for HIGH.
R_LOW - Static variable in class weka.clusterers.XMeans
Index in ranges for LOW.
R_MAX - Static variable in class weka.core.NormalizableDistance
Index in ranges for MAX.
R_MIN - Static variable in class weka.core.NormalizableDistance
Index in ranges for MIN.
R_WIDTH - Static variable in class weka.clusterers.XMeans
Index in ranges for WIDTH.
R_WIDTH - Static variable in class weka.core.NormalizableDistance
Index in ranges for WIDTH.
RacedIncrementalLogitBoost - Class in weka.classifiers.meta
Classifier for incremental learning of large datasets by way of racing logit-boosted committees.

For more information see:

Eibe Frank, Geoffrey Holmes, Richard Kirkby, Mark Hall: Racing committees for large datasets.
RacedIncrementalLogitBoost() - Constructor for class weka.classifiers.meta.RacedIncrementalLogitBoost
Constructor.
RacedIncrementalLogitBoost.Committee - Class in weka.classifiers.meta
Class representing a committee of LogitBoosted models
RaceSearch - Class in weka.attributeSelection
Races the cross validation error of competing attribute subsets.
RaceSearch() - Constructor for class weka.attributeSelection.RaceSearch
 
raceTypeTipText() - Method in class weka.attributeSelection.RaceSearch
Returns the tip text for this property
radiusesTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster
Returns the tip text for this property
rand - Variable in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
The random number generator for selecting the first anchor point randomly (if selecting randomly).
randEntropy - Variable in class weka.classifiers.lazy.kstar.KStarWrapper
used/reused to hold the random entropy
Random() - Constructor for class weka.core.Debug.Random
Creates a new random number generator.
Random(long) - Constructor for class weka.core.Debug.Random
Creates a new random number generator using a single long seed.
Random(boolean) - Constructor for class weka.core.Debug.Random
Creates a new random number generator.
Random(long, boolean) - Constructor for class weka.core.Debug.Random
Creates a new random number generator using a single long seed.
random(int) - Static method in class weka.core.matrix.DoubleVector
Returns a random vector of uniform distribution
random(int, int) - Static method in class weka.core.matrix.Matrix
Generate matrix with random elements
RANDOM - Static variable in class weka.datagenerators.clusterers.BIRCHCluster
Constant set for choice of pattern.
RANDOM - Static variable in class weka.filters.supervised.attribute.ClassOrder
The class values are sorted in random order
randomCARule(int, int, Random) - Method in class weka.associations.PriorEstimation
Constructs an item set of certain length randomly.
RandomCommittee - Class in weka.classifiers.meta
Class for building an ensemble of randomizable base classifiers.
RandomCommittee() - Constructor for class weka.classifiers.meta.RandomCommittee
Constructor.
RandomForest - Class in weka.classifiers.trees
Class for constructing a forest of random trees.

For more information see:

Leo Breiman (2001).
RandomForest() - Constructor for class weka.classifiers.trees.RandomForest
 
Randomizable - Interface in weka.core
Interface to something that has random behaviour that is able to be seeded with an integer.
RandomizableClassifier - Class in weka.classifiers
Abstract utility class for handling settings common to randomizable classifiers.
RandomizableClassifier() - Constructor for class weka.classifiers.RandomizableClassifier
 
RandomizableClusterer - Class in weka.clusterers
Abstract utility class for handling settings common to randomizable clusterers.
RandomizableClusterer() - Constructor for class weka.clusterers.RandomizableClusterer
 
RandomizableDensityBasedClusterer - Class in weka.clusterers
Abstract utility class for handling settings common to randomizable clusterers.
RandomizableDensityBasedClusterer() - Constructor for class weka.clusterers.RandomizableDensityBasedClusterer
 
RandomizableIteratedSingleClassifierEnhancer - Class in weka.classifiers
Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from a single base learner.
RandomizableIteratedSingleClassifierEnhancer() - Constructor for class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
 
RandomizableMultipleClassifiersCombiner - Class in weka.classifiers
Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from multiple classifiers based on a given random number seed.
RandomizableMultipleClassifiersCombiner() - Constructor for class weka.classifiers.RandomizableMultipleClassifiersCombiner
 
RandomizableSingleClassifierEnhancer - Class in weka.classifiers
Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from a single base learner.
RandomizableSingleClassifierEnhancer() - Constructor for class weka.classifiers.RandomizableSingleClassifierEnhancer
 
RandomizableSingleClustererEnhancer - Class in weka.clusterers
Abstract utility class for handling settings common to randomizable clusterers.
RandomizableSingleClustererEnhancer() - Constructor for class weka.clusterers.RandomizableSingleClustererEnhancer
 
randomize(int[], Random) - Method in class weka.classifiers.BVDecomposeSegCVSub
Accepts an array of ints and randomises the values in the array, using the random seed.
randomize(Random) - Method in class weka.core.Instances
Shuffles the instances in the set so that they are ordered randomly.
Randomize - Class in weka.filters.unsupervised.instance
Randomly shuffles the order of instances passed through it.
Randomize() - Constructor for class weka.filters.unsupervised.instance.Randomize
 
RANDOMIZED - Static variable in class weka.datagenerators.clusterers.BIRCHCluster
Constant set for input order (default)
randomizeDataTipText() - Method in class weka.experiment.RandomSplitResultProducer
Returns the tip text for this property
randomizeTipText() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
Returns the tip text for this property
randomNormal(int, int) - Static method in class weka.classifiers.functions.pace.PaceMatrix
Generate matrix with standard-normally distributed random elements
randomOrderTipText() - Method in class weka.classifiers.bayes.net.search.global.K2
 
randomOrderTipText() - Method in class weka.classifiers.bayes.net.search.local.K2
 
RandomProjection - Class in weka.filters.unsupervised.attribute
Reduces the dimensionality of the data by projecting it onto a lower dimensional subspace using a random matrix with columns of unit length (i.e.
RandomProjection() - Constructor for class weka.filters.unsupervised.attribute.RandomProjection
 
RandomRBF - Class in weka.datagenerators.classifiers.classification
RandomRBF data is generated by first creating a random set of centers for each class.
RandomRBF() - Constructor for class weka.datagenerators.classifiers.classification.RandomRBF
initializes the generator with default values
randomRule(int, int, Random) - Method in class weka.associations.PriorEstimation
Constructs an item set of certain length randomly.
RandomSearch - Class in weka.attributeSelection
RandomSearch :

Performs a Random search in the space of attribute subsets.
RandomSearch() - Constructor for class weka.attributeSelection.RandomSearch
Constructor
randomSeedTipText() - Method in class weka.classifiers.functions.LeastMedSq
Returns the tip text for this property
randomSeedTipText() - Method in class weka.classifiers.functions.SMO
Returns the tip text for this property
randomSeedTipText() - Method in class weka.classifiers.mi.MISMO
Returns the tip text for this property
randomSeedTipText() - Method in class weka.classifiers.trees.ADTree
 
randomSeedTipText() - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
Returns the tip text for this property.
randomSeedTipText() - Method in class weka.filters.supervised.instance.Resample
Returns the tip text for this property.
randomSeedTipText() - Method in class weka.filters.supervised.instance.SMOTE
Returns the tip text for this property.
randomSeedTipText() - Method in class weka.filters.supervised.instance.SpreadSubsample
Returns the tip text for this property
randomSeedTipText() - Method in class weka.filters.unsupervised.attribute.AddNoise
Returns the tip text for this property
randomSeedTipText() - Method in class weka.filters.unsupervised.attribute.RandomProjection
Returns the tip text for this property
randomSeedTipText() - Method in class weka.filters.unsupervised.instance.Randomize
Returns the tip text for this property
randomSeedTipText() - Method in class weka.filters.unsupervised.instance.Resample
Returns the tip text for this property
randomSeedTipText() - Method in class weka.filters.unsupervised.instance.ReservoirSample
Returns the tip text for this property
RandomSplitResultProducer - Class in weka.experiment
Generates a single train/test split and calls the appropriate SplitEvaluator to generate some results.
RandomSplitResultProducer() - Constructor for class weka.experiment.RandomSplitResultProducer
 
RandomSubset - Class in weka.filters.unsupervised.attribute
Chooses a random subset of attributes, either an absolute number or a percentage.
RandomSubset() - Constructor for class weka.filters.unsupervised.attribute.RandomSubset
 
RandomSubSpace - Class in weka.classifiers.meta
This method constructs a decision tree based classifier that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.
RandomSubSpace() - Constructor for class weka.classifiers.meta.RandomSubSpace
Constructor.
randomSubSpace(Integer[], int, int, Random) - Method in class weka.classifiers.meta.RandomSubSpace
generates an index string describing a random subspace, suitable for the Remove filter.
randomTipText() - Method in class weka.datagenerators.DataGenerator
Returns the tip text for this property
RandomTree - Class in weka.classifiers.trees
Class for constructing a tree that considers K randomly chosen attributes at each node.
RandomTree() - Constructor for class weka.classifiers.trees.RandomTree
 
RandomVariates - Class in weka.core
Class implementing some simple random variates generator.
RandomVariates() - Constructor for class weka.core.RandomVariates
Simply the constructor of super class
RandomVariates(long) - Constructor for class weka.core.RandomVariates
Simply the constructor of super class
randomWidthFactorTipText() - Method in class weka.classifiers.meta.MultiClassClassifier
 
Range - Class in weka.core
Class representing a range of cardinal numbers.
Range() - Constructor for class weka.core.Range
Default constructor.
Range(String) - Constructor for class weka.core.Range
Constructor to set initial range.
RANGE_BOUNDS - Static variable in class weka.classifiers.meta.ThresholdSelector
Correct based on min/max observed
RANGE_NONE - Static variable in class weka.classifiers.meta.ThresholdSelector
no range correction
rangeCorrectionTipText() - Method in class weka.classifiers.meta.ThresholdSelector
 
rangeLower(String) - Method in class weka.core.Range
Translates a range into it's lower index.
rangeSingle(String) - Method in class weka.core.Range
Translates a single string selection into it's internal 0-based equivalent
rangesSet() - Method in class weka.core.NormalizableDistance
Check if ranges are set.
rangesTipText() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
Returns the tip text for this property.
rangeTipText() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Returns the tip text for this property
rangeUpper(String) - Method in class weka.core.Range
Translates a range into it's upper index.
rank() - Method in class weka.core.matrix.Matrix
Matrix rank
rank() - Method in class weka.core.matrix.SingularValueDecomposition
Effective numerical matrix rank
rankAttributes(Instances, SubsetEvaluator, boolean) - Method in class weka.attributeSelection.LFSMethods
 
RankEachAttribute() - Method in class weka.attributeSelection.ScatterSearchV1
Rank all the attributes individually acording to their merits
rankedAttributes() - Method in class weka.attributeSelection.AttributeSelection
get the final ranking of the attributes.
rankedAttributes() - Method in class weka.attributeSelection.GreedyStepwise
Produces a ranked list of attributes.
rankedAttributes() - Method in class weka.attributeSelection.RaceSearch
 
rankedAttributes() - Method in interface weka.attributeSelection.RankedOutputSearch
Returns a X by 2 list of attribute indexes and corresponding evaluations from best (highest) to worst.
rankedAttributes() - Method in class weka.attributeSelection.Ranker
Sorts the evaluated attribute list
RankedOutputSearch - Interface in weka.attributeSelection
Interface for search methods capable of producing a ranked list of attributes.
Ranker - Class in weka.attributeSelection
Ranker :

Ranks attributes by their individual evaluations.
Ranker() - Constructor for class weka.attributeSelection.Ranker
Constructor
RankSearch - Class in weka.attributeSelection
RankSearch :

Uses an attribute/subset evaluator to rank all attributes.
RankSearch() - Constructor for class weka.attributeSelection.RankSearch
Constructor
rankTipText() - Method in class weka.attributeSelection.LatentSemanticAnalysis
Returns the tip text for this property
rawOutputTipText() - Method in class weka.experiment.CrossValidationResultProducer
Returns the tip text for this property
rawOutputTipText() - Method in class weka.experiment.RandomSplitResultProducer
Returns the tip text for this property
RBFKernel - Class in weka.classifiers.functions.supportVector
The RBF kernel.
RBFKernel() - Constructor for class weka.classifiers.functions.supportVector.RBFKernel
default constructor - does nothing.
RBFKernel(Instances, int, double) - Constructor for class weka.classifiers.functions.supportVector.RBFKernel
Constructor.
RBFNetwork - Class in weka.classifiers.functions
Class that implements a normalized Gaussian radial basisbasis function network.
It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that.
RBFNetwork() - Constructor for class weka.classifiers.functions.RBFNetwork
 
rbind(PaceMatrix) - Method in class weka.classifiers.functions.pace.PaceMatrix
Returns a new matrix which binds two matrices together with rows.
rchisq(int, double, Random) - Static method in class weka.core.matrix.Maths
Generates a sample of a Chi-square distribution.
RDG1 - Class in weka.datagenerators.classifiers.classification
A data generator that produces data randomly by producing a decision list.
The decision list consists of rules.
Instances are generated randomly one by one.
RDG1() - Constructor for class weka.datagenerators.classifiers.classification.RDG1
initializes the generator with default values
read(String) - Static method in class weka.core.converters.ConverterUtils.DataSource
convencience method for loading a dataset in batch mode.
read(InputStream) - Static method in class weka.core.converters.ConverterUtils.DataSource
convencience method for loading a dataset in batch mode from a stream.
read(Loader) - Static method in class weka.core.converters.ConverterUtils.DataSource
convencience method for loading a dataset in batch mode.
read(BufferedReader) - Static method in class weka.core.matrix.Matrix
Read a matrix from a stream.
read(String) - Static method in class weka.core.SerializationHelper
deserializes the given file and returns the object from it.
read(InputStream) - Static method in class weka.core.SerializationHelper
deserializes from the given stream and returns the object from it.
read(String) - Method in class weka.core.Stopwords
Generates a new Stopwords object from the given file
read(File) - Method in class weka.core.Stopwords
Generates a new Stopwords object from the given file
read(BufferedReader) - Method in class weka.core.Stopwords
Generates a new Stopwords object from the reader.
read(String) - Static method in class weka.core.xml.KOML
reads the XML-serialized object from the given file
read(File) - Static method in class weka.core.xml.KOML
reads the XML-serialized object from the given file
read(InputStream) - Static method in class weka.core.xml.KOML
reads the XML-serialized object from a stream
read(String) - Method in class weka.core.xml.XMLDocument
parses the given XML string (can be XML or a filename) and returns a DOM Document.
read(File) - Method in class weka.core.xml.XMLDocument
parses the given file and returns a DOM document.
read(InputStream) - Method in class weka.core.xml.XMLDocument
parses the given stream and returns a DOM document.
read(Reader) - Method in class weka.core.xml.XMLDocument
parses the given reader and returns a DOM document.
read(String) - Method in class weka.core.xml.XMLSerialization
parses the given XML string (can be XML or a filename) and returns an Object generated from the representation
read(File) - Method in class weka.core.xml.XMLSerialization
parses the given file and returns a DOM document
read(InputStream) - Method in class weka.core.xml.XMLSerialization
parses the given stream and returns a DOM document
read(Reader) - Method in class weka.core.xml.XMLSerialization
parses the given reader and returns a DOM document
read() - Method in class weka.core.xml.XMLSerializationMethodHandler
returns the handler for read methods
read(String) - Static method in class weka.core.xml.XStream
reads the XML-serialized object from the given file
read(File) - Static method in class weka.core.xml.XStream
reads the XML-serialized object from the given file
read(InputStream) - Static method in class weka.core.xml.XStream
reads the XML-serialized object from the given input stream
read(Reader) - Static method in class weka.core.xml.XStream
reads the XML-serialized object from the given Reader
read(String) - Static method in class weka.experiment.Experiment
Loads an experiment from a file.
readAll(String) - Static method in class weka.core.SerializationHelper
deserializes the given file and returns the objects from it.
readAll(InputStream) - Static method in class weka.core.SerializationHelper
deserializes from the given stream and returns the object from it.
readBeanConnection(Element) - Method in class weka.gui.beans.xml.XMLBeans
builds the BeanConnection from the given DOM node.
readBeanInstance(Element) - Method in class weka.gui.beans.xml.XMLBeans
builds the BeanInstance from the given DOM node.
readBeanVisual(Element) - Method in class weka.gui.beans.xml.XMLBeans
builds the BeanVisual from the given DOM node.
readBIF(String) - Method in class weka.gui.graphvisualizer.GraphVisualizer
BIF reader
Reads a graph description in XMLBIF03 from a string
readBIF(InputStream) - Method in class weka.gui.graphvisualizer.GraphVisualizer
BIF reader
Reads a graph description in XMLBIF03 from an InputStrem
readBIFFromFile(String) - Method in class weka.classifiers.bayes.net.GUI
BIF reader
Reads a graph description in XMLBIF03 from an file with name sFileName
readBinary(String) - Static method in class weka.core.xml.SerialUIDChanger
loads a serialized object and returns it
readBooleanFromXML(Element) - Method in class weka.core.xml.XMLSerialization
builds the primitive from the given DOM node.
readByteFromXML(Element) - Method in class weka.core.xml.XMLSerialization
builds the primitive from the given DOM node.
readCharFromXML(Element) - Method in class weka.core.xml.XMLSerialization
builds the primitive from the given DOM node.
readCollection(Element) - Method in class weka.core.xml.XMLBasicSerialization
builds the Collection from the given DOM node.
readColor(Element) - Method in class weka.gui.beans.xml.XMLBeans
builds the Color from the given DOM node.
readColorUIResource(Element) - Method in class weka.gui.beans.xml.XMLBeans
builds the ColorUIResource from the given DOM node.
readCostMatrixOld(Element) - Method in class weka.core.xml.XMLBasicSerialization
builds the Matrix (old) from the given DOM node.
readDefaultListModel(Element) - Method in class weka.core.xml.XMLBasicSerialization
builds the DefaultListModel from the given DOM node.
readDimension(Element) - Method in class weka.gui.beans.xml.XMLBeans
builds the Dimension from the given DOM node.
readDOT(Reader) - Method in class weka.gui.graphvisualizer.GraphVisualizer
Dot reader
Reads a graph description in DOT format from a string
readDoubleFromXML(Element) - Method in class weka.core.xml.XMLSerialization
builds the primitive from the given DOM node.
readFactorsAndCovariates(Element, String) - Method in class weka.classifiers.pmml.consumer.GeneralRegression
Read the lists of factors and covariates.
readFloatFromXML(Element) - Method in class weka.core.xml.XMLSerialization
builds the primitive from the given DOM node.
readFont(Element) - Method in class weka.gui.beans.xml.XMLBeans
builds the Font from the given DOM node.
readFontUIResource(Element) - Method in class weka.gui.beans.xml.XMLBeans
builds the FontUIResource from the given DOM node.
readFromXML(Object, String, Element) - Method in class weka.core.xml.XMLSerialization
adds the specific node to the object via a set method
readFromXML(Element) - Method in class weka.core.xml.XMLSerialization
builds the object from the given DOM node.
readHeader(int) - Method in class weka.core.converters.ArffLoader.ArffReader
Reads and stores header of an ARFF file.
readInstance(Instances) - Method in class weka.core.converters.ArffLoader.ArffReader
Reads a single instance using the tokenizer and returns it.
readInstance(Instances, boolean) - Method in class weka.core.converters.ArffLoader.ArffReader
Reads a single instance using the tokenizer and returns it.
readInstance(Reader) - Method in class weka.core.Instances
Deprecated.
instead of using this method in conjunction with the readInstance(Reader) method, one should use the ArffLoader or DataSource class instead.
readIntFromXML(Element) - Method in class weka.core.xml.XMLSerialization
builds the primitive from the given DOM node.
readLoader(Element) - Method in class weka.gui.beans.xml.XMLBeans
builds the Loader from the given DOM node.
readLongFromXML(Element) - Method in class weka.core.xml.XMLSerialization
builds the primitive from the given DOM node.
readMap(Element) - Method in class weka.core.xml.XMLBasicSerialization
builds the Map from the given DOM node.
readMatrix(Element) - Method in class weka.core.xml.XMLBasicSerialization
builds the Matrix from the given DOM node.
readMatrixOld(Element) - Method in class weka.core.xml.XMLBasicSerialization
builds the Matrix (old) from the given DOM node.
readMetaBean(Element) - Method in class weka.gui.beans.xml.XMLBeans
builds the MetaBean from the given DOM node.
readOldFormat(Reader) - Method in class weka.classifiers.CostMatrix
Loads a cost matrix in the old format from a reader.
readParameterList(Element) - Method in class weka.classifiers.pmml.consumer.GeneralRegression
Read the list of parameters.
readPoint(Element) - Method in class weka.gui.beans.xml.XMLBeans
builds the Point from the given DOM node.
readPostProcess(Object) - Method in class weka.core.xml.XMLSerialization
additional post-processing can happen in derived classes after reading from XML.
readPostProcess(Object) - Method in class weka.experiment.xml.XMLExperiment
additional post-processing can happen in derived classes after reading from XML.
readPostProcess(Object) - Method in class weka.gui.beans.xml.XMLBeans
additional post-processing can happen in derived classes after reading from XML.
readPPMatrix(Element) - Method in class weka.classifiers.pmml.consumer.GeneralRegression
Read the PPMatrix from the xml.
readPreProcess(Document) - Method in class weka.core.xml.XMLSerialization
additional pre-processing can happen in derived classes before the actual reading from XML (working on the raw XML).
readPreProcess(Document) - Method in class weka.gui.beans.xml.XMLBeans
additional pre-processing can happen in derived classes before the actual reading from XML (working on the raw XML).
readProperties(String) - Static method in class weka.core.Utils
Reads properties that inherit from three locations.
readPropertyNode(Element) - Method in class weka.experiment.xml.XMLExperiment
builds the PropertyNode from the given DOM node.
readSaver(Element) - Method in class weka.gui.beans.xml.XMLBeans
builds the Saver from the given DOM node.
readShortFromXML(Element) - Method in class weka.core.xml.XMLSerialization
builds the primitive from the given DOM node.
readTillEOL() - Method in class weka.core.converters.ArffLoader.ArffReader
Reads and skips all tokens before next end of line token.
realCount - Variable in class weka.core.AttributeStats
The number of real-like values (i.e.
rearrangePoints(int[], int, int, int, double) - Method in class weka.core.neighboursearch.kdtrees.KMeansInpiredMethod
Re-arranges the indices array so that in the portion of the array belonging to the node to be split, the points <= to the splitVal are on the left of the portion and those > the splitVal are on the right.
rearrangePoints(int[], int, int, int, double) - Method in class weka.core.neighboursearch.kdtrees.MidPointOfWidestDimension
Re-arranges the indices array such that the points <= to the splitVal are on the left of the array and those > the splitVal are on the right.
rearrangePoints(int[], int, int, int, double) - Method in class weka.core.neighboursearch.kdtrees.SlidingMidPointOfWidestSide
Re-arranges the indices array such that the points <= to the splitVal are on the left of the array and those > the splitVal are on the right.
rebuildBeanConnections(Vector, Object) - Method in class weka.gui.beans.xml.XMLBeans
rebuilds all the connections for a certain key in the hashtable.
recall(int) - Method in class weka.classifiers.Evaluation
Calculate the recall with respect to a particular class.
RECALL - Static variable in class weka.classifiers.meta.ThresholdSelector
recall
RECALL_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
attribute name: Recall
recomputeCenters(Instances, int[][], Instances) - Method in class weka.clusterers.XMeans
Recompute the new centers.
recomputeCentersFast(Instances, int[][], Instances) - Method in class weka.clusterers.XMeans
Recompute the new centers - 2nd version Same as recomputeCenters, but does not check if center stays the same.
RECTANGLE - Static variable in class weka.gui.visualize.VisualizePanelEvent
 
redo() - Method in class weka.classifiers.bayes.net.EditableBayesNet
redo the last edit action performed on the network.
reduce_table() - Method in class weka.core.mathematicalexpression.Parser
Access to reduce_goto table.
reduce_table() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
Access to reduce_goto table.
reducedErrorPrune() - Method in class weka.classifiers.trees.REPTree.Tree
Prunes the tree using the hold-out data (bottom-up).
reducedErrorPruningTipText() - Method in class weka.classifiers.rules.PART
Returns the tip text for this property
reducedErrorPruningTipText() - Method in class weka.classifiers.trees.J48
Returns the tip text for this property
reduceDimensionality(Instances) - Method in class weka.attributeSelection.AttributeSelection
reduce the dimensionality of a set of instances to include only those attributes chosen by the last run of attribute selection.
reduceDimensionality(Instance) - Method in class weka.attributeSelection.AttributeSelection
reduce the dimensionality of a single instance to include only those attributes chosen by the last run of attribute selection.
reduceDL(double, boolean) - Method in class weka.classifiers.rules.RuleStats
Try to reduce the DL of the ruleset by testing removing the rules one by one in reverse order and update all the stats
reduceMatrix(double[][]) - Static method in class weka.core.ContingencyTables
Reduces a matrix by deleting all zero rows and columns.
reevaluateModel(String, Classifier, Instances) - Method in class weka.gui.explorer.ClassifierPanel
Re-evaluates the named classifier with the current test set.
reevaluateModel(String, Clusterer, Instances, int[]) - Method in class weka.gui.explorer.ClustererPanel
Re-evaluates the named clusterer with the current test set.
ReferenceInstances - Class in weka.classifiers.trees.adtree
Simple class that extends the Instances class making it possible to create subsets of instances that reference their source set.
ReferenceInstances(Instances, int) - Constructor for class weka.classifiers.trees.adtree.ReferenceInstances
Creates an empty set of instances.
refine(ArrayList) - Method in class weka.associations.tertius.Rule
Refine a rule by adding literal from a set of predictes.
refineOwners(KDTreeNode, Instances, int[]) - Method in class weka.core.neighboursearch.KDTree
Refines the ownerlist.
refresh() - Method in class weka.gui.arffviewer.ArffViewer
validates and repaints the frame
refresh() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
validates and repaints the frame
refreshFreqTipText() - Method in class weka.gui.beans.StripChart
GUI Tip text
register(Object, Class, String) - Method in class weka.core.xml.XMLSerializationMethodHandler
adds read and write methods for the given class, i.e., read&;lt;name> and write<name> ("name" is prefixed by read and write)
registerEditors() - Static method in class weka.gui.GenericObjectEditor
registers all the editors in Weka.
RegOptimizer - Class in weka.classifiers.functions.supportVector
Base class implementation for learning algorithm of SMOreg Valid options are:

RegOptimizer() - Constructor for class weka.classifiers.functions.supportVector.RegOptimizer
the default constructor
regOptimizerTipText() - Method in class weka.classifiers.functions.SMOreg
Returns the tip text for this property
regress(Attribute, Instances, boolean) - Method in class weka.classifiers.functions.IsotonicRegression
Does the actual regression.
Regression - Class in weka.classifiers.pmml.consumer
Class implementing import of PMML Regression model.
Regression(Element, Instances, MiningSchema) - Constructor for class weka.classifiers.pmml.consumer.Regression
Constructs a new PMML Regression.
regression(Matrix, double) - Method in class weka.core.matrix.Matrix
Performs a (ridged) linear regression.
regression(Matrix, double[], double) - Method in class weka.core.matrix.Matrix
Performs a weighted (ridged) linear regression.
regression(Matrix, double) - Method in class weka.core.Matrix
Deprecated.
Performs a (ridged) linear regression.
regression(Matrix, double[], double) - Method in class weka.core.Matrix
Deprecated.
Performs a weighted (ridged) linear regression.
RegressionByDiscretization - Class in weka.classifiers.meta
A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized.
RegressionByDiscretization() - Constructor for class weka.classifiers.meta.RegressionByDiscretization
Default constructor.
RegressionGenerator - Class in weka.datagenerators
Abstract class for data generators for regression classifiers.
RegressionGenerator() - Constructor for class weka.datagenerators.RegressionGenerator
initializes the generator with default values
RegressionSplitEvaluator - Class in weka.experiment
A SplitEvaluator that produces results for a classification scheme on a numeric class attribute.
RegressionSplitEvaluator() - Constructor for class weka.experiment.RegressionSplitEvaluator
No args constructor.
RegSMO - Class in weka.classifiers.functions.supportVector
Implementation of SMO for support vector regression as described in :

A.J.
RegSMO() - Constructor for class weka.classifiers.functions.supportVector.RegSMO
default constructor
RegSMOImproved - Class in weka.classifiers.functions.supportVector
Learn SVM for regression using SMO with Shevade, Keerthi, et al.
RegSMOImproved() - Constructor for class weka.classifiers.functions.supportVector.RegSMOImproved
 
REGULAR_CONNECTION - Static variable in class weka.gui.beans.xml.XMLBeans
the identifier for regular BeanConnections
relabelTipText() - Method in class weka.classifiers.trees.J48graft
Returns the tip text for this property
RELAGGS - Class in weka.filters.unsupervised.attribute
A propositionalization filter inspired by the RELAGGS algorithm.
It processes all relational attributes that fall into the user defined range (all others are skipped, i.e., not added to the output).
RELAGGS() - Constructor for class weka.filters.unsupervised.attribute.RELAGGS
 
relation() - Method in class weka.core.Attribute
Returns the header info for a relation-valued attribute, null if the attribute is not relation-valued.
relation(int) - Method in class weka.core.Attribute
Returns a value of a relation-valued attribute.
RELATION_NAME - Static variable in class weka.classifiers.evaluation.CostCurve
The name of the relation used in cost curve datasets
RELATION_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
The name of the relation used in threshold curve datasets
RELATIONAL - Static variable in class weka.core.Attribute
Constant set for relation-valued attributes.
RelationalCellEditor(int, int) - Constructor for class weka.gui.arffviewer.ArffTable.RelationalCellEditor
initializes the editor
RelationalLocator - Class in weka.core
This class locates and records the indices of relational attributes,
RelationalLocator(Instances) - Constructor for class weka.core.RelationalLocator
Initializes the RelationalLocator with the given data.
RelationalLocator(Instances, int, int) - Constructor for class weka.core.RelationalLocator
Initializes the RelationalLocator with the given data.
RelationalLocator(Instances, int[]) - Constructor for class weka.core.RelationalLocator
Initializes the RelationalLocator with the given data.
relationalValue(int) - Method in class weka.core.Instance
Returns the relational value of a relational attribute.
relationalValue(Attribute) - Method in class weka.core.Instance
Returns the relational value of a relational attribute.
relationForTableNameTipText() - Method in class weka.core.converters.DatabaseSaver
Returns the tip text fo this property.
relationName() - Method in class weka.core.Instances
Returns the relation's name.
relationNameTipText() - Method in class weka.datagenerators.DataGenerator
Returns the tip text for this property
relativeAbsoluteError() - Method in class weka.classifiers.Evaluation
Returns the relative absolute error.
relativeDL(int, double, boolean) - Method in class weka.classifiers.rules.RuleStats
The description length (DL) of the ruleset relative to if the rule in the given position is deleted, which is obtained by:
MDL if the rule exists - MDL if the rule does not exist
Note the minimal possible DL of the ruleset is calculated(i.e.
ReliefFAttributeEval - Class in weka.attributeSelection
ReliefFAttributeEval :

Evaluates the worth of an attribute by repeatedly sampling an instance and considering the value of the given attribute for the nearest instance of the same and different class.
ReliefFAttributeEval() - Constructor for class weka.attributeSelection.ReliefFAttributeEval
Constructor
RemoteBoundaryVisualizerSubTask - Class in weka.gui.boundaryvisualizer
Class that encapsulates a sub task for distributed boundary visualization.
RemoteBoundaryVisualizerSubTask() - Constructor for class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
RemoteEngine - Class in weka.experiment
A general purpose server for executing Task objects sent via RMI.
RemoteEngine(String) - Constructor for class weka.experiment.RemoteEngine
Constructor
RemoteExperiment - Class in weka.experiment
Holds all the necessary configuration information for a distributed experiment.
RemoteExperiment() - Constructor for class weka.experiment.RemoteExperiment
Construct a new RemoteExperiment using an empty Experiment as base Experiment
RemoteExperiment(Experiment) - Constructor for class weka.experiment.RemoteExperiment
Construct a new RemoteExperiment using a base Experiment
RemoteExperimentEvent - Class in weka.experiment
Class encapsulating information on progress of a remote experiment
RemoteExperimentEvent(boolean, boolean, boolean, String) - Constructor for class weka.experiment.RemoteExperimentEvent
Constructor
RemoteExperimentListener - Interface in weka.experiment
Interface for classes that want to listen for updates on RemoteExperiment progress
remoteExperimentStatus(RemoteExperimentEvent) - Method in interface weka.experiment.RemoteExperimentListener
Called when progress has been made in a remote experiment
RemoteExperimentSubTask - Class in weka.experiment
Class to encapsulate an experiment as a task that can be executed on a remote host.
RemoteExperimentSubTask() - Constructor for class weka.experiment.RemoteExperimentSubTask
 
RemoteResult - Class in weka.gui.boundaryvisualizer
Class that encapsulates a result (and progress info) for part of a distributed boundary visualization.
RemoteResult(int, int) - Constructor for class weka.gui.boundaryvisualizer.RemoteResult
Creates a new RemoteResult instance.
remove() - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListInverseIterator
 
remove() - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListIterator
 
remove(int) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList
Removes an element at the specified index from the list.
remove(Object) - Method in class weka.core.ProtectedProperties
Overrides a method to prevent the properties from being modified.
remove(String) - Method in class weka.core.Stopwords
removes the word from the stopword list
remove(PrintStream) - Method in class weka.core.Tee
removes the given PrintStream from the list.
remove(int) - Method in class weka.core.Tee
removes the given PrintStream from the list.
remove(Object) - Method in class weka.core.Trie
Removes a single instance of the specified element from this collection, if it is present.
remove() - Method in class weka.core.Trie.TrieIterator
ignored
remove(Character) - Method in class weka.core.Trie.TrieNode
removes the given characted from its children
remove(String) - Method in class weka.core.Trie.TrieNode
Removes a suffix from the trie.
remove(String) - Method in class weka.core.xml.MethodHandler
removes the method for the property specified by the display name from its internal list.
remove(Class) - Method in class weka.core.xml.MethodHandler
removes the method for the specified class from its internal list.
Remove - Class in weka.filters.unsupervised.attribute
A filter that removes a range of attributes from the dataset.
Remove() - Constructor for class weka.filters.unsupervised.attribute.Remove
Constructor so that we can initialize the Range variable properly.
remove() - Method in class weka.gui.beans.BeanConnection
Remove this connection
remove(int) - Method in class weka.gui.CheckBoxList.CheckBoxListModel
Removes the element at the specified position in this list.
REMOVE_CHILDREN - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
 
REMOVE_POINT_RADIUS - Static variable in class weka.gui.boundaryvisualizer.BoundaryPanel
The distance we can click away from a point in the GUI and still remove it.
removeActionListener(ActionListener) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Remove a listener
removeAll(Collection<?>) - Method in class weka.core.Trie
Removes all this collection's elements that are also contained in the specified collection
removeAllBeansFromContainer(JComponent) - Static method in class weka.gui.beans.BeanInstance
Removes all beans from containing component
removeAllButton - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
removeAllElements() - Method in class weka.core.FastVector
Removes all components from this vector and sets its size to zero.
removeAllElements() - Method in class weka.core.Queue
Removes all objects from the queue m_Tail.m_Next.
removeAllInputs() - Method in class weka.classifiers.functions.neural.NeuralConnection
This function will remove all the inputs to this unit.
removeAllInputs() - Method in class weka.classifiers.functions.neural.NeuralNode
This function will remove all the inputs to this unit.
removeAllInstances() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Deletes all training instances from our dataset.
removeAllMissingColsTipText() - Method in class weka.associations.Apriori
Returns the tip text for this property
removeAllOutputs() - Method in class weka.classifiers.functions.neural.NeuralConnection
This function will remove all outputs to this unit.
removeAllowed(Class, String) - Method in class weka.core.xml.PropertyHandler
removes the given property (display name) for the specified class from the list of allowed properties.
removeAllPlots() - Method in class weka.gui.visualize.Plot2D
Clears all plots
removeAllPlots() - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
Removes all the plots.
removeAllPlots() - Method in class weka.gui.visualize.VisualizePanel
Removes all the plots.
removeAllSpecifiers() - Method in class weka.experiment.PairedTTester.DatasetSpecifiers
Removes all specifiers.
removeBatchClassifierListener(BatchClassifierListener) - Method in class weka.gui.beans.Classifier
Remove a batch classifier listener
removeBatchClustererListener(BatchClustererListener) - Method in class weka.gui.beans.Clusterer
Remove a batch clusterer listener
removeBean(JComponent) - Method in class weka.gui.beans.BeanInstance
Remove this bean from the list of beans and from the containing component
removeBlacklist(String[]) - Method in class weka.datagenerators.DataGenerator
removes all the options from the options array that are blacklisted
removeCancelListener(ActionListener) - Method in class weka.gui.GenericObjectEditor.GOEPanel
This is used to remove an action listener from the cancel button.
removeCapabilitiesFilter() - Method in class weka.gui.GenericObjectEditor
Removes the current Capabilities filter.
removeCapabilitiesFilterListener(Explorer.CapabilitiesFilterChangeListener) - Method in class weka.gui.explorer.Explorer
Removes the specified listener from the set of listeners if it is present.
removeChangeListener(ChangeListener) - Method in class weka.gui.arffviewer.ArffPanel
Removes a ChangeListener from the panel
removeChangeListener(ChangeListener) - Method in class weka.gui.arffviewer.ArffTable
Removes a ChangeListener from the panel
removeChartListener(ChartListener) - Method in class weka.gui.beans.IncrementalClassifierEvaluator
Remove a chart listener
removeChildFrame(Container) - Method in class weka.gui.GUIChooser
tries to remove the child frame, it returns true if it could do such.
removeChildFrame(Container) - Method in class weka.gui.Main
tries to remove the child frame, it returns true if it could do such.
removeClassColumnTipText() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
Returns the tip text for this property
removeConnectionListener(ConnectionListener) - Method in class weka.gui.sql.ConnectionPanel
removes the given listener from the list of listeners.
removeConnectionListener(ConnectionListener) - Method in class weka.gui.sql.SqlViewer
removes the given listener from the list of listeners.
removeConnections(BeanInstance) - Static method in class weka.gui.beans.BeanConnection
Remove all connections for a bean.
removeCount(int) - Method in class weka.associations.FPGrowth.ShadowCounts
Remove the count at the given recursion level.
removeCycles() - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
The following two methods remove cycles from the graph.
removeDataFormatListener(DataFormatListener) - Method in class weka.gui.beans.ClassAssigner
 
removeDataFormatListener(DataFormatListener) - Method in class weka.gui.beans.ClassValuePicker
 
removeDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.AbstractDataSource
Remove a listener
removeDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.ClassAssigner
 
removeDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.ClassValuePicker
 
removeDataSourceListener(DataSourceListener) - Method in interface weka.gui.beans.DataSource
Remove a data source listener
removeDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.DataVisualizer
Remove a listener
removeDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.Filter
Remove a data source listener
removeDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.InstanceStreamToBatchMaker
 
removeDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.Loader
Remove a listener
removeDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.PredictionAppender
Remove a datasource listener
removedPercentageTipText() - Method in class weka.classifiers.meta.RotationForest
Returns the tip text for this property
removeElement(Object) - Method in class weka.gui.CheckBoxList.CheckBoxListModel
Removes the first (lowest-indexed) occurrence of the argument from this list.
removeElementAt(int) - Method in class weka.core.FastVector
Deletes an element from this vector.
removeExtAttributes(Instances) - Method in class weka.classifiers.trees.ft.FTtree
Removes extended attributes in current dataset or instance
removeFilterName(String) - Method in class weka.experiment.ResultMatrix
removes the filter classname from the given string if it should be removed, otherwise leaves the string alone
removeFirst() - Method in class weka.associations.tertius.SimpleLinkedList
 
RemoveFolds - Class in weka.filters.unsupervised.instance
This filter takes a dataset and outputs a specified fold for cross validation.
RemoveFolds() - Constructor for class weka.filters.unsupervised.instance.RemoveFolds
 
RemoveFrequentValues - Class in weka.filters.unsupervised.instance
Determines which values (frequent or infrequent ones) of an (nominal) attribute are retained and filters the instances accordingly.
RemoveFrequentValues() - Constructor for class weka.filters.unsupervised.instance.RemoveFrequentValues
 
removeGraphListener(GraphListener) - Method in class weka.gui.beans.Associator
Remove a graph listener
removeGraphListener(GraphListener) - Method in class weka.gui.beans.Classifier
Remove a graph listener
removeGraphListener(GraphListener) - Method in class weka.gui.beans.Clusterer
Remove a graph listener
removeHistoryChangedListener(HistoryChangedListener) - Method in class weka.gui.sql.ConnectionPanel
removes the given listener from the list of listeners.
removeHistoryChangedListener(HistoryChangedListener) - Method in class weka.gui.sql.QueryPanel
removes the given listener from the list of listeners.
removeHistoryChangedListener(HistoryChangedListener) - Method in class weka.gui.sql.SqlViewer
removes the given listener from the list of listeners.
removeIgnored(String) - Method in class weka.core.xml.PropertyHandler
removes the given display name from the ignore list.
removeIgnored(Class, String) - Method in class weka.core.xml.PropertyHandler
removes the given display name from the ignore list of the class.
removeIgnored(Instances) - Method in class weka.filters.unsupervised.attribute.AddCluster
filters all attributes that should be ignored
removeIgnored(Instances) - Method in class weka.filters.unsupervised.attribute.ClusterMembership
filters all attributes that should be ignored
removeIncrementalClassifierListener(IncrementalClassifierListener) - Method in class weka.gui.beans.Classifier
Remove an incremental classifier listener
removeInstanceListener(InstanceListener) - Method in class weka.gui.beans.AbstractDataSource
Remove an instance listener
removeInstanceListener(InstanceListener) - Method in class weka.gui.beans.ClassAssigner
 
removeInstanceListener(InstanceListener) - Method in interface weka.gui.beans.DataSource
Remove an instance listener
removeInstanceListener(InstanceListener) - Method in class weka.gui.beans.Filter
Remove an instance listener
removeInstanceListener(InstanceListener) - Method in class weka.gui.beans.InstanceStreamToBatchMaker
 
removeInstanceListener(InstanceListener) - Method in class weka.gui.beans.Loader
Remove an instance listener
removeInstanceListener(InstanceListener) - Method in class weka.gui.beans.PredictionAppender
Remove an instance listener
removeInstanceListener(InstanceListener) - Method in class weka.gui.streams.InstanceJoiner
 
removeInstanceListener(InstanceListener) - Method in class weka.gui.streams.InstanceLoader
 
removeInstanceListener(InstanceListener) - Method in interface weka.gui.streams.InstanceProducer
 
removeInstances(Instances, int) - Method in class weka.classifiers.meta.Decorate
Removes a specified number of instances from the given set of instances.
removeLast() - Method in class weka.classifiers.rules.RuleStats
Remove the last rule in the ruleset as well as it's stats.
removeLayoutCompleteEventListener(LayoutCompleteEventListener) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
Method to remove a LayoutCompleteEventListener.
removeLayoutCompleteEventListener(LayoutCompleteEventListener) - Method in interface weka.gui.graphvisualizer.LayoutEngine
This method removes a LayoutCompleteEventListener from the LayoutEngine.
removeLinkAt(int) - Method in class weka.attributeSelection.BestFirst.LinkedList2
removes an element (Link) at a specific index from the list.
removeLinkAt(int) - Method in class weka.attributeSelection.LFSMethods.LinkedList2
removes an element (Link) at a specific index from the list.
RemoveMisclassified - Class in weka.filters.unsupervised.instance
A filter that removes instances which are incorrectly classified.
RemoveMisclassified() - Constructor for class weka.filters.unsupervised.instance.RemoveMisclassified
 
removeMissingColumns(Instances) - Method in class weka.associations.Apriori
Removes columns that are all missing from the data
removeNotesFrame() - Method in class weka.gui.experiment.SetupPanel
Deletes the notes frame.
removeNotesFrame() - Method in class weka.gui.experiment.SimpleSetupPanel
Deletes the notes frame.
removeNotify() - Method in class weka.gui.PropertyPanel
Cleans up when the panel is destroyed.
removeOkListener(ActionListener) - Method in class weka.gui.GenericObjectEditor.GOEPanel
This is used to remove an action listener from the ok button.
removeOldClassTipText() - Method in class weka.filters.supervised.attribute.AddClassification
Returns the tip text for this property.
RemovePercentage - Class in weka.filters.unsupervised.instance
A filter that removes a given percentage of a dataset.
RemovePercentage() - Constructor for class weka.filters.unsupervised.instance.RemovePercentage
 
removeProjectedCount(int) - Method in class weka.associations.FPGrowth.FPTreeNode
Remove the projected count at the given recursion level for this node.
removePropertyChangeListener(String, PropertyChangeListener) - Method in class weka.gui.beans.AbstractDataSource
Remove a property change listener from this bean
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.AssociatorCustomizer
Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.BeanVisual
Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.ClassAssignerCustomizer
Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.ClassifierCustomizer
Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.ClassValuePickerCustomizer
Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.ClustererCustomizer
Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.CrossValidationFoldMakerCustomizer
Remove a property change listener
removePropertyChangeListener(String, PropertyChangeListener) - Method in class weka.gui.beans.DataVisualizer
Remove a property change listener from this bean
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.FilterCustomizer
Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.IncrementalClassifierEvaluatorCustomizer
Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.LoaderCustomizer
Remove a property change listener
removePropertyChangeListener(String, PropertyChangeListener) - Method in class weka.gui.beans.ModelPerformanceChart
Remove a property change listener from this bean
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.PredictionAppenderCustomizer
Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.SaverCustomizer
Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.SerializedModelSaverCustomizer
Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.StripChartCustomizer
Remove a property change listener
removePropertyChangeListener(String, PropertyChangeListener) - Method in class weka.gui.beans.TextViewer
Remove a property change listener from this bean
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.TrainTestSplitMakerCustomizer
Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.CostMatrixEditor
Removes an object from the list of those that wish to be informed when the cost matrix changes.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.experiment.SetupPanel
Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.experiment.SimpleSetupPanel
Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.explorer.PreprocessPanel
Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.GenericArrayEditor
Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.GenericObjectEditor
Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.PropertySheetPanel
Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.SetInstancesPanel
Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.SimpleDateFormatEditor
Removes an object from the list of those that wish to be informed when the date format changes.
removePropertyChangeListenersSubFlow(PropertyChangeListener) - Method in class weka.gui.beans.MetaBean
 
removeQueryExecuteListener(QueryExecuteListener) - Method in class weka.gui.sql.QueryPanel
removes the given listener from the list of listeners.
removeQueryExecuteListener(QueryExecuteListener) - Method in class weka.gui.sql.SqlViewer
removes the given listener from the list of listeners.
RemoveRange - Class in weka.filters.unsupervised.instance
A filter that removes a given range of instances of a dataset.
RemoveRange() - Constructor for class weka.filters.unsupervised.instance.RemoveRange
 
removeRedundant(RuleItem) - Method in class weka.associations.RuleGeneration
Method that removes redundant rules out of the list of the best rules.
removeResult(String) - Method in class weka.gui.ResultHistoryPanel
Removes one of the result buffers from the history.
removeResultChangedListener(ResultChangedListener) - Method in class weka.gui.sql.ResultPanel
removes the given listener from the list of listeners
removeResultChangedListener(ResultChangedListener) - Method in class weka.gui.sql.SqlViewer
removes the given listener from the list of listeners.
removeSubstring(String, String) - Static method in class weka.core.Utils
Removes all occurrences of a string from another string.
removeTableModelListener(TableModelListener) - Method in class weka.gui.arffviewer.ArffSortedTableModel
removes a listener from the list that is notified each time a change to the data model occurs
removeTableModelListener(TableModelListener) - Method in class weka.gui.arffviewer.ArffTableModel
removes a listener from the list that is notified each time a change to the data model occurs
removeTableModelListener(TableModelListener) - Method in class weka.gui.sql.ResultSetTableModel
removes a listener from the list that is notified each time a change to the data model occurs.
removeTestSetListener(TestSetListener) - Method in class weka.gui.beans.AbstractTestSetProducer
Remove a listener for test sets
removeTestSetListener(TestSetListener) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
Remove a test set listener
removeTestSetListener(TestSetListener) - Method in class weka.gui.beans.ClassAssigner
 
removeTestSetListener(TestSetListener) - Method in class weka.gui.beans.Filter
Remove a test set listener
removeTestSetListener(TestSetListener) - Method in class weka.gui.beans.PredictionAppender
Remove a test set listener
removeTestSetListener(TestSetListener) - Method in interface weka.gui.beans.TestSetProducer
Remove a listener for test set events
removeTextListener(TextListener) - Method in class weka.gui.beans.Associator
Remove a text listener
removeTextListener(TextListener) - Method in class weka.gui.beans.Classifier
Remove a text listener
removeTextListener(TextListener) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator
Remove a text listener
removeTextListener(TextListener) - Method in class weka.gui.beans.Clusterer
Remove a text listener
removeTextListener(TextListener) - Method in class weka.gui.beans.ClustererPerformanceEvaluator
Remove a text listener
removeTextListener(TextListener) - Method in class weka.gui.beans.IncrementalClassifierEvaluator
Remove a text listener
removeTextListener(TextListener) - Method in class weka.gui.beans.TextViewer
Remove a text listener
removeThresholdDataListener(ThresholdDataListener) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator
Remove a Threshold data listener
removeTrainingInstanceFromMouseLocation(int, int) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Removes a single training instance from our dataset, if there is one that is close enough to the specified mouse location.
removeTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
Remove a training set listener
removeTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.AbstractTrainingSetProducer
Remove a training set listener
removeTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.ClassAssigner
 
removeTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.Filter
Remove a training set listener
removeTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.PredictionAppender
Remove a training set listener
removeTrainingSetListener(TrainingSetListener) - Method in interface weka.gui.beans.TrainingSetProducer
Remove a training set listener
RemoveType - Class in weka.filters.unsupervised.attribute
Removes attributes of a given type.
RemoveType() - Constructor for class weka.filters.unsupervised.attribute.RemoveType
 
removeUnusedTipText() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
Returns the tip text for this property.
RemoveUseless - Class in weka.filters.unsupervised.attribute
This filter removes attributes that do not vary at all or that vary too much.
RemoveUseless() - Constructor for class weka.filters.unsupervised.attribute.RemoveUseless
 
removeUserToolBarBeans(Vector) - Method in class weka.gui.beans.xml.XMLBeans
removes the given meta beans from the layout, since they're only listed in the user toolbar
removeVariable(String) - Method in class weka.core.Environment
Remove a named variable from the map.
removeVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.AbstractDataSource
Remove a vetoable change listener from this bean
removeVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.CostBenefitAnalysis
 
removeVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.DataVisualizer
Remove a vetoable change listener from this bean
removeVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.GraphViewer
Remove a vetoable change listener from this bean
removeVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.ModelPerformanceChart
Remove a vetoable change listener from this bean
removeVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.TextViewer
Remove a vetoable change listener from this bean
removeVisualizableErrorListener(VisualizableErrorListener) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator
Remove a visualizable error listener
RemoveWithValues - Class in weka.filters.unsupervised.instance
Filters instances according to the value of an attribute.
RemoveWithValues() - Constructor for class weka.filters.unsupervised.instance.RemoveWithValues
Default constructor
renameAttribute(int, String) - Method in class weka.core.Instances
Renames an attribute.
renameAttribute(Attribute, String) - Method in class weka.core.Instances
Renames an attribute.
renameAttribute() - Method in class weka.gui.arffviewer.ArffPanel
renames the current attribute
renameAttribute() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
renames the current selected Attribute
renameAttributeAt(int, String) - Method in class weka.gui.arffviewer.ArffSortedTableModel
renames the attribute at the given col index
renameAttributeAt(int, String) - Method in class weka.gui.arffviewer.ArffTableModel
renames the attribute at the given col index
renameAttributes(Instances, String) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
renames all the attributes in the dataset (excluding the class if present) by adding the prefix to the name.
renameAttributeValue(int, int, String) - Method in class weka.core.Instances
Renames the value of a nominal (or string) attribute value.
renameAttributeValue(Attribute, String, String) - Method in class weka.core.Instances
Renames the value of a nominal (or string) attribute value.
renameNodeValue(int, String, String) - Method in class weka.classifiers.bayes.net.EditableBayesNet
change the name of a value of a node
Reorder - Class in weka.filters.unsupervised.attribute
A filter that generates output with a new order of the attributes.
Reorder() - Constructor for class weka.filters.unsupervised.attribute.Reorder
 
RepeatedHillClimber - Class in weka.classifiers.bayes.net.search.global
This Bayes Network learning algorithm repeatedly uses hill climbing starting with a randomly generated network structure and return the best structure of the various runs.
RepeatedHillClimber() - Constructor for class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
 
RepeatedHillClimber - Class in weka.classifiers.bayes.net.search.local
This Bayes Network learning algorithm repeatedly uses hill climbing starting with a randomly generated network structure and return the best structure of the various runs.
RepeatedHillClimber() - Constructor for class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
 
repeatLiteralsTipText() - Method in class weka.associations.Tertius
Returns the tip text for this property.
replaceAllBy(Stack<T>) - Method in class weka.core.neighboursearch.covertrees.Stack
Replace all elements in the stack with the elements of another given stack.
replaceMissingTipText() - Method in class weka.filters.supervised.attribute.PLSFilter
Returns the tip text for this property
replaceMissingValues(double[]) - Method in class weka.core.BinarySparseInstance
Does nothing, since we don't support missing values.
replaceMissingValues(double[]) - Method in class weka.core.Instance
Replaces all missing values in the instance with the values contained in the given array.
replaceMissingValues(double[]) - Method in class weka.core.SparseInstance
Replaces all missing values in the instance with the values contained in the given array.
ReplaceMissingValues - Class in weka.filters.unsupervised.attribute
Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.
ReplaceMissingValues() - Constructor for class weka.filters.unsupervised.attribute.ReplaceMissingValues
 
replaceMissingValuesTipText() - Method in class weka.filters.unsupervised.attribute.RandomProjection
Returns the tip text for this property
replaceSubstring(String, String, String) - Static method in class weka.core.Utils
Replaces with a new string, all occurrences of a string from another string.
replot() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Quickly replot the display using cached probability estimates
reportFrequencyTipText() - Method in class weka.attributeSelection.GeneticSearch
Returns the tip text for this property
REPTree - Class in weka.classifiers.trees
Fast decision tree learner.
REPTree() - Constructor for class weka.classifiers.trees.REPTree
 
REPTree.Tree - Class in weka.classifiers.trees
An inner class for building and storing the tree structure
repulsionTipText() - Method in class weka.clusterers.CLOPE
Returns the tip text for this property
resample(Random) - Method in class weka.core.Instances
Creates a new dataset of the same size using random sampling with replacement.
Resample - Class in weka.filters.supervised.instance
Produces a random subsample of a dataset using either sampling with replacement or without replacement.
The original dataset must fit entirely in memory.
Resample() - Constructor for class weka.filters.supervised.instance.Resample
 
Resample - Class in weka.filters.unsupervised.instance
Produces a random subsample of a dataset using either sampling with replacement or without replacement.
Resample() - Constructor for class weka.filters.unsupervised.instance.Resample
 
resampleWithWeights(Instances, Random, boolean[]) - Method in class weka.classifiers.meta.Bagging
Creates a new dataset of the same size using random sampling with replacement according to the given weight vector.
resampleWithWeights(Random) - Method in class weka.core.Instances
Creates a new dataset of the same size using random sampling with replacement according to the current instance weights.
resampleWithWeights(Random, double[]) - Method in class weka.core.Instances
Creates a new dataset of the same size using random sampling with replacement according to the given weight vector.
ReservoirSample - Class in weka.filters.unsupervised.instance
Produces a random subsample of a dataset using the reservoir sampling Algorithm "R" by Vitter.
ReservoirSample() - Constructor for class weka.filters.unsupervised.instance.ReservoirSample
 
reset() - Method in class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd
Call this to reset the value and error for this unit, ready for the next run.
reset() - Method in class weka.classifiers.functions.neural.NeuralConnection
Call this to reset the unit for another run.
reset() - Method in class weka.classifiers.functions.neural.NeuralNode
Call this to reset the value and error for this unit, ready for the next run.
reset() - Method in class weka.classifiers.functions.SPegasos
Reset the classifier.
reset() - Method in class weka.core.converters.AbstractFileLoader
Resets the loader ready to read a new data set
reset() - Method in class weka.core.converters.AbstractLoader
Default implementation sets retrieval mode to NONE
reset() - Method in class weka.core.converters.ArffLoader
Resets the Loader ready to read a new data set or the same data set again.
reset() - Method in class weka.core.converters.C45Loader
Resets the Loader ready to read a new data set or the same data set again.
reset() - Method in class weka.core.converters.ConverterUtils.DataSource
resets the loader.
reset() - Method in class weka.core.converters.CSVLoader
Resets the Loader ready to read a new data set or the same data set again.
reset() - Method in class weka.core.converters.DatabaseLoader
Resets the Loader ready to read a new data set
reset() - Method in class weka.core.converters.LibSVMLoader
Resets the Loader ready to read a new data set.
reset() - Method in interface weka.core.converters.Loader
Resets the Loader object ready to begin loading.
reset() - Method in class weka.core.converters.SerializedInstancesLoader
Resets the Loader ready to read a new data set
reset() - Method in class weka.core.converters.SVMLightLoader
Resets the Loader ready to read a new data set.
reset() - Method in class weka.core.converters.TextDirectoryLoader
Resets the loader ready to read a new data set
reset() - Method in class weka.core.converters.XRFFLoader
Resets the Loader ready to read a new data set
reset() - Method in class weka.core.neighboursearch.PerformanceStats
Resets all internal fields/counters.
reset() - Method in class weka.core.neighboursearch.TreePerformanceStats
Resets all internal fields/counters.
reset() - Method in class weka.filters.MultiFilter
resets the filter, i.e., m_NewBatch to true and m_FirstBatchDone to false.
reset() - Method in class weka.filters.SimpleFilter
resets the filter, i.e., m_NewBatch to true and m_FirstBatchDone to false.
reset() - Method in class weka.filters.supervised.attribute.AddClassification
resets the filter, i.e., m_ActualClassifier to null.
reset() - Method in class weka.filters.unsupervised.attribute.KernelFilter
resets the filter, i.e., m_NewBatch to true and m_FirstBatchDone to false.
reset() - Static method in class weka.gui.beans.BeanConnection
Reset the list of connections
reset(JComponent) - Static method in class weka.gui.beans.BeanInstance
Reset the list of beans
resetAttIndex(boolean) - Method in class weka.classifiers.lazy.LBR.Indexes
Resets the boolean value in AttIndexes array
resetAttIndexTo(LBR.Indexes) - Method in class weka.classifiers.lazy.LBR.Indexes
Resets the boolean value in AttIndexes array based on another set of Indexes
resetConsumed() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
reset consumation counts
resetDatasetBasedOn(LBR.Indexes) - Method in class weka.classifiers.lazy.LBR.Indexes
Resets the boolean values in Attribute and Instance array to reflect an empty dataset withthe same attributes set as in the incoming Indexes Object
resetDistribution(Instances) - Method in class weka.classifiers.trees.j48.BinC45Split
Sets distribution associated with model.
resetDistribution(Instances) - Method in class weka.classifiers.trees.j48.C45Split
Sets distribution associated with model.
resetDistribution(Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Sets distribution associated with model.
resetHistory() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Clears any instances from the history queue.
resetID() - Static method in class weka.classifiers.trees.j48.ClassifierTree
Resets the unique node ID counter (e.g.
resetID() - Static method in class weka.classifiers.trees.REPTree
resets the counter for the nodes
resetInstanceIndex(boolean) - Method in class weka.classifiers.lazy.LBR.Indexes
Resets the boolean value in the Instance Indexes array to a specified value
resetOptions() - Method in class weka.associations.Apriori
Resets the options to the default values.
resetOptions() - Method in class weka.associations.FPGrowth
Reset all options to their default values.
resetOptions() - Method in class weka.associations.GeneralizedSequentialPatterns
Resets the algorithm's options to the default values.
resetOptions() - Method in class weka.associations.PredictiveApriori
Resets the options to the default values.
resetOptions() - Method in class weka.associations.Tertius
Resets the options to the default values.
resetOptions() - Method in class weka.attributeSelection.BestFirst
Reset options to default values
resetOptions() - Method in class weka.attributeSelection.CfsSubsetEval
 
resetOptions() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
Reset options to their default values
resetOptions() - Method in class weka.attributeSelection.ClassifierSubsetEval
reset to defaults
resetOptions() - Method in class weka.attributeSelection.GainRatioAttributeEval
reset options to default values
resetOptions() - Method in class weka.attributeSelection.GreedyStepwise
Resets options
resetOptions() - Method in class weka.attributeSelection.InfoGainAttributeEval
Reset options to their default values
resetOptions() - Method in class weka.attributeSelection.LinearForwardSelection
Reset options to default values
resetOptions() - Method in class weka.attributeSelection.OneRAttributeEval
rests to defaults.
resetOptions() - Method in class weka.attributeSelection.RaceSearch
Reset the search method.
resetOptions() - Method in class weka.attributeSelection.Ranker
Resets stuff to default values
resetOptions() - Method in class weka.attributeSelection.RankSearch
Reset the search method.
resetOptions() - Method in class weka.attributeSelection.ReliefFAttributeEval
Reset options to their default values
resetOptions() - Method in class weka.attributeSelection.ScatterSearchV1
 
resetOptions() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Reset options to default values
resetOptions() - Method in class weka.attributeSelection.SVMAttributeEval
Resets options to defaults.
resetOptions() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
set options to default values
resetOptions() - Method in class weka.attributeSelection.WrapperSubsetEval
 
resetOptions() - Method in class weka.classifiers.rules.DecisionTable
Resets the options.
resetOptions() - Method in class weka.clusterers.EM
Reset to default options
resetOptions() - Method in class weka.core.converters.AbstractFileSaver
resets the options
resetOptions() - Method in class weka.core.converters.AbstractSaver
resets the options
resetOptions() - Method in class weka.core.converters.ArffSaver
Resets the Saver
resetOptions() - Method in class weka.core.converters.C45Saver
Resets the Saver
resetOptions() - Method in class weka.core.converters.CSVSaver
Resets the Saver
resetOptions() - Method in class weka.core.converters.DatabaseSaver
Resets the Saver ready to save a new data set.
resetOptions() - Method in class weka.core.converters.LibSVMSaver
Resets the Saver
resetOptions() - Method in class weka.core.converters.SerializedInstancesSaver
Resets the Saver.
resetOptions() - Method in class weka.core.converters.SVMLightSaver
Resets the Saver.
resetOptions() - Method in class weka.core.converters.XRFFSaver
Resets the Saver
resetOptions() - Method in class weka.filters.supervised.attribute.AttributeSelection
set options to their default values
resetQueue() - Method in class weka.filters.Filter
Clears the output queue.
resetStructure() - Method in class weka.core.converters.AbstractSaver
Resets the structure (header information of the instances)
resetStructure() - Method in class weka.core.converters.DatabaseLoader
Resets the structure of instances
resetTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
resetWriter() - Method in class weka.core.converters.AbstractFileSaver
Sets the writer to null.
resetWriter() - Method in class weka.core.converters.SerializedInstancesSaver
Resets the writer, setting writer and objectstream to null.
ResidualModelSelection - Class in weka.classifiers.trees.lmt
Helper class for logistic model trees (weka.classifiers.trees.lmt.LMT) to implement the splitting criterion based on residuals.
ResidualModelSelection(int) - Constructor for class weka.classifiers.trees.lmt.ResidualModelSelection
Constructor to create ResidualModelSelection object.
ResidualSplit - Class in weka.classifiers.trees.lmt
Helper class for logistic model trees (weka.classifiers.trees.lmt.LMT) to implement the splitting criterion based on residuals of the LogitBoost algorithm.
ResidualSplit(int) - Constructor for class weka.classifiers.trees.lmt.ResidualSplit
Creates a split object
restoreBeans() - Method in class weka.gui.beans.MetaBean
 
restoreWeights() - Method in class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd
Call this to have the connection restore from the saved weights.
restoreWeights() - Method in class weka.classifiers.functions.neural.NeuralConnection
Call this to have the connection restore from the saved weights.
restoreWeights() - Method in class weka.classifiers.functions.neural.NeuralNode
Call this to have the connection restore from the saved weights.
restoreWindows() - Method in class weka.gui.Main
restores all windows.
resultChanged(ResultChangedEvent) - Method in interface weka.gui.sql.event.ResultChangedListener
This method gets called when a query has been executed.
resultChanged(ResultChangedEvent) - Method in class weka.gui.sql.SqlViewer
This method gets called when a query has been executed.
resultChanged(ResultChangedEvent) - Method in class weka.gui.sql.SqlViewerDialog
This method gets called when a query has been executed.
ResultChangedEvent - Class in weka.gui.sql.event
An event that is generated when a different Result is activated in the ResultPanel.
ResultChangedEvent(Object, String, String, String, String) - Constructor for class weka.gui.sql.event.ResultChangedEvent
constructs the event
ResultChangedListener - Interface in weka.gui.sql.event
A listener that is notified if another Result is activated in the ResultPanel.
ResultHistoryPanel - Class in weka.gui
A component that accepts named stringbuffers and displays the name in a list box.
ResultHistoryPanel(JTextComponent) - Constructor for class weka.gui.ResultHistoryPanel
Create the result history object
ResultHistoryPanel.RKeyAdapter - Class in weka.gui
Extension of KeyAdapter that implements Serializable.
ResultHistoryPanel.RMouseAdapter - Class in weka.gui
Extension of MouseAdapter that implements Serializable.
ResultListener - Interface in weka.experiment
Interface for objects able to listen for results obtained by a ResultProducer
ResultMatrix - Class in weka.experiment
This matrix is a container for the datasets and classifier setups and their statistics.
ResultMatrix() - Constructor for class weka.experiment.ResultMatrix
initializes the matrix as 1x1 matrix
ResultMatrix(int, int) - Constructor for class weka.experiment.ResultMatrix
initializes the matrix with the given dimensions
ResultMatrix(ResultMatrix) - Constructor for class weka.experiment.ResultMatrix
initializes the matrix with the values from the given matrix
ResultMatrixCSV - Class in weka.experiment
This matrix is a container for the datasets and classifier setups and their statistics.
ResultMatrixCSV() - Constructor for class weka.experiment.ResultMatrixCSV
initializes the matrix as 1x1 matrix
ResultMatrixCSV(int, int) - Constructor for class weka.experiment.ResultMatrixCSV
initializes the matrix with the given dimensions
ResultMatrixCSV(ResultMatrix) - Constructor for class weka.experiment.ResultMatrixCSV
initializes the matrix with the values from the given matrix
ResultMatrixGnuPlot - Class in weka.experiment
This matrix is a container for the datasets and classifier setups and their statistics.
ResultMatrixGnuPlot() - Constructor for class weka.experiment.ResultMatrixGnuPlot
initializes the matrix as 1x1 matrix
ResultMatrixGnuPlot(int, int) - Constructor for class weka.experiment.ResultMatrixGnuPlot
initializes the matrix with the given dimensions
ResultMatrixGnuPlot(ResultMatrix) - Constructor for class weka.experiment.ResultMatrixGnuPlot
initializes the matrix with the values from the given matrix
ResultMatrixHTML - Class in weka.experiment
This matrix is a container for the datasets and classifier setups and their statistics.
ResultMatrixHTML() - Constructor for class weka.experiment.ResultMatrixHTML
initializes the matrix as 1x1 matrix
ResultMatrixHTML(int, int) - Constructor for class weka.experiment.ResultMatrixHTML
initializes the matrix with the given dimensions
ResultMatrixHTML(ResultMatrix) - Constructor for class weka.experiment.ResultMatrixHTML
initializes the matrix with the values from the given matrix
ResultMatrixLatex - Class in weka.experiment
This matrix is a container for the datasets and classifier setups and their statistics.
ResultMatrixLatex() - Constructor for class weka.experiment.ResultMatrixLatex
initializes the matrix as 1x1 matrix
ResultMatrixLatex(int, int) - Constructor for class weka.experiment.ResultMatrixLatex
initializes the matrix with the given dimensions
ResultMatrixLatex(ResultMatrix) - Constructor for class weka.experiment.ResultMatrixLatex
initializes the matrix with the values from the given matrix
ResultMatrixPlainText - Class in weka.experiment
This matrix is a container for the datasets and classifier setups and their statistics.
ResultMatrixPlainText() - Constructor for class weka.experiment.ResultMatrixPlainText
initializes the matrix as 1x1 matrix
ResultMatrixPlainText(int, int) - Constructor for class weka.experiment.ResultMatrixPlainText
initializes the matrix with the given dimensions
ResultMatrixPlainText(ResultMatrix) - Constructor for class weka.experiment.ResultMatrixPlainText
initializes the matrix with the values from the given matrix
ResultMatrixSignificance - Class in weka.experiment
This matrix is a container for the datasets and classifier setups and their statistics.
ResultMatrixSignificance() - Constructor for class weka.experiment.ResultMatrixSignificance
initializes the matrix as 1x1 matrix
ResultMatrixSignificance(int, int) - Constructor for class weka.experiment.ResultMatrixSignificance
initializes the matrix with the given dimensions
ResultMatrixSignificance(ResultMatrix) - Constructor for class weka.experiment.ResultMatrixSignificance
initializes the matrix with the values from the given matrix
ResultPanel - Class in weka.gui.sql
Represents a panel for displaying the results of a query in table format.
ResultPanel(JFrame) - Constructor for class weka.gui.sql.ResultPanel
initializes the panel
ResultProducer - Interface in weka.experiment
This interface defines the methods required for an object that produces results for different randomizations of a dataset.
resultProducerTipText() - Method in class weka.experiment.AveragingResultProducer
Returns the tip text for this property
resultProducerTipText() - Method in class weka.experiment.DatabaseResultProducer
Returns the tip text for this property
resultProducerTipText() - Method in class weka.experiment.LearningRateResultProducer
Returns the tip text for this property
Resultset(Instance) - Constructor for class weka.experiment.PairedTTester.Resultset
Constructir
ResultSetHelper - Class in weka.gui.sql
Represents an extended JTable, containing a table model based on a ResultSet and the corresponding query.
ResultSetHelper(ResultSet) - Constructor for class weka.gui.sql.ResultSetHelper
initializes the helper, with unlimited number of rows.
ResultSetHelper(ResultSet, int) - Constructor for class weka.gui.sql.ResultSetHelper
initializes the helper, with the given maximum number of rows (less than 1 means unlimited).
resultsetKey() - Method in class weka.experiment.PairedTTester
Creates a key that maps resultset numbers to their descriptions.
resultsetKey() - Method in interface weka.experiment.Tester
Creates a key that maps resultset numbers to their descriptions.
ResultSetTable - Class in weka.gui.sql
Represents an extended JTable, containing a table model based on a ResultSet and the corresponding query.
ResultSetTable(String, String, String, String, ResultSetTableModel) - Constructor for class weka.gui.sql.ResultSetTable
initializes the table
ResultSetTableCellRenderer - Class in weka.gui.sql
Handles the background colors for missing values differently than the DefaultTableCellRenderer.
ResultSetTableCellRenderer() - Constructor for class weka.gui.sql.ResultSetTableCellRenderer
initializes the Renderer with a standard color
ResultSetTableCellRenderer(Color, Color) - Constructor for class weka.gui.sql.ResultSetTableCellRenderer
initializes the Renderer with the given colors
ResultSetTableModel - Class in weka.gui.sql
The model for an SQL ResultSet.
ResultSetTableModel(ResultSet) - Constructor for class weka.gui.sql.ResultSetTableModel
initializes the model, retrieves all rows.
ResultSetTableModel(ResultSet, int) - Constructor for class weka.gui.sql.ResultSetTableModel
initializes the model, retrieves only the given amount of rows (0 means all).
ResultsPanel - Class in weka.gui.experiment
This panel controls simple analysis of experimental results.
ResultsPanel() - Constructor for class weka.gui.experiment.ResultsPanel
Creates the results panel with no initial experiment.
ResultVectorTableModel - Class in weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI
ResultVectorTableModel.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht
Date: Sep 12, 2004
Time: 9:23:31 PM
$ Revision 1.4 $
ResultVectorTableModel(FastVector) - Constructor for class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.ResultVectorTableModel
Constructs a default DefaultTableModel which is a table of zero columns and zero rows.
retainAll(Collection<?>) - Method in class weka.core.Trie
Retains only the elements in this collection that are contained in the specified collection
retrieveDir() - Method in class weka.core.converters.AbstractFileSaver
Gets the directory
retrieveDir() - Method in class weka.core.converters.AbstractSaver
Default implementation throws an IOException.
retrieveDir() - Method in interface weka.core.converters.Saver
Gets the driectory of the output file This method is used in the KnowledgeFlow GUI.
retrieveFile() - Method in class weka.core.converters.AbstractFileLoader
get the File specified as the source
retrieveFile() - Method in class weka.core.converters.AbstractFileSaver
Gets the destination file.
retrieveFile() - Method in class weka.core.converters.ArffLoader
get the File specified as the source
retrieveFile() - Method in interface weka.core.converters.FileSourcedConverter
Return the current source file/ destination file
retrieveInstances() - Method in class weka.experiment.InstanceQuery
Makes a database query using the query set through the -Q option to convert a table into a set of instances
retrieveInstances(String) - Method in class weka.experiment.InstanceQuery
Makes a database query to convert a table into a set of instances
retrieveURL() - Method in class weka.core.converters.ArffLoader
Return the current url
retrieveURL() - Method in class weka.core.converters.LibSVMLoader
Return the current url.
retrieveURL() - Method in class weka.core.converters.SVMLightLoader
Return the current url.
retrieveURL() - Method in interface weka.core.converters.URLSourcedLoader
Return the current url
retrieveURL() - Method in class weka.core.converters.XRFFLoader
Return the current url
returnLeaves(FastVector[]) - Method in class weka.classifiers.trees.m5.RuleNode
Return a list containing all the leaves in the tree
rev() - Method in class weka.core.matrix.DoubleVector
Returns the reverse vector
reverseArcMakesSense(BayesNet, Instances, int, int) - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
reverseArcMakesSense checks whether the arc from iAttributeTail to iAttributeHead exists and reversing does not introduce a cycle
REVERSED - Static variable in interface weka.gui.graphvisualizer.GraphConstants
Types of Edges
reversedArcs(BayesNet) - Method in class weka.classifiers.bayes.net.BIFReader
Count nr of reversed arcs from other network compared to current network
revertNewLines(String) - Static method in class weka.core.Utils
Reverts \r and \n in a string into carriage returns and new lines.
REVISION - Static variable in class weka.core.Version
the revision
RevisionHandler - Interface in weka.core
For classes that should return their source control revision.
RevisionUtils - Class in weka.core
Contains utility functions for handling revisions.
RevisionUtils() - Constructor for class weka.core.RevisionUtils
 
RevisionUtils.Type - Enum in weka.core
Enumeration of source control types.
ridgeTipText() - Method in class weka.classifiers.functions.LinearRegression
Returns the tip text for this property
ridgeTipText() - Method in class weka.classifiers.functions.Logistic
Returns the tip text for this property
ridgeTipText() - Method in class weka.classifiers.functions.RBFNetwork
Returns the tip text for this property
ridgeTipText() - Method in class weka.classifiers.mi.MILR
Returns the tip text for this property
Ridor - Class in weka.classifiers.rules
An implementation of a RIpple-DOwn Rule learner.

It generates a default rule first and then the exceptions for the default rule with the least (weighted) error rate.
Ridor() - Constructor for class weka.classifiers.rules.Ridor
 
RIGHT - Static variable in class weka.classifiers.trees.m5.Rule
 
RIGHT_PARENTHESES - Variable in class weka.experiment.ResultMatrix
the right parentheses for enumerating cols/rows
rightNode() - Method in class weka.classifiers.trees.m5.RuleNode
Get the right child of this node
rightSide(int, Instances) - Method in class weka.classifiers.trees.j48.BinC45Split
Prints the condition satisfied by instances in a subset.
rightSide(int, Instances) - Method in class weka.classifiers.trees.j48.C45Split
Prints the condition satisfied by instances in a subset.
rightSide(int, Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Prints left side of condition satisfied by instances in subset index.
rightSide(int, Instances) - Method in class weka.classifiers.trees.j48.GraftSplit
Prints condition satisfied by instances in subset index.
rightSide(int, Instances) - Method in class weka.classifiers.trees.j48.NBTreeNoSplit
Does nothing because no condition has to be satisfied.
rightSide(int, Instances) - Method in class weka.classifiers.trees.j48.NBTreeSplit
Prints the condition satisfied by instances in a subset.
rightSide(int, Instances) - Method in class weka.classifiers.trees.j48.NoSplit
Does nothing because no condition has to be satisfied.
rightSide(int, Instances) - Method in class weka.classifiers.trees.lmt.ResidualSplit
Prints the condition satisfied by instances in a subset.
RINT - Static variable in interface weka.core.mathematicalexpression.sym
 
RINT - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
RipperRule() - Constructor for class weka.classifiers.rules.JRip.RipperRule
Constructor
RKeyAdapter() - Constructor for class weka.gui.ResultHistoryPanel.RKeyAdapter
 
rmCoveredBySuccessives(Instances, FastVector, int) - Static method in class weka.classifiers.rules.RuleStats
Static utility function to count the data covered by the rules after the given index in the given rules, and then remove them.
RMouseAdapter() - Constructor for class weka.gui.ResultHistoryPanel.RMouseAdapter
 
rndmNum(boolean) - Method in class weka.filters.unsupervised.attribute.RandomProjection
returns a double x such that
x = sqrt(3) * { -1 with prob.
rnorm(int, double, double, Random) - Static method in class weka.core.matrix.Maths
Generates a sample of a normal distribution.
rocAnalysisTipText() - Method in class weka.associations.Tertius
Returns the tip text for this property.
rocToString() - Method in class weka.associations.tertius.Rule
Return a String giving the TP-rate and FP-rate of this rule.
ROOT_FINDER_ACCURACY - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
 
ROOT_FINDER_MAX_ITER - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
How close the root finder for numeric and nominal have to get
ROOT_NODE - Static variable in class weka.core.xml.XMLOptions
the root node.
ROOT_NODE - Static variable in class weka.core.xml.XMLSerialization
the root node of the XML document
rootMeanPriorSquaredError() - Method in class weka.classifiers.Evaluation
Returns the root mean prior squared error.
rootMeanSquaredError() - Method in class weka.classifiers.Evaluation
Returns the root mean squared error.
rootMeanSquaredError() - Method in class weka.classifiers.trees.m5.RuleNode
Get the root mean squared error at this node
rootRelativeSquaredError() - Method in class weka.classifiers.Evaluation
Returns the root relative squared error if the class is numeric.
rotate(double, double, double) - Method in class weka.gui.visualize.PostscriptGraphics
 
rotate(double) - Method in class weka.gui.visualize.PostscriptGraphics
 
RotationForest - Class in weka.classifiers.meta
Class for construction a Rotation Forest.
RotationForest() - Constructor for class weka.classifiers.meta.RotationForest
Constructor.
round(double) - Static method in class weka.core.Utils
Rounds a double to the next nearest integer value.
roundDouble(double, int) - Static method in class weka.core.Utils
Rounds a double to the given number of decimal places.
row(int) - Method in class weka.classifiers.meta.GridSearch.Grid
returns an Enumeration over all pairs in the given row
RPAREN - Static variable in interface weka.core.mathematicalexpression.sym
 
RPAREN - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
rsolve(PaceMatrix, IntVector, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Solves upper-triangular equation
R x = b
On output, the solution is stored in b
RtoP(double[], int) - Static method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Convert from function responses to probabilities
Rule - Class in weka.associations.tertius
Class representing a rule with a body and a head.
Rule(boolean, int, boolean, boolean, boolean, boolean) - Constructor for class weka.associations.tertius.Rule
Constructor for a rule when the counter-instances are not stored, giving all the constraints applied to this rule.
Rule(Instances, boolean, int, boolean, boolean, boolean, boolean) - Constructor for class weka.associations.tertius.Rule
Constructor for a rule when the counter-instances are stored, giving all the constraints applied to this rule.
Rule - Class in weka.classifiers.rules
Abstract class of generic rule
Rule() - Constructor for class weka.classifiers.rules.Rule
 
Rule - Class in weka.classifiers.trees.m5
Generates a single m5 tree or rule
Rule() - Constructor for class weka.classifiers.trees.m5.Rule
Constructor declaration
RuleGeneration - Class in weka.associations
Class implementing the rule generation procedure of the predictive apriori algorithm.
RuleGeneration(ItemSet) - Constructor for class weka.associations.RuleGeneration
Constructor
RuleItem - Class in weka.associations
Class for storing an (class) association rule.
RuleItem() - Constructor for class weka.associations.RuleItem
Constructor for an empty RuleItem
RuleItem(RuleItem) - Constructor for class weka.associations.RuleItem
Constructor that generates a RuleItem out of a given one
RuleItem(ItemSet, ItemSet, int, int, double[], Hashtable) - Constructor for class weka.associations.RuleItem
Constructor
RuleNode - Class in weka.classifiers.trees.m5
Constructs a node for use in an m5 tree or rule
RuleNode(double, double, RuleNode) - Constructor for class weka.classifiers.trees.m5.RuleNode
Creates a new RuleNode instance.
rulesetForOneClass(double, Instances, double, double) - Method in class weka.classifiers.rules.JRip
Build a ruleset for the given class according to the given data
rulesMustContainTipText() - Method in class weka.associations.FPGrowth
Returns the tip text for this property
RuleStats - Class in weka.classifiers.rules
This class implements the statistics functions used in the propositional rule learner, from the simpler ones like count of true/false positive/negatives, filter data based on the ruleset, etc.
RuleStats() - Constructor for class weka.classifiers.rules.RuleStats
Default constructor
RuleStats(Instances, FastVector) - Constructor for class weka.classifiers.rules.RuleStats
Constructor that provides ruleset and data
run() - Method in class weka.associations.Tertius
Run the search.
run() - Method in class weka.gui.beans.Classifier.TrainingTask
 
run() - Method in class weka.gui.beans.FlowRunner
Launch all loaded KnowledgeFlow
run() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel.PlotThread
 
run() - Method in class weka.gui.MemoryUsagePanel.MemoryMonitor
The run method.
RUN_FIELD_NAME - Static variable in class weka.experiment.CrossValidationResultProducer
The name of the key field containing the run number
RUN_FIELD_NAME - Static variable in class weka.experiment.RandomSplitResultProducer
The name of the key field containing the run number
runAssociator(Associator, String[]) - Static method in class weka.associations.AbstractAssociator
runs the associator with the given commandline options
runBasicTest(boolean, boolean, boolean, boolean, boolean, boolean, int, int, boolean, boolean, int, int, FastVector) - Method in class weka.associations.CheckAssociator
Runs a text on the datasets with the given characteristics.
runBasicTest(boolean, boolean, boolean, boolean, boolean, boolean, int, int, int, boolean, boolean, int, int, FastVector) - Method in class weka.associations.CheckAssociator
Runs a text on the datasets with the given characteristics.
runBasicTest(boolean, boolean, boolean, boolean, boolean, boolean, int, int, boolean, boolean, int, int, FastVector) - Method in class weka.attributeSelection.CheckAttributeSelection
Runs a text on the datasets with the given characteristics.
runBasicTest(boolean, boolean, boolean, boolean, boolean, boolean, int, int, int, boolean, boolean, int, int, FastVector) - Method in class weka.attributeSelection.CheckAttributeSelection
Runs a text on the datasets with the given characteristics.
runBasicTest(boolean, boolean, boolean, boolean, boolean, boolean, int, int, boolean, boolean, int, int, int, FastVector) - Method in class weka.classifiers.CheckClassifier
Runs a text on the datasets with the given characteristics.
runBasicTest(boolean, boolean, boolean, boolean, boolean, boolean, int, int, int, boolean, boolean, int, int, int, FastVector) - Method in class weka.classifiers.CheckClassifier
Runs a text on the datasets with the given characteristics.
runBasicTest(boolean, boolean, boolean, boolean, boolean, boolean, int, int, boolean, boolean, int, int, FastVector) - Method in class weka.classifiers.functions.supportVector.CheckKernel
Runs a text on the datasets with the given characteristics.
runBasicTest(boolean, boolean, boolean, boolean, boolean, boolean, int, int, int, boolean, boolean, int, int, FastVector) - Method in class weka.classifiers.functions.supportVector.CheckKernel
Runs a text on the datasets with the given characteristics.
runBasicTest(boolean, boolean, boolean, boolean, boolean, boolean, int, boolean, int, FastVector) - Method in class weka.clusterers.CheckClusterer
Runs a text on the datasets with the given characteristics.
runBasicTest(CheckEstimator.AttrTypes, int, int, int, int, boolean, boolean, int, int, int, FastVector) - Method in class weka.estimators.CheckEstimator
Runs a text on the datasets with the given characteristics.
runBasicTest(CheckEstimator.AttrTypes, int, int, int, int, int, boolean, boolean, int, int, int, FastVector) - Method in class weka.estimators.CheckEstimator
Runs a text on the datasets with the given characteristics.
runCheck(Check, String[]) - Static method in class weka.core.Check
runs the CheckScheme with the given options
runClassifier(Classifier, String[]) - Static method in class weka.classifiers.Classifier
runs the classifier instance with the given options.
runClusterer(Clusterer, String[]) - Static method in class weka.clusterers.AbstractClusterer
runs the clusterer instance with the given options.
runCommand(String) - Method in class weka.gui.SimpleCLIPanel
Executes a simple cli command.
runDataGenerator(DataGenerator, String[]) - Static method in class weka.datagenerators.DataGenerator
runs the datagenerator instance with the given options.
runEvaluator(ASEvaluation, String[]) - Static method in class weka.attributeSelection.ASEvaluation
runs the evaluator with the given commandline options
runExperiment() - Method in class weka.experiment.Experiment
Runs all iterations of the experiment, continuing past errors.
runExperiment() - Method in class weka.experiment.RemoteExperiment
Overides runExperiment in Experiment
runFileLoader(AbstractFileLoader, String[]) - Static method in class weka.core.converters.AbstractFileLoader
runs the given loader with the provided options
runFileSaver(AbstractFileSaver, String[]) - Static method in class weka.core.converters.AbstractFileSaver
runs the given saver with the specified options
runFilter(Filter, String[]) - Static method in class weka.filters.Filter
runs the filter instance with the given options.
runJavadoc(Javadoc, String[]) - Static method in class weka.core.Javadoc
runs the javadoc producer with the given commandline options
RunNumberPanel - Class in weka.gui.experiment
This panel controls configuration of lower and upper run numbers in an experiment.
RunNumberPanel() - Constructor for class weka.gui.experiment.RunNumberPanel
Creates the panel with no initial experiment.
RunNumberPanel(Experiment) - Constructor for class weka.gui.experiment.RunNumberPanel
Creates the panel with the supplied initial experiment.
RunPanel - Class in weka.gui.experiment
This panel controls the running of an experiment.
RunPanel() - Constructor for class weka.gui.experiment.RunPanel
Creates the run panel with no initial experiment.
RunPanel(Experiment) - Constructor for class weka.gui.experiment.RunPanel
Creates the panel with the supplied initial experiment.
runSequentially(TreeMap<Integer, Startable>) - Method in class weka.gui.beans.FlowRunner
 
runsTipText() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
 
runsTipText() - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
 
runsTipText() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
 
runsTipText() - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
 
runsTipText() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
 
runsTipText() - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
 
runsTipText() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
 
runsTipText() - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
 
runTests(boolean, boolean, boolean) - Method in class weka.clusterers.CheckClusterer
Run a battery of tests
runTokenizer(Tokenizer, String[]) - Static method in class weka.core.tokenizers.Tokenizer
initializes the given tokenizer with the given options and runs the tokenizer over all the remaining strings in the options array.

S

s_fileFormatsAvailable - Static variable in class weka.gui.beans.SerializedModelSaver
Available file formats.
s_startupListeners - Static variable in class weka.gui.beans.KnowledgeFlowApp
 
sameClauseAs(Rule) - Method in class weka.associations.tertius.Rule
Test if this rule and another rule correspond to the same clause.
sameClauseTipText() - Method in class weka.associations.Tertius
Returns the tip text for this property.
SAMPLE_SIZE_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
attribute name: Sample Size
sampleSizePercentTipText() - Method in class weka.classifiers.meta.GridSearch
Returns the tip text for this property
sampleSizePercentTipText() - Method in class weka.filters.supervised.instance.Resample
Returns the tip text for this property.
sampleSizePercentTipText() - Method in class weka.filters.unsupervised.instance.Resample
Returns the tip text for this property
sampleSizeTipText() - Method in class weka.attributeSelection.ReliefFAttributeEval
Returns the tip text for this property
sampleSizeTipText() - Method in class weka.classifiers.functions.LeastMedSq
Returns the tip text for this property
sampleSizeTipText() - Method in class weka.filters.unsupervised.instance.ReservoirSample
Returns the tip text for this property
sanitizeFilename(String) - Method in class weka.gui.beans.Saver
makes sure that the filename is valid, i.e., replaces slashes, backslashes and colons with underscores ("_").
sanitizeFilename(String) - Method in class weka.gui.beans.SerializedModelSaver
makes sure that the filename is valid, i.e., replaces slashes, backslashes and colons with underscores ("_").
satisfies(Instance) - Method in class weka.associations.tertius.AttributeValueLiteral
 
satisfies(Instance) - Method in class weka.associations.tertius.Literal
 
save(StringBuffer) - Method in class weka.gui.SaveBuffer
Save a buffer
saveBatch() - Method in class weka.gui.beans.Saver
Saves instances in batch mode
saveBinary(File, Object, Instances) - Static method in class weka.gui.beans.SerializedModelSaver
Save a model in binary form.
saveBuffer() - Method in class weka.gui.experiment.ResultsPanel
Save the currently selected result buffer to a file.
saveBuffer(String) - Method in class weka.gui.explorer.AssociationsPanel
Save the currently selected associator output to a file.
saveBuffer(String) - Method in class weka.gui.explorer.AttributeSelectionPanel
Save the named buffer to a file.
saveBuffer(String) - Method in class weka.gui.explorer.ClassifierPanel
Save the currently selected classifier output to a file.
saveBuffer(String) - Method in class weka.gui.explorer.ClustererPanel
Save the currently selected clusterer output to a file.
SaveBuffer - Class in weka.gui
This class handles the saving of StringBuffers to files.
SaveBuffer(Logger, Component) - Constructor for class weka.gui.SaveBuffer
Constructor
saveChanges() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
if the file is changed it pops up a dialog whether to change the settings.
saveChanges(boolean) - Method in class weka.gui.arffviewer.ArffViewerMainPanel
if the file is changed it pops up a dialog whether to change the settings.
saveClassifier(String, Classifier, Instances) - Method in class weka.gui.explorer.ClassifierPanel
Saves the currently selected classifier
saveClusterer(String, Clusterer, Instances, int[]) - Method in class weka.gui.explorer.ClustererPanel
Saves the currently selected clusterer
saveComponent() - Method in class weka.gui.visualize.PrintableComponent
displays a save dialog for saving the panel to a file.
saveComponent() - Method in interface weka.gui.visualize.PrintableHandler
displays a save dialog for saving the component to a file.
saveComponent() - Method in class weka.gui.visualize.PrintablePanel
displays a save dialog for saving the panel to a file.
saveFile() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
saves the current data into a file
saveFileAs() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
saves the current data into a new file
saveHistory() - Method in class weka.gui.SimpleCLIPanel
saves the current command history in the user's home directory.
saveHistory() - Method in class weka.gui.sql.SqlViewer
saves the history properties of the SqlViewer in the user's home directory.
saveImage(String) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
 
saveInstanceDataTipText() - Method in class weka.classifiers.trees.ADTree
 
saveInstanceDataTipText() - Method in class weka.classifiers.trees.J48
Returns the tip text for this property
saveInstanceDataTipText() - Method in class weka.classifiers.trees.J48graft
Returns the tip text for this property
saveInstanceDataTipText() - Method in class weka.clusterers.Cobweb
Returns the tip text for this property
saveInstancesTipText() - Method in class weka.classifiers.trees.M5P
Returns the tip text for this property
saveInstancesToFile(AbstractFileSaver, Instances) - Method in class weka.gui.explorer.PreprocessPanel
saves the data with the specified saver
saveKOML(File, Object, Instances) - Static method in class weka.gui.beans.SerializedModelSaver
Save a model in KOML deep object serialized XML form.
saveLayout(OutputStream) - Method in class weka.gui.beans.KnowledgeFlowApp
Save the knowledge flow into the OutputStream passed at input.
saveModel() - Method in class weka.gui.beans.Classifier
 
saveModel() - Method in class weka.gui.beans.Clusterer
 
saveObject(Object) - Method in class weka.gui.GenericObjectEditor.GOEPanel
Saves an object to a file selected by the user.
Saver - Interface in weka.core.converters
Interface to something that can save Instances to an output destination in some format.
Saver - Class in weka.gui.beans
Saves data sets using weka.core.converter classes
Saver() - Constructor for class weka.gui.beans.Saver
Contsructor
SAVER_DIALOG - Static variable in class weka.gui.ConverterFileChooser
the saver dialog
SaverBeanInfo - Class in weka.gui.beans
Bean info class for the saver bean
SaverBeanInfo() - Constructor for class weka.gui.beans.SaverBeanInfo
 
SaverCustomizer - Class in weka.gui.beans
GUI Customizer for the saver bean
SaverCustomizer() - Constructor for class weka.gui.beans.SaverCustomizer
Constructor
saveSize() - Method in class weka.gui.sql.SqlViewer
obtains the size of the panel and saves it in the history.
saveToFile(String, Object) - Static method in class weka.core.Debug
writes the serialized object to the speicified file
saveTransformedData(Instances) - Method in class weka.gui.explorer.AttributeSelectionPanel
Popup a SaveDialog for saving the transformed data
saveWeights() - Method in class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd
Call this to have the connection save the current weights.
saveWeights() - Method in class weka.classifiers.functions.neural.NeuralConnection
Call this to have the connection save the current weights.
saveWeights() - Method in class weka.classifiers.functions.neural.NeuralNode
Call this to have the connection save the current weights.
saveWorkingInstancesToFileQ() - Method in class weka.gui.explorer.PreprocessPanel
Queries the user for a file to save instances as, then saves the instances in a background process.
saveXStream(File, Object, Instances) - Static method in class weka.gui.beans.SerializedModelSaver
Save a model in XStream deep object serialized XML form.
scalarMultiply(double) - Method in class weka.core.AlgVector
Computes the scalar product of this vector with a scalar
scale(int) - Method in class weka.gui.beans.BeanVisual
Reduce this BeanVisual's icon size by the given factor
scale(double, double) - Method in class weka.gui.visualize.PostscriptGraphics
 
scaleTipText() - Method in class weka.filters.unsupervised.attribute.Normalize
Returns the tip text for this property.
Scanner - Class in weka.core.mathematicalexpression
A scanner for mathematical expressions.
Scanner(InputStream, SymbolFactory) - Constructor for class weka.core.mathematicalexpression.Scanner
 
Scanner(Reader) - Constructor for class weka.core.mathematicalexpression.Scanner
Creates a new scanner There is also a java.io.InputStream version of this constructor.
Scanner(InputStream) - Constructor for class weka.core.mathematicalexpression.Scanner
Creates a new scanner.
Scanner - Class in weka.filters.unsupervised.instance.subsetbyexpression
A scanner for evaluating whether an Instance is to be included in a subset or not.
Scanner(InputStream, SymbolFactory) - Constructor for class weka.filters.unsupervised.instance.subsetbyexpression.Scanner
 
Scanner(Reader) - Constructor for class weka.filters.unsupervised.instance.subsetbyexpression.Scanner
Creates a new scanner There is also a java.io.InputStream version of this constructor.
Scanner(InputStream) - Constructor for class weka.filters.unsupervised.instance.subsetbyexpression.Scanner
Creates a new scanner.
ScatterPlotMatrix - Class in weka.gui.beans
Bean that encapsulates weka.gui.visualize.MatrixPanel for displaying a scatter plot matrix.
ScatterPlotMatrix() - Constructor for class weka.gui.beans.ScatterPlotMatrix
 
ScatterPlotMatrixBeanInfo - Class in weka.gui.beans
Bean info class for the scatter plot matrix bean
ScatterPlotMatrixBeanInfo() - Constructor for class weka.gui.beans.ScatterPlotMatrixBeanInfo
 
ScatterSearchV1 - Class in weka.attributeSelection
Class for performing the Sequential Scatter Search.
ScatterSearchV1() - Constructor for class weka.attributeSelection.ScatterSearchV1
 
ScatterSearchV1.Subset - Class in weka.attributeSelection
 
Scoreable - Interface in weka.classifiers.bayes.net.search.local
Interface for allowing to score a classifier
scoreTypeTipText() - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
 
scrollToVisible(int, int) - Method in class weka.gui.JTableHelper
Assumes table is contained in a JScrollPane.
scrollToVisible(JTable, int, int) - Static method in class weka.gui.JTableHelper
Assumes table is contained in a JScrollPane.
search() - Method in class weka.associations.Tertius
Search in the space of hypotheses the rules that have the highest confirmation.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.ASSearch
Searches the attribute subset/ranking space.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.BestFirst
Searches the attribute subset space by best first search
search(ASSearch, ASEvaluation, Instances) - Method in class weka.attributeSelection.CheckAttributeSelection
Performs a attribute selection with the given search and evaluation scheme on the provided data.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.ExhaustiveSearch
Searches the attribute subset space using an exhaustive search.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.GeneticSearch
Searches the attribute subset space using a genetic algorithm.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.GreedyStepwise
Searches the attribute subset space by forward selection.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.LinearForwardSelection
Searches the attribute subset space by linear forward selection
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.RaceSearch
Searches the attribute subset space by racing cross validation errors of competing subsets
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.RandomSearch
Searches the attribute subset space randomly.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.Ranker
Kind of a dummy search algorithm.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.RankSearch
Ranks attributes using the specified attribute evaluator and then searches the ranking using the supplied subset evaluator.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.ScatterSearchV1
Searches the attribute subset space using Scatter Search.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Searches the attribute subset space by subset size forward selection
search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm
Search for Bayes network structure using ICS algorithm
search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
search determines the network structure/graph of the network with a genetic search algorithm.
search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.global.HillClimber
search determines the network structure/graph of the network with the Taby algorithm.
search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.global.K2
search determines the network structure/graph of the network with the K2 algorithm, restricted by its initial structure (which can be an empty graph, or a Naive Bayes graph.
search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
search determines the network structure/graph of the network with the repeated hill climbing.
search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
 
search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
search determines the network structure/graph of the network with the Tabu search algorithm.
search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
search determines the network structure/graph of the network with a genetic search algorithm.
search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.local.HillClimber
search determines the network structure/graph of the network with the Taby algorithm.
search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.local.K2
search determines the network structure/graph of the network with the K2 algorithm, restricted by its initial structure (which can be an empty graph, or a Naive Bayes graph.
search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
search determines the network structure/graph of the network
search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
search determines the network structure/graph of the network with the repeated hill climbing.
search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
 
search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
search determines the network structure/graph of the network with the Tabu search algorithm.
search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
 
search(ASEvaluation, Instances) - Method in class weka.classifiers.rules.DTNB.BackwardsWithDelete
 
search() - Method in class weka.gui.arffviewer.ArffPanel
searches for a string in the cells
search() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
searches for a string in the cells
search(Vector, String) - Method in class weka.gui.HierarchyPropertyParser
Helper function to search for the given target string in a given vector in which the elements' value may hopefully is equal to the target.
SEARCH_METHOD_FLOATING - Static variable in class weka.attributeSelection.LinearForwardSelection
 
SEARCH_METHOD_FORWARD - Static variable in class weka.attributeSelection.LinearForwardSelection
search directions
SearchAlgorithm - Class in weka.classifiers.bayes.net.search
This is the base class for all search algorithms for learning Bayes networks.
SearchAlgorithm() - Constructor for class weka.classifiers.bayes.net.search.SearchAlgorithm
c'tor
searchAlgorithmTipText() - Method in class weka.classifiers.bayes.BayesNet
 
searchBackwardsTipText() - Method in class weka.attributeSelection.GreedyStepwise
Returns the tip text for this property
searchFinish() - Method in class weka.core.neighboursearch.PerformanceStats
Signals end of the nearest neighbour search.
searchFinish() - Method in class weka.core.neighboursearch.TreePerformanceStats
Signals end of the nearest neighbour search.
SEARCHPATH_ALL - Static variable in class weka.classifiers.trees.ADTree
search mode: Expand all paths
SEARCHPATH_HEAVIEST - Static variable in class weka.classifiers.trees.ADTree
search mode: Expand the heaviest path
SEARCHPATH_RANDOM - Static variable in class weka.classifiers.trees.ADTree
search mode: Expand a random path
SEARCHPATH_ZPURE - Static variable in class weka.classifiers.trees.ADTree
search mode: Expand the best z-pure path
searchPathTipText() - Method in class weka.classifiers.trees.ADTree
 
searchPercentTipText() - Method in class weka.attributeSelection.RandomSearch
Returns the tip text for this property
searchPoints(int, int, boolean) - Method in class weka.gui.visualize.Plot2D
Pops up a window displaying attribute information on any instances at a point+-plotting_point_size (in panel coordinates)
searchStart() - Method in class weka.core.neighboursearch.PerformanceStats
Signals start of the nearest neighbour search.
searchStart() - Method in class weka.core.neighboursearch.TreePerformanceStats
Signals start of the nearest neighbour search.
searchTerminationTipText() - Method in class weka.attributeSelection.BestFirst
Returns the tip text for this property
searchTerminationTipText() - Method in class weka.attributeSelection.LinearForwardSelection
Returns the tip text for this property
searchTipText() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Returns the tip text for this property
searchTipText() - Method in class weka.classifiers.rules.DecisionTable
Returns the tip text for this property
searchTipText() - Method in class weka.filters.supervised.attribute.AttributeSelection
Returns the tip text for this property
secondChoiceHeuristic(int) - Method in class weka.classifiers.functions.supportVector.RegSMO
applies heuristic for finding candidate that is expected to lead to good gain when applying takeStep together with second candidate.
secondInstanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceJoiner
 
secondInstanceProduced(InstanceEvent) - Method in interface weka.gui.streams.SerialInstanceListener
 
secondValueIndexTipText() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
 
secondValueIndexTipText() - Method in class weka.filters.unsupervised.attribute.SwapValues
 
SEED - Static variable in class weka.associations.PriorEstimation
The random seed used for the random rule generation step.
seedTipText() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
Returns the tip text for this property
seedTipText() - Method in class weka.attributeSelection.GeneticSearch
Returns the tip text for this property
seedTipText() - Method in class weka.attributeSelection.OneRAttributeEval
Returns a string for this option suitable for display in the gui as a tip text
seedTipText() - Method in class weka.attributeSelection.RandomSearch
Returns the tip text for this property
seedTipText() - Method in class weka.attributeSelection.ReliefFAttributeEval
Returns the tip text for this property
seedTipText() - Method in class weka.attributeSelection.ScatterSearchV1
Returns the tip text for this property
seedTipText() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Returns the tip text for this property
seedTipText() - Method in class weka.attributeSelection.WrapperSubsetEval
Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
 
seedTipText() - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
 
seedTipText() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
 
seedTipText() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
 
seedTipText() - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
 
seedTipText() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
 
seedTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
seedTipText() - Method in class weka.classifiers.functions.supportVector.RegOptimizer
Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.functions.VotedPerceptron
Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.functions.Winnow
Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.meta.MultiScheme
Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.RandomizableClassifier
Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.RandomizableMultipleClassifiersCombiner
Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.RandomizableSingleClassifierEnhancer
Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.rules.ConjunctiveRule
Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.rules.JRip
Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.rules.PART
Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.rules.Ridor
Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.trees.J48
Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.trees.RandomForest
Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.trees.RandomTree
Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.trees.REPTree
Returns the tip text for this property
seedTipText() - Method in class weka.clusterers.Cobweb
Returns the tip text for this property
seedTipText() - Method in class weka.clusterers.RandomizableClusterer
Returns the tip text for this property
seedTipText() - Method in class weka.clusterers.RandomizableDensityBasedClusterer
Returns the tip text for this property
seedTipText() - Method in class weka.clusterers.RandomizableSingleClustererEnhancer
Returns the tip text for this property
seedTipText() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Returns the tip text for this property.
seedTipText() - Method in class weka.datagenerators.DataGenerator
Returns the tip text for this property
seedTipText() - Method in class weka.filters.supervised.attribute.ClassOrder
Returns the tip text for this property
seedTipText() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Returns the tip text for this property
seedTipText() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
Returns the tip text for this property
seedTipText() - Method in class weka.filters.unsupervised.attribute.RandomSubset
Returns the tip text for this property.
seedTipText() - Method in class weka.filters.unsupervised.instance.RemoveFolds
Returns the tip text for this property
seedTipText() - Method in class weka.gui.beans.CrossValidationFoldMaker
Tip text for this property
seedTipText() - Method in class weka.gui.beans.TrainTestSplitMaker
Tip text for this property
select(int, int, int, int) - Method in class weka.core.Instances
Implements computation of the kth-smallest element according to Manber's "Introduction to Algorithms".
select(double[], int[], int, int, int, int) - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
Implements computation of the kth-smallest element according to Manber's "Introduction to Algorithms".
select(int, int[], int, int, int) - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
Implements computation of the kth-smallest element according to Manber's "Introduction to Algorithms".
select(int, int[], int, int, int) - Method in class weka.core.neighboursearch.kdtrees.MedianOfWidestDimension
Implements computation of the kth-smallest element according to Manber's "Introduction to Algorithms".
select(String) - Method in class weka.experiment.DatabaseUtils
Executes a SQL SELECT query that returns a ResultSet.
SelectAttributes(ASEvaluation, String[]) - Static method in class weka.attributeSelection.AttributeSelection
Perform attribute selection with a particular evaluator and a set of options specifying search method and input file etc.
SelectAttributes(Instances) - Method in class weka.attributeSelection.AttributeSelection
Perform attribute selection on the supplied training instances.
SelectAttributes(ASEvaluation, String[], Instances) - Static method in class weka.attributeSelection.AttributeSelection
Perform attribute selection with a particular evaluator and a set of options specifying search method and options for the search method and evaluator.
selectAttributesCVSplit(Instances) - Method in class weka.attributeSelection.AttributeSelection
Select attributes for a split of the data.
selectClasses(int, Random) - Method in class weka.classifiers.meta.RotationForest
Selects a non-empty subset of the classes
selectedAttributes() - Method in class weka.attributeSelection.AttributeSelection
get the final selected set of attributes.
SelectedTag - Class in weka.core
Represents a selected value from a finite set of values, where each value is a Tag (i.e.
SelectedTag(int, Tag[]) - Constructor for class weka.core.SelectedTag
Creates a new SelectedTag instance.
SelectedTag(String, Tag[]) - Constructor for class weka.core.SelectedTag
Creates a new SelectedTag instance.
SelectedTagEditor - Class in weka.gui
A PropertyEditor that uses tags, where the tags are obtained from a weka.core.SelectedTag object.
SelectedTagEditor() - Constructor for class weka.gui.SelectedTagEditor
 
selectIndexProbabilistically(double[]) - Method in class weka.classifiers.meta.Decorate
Given cumulative probabilities select a nominal attribute value index
SELECTING - Static variable in class weka.gui.beans.KnowledgeFlowApp
 
SELECTION_BACKWARD - Static variable in class weka.attributeSelection.BestFirst
search direction: backward
SELECTION_BIDIRECTIONAL - Static variable in class weka.attributeSelection.BestFirst
search direction: bidirectional
SELECTION_FORWARD - Static variable in class weka.attributeSelection.BestFirst
search direction: forward
SELECTION_GREEDY - Static variable in class weka.classifiers.functions.LinearRegression
Attribute selection method: Greedy method
SELECTION_M5 - Static variable in class weka.classifiers.functions.LinearRegression
Attribute selection method: M5 method
SELECTION_NONE - Static variable in class weka.classifiers.functions.LinearRegression
Attribute selection method: No attribute selection
selectionThresholdTipText() - Method in class weka.attributeSelection.RaceSearch
Returns the tip text for this property
selectModel(Instances) - Method in class weka.classifiers.trees.j48.BinC45ModelSelection
Selects C4.5-type split for the given dataset.
selectModel(Instances, Instances) - Method in class weka.classifiers.trees.j48.BinC45ModelSelection
Selects C4.5-type split for the given dataset.
selectModel(Instances) - Method in class weka.classifiers.trees.j48.C45ModelSelection
Selects C4.5-type split for the given dataset.
selectModel(Instances, Instances) - Method in class weka.classifiers.trees.j48.C45ModelSelection
Selects C4.5-type split for the given dataset.
selectModel(Instances) - Method in class weka.classifiers.trees.j48.ModelSelection
Selects a model for the given dataset.
selectModel(Instances, Instances) - Method in class weka.classifiers.trees.j48.ModelSelection
Selects a model for the given train data using the given test data
selectModel(Instances) - Method in class weka.classifiers.trees.j48.NBTreeModelSelection
Selects NBTree-type split for the given dataset.
selectModel(Instances, Instances) - Method in class weka.classifiers.trees.j48.NBTreeModelSelection
Selects NBTree-type split for the given dataset.
selectModel(Instances, double[][], double[][]) - Method in class weka.classifiers.trees.lmt.ResidualModelSelection
Selects split based on residuals for the given dataset.
selectModel(Instances) - Method in class weka.classifiers.trees.lmt.ResidualModelSelection
Method not in use
selectModel(Instances, Instances) - Method in class weka.classifiers.trees.lmt.ResidualModelSelection
Method not in use
selectPattern() - Method in class weka.gui.ListSelectorDialog
opens a separate dialog for entering a regex pattern for selecting elements from the provided list
selectProperty() - Method in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Gets the user to select a property of the current resultproducer.
selectRegressions(SimpleLinearRegression[][]) - Method in class weka.classifiers.trees.lmt.LogisticBase
Helper function for cutting back m_regressions to the set of classifiers (corresponsing to the number of LogitBoost iterations) that gave the smallest error.
selectWeightQuantile(Instances, double) - Method in class weka.classifiers.meta.AdaBoostM1
Select only instances with weights that contribute to the specified quantile of the weight distribution
selectWeightQuantile(Instances, double) - Method in class weka.classifiers.meta.LogitBoost
Select only instances with weights that contribute to the specified quantile of the weight distribution
SEND_INSTANCES - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
Command to remove instances from this node and send them to the VisualizePanel.
separable(DoubleVector, int, int, double) - Method in class weka.classifiers.functions.pace.ChisqMixture
Return true if a value can be considered for mixture estimation separately from the data indexed between i0 and i1
separable(DoubleVector, int, int, double) - Method in class weka.classifiers.functions.pace.MixtureDistribution
Return true if a value can be considered for mixture estimatino separately from the data indexed between i0 and i1
separable(DoubleVector, int, int, double) - Method in class weka.classifiers.functions.pace.NormalMixture
Return true if a value can be considered for mixture estimatino separately from the data indexed between i0 and i1
separatingThreshold - Variable in class weka.classifiers.functions.pace.ChisqMixture
the separating threshold value
separatingThreshold - Variable in class weka.classifiers.functions.pace.NormalMixture
the separating threshold
seq(int, int) - Static method in class weka.core.matrix.IntVector
Generates an IntVector that stores all integers inclusively between two integers.
Sequence - Class in weka.associations.gsp
Class representing a sequence of elements/itemsets.
Sequence() - Constructor for class weka.associations.gsp.Sequence
Constructor.
Sequence(FastVector) - Constructor for class weka.associations.gsp.Sequence
Constructor accepting a set of elements as parameter.
Sequence(int) - Constructor for class weka.associations.gsp.Sequence
Constructor accepting an int value as parameter to set the support count.
SequentialDatabase - Class in weka.clusterers.forOPTICSAndDBScan.Databases
SequentialDatabase.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 20, 2004
Time: 1:23:38 PM
$ Revision 1.4 $
SequentialDatabase(Instances) - Constructor for class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase
Constructs a new sequential database and holds the original instances
SERFileFilter - Class in weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI
SERFileFilter.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht
Date: Sep 15, 2004
Time: 6:54:56 PM
$ Revision 1.4 $
SERFileFilter(String, String) - Constructor for class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERFileFilter
 
SERIAL_VERSION_UID - Static variable in class weka.core.SerializationHelper
the field name of serialVersionUID.
SerialInstanceListener - Interface in weka.gui.streams
Defines an interface for objects able to produce two output streams of instances.
SerializationHelper - Class in weka.core
A helper class for determining serialVersionUIDs and checking whether classes contain one and/or need one.
SerializationHelper() - Constructor for class weka.core.SerializationHelper
 
serialize(Object) - Static method in class weka.core.xml.XStream
Serializes the supplied object xml
SERIALIZED_OBJ_FILE_EXTENSION - Static variable in class weka.core.Instances
The filename extension that should be used for bin.
SerializedClassifier - Class in weka.classifiers.misc
A wrapper around a serialized classifier model.
SerializedClassifier() - Constructor for class weka.classifiers.misc.SerializedClassifier
 
serializedClassifierFileTipText() - Method in class weka.filters.supervised.attribute.AddClassification
Returns the tip text for this property.
SerializedInstancesLoader - Class in weka.core.converters
Reads a source that contains serialized Instances.
SerializedInstancesLoader() - Constructor for class weka.core.converters.SerializedInstancesLoader
 
SerializedInstancesSaver - Class in weka.core.converters
Serializes the instances to a file with extension bsi.
SerializedInstancesSaver() - Constructor for class weka.core.converters.SerializedInstancesSaver
Constructor.
SerializedModelSaver - Class in weka.gui.beans
A bean that saves serialized models
SerializedModelSaver() - Constructor for class weka.gui.beans.SerializedModelSaver
Constructor.
SerializedModelSaverBeanInfo - Class in weka.gui.beans
Bean info class for the serialized model saver bean
SerializedModelSaverBeanInfo() - Constructor for class weka.gui.beans.SerializedModelSaverBeanInfo
 
SerializedModelSaverCustomizer - Class in weka.gui.beans
GUI Customizer for the SerializedModelSaver bean
SerializedModelSaverCustomizer() - Constructor for class weka.gui.beans.SerializedModelSaverCustomizer
Constructor
SerializedObject - Class in weka.core
Class for storing an object in serialized form in memory.
SerializedObject(Object) - Constructor for class weka.core.SerializedObject
Creates a new serialized object (without compression).
SerializedObject(Object, boolean) - Constructor for class weka.core.SerializedObject
Creates a new serialized object.
serializePMMLModel(PMMLModel, String) - Static method in class weka.core.pmml.PMMLFactory
Serialize a PMMLModel object that encapsulates a PMML model
serializePMMLModel(PMMLModel, File) - Static method in class weka.core.pmml.PMMLFactory
Serialize a PMMLModel object that encapsulates a PMML model
serializePMMLModel(PMMLModel, OutputStream) - Static method in class weka.core.pmml.PMMLFactory
Serialize a PMMLModel object that encapsulates a PMML model
SerialUIDChanger - Class in weka.core.xml
This class enables one to change the UID of a serialized object and therefore not losing the data stored in the binary format.
SerialUIDChanger() - Constructor for class weka.core.xml.SerialUIDChanger
 
serialVersionUID - Static variable in class weka.classifiers.functions.LibLINEAR
serial UID
serialVersionUID - Static variable in class weka.classifiers.functions.LibSVM
serial UID
SERObject - Class in weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI
SERObject.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht
Date: Sep 15, 2004
Time: 9:43:00 PM
$ Revision 1.4 $
SERObject(FastVector, int, int, double, int, boolean, String, String, int, String) - Constructor for class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject
 
set(int) - Method in class weka.attributeSelection.GeneticSearch.GABitSet
set a bit in the chromosome
set(int, double) - Method in class weka.core.matrix.DoubleVector
Set a single element.
set(double) - Method in class weka.core.matrix.DoubleVector
Set all elements to a value
set(int, int, double) - Method in class weka.core.matrix.DoubleVector
Set some elements to a value
set(int, int, double[], int) - Method in class weka.core.matrix.DoubleVector
Set some elements using a 2-D array
set(DoubleVector) - Method in class weka.core.matrix.DoubleVector
Set the elements using a DoubleVector
set(int, int, DoubleVector, int) - Method in class weka.core.matrix.DoubleVector
Set some elements using a DoubleVector.
set(int) - Method in class weka.core.matrix.IntVector
Sets the value of an element.
set(int, int, int[], int) - Method in class weka.core.matrix.IntVector
Sets the values of elements from an int array.
set(int, int, IntVector, int) - Method in class weka.core.matrix.IntVector
Sets the values of elements from another IntVector.
set(IntVector) - Method in class weka.core.matrix.IntVector
Sets the values of elements from another IntVector.
set(int, int) - Method in class weka.core.matrix.IntVector
Sets a single element.
set(int, int, double) - Method in class weka.core.matrix.Matrix
Set a single element.
set(int, T) - Method in class weka.core.neighboursearch.covertrees.Stack
Sets the ith element in the stack.
set(int, Object) - Method in class weka.gui.CheckBoxList.CheckBoxListModel
Replaces the element at the specified position in this list with the specified element.
setAcuity(double) - Method in class weka.clusterers.Cobweb
set the acuity.
setAdditionalMeasures(String[]) - Method in class weka.experiment.AveragingResultProducer
Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.ClassifierSplitEvaluator
Set a list of method names for additional measures to look for in Classifiers.
setAdditionalMeasures(String[]) - Method in class weka.experiment.CrossValidationResultProducer
Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.DatabaseResultProducer
Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
Set a list of method names for additional measures to look for in Classifiers.
setAdditionalMeasures(String[]) - Method in class weka.experiment.LearningRateResultProducer
Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.RandomSplitResultProducer
Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.RegressionSplitEvaluator
Set a list of method names for additional measures to look for in Classifiers.
setAdditionalMeasures(String[]) - Method in interface weka.experiment.ResultProducer
Sets a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in interface weka.experiment.SplitEvaluator
Sets a list of method names for additional measures to look for in SplitEvaluators.
setAdjustWeights(boolean) - Method in class weka.filters.supervised.instance.SpreadSubsample
Sets whether the instance weights will be adjusted to maintain total weight per class.
setAdvanceDataSetFirst(boolean) - Method in class weka.experiment.Experiment
Set the value of m_AdvanceDataSetFirst.
setAlgorithm(SelectedTag) - Method in class weka.filters.supervised.attribute.PLSFilter
Sets the type of algorithm to use
setAlgorithm(SelectedTag) - Method in class weka.filters.unsupervised.attribute.Wavelet
Sets the type of algorithm to use
setAlgorithmType(SelectedTag) - Method in class weka.classifiers.mi.MILR
Sets the algorithm type.
setAllowUnclassifiedInstances(boolean) - Method in class weka.classifiers.trees.RandomTree
Set the value of AllowUnclassifiedInstances.
setAlpha(double) - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator
Set prior used in probability table estimation
setAlpha(double) - Method in class weka.classifiers.functions.Winnow
Set the value of Alpha.
setAmplitude(double) - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Sets the amplitude multiplier.
setAnimated() - Method in class weka.gui.beans.BeanVisual
Set the animated version of the icon
setAppendPredictedProbabilities(boolean) - Method in class weka.gui.beans.PredictionAppender
Set whether to append predicted probabilities rather than class value (for discrete class data sets)
setAppropriateNodeSize() - Method in class weka.classifiers.bayes.net.GUI
This method sets the node size that is appropriate considering the maximum label size that is present.
setAppropriateNodeSize() - Method in class weka.gui.graphvisualizer.GraphVisualizer
This method sets the node size that is appropriate considering the maximum label size that is present.
setAppropriateSize() - Method in class weka.classifiers.bayes.net.GUI
Sets the preferred size for m_GraphPanel GraphPanel to the minimum size that is neccessary to display the graph.
setAppropriateSize() - Method in class weka.gui.graphvisualizer.GraphVisualizer
Sets the preferred size for m_gp GraphPanel to the minimum size that is neccessary to display the graph.
setArffFile(String) - Method in class weka.gui.streams.InstanceLoader
 
setArffFile(String) - Method in class weka.gui.streams.InstanceSavePanel
 
setArray(int[]) - Method in class weka.core.matrix.IntVector
Sets the internal one-dimensional array.
setArtificialSize(double) - Method in class weka.classifiers.meta.Decorate
Sets factor that determines number of artificial examples to generate.
setAssociatedConnections(Vector) - Method in class weka.gui.beans.MetaBean
 
setAssociator(Associator) - Method in class weka.associations.CheckAssociator
Set the associator to test.
setAssociator(Associator) - Method in class weka.associations.SingleAssociatorEnhancer
Set the base associator.
setAssociator(Associator) - Method in class weka.gui.beans.Associator
Set the associator for this wrapper
setAsText(String) - Method in class weka.gui.CostMatrixEditor
Some objects can be represented as text, but a cost matrix cannot.
setAsText(String) - Method in class weka.gui.GenericArrayEditor
Returns null as we don't support getting/setting values as text.
setAsText(String) - Method in class weka.gui.GenericObjectEditor
Returns null as we don't support getting/setting values as text.
setAsText(String) - Method in class weka.gui.SelectedTagEditor
Sets the current property value as text.
setAsText(String) - Method in class weka.gui.SimpleDateFormatEditor
Sets the date format string.
setAttIndex(int, boolean) - Method in class weka.classifiers.lazy.LBR.Indexes
Changes the boolean value at the specified index in the AttIndexes array
setAttList_Irr(boolean[]) - Method in class weka.datagenerators.classifiers.classification.RDG1
Sets the array that defines which of the attributes are seen to be irrelevant.
setAttribute(int) - Method in class weka.gui.AttributeSummaryPanel
Sets the attribute that statistics will be displayed for.
setAttribute(int) - Method in class weka.gui.AttributeVisualizationPanel
Tells the panel which attribute to visualize.
setAttributeEvaluator(ASEvaluation) - Method in class weka.attributeSelection.FilteredAttributeEval
Set the attribute evaluator to use
setAttributeEvaluator(ASEvaluation) - Method in class weka.attributeSelection.RaceSearch
Set the attribute evaluator to use for generating the ranking.
setAttributeEvaluator(ASEvaluation) - Method in class weka.attributeSelection.RankSearch
Set the attribute evaluator to use for generating the ranking.
setAttributeID(int) - Method in class weka.experiment.ClassifierSplitEvaluator
Set the index of Attibute Identifying the instances
setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.Add
Sets index of the attribute used.
setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.AddNoise
Sets index of the attribute used.
setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.AddValues
Sets index of the attribute used.
setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
Sets the index of the attribute used.
setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.MakeIndicator
Sets index of the attribute used.
setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
Sets index of the attribute used.
setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.SwapValues
Sets index of the attribute used.
setAttributeIndex(String) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Sets index of the attribute used.
setAttributeIndex(String) - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Sets index of the attribute used.
setAttributeIndexes(String) - Method in class weka.filters.unsupervised.attribute.NominalToString
Sets index of the attribute used.
setAttributeIndices(String) - Method in interface weka.core.DistanceFunction
Sets the range of attributes to use in the calculation of the distance.
setAttributeIndices(String) - Method in class weka.core.NormalizableDistance
Sets the range of attributes to use in the calculation of the distance.
setAttributeIndices(String) - Method in class weka.filters.supervised.attribute.Discretize
Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Set which attributes are to be copied (or kept if invert is true)
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.Copy
Set which attributes are to be copied (or kept if invert is true)
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.Discretize
Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.FirstOrder
Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Sets which attributes are to be used for interquartile calculations and outlier/extreme value detection (only numeric attributes among the selection will be used).
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.NominalToBinary
Sets which attributes are to be acted on.
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Sets the columns to use, e.g., first-last or first-3,5-last
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.NumericToNominal
Sets which attributes are to be "nominalized" (only numeric attributes among the selection will be transformed).
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.NumericTransform
Set which attributes are to be transformed (or kept if invert is true).
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.Remove
Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.Reorder
Set which attributes are to be copied (or kept if invert is true)
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Sets which attributes are to be worked on.
setAttributeIndicesArray(int[]) - Method in class weka.filters.supervised.attribute.Discretize
Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Set which attributes are to be copied (or kept if invert is true)
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.Copy
Set which attributes are to be copied (or kept if invert is true)
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.Discretize
Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.FirstOrder
Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Sets which attributes are to be used for interquartile calculations and outlier/extreme value detection (only numeric attributes among the selection will be used).
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.NumericToNominal
Sets which attributes are to be transoformed to nominal.
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.NumericTransform
Set which attributes are to be transformed (or kept if invert is true)
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.Remove
Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.Reorder
Set which attributes are to be copied (or kept if invert is true)
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Sets which attributes are to be processed.
setAttributeName(String) - Method in class weka.filters.unsupervised.attribute.Add
Set the new attribute's name.
setAttributeName(String) - Method in class weka.filters.unsupervised.attribute.AddID
Set the new attribute's name
setAttributeNamePrefix(String) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Set the attribute name prefix.
setAttributeRange(String) - Method in class weka.filters.unsupervised.attribute.StringToNominal
Sets range of indices of the attributes used.
setAttributeSelectionMethod(SelectedTag) - Method in class weka.classifiers.functions.LinearRegression
Sets the method used to select attributes for use in the linear regression.
setAttributeType(SelectedTag) - Method in class weka.filters.unsupervised.attribute.Add
Sets the type of attribute to generate.
setAttributeType(SelectedTag) - Method in class weka.filters.unsupervised.attribute.RemoveType
Sets the attribute type to be deleted by the filter.
setAttributeTypes(Hashtable) - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
Sets the attribute - attribute-type relation to use.
setAttributeTypeString(String) - Method in class weka.filters.unsupervised.attribute.RemoveType
Sets the attribute type to be deleted by the filter.
setAttrIndexRange(String) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Sets which attributes are used in the cluster attributes among the selection will be discretized.
setAtts(int[], boolean) - Method in class weka.classifiers.lazy.LBR.Indexes
Changes the boolean value at the specified index in the InstIndexes array
setAttsToEliminatePerIteration(int) - Method in class weka.attributeSelection.SVMAttributeEval
Set the constant rate of attribute elimination per iteration
setAutoBuild(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron
This will set whether the network is automatically built or if it is left up to the user.
setAutoKeyGeneration(boolean) - Method in class weka.core.converters.DatabaseSaver
En/Dis-ables the automatic generation of a primary key.
setBackground(Color) - Method in class weka.gui.visualize.BMPWriter
sets the background color to use in creating the JPEG
setBackground(Color) - Method in class weka.gui.visualize.JPEGWriter
sets the background color to use in creating the JPEG.
setBackground(Color) - Method in class weka.gui.visualize.PNGWriter
sets the background color to use in creating the JPEG
setBackground(Color) - Method in class weka.gui.visualize.PostscriptGraphics
 
setBagSizePercent(int) - Method in class weka.classifiers.meta.Bagging
Sets the size of each bag, as a percentage of the training set size.
setBagSizePercent(int) - Method in class weka.classifiers.meta.MetaCost
Sets the size of each bag, as a percentage of the training set size.
setBalanceClass(boolean) - Method in class weka.datagenerators.classifiers.classification.Agrawal
Sets whether the class is balanced.
setBalanced(boolean) - Method in class weka.classifiers.functions.Winnow
Set the value of Balanced.
setBallSplitter(BallSplitter) - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor
Sets the ball splitting algorithm to be used by the TopDown constructor.
setBallTreeConstructor(BallTreeConstructor) - Method in class weka.core.neighboursearch.BallTree
Sets the BallTreeConstructor for building the BallTree (default TopDownConstructor).
setBase(double) - Method in class weka.core.neighboursearch.CoverTree
Sets the base to use for expansion constant.
setBaseExperiment(Experiment) - Method in class weka.experiment.RemoteExperiment
Set the base experiment.
setBeanConnection(BeanConnection, Vector) - Method in class weka.gui.beans.xml.XMLBeans
puts the given BeanConnection onto the next null in the given Vector, or at the end of the list, if no null is found.
setBeanContext(BeanContext) - Method in class weka.gui.beans.AbstractDataSource
Set a bean context for this bean
setBeanContext(BeanContext) - Method in class weka.gui.beans.CostBenefitAnalysis
 
setBeanContext(BeanContext) - Method in class weka.gui.beans.DataVisualizer
Set a bean context for this bean
setBeanContext(BeanContext) - Method in class weka.gui.beans.GraphViewer
Set a bean context for this bean
setBeanContext(BeanContext) - Method in class weka.gui.beans.Loader
Set a bean context for this bean
setBeanContext(BeanContext) - Method in class weka.gui.beans.ModelPerformanceChart
Set a bean context for this bean
setBeanContext(BeanContext) - Method in class weka.gui.beans.TextViewer
Set a bean context for this bean
setBeanInstances(Vector, JComponent) - Static method in class weka.gui.beans.BeanInstance
Describe setBeanInstances method here.
setBeta(double) - Method in class weka.classifiers.functions.Winnow
Set the value of Beta.
setBias(double) - Method in class weka.classifiers.functions.LibLINEAR
Sets bias term value (default 1) No bias term is added if value < 0
setBias(double) - Method in class weka.classifiers.misc.VFI
Set the value of the exponential bias towards more confident intervals
setBiasToUniformClass(double) - Method in class weka.filters.supervised.instance.Resample
Sets the bias towards a uniform class.
setBIFFile(String) - Method in class weka.classifiers.bayes.BayesNet
Set name of network in BIF file to compare with
setBIFFile(String) - Method in class weka.classifiers.bayes.net.search.fixed.FromFile
Set name of network in BIF file to read structure from
setBinarizeNumericAttributes(boolean) - Method in class weka.attributeSelection.ChiSquaredAttributeEval
Binarize numeric attributes.
setBinarizeNumericAttributes(boolean) - Method in class weka.attributeSelection.InfoGainAttributeEval
Binarize numeric attributes.
setBinaryAttributesNominal(boolean) - Method in class weka.filters.supervised.attribute.NominalToBinary
Sets if binary attributes are to be treates as nominal ones.
setBinaryAttributesNominal(boolean) - Method in class weka.filters.unsupervised.attribute.NominalToBinary
Sets if binary attributes are to be treates as nominal ones.
setBinarySplits(boolean) - Method in class weka.classifiers.rules.PART
Set the value of binarySplits.
setBinarySplits(boolean) - Method in class weka.classifiers.trees.J48
Set the value of binarySplits.
setBinarySplits(boolean) - Method in class weka.classifiers.trees.J48graft
Set the value of binarySplits.
setBins(int) - Method in class weka.filters.unsupervised.attribute.Discretize
Sets the number of bins to divide each selected numeric attribute into
setBins(int) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Ignored
setBinSplit(boolean) - Method in class weka.classifiers.trees.FT
Set the value of binarySplits.
setBinValue(double) - Method in class weka.clusterers.XMeans
Sets the distance value between true and false of binary attributes.
setBlendFactor(int) - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute
Set the blending factor
setBlendMethod(int) - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute
Set the blending method
setBooleanCols(Range) - Method in class weka.datagenerators.ClusterGenerator
Sets which attributes are boolean.
setBooleanIndices(String) - Method in class weka.datagenerators.ClusterGenerator
Sets which attributes are boolean
setBuildLogisticModels(boolean) - Method in class weka.classifiers.functions.SMO
Set the value of buildLogisticModels.
setBuildLogisticModels(boolean) - Method in class weka.classifiers.mi.MISMO
Set the value of buildLogisticModels.
setBuildRegressionTree(boolean) - Method in class weka.classifiers.trees.m5.M5Base
Set the value of regressionTree.
setButtons() - Method in class weka.gui.sql.ConnectionPanel
sets the buttons according to the connected-state.
setButtons(ListSelectionEvent) - Method in class weka.gui.sql.InfoPanel
sets the state of the buttons according to the selection state of the JList
setButtons() - Method in class weka.gui.sql.QueryPanel
sets the buttons according to the connected-state.
setButtons() - Method in class weka.gui.sql.ResultPanel
sets the state of the buttons
setButtons() - Method in class weka.gui.ViewerDialog
sets the state of the buttons
setC(double) - Method in class weka.classifiers.functions.SMO
Set the value of C.
setC(double) - Method in class weka.classifiers.functions.SMOreg
Set the value of C.
setC(double) - Method in class weka.classifiers.mi.MISMO
Set the value of C.
setC(double) - Method in class weka.classifiers.mi.MISVM
Set the value of C.
setCacheKeyName(String) - Method in class weka.experiment.DatabaseResultListener
Set the value of CacheKeyName.
setCacheSize(double) - Method in class weka.classifiers.functions.LibSVM
Sets cache memory size in MB (default 40)
setCacheSize(int) - Method in class weka.classifiers.functions.supportVector.CachedKernel
Sets the size of the cache to use (a prime number)
setCacheSize(int) - Method in class weka.classifiers.functions.supportVector.StringKernel
Sets the size of the cache to use (a prime number)
setCalcOutOfBag(boolean) - Method in class weka.classifiers.meta.Bagging
Set whether the out of bag error is calculated.
setCalculateStdDevs(boolean) - Method in class weka.experiment.AveragingResultProducer
Set the value of CalculateStdDevs.
setCancelButton(boolean) - Method in class weka.gui.GenericObjectEditor.GOEPanel
Enables/disables the cancel button.
setCanChangeClassInDialog(boolean) - Method in class weka.gui.GenericObjectEditor
Sets whether the user can change the class in the dialog.
setCapabilities(Capabilities) - Method in class weka.core.FindWithCapabilities
Uses the given Capabilities for the search.
setCapabilities(Capabilities) - Method in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
sets the initial capabilities.
setCapabilitiesFilter(Capabilities) - Method in class weka.gui.ConverterFileChooser
sets the capabilities that the savers must have.
setCapabilitiesFilter(Capabilities) - Method in class weka.gui.GenericObjectEditor
Sets the capabilities to use for filtering.
setCapacity(int) - Method in class weka.core.FastVector
Sets the vector's capacity to the given value.
setCapacity(int) - Method in class weka.core.matrix.DoubleVector
Sets the capacity of the vector
setCapacity(int) - Method in class weka.core.matrix.IntVector
Sets the capacity of the vector
setCar(boolean) - Method in class weka.associations.Apriori
Sets class association rule mining
setCar(boolean) - Method in class weka.associations.PredictiveApriori
Sets class association rule mining
setCardinality(int) - Method in class weka.datagenerators.classifiers.classification.BayesNet
Sets the cardinality of the attributes (incl class attribute)
setCell(int, int, Object) - Method in class weka.classifiers.CostMatrix
Set the value of a particular cell in the matrix
setCellValue(double, double, double, int) - Method in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell
Set the value of a cell.
setCenter(double) - Method in class weka.gui.treevisualizer.Node
Set the value of center.
setCenterData(boolean) - Method in class weka.attributeSelection.PrincipalComponents
Set whether to center (rather than standardize) the data.
setCenterData(boolean) - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
Set whether to center (rather than standardize) the data.
setCenteredLocation() - Method in class weka.gui.arffviewer.ArffViewer
positions the window at the center of the screen
setChanged(boolean) - Method in class weka.gui.arffviewer.ArffPanel
can only reset the changed state to FALSE
setChar(Character) - Method in class weka.core.Trie.TrieNode
sets the character this node represents
setCharSet(String) - Method in class weka.core.converters.TextDirectoryLoader
Set the character set to use when reading text files (an empty string indicates that the default character set will be used).
setChecked(boolean) - Method in class weka.gui.CheckBoxList.CheckBoxListItem
sets the checked state of the item
setChecked(int, boolean) - Method in class weka.gui.CheckBoxList.CheckBoxListModel
sets the checked state of the element at the given index
setChecked(int, boolean) - Method in class weka.gui.CheckBoxList
sets the checked state of the element at the given index
setCheckErrorRate(boolean) - Method in class weka.classifiers.rules.JRip
Sets whether to check for error rate is in stopping criterion
setChecksTurnedOff(boolean) - Method in class weka.classifiers.functions.SMO
Disables or enables the checks (which could be time-consuming).
setChecksTurnedOff(boolean) - Method in class weka.classifiers.functions.supportVector.Kernel
Disables or enables the checks (which could be time-consuming).
setChecksTurnedOff(boolean) - Method in class weka.classifiers.mi.MISMO
Disables or enables the checks (which could be time-consuming).
setChecksTurnedOff(boolean) - Method in class weka.filters.unsupervised.attribute.KernelFilter
Disables or enables the checks (which could be time-consuming).
setChildForBranch(int, PredictionNode) - Method in class weka.classifiers.trees.adtree.Splitter
Sets the child for a branch of the split.
setChildForBranch(int, PredictionNode) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit
Sets the child for a branch of the split.
setChildForBranch(int, PredictionNode) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit
Sets the child for a branch of the split.
setChildForBranch(int, LADTree.PredictionNode) - Method in class weka.classifiers.trees.LADTree.Splitter
 
setChildForBranch(int, LADTree.PredictionNode) - Method in class weka.classifiers.trees.LADTree.TwoWayNominalSplit
 
setChildForBranch(int, LADTree.PredictionNode) - Method in class weka.classifiers.trees.LADTree.TwoWayNumericSplit
 
setChromosome(BitSet) - Method in class weka.attributeSelection.GeneticSearch.GABitSet
set the chromosome
setCindex(int, double, double) - Method in class weka.gui.visualize.AttributePanel
Set the index of the attribute by which to colour the data.
setCindex(int) - Method in class weka.gui.visualize.AttributePanel
Set the index of the attribute by which to colour the data.
setCindex(int) - Method in class weka.gui.visualize.ClassPanel
Set the index of the attribute to display coloured labels for
setCindex(int) - Method in class weka.gui.visualize.Plot2D
Set the index of the attribute to use for colouring
setCindex(int) - Method in class weka.gui.visualize.PlotData2D
Set the colouring index of the data
setCindex(int) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
Set the index of the attribute to use for colouring
setClass(Attribute) - Method in class weka.core.Instances
Sets the class attribute.
setClassColumn(String) - Method in class weka.gui.beans.ClassAssigner
 
setClassFlag(boolean) - Method in class weka.datagenerators.ClusterGenerator
Sets the class flag, if class flag is set, the cluster is listed as class atrribute in an extra attribute.
setClassForIRStatistics(int) - Method in class weka.experiment.ClassifierSplitEvaluator
Set the value of ClassForIRStatistics.
setClassification(boolean) - Method in class weka.associations.Tertius
Set the value of classification.
setClassifier(Classifier) - Method in class weka.attributeSelection.ClassifierSubsetEval
Set the classifier to use for accuracy estimation
setClassifier(Classifier) - Method in class weka.attributeSelection.WrapperSubsetEval
Set the classifier to use for accuracy estimation
setClassifier(Classifier) - Method in class weka.classifiers.BVDecompose
Set the classifiers being analysed
setClassifier(Classifier) - Method in class weka.classifiers.BVDecomposeSegCVSub
Set the classifiers being analysed
setClassifier(Classifier) - Method in class weka.classifiers.CheckClassifier
Set the classifier for boosting.
setClassifier(Classifier) - Method in class weka.classifiers.CheckSource
Sets the classifier to use for the comparison.
setClassifier(Classifier) - Method in class weka.classifiers.meta.GridSearch
Set the base learner.
setClassifier(Classifier) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Set the base learner.
setClassifier(Classifier) - Method in class weka.classifiers.SingleClassifierEnhancer
Set the base learner.
setClassifier(Classifier) - Method in class weka.experiment.ClassifierSplitEvaluator
Sets the classifier.
setClassifier(Classifier) - Method in class weka.experiment.RegressionSplitEvaluator
Sets the classifier.
setClassifier(Classifier) - Method in class weka.filters.supervised.attribute.AddClassification
Sets the classifier to classify instances with.
setClassifier(Classifier) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Sets the classifier to classify instances with.
setClassifier(Classifier) - Method in class weka.gui.beans.BatchClassifierEvent
Set the classifier
setClassifier(Classifier) - Method in class weka.gui.beans.IncrementalClassifierEvent
 
setClassifier(Classifier) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Set the classifier to use.
setClassifier(Classifier) - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
Set a classifier to use
setClassifier(Classifier) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
Set the classifier to use
setClassifierName(String) - Method in class weka.experiment.ClassifierSplitEvaluator
Set the Classifier to use, given it's class name.
setClassifierName(String) - Method in class weka.experiment.RegressionSplitEvaluator
Set the Classifier to use, given it's class name.
setClassifiers(Classifier[]) - Method in class weka.classifiers.meta.MultiScheme
Sets the list of possible classifers to choose from.
setClassifiers(Classifier[]) - Method in class weka.classifiers.MultipleClassifiersCombiner
Sets the list of possible classifers to choose from.
setClassifierTemplate(Classifier) - Method in class weka.gui.beans.Classifier
Set the classifier for this wrapper
setClassifyIterations(int) - Method in class weka.classifiers.BVDecomposeSegCVSub
Sets the number of times an instance is classified
setClassIndex(int) - Method in class weka.associations.Apriori
Sets the class index
setClassIndex(int) - Method in interface weka.associations.CARuleMiner
Sets the class index for the class association rule miner
setClassIndex(int) - Method in class weka.associations.FilteredAssociator
Sets the class index
setClassIndex(int) - Method in class weka.associations.PredictiveApriori
Sets the class index
setClassIndex(int) - Method in class weka.associations.Tertius
Set the value of classIndex.
setClassIndex(int) - Method in class weka.classifiers.BVDecompose
Sets index of attribute to discretize on
setClassIndex(int) - Method in class weka.classifiers.BVDecomposeSegCVSub
Sets index of attribute to discretize on
setClassIndex(int) - Method in class weka.classifiers.CheckSource
Sets the class index of the dataset.
setClassIndex(String) - Method in class weka.core.converters.LibSVMSaver
Sets index of the class attribute.
setClassIndex(String) - Method in class weka.core.converters.SVMLightSaver
Sets index of the class attribute.
setClassIndex(String) - Method in class weka.core.converters.XRFFSaver
Sets index of the class attribute.
setClassIndex(String) - Method in class weka.core.FindWithCapabilities
sets the class index, -1 for none, first and last are also valid.
setClassIndex(int) - Method in class weka.core.Instances
Sets the class index of the set.
setClassIndex(int) - Method in class weka.core.TestInstances
sets the class index (0-based)
setClassIndex(int) - Method in class weka.filters.CheckSource
Sets the class index of the dataset.
setClassIndex(String) - Method in class weka.filters.unsupervised.attribute.ClassAssigner
sets the class index.
setClassIndex(int) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Sets the attribute on which misclassifications are based.
setClassMissing() - Method in class weka.core.Instance
Sets the class value of an instance to be "missing".
setClassname(String) - Method in class weka.core.AllJavadoc
sets the classname of the class to generate the Javadoc for
setClassname(String) - Method in class weka.core.Javadoc
sets the classname of the class to generate the Javadoc for
setClassname(String) - Method in class weka.core.ListOptions
sets the classname of the class to generate the Javadoc for
setClassName(String) - Method in class weka.filters.unsupervised.attribute.NumericTransform
Sets the class containing the transformation method.
setClassOrder(int) - Method in class weka.filters.supervised.attribute.ClassOrder
Set the wanted class order
setClassType(int) - Method in class weka.core.TestInstances
sets the class attribute type
setClassType(Class) - Method in class weka.gui.GenericObjectEditor
Sets the class of values that can be edited.
setClassValue(double) - Method in class weka.core.Instance
Sets the class value of an instance to the given value (internal floating-point format).
setClassValue(String) - Method in class weka.core.Instance
Sets the class value of an instance to the given value.
setClassValue(String) - Method in class weka.filters.supervised.instance.SMOTE
Sets the index of the class value to which SMOTE should be applied.
setClassValue(String) - Method in class weka.gui.beans.ClassValuePicker
Set the class value index considered to be the "positive" class value.
setClearEachDataset(boolean) - Method in class weka.gui.streams.InstanceViewer
 
setClip(int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics
Not implemented
setClip(Shape) - Method in class weka.gui.visualize.PostscriptGraphics
Not implemented
setCloseTo(double) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Set the "close to" number.
setCloseToDefault(double) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Set the "close to" default.
setCloseToTolerance(double) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Set the "close to" Tolerance.
setClusterDefinitions(ClusterDefinition[]) - Method in class weka.datagenerators.clusterers.SubspaceCluster
sets the clusters to use
setClusterer(Clusterer) - Method in class weka.attributeSelection.UnsupervisedSubsetEvaluator
Set the clusterer to use
setClusterer(Clusterer) - Method in class weka.classifiers.meta.ClassificationViaClustering
Set the base clusterer.
setClusterer(Clusterer) - Method in class weka.clusterers.CheckClusterer
Set the clusterer for testing.
setClusterer(Clusterer) - Method in class weka.clusterers.ClusterEvaluation
set the clusterer
setClusterer(Clusterer) - Method in class weka.clusterers.MakeDensityBasedClusterer
Sets the clusterer to wrap.
setClusterer(Clusterer) - Method in class weka.clusterers.SingleClustererEnhancer
Set the base clusterer.
setClusterer(DensityBasedClusterer) - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
Sets the clusterer.
setClusterer(Clusterer) - Method in class weka.filters.unsupervised.attribute.AddCluster
Sets the clusterer to assign clusters with.
setClusterer(Clusterer) - Method in class weka.gui.beans.Clusterer
Set the clusterer for this wrapper
setClustererName(String) - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
Set the Clusterer to use, given it's class name.
setClusteringSeed(int) - Method in class weka.classifiers.functions.RBFNetwork
Set the random seed to be passed on to K-means.
setClusterLabel(int) - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
Sets the clusterID (cluster), to which this DataObject belongs to
setClusterLabel(int) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
Sets the clusterID (cluster), to which this DataObject belongs to
setClusterLabel(int) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
Sets the clusterID (cluster), to which this DataObject belongs to
setClusterSubType(SelectedTag) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Sets the cluster sub type.
setClusterType(SelectedTag) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Sets the cluster type.
setCoef0(double) - Method in class weka.classifiers.functions.LibSVM
Sets coef (default 0)
setColHidden(int, boolean) - Method in class weka.experiment.ResultMatrix
sets the hidden status of the column (if the index is valid)
setColName(int, String) - Method in class weka.experiment.ResultMatrix
sets the name of the column (if the index is valid)
setColNameWidth(int) - Method in class weka.experiment.ResultMatrix
sets the width for the column names (0 = optimal)
setColor(Color) - Method in class weka.gui.treevisualizer.Node
Set the value of color.
setColor(Color) - Method in class weka.gui.visualize.PostscriptGraphics
Set current pen color.
setColOrder(int[]) - Method in class weka.experiment.ResultMatrix
sets the ordering of the columns, null means default
setColoringIndex(int) - Method in class weka.gui.AttributeVisualizationPanel
Set the coloring (class) index for the plot
setColoringIndex(int) - Method in class weka.gui.beans.AttributeSummarizer
Set the coloring index for the attribute summary plots
setColors(FastVector) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Set a vector of Color objects for the classes
setColourIndex(int) - Method in class weka.gui.visualize.VisualizePanel
Sets the index used for colouring.
setColours(FastVector) - Method in class weka.gui.visualize.AttributePanel
Sets a list of colours to use for colouring data points
setColours(FastVector) - Method in class weka.gui.visualize.ClassPanel
Set a list of colours to use for colouring labels
setColours(FastVector) - Method in class weka.gui.visualize.Plot2D
Set a list of colours to use when colouring points according to class values or cluster numbers
setColours(FastVector) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
Set a list of colours to use for plotting points
setColumn(int, double[]) - Method in class weka.core.Matrix
Deprecated.
Sets a column of the matrix to the given column.
setColumnDimension(int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Set the column dimenion of the matrix
setCombination(SelectedTag) - Method in class weka.attributeSelection.ScatterSearchV1
Set the kind of combination
setCombinationRule(SelectedTag) - Method in class weka.classifiers.meta.Vote
Sets the combination rule to use.
setComboSizes() - Method in class weka.gui.experiment.ResultsPanel
Sets the combo-boxes to a fixed size so they don't take up too much room that would be better devoted to the test output box.
setComplexityParameter(double) - Method in class weka.attributeSelection.SVMAttributeEval
Set the value of C for SMO
setComponent(JComponent) - Method in class weka.gui.visualize.JComponentWriter
sets the component to print to an output format
setComposite(Composite) - Method in class weka.gui.visualize.PostscriptGraphics
 
setCompressOutput(boolean) - Method in class weka.core.converters.ArffSaver
Sets whether to compress the output.
setCompressOutput(boolean) - Method in class weka.core.converters.XRFFSaver
Sets whether to compress the output.
setConfidenceFactor(float) - Method in class weka.classifiers.rules.PART
Set the value of CF.
setConfidenceFactor(float) - Method in class weka.classifiers.trees.J48
Set the value of CF.
setConfidenceFactor(float) - Method in class weka.classifiers.trees.J48graft
Set the value of CF.
setConfirmationThreshold(double) - Method in class weka.associations.Tertius
Set the value of confirmationThreshold.
setConfirmationValues(int) - Method in class weka.associations.Tertius
Set the value of confirmationValues.
setConfirmExit(boolean) - Method in class weka.gui.arffviewer.ArffViewer
whether to present a MessageBox on Exit or not
setConfirmExit(boolean) - Method in class weka.gui.arffviewer.ArffViewerMainPanel
whether to present a MessageBox on Exit or not
setConnections(Vector) - Static method in class weka.gui.beans.BeanConnection
Describe setConnections method here.
setConnectPoints(boolean[]) - Method in class weka.gui.visualize.PlotData2D
Set whether consecutive points should be connected by lines
setConnectPoints(FastVector) - Method in class weka.gui.visualize.PlotData2D
Set whether consecutive points should be connected by lines
setConsequent(double) - Method in class weka.classifiers.rules.JRip.RipperRule
Sets the internal representation of the class label to be predicted
setConservativeForwardSelection(boolean) - Method in class weka.attributeSelection.GreedyStepwise
Set whether attributes should continue to be added during a forward search as long as merit does not decrease
setContainChildBalls(boolean) - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
Sets whether if a parent ball should completely enclose its two child balls.
setConvertNominal(boolean) - Method in class weka.classifiers.trees.LMT
Set the value of convertNominal.
setConvertNominalToBinary(boolean) - Method in class weka.classifiers.functions.LibLINEAR
Whether to turn on conversion of nominal attributes to binary.
setCoreConvertersOnly(boolean) - Method in class weka.gui.ConverterFileChooser
Whether to display only the hardocded core converters.
setCoreDistance(double) - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
Sets a new coreDistance for this dataObject
setCoreDistance(double) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
Sets a new coreDistance for this dataObject
setCoreDistance(double) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
Sets a new coreDistance for this dataObject
setCoreDistanceColor(Color) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel
Sets a new color for the coreDistance
setCost(double) - Method in class weka.classifiers.functions.LibLINEAR
Sets the cost parameter C (default 1)
setCost(double) - Method in class weka.classifiers.functions.LibSVM
Sets the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
setCostMatrix(CostMatrix) - Method in class weka.attributeSelection.CostSensitiveASEvaluation
Sets the misclassification cost matrix.
setCostMatrix(CostMatrix) - Method in class weka.classifiers.meta.CostSensitiveClassifier
Sets the misclassification cost matrix.
setCostMatrix(CostMatrix) - Method in class weka.classifiers.meta.MetaCost
Sets the misclassification cost matrix.
setCostMatrixSource(SelectedTag) - Method in class weka.attributeSelection.CostSensitiveASEvaluation
Sets the source location of the cost matrix.
setCostMatrixSource(SelectedTag) - Method in class weka.classifiers.meta.CostSensitiveClassifier
Sets the source location of the cost matrix.
setCostMatrixSource(SelectedTag) - Method in class weka.classifiers.meta.MetaCost
Sets the source location of the cost matrix.
setCount(int, double) - Method in class weka.experiment.ResultMatrix
sets the count for the row (if the index is valid)
setCounter(int) - Method in class weka.associations.ItemSet
Sets the counter
setCountWidth(int) - Method in class weka.experiment.ResultMatrix
sets the width for the counts (0 = optimal)
setCreatorApplication(Document) - Method in class weka.classifiers.pmml.consumer.PMMLClassifier
Set the name of the application (if specified) that created this model
setCreatorApplication(Document) - Method in interface weka.core.pmml.PMMLModel
Set the name of the application (if specified) that created this.
setCriticalValue(int) - Method in class weka.classifiers.bayes.AODEsr
Sets the critical value
setCrossoverProb(double) - Method in class weka.attributeSelection.GeneticSearch
set the probability of crossover
setCrossVal(int) - Method in class weka.classifiers.rules.DecisionTable
Sets the number of folds for cross validation (1 = leave one out)
setCrossValidate(boolean) - Method in class weka.classifiers.lazy.IBk
Sets whether hold-one-out cross-validation will be used to select the best k value.
setCurrentFilename(String) - Method in class weka.gui.arffviewer.ArffViewerMainPanel
sets the filename of the current tab
setCurrentInstance(Instance) - Method in class weka.gui.beans.IncrementalClassifierEvent
Set the current instance for this event
setCurveData(PlotData2D, Attribute) - Method in class weka.gui.beans.CostBenefitAnalysis
Set the threshold curve data to use.
setCustomColour(Color) - Method in class weka.gui.visualize.PlotData2D
Set a custom colour to use for this plot.
setCustomHeight(int) - Method in class weka.gui.visualize.JComponentWriter
sets the custom height to use
setCustomName(String) - Method in class weka.gui.beans.Associator
Set a custom (descriptive) name for this bean
setCustomName(String) - Method in interface weka.gui.beans.BeanCommon
Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.ClassAssigner
Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.Classifier
Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator
Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.ClassValuePicker
Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.Clusterer
Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.ClustererPerformanceEvaluator
Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.CostBenefitAnalysis
Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.CrossValidationFoldMaker
Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.Filter
Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.IncrementalClassifierEvaluator
Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.InstanceStreamToBatchMaker
Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.Loader
Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.MetaBean
Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.PredictionAppender
Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.Saver
Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.SerializedModelSaver
Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.StripChart
Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.TestSetMaker
Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.TextViewer
Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.TrainingSetMaker
Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.TrainTestSplitMaker
Set a custom (descriptive) name for this bean
setCustomWidth(int) - Method in class weka.gui.visualize.JComponentWriter
sets the custom width to use
setCutoff(double) - Method in class weka.clusterers.Cobweb
set the cutoff
setCutOffFactor(double) - Method in class weka.clusterers.XMeans
Sets a new cutoff factor.
setCVisible(boolean) - Method in class weka.gui.treevisualizer.Node
Sets all the children of this node either to visible or invisible
setCVParameters(Object[]) - Method in class weka.classifiers.meta.CVParameterSelection
Set method for CVParameters.
setCVType(SelectedTag) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
set cross validation strategy to be used in searching for networks.
setData(Instances) - Method in class weka.classifiers.bayes.net.EditableBayesNet
Assuming a network structure is defined and we want to learn from data, the data set must be put if correct order first and possibly discretized/missing values filled in before proceeding to CPT learning.
setData(Instances) - Method in class weka.classifiers.rules.RuleStats
Set the data of the stats, overwriting the old one if any
setDatabase_distanceType(String) - Method in class weka.clusterers.DBScan
Sets a new distance-type
setDatabase_distanceType(String) - Method in class weka.clusterers.OPTICS
Sets a new distance-type
setDatabase_Type(String) - Method in class weka.clusterers.DBScan
Sets a new database-type
setDatabase_Type(String) - Method in class weka.clusterers.OPTICS
Sets a new database-type
setDatabaseOutput(File) - Method in class weka.clusterers.OPTICS
Sets the the file to save the generated database to.
setDatabaseURL(String) - Method in class weka.experiment.DatabaseUtils
Set the value of DatabaseURL.
setDataFileName(String) - Method in class weka.classifiers.BVDecompose
Sets the name of the data file used for the decomposition
setDataFileName(String) - Method in class weka.classifiers.BVDecomposeSegCVSub
Sets the name of the dataset file.
setDataGenerator(DataGenerator) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Set the data generator to use for generating new instances
setDataGenerator(DataGenerator) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
Set the density estimator to use
setDataPoint(double[]) - Method in class weka.gui.beans.ChartEvent
Set the data point
setDataSeqID(int) - Method in class weka.associations.GeneralizedSequentialPatterns
Sets the attribute representing the data sequence ID.
setDataset(File) - Method in class weka.classifiers.CheckSource
Sets the dataset to use for testing.
setDataset(Instances) - Method in class weka.core.Instance
Sets the reference to the dataset.
setDataset(File) - Method in class weka.filters.CheckSource
Sets the dataset to use for testing.
setDataSet(PlotData2D, Attribute) - Method in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
Set the threshold data for the panel to use.
setDatasetFormat(Instances) - Method in class weka.datagenerators.DataGenerator
Sets the format of the dataset that is to be generated.
setDatasetKeyColumns(Range) - Method in class weka.experiment.PairedTTester
Set the value of DatasetKeyColumns.
setDatasetKeyColumns(Range) - Method in interface weka.experiment.Tester
Set the value of DatasetKeyColumns.
setDatasetKeyFromDialog() - Method in class weka.gui.experiment.ResultsPanel
 
setDatasets(DefaultListModel) - Method in class weka.experiment.Experiment
Set the datasets to use in the experiment
setDatasets(DefaultListModel) - Method in class weka.experiment.RemoteExperiment
Set the datasets to use in the experiment
setDataType(int) - Method in class weka.gui.beans.xml.XMLBeans
sets what kind of data is to be read/written
setDateAttributes(String) - Method in class weka.core.converters.CSVLoader
Set the attribute range to be forced to type date.
setDateFormat(String) - Method in class weka.core.converters.CSVLoader
Set the format to use for parsing date values.
setDateFormat(String) - Method in class weka.filters.unsupervised.attribute.Add
Set the date format, complying to ISO-8601.
setDateFormat(String) - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
Sets the output date format.
setDateFormat(SimpleDateFormat) - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
Sets the output date format.
setDB(boolean) - Method in class weka.gui.beans.Loader
 
setDebug(boolean) - Method in class weka.associations.GeneralizedSequentialPatterns
Set debugging mode.
setDebug(boolean) - Method in class weka.attributeSelection.RaceSearch
Set whether verbose output should be generated.
setDebug(boolean) - Method in class weka.attributeSelection.ScatterSearchV1
Set whether verbose output should be generated.
setDebug(boolean) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
 
setDebug(boolean) - Method in class weka.classifiers.BVDecompose
Sets debugging mode
setDebug(boolean) - Method in class weka.classifiers.BVDecomposeSegCVSub
Sets debugging mode
setDebug(boolean) - Method in class weka.classifiers.Classifier
Set debugging mode.
setDebug(boolean) - Method in class weka.classifiers.functions.LeastMedSq
sets whether or not debugging output shouild be printed
setDebug(boolean) - Method in class weka.classifiers.functions.LinearRegression
Controls whether debugging output will be printed
setDebug(boolean) - Method in class weka.classifiers.functions.Logistic
Sets whether debugging output will be printed.
setDebug(boolean) - Method in class weka.classifiers.functions.PaceRegression
Controls whether debugging output will be printed
setDebug(boolean) - Method in class weka.classifiers.functions.supportVector.Kernel
Enables or disables the output of debug information (if the derived kernel supports that)
setDebug(boolean) - Method in class weka.classifiers.meta.MultiScheme
Set debugging mode
setDebug(boolean) - Method in class weka.classifiers.rules.JRip
Sets whether debug information is output to the console
setDebug(boolean) - Method in class weka.clusterers.EM
Set debug mode - verbose output
setDebug(boolean) - Method in class weka.clusterers.HierarchicalClusterer
Set debugging mode.
setDebug(boolean) - Method in class weka.clusterers.sIB
Set debug mode - verbose output
setDebug(boolean) - Method in class weka.core.Check
Set debugging mode
setDebug(boolean) - Method in class weka.core.converters.TextDirectoryLoader
Sets whether to print some debug information.
setDebug(boolean) - Method in class weka.core.Debug.Random
sets whether to print the generated random values or not
setDebug(boolean) - Method in class weka.core.Optimization
Set whether in debug mode
setDebug(boolean) - Method in class weka.datagenerators.DataGenerator
Sets the debug flag.
setDebug(boolean) - Method in class weka.estimators.CheckEstimator
Set debugging mode
setDebug(boolean) - Method in class weka.estimators.Estimator
Set debugging mode.
setDebug(boolean) - Method in class weka.experiment.DatabaseUtils
Sets whether there should be printed some debugging output to stderr or not.
setDebug(boolean) - Method in class weka.filters.SimpleFilter
Sets the debugging mode
setDebug(boolean) - Method in class weka.filters.unsupervised.attribute.AddExpression
Set debug mode.
setDebug(boolean) - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion
sets debug mode on/off.
setDebug(boolean) - Method in class weka.gui.streams.InstanceCounter
 
setDebug(boolean) - Method in class weka.gui.streams.InstanceJoiner
 
setDebug(boolean) - Method in class weka.gui.streams.InstanceLoader
 
setDebug(boolean) - Method in class weka.gui.streams.InstanceSavePanel
 
setDebug(boolean) - Method in class weka.gui.streams.InstanceTable
 
setDebug(boolean) - Method in class weka.gui.streams.InstanceViewer
 
setDebugLevel(int) - Method in class weka.clusterers.XMeans
Sets the debug level.
setDebugVectorsFile(File) - Method in class weka.clusterers.XMeans
Sets the file that has the random vectors stored.
setDecay(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron
 
setDecimals(int) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Set the number of decimals to round to.
setDefaultColourList(Color[]) - Method in class weka.gui.visualize.AttributePanel
 
setDefaultColourList(Color[]) - Method in class weka.gui.visualize.ClassPanel
 
setDefaults() - Method in class weka.datagenerators.ClusterDefinition
sets the default values
setDefaults() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
sets the default values
setDefaultValue() - Method in class weka.gui.GenericObjectEditor
Sets the current object to be the default, taken as the first item in the chooser.
setDefaultWeight(double) - Method in class weka.classifiers.functions.Winnow
Set the value of defaultWeight.
setDegree(int) - Method in class weka.classifiers.functions.LibSVM
Sets the degree of the kernel
setDegreesOfFreedom(int) - Method in class weka.experiment.PairedStats
Sets the degrees of freedom (if calibration is required).
setDeleteEmptyBins(boolean) - Method in class weka.classifiers.meta.RegressionByDiscretization
Sets the number of bins to divide each selected numeric attribute into
setDelimiters(String) - Method in class weka.core.tokenizers.CharacterDelimitedTokenizer
Set the value of delimiters.
setDelta(double) - Method in class weka.associations.Apriori
Set the value of delta.
setDelta(double) - Method in class weka.associations.FPGrowth
Set the value of delta.
setDelta(double) - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
Sets the m_fDelta.
setDelta(double) - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
Sets the m_fDelta.
setDensityBasedClusterer(DensityBasedClusterer) - Method in class weka.filters.unsupervised.attribute.ClusterMembership
Set the clusterer for use in filtering
setDerived(int) - Method in class weka.gui.AttributeSummaryPanel
Sets the gui elements for fields that are stored in the AttributeStats structure.
setDescendantPopulationSize(int) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
 
setDescendantPopulationSize(int) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
 
setDescendents(ArrayList, C45PruneableClassifierTreeG) - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG
add the grafted nodes at originalLeaf's position in tree.
setDesign(boolean) - Method in class weka.gui.beans.AttributeSummarizer
Set whether the appearance of this bean should be design or application
setDesignatedClass(SelectedTag) - Method in class weka.classifiers.meta.ThresholdSelector
Sets the method to determine which class value to optimize.
setDesiredSize(int) - Method in class weka.classifiers.meta.Decorate
Sets the desired size of the committee.
setDesiredWeightOfInstancesPerInterval(double) - Method in class weka.filters.unsupervised.attribute.Discretize
Set the DesiredWeightOfInstancesPerInterval value.
setDestination(File) - Method in class weka.core.converters.AbstractFileSaver
Sets the destination file (and directories if necessary).
setDestination(OutputStream) - Method in class weka.core.converters.AbstractFileSaver
Sets the destination output stream.
setDestination(File) - Method in class weka.core.converters.AbstractSaver
Default implementation throws an IOException.
setDestination(OutputStream) - Method in class weka.core.converters.AbstractSaver
Default implementation throws an IOException.
setDestination(OutputStream) - Method in class weka.core.converters.ArffSaver
Sets the destination output stream.
setDestination(String, String, String) - Method in class weka.core.converters.DatabaseSaver
Sets the database url.
setDestination(String) - Method in class weka.core.converters.DatabaseSaver
Sets the database url.
setDestination() - Method in class weka.core.converters.DatabaseSaver
Sets the database url using the DatabaseUtils file.
setDestination(File) - Method in interface weka.core.converters.Saver
Resets the Saver object and sets the destination to be the supplied File object.
setDestination(OutputStream) - Method in interface weka.core.converters.Saver
Resets the Saver object and sets the destination to be the supplied InputStream.
setDestination(OutputStream) - Method in class weka.core.converters.SerializedInstancesSaver
Sets the destination output stream.
setDestination(OutputStream) - Method in class weka.core.converters.XRFFSaver
Sets the destination output stream.
setDetectionPerAttribute(boolean) - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Set whether an Outlier/ExtremeValue attribute pair is generated for each numeric attribute ("true") or just one pair for all numeric attributes together ("false").
setDir(String) - Method in class weka.core.converters.AbstractFileSaver
Sets the directory where the instances should be stored
setDir(String) - Method in class weka.core.converters.AbstractSaver
Default implementation throws an IOException.
setDir(String) - Method in interface weka.core.converters.Saver
Sets the directory of the output file.
setDir(String) - Method in class weka.core.Javadoc
sets the dir containing the file that is to be updated.
setDirAndPrefix(String, String) - Method in class weka.core.converters.AbstractFileSaver
Sets the directory and the file prefix.
setDirAndPrefix(String, String) - Method in class weka.core.converters.AbstractSaver
Default implementation throws an IOException.
setDirAndPrefix(String, String) - Method in interface weka.core.converters.Saver
Sets the file prefix and the directory.
setDirection(SelectedTag) - Method in class weka.attributeSelection.BestFirst
Set the search direction
setDirectory(File) - Method in class weka.core.converters.TextDirectoryLoader
sets the source directory
setDirectory(File) - Method in class weka.gui.beans.SerializedModelSaver
Set the directory that the model(s) will be saved into.
setDiscretizeBin(int) - Method in class weka.classifiers.mi.MIBoost
Set the number of bins in discretization
setDisplayConnectors(boolean) - Method in class weka.gui.beans.BeanVisual
Turn on/off the connector points
setDisplayConnectors(boolean, Color) - Method in class weka.gui.beans.BeanVisual
Turn on/off the connector points
setDisplayedFromDialog() - Method in class weka.gui.experiment.ResultsPanel
 
setDisplayedResultsets(int[]) - Method in class weka.experiment.PairedTTester
Sets the indicies of the datasets to display (null means all).
setDisplayedResultsets(int[]) - Method in interface weka.experiment.Tester
Sets the indicies of the datasets to display (null means all).
setDisplayModelInOldFormat(boolean) - Method in class weka.classifiers.bayes.NaiveBayes
Set whether to display model output in the old, original format.
setDisplayModelInOldFormat(boolean) - Method in class weka.clusterers.EM
Set whether to display model output in the old, original format.
setDisplayRules(boolean) - Method in class weka.classifiers.rules.DecisionTable
Sets whether rules are to be printed
setDisplayStdDevs(boolean) - Method in class weka.clusterers.SimpleKMeans
Sets whether standard deviations and nominal count Should be displayed in the clustering output
setDistanceF(DistanceFunction) - Method in class weka.clusterers.XMeans
gets the "binary" distance value.
setDistanceFunction(DistanceFunction) - Method in class weka.clusterers.HierarchicalClusterer
 
setDistanceFunction(DistanceFunction) - Method in class weka.clusterers.SimpleKMeans
sets the distance function to use for instance comparison.
setDistanceFunction(DistanceFunction) - Method in class weka.core.neighboursearch.CoverTree
Sets the distance function to use for nearest neighbour search.
setDistanceFunction(DistanceFunction) - Method in class weka.core.neighboursearch.KDTree
sets the distance function to use for nearest neighbour search.
setDistanceFunction(DistanceFunction) - Method in class weka.core.neighboursearch.NearestNeighbourSearch
sets the distance function to use for nearest neighbour search.
setDistanceIsBranchLength(boolean) - Method in class weka.clusterers.HierarchicalClusterer
 
setDistanceWeighting(SelectedTag) - Method in class weka.classifiers.lazy.IBk
Sets the distance weighting method used.
setDistMult(double) - Method in class weka.datagenerators.clusterers.BIRCHCluster
Sets the distance multiplier.
setDistribution(String, double[][]) - Method in class weka.classifiers.bayes.net.EditableBayesNet
specify distribution of a node
setDistribution(int, double[][]) - Method in class weka.classifiers.bayes.net.EditableBayesNet
specify distribution of a node
setDistribution(SelectedTag) - Method in class weka.filters.unsupervised.attribute.RandomProjection
Sets the distribution to use for calculating the random matrix
setDistributionSpread(double) - Method in class weka.filters.supervised.instance.SpreadSubsample
Sets the value for the distribution spread
setDocType(String) - Method in class weka.core.xml.XMLDocument
sets the DOCTYPE-String to use in the XML output.
setDocument(Document) - Method in class weka.core.xml.XMLDocument
sets the DOM document to use.
setDoNotOperateOnPerClassBasis(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Set the DoNotOperateOnPerClassBasis value.
setDoNotReplaceMissingValues(boolean) - Method in class weka.classifiers.functions.LibLINEAR
Whether to turn off automatic replacement of missing values.
setDoNotReplaceMissingValues(boolean) - Method in class weka.classifiers.functions.LibSVM
Whether to turn off automatic replacement of missing values.
setDontFilterAfterFirstBatch(boolean) - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Set whether to apply the filter to instances that arrive once the first (training) batch has been seen.
setDontNormalize(boolean) - Method in class weka.classifiers.functions.SPegasos
Turn normalization off/on.
setDontNormalize(boolean) - Method in class weka.core.NormalizableDistance
Sets whether if the attribute values are to be normalized in distance calculation.
setDontReplaceMissing(boolean) - Method in class weka.classifiers.functions.SPegasos
Turn global replacement of missing values off/on.
setDontReplaceMissingValues(boolean) - Method in class weka.clusterers.SimpleKMeans
Sets whether missing values are to be replaced
setElement(int, int, double) - Method in class weka.classifiers.CostMatrix
Set the value of a cell as a double
setElement(int, double) - Method in class weka.core.AlgVector
Sets an element of the matrix to the given value.
setElement(int, int, double) - Method in class weka.core.Matrix
Deprecated.
Sets an element of the matrix to the given value.
setElementAt(Object, int) - Method in class weka.core.FastVector
Sets the element at the given index.
setElementAt(Object, int) - Method in class weka.gui.CheckBoxList.CheckBoxListModel
Sets the component at the specified index of this list to be the specified object.
setElements(FastVector) - Method in class weka.associations.gsp.Sequence
Sets the Elements of the Sequence.
setElements(double[]) - Method in class weka.core.AlgVector
Sets the elements of the vector to values of the given array.
setEliminateColinearAttributes(boolean) - Method in class weka.classifiers.functions.LinearRegression
Set the value of EliminateColinearAttributes.
setEnabled(boolean) - Method in class weka.core.Debug
sets whether the logging is enabled or not
setEnabled(boolean) - Method in class weka.core.Memory
sets whether the memory management is enabled
setEnabled(boolean) - Method in class weka.gui.GenericObjectEditor
Sets whether the editor is "enabled", meaning that the current values will be painted.
setEnabled(boolean) - Method in class weka.gui.PropertyPanel
Passes on enabled/disabled status to the custom panel (if one is set).
setEnclosureCharacters(String) - Method in class weka.core.converters.CSVLoader
Set the character(s) to use/recognize as string enclosures
setEntropicAutoBlend(boolean) - Method in class weka.classifiers.lazy.KStar
Set whether entropic blending is to be used.
setEnumerateColNames(boolean) - Method in class weka.experiment.ResultMatrix
sets whether the column names are prefixed with "(x)" where "x" is the index
setEnumerateRowNames(boolean) - Method in class weka.experiment.ResultMatrix
sets whether to the row names or numbers instead are enumerateed
setEnvironment(Environment) - Method in class weka.core.converters.AbstractFileLoader
Set the environment variables to use.
setEnvironment(Environment) - Method in class weka.core.converters.AbstractFileSaver
Set the environment variables to use.
setEnvironment(Environment) - Method in interface weka.core.EnvironmentHandler
Set environment variables to use.
setEnvironment(Environment) - Method in class weka.gui.beans.FlowRunner
Set the environment variables to use.
setEnvironment(Environment) - Method in class weka.gui.beans.KnowledgeFlowApp
Set the environment variables to use.
setEnvironment(Environment) - Method in class weka.gui.beans.Loader
Set environment variables to use.
setEnvironment(Environment) - Method in class weka.gui.beans.Saver
Set environment variables to use.
setEnvironment(Environment) - Method in class weka.gui.beans.SerializedModelSaver
Set environment variables to use.
setEpochs(int) - Method in class weka.classifiers.functions.SPegasos
Set the number of epochs to use
setEps(double) - Method in class weka.classifiers.functions.LibLINEAR
Sets tolerance of termination criterion (default 0.001)
setEps(double) - Method in class weka.classifiers.functions.LibSVM
Sets tolerance of termination criterion (default 0.001)
setEpsilon(double) - Method in class weka.classifiers.functions.SMO
Set the value of epsilon.
setEpsilon(double) - Method in class weka.classifiers.functions.supportVector.RegSMO
Set the value of epsilon.
setEpsilon(double) - Method in class weka.classifiers.mi.MISMO
Set the value of epsilon.
setEpsilon(double) - Method in class weka.clusterers.DBScan
Sets a new value for epsilon
setEpsilon(double) - Method in class weka.clusterers.OPTICS
Sets a new value for epsilon
setEpsilonParameter(double) - Method in class weka.attributeSelection.SVMAttributeEval
Set the value of P for SMO
setEpsilonParameter(double) - Method in class weka.classifiers.functions.supportVector.RegOptimizer
Set the value of epsilon parameter of the epsilon insensitive loss function.
setErrorOnProbabilities(boolean) - Method in class weka.classifiers.functions.SimpleLogistic
Set the value of errorOnProbabilities.
setErrorOnProbabilities(boolean) - Method in class weka.classifiers.trees.FT
Set the value of errorOnProbabilities.
setErrorOnProbabilities(boolean) - Method in class weka.classifiers.trees.LMT
Set the value of errorOnProbabilities.
setEstimator(BayesNetEstimator) - Method in class weka.classifiers.bayes.BayesNet
Set the Estimator Algorithm used in calculating the CPTs
setEstimator(SelectedTag) - Method in class weka.classifiers.functions.PaceRegression
Sets the estimator.
setEstimator(Estimator) - Method in class weka.estimators.CheckEstimator
Set the estimator for boosting.
setEuclideanDistanceFunction(EuclideanDistance) - Method in class weka.core.neighboursearch.balltrees.BallSplitter
Sets the distance function used to (or to be used to) build the tree.
setEuclideanDistanceFunction(EuclideanDistance) - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
Sets the distance function to use to build the tree.
setEuclideanDistanceFunction(EuclideanDistance) - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
Sets the EuclideanDistance object to use for splitting nodes.
setEvaluation(SelectedTag) - Method in class weka.classifiers.meta.GridSearch
Sets the criterion to use for evaluating the classifier performance.
setEvaluationMeasure(SelectedTag) - Method in class weka.classifiers.rules.DecisionTable
Sets the performance evaluation measure to use for selecting attributes for the decision table
setEvaluationMode(SelectedTag) - Method in class weka.classifiers.meta.ThresholdSelector
Sets the evaluation mode used.
setEvaluator(ASEvaluation) - Method in class weka.attributeSelection.AttributeSelection
set the attribute/subset evaluator
setEvaluator(ASEvaluation) - Method in class weka.attributeSelection.CheckAttributeSelection
Set the evaluator to test.
setEvaluator(ASEvaluation) - Method in class weka.attributeSelection.CostSensitiveASEvaluation
Set the base evaluator.
setEvaluator(ASEvaluation) - Method in class weka.attributeSelection.CostSensitiveAttributeEval
Set the base evaluator.
setEvaluator(ASEvaluation) - Method in class weka.attributeSelection.CostSensitiveSubsetEval
Set the base evaluator.
setEvaluator(ASEvaluation) - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Sets the attribute evaluator
setEvaluator(ASEvaluation) - Method in class weka.filters.supervised.attribute.AttributeSelection
set attribute/subset evaluator
setEvalUsingTrainingData(boolean) - Method in class weka.attributeSelection.OneRAttributeEval
Use the training data to evaluate attributes rather than cross validation
setEvents(int[]) - Method in class weka.associations.gsp.Element
Sets the events Array of an Element.
setEvidence(int, int) - Method in class weka.classifiers.bayes.net.EditableBayesNet
set evidence state of a node.
setEvidence(int, int) - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode
 
setEvidence(int, int) - Method in class weka.classifiers.bayes.net.MarginCalculator
 
setExclusive(boolean) - Method in class weka.classifiers.rules.ConjunctiveRule
Sets whether exclusive expressions for nominal attributes splits are considered
setExecutionSlots(int) - Method in class weka.gui.beans.Classifier
Set the number of execution slots (threads) to use to train models with.
setExecutionStatus(int) - Method in class weka.experiment.TaskStatusInfo
Set the execution status of this Task.
setExitIfNoWindowsOpen(boolean) - Static method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
Sets whether System.exit gets called when no more windows are open.
setExitOnClose(boolean) - Method in class weka.gui.arffviewer.ArffViewer
whether to do a System.exit(0) on close
setExitOnClose(boolean) - Method in class weka.gui.arffviewer.ArffViewerMainPanel
whether to do a System.exit(0) on close
setExpectedResultsPerAverage(int) - Method in class weka.experiment.AveragingResultProducer
Set the value of ExpectedResultsPerAverage.
setExperiment(Experiment) - Method in class weka.experiment.RemoteExperimentSubTask
Set the experiment for this sub task
setExperiment(Experiment) - Method in class weka.gui.experiment.AlgorithmListPanel
Tells the panel to act on a new experiment.
setExperiment(Experiment) - Method in class weka.gui.experiment.DatasetListPanel
Tells the panel to act on a new experiment.
setExperiment(Experiment) - Method in class weka.gui.experiment.DistributeExperimentPanel
Sets the experiment to be configured.
setExperiment(Experiment) - Method in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Sets the experiment which will have the custom properties edited.
setExperiment(RemoteExperiment) - Method in class weka.gui.experiment.HostListPanel
Tells the panel to act on a new experiment.
setExperiment(Experiment) - Method in class weka.gui.experiment.ResultsPanel
Tells the panel to use a new experiment.
setExperiment(Experiment) - Method in class weka.gui.experiment.RunNumberPanel
Sets the experiment to be configured.
setExperiment(Experiment) - Method in class weka.gui.experiment.RunPanel
Sets the experiment the panel operates on.
setExperiment(Experiment) - Method in class weka.gui.experiment.SetupPanel
Sets the experiment to configure.
setExperiment(Experiment) - Method in class weka.gui.experiment.SimpleSetupPanel
Sets the experiment to configure.
setExplicitPropsFile(boolean) - Method in class weka.gui.GenericPropertiesCreator
if FALSE, the locating of a props-file of the Utils-class is used, otherwise it's tried to load the specified file
setExplorer(Explorer) - Method in class weka.gui.explorer.AssociationsPanel
Sets the Explorer to use as parent frame (used for sending notifications about changes in the data)
setExplorer(Explorer) - Method in class weka.gui.explorer.AttributeSelectionPanel
Sets the Explorer to use as parent frame (used for sending notifications about changes in the data)
setExplorer(Explorer) - Method in class weka.gui.explorer.ClassifierPanel
Sets the Explorer to use as parent frame (used for sending notifications about changes in the data)
setExplorer(Explorer) - Method in class weka.gui.explorer.ClustererPanel
Sets the Explorer to use as parent frame (used for sending notifications about changes in the data)
setExplorer(Explorer) - Method in interface weka.gui.explorer.Explorer.ExplorerPanel
Sets the Explorer to use as parent frame (used for sending notifications about changes in the data)
setExplorer(Explorer) - Method in class weka.gui.explorer.PreprocessPanel
Sets the Explorer to use as parent frame (used for sending notifications about changes in the data)
setExplorer(Explorer) - Method in class weka.gui.explorer.VisualizePanel
Sets the Explorer to use as parent frame (used for sending notifications about changes in the data)
setExponent(double) - Method in class weka.classifiers.functions.supportVector.NormalizedPolyKernel
Sets the exponent value (must be different from 1.0).
setExponent(double) - Method in class weka.classifiers.functions.supportVector.PolyKernel
Sets the exponent value.
setExponent(double) - Method in class weka.classifiers.functions.VotedPerceptron
Set the value of exponent.
setExpression(String) - Method in class weka.datagenerators.classifiers.regression.Expression
Sets the mathematical expression to generate y out of x.
setExpression(String) - Method in class weka.filters.unsupervised.attribute.AddExpression
Set the expression to apply
setExpression(String) - Method in class weka.filters.unsupervised.attribute.MathExpression
Set the expression to apply
setExpression(String) - Method in class weka.filters.unsupervised.instance.SubsetByExpression
Sets the expression used for filtering.
setExtremeValuesAsOutliers(boolean) - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Set whether extreme values are also tagged as outliers.
setExtremeValuesFactor(double) - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Sets the factor for determining the thresholds for extreme values.
setFalseNegative(double) - Method in class weka.classifiers.evaluation.TwoClassStats
Sets the number of positive instances predicted as negative
setFalsePositive(double) - Method in class weka.classifiers.evaluation.TwoClassStats
Sets the number of negative instances predicted as positive
setFastRegression(boolean) - Method in class weka.classifiers.trees.LMT
Set the value of fastRegression.
setField(Object, String, Object) - Method in class weka.classifiers.functions.LibLINEAR
sets the specified field
setField(Object, String, int, Object) - Method in class weka.classifiers.functions.LibLINEAR
sets the specified field in an array
setField(Object, String, Object) - Method in class weka.classifiers.functions.LibSVM
sets the specified field
setField(Object, String, int, Object) - Method in class weka.classifiers.functions.LibSVM
sets the specified field in an array
setFieldDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.DerivedFieldMetaInfo
Upadate the field definitions for this derived field
setFieldDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.Discretize
Set the field definitions for this Expression to use
setFieldDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.Expression
Set the field definitions for this Expression to use
setFieldDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.FieldRef
 
setFieldDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.NormContinuous
Set the field definitions for this Expression to use
setFieldDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.NormDiscrete
Set the field definitions for this Expression to use
setFile(File) - Method in class weka.core.converters.AbstractFileLoader
sets the source File
setFile(File) - Method in class weka.core.converters.AbstractFileSaver
Sets the destination file.
setFile(File) - Method in class weka.core.converters.AbstractSaver
Default implementation throws an IOException.
setFile(File) - Method in class weka.core.converters.ArffLoader
sets the source File
setFile(File) - Method in class weka.core.converters.ArffSaver
Sets the destination file.
setFile(File) - Method in interface weka.core.converters.FileSourcedConverter
Set the file to load from/ to save in
setFile(File) - Method in interface weka.core.converters.Saver
Sets the output file
setFile(File) - Method in class weka.core.converters.XRFFSaver
Sets the destination file.
setFile(File) - Method in class weka.gui.visualize.JComponentWriter
sets the file to store the output in
setFileExtension(String) - Method in class weka.core.converters.AbstractFileSaver
Sets ihe file extension.
setFileFormat(Tag) - Method in class weka.gui.beans.SerializedModelSaver
Set the file format to use for saving.
setFileMustExist(boolean) - Method in class weka.gui.ConverterFileChooser
Whether the selected file must exst (only open dialog).
setFilename(String) - Method in class weka.core.FindWithCapabilities
Sets the dataset filename to base the capabilities on.
setFilename(String) - Method in class weka.gui.arffviewer.ArffPanel
sets the filename
setFilename(int, String) - Method in class weka.gui.arffviewer.ArffViewerMainPanel
sets the filename of the specified panel
setFilePrefix(String) - Method in class weka.core.converters.AbstractFileSaver
Sets the file name prefix
setFilePrefix(String) - Method in class weka.core.converters.AbstractSaver
Default implementation throws an IOException.
setFilePrefix(String) - Method in interface weka.core.converters.Saver
Sets the file prefix.
setFillWithMissing(boolean) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Sets whether missing values should be used rather than removing instances where the translated value is not known (due to border effects).
setFilter(Filter) - Method in class weka.associations.FilteredAssociator
Sets the filter
setFilter(Filter) - Method in class weka.attributeSelection.FilteredAttributeEval
Set the filter to use
setFilter(Filter) - Method in class weka.attributeSelection.FilteredSubsetEval
Set the filter to use
setFilter(Filter) - Method in class weka.classifiers.functions.PLSClassifier
Set the PLS filter (only used for setup).
setFilter(Filter) - Method in class weka.classifiers.meta.FilteredClassifier
Sets the filter
setFilter(Filter) - Method in class weka.classifiers.meta.GridSearch
Set the kernel filter (only used for setup).
setFilter(Filter) - Method in class weka.clusterers.FilteredClusterer
Sets the filter.
setFilter(Filter) - Method in class weka.filters.CheckSource
Sets the filter to use for the comparison.
setFilter(Filter) - Method in class weka.filters.unsupervised.attribute.Wavelet
Set the preprocessing filter (only used for setup).
setFilter(Filter) - Method in class weka.gui.beans.Filter
Set the filter to be wrapped by this bean
setFilterAfterFirstBatch(boolean) - Method in class weka.filters.unsupervised.instance.SubsetByExpression
Set whether to apply the filter to instances that arrive once the first (training) batch has been seen.
setFilterAttributes(String) - Method in class weka.associations.GeneralizedSequentialPatterns
Sets the String containing the attributes which are used for output filtering.
setFilters(Filter[]) - Method in class weka.filters.MultiFilter
Sets the list of possible filters to choose from.
setFilters(Filter[]) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
Sets the list of possible filters to choose from.
setFilterType(SelectedTag) - Method in class weka.attributeSelection.SVMAttributeEval
The filtering mode to pass to SMO
setFilterType(SelectedTag) - Method in class weka.classifiers.functions.GaussianProcesses
Sets how the training data will be transformed.
setFilterType(SelectedTag) - Method in class weka.classifiers.functions.SMO
Sets how the training data will be transformed.
setFilterType(SelectedTag) - Method in class weka.classifiers.functions.SMOreg
Sets how the training data will be transformed.
setFilterType(SelectedTag) - Method in class weka.classifiers.mi.MDD
Sets how the training data will be transformed.
setFilterType(SelectedTag) - Method in class weka.classifiers.mi.MIDD
Sets how the training data will be transformed.
setFilterType(SelectedTag) - Method in class weka.classifiers.mi.MIEMDD
Sets how the training data will be transformed.
setFilterType(SelectedTag) - Method in class weka.classifiers.mi.MIOptimalBall
Sets how the training data will be transformed.
setFilterType(SelectedTag) - Method in class weka.classifiers.mi.MISMO
Sets how the training data will be transformed.
setFilterType(SelectedTag) - Method in class weka.classifiers.mi.MISVM
Sets how the training data will be transformed.
setFindAllRulesForSupportLevel(boolean) - Method in class weka.associations.FPGrowth
If true then turn off the iterative support reduction method of finding x rules that meet the minimum support and metric thresholds and just return all the rules that meet the lower bound on minimum support and the minimum metric.
setFindNumBins(boolean) - Method in class weka.filters.unsupervised.attribute.Discretize
Set the value of FindNumBins.
setFindNumBins(boolean) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Set the value of FindNumBins.
setFirstValueIndex(String) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
Sets index of the first value used.
setFirstValueIndex(String) - Method in class weka.filters.unsupervised.attribute.SwapValues
Sets index of the first value used.
setFitness(double) - Method in class weka.attributeSelection.GeneticSearch.GABitSet
sets the scaled fitness
setFlags() - Method in class weka.core.Range
Sets the flags array.
setFlow(Vector) - Method in class weka.gui.beans.KnowledgeFlowApp
Set the flow for the KnowledgeFlow to edit.
setFlows(Vector) - Method in class weka.gui.beans.FlowRunner
Set the vector holding the flows(s) to run
setFocus() - Method in class weka.gui.sql.ConnectionPanel
sets the focus in a designated control.
setFocus() - Method in class weka.gui.sql.InfoPanel
sets the focus in a designated control
setFocus() - Method in class weka.gui.sql.QueryPanel
sets the focus in a designated control.
setFocus() - Method in class weka.gui.sql.ResultPanel
sets the focus in a designated control
setFold(int) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Selects a fold.
setFold(int) - Method in class weka.filters.unsupervised.instance.RemoveFolds
Selects a fold.
setFoldColumn(int) - Method in class weka.experiment.PairedTTester
Set the value of FoldColumn.
setFoldColumn(int) - Method in interface weka.experiment.Tester
Set the value of FoldColumn.
setFolds(int) - Method in class weka.attributeSelection.AttributeSelection
set the number of folds for cross validation
setFolds(int) - Method in class weka.attributeSelection.OneRAttributeEval
Set the number of folds to use for cross validation
setFolds(int) - Method in class weka.attributeSelection.WrapperSubsetEval
Set the number of folds to use for accuracy estimation
setFolds(int) - Method in class weka.classifiers.rules.ConjunctiveRule
the number of folds to use
setFolds(int) - Method in class weka.classifiers.rules.JRip
Sets the number of folds to use
setFolds(int) - Method in class weka.classifiers.rules.Ridor
 
setFolds(int) - Method in class weka.gui.beans.CrossValidationFoldMaker
Set the number of folds for the cross validation
setFoldsType(SelectedTag) - Method in class weka.attributeSelection.RaceSearch
Set the xfold type
setFont(Font) - Method in class weka.gui.visualize.PostscriptGraphics
Set current font.
setFormat(String) - Method in class weka.core.Debug.Timestamp
sets the format for the timestamp
setFormat() - Method in class weka.gui.experiment.OutputFormatDialog
sets the class of the chosen result matrix.
setForwardSelectionMethod(SelectedTag) - Method in class weka.attributeSelection.LinearForwardSelection
Set the search direction
setFrequencyLimit(int) - Method in class weka.classifiers.bayes.AODE
Sets the frequency limit
setFrequencyLimit(int) - Method in class weka.classifiers.bayes.AODEsr
Sets the frequency limit
setFrequencyThreshold(double) - Method in class weka.associations.Tertius
Set the value of frequencyThreshold.
setFunction(SelectedTag) - Method in class weka.datagenerators.classifiers.classification.Agrawal
Sets the function for generating the data.
setFunctionValue(int, double) - Method in class weka.classifiers.functions.pace.DiscreteFunction
Sets a particular function value
setGamma(double) - Method in class weka.classifiers.functions.LibSVM
Sets gamma (default = 1/no of attributes)
setGamma(double) - Method in class weka.classifiers.functions.supportVector.RBFKernel
Sets the gamma value.
setGenerateRanking(boolean) - Method in class weka.attributeSelection.GreedyStepwise
Records whether the user has requested a ranked list of attributes.
setGenerateRanking(boolean) - Method in class weka.attributeSelection.RaceSearch
Records whether the user has requested a ranked list of attributes.
setGenerateRanking(boolean) - Method in interface weka.attributeSelection.RankedOutputSearch
Sets whether or not ranking is to be performed.
setGenerateRanking(boolean) - Method in class weka.attributeSelection.Ranker
This is a dummy set method---Ranker is ONLY capable of producing a ranked list of attributes for attribute evaluators.
setGenerateRules(boolean) - Method in class weka.classifiers.trees.m5.M5Base
Generate rules (decision list) rather than a tree
setGenerator(DataGenerator) - Method in class weka.gui.explorer.DataGeneratorPanel
sets the generator to use initially
setGeneratorOption(BayesNetGenerator, String, String) - Method in class weka.datagenerators.classifiers.classification.BayesNet
sets a specific option/value of the generator (option must be w/o then '-')
setGeneratorOption(String, String) - Method in class weka.datagenerators.classifiers.classification.BayesNet
sets a specific option/value of the generator (option must be w/o then '-')
setGeneratorOptions(BayesNetGenerator, Vector) - Method in class weka.datagenerators.classifiers.classification.BayesNet
sets the given options of the BayesNetGenerator
setGeneratorOptions(Vector) - Method in class weka.datagenerators.classifiers.classification.BayesNet
sets the given options of the BayesNetGenerator
setGeneratorSamplesBase(double) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Set the base for computing the number of samples to obtain from each generator.
setGeneratorSamplesBase(double) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
Set the base for computing the number of samples to obtain from each generator.
setGlobalBlend(int) - Method in class weka.classifiers.lazy.KStar
Set the global blend parameter
setGlobalModel(NBTreeNoSplit) - Method in class weka.classifiers.trees.j48.NBTreeSplit
Set the global naive bayes model for this node
setGridIsExtendable(boolean) - Method in class weka.classifiers.meta.GridSearch
Set whether the grid can be extended dynamically.
setGridWidth(int) - Method in class weka.gui.beans.AttributeSummarizer
Set the width of the grid of plots
setGroupIdentifier(long) - Method in class weka.gui.beans.BatchClassifierEvent
 
setGUI(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron
This will set whether A GUI is brought up to allow interaction by the user with the neural network during training.
setGUIType(SelectedTag) - Method in class weka.gui.Main
Sets the type of GUI to use.
setHandler(CapabilitiesHandler) - Method in class weka.core.FindWithCapabilities
sets the Capabilities handler to generate the data for.
setHandler(CapabilitiesHandler) - Method in class weka.core.TestInstances
sets the Capabilities handler to generate the data for
setHandleRightClicks(boolean) - Method in class weka.gui.ResultHistoryPanel
Set whether the result history list should handle right clicks or whether the parent object will handle them.
setHashtable(Hashtable) - Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
Set hashtable from END.
setHashtable(Hashtable) - Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
Set hashtable from END.
setHashtable(Hashtable) - Method in class weka.classifiers.meta.nestedDichotomies.ND
Set hashtable from END.
setHDRank(int) - Method in class weka.classifiers.mi.CitationKNN
Sets the rank associated to the Hausdorff distance
setHeader(int) - Method in class weka.gui.AttributeSummaryPanel
Sets the labels for fields we can determine just from the instance header.
setHeuristic(boolean) - Method in class weka.classifiers.trees.BFTree
Set if use heuristic search for nominal attributes in multi-class problems.
setHeuristic(boolean) - Method in class weka.classifiers.trees.SimpleCart
Set if use heuristic search for nominal attributes in multi-class problems.
setHeuristicStop(int) - Method in class weka.classifiers.functions.SimpleLogistic
Set the value of heuristicStop.
setHeuristicStop(int) - Method in class weka.classifiers.trees.lmt.LogisticBase
Sets the option "heuristicStop".
setHidden(boolean) - Method in class weka.gui.beans.BeanConnection
Make this connection invisible on the display
setHiddenLayers(String) - Method in class weka.classifiers.functions.MultilayerPerceptron
This will set what the hidden layers are made up of when auto build is enabled.
setHighlight(String) - Method in class weka.gui.treevisualizer.TreeVisualizer
Set the highlight for the node with the given id
setHistory(DefaultListModel) - Method in class weka.gui.sql.ConnectionPanel
sets the local history to the given one.
setHistory(DefaultListModel) - Method in class weka.gui.sql.QueryPanel
sets the local history to the given one.
setHoldOutFile(File) - Method in class weka.attributeSelection.ClassifierSubsetEval
Set the file that contains hold out/test instances
setHornClauses(boolean) - Method in class weka.associations.Tertius
Set the value of hornClauses.
setHyperparameterRange(String) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Set the range of hyperparameter values to consider during CV-based selection
setHyperparameterSelection(SelectedTag) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Set the method used to select the hyperparameter
setHyperparameterValue(double) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Set the hyperparameter value.
setID(int) - Method in class weka.core.Tag
Sets the numeric ID of the Tag.
setIDFTransform(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Sets whether if the word frequencies in a document should be transformed into:
fij*log(num of Docs/num of Docs with word i)
where fij is the frequency of word i in document(instance) j.
setIDIndex(String) - Method in class weka.filters.unsupervised.attribute.AddID
Sets index of the attribute used.
setIDStr(String) - Method in class weka.core.Tag
Sets the string ID of the Tag.
setIgnoreClass(boolean) - Method in class weka.filters.unsupervised.attribute.PotentialClassIgnorer
Set the IgnoreClass value.
setIgnoredAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.AddCluster
Sets the ranges of attributes to be ignored.
setIgnoredAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.ClusterMembership
Sets the ranges of attributes to be ignored.
setIgnoredProperties(String) - Method in class weka.core.CheckGOE
Sets the properties to ignore in checkToolTips().
setIgnoreRange(String) - Method in class weka.filters.unsupervised.attribute.MathExpression
Set which attributes are to be ignored
setIncludeClass(boolean) - Method in class weka.core.InstanceComparator
sets whether the class should be included (= TRUE) in the comparison
setIncludeClass(boolean) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Sets whether the class can be cleaned, too.
setIndex(int) - Method in class weka.core.pmml.MiningFieldMetaInfo
Set the index of this field in the mining schema Instances
setInitAsNaiveBayes(boolean) - Method in class weka.classifiers.bayes.net.search.global.HillClimber
Sets whether to init as naive bayes
setInitAsNaiveBayes(boolean) - Method in class weka.classifiers.bayes.net.search.global.K2
Sets whether to init as naive bayes
setInitAsNaiveBayes(boolean) - Method in class weka.classifiers.bayes.net.search.local.HillClimber
Sets whether to init as naive bayes
setInitAsNaiveBayes(boolean) - Method in class weka.classifiers.bayes.net.search.local.K2
Sets whether to init as naive bayes
setInitFile(File) - Method in class weka.filters.unsupervised.attribute.KernelFilter
Sets the file to initialize the filter with, can be null.
setInitFileClassIndex(String) - Method in class weka.filters.unsupervised.attribute.KernelFilter
Sets class index of the file to initialize the filter with.
setInitialAnchorRandom(boolean) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Sets whether if the initial anchor is chosen randomly.
setInputCenterFile(File) - Method in class weka.clusterers.XMeans
Sets the file to read the list of centers from.
setInputFilename(String) - Method in class weka.gui.GenericPropertiesCreator
sets the file to get the information about the packages from.
setInputFormat(Instances) - Method in class weka.filters.AllFilter
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.Filter
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.SimpleFilter
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.attribute.ClassOrder
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.attribute.Discretize
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.attribute.NominalToBinary
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.instance.Resample
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.instance.SMOTE
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.instance.SpreadSubsample
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Add
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AddCluster
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AddExpression
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AddID
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AddNoise
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AddValues
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Center
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.ClusterMembership
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Copy
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Discretize
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.FirstOrder
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.MakeIndicator
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.MathExpression
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.NominalToBinary
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.NominalToString
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Normalize
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.NumericToBinary
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.NumericTransform
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Obfuscate
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.PotentialClassIgnorer
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.RandomProjection
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Remove
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.RemoveType
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.RemoveUseless
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Reorder
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Standardize
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.StringToNominal
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.SwapValues
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.TimeSeriesDelta
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.NonSparseToSparse
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.Normalize
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.Randomize
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveFolds
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemovePercentage
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveRange
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.Resample
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.ReservoirSample
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.SparseToNonSparse
Sets the format of the input instances.
setInputOrder(SelectedTag) - Method in class weka.datagenerators.clusterers.BIRCHCluster
Sets the input order.
setInputs(Vector) - Method in class weka.gui.beans.MetaBean
 
setInstance(Instance) - Method in class weka.gui.beans.InstanceEvent
Set the instance
setInstanceIndex(int, boolean) - Method in class weka.classifiers.lazy.LBR.Indexes
Changes the boolean value at the specified index in the InstIndexes array
setInstanceList(int[]) - Method in class weka.core.neighboursearch.balltrees.BallSplitter
Sets the master index array containing indices of the training instances.
setInstanceList(int[]) - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
Sets the master index array that points to instances in m_Instances, so that only this array is manipulated, and m_Instances is left untouched.
setInstanceList(int[]) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Sets the master index array that points to instances in m_Instances, so that only this array is manipulated, and m_Instances is left untouched.
setInstanceList(int[]) - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
Sets the master index array containing indices of the training instances.
setInstanceRange(int) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Sets the number of instances forward to translate values between.
setInstances(Instances) - Method in class weka.core.converters.AbstractSaver
Sets instances that should be stored.
setInstances(Instances) - Method in class weka.core.converters.LibSVMSaver
Sets instances that should be stored.
setInstances(Instances) - Method in interface weka.core.converters.Saver
Sets the instances to be saved
setInstances(Instances) - Method in class weka.core.converters.SVMLightSaver
Sets instances that should be stored.
setInstances(Instances) - Method in class weka.core.converters.XRFFSaver
Sets instances that should be stored.
setInstances(Instances) - Method in interface weka.core.DistanceFunction
Sets the instances.
setInstances(Instances) - Method in class weka.core.neighboursearch.BallTree
Builds the BallTree based on the given set of instances.
setInstances(Instances) - Method in class weka.core.neighboursearch.balltrees.BallSplitter
Sets the training instances on which the tree is (or is to be) built.
setInstances(Instances) - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
Sets the instances on which the tree is to be built.
setInstances(Instances) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Sets the instances on which the tree is to be built.
setInstances(Instances) - Method in class weka.core.neighboursearch.CoverTree
Builds the Cover Tree on the given set of instances.
setInstances(Instances) - Method in class weka.core.neighboursearch.KDTree
Builds the KDTree on the given set of instances.
setInstances(Instances) - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
Sets the training instances on which the tree is (or is to be) built.
setInstances(Instances) - Method in class weka.core.neighboursearch.LinearNNSearch
Sets the instances comprising the current neighbourhood.
setInstances(Instances) - Method in class weka.core.neighboursearch.NearestNeighbourSearch
Sets the instances.
setInstances(Instances) - Method in class weka.core.NormalizableDistance
Sets the instances.
setInstances(Instances) - Method in class weka.core.xml.XMLInstances
builds up the XML structure based on the given data
setInstances(Instances) - Method in class weka.experiment.AveragingResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.CrossValidationResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.DatabaseResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.LearningRateResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.PairedTTester
Set the value of Instances.
setInstances(Instances) - Method in class weka.experiment.RandomSplitResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in interface weka.experiment.ResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in interface weka.experiment.Tester
Set the value of Instances.
setInstances(Instances) - Method in class weka.gui.arffviewer.ArffPanel
displays the given instances, i.e.
setInstances(Instances) - Method in class weka.gui.arffviewer.ArffSortedTableModel
sets the data
setInstances(Instances) - Method in class weka.gui.arffviewer.ArffTableModel
sets the data
setInstances(Instances) - Method in class weka.gui.AttributeListPanel
Sets the instances who's attribute names will be displayed.
setInstances(Instances) - Method in class weka.gui.AttributeSelectionPanel
Sets the instances who's attribute names will be displayed.
setInstances(Instances) - Method in class weka.gui.AttributeSummaryPanel
Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.AttributeVisualizationPanel
Sets the instances for use
setInstances(Instances) - Method in class weka.gui.beans.AttributeSummarizer
Set instances for this bean.
setInstances(Instances) - Method in class weka.gui.beans.DataVisualizer
Set instances for this bean.
setInstances(Instances) - Method in class weka.gui.beans.ScatterPlotMatrix
Set instances for this bean.
setInstances(Instances) - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
Set the training instances
setInstances(Instances) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
Set the training data
setInstances(Instances) - Method in class weka.gui.experiment.ResultsPanel
Sets up the panel with a new set of instances, attempting to guess the correct settings for various columns.
setInstances(Instances) - Method in class weka.gui.explorer.AssociationsPanel
Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.explorer.AttributeSelectionPanel
Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.explorer.ClassifierPanel
Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.explorer.ClustererPanel
Tells the panel to use a new set of instances.
setInstances(Instances) - Method in interface weka.gui.explorer.Explorer.ExplorerPanel
Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.explorer.PreprocessPanel
Tells the panel to use a new base set of instances.
setInstances(Instances) - Method in class weka.gui.InstancesSummaryPanel
Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.SetInstancesPanel
Updates the set of instances that is currently held by the panel
setInstances(Instances) - Method in class weka.gui.ViewerDialog
sets the instances to display
setInstances(Instances) - Method in class weka.gui.visualize.AttributePanel
This sets the instances to be drawn into the attribute panel
setInstances(Instances) - Method in class weka.gui.visualize.ClassPanel
Set the instances.
setInstances(Instances) - Method in class weka.gui.visualize.MatrixPanel
This method changes the Instances object of this class to a new one.
setInstances(Instances) - Method in class weka.gui.visualize.Plot2D
Sets the master plot from a set of instances
setInstances(Instances) - Method in class weka.gui.visualize.VisualizePanel
Tells the panel to use a new set of instances.
setInstancesFromDatabaseTable(String) - Method in class weka.gui.experiment.ResultsPanel
Queries a database to load results from the specified table name.
setInstancesFromDB(InstanceQuery) - Method in class weka.gui.explorer.PreprocessPanel
Loads instances from a database
setInstancesFromDBaseQuery() - Method in class weka.gui.experiment.ResultsPanel
Queries the user enough to make a database query to retrieve experiment results.
setInstancesFromDBQ(String, String, String, String) - Method in class weka.gui.explorer.PreprocessPanel
Loads instances from an SQL query the user provided with the SqlViewerDialog, then loads the instances in a background process.
setInstancesFromExp(Experiment) - Method in class weka.gui.experiment.ResultsPanel
Examines the supplied experiment to determine the results destination and attempts to load the results.
setInstancesFromFile(File) - Method in class weka.gui.experiment.ResultsPanel
Loads results from a set of instances contained in the supplied file.
setInstancesFromFile(AbstractFileLoader) - Method in class weka.gui.explorer.PreprocessPanel
Loads results from a set of instances retrieved with the supplied loader.
setInstancesFromFile(File) - Method in class weka.gui.SetInstancesPanel
Loads results from a set of instances contained in the supplied file.
setInstancesFromFileQ() - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
Queries the user for a file to load instances from, then loads the instances in a background process.
setInstancesFromFileQ() - Method in class weka.gui.explorer.PreprocessPanel
Queries the user for a file to load instances from, then loads the instances in a background process.
setInstancesFromFileQ() - Method in class weka.gui.SetInstancesPanel
Queries the user for a file to load instances from, then loads the instances in a background process.
setInstancesFromURL(URL) - Method in class weka.gui.explorer.PreprocessPanel
Loads instances from a URL.
setInstancesFromURL(URL) - Method in class weka.gui.SetInstancesPanel
Loads instances from a URL.
setInstancesFromURLQ() - Method in class weka.gui.explorer.PreprocessPanel
Queries the user for a URL to load instances from, then loads the instances in a background process.
setInstancesFromURLQ() - Method in class weka.gui.SetInstancesPanel
Queries the user for a URL to load instances from, then loads the instances in a background process.
setInstancesIndices(String) - Method in class weka.filters.unsupervised.instance.RemoveRange
Sets the ranges of instances to be selected.
SetInstancesPanel - Class in weka.gui
A panel that displays an instance summary for a set of instances and lets the user open a set of instances from either a file or URL.
SetInstancesPanel() - Constructor for class weka.gui.SetInstancesPanel
Create the panel.
setInstNums(String) - Method in class weka.datagenerators.clusterers.BIRCHCluster
Sets the upper and lower boundary for instances per cluster.
setInstNums(String) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Sets the upper and lower boundary for instances for this cluster.
setInsts(int[], boolean) - Method in class weka.classifiers.lazy.LBR.Indexes
Changes the boolean value at the specified index in the InstIndexes array
setInterAnchorDistances(Vector, MiddleOutConstructor.TempNode, Vector) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Sets the distances of a supplied new anchor to all the rest of the previous anchor points.
setInternalCacheSize(int) - Method in class weka.classifiers.functions.supportVector.StringKernel
sets the size of the internal cache for intermediate results.
setInternals(boolean) - Method in class weka.classifiers.bayes.WAODE
Sets whether internals about classifier are printed via toString().
setInterval(int) - Method in class weka.gui.MemoryUsagePanel.MemoryMonitor
Sets the refresh interval in msecs.
setInvert(boolean) - Method in class weka.core.Range
Sets whether the range sense is inverted, i.e.
setInvert(boolean) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Set whether selection is inverted.
setInvertSelection(boolean) - Method in interface weka.core.DistanceFunction
Sets whether the matching sense of attribute indices is inverted or not.
setInvertSelection(boolean) - Method in class weka.core.NormalizableDistance
Sets whether the matching sense of attribute indices is inverted or not.
setInvertSelection(boolean) - Method in class weka.filters.supervised.attribute.Discretize
Sets whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.supervised.instance.Resample
Sets whether the selection is inverted (only if instances are drawn WIHTOUT replacement).
setInvertSelection(boolean) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Sets if selection is to be inverted.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Set whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.Copy
Set whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.Discretize
Sets whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.MathExpression
Set whether selected columns should be select or unselect.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.NominalToBinary
Sets whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Sets whether the selection of the indices is inverted or not
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.NumericToNominal
Sets whether selected columns should be worked on or all the others apart from these.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.NumericTransform
Set whether selected columns should be transformed or not.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.RELAGGS
Sets whether selected columns should be processed or skipped.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.Remove
Set whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.RemoveType
Set whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Sets whether selected columns should be processed or skipped.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.instance.RemoveFolds
Sets if selection is to be inverted.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Set whether selected values should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.instance.RemovePercentage
Sets if selection is to be inverted.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.instance.RemoveRange
Sets if selection is to be inverted.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Set whether selected values should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.instance.Resample
Sets whether the selection is inverted (only if instances are drawn WIHTOUT replacement).
setItem(int[]) - Method in class weka.associations.ItemSet
Sets an item sets
setItemAt(int, int) - Method in class weka.associations.ItemSet
Sets the index of an attribute value
setJitter(int) - Method in class weka.gui.visualize.Plot2D
Set level of jitter and repaint the plot using the new jitter value
setJitter(int) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
Set level of jitter and repaint the plot using the new jitter value
setKDTree(KDTree) - Method in class weka.clusterers.XMeans
Sets the KDTree class.
setKernel(Kernel) - Method in class weka.classifiers.functions.GaussianProcesses
Sets the kernel to use.
setKernel(Kernel) - Method in class weka.classifiers.functions.SMO.BinarySMO
sets the kernel to use
setKernel(Kernel) - Method in class weka.classifiers.functions.SMO
sets the kernel to use
setKernel(Kernel) - Method in class weka.classifiers.functions.SMOreg
sets the kernel to use
setKernel(Kernel) - Method in class weka.classifiers.functions.supportVector.CheckKernel
Set the lernel to test.
setKernel(Kernel) - Method in class weka.classifiers.mi.MISMO.BinaryMISMO
sets the kernel to use
setKernel(Kernel) - Method in class weka.classifiers.mi.MISMO
Sets the kernel to use.
setKernel(Kernel) - Method in class weka.classifiers.mi.MISVM
Sets the kernel to use.
setKernel(Kernel) - Method in class weka.filters.unsupervised.attribute.KernelFilter
Sets the kernel to use.
setKernelBandwidth(int) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator
Set the kernel bandwidth (number of nearest neighbours to cover)
setKernelFactorExpression(String) - Method in class weka.filters.unsupervised.attribute.KernelFilter
Sets the expression for the kernel.
setKernelMatrix(Matrix) - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
Set the kernel matrix.
setKernelMatrixFile(File) - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
Sets the file holding the kernel matrix
setKernelType(SelectedTag) - Method in class weka.classifiers.functions.LibSVM
Sets type of kernel function (default KERNELTYPE_RBF)
setKey(String) - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
Sets the key for this DataObject
setKey(String) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
Sets the key for this DataObject
setKey(String) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
Sets the key for this DataObject
setKeyFieldName(String) - Method in class weka.experiment.AveragingResultProducer
Set the value of KeyFieldName.
setKeys(String) - Method in class weka.core.converters.DatabaseLoader
Sets the key columns of a database table
setKeywords(String) - Method in class weka.experiment.DatabaseUtils
Sets the keywords (comma-separated list) to use.
setKeywordsMaskChar(String) - Method in class weka.experiment.DatabaseUtils
Sets the mask character to append to table or attribute names that are a reserved keyword.
setKNN(int) - Method in class weka.classifiers.lazy.IBk
Set the number of neighbours the learner is to use.
setKNN(int) - Method in class weka.classifiers.lazy.LWL
Sets the number of neighbours used for kernel bandwidth setting.
setKValue(int) - Method in class weka.classifiers.trees.RandomTree
Set the value of K.
setLabels(String) - Method in class weka.filters.unsupervised.attribute.AddValues
Sets the comma-separated list of labels.
setLambda(double) - Method in class weka.classifiers.functions.SPegasos
Set the value of lambda to use
setLambda(double) - Method in class weka.classifiers.functions.supportVector.StringKernel
Sets the lambda constant used in the string kernel
setLearningRate(double) - Method in class weka.classifiers.functions.MultilayerPerceptron
The learning rate can be set using this command.
setLegendText(Vector) - Method in class weka.gui.beans.ChartEvent
Set the legend text vector
setLikelihoodThreshold(double) - Method in class weka.classifiers.meta.LogitBoost
Set the value of Precision.
setLink(boolean, int) - Method in class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd
Call this function to set What this end unit represents.
setLinkType(SelectedTag) - Method in class weka.clusterers.HierarchicalClusterer
 
setListData(Object[]) - Method in class weka.gui.CheckBoxList
Constructs a CheckBoxListModel from an array of objects and then applies setModel to it.
setListData(Vector) - Method in class weka.gui.CheckBoxList
Constructs a CheckBoxListModel from a Vector and then applies setModel to it.
setLNorm(double) - Method in class weka.filters.unsupervised.instance.Normalize
Set the L-norm to used
setLoader(Loader) - Method in class weka.gui.beans.Loader
Set the loader to use
setLocallyPredictive(boolean) - Method in class weka.attributeSelection.CfsSubsetEval
Include locally predictive attributes
setLocationProbs(int, double[]) - Method in class weka.gui.boundaryvisualizer.RemoteResult
Store the classifier's distribution for a particular pixel in the visualization
setLog(Logger) - Method in class weka.classifiers.pmml.consumer.PMMLClassifier
Set a logger to use.
setLog(Debug.Log) - Method in class weka.core.Debug.Random
the log to use, if it is null then stdout is used
setLog(Logger) - Method in interface weka.core.pmml.PMMLModel
Set a logger to use.
setLog(Logger) - Method in class weka.gui.beans.AbstractDataSink
Set a log for this bean
setLog(Logger) - Method in class weka.gui.beans.AbstractEvaluator
Set a logger
setLog(Logger) - Method in class weka.gui.beans.AbstractTestSetProducer
Set a logger
setLog(Logger) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
Set a log for this bean
setLog(Logger) - Method in class weka.gui.beans.AbstractTrainingSetProducer
Set a logger
setLog(Logger) - Method in class weka.gui.beans.Associator
Set a logger
setLog(Logger) - Method in interface weka.gui.beans.BeanCommon
Set a logger
setLog(Logger) - Method in class weka.gui.beans.ClassAssigner
 
setLog(Logger) - Method in class weka.gui.beans.Classifier
Set a logger
setLog(Logger) - Method in class weka.gui.beans.ClassValuePicker
 
setLog(Logger) - Method in class weka.gui.beans.Clusterer
Set a logger
setLog(Logger) - Method in class weka.gui.beans.CostBenefitAnalysis
Set a logger
setLog(Logger) - Method in class weka.gui.beans.Filter
Set a logger
setLog(Logger) - Method in class weka.gui.beans.FlowRunner
 
setLog(Logger) - Method in class weka.gui.beans.InstanceStreamToBatchMaker
Set a logger
setLog(Logger) - Method in class weka.gui.beans.Loader
Set a logger
setLog(Logger) - Method in interface weka.gui.beans.LogWriter
Set a logger
setLog(Logger) - Method in class weka.gui.beans.MetaBean
Set a logger
setLog(Logger) - Method in class weka.gui.beans.PredictionAppender
Set a logger
setLog(Logger) - Method in class weka.gui.beans.SerializedModelSaver
Set a log for this bean.
setLog(Logger) - Method in class weka.gui.beans.StripChart
Set a logger
setLog(Logger) - Method in class weka.gui.beans.TextViewer
Set a logger
setLog(Logger) - Method in class weka.gui.explorer.AssociationsPanel
Sets the Logger to receive informational messages
setLog(Logger) - Method in class weka.gui.explorer.AttributeSelectionPanel
Sets the Logger to receive informational messages
setLog(Logger) - Method in class weka.gui.explorer.ClassifierPanel
Sets the Logger to receive informational messages
setLog(Logger) - Method in class weka.gui.explorer.ClustererPanel
Sets the Logger to receive informational messages
setLog(Logger) - Method in class weka.gui.explorer.DataGeneratorPanel
Sets the Logger to receive informational messages
setLog(Logger) - Method in interface weka.gui.explorer.Explorer.LogHandler
Sets the Logger to receive informational messages
setLog(Logger) - Method in class weka.gui.explorer.PreprocessPanel
Sets the Logger to receive informational messages
setLog(Logger) - Method in class weka.gui.visualize.VisualizePanel
Sets the Logger to receive informational messages
setLogFile(File) - Method in class weka.classifiers.meta.GridSearch
Sets the log file to use.
setLookAndFeel(String) - Static method in class weka.gui.LookAndFeel
sets the look and feel to the specified class
setLookAndFeel() - Static method in class weka.gui.LookAndFeel
sets the look and feel to the one in the props-file or if not set the default one of the system
setLookupCacheSize(int) - Method in class weka.attributeSelection.BestFirst
Set the maximum size of the evaluated subset cache (hashtable).
setLookupCacheSize(int) - Method in class weka.attributeSelection.LinearForwardSelection
Set the maximum size of the evaluated subset cache (hashtable).
setLookupCacheSize(int) - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Set the maximum size of the evaluated subset cache (hashtable).
setLoss(double) - Method in class weka.classifiers.functions.LibSVM
Sets the epsilon in loss function of epsilon-SVR (default 0.1)
setLossFunction(SelectedTag) - Method in class weka.classifiers.functions.SPegasos
Set the loss function to use.
setLowerBoundMinSupport(double) - Method in class weka.associations.Apriori
Set the value of lowerBoundMinSupport.
setLowerBoundMinSupport(double) - Method in class weka.associations.FPGrowth
Set the value of lowerBoundMinSupport.
setLowerCaseTokens(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Sets whether if the tokens are to be downcased or not.
setLowerSize(int) - Method in class weka.experiment.LearningRateResultProducer
Set the value of LowerSize.
setMajorityClass(boolean) - Method in class weka.classifiers.rules.Ridor
 
setMakeBinary(boolean) - Method in class weka.filters.supervised.attribute.Discretize
Sets whether binary attributes should be made for discretized ones.
setMakeBinary(boolean) - Method in class weka.filters.unsupervised.attribute.Discretize
Sets whether binary attributes should be made for discretized ones.
setManualThresholdValue(double) - Method in class weka.classifiers.meta.ThresholdSelector
Sets the value for a manual threshold.
setMargin(int, double[]) - Method in class weka.classifiers.bayes.net.EditableBayesNet
set marginal distibution for a node
setMarkovBlanketClassifier(boolean) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
 
setMarkovBlanketClassifier(boolean) - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
 
setMarkovBlanketClassifier(boolean) - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
 
setMasterPlot(PlotData2D) - Method in class weka.gui.visualize.Plot2D
Set the master plot.
setMasterPlot(PlotData2D) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
Clears all existing plots and sets a new master plot
setMasterPlot(PlotData2D) - Method in class weka.gui.visualize.VisualizePanel
Set the master plot for the visualize panel
setMatchMissingValues(boolean) - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Sets whether missing values are counted as a match.
setMatrix(int, int, int, int, double) - Method in class weka.classifiers.functions.pace.PaceMatrix
Set the submatrix A[i0:i1][j0:j1] with a same value
setMatrix(int, int, int, DoubleVector) - Method in class weka.classifiers.functions.pace.PaceMatrix
Set the submatrix A[i0:i1][j] with the values stored in a DoubleVector
setMatrix(double[], boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix
Set the whole matrix from a 1-D array
setMatrix(int, int, int, int, Matrix) - Method in class weka.core.matrix.Matrix
Set a submatrix.
setMatrix(int[], int[], Matrix) - Method in class weka.core.matrix.Matrix
Set a submatrix.
setMatrix(int[], int, int, Matrix) - Method in class weka.core.matrix.Matrix
Set a submatrix.
setMatrix(int, int, int[], Matrix) - Method in class weka.core.matrix.Matrix
Set a submatrix.
setMax(double) - Method in class weka.gui.beans.ChartEvent
Set the max y value
setMaxBoostingIterations(int) - Method in class weka.classifiers.functions.SimpleLogistic
Set the value of maxBoostingIterations.
setMaxCardinality(int) - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm
sets the cardinality
setMaxCardinality(int) - Method in class weka.filters.unsupervised.attribute.RELAGGS
Sets the maximum number of values allowed for nominal attributes, before they're skipped.
setMaxChunkSize(int) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Set the maximum chunk size
setMaxCount(double) - Method in class weka.filters.supervised.instance.SpreadSubsample
Sets the value for the max count
setMaxDefault(double) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Set the naximum default.
setMaxDepth(int) - Method in class weka.classifiers.trees.RandomForest
Set the maximum depth of the tree, 0 for unlimited.
setMaxDepth(int) - Method in class weka.classifiers.trees.RandomTree
Set the maximum depth of the tree, 0 for unlimited.
setMaxDepth(int) - Method in class weka.classifiers.trees.REPTree
Set the value of MaxDepth.
setMaxGenerations(int) - Method in class weka.attributeSelection.GeneticSearch
set the number of generations to evaluate
setMaxGridExtensions(int) - Method in class weka.classifiers.meta.GridSearch
Sets the maximum number of grid extensions, -1 for unlimited.
setMaxGroup(int) - Method in class weka.classifiers.meta.RotationForest
Sets the maximum size of a group.
setMaximumAttributeNames(int) - Method in class weka.attributeSelection.LatentSemanticAnalysis
Sets maximum number of attributes to include in transformed attribute names.
setMaximumAttributeNames(int) - Method in class weka.attributeSelection.PrincipalComponents
Sets maximum number of attributes to include in transformed attribute names.
setMaximumAttributeNames(int) - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
Sets maximum number of attributes to include in transformed attribute names.
setMaximumAttributes(int) - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
Sets maximum number of PC attributes to retain.
setMaximumVariancePercentageAllowed(double) - Method in class weka.filters.unsupervised.attribute.RemoveUseless
Sets the maximum variance attributes are allowed to have before they are deleted by the filter.
setMaxInstancesInLeaf(int) - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
Sets the maximum number of instances allowed in a leaf.
setMaxInstancesInLeaf(int) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Sets the maximum number of instances allowed in a leaf.
setMaxInstInLeaf(int) - Method in class weka.core.neighboursearch.KDTree
Sets the maximum number of instances in a leaf.
setMaxInstNum(int) - Method in class weka.datagenerators.clusterers.BIRCHCluster
Sets the upper boundary for instances per cluster.
setMaxInstNum(int) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Sets the upper boundary for instances per cluster.
setMaxIteration(int) - Method in class weka.core.Optimization
Set the maximal number of iterations in searching (Default 200)
setMaxIterations(int) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Set the maximum number of iterations to perform
setMaxIterations(int) - Method in class weka.classifiers.mi.MIBoost
Set the maximum number of boost iterations
setMaxIterations(int) - Method in class weka.classifiers.mi.MISVM
Sets the maximum number of iterations.
setMaxIterations(int) - Method in class weka.classifiers.trees.lmt.LogisticBase
Sets the parameter "maxIterations".
setMaxIterations(int) - Method in class weka.clusterers.EM
Set the maximum number of iterations to perform
setMaxIterations(int) - Method in class weka.clusterers.sIB
Set the max number of iterations
setMaxIterations(int) - Method in class weka.clusterers.SimpleKMeans
set the maximum number of iterations to be executed
setMaxIterations(int) - Method in class weka.clusterers.XMeans
Sets the maximum number of iterations to perform.
setMaxIterations(int) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Sets the maximum number of cleansing iterations to perform - < 1 means go until fully cleansed
setMaxIts(int) - Method in class weka.classifiers.functions.Logistic
Set the value of MaxIts.
setMaxIts(int) - Method in class weka.classifiers.functions.RBFNetwork
Set the value of MaxIts.
setMaxK(int) - Method in class weka.classifiers.functions.VotedPerceptron
Set the value of maxK.
setMaxKMeans(int) - Method in class weka.clusterers.XMeans
Set the maximum number of iterations to perform in KMeans.
setMaxKMeansForChildren(int) - Method in class weka.clusterers.XMeans
Sets the maximum number of iterations KMeans that is performed on the child centers.
setMaxNrOfParents(int) - Method in class weka.classifiers.bayes.net.search.global.HillClimber
Sets the max number of parents
setMaxNrOfParents(int) - Method in class weka.classifiers.bayes.net.search.global.K2
Sets the max number of parents
setMaxNrOfParents(int) - Method in class weka.classifiers.bayes.net.search.local.HillClimber
Sets the max number of parents
setMaxNrOfParents(int) - Method in class weka.classifiers.bayes.net.search.local.K2
Sets the max number of parents
setMaxNrOfParents(int) - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
Sets the max number of parents
setMaxNumberOfItems(int) - Method in class weka.associations.FPGrowth
Set the maximum number of items to include in large items sets.
setMaxNumClusters(int) - Method in class weka.clusterers.XMeans
Sets the maximum number of clusters to generate.
setMaxPlots(int) - Method in class weka.gui.beans.AttributeSummarizer
Set the maximum number of plots to display
setMaxRadius(double) - Method in class weka.datagenerators.clusterers.BIRCHCluster
Sets the upper boundary for the radiuses of the clusters.
setMaxRange(double) - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Sets the upper boundary for the range of x
setMaxRelativeLeafRadius(double) - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
Sets the maximum relative radius, allowed for a leaf node.
setMaxRows(int) - Method in class weka.gui.sql.QueryPanel
sets the maximum number of rows to display.
setMaxRuleSize(int) - Method in class weka.datagenerators.classifiers.classification.RDG1
Sets the maximum number of tests in rules.
setMaxSubsequenceLength(int) - Method in class weka.classifiers.functions.supportVector.StringKernel
Sets the maximum length of the subsequence.
setMaxThreshold(double) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Set the maximum threshold.
setMDLTheoryWeight(double) - Method in class weka.classifiers.rules.RuleStats
Set the weight of theory in MDL calcualtion
setMean(int, int, double) - Method in class weka.experiment.ResultMatrix
sets the mean at the given position (if the position is valid)
setMeanPrec(int) - Method in class weka.experiment.ResultMatrix
sets the precision for the means
setMeanPrec(int) - Method in class weka.gui.experiment.OutputFormatDialog
Sets the precision of the mean output.
setMeanSquared(boolean) - Method in class weka.classifiers.lazy.IBk
Sets whether the mean squared error is used rather than mean absolute error when doing cross-validation.
setMeanStddev(String) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Sets mean and standarddeviation.
setMeanWidth(int) - Method in class weka.experiment.ResultMatrix
sets the width for the mean (0 = optimal)
setMeasure(SelectedTag) - Method in class weka.classifiers.meta.ThresholdSelector
set measure used for determining threshold
setMeasurePerformance(boolean) - Method in class weka.core.neighboursearch.BallTree
Sets whether to calculate the performance statistics or not.
setMeasurePerformance(boolean) - Method in class weka.core.neighboursearch.KDTree
Sets whether to calculate the performance statistics or not.
setMeasurePerformance(boolean) - Method in class weka.core.neighboursearch.NearestNeighbourSearch
Sets whether to calculate the performance statistics or not.
setMestWeight(double) - Method in class weka.classifiers.bayes.AODEsr
Sets the weight for m-estimate
setMetaClassifier(Classifier) - Method in class weka.classifiers.meta.Stacking
Adds meta classifier
setMethod(NeuralMethod) - Method in class weka.classifiers.functions.neural.NeuralNode
Set how this node should operate (note that the neural method has no internal state, so the same object can be used by any number of nodes.
setMethod(SelectedTag) - Method in class weka.classifiers.meta.MultiClassClassifier
Sets the method used.
setMethod(SelectedTag) - Method in class weka.classifiers.mi.MIWrapper
Set the method used in testing.
setMethodName(String) - Method in class weka.filters.unsupervised.attribute.NumericTransform
Set the transformation method.
setMetricType(SelectedTag) - Method in class weka.associations.Apriori
Set the metric type for ranking rules
setMetricType(SelectedTag) - Method in class weka.associations.FPGrowth
Set the metric type to use.
setMin(double) - Method in class weka.gui.beans.ChartEvent
Set the min y value
setMinBoxRelWidth(double) - Method in class weka.core.neighboursearch.KDTree
Sets the minimum relative box width.
setMinBucketSize(int) - Method in class weka.classifiers.rules.OneR
Set the value of minBucketSize.
setMinChange(int) - Method in class weka.clusterers.sIB
set the minimum number of changes
setMinChunkSize(int) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Set the minimum chunk size
setMinDefault(double) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Set the minimum default.
setMinGroup(int) - Method in class weka.classifiers.meta.RotationForest
Sets the minimum size of a group.
setMinimax(boolean) - Method in class weka.classifiers.mi.MISMO
Set if the MIMinimax feature space is to be used.
setMinimizeExpectedCost(boolean) - Method in class weka.classifiers.meta.CostSensitiveClassifier
Set the value of MinimizeExpectedCost.
setMinimumBucketSize(int) - Method in class weka.attributeSelection.OneRAttributeEval
Set the minumum bucket size used by OneR
setMinimumNumberInstances(int) - Method in class weka.core.Capabilities
sets the minimum number of instances that have to be in the dataset
setMiningSchemaInstances(Instances) - Method in class weka.core.pmml.MiningFieldMetaInfo
Set the Instances that represent the mining schema.
setMinInstNum(int) - Method in class weka.datagenerators.clusterers.BIRCHCluster
Sets the lower boundary for instances per cluster.
setMinInstNum(int) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Sets the lower boundary for instances per cluster.
setMinMaxValues() - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database
Sets the minimum and maximum values for each attribute in different arrays by walking through every DataObject of the database
setMinMaxValues() - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase
Sets the minimum and maximum values for each attribute in different arrays by walking through every DataObject of the database
setMinMaxX(double, double) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
Set the minimum and maximum values of the x axis fixed dimension
setMinMaxY(double, double) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
Set the minimum and maximum values of the y axis fixed dimension
setMinMetric(double) - Method in class weka.associations.Apriori
Set the value of minConfidence.
setMinMetric(double) - Method in class weka.associations.FPGrowth
Set the value of minConfidence.
setMinNo(double) - Method in class weka.classifiers.rules.ConjunctiveRule
Sets the minimum total weight of the instances in a rule
setMinNo(double) - Method in class weka.classifiers.rules.JRip
Sets the minimum total weight of the instances in a rule
setMinNo(double) - Method in class weka.classifiers.rules.Ridor
 
setMinNum(double) - Method in class weka.classifiers.trees.RandomTree
Set the value of MinNum.
setMinNum(double) - Method in class weka.classifiers.trees.REPTree
Set the value of MinNum.
setMinNumClusters(int) - Method in class weka.clusterers.XMeans
Sets the minimum number of clusters to generate.
setMinNumInstances(int) - Method in class weka.classifiers.trees.FT
Set the value of minNumInstances.
setMinNumInstances(int) - Method in class weka.classifiers.trees.LMT
Set the value of minNumInstances.
setMinNumInstances(double) - Method in class weka.classifiers.trees.m5.M5Base
Set the minimum number of instances to allow at a leaf node
setMinNumInstances(double) - Method in class weka.classifiers.trees.m5.Rule
Set the minumum number of instances to allow at a leaf node
setMinNumInstances(double) - Method in class weka.classifiers.trees.m5.RuleNode
Set the minumum number of instances to allow at a leaf node
setMinNumObj(int) - Method in class weka.classifiers.rules.PART
Set the value of minNumObj.
setMinNumObj(int) - Method in class weka.classifiers.trees.BFTree
Set minimal number of instances at the terminal nodes.
setMinNumObj(int) - Method in class weka.classifiers.trees.J48
Set the value of minNumObj.
setMinNumObj(int) - Method in class weka.classifiers.trees.J48graft
Set the value of minNumObj.
setMinNumObj(double) - Method in class weka.classifiers.trees.SimpleCart
Set minimal number of instances at the terminal nodes.
setMinPoints(int) - Method in class weka.clusterers.DBScan
Sets a new value for minPoints
setMinPoints(int) - Method in class weka.clusterers.OPTICS
Sets a new value for minPoints
setMinRadius(double) - Method in class weka.datagenerators.clusterers.BIRCHCluster
Sets the lower boundary for the radiuses of the clusters.
setMinRange(double) - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Sets the lower boundary for the range of x
setMinRuleSize(int) - Method in class weka.datagenerators.classifiers.classification.RDG1
Sets the minimum number of tests in rules.
setMinStdDev(double) - Method in class weka.classifiers.functions.RBFNetwork
Set the MinStdDev value.
setMinStdDev(double) - Method in class weka.clusterers.EM
Set the minimum value for standard deviation when calculating normal density.
setMinStdDev(double) - Method in class weka.clusterers.MakeDensityBasedClusterer
Set the minimum value for standard deviation when calculating normal density.
setMinStdDevPerAtt(double[]) - Method in class weka.clusterers.EM
 
setMinSupport(double) - Method in class weka.associations.GeneralizedSequentialPatterns
Sets the minimum support threshold.
setMinTermFreq(int) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Set the MinTermFreq value.
setMinThreshold(double) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Set the minimum threshold.
setMinVarianceProp(double) - Method in class weka.classifiers.trees.REPTree
Set the value of MinVarianceProp.
setMissing(int) - Method in class weka.core.Instance
Sets a specific value to be "missing".
setMissing(Attribute) - Method in class weka.core.Instance
Sets a specific value to be "missing".
setMissingMerge(boolean) - Method in class weka.attributeSelection.ChiSquaredAttributeEval
distribute the counts for missing values across observed values
setMissingMerge(boolean) - Method in class weka.attributeSelection.GainRatioAttributeEval
distribute the counts for missing values across observed values
setMissingMerge(boolean) - Method in class weka.attributeSelection.InfoGainAttributeEval
distribute the counts for missing values across observed values
setMissingMerge(boolean) - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
distribute the counts for missing values across observed values
setMissingMode(int) - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute
Set the missing value mode.
setMissingMode(SelectedTag) - Method in class weka.classifiers.lazy.KStar
Sets the method to use for handling missing values.
setMissingSeparate(boolean) - Method in class weka.attributeSelection.CfsSubsetEval
Treat missing as a separate value
setMissingValue(String) - Method in class weka.core.converters.CSVLoader
Sets the placeholder for missing values.
setMissingValues(SelectedTag) - Method in class weka.associations.Tertius
Set the value of missingValues.
setMixingDistribution(DiscreteFunction) - Method in class weka.classifiers.functions.pace.MixtureDistribution
Sets the mixing distribution
setModel(Classifier) - Method in class weka.classifiers.misc.SerializedClassifier
Sets the fully built model to use, if one doesn't want to load a model from a file or already deserialized a model from somewhere else.
setModel(TableModel) - Method in class weka.gui.arffviewer.ArffTable
sets the new model
setModel(ListModel) - Method in class weka.gui.CheckBoxList
sets the model - must be an instance of CheckBoxListModel
setModel(TableModel) - Method in class weka.gui.SortedTableModel
sets the model to use
setModelFile(File) - Method in class weka.classifiers.misc.SerializedClassifier
Sets the file containing the serialized model.
setModelType(SelectedTag) - Method in class weka.classifiers.trees.FT
Set the Functional Tree type.
setModePanel(SetupModePanel) - Method in class weka.gui.experiment.SimpleSetupPanel
Sets the panel used to switch between simple and advanced modes.
setModifyHeader(boolean) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Sets whether the header will be modified when selecting on nominal attributes.
setModifyHeader(boolean) - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Sets whether the header will be modified when selecting on nominal attributes.
setMomentum(double) - Method in class weka.classifiers.functions.MultilayerPerceptron
The momentum can be set using this command.
setMultiInstance(boolean) - Method in class weka.core.TestInstances
sets whether multi-instance data should be generated (with a fixed data structure)
setMultinomialWord(boolean) - Method in class weka.classifiers.bayes.DMNBtext
Sets whether use binary text representation
setMutationProb(double) - Method in class weka.attributeSelection.GeneticSearch
set the probability of mutation
setName(String) - Method in class weka.filters.unsupervised.attribute.AddExpression
Set the name for the new attribute.
setName(String) - Method in class weka.gui.visualize.VisualizePanel
Set a name for this plot
setNearestNeighbors(int) - Method in class weka.filters.supervised.instance.SMOTE
Sets the number of nearest neighbors to use.
setNearestNeighbourSearchAlgorithm(NearestNeighbourSearch) - Method in class weka.classifiers.lazy.IBk
Sets the nearestNeighbourSearch algorithm to be used for finding nearest neighbour(s).
setNearestNeighbourSearchAlgorithm(NearestNeighbourSearch) - Method in class weka.classifiers.lazy.LWL
Sets the nearestNeighbourSearch algorithm to be used for finding nearest neighbour(s).
setNegation(Literal) - Method in class weka.associations.tertius.Literal
 
setNegation(SelectedTag) - Method in class weka.associations.Tertius
Set the value of negation.
setNewToolTip(String) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel
Displays a toolTip for the selected DataObject
setNGramMaxSize(int) - Method in class weka.core.tokenizers.NGramTokenizer
Sets the max size of the Ngram.
setNGramMinSize(int) - Method in class weka.core.tokenizers.NGramTokenizer
Sets the min size of the Ngram.
setNoClass(boolean) - Method in class weka.core.TestInstances
whether to have no class, e.g., for clusterers; otherwise the class attribute index is set to last
setNodeName(int, String) - Method in class weka.classifiers.bayes.net.EditableBayesNet
change the name of a node
setNodesEdges(FastVector, FastVector) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
Sets the nodes and edges for this LayoutEngine.
setNodesEdges(FastVector, FastVector) - Method in interface weka.gui.graphvisualizer.LayoutEngine
This method sets the nodes and edges vectors of the LayoutEngine
setNodeSize(int, int) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
Sets the size of a node.
setNodeSize(int, int) - Method in interface weka.gui.graphvisualizer.LayoutEngine
This method sets the allowed size of the node
setNodeSplitter(KDTreeNodeSplitter) - Method in class weka.core.neighboursearch.KDTree
Sets the splitting method to use to split the nodes of the KDTree.
setNodeWidthNormalization(boolean) - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
Sets whether if a nodes region is normalized or not.
setNoise(double) - Method in class weka.classifiers.functions.GaussianProcesses
Set the level of Gaussian Noise.
setNoisePercent(double) - Method in class weka.datagenerators.classifiers.classification.LED24
Sets the noise percentage.
setNoiseRate(double) - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Sets the gaussian noise rate.
setNoiseRate(double) - Method in class weka.datagenerators.clusterers.BIRCHCluster
Sets the percentage of noise set.
setNoiseRate(double) - Method in class weka.datagenerators.clusterers.SubspaceCluster
Sets the percentage of noise set.
setNoiseThreshold(double) - Method in class weka.associations.Tertius
Set the value of noiseThreshold.
setNoiseVariance(double) - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Sets the noise variance
setNominal() - Method in class weka.gui.visualize.ClassPanel
Sets the legend to be for a nominal variable
setNominalAttributes(String) - Method in class weka.core.converters.CSVLoader
Sets the attribute range to be forced to type nominal.
setNominalCols(Range) - Method in class weka.datagenerators.ClusterGenerator
Sets which attributes are nominal.
setNominalIndices(String) - Method in class weka.datagenerators.ClusterGenerator
Sets which attributes are nominal
setNominalIndices(String) - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Set which nominal labels are to be included in the selection.
setNominalIndicesArr(int[]) - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Set which values of a nominal attribute are to be used for selection.
setNominalLabels(String) - Method in class weka.filters.unsupervised.attribute.Add
Set the labels for nominal attribute creation.
setNominalToBinaryFilter(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron
 
setNoPruning(boolean) - Method in class weka.classifiers.trees.REPTree
Set the value of NoPruning.
setNoReplacement(boolean) - Method in class weka.filters.supervised.instance.Resample
Sets whether instances are drawn with or with out replacement.
setNoReplacement(boolean) - Method in class weka.filters.unsupervised.instance.Resample
Sets whether instances are drawn with or with out replacement.
setNorm(double) - Method in class weka.filters.unsupervised.instance.Normalize
Set the norm of the instances
setNormalize(boolean) - Method in class weka.attributeSelection.LatentSemanticAnalysis
Set whether input data will be normalized.
setNormalize(boolean) - Method in class weka.classifiers.functions.LibLINEAR
whether to normalize input data
setNormalize(boolean) - Method in class weka.classifiers.functions.LibSVM
whether to normalize input data
setNormalizeAttributes(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron
 
setNormalizeData(boolean) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Set whether to normalize the data or not
setNormalizeDimWidths(boolean) - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
Should we normalize the widths(ranges) of the dimensions (attributes) before selecting the widest one.
setNormalizeDocLength(SelectedTag) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Sets whether if the word frequencies for a document (instance) should be normalized or not.
setNormalizeNodeWidth(boolean) - Method in class weka.core.neighboursearch.KDTree
Sets the flag for normalizing the widths of a KDTree Node by the width of the dimension in the universe.
setNormalizeNumericClass(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron
 
setNormalizeWordWeights(boolean) - Method in class weka.classifiers.bayes.ComplementNaiveBayes
Sets whether if the word weights for each class should be normalized
setNotCapabilities(Capabilities) - Method in class weka.core.FindWithCapabilities
Uses the given "not to have" Capabilities for the search.
setNotes(String) - Method in class weka.experiment.Experiment
Set the user notes.
setNotes(String) - Method in class weka.experiment.RemoteExperiment
Set the user notes.
setNotificationEnabled(boolean) - Method in class weka.gui.arffviewer.ArffSortedTableModel
sets whether the notification of changes is enabled
setNotificationEnabled(boolean) - Method in class weka.gui.arffviewer.ArffTableModel
sets whether the notification of changes is enabled
setNotUnifyNorm(boolean) - Method in class weka.clusterers.sIB
Set whether to normalize instances to unify prior probability before building the clusterer
setNrOfGoodOperations(int) - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
Sets the number of "good operations"
setNrOfLookAheadSteps(int) - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
Sets the number of look-ahead steps
setNu(double) - Method in class weka.classifiers.functions.LibSVM
Sets nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
setNumAllConds(double) - Method in class weka.classifiers.rules.RuleStats
Set the number of all conditions that could appear in a rule in this RuleStats object, if the number set is smaller than 0 (typically -1), then it calcualtes based on the data store
setNumAntds(int) - Method in class weka.classifiers.rules.ConjunctiveRule
Sets the number of antecedants
setNumArcs(int) - Method in class weka.datagenerators.classifiers.classification.BayesNet
Sets the number of arcs for the bayesian net
setNumAttemptsOfGeneOption(int) - Method in class weka.classifiers.rules.NNge
Sets the number of attempts for generalisation.
setNumAttributes(int) - Method in class weka.datagenerators.classifiers.classification.BayesNet
Sets the number of attributes the dataset should have.
setNumAttributes(int) - Method in class weka.datagenerators.classifiers.classification.RandomRBF
Sets the number of attributes the dataset should have.
setNumAttributes(int) - Method in class weka.datagenerators.classifiers.classification.RDG1
Sets the number of attributes the dataset should have.
setNumAttributes(int) - Method in class weka.datagenerators.clusterers.SubspaceCluster
Sets the number of attributes the dataset should have.
setNumAttributes(int) - Method in class weka.datagenerators.ClusterGenerator
Sets the number of attributes the dataset should have.
setNumAttributes(double) - Method in class weka.filters.unsupervised.attribute.RandomSubset
Set the number of attributes.
setNumberLiterals(int) - Method in class weka.associations.Tertius
Set the value of numberLiterals.
setNumberOfAttributes(int) - Method in class weka.filters.unsupervised.attribute.RandomProjection
Sets the number of attributes (dimensions) the data should be reduced to
setNumberOfGroups(boolean) - Method in class weka.classifiers.meta.RotationForest
Set whether minGroup and maxGroup refer to the number of groups or their size
setNumBins(int) - Method in class weka.classifiers.meta.RegressionByDiscretization
Sets the number of bins to divide each selected numeric attribute into
setNumBoostingIterations(int) - Method in class weka.classifiers.functions.SimpleLogistic
Set the value of numBoostingIterations.
setNumBoostingIterations(int) - Method in class weka.classifiers.trees.FT
Set the value of numBoostingIterations.
setNumBoostingIterations(int) - Method in class weka.classifiers.trees.LMT
Set the value of numBoostingIterations.
setNumCentroids(int) - Method in class weka.datagenerators.classifiers.classification.RandomRBF
Sets the number of centroids to use.
setNumCiters(int) - Method in class weka.classifiers.mi.CitationKNN
Sets the number of citers considered to estimate the class prediction of tests bags
setNumClasses(int) - Method in class weka.core.TestInstances
sets the number of classes
setNumClasses(int) - Method in class weka.datagenerators.classifiers.classification.RandomRBF
Sets the number of classes the dataset should have.
setNumClasses(int) - Method in class weka.datagenerators.classifiers.classification.RDG1
Sets the number of classes the dataset should have.
setNumClusters(int) - Method in class weka.classifiers.functions.RBFNetwork
Set the number of clusters for K-means to generate.
setNumClusters(int) - Method in class weka.clusterers.EM
Set the number of clusters (-1 to select by CV).
setNumClusters(int) - Method in class weka.clusterers.FarthestFirst
set the number of clusters to generate
setNumClusters(int) - Method in class weka.clusterers.HierarchicalClusterer
 
setNumClusters(int) - Method in class weka.clusterers.MakeDensityBasedClusterer
Set the number of clusters to generate.
setNumClusters(int) - Method in interface weka.clusterers.NumberOfClustersRequestable
Set the number of clusters to generate
setNumClusters(int) - Method in class weka.clusterers.sIB
Set the number of clusters
setNumClusters(int) - Method in class weka.clusterers.SimpleKMeans
set the number of clusters to generate
setNumClusters(int) - Method in class weka.datagenerators.clusterers.BIRCHCluster
Sets the number of clusters the dataset should have.
setNumComponents(int) - Method in class weka.filters.supervised.attribute.PLSFilter
sets the maximum number of attributes to use.
setNumCycles(int) - Method in class weka.datagenerators.clusterers.BIRCHCluster
Sets the the number of cycles.
setNumDate(int) - Method in class weka.core.CheckScheme
sets the number of data attributes
setNumDate(int) - Method in class weka.core.TestInstances
sets the number of date attributes
setNumeric(boolean) - Method in class weka.filters.unsupervised.attribute.MakeIndicator
Sets if the new Attribute is to be numeric.
setNumeric() - Method in class weka.gui.visualize.ClassPanel
Sets the legend to be for a numeric variable
setNumericPriorsFromBuffer() - Method in class weka.classifiers.Evaluation
Sets up the priors for numeric class attributes from the training class values that have been seen so far.
setNumExamples(int) - Method in class weka.datagenerators.ClassificationGenerator
Sets the number of examples, given by option.
setNumExamples(int) - Method in class weka.datagenerators.classifiers.classification.BayesNet
Sets the number of examples, given by option.
setNumExamples(int) - Method in class weka.datagenerators.RegressionGenerator
Sets the number of examples, given by option.
setNumExamplesAct(int) - Method in class weka.datagenerators.DataGenerator
Sets the number of examples the dataset should have.
setNumFeatures(int) - Method in class weka.classifiers.trees.RandomForest
Set the number of features to use in random selection.
setNumFoldersMIOption(int) - Method in class weka.classifiers.rules.NNge
Sets the number of folder for mutual information.
setNumFolds(int) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Set the number of folds to use for CV-based hyperparameter selection
setNumFolds(int) - Method in class weka.classifiers.functions.SMO
Set the value of numFolds.
setNumFolds(int) - Method in class weka.classifiers.meta.CVParameterSelection
Sets the number of folds for the cross-validation.
setNumFolds(int) - Method in class weka.classifiers.meta.Dagging
Sets the number of folds to use for splitting the training set.
setNumFolds(int) - Method in class weka.classifiers.meta.LogitBoost
Set the value of NumFolds.
setNumFolds(int) - Method in class weka.classifiers.meta.MultiScheme
Sets the number of folds for cross-validation.
setNumFolds(int) - Method in class weka.classifiers.meta.Stacking
Sets the number of folds for the cross-validation.
setNumFolds(int) - Method in class weka.classifiers.mi.MISMO
Set the value of numFolds.
setNumFolds(int) - Method in class weka.classifiers.rules.PART
Set the value of numFolds.
setNumFolds(int) - Method in class weka.classifiers.trees.J48
Set the value of numFolds.
setNumFolds(int) - Method in class weka.classifiers.trees.RandomTree
Set the value of NumFolds.
setNumFolds(int) - Method in class weka.classifiers.trees.REPTree
Set the value of NumFolds.
setNumFolds(int) - Method in class weka.experiment.CrossValidationResultProducer
Set the value of NumFolds.
setNumFolds(int) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Sets the number of folds the dataset is split into.
setNumFolds(int) - Method in class weka.filters.unsupervised.instance.RemoveFolds
Sets the number of folds the dataset is split into.
setNumFolds(int) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Sets the number of cross-validation folds to use - < 2 means no cross-validation.
setNumFoldsPruning(int) - Method in class weka.classifiers.trees.BFTree
Set number of folds in internal cross-validation.
setNumFoldsPruning(int) - Method in class weka.classifiers.trees.SimpleCart
Set number of folds in internal cross-validation.
setNumInstances(int) - Method in class weka.core.CheckScheme
Sets the number of instances to use in the datasets (some classifiers might require more instances).
setNumInstances(int) - Method in class weka.core.TestInstances
sets the number of instances to produce
setNumInstances(Random) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Sets the real number of instances for this cluster.
setNumInstances(int) - Method in class weka.estimators.CheckEstimator
Sets the number of instances to use in the datasets (some estimators might require more instances).
setNumInstancesRelational(int) - Method in class weka.core.CheckScheme
sets the number of instances in relational/bag attributes to produce
setNumInstancesRelational(int) - Method in class weka.core.TestInstances
sets the number of instances in relational/bag attributes to produce
setNumIrrelevant(int) - Method in class weka.datagenerators.classifiers.classification.RDG1
Sets the number of irrelevant attributes.
setNumIterations(int) - Method in class weka.classifiers.bayes.DMNBtext
Sets the number of iterations to be performed
setNumIterations(int) - Method in class weka.classifiers.functions.VotedPerceptron
Set the value of NumIterations.
setNumIterations(int) - Method in class weka.classifiers.functions.Winnow
Set the value of numIterations.
setNumIterations(int) - Method in class weka.classifiers.IteratedSingleClassifierEnhancer
Sets the number of bagging iterations
setNumIterations(int) - Method in class weka.classifiers.meta.MetaCost
Sets the number of bagging iterations
setNumNeighbours(int) - Method in class weka.attributeSelection.ReliefFAttributeEval
Set the number of nearest neighbours
setNumNeighbours(int) - Method in class weka.classifiers.mi.MINND
Sets the number of nearest neighbours to estimate the class prediction of tests bags
setNumNominal(int) - Method in class weka.core.CheckScheme
sets the number of nominal attributes
setNumNominal(int) - Method in class weka.core.TestInstances
sets the number of nominal attributes
setNumNominalValues(int) - Method in class weka.core.TestInstances
sets the number of values for nominal attributes
setNumNumeric(int) - Method in class weka.core.CheckScheme
sets the number of numeric attributes
setNumNumeric(int) - Method in class weka.core.TestInstances
sets the number of numeric attributes
setNumNumeric(int) - Method in class weka.datagenerators.classifiers.classification.RDG1
Sets the number of numerical attributes.
setNumOfBoostingIterations(int) - Method in class weka.classifiers.trees.ADTree
Sets the number of boosting iterations.
setNumOfBoostingIterations(int) - Method in class weka.classifiers.trees.LADTree
Sets the number of boosting iterations.
setNumReferences(int) - Method in class weka.classifiers.mi.CitationKNN
Sets the number of references considered to estimate the class prediction of tests bags
setNumRelational(int) - Method in class weka.core.CheckScheme
sets the number of relational attributes
setNumRelational(int) - Method in class weka.core.TestInstances
sets the number of relational attributes
setNumRelationalDate(int) - Method in class weka.core.TestInstances
sets the number of date attributes in a relational attribute
setNumRelationalNominal(int) - Method in class weka.core.TestInstances
sets the number of nominal attributes in a relational attribute
setNumRelationalNominalValues(int) - Method in class weka.core.TestInstances
sets the number of values for nominal attributes in a relational attribute
setNumRelationalNumeric(int) - Method in class weka.core.TestInstances
sets the number of numeric attributes in a relational attribute
setNumRelationalString(int) - Method in class weka.core.TestInstances
sets the number of string attributes in a relational attribute
setNumRestarts(int) - Method in class weka.clusterers.sIB
Set the number of restarts
setNumRules(int) - Method in class weka.associations.Apriori
Set the value of numRules.
setNumRules(int) - Method in class weka.associations.PredictiveApriori
Set the value of required rules.
setNumRulesToFind(int) - Method in class weka.associations.FPGrowth
Set the desired number of rules to find.
setNumRuns(int) - Method in class weka.classifiers.meta.LogitBoost
Set the value of NumRuns.
setNumSamplesPerRegion(int) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Set the number of points to uniformly sample from a region (fixed dimensions).
setNumSamplesPerRegion(int) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
Set the number of points to uniformly sample from a region (fixed dimensions).
setNumString(int) - Method in class weka.core.CheckScheme
sets the number of string attributes
setNumString(int) - Method in class weka.core.TestInstances
sets the number of string attributes
setNumSubCmtys(int) - Method in class weka.classifiers.meta.MultiBoostAB
Set the number of sub committees to use
setNumSubsetSizeCVFolds(int) - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Set the number of cross validation folds for subset size determination (default = 5).
setNumTestingNoises(int) - Method in class weka.classifiers.mi.MINND
Sets The number of nearest neighbour exemplars in the selection of noises in the test data
setNumToSelect(int) - Method in class weka.attributeSelection.GreedyStepwise
Specify the number of attributes to select from the ranked list (if generating a ranking).
setNumToSelect(int) - Method in class weka.attributeSelection.RaceSearch
Specify the number of attributes to select from the ranked list (if generating a ranking).
setNumToSelect(int) - Method in interface weka.attributeSelection.RankedOutputSearch
Specify the number of attributes to select from the ranked list.
setNumToSelect(int) - Method in class weka.attributeSelection.Ranker
Specify the number of attributes to select from the ranked list.
setNumTrainingNoises(int) - Method in class weka.classifiers.mi.MINND
Sets the number of nearest neighbour instances in the selection of noises in the training data
setNumTrees(int) - Method in class weka.classifiers.trees.RandomForest
Set the value of numTrees.
setNumUsedAttributes(int) - Method in class weka.attributeSelection.LinearForwardSelection
Set the number of top-ranked attributes that taken into account by the search process.
setNumUsedAttributes(int) - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Set the number of top-ranked attributes that taken into account by the search process.
setNumValues(int) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Sets how many values are retained
setNumXValFolds(int) - Method in class weka.classifiers.meta.ThresholdSelector
Set the number of folds used for cross-validation.
setObject(Object) - Method in class weka.core.CheckGOE
Set the object to work on..
setObject(Object) - Method in class weka.gui.beans.AssociatorCustomizer
Set the classifier object to be edited
setObject(Object) - Method in class weka.gui.beans.ClassAssignerCustomizer
Set the bean to be edited
setObject(Object) - Method in class weka.gui.beans.ClassifierCustomizer
Set the classifier object to be edited
setObject(Object) - Method in class weka.gui.beans.ClassValuePickerCustomizer
Set the bean to be edited
setObject(Object) - Method in class weka.gui.beans.ClustererCustomizer
Set the Clusterer object to be edited
setObject(Object) - Method in class weka.gui.beans.CrossValidationFoldMakerCustomizer
Set the object to be edited
setObject(Object) - Method in class weka.gui.beans.FilterCustomizer
Set the filter bean to be edited
setObject(Object) - Method in class weka.gui.beans.IncrementalClassifierEvaluatorCustomizer
Set the object to be edited
setObject(Object) - Method in class weka.gui.beans.LoaderCustomizer
Set the loader to be customized
setObject(Object) - Method in class weka.gui.beans.PredictionAppenderCustomizer
Set the object to be edited
setObject(Object) - Method in class weka.gui.beans.SaverCustomizer
Set the saver to be customized
setObject(Object) - Method in class weka.gui.beans.SerializedModelSaverCustomizer
Set the model saver to be customized
setObject(Object) - Method in class weka.gui.beans.StripChartCustomizer
Set the StripChart object to be customized
setObject(Object) - Method in class weka.gui.beans.TrainTestSplitMakerCustomizer
Set the TrainTestSplitMaker to be customized
setObject(Object) - Method in class weka.gui.GenericObjectEditor
Sets the current Object.
setObjective(double) - Method in class weka.attributeSelection.GeneticSearch.GABitSet
sets the objective merit value
setOfSequencesToString(FastVector, Instances, FastVector) - Static method in class weka.associations.gsp.Sequence
Returns a String representation of a set of Sequences where the numeric value of each event/item is represented by its respective nominal value.
setOkButtonText(String) - Method in class weka.gui.GenericObjectEditor.GOEPanel
Allows customization of the action label on the dialog.
setOmega(double) - Method in class weka.classifiers.functions.supportVector.Puk
Sets the omega value.
setOn(boolean) - Method in class weka.gui.visualize.ClassPanel
Enables the panel
setOnDemandDirectory(File) - Method in class weka.attributeSelection.CostSensitiveASEvaluation
Sets the directory that will be searched for cost files when loading on demand.
setOnDemandDirectory(File) - Method in class weka.classifiers.meta.CostSensitiveClassifier
Sets the directory that will be searched for cost files when loading on demand.
setOnDemandDirectory(File) - Method in class weka.classifiers.meta.MetaCost
Sets the directory that will be searched for cost files when loading on demand.
setOnDemandDirectory(File) - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
Sets the directory that will be searched for cost files when loading on demand.
setOptimalColumnWidth(int) - Method in class weka.gui.JTableHelper
sets the optimal column width for the given column
setOptimalColumnWidth(JTable, int) - Static method in class weka.gui.JTableHelper
sets the optimal column width for the given column
setOptimalColumnWidth() - Method in class weka.gui.JTableHelper
sets the optimal column width for all columns
setOptimalColumnWidth(JTable) - Static method in class weka.gui.JTableHelper
sets the optimal column width for alls column if the given table
setOptimalColWidth() - Method in class weka.gui.arffviewer.ArffPanel
calculates the optimal column width for the current column
setOptimalColWidths() - Method in class weka.gui.arffviewer.ArffPanel
calculates the optimal column widths for all columns
setOptimalColWidths() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
sets the optimal column width for all columns
setOptimalHeaderWidth(int) - Method in class weka.gui.JTableHelper
sets the optimal header width for the given column
setOptimalHeaderWidth(JTable, int) - Static method in class weka.gui.JTableHelper
sets the optimal header width for the given column
setOptimalHeaderWidth() - Method in class weka.gui.JTableHelper
sets the optimal header width for all columns
setOptimalHeaderWidth(JTable) - Static method in class weka.gui.JTableHelper
sets the optimal header width for alls column if the given table
setOptimizations(int) - Method in class weka.classifiers.rules.JRip
Sets the number of optimization runs
setOptionHandler(OptionHandler) - Method in class weka.core.CheckOptionHandler
Set the OptionHandler to work on..
setOptions(String[]) - Method in class weka.associations.Apriori
Parses a given list of options.
setOptions(String[]) - Method in class weka.associations.CheckAssociator
Parses a given list of options.
setOptions(String[]) - Method in class weka.associations.FilteredAssociator
Parses a given list of options.
setOptions(String[]) - Method in class weka.associations.FPGrowth
Parses a given list of options.
setOptions(String[]) - Method in class weka.associations.GeneralizedSequentialPatterns
Parses a given list of options.
setOptions(String[]) - Method in class weka.associations.PredictiveApriori
Parses a given list of options.
setOptions(String[]) - Method in class weka.associations.SingleAssociatorEnhancer
Parses a given list of options.
setOptions(String[]) - Method in class weka.associations.Tertius
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.BestFirst
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.CfsSubsetEval
Parses and sets a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.CheckAttributeSelection
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.ChiSquaredAttributeEval
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.ClassifierSubsetEval
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.CostSensitiveASEvaluation
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.ExhaustiveSearch
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.FilteredAttributeEval
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.FilteredSubsetEval
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.GainRatioAttributeEval
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.GeneticSearch
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.GreedyStepwise
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.InfoGainAttributeEval
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.LatentSemanticAnalysis
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.LinearForwardSelection
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.OneRAttributeEval
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.PrincipalComponents
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.RaceSearch
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.RandomSearch
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.Ranker
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.RankSearch
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.ReliefFAttributeEval
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.ScatterSearchV1
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.SVMAttributeEval
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.WrapperSubsetEval
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.AODE
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.AODEsr
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.BayesNet
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.ComplementNaiveBayes
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.DMNBtext
 
setOptions(String[]) - Method in class weka.classifiers.bayes.NaiveBayes
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.BayesNetGenerator
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.fixed.FromFile
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.global.HillClimber
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.global.K2
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.global.TAN
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.HillClimber
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.K2
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.TAN
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.WAODE
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.BVDecompose
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.BVDecomposeSegCVSub
Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.classifiers.CheckClassifier
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.CheckSource
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.Classifier
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.GaussianProcesses
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.LeastMedSq
Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.classifiers.functions.LibLINEAR
Sets the classifier options

Valid options are:

setOptions(String[]) - Method in class weka.classifiers.functions.LibSVM
Sets the classifier options

Valid options are:

setOptions(String[]) - Method in class weka.classifiers.functions.LinearRegression
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.Logistic
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.MultilayerPerceptron
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.PaceRegression
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.PLSClassifier
Parses the options for this object.
setOptions(String[]) - Method in class weka.classifiers.functions.RBFNetwork
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.SimpleLogistic
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.SMO
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.SMOreg
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.SPegasos
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.CachedKernel
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.CheckKernel
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.Kernel
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.PolyKernel
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.Puk
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.RBFKernel
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.RegOptimizer
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.RegSMO
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.StringKernel
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.VotedPerceptron
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.Winnow
Parses a given list of options.

Valid options are:

setOptions(String[]) - Method in class weka.classifiers.IteratedSingleClassifierEnhancer
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.lazy.IBk
Parses a given list of options.
setOptions(int, int, int) - Method in class weka.classifiers.lazy.kstar.KStarNominalAttribute
Sets the options.
setOptions(int, int, int) - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute
Set options.
setOptions(String[]) - Method in class weka.classifiers.lazy.KStar
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.lazy.LWL
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.AdaBoostM1
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.AdditiveRegression
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.Bagging
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.ClassificationViaClustering
Parses the options for this object.
setOptions(String[]) - Method in class weka.classifiers.meta.CostSensitiveClassifier
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.CVParameterSelection
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.Dagging
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.Decorate
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.FilteredClassifier
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.GridSearch
Parses the options for this object.
setOptions(String[]) - Method in class weka.classifiers.meta.LogitBoost
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.MetaCost
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.MultiBoostAB
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.MultiClassClassifier
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.MultiScheme
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.OrdinalClassClassifier
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.RandomSubSpace
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.RegressionByDiscretization
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.RotationForest
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.Stacking
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.ThresholdSelector
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.Vote
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.mi.CitationKNN
Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.classifiers.mi.MDD
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.mi.MIBoost
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.mi.MIDD
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.mi.MIEMDD
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.mi.MILR
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.mi.MINND
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.mi.MIOptimalBall
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.mi.MISMO
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.mi.MISVM
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.mi.MIWrapper
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.mi.SimpleMI
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.misc.SerializedClassifier
Parses the options for this object.
setOptions(String[]) - Method in class weka.classifiers.misc.VFI
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.MultipleClassifiersCombiner
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.RandomizableClassifier
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.RandomizableMultipleClassifiersCombiner
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.RandomizableSingleClassifierEnhancer
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.rules.ConjunctiveRule
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.rules.DecisionTable
Parses the options for this object.
setOptions(String[]) - Method in class weka.classifiers.rules.DTNB
Parses the options for this object.
setOptions(String[]) - Method in class weka.classifiers.rules.JRip
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.rules.NNge
Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.classifiers.rules.OneR
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.rules.PART
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.rules.Ridor
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.SingleClassifierEnhancer
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.ADTree
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.BFTree
Parses the options for this object.
setOptions(String[]) - Method in class weka.classifiers.trees.FT
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.J48
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.J48graft
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.LADTree
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.LMT
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.m5.M5Base
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.M5P
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.RandomForest
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.RandomTree
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.REPTree
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.SimpleCart
Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.CheckClusterer
Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.CLOPE
Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.Cobweb
Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.DBScan
Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.clusterers.EM
Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.FarthestFirst
Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.FilteredClusterer
Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.HierarchicalClusterer
Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.MakeDensityBasedClusterer
Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.OPTICS
Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.clusterers.RandomizableClusterer
Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.RandomizableDensityBasedClusterer
Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.RandomizableSingleClustererEnhancer
Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.sIB
Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.SimpleKMeans
Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.SingleClustererEnhancer
Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.XMeans
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.Check
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.CheckGOE
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.CheckOptionHandler
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.CheckScheme
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.converters.AbstractFileSaver
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.converters.ArffSaver
Parses the options for this object.
setOptions(String[]) - Method in class weka.core.converters.C45Saver
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.converters.CSVLoader
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.converters.DatabaseLoader
Sets the options.
setOptions(String[]) - Method in class weka.core.converters.DatabaseSaver
Sets the options.
setOptions(String[]) - Method in class weka.core.converters.LibSVMSaver
Parses the options for this object.
setOptions(String[]) - Method in class weka.core.converters.SVMLightSaver
Parses the options for this object.
setOptions(String[]) - Method in class weka.core.converters.TextDirectoryLoader
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.converters.XRFFSaver
Parses the options for this object.
setOptions(String[]) - Method in class weka.core.FindWithCapabilities
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.Javadoc
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.ListOptions
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.BallTree
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.balltrees.BallSplitter
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.CoverTree
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.KDTree
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.LinearNNSearch
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.NearestNeighbourSearch
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.NormalizableDistance
Parses a given list of options.
setOptions(String[]) - Method in interface weka.core.OptionHandler
Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.core.OptionHandlerJavadoc
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.stemmers.SnowballStemmer
Parses the options.
setOptions(String[]) - Method in class weka.core.TechnicalInformationHandlerJavadoc
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.TestInstances
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.tokenizers.CharacterDelimitedTokenizer
Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.core.tokenizers.NGramTokenizer
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.tokenizers.Tokenizer
Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.datagenerators.ClassificationGenerator
Sets the options.
setOptions(String[]) - Method in class weka.datagenerators.classifiers.classification.Agrawal
Parses a list of options for this object.
setOptions(String[]) - Method in class weka.datagenerators.classifiers.classification.BayesNet
Parses a list of options for this object.
setOptions(String[]) - Method in class weka.datagenerators.classifiers.classification.LED24
Parses a list of options for this object.
setOptions(String[]) - Method in class weka.datagenerators.classifiers.classification.RandomRBF
Parses a list of options for this object.
setOptions(String[]) - Method in class weka.datagenerators.classifiers.classification.RDG1
Parses a list of options for this object.
setOptions(String[]) - Method in class weka.datagenerators.classifiers.regression.Expression
Parses a list of options for this object.
setOptions(String[]) - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Parses a list of options for this object.
setOptions(String[]) - Method in class weka.datagenerators.ClusterDefinition
Parses a list of options for this object.
setOptions(String[]) - Method in class weka.datagenerators.clusterers.BIRCHCluster
Parses a list of options for this object.
setOptions(String[]) - Method in class weka.datagenerators.clusterers.SubspaceCluster
Parses a list of options for this object.
setOptions(String[]) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Parses a list of options for this object.
setOptions(String[]) - Method in class weka.datagenerators.ClusterGenerator
Sets the options.
setOptions(String[]) - Method in class weka.datagenerators.DataGenerator
Parses a list of options for this object.
setOptions(String[]) - Method in class weka.datagenerators.RegressionGenerator
Sets the options.
setOptions(String[]) - Method in class weka.estimators.CheckEstimator
Parses a given list of options.
setOptions(String[]) - Method in class weka.estimators.Estimator
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.AveragingResultProducer
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.ClassifierSplitEvaluator
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.CrossValidationResultProducer
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.CSVResultListener
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.DatabaseResultProducer
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.Experiment
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.InstanceQuery
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.LearningRateResultProducer
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.PairedTTester
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.RandomSplitResultProducer
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.RegressionSplitEvaluator
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.CheckSource
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.MultiFilter
Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.SimpleFilter
Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.supervised.attribute.AddClassification
Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.supervised.attribute.AttributeSelection
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.supervised.attribute.ClassOrder
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.supervised.attribute.Discretize
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.supervised.attribute.NominalToBinary
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.supervised.attribute.PLSFilter
Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.supervised.instance.Resample
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.supervised.instance.SMOTE
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.supervised.instance.SpreadSubsample
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Parses a given list of options controlling the behaviour of this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Add
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.AddCluster
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.AddExpression
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.AddID
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.AddNoise
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.AddValues
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.ClassAssigner
Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.ClusterMembership
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Copy
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Discretize
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.FirstOrder
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.KernelFilter
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.MakeIndicator
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.MathExpression
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.NominalToBinary
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.NominalToString
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Normalize
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.NumericToNominal
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.NumericTransform
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.PotentialClassIgnorer
Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.RandomProjection
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.RandomSubset
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.RELAGGS
Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Remove
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.RemoveType
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.RemoveUseless
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Reorder
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.StringToNominal
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.SwapValues
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Wavelet
Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.Normalize
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.Randomize
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemoveFolds
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemovePercentage
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemoveRange
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.Resample
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.ReservoirSample
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.SubsetByExpression
Parses a given list of options.
setOptions(String[]) - Method in class weka.gui.Main
Parses the options for this object.
setOriginalCoords(Vector) - Method in class weka.gui.beans.MetaBean
sets the vector containing the original coordinates (instances of class Point) for the inputs
setOutlierFactor(double) - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Sets the factor for determining the thresholds for outliers.
setOutput(PrintWriter) - Method in class weka.datagenerators.DataGenerator
Sets the print writer.
setOutputCenterFile(File) - Method in class weka.clusterers.XMeans
Sets file to write the list of centers to.
setOutputClassification(boolean) - Method in class weka.filters.supervised.attribute.AddClassification
Set whether the classification of the classifier is output.
setOutputDistribution(boolean) - Method in class weka.filters.supervised.attribute.AddClassification
Set whether the Distribution of the classifier is output.
setOutputErrorFlag(boolean) - Method in class weka.filters.supervised.attribute.AddClassification
Set whether the classification of the classifier is output.
setOutputFile(File) - Method in class weka.experiment.CrossValidationResultProducer
Set the value of OutputFile.
setOutputFile(File) - Method in class weka.experiment.CSVResultListener
Set the value of OutputFile.
setOutputFile(File) - Method in class weka.experiment.RandomSplitResultProducer
Set the value of OutputFile.
setOutputFilename(boolean) - Method in class weka.core.converters.TextDirectoryLoader
Sets whether the filename will be stored as an extra attribute.
setOutputFileName(String) - Method in class weka.experiment.CSVResultListener
Set the value of OutputFileName.
setOutputFilename(String) - Method in class weka.gui.GenericPropertiesCreator
sets the file to output the properties for the GEO to
setOutputFormat(int) - Method in class weka.core.Debug.Clock
sets the format of the output
setOutputFormat(Instances) - Method in class weka.filters.Filter
Sets the format of output instances.
setOutputFormat() - Method in class weka.filters.supervised.attribute.AttributeSelection
Set the output format.
setOutputFormat() - Method in class weka.filters.supervised.attribute.Discretize
Set the output format.
setOutputFormat() - Method in class weka.filters.unsupervised.attribute.Discretize
Set the output format.
setOutputFormat() - Method in class weka.filters.unsupervised.attribute.RandomProjection
Sets the output format
setOutputFormat() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Set the output format.
setOutputFormatFromDialog() - Method in class weka.gui.experiment.ResultsPanel
displays the Dialog for the output format and sets the chosen settings, if the user approves.
setOutputItemSets(boolean) - Method in class weka.associations.Apriori
Sets whether itemsets are output as well
setOutputOffsetMultiplier(boolean) - Method in class weka.filters.unsupervised.attribute.InterquartileRange
Set whether an additional attribute "Offset" is generated per Outlier/ExtremeValue attribute pair that lists the multiplier the value is off the median: value = median + 'multiplier' * IQR.
setOutputPerClassInfoRetrievalStats(boolean) - Method in class weka.gui.beans.IncrementalClassifierEvaluator
Set whether to output per-class information retrieval statistics (nominal class only).
setOutputs(Vector) - Method in class weka.gui.beans.MetaBean
 
setOutputTypes(String) - Method in class weka.core.Debug.DBO
Switches the outputs on that are requested from the option O
setOutputWordCounts(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Sets whether output instances contain 0 or 1 indicating word presence, or word counts.
setOverwriteWarning(boolean) - Method in class weka.gui.ConverterFileChooser
Whether a warning is popped up if the file that is to be saved already exists (only save dialog).
setOwner(CapabilitiesHandler) - Method in class weka.core.Capabilities
sets the owner of this capabilities object
setP(double) - Method in class weka.classifiers.BVDecomposeSegCVSub
Set the proportion of instances that are common between two training sets used to train a classifier.
setPadding(SelectedTag) - Method in class weka.filters.unsupervised.attribute.Wavelet
Sets the type of Padding to use
setPaint(Paint) - Method in class weka.gui.visualize.PostscriptGraphics
 
setPaintMode() - Method in class weka.gui.visualize.PostscriptGraphics
Not implemented
setPanelHeight(int) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
Set the height of the visualization
setPanelWidth(int) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
Set the width of the visualization
setParameterDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.BuiltInArithmetic
Set the structure of the parameters that are expected as input by this function.
setParameterDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.BuiltInMath
Set the structure of the parameters that are expected as input by this function.
setParameterDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.BuiltInString
Set the structure of the parameters that are expected as input by this function.
setParameterDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.DefineFunction
Set the structure of the parameters that are expected as input by this function.
setParameterDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.Function
Set the structure of the parameters that are expected as input by this function.
SetParent(int, int) - Method in class weka.classifiers.bayes.net.ParentSet
sets index parent of parent specified by index
setParent(ClusterGenerator) - Method in class weka.datagenerators.ClusterDefinition
sets the parent datagenerator this cluster belongs to
setParent(SubspaceCluster) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
sets the parent datagenerator this cluster belongs to
setParent(Container) - Method in class weka.gui.arffviewer.ArffViewerMainPanel
sets the new parent frame
setParent(Edge) - Method in class weka.gui.treevisualizer.Node
Set the value of parent.
setParentFrame(JFrame) - Method in class weka.gui.beans.AssociatorCustomizer
 
setParentFrame(JFrame) - Method in class weka.gui.beans.ClassAssignerCustomizer
 
setParentFrame(JFrame) - Method in class weka.gui.beans.ClassifierCustomizer
 
setParentFrame(JFrame) - Method in class weka.gui.beans.ClassValuePickerCustomizer
 
setParentFrame(JFrame) - Method in class weka.gui.beans.ClustererCustomizer
 
setParentFrame(JFrame) - Method in interface weka.gui.beans.CustomizerCloseRequester
A reference to the parent is passed in
setParentFrame(JFrame) - Method in class weka.gui.beans.FilterCustomizer
 
setParentFrame(JFrame) - Method in class weka.gui.beans.LoaderCustomizer
 
setParentFrame(JFrame) - Method in class weka.gui.beans.SaverCustomizer
 
setParentFrame(JFrame) - Method in class weka.gui.beans.SerializedModelSaverCustomizer
 
setParentFrame(JFrame) - Method in class weka.gui.SetInstancesPanel
Sets the frame, this panel resides in.
setParentSeparator(MarginCalculator.JunctionTreeSeparator) - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode
 
setPassword(String) - Method in interface weka.core.converters.DatabaseConverter
 
setPassword(String) - Method in class weka.core.converters.DatabaseLoader
Sets user password for the database
setPassword(String) - Method in class weka.core.converters.DatabaseSaver
Sets the database password.
setPassword(String) - Method in class weka.experiment.DatabaseUtils
Set the database password.
setPassword(String) - Method in class weka.gui.sql.ConnectionPanel
sets the Password.
setPattern(SelectedTag) - Method in class weka.datagenerators.clusterers.BIRCHCluster
Sets the pattern type.
setPercent(int) - Method in class weka.filters.unsupervised.attribute.AddNoise
Sets the size of noise data, as a percentage of the original set.
setPercent(double) - Method in class weka.filters.unsupervised.attribute.RandomProjection
Sets the percent the attributes (dimensions) of the data should be reduced to
setPercent() - Method in class weka.gui.visualize.MatrixPanel
Calculates the percentage to resample
setPercentage(double) - Method in class weka.filters.supervised.instance.SMOTE
Sets the percentage of SMOTE instances to create.
setPercentage(double) - Method in class weka.filters.unsupervised.instance.RemovePercentage
Sets the percentage of intances to select.
setPercentCompleted(int) - Method in class weka.gui.boundaryvisualizer.RemoteResult
Set the progress for this row so far
setPercentThreshold(int) - Method in class weka.attributeSelection.SVMAttributeEval
Set the threshold below which percentage elimination reverts to constant elimination.
setPercentToEliminatePerIteration(int) - Method in class weka.attributeSelection.SVMAttributeEval
Set the percentage of attributes to eliminate per iteration
setPerformPrediction(boolean) - Method in class weka.filters.supervised.attribute.PLSFilter
Sets whether to update the class attribute with the predicted value.
setPerformRanking(boolean) - Method in class weka.attributeSelection.LinearForwardSelection
Perform initial ranking to select top-ranked attributes.
setPerformRanking(boolean) - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Perform initial ranking to select top-ranked attributes.
setPeriodicPruning(double) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Sets the rate at which the dictionary is periodically pruned, as a percentage of the dataset size.
setPerturbationFraction(double) - Method in class weka.datagenerators.classifiers.classification.Agrawal
Sets the perturbation fraction.
setPivot(Instance) - Method in class weka.core.neighboursearch.balltrees.BallNode
Sets the pivot/centre of this nodes ball.
setPixHeight(double) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
Set the height of a pixel
setPixWidth(double) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
Set the width of a pixel
setPlotCompanion(Plot2DCompanion) - Method in class weka.gui.visualize.Plot2D
Set a companion class.
setPlotList(FastVector) - Method in class weka.gui.visualize.LegendPanel
Set the list of plots to generate legend entries for
setPlotName(String) - Method in class weka.gui.visualize.PlotData2D
Set the name of this plot
setPlotNameHTML(String) - Method in class weka.gui.visualize.PlotData2D
Set the plot name for use in a tool tip text.
setPlotTrainingData(boolean) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Set whether to superimpose the training data plot
setPlus(int, int, double) - Method in class weka.classifiers.functions.pace.PaceMatrix
Add a value to an element and reset the element
setPlus(int, double) - Method in class weka.core.matrix.DoubleVector
Adds a value to an element
setPMMLVersion(Document) - Method in class weka.classifiers.pmml.consumer.PMMLClassifier
Set the version of PMML used for this model.
setPMMLVersion(Document) - Method in interface weka.core.pmml.PMMLModel
Set the version of the PMML.
setPoints(MiddleOutConstructor.TempNode, int, int, int[]) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Sets the points of an anchor node.
setPointValue(int, double) - Method in class weka.classifiers.functions.pace.DiscreteFunction
Sets a particular point value
setPopulationSize(int) - Method in class weka.attributeSelection.GeneticSearch
set the population size
setPopulationSize(int) - Method in class weka.attributeSelection.ScatterSearchV1
Set the population size
setPopulationSize(int) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
 
setPopulationSize(int) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
 
setPopup(JPopupMenu) - Method in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
sets the JPopupMenu to display again after closing the dialog.
setPosition(int, int, int) - Method in class weka.classifiers.bayes.net.EditableBayesNet
set position of node
setPosition(int, int, int, FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet
Set position of node.
setPositiveIndex(int) - Method in class weka.associations.FPGrowth
Set the index of the attribute value to consider as positive for binary attributes in normal dense instances.
setPostProcessor(CheckScheme.PostProcessor) - Method in class weka.core.CheckScheme
sets the PostProcessor to use
setPostProcessor(CheckEstimator.PostProcessor) - Method in class weka.estimators.CheckEstimator
sets the PostProcessor to use
setPredTargetColumn(boolean) - Method in class weka.experiment.ClassifierSplitEvaluator
Set the flag for prediction and target output.
setPreferredScrollableViewportSize(Dimension) - Method in class weka.gui.AttributeSelectionPanel
 
setPrefix(String) - Method in class weka.gui.beans.SerializedModelSaver
Set the prefix to prepend to the model file names.
setPreprocessing(SelectedTag) - Method in class weka.filters.supervised.attribute.PLSFilter
Sets the type of preprocessing to use
setPreprocessing(Filter) - Method in class weka.filters.unsupervised.attribute.KernelFilter
Sets the filter to use for preprocessing (use the AllFilter for no preprocessing)
setPreserveInstancesOrder(boolean) - Method in class weka.clusterers.SimpleKMeans
Sets whether order of instances must be preserved
setPrintColNames(boolean) - Method in class weka.experiment.ResultMatrix
sets whether the column names or numbers instead are printed.
setPrintNewick(boolean) - Method in class weka.clusterers.HierarchicalClusterer
 
setPrintRowNames(boolean) - Method in class weka.experiment.ResultMatrix
sets whether the row names or numbers instead are printed deactivating automatically sets m_EnumerateColNames to TRUE.
setPriorClass(SelectedTag) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Set the type of prior to use.
setPriors(Instances) - Method in class weka.classifiers.Evaluation
Sets the class prior probabilities
setProbabilityEstimates(boolean) - Method in class weka.classifiers.functions.LibLINEAR
Returns whether probability estimates are generated instead of -1/+1 for classification problems.
setProbabilityEstimates(boolean) - Method in class weka.classifiers.functions.LibSVM
Returns whether probability estimates are generated instead of -1/+1 for classification problems.
setProcessed(boolean) - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
Marks this dataObject as processed
setProcessed(boolean) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
Marks this dataObject as processed
setProcessed(boolean) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
Marks this dataObject as processed
setProjectionFilter(Filter) - Method in class weka.classifiers.meta.RotationForest
Sets the filter used to project the data.
setProlog(boolean) - Method in class weka.core.OptionHandlerJavadoc
sets whether to add the "Valid options are..." prolog
setProlog(boolean) - Method in class weka.core.TechnicalInformationHandlerJavadoc
sets whether to add the "Valid options are..." prolog
setProperty(String, String) - Method in class weka.core.ProtectedProperties
Overrides a method to prevent the properties from being modified.
setProperty(int, Object) - Method in class weka.experiment.Experiment
Recursively sets the custom property value, by setting all values along the property path.
setPropertyArray(Object) - Method in class weka.experiment.Experiment
Sets the array of values to set the custom property to.
setPropertyArray(Object) - Method in class weka.experiment.RemoteExperiment
Sets the array of values to set the custom property to.
setPropertyPath(PropertyNode[]) - Method in class weka.experiment.Experiment
Sets the path of properties taken to get to the custom property to iterate over.
setPropertyPath(PropertyNode[]) - Method in class weka.experiment.RemoteExperiment
Sets the path of properties taken to get to the custom property to iterate over.
setPruningMethod(SelectedTag) - Method in class weka.classifiers.functions.supportVector.StringKernel
Sets the method used to for pruning.
setPruningStrategy(SelectedTag) - Method in class weka.classifiers.trees.BFTree
Sets the pruning strategy.
setPruningType(SelectedTag) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Set the pruning type
setQuality(float) - Method in class weka.gui.visualize.JPEGWriter
sets the quality the JPEG is saved in.
setQuery(String) - Method in class weka.core.converters.DatabaseLoader
Sets the query to execute against the database
setQuery(String) - Method in class weka.experiment.InstanceQuery
Set the query to execute against the database
setQuery(String) - Method in class weka.gui.sql.QueryPanel
sets the query in the textarea.
setQueryPanel(QueryPanel) - Method in class weka.gui.sql.ResultPanel
sets the QueryPanel to use for displaying the query
setRaceType(SelectedTag) - Method in class weka.attributeSelection.RaceSearch
Set the race type
setRadius(double) - Method in class weka.core.neighboursearch.balltrees.BallNode
Sets the radius of the node's ball.
setRadiuses(String) - Method in class weka.datagenerators.clusterers.BIRCHCluster
Sets the upper and lower boundary for the radius of the clusters.
setRandom(Random) - Method in class weka.datagenerators.DataGenerator
Sets the random generator.
setRandomize(boolean) - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
Sets whether the order of the generated data is randomized
setRandomizeData(boolean) - Method in class weka.experiment.RandomSplitResultProducer
Set to true if dataset is to be randomized
setRandomOrder(boolean) - Method in class weka.classifiers.bayes.net.search.global.K2
Set random order flag
setRandomOrder(boolean) - Method in class weka.classifiers.bayes.net.search.local.K2
Set random order flag
setRandomSeed(long) - Method in class weka.classifiers.functions.LeastMedSq
Set the seed for the random number generator
setRandomSeed(int) - Method in class weka.classifiers.functions.SMO
Set the value of randomSeed.
setRandomSeed(int) - Method in class weka.classifiers.mi.MISMO
Set the value of randomSeed.
setRandomSeed(int) - Method in class weka.classifiers.trees.ADTree
Sets random seed for a random walk.
setRandomSeed(int) - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
Sets the seed for random number generator.
setRandomSeed(int) - Method in class weka.filters.supervised.instance.Resample
Sets the random number seed.
setRandomSeed(int) - Method in class weka.filters.supervised.instance.SMOTE
Sets the random number seed.
setRandomSeed(int) - Method in class weka.filters.supervised.instance.SpreadSubsample
Sets the random number seed.
setRandomSeed(int) - Method in class weka.filters.unsupervised.attribute.AddNoise
Sets the random number seed.
setRandomSeed(long) - Method in class weka.filters.unsupervised.attribute.RandomProjection
Sets the random seed of the random number generator
setRandomSeed(int) - Method in class weka.filters.unsupervised.instance.Randomize
Set the random number generator seed value.
setRandomSeed(int) - Method in class weka.filters.unsupervised.instance.Resample
Sets the random number seed.
setRandomSeed(int) - Method in class weka.filters.unsupervised.instance.ReservoirSample
Sets the random number seed.
setRandomWidthFactor(double) - Method in class weka.classifiers.meta.MultiClassClassifier
Sets the multiplier when generating random codes.
setRange(String) - Method in class weka.datagenerators.classifiers.regression.MexicanHat
Sets the upper and lower boundary for the range of x
setRangeCorrection(SelectedTag) - Method in class weka.classifiers.meta.ThresholdSelector
Sets the confidence range correction mode used.
setRanges(String) - Method in class weka.core.Range
Sets the ranges from a string representation.
setRanges(Range[]) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
Sets the list of possible Ranges to choose from.
setRank(double) - Method in class weka.attributeSelection.LatentSemanticAnalysis
Sets the desired matrix rank (or coverage proportion) for feature-space reduction
setRanking(boolean) - Method in class weka.attributeSelection.AttributeSelection
produce a ranking (if possible with the set search and evaluator)
setRanking(int[][]) - Method in class weka.experiment.ResultMatrix
sets the ranking data based on the wins
setRawOutput(boolean) - Method in class weka.experiment.CrossValidationResultProducer
Set to true if raw split evaluator output is to be saved
setRawOutput(boolean) - Method in class weka.experiment.RandomSplitResultProducer
Set to true if raw split evaluator output is to be saved
setReachabilityDistance(double) - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
Sets a new reachability-distance for this dataObject
setReachabilityDistance(double) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
Sets a new reachability-distance for this dataObject
setReachabilityDistance(double) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
Sets a new reachability-distance for this dataObject
setReachabilityDistanceColor(Color) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel
Sets a new color for the reachabilityDistance
setReadable(String) - Method in class weka.core.Tag
Sets the string description of the Tag.
setReadIncrementally(boolean) - Method in class weka.gui.SetInstancesPanel
Sets whether or not instances should be read incrementally by the Loader.
setReadOnly(boolean) - Method in class weka.gui.arffviewer.ArffPanel
sets whether the model is read-only
setReadOnly(boolean) - Method in class weka.gui.arffviewer.ArffSortedTableModel
sets whether the model is read-only
setReadOnly(boolean) - Method in class weka.gui.arffviewer.ArffTable
sets whether the model is read-only
setReadOnly(boolean) - Method in class weka.gui.arffviewer.ArffTableModel
sets whether the model is read-only
setReducedErrorPruning(boolean) - Method in class weka.classifiers.rules.PART
Set the value of reducedErrorPruning.
setReducedErrorPruning(boolean) - Method in class weka.classifiers.trees.J48
Set the value of reducedErrorPruning.
setRefer(String) - Method in class weka.gui.treevisualizer.Node
Set the value of refer.
setRefreshFreq(int) - Method in class weka.gui.beans.StripChart
Set how often (in x axis points) to refresh the display
setRegOptimizer(RegOptimizer) - Method in class weka.classifiers.functions.SMOreg
sets the learning algorithm
setRegressionTree(boolean) - Method in class weka.classifiers.trees.m5.Rule
Set the value of regressionTree.
setRegressionTree(boolean) - Method in class weka.classifiers.trees.m5.RuleNode
Set the value of regressionTree.
setRelabel(boolean) - Method in class weka.classifiers.trees.J48graft
Set the value of relabelling.
setRelation(String) - Method in class weka.core.TestInstances
sets the name of the relation
setRelationalClassFormat(Instances) - Method in class weka.core.TestInstances
sets the structure for the relational class attribute
setRelationalFormat(int, Instances) - Method in class weka.core.TestInstances
sets the structure for the bags for the relational attribute
setRelationForTableName(boolean) - Method in class weka.core.converters.DatabaseSaver
En/Dis-ables that the relation name is used for the name of the table (default enabled).
setRelationName(String) - Method in class weka.core.Instances
Sets the relation's name.
setRelationName(String) - Method in class weka.datagenerators.DataGenerator
Sets the relation name the dataset should have.
setRelationNameForFilename(boolean) - Method in class weka.gui.beans.Saver
Set whether to use the relation name as the primary part of the filename.
setRemoteHosts(DefaultListModel) - Method in class weka.experiment.RemoteExperiment
Set the list of remote host names
setRemoteHosts(Vector) - Method in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
Set a list of host names of machines to distribute processing to
setRemoveAllMissingCols(boolean) - Method in class weka.associations.Apriori
Remove columns containing all missing values.
setRemoveClassColumn(boolean) - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
Set whether the class column should be removed from the data.
setRemovedPercentage(int) - Method in class weka.classifiers.meta.RotationForest
Sets the percentage of instance to be removed
setRemoveFilterName(boolean) - Method in class weka.experiment.ResultMatrix
sets whether to remove the filter classname from the dataset name
setRemoveFilterName(boolean) - Method in class weka.gui.experiment.OutputFormatDialog
sets whether to remove the filter classname from the dataset name.
setRemoveOldClass(boolean) - Method in class weka.filters.supervised.attribute.AddClassification
Set whether the old class attribute is removed.
setRemoveUnused(boolean) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
Sets whether unused attributes (ones that are not covered by any of the ranges) are removed from the output.
setRenderingHint(RenderingHints.Key, Object) - Method in class weka.gui.visualize.PostscriptGraphics
 
setRenderingHints(Map) - Method in class weka.gui.visualize.PostscriptGraphics
 
setRepeatLiterals(boolean) - Method in class weka.associations.Tertius
Set the value of repeatLiterals.
setReplaceMissing(boolean) - Method in class weka.filters.supervised.attribute.PLSFilter
Sets whether to replace missing values.
setReplaceMissingValues(boolean) - Method in class weka.filters.unsupervised.attribute.RandomProjection
Sets either to use replace missing values filter or not
setReportFrequency(int) - Method in class weka.attributeSelection.GeneticSearch
set how often reports are generated
setRepulsion(double) - Method in class weka.clusterers.CLOPE
set the repulsion
setReset(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron
This sets the network up to be able to reset itself with the current settings and the learning rate at half of what it is currently.
setReset(boolean) - Method in class weka.gui.beans.ChartEvent
Set the reset flag
setResult(Double) - Method in class weka.core.mathematicalexpression.Parser
Sets the result of the evaluation.
setResult(Boolean) - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
Sets the result of the evaluation.
setResultKeyFromDialog() - Method in class weka.gui.experiment.ResultsPanel
 
setResultListener(ResultListener) - Method in class weka.experiment.AveragingResultProducer
Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.CrossValidationResultProducer
Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.DatabaseResultProducer
Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.Experiment
Sets the result listener where results will be sent.
setResultListener(ResultListener) - Method in class weka.experiment.LearningRateResultProducer
Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.RandomSplitResultProducer
Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.RemoteExperiment
Sets the result listener where results will be sent.
setResultListener(ResultListener) - Method in interface weka.experiment.ResultProducer
Sets the object to send results of each run to.
setResultMatrix(ResultMatrix) - Method in class weka.experiment.PairedTTester
Sets the matrix to use to produce the output.
setResultMatrix(ResultMatrix) - Method in interface weka.experiment.Tester
Sets the matrix to use to produce the output.
setResultMatrix(Class) - Method in class weka.gui.experiment.OutputFormatDialog
Sets the matrix to use as initial selected output format.
setResultProducer(ResultProducer) - Method in class weka.experiment.AveragingResultProducer
Set the ResultProducer.
setResultProducer(ResultProducer) - Method in class weka.experiment.DatabaseResultProducer
Set the ResultProducer.
setResultProducer(ResultProducer) - Method in class weka.experiment.Experiment
Set the result producer used for the current experiment.
setResultProducer(ResultProducer) - Method in class weka.experiment.LearningRateResultProducer
Set the ResultProducer.
setResultProducer(ResultProducer) - Method in class weka.experiment.RemoteExperiment
Set the result producer used for the current experiment.
setResultsetKeyColumns(Range) - Method in class weka.experiment.PairedTTester
Set the value of ResultsetKeyColumns.
setResultsetKeyColumns(Range) - Method in interface weka.experiment.Tester
Set the value of ResultsetKeyColumns.
setResultsPanel(ResultsPanel) - Method in class weka.gui.experiment.RunPanel
Sets the pointer to the results panel.
setResultVector(FastVector) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel
Sets a new resultVector
setRetrieval(int) - Method in class weka.core.converters.AbstractLoader
Sets the retrieval mode.
setRetrieval(int) - Method in class weka.core.converters.AbstractSaver
Sets the retrieval mode.
setRetrieval(int) - Method in interface weka.core.converters.Loader
Sets the retrieval mode.
setRetrieval(int) - Method in interface weka.core.converters.Saver
Sets the retrieval mode
setRidge(double) - Method in class weka.classifiers.functions.LinearRegression
Set the value of Ridge.
setRidge(double) - Method in class weka.classifiers.functions.Logistic
Sets the ridge in the log-likelihood.
setRidge(double) - Method in class weka.classifiers.functions.RBFNetwork
Sets the ridge value for logistic or linear regression.
setRidge(double) - Method in class weka.classifiers.mi.MILR
Sets the ridge in the log-likelihood.
setRocAnalysis(boolean) - Method in class weka.associations.Tertius
Set the value of rocAnalysis.
setROCString(String) - Method in class weka.gui.visualize.ThresholdVisualizePanel
Set the string with ROC area
setRoot(boolean) - Method in class weka.gui.treevisualizer.Node
Set the value of root.
setRootNode(String) - Method in class weka.core.xml.XMLDocument
sets the root node to use in the XML output.
setRow(int, double[]) - Method in class weka.core.Matrix
Deprecated.
Sets a row of the matrix to the given row.
setRowDimension(int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Set the row dimenion of the matrix
setRowHidden(int, boolean) - Method in class weka.experiment.ResultMatrix
sets the hidden status of the row (if the index is valid)
setRowName(int, String) - Method in class weka.experiment.ResultMatrix
sets the name of the row (if the index is valid)
setRowNameWidth(int) - Method in class weka.experiment.ResultMatrix
sets the width for the row names (0 = optimal)
setRowNumber(int) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
Set the row number for this sub task
setRowOrder(int[]) - Method in class weka.experiment.ResultMatrix
sets the ordering of the rows, null means default
setRsource(String) - Method in class weka.gui.treevisualizer.Edge
Set the value of rsource.
setRtarget(String) - Method in class weka.gui.treevisualizer.Edge
Set the value of rtarget.
setRuleset(FastVector) - Method in class weka.classifiers.rules.RuleStats
Set the ruleset of the stats, overwriting the old one if any
setRulesMustContain(String) - Method in class weka.associations.FPGrowth
Set the comma separated list of items that rules must contain in order to be output.
setRunColumn(int) - Method in class weka.experiment.PairedTTester
Set the value of RunColumn.
setRunColumn(int) - Method in interface weka.experiment.Tester
Set the value of RunColumn.
setRunLower(int) - Method in class weka.experiment.Experiment
Set the lower run number for the experiment.
setRunLower(int) - Method in class weka.experiment.RemoteExperiment
Set the lower run number for the experiment.
setRuns(int) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
Sets the number of runs
setRuns(int) - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
Sets the number of runs
setRuns(int) - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
Sets the m_nRuns.
setRuns(int) - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
Sets the number of runs
setRuns(int) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
Sets the number of runs
setRuns(int) - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
Sets the number of runs
setRuns(int) - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
Sets the m_nRuns.
setRuns(int) - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
Sets the number of runs
setRunUpper(int) - Method in class weka.experiment.Experiment
Set the upper run number for the experiment.
setRunUpper(int) - Method in class weka.experiment.RemoteExperiment
Set the upper run number for the experiment.
setSampleSize(int) - Method in class weka.attributeSelection.ReliefFAttributeEval
Set the number of instances to sample for attribute estimation
setSampleSize(int) - Method in class weka.classifiers.functions.LeastMedSq
sets number of samples
setSampleSize(int) - Method in class weka.filters.unsupervised.instance.ReservoirSample
Sets the size of the subsample.
setSampleSizePercent(double) - Method in class weka.classifiers.meta.GridSearch
Sets the sample size for the initial grid search.
setSampleSizePercent(double) - Method in class weka.filters.supervised.instance.Resample
Sets the size of the subsample, as a percentage of the original set.
setSampleSizePercent(double) - Method in class weka.filters.unsupervised.instance.Resample
Sets the size of the subsample, as a percentage of the original set.
setSaveDialogTitle(String) - Method in class weka.gui.visualize.PrintableComponent
sets the title for the save dialog.
setSaveDialogTitle(String) - Method in interface weka.gui.visualize.PrintableHandler
sets the title for the save dialog
setSaveDialogTitle(String) - Method in class weka.gui.visualize.PrintablePanel
sets the title for the save dialog
setSaveInstanceData(boolean) - Method in class weka.classifiers.trees.ADTree
Sets whether the tree is to save instance data.
setSaveInstanceData(boolean) - Method in class weka.classifiers.trees.J48
Set whether instance data is to be saved.
setSaveInstanceData(boolean) - Method in class weka.classifiers.trees.J48graft
Set whether instance data is to be saved.
setSaveInstanceData(boolean) - Method in class weka.clusterers.Cobweb
Set the value of saveInstances.
setSaveInstances(boolean) - Method in class weka.classifiers.trees.m5.Rule
Sets whether instances at each node in an M5 tree should be saved for visualization purposes.
setSaveInstances(boolean) - Method in class weka.classifiers.trees.m5.RuleNode
Set whether to save instances for visualization purposes.
setSaveInstances(boolean) - Method in class weka.classifiers.trees.M5P
Set whether to save instance data at each node in the tree for visualization purposes
setSaverTemplate(Saver) - Method in class weka.gui.beans.Saver
Set the loader to use
setScale(double) - Method in class weka.filters.unsupervised.attribute.Normalize
Sets the scaling factor.
setScale(double, double) - Method in class weka.gui.visualize.JComponentWriter
sets the scale factor - is ignored since we always create a screenshot!
setScale(double, double) - Method in class weka.gui.visualize.PrintableComponent
sets the scale factor.
setScale(double, double) - Method in interface weka.gui.visualize.PrintableHandler
sets the scale factor
setScale(double, double) - Method in class weka.gui.visualize.PrintablePanel
sets the scale factor
setScalingEnabled(boolean) - Method in class weka.gui.visualize.JComponentWriter
sets whether to enable scaling
setScoreType(SelectedTag) - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
set quality measure to be used in searching for networks.
setSearch(ASSearch) - Method in class weka.attributeSelection.AttributeSelection
set the search method
setSearch(ASSearch) - Method in class weka.attributeSelection.CheckAttributeSelection
Set the search method to test.
setSearch(ASSearch) - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Sets the search method
setSearch(ASSearch) - Method in class weka.classifiers.rules.DecisionTable
Sets the search method to use
setSearch(ASSearch) - Method in class weka.classifiers.rules.DTNB
Sets the search method to use
setSearch(ASSearch) - Method in class weka.filters.supervised.attribute.AttributeSelection
Set search class
setSearchAlgorithm(SearchAlgorithm) - Method in class weka.classifiers.bayes.BayesNet
Set the SearchAlgorithm used in searching for network structures.
setSearchBackwards(boolean) - Method in class weka.attributeSelection.GreedyStepwise
Set whether to search backwards instead of forwards
setSearchPath(SelectedTag) - Method in class weka.classifiers.trees.ADTree
Sets the method of searching the tree for a new insertion.
setSearchPercent(double) - Method in class weka.attributeSelection.RandomSearch
set the percentage of the search space to consider
setSearchString(String) - Method in class weka.gui.arffviewer.ArffTable
sets the search string to look for in the table, NULL or "" disables the search
setSearchTermination(int) - Method in class weka.attributeSelection.BestFirst
Set the numnber of non-improving nodes to consider before terminating search.
setSearchTermination(int) - Method in class weka.attributeSelection.LinearForwardSelection
Set the numnber of non-improving nodes to consider before terminating search.
setSecondValueIndex(String) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
Sets index of the second value used.
setSecondValueIndex(String) - Method in class weka.filters.unsupervised.attribute.SwapValues
Sets index of the second value used.
setSeed(int) - Method in class weka.attributeSelection.AttributeSelection
set the seed for use in cross validation
setSeed(int) - Method in class weka.attributeSelection.CostSensitiveASEvaluation
Set the seed for random number generation.
setSeed(int) - Method in class weka.attributeSelection.GeneticSearch
set the seed for random number generation
setSeed(int) - Method in class weka.attributeSelection.OneRAttributeEval
Set the random number seed for cross validation
setSeed(int) - Method in class weka.attributeSelection.RandomSearch
Set the random seed to use
setSeed(int) - Method in class weka.attributeSelection.ReliefFAttributeEval
Set the random number seed for randomly sampling instances.
setSeed(int) - Method in class weka.attributeSelection.ScatterSearchV1
set the seed for random number generation
setSeed(int) - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Seed for cross validation subset size determination.
setSeed(int) - Method in class weka.attributeSelection.WrapperSubsetEval
Set the seed to use for cross validation
setSeed(int) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Set the seed for randomizing the instances for CV-based hyperparameter selection
setSeed(int) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
Sets the random number seed
setSeed(int) - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
Sets the random number seed
setSeed(int) - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
Sets the random number seed
setSeed(int) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
Sets the random number seed
setSeed(int) - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
Sets the random number seed
setSeed(int) - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
Sets the random number seed
setSeed(int) - Method in class weka.classifiers.BVDecompose
Sets the random number seed
setSeed(int) - Method in class weka.classifiers.BVDecomposeSegCVSub
Sets the random number seed
setSeed(int) - Method in class weka.classifiers.evaluation.EvaluationUtils
Sets the seed for randomization during cross-validation
setSeed(int) - Method in class weka.classifiers.functions.MultilayerPerceptron
This seeds the random number generator, that is used when a random number is needed for the network.
setSeed(int) - Method in class weka.classifiers.functions.supportVector.RegOptimizer
Sets the seed value for the random number generator
setSeed(int) - Method in class weka.classifiers.functions.VotedPerceptron
Set the value of Seed.
setSeed(int) - Method in class weka.classifiers.functions.Winnow
Set the value of Seed.
setSeed(int) - Method in class weka.classifiers.meta.MultiScheme
Sets the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.RandomizableClassifier
Set the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
Set the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.RandomizableMultipleClassifiersCombiner
Set the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.RandomizableSingleClassifierEnhancer
Set the seed for random number generation.
setSeed(long) - Method in class weka.classifiers.rules.ConjunctiveRule
sets the seed for randomizing the data
setSeed(long) - Method in class weka.classifiers.rules.JRip
Sets the seed value to use in randomizing the data
setSeed(int) - Method in class weka.classifiers.rules.PART
Set the value of Seed.
setSeed(int) - Method in class weka.classifiers.rules.Ridor
 
setSeed(int) - Method in class weka.classifiers.trees.J48
Set the value of Seed.
setSeed(int) - Method in class weka.classifiers.trees.RandomForest
Set the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.trees.RandomTree
Set the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.trees.REPTree
Set the value of Seed.
setSeed(int) - Method in class weka.clusterers.RandomizableClusterer
Set the seed for random number generation.
setSeed(int) - Method in class weka.clusterers.RandomizableDensityBasedClusterer
Set the seed for random number generation.
setSeed(int) - Method in class weka.clusterers.RandomizableSingleClustererEnhancer
Set the seed for random number generation.
setSeed(long) - Method in class weka.core.Debug.Random
Sets the seed of this random number generator using a single long seed.
setSeed(int) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Sets the seed for random number generator (that is used for selecting the first anchor point randomly).
setSeed(int) - Method in interface weka.core.Randomizable
Set the seed for random number generation.
setSeed(int) - Method in class weka.core.TestInstances
sets the seed value for the random number generator
setSeed(int) - Method in class weka.datagenerators.DataGenerator
Sets the random number seed.
setSeed(long) - Method in class weka.filters.supervised.attribute.ClassOrder
Set randomization seed
setSeed(long) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Sets the random number seed for shuffling the dataset.
setSeed(int) - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
Sets the new seed for randomizing the order of the generated data
setSeed(int) - Method in class weka.filters.unsupervised.attribute.RandomSubset
Set the seed value for the random number generator.
setSeed(long) - Method in class weka.filters.unsupervised.instance.RemoveFolds
Sets the random number seed for shuffling the dataset.
setSeed(int) - Method in class weka.gui.beans.CrossValidationFoldMaker
Set the seed
setSeed(int) - Method in class weka.gui.beans.TrainTestSplitMaker
Set the random seed
setSeed(int) - Method in interface weka.gui.boundaryvisualizer.DataGenerator
Set a seed for random number generation (if needed).
setSeed(int) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator
Initializes a new random number generator using the supplied seed.
setSelectedAttributes(boolean[]) - Method in class weka.gui.AttributeSelectionPanel
Set the selected attributes in the widget.
setSelectedColumn(int) - Method in class weka.gui.arffviewer.ArffTable
sets the selected column
setSelectedItem(JComboBox, String) - Method in class weka.gui.experiment.ResultsPanel
Sets the selected item of an combobox, since using setSelectedItem(...) doesn't work, if one checks object references!
setSelectedItem(JComboBox, String) - Method in class weka.gui.experiment.SimpleSetupPanel
Sets the selected item of an combobox, since using setSelectedItem(...) doesn't work, if one checks object references!
setSelectedRange(String) - Method in class weka.filters.unsupervised.attribute.RELAGGS
Set the range of attributes to process.
setSelectedRange(String) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Set the value of m_SelectedRange.
setSelectionThreshold(double) - Method in class weka.attributeSelection.RaceSearch
Set the threshold by which the AttributeSelection module can discard attributes.
setSeparatingThreshold(double) - Method in class weka.classifiers.functions.pace.ChisqMixture
Sets the separating threshold value
setSeparatingThreshold(double) - Method in class weka.classifiers.functions.pace.NormalMixture
Sets the separating threshold value
setSeperator(String) - Method in class weka.gui.HierarchyPropertyParser
Set the seperator between levels.
setSequentialAttIndex(boolean) - Method in class weka.classifiers.lazy.LBR.Indexes
A Sequential Attribute index is all those Attributes that are set to the specified value placed in a sequential array.
setSequentialDataset(boolean) - Method in class weka.classifiers.lazy.LBR.Indexes
Sets both the Instance and Attribute indexes to a specified value
setSequentialInstanceIndex(boolean) - Method in class weka.classifiers.lazy.LBR.Indexes
A Sequential Instance index is all those Instances that are set to the specified value placed in a sequential array.
setSerializedClassifierFile(File) - Method in class weka.filters.supervised.attribute.AddClassification
Sets the file pointing to a serialized, trained classifier.
setShape(int) - Method in class weka.gui.treevisualizer.Node
Set the value of shape.
setShapes(FastVector) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
This can be used to set the shapes that should appear.
setShapes(FastVector) - Method in class weka.gui.visualize.VisualizePanel
This will set the shapes for the instances.
setShapeSize(int[]) - Method in class weka.gui.visualize.PlotData2D
Set the shape sizes for the plot data
setShapeSize(FastVector) - Method in class weka.gui.visualize.PlotData2D
Set the shape sizes for the plot data
setShapeType(int[]) - Method in class weka.gui.visualize.PlotData2D
Set the shape type for the plot data
setShapeType(FastVector) - Method in class weka.gui.visualize.PlotData2D
Set the shape type for the plot data
setShowAttBars(boolean) - Method in class weka.gui.visualize.VisualizePanel
Set whether the attribute bars should be shown or not.
setShowAverage(boolean) - Method in class weka.experiment.ResultMatrix
sets whether to display the average per column or not
setShowAverage(boolean) - Method in class weka.gui.experiment.OutputFormatDialog
sets whether the average for each column is displayed.
setShowClassPanel(boolean) - Method in class weka.gui.visualize.VisualizePanel
Set whether the class panel should be shown or not.
setShowCoreDistances(boolean) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel
Sets the flag for showCoreDistances
setShowGUI(boolean) - Method in class weka.clusterers.OPTICS
Sets the flag for displaying the GUI.
setShowReachabilityDistances(boolean) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel
Sets the flag for showReachabilityDistances
setShowStdDev(boolean) - Method in class weka.experiment.ResultMatrix
sets whether to display the std deviations or not
setShowStdDev(boolean) - Method in class weka.experiment.ResultMatrixSignificance
sets whether to display the std deviations or not - always false!
setShowStdDevs(boolean) - Method in class weka.experiment.PairedTTester
Set whether standard deviations are displayed or not.
setShowStdDevs(boolean) - Method in interface weka.experiment.Tester
Set whether standard deviations are displayed or not.
setShrinkage(double) - Method in class weka.classifiers.meta.AdditiveRegression
Set the shrinkage parameter
setShrinkage(double) - Method in class weka.classifiers.meta.LogitBoost
Set the value of Shrinkage.
setShrinking(boolean) - Method in class weka.classifiers.functions.LibSVM
whether to use the shrinking heuristics
setShuffle(int) - Method in class weka.classifiers.rules.Ridor
 
setSigma(int) - Method in class weka.attributeSelection.ReliefFAttributeEval
Sets the sigma value.
setSigma(double) - Method in class weka.classifiers.functions.supportVector.Puk
Sets the sigma value.
setSignificance(int, int, int) - Method in class weka.experiment.ResultMatrix
sets the significance at the given position (if the position is valid)
setSignificanceLevel(double) - Method in class weka.associations.Apriori
Set the value of significanceLevel.
setSignificanceLevel(double) - Method in class weka.attributeSelection.RaceSearch
Sets the significance level to use
setSignificanceLevel(double) - Method in class weka.experiment.PairedTTester
Set the value of SignificanceLevel.
setSignificanceLevel(double) - Method in interface weka.experiment.Tester
Set the value of SignificanceLevel.
setSignificanceWidth(int) - Method in class weka.experiment.ResultMatrix
sets the width for the significance (0 = optimal)
setSilent(boolean) - Method in class weka.core.AllJavadoc
sets whether to suppress output in the console
setSilent(boolean) - Method in class weka.core.Check
Set slient mode, i.e., no output at all to stdout
setSilent(boolean) - Method in class weka.core.Javadoc
sets whether to suppress output in the console
setSilent(boolean) - Method in class weka.estimators.CheckEstimator
Set slient mode, i.e., no output at all to stdout
setSindex(int) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
Set the index of the attribute to use for the shape.
setSIndex(int) - Method in class weka.gui.visualize.VisualizePanel
Set the shape for creating splits.
setSingle(String) - Method in class weka.gui.ResultHistoryPanel
Sets the single-click display to view the named result.
setSingleIndex(String) - Method in class weka.core.SingleIndex
Sets the index from a string representation.
setSize(int) - Method in class weka.core.matrix.DoubleVector
Sets the size of the vector
setSize(int) - Method in class weka.core.matrix.IntVector
Sets the size of the vector.
setSize(int, int) - Method in class weka.experiment.ResultMatrix
clears the content of the matrix and sets the new size
setSizePer(double) - Method in class weka.classifiers.trees.BFTree
Set training set size.
setSizePer(double) - Method in class weka.classifiers.trees.SimpleCart
Set training set size.
setSkipIdentical(boolean) - Method in class weka.core.neighboursearch.LinearNNSearch
Sets the property to skip identical instances (with distance zero from the target) from the set of neighbours returned.
setSmoothing(boolean) - Method in class weka.classifiers.trees.m5.Rule
Smooth predictions
setSmoothingParameter(double) - Method in class weka.classifiers.bayes.ComplementNaiveBayes
Sets the smoothing value used to avoid zero WordGivenClass probabilities
setSMOReg(SMOreg) - Method in class weka.classifiers.functions.supportVector.RegOptimizer
sets the parent SVM
setSort(boolean) - Method in class weka.filters.unsupervised.attribute.AddValues
Sets whether the labels are sorted.
setSortColumn(int) - Method in class weka.experiment.PairedTTester
Set the column to sort on, -1 means the default sorting.
setSortColumn(int) - Method in interface weka.experiment.Tester
Set the column to sort on, -1 means the default sorting.
setSource(File) - Method in class weka.core.converters.AbstractFileLoader
Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(File) - Method in class weka.core.converters.AbstractLoader
Default implementation throws an IOException.
setSource(InputStream) - Method in class weka.core.converters.AbstractLoader
Default implementation throws an IOException.
setSource(URL) - Method in class weka.core.converters.ArffLoader
Resets the Loader object and sets the source of the data set to be the supplied url.
setSource(InputStream) - Method in class weka.core.converters.ArffLoader
Resets the Loader object and sets the source of the data set to be the supplied InputStream.
setSource(File) - Method in class weka.core.converters.C45Loader
Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(InputStream) - Method in class weka.core.converters.CSVLoader
Resets the Loader object and sets the source of the data set to be the supplied Stream object.
setSource(File) - Method in class weka.core.converters.CSVLoader
Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(String, String, String) - Method in class weka.core.converters.DatabaseLoader
Sets the database url, user and pw
setSource(String) - Method in class weka.core.converters.DatabaseLoader
Sets the database url
setSource() - Method in class weka.core.converters.DatabaseLoader
Sets the database url using the DatabaseUtils file
setSource(URL) - Method in class weka.core.converters.LibSVMLoader
Resets the Loader object and sets the source of the data set to be the supplied url.
setSource(InputStream) - Method in class weka.core.converters.LibSVMLoader
Resets the Loader object and sets the source of the data set to be the supplied InputStream.
setSource(File) - Method in interface weka.core.converters.Loader
Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(InputStream) - Method in interface weka.core.converters.Loader
Resets the Loader object and sets the source of the data set to be the supplied InputStream.
setSource(InputStream) - Method in class weka.core.converters.SerializedInstancesLoader
Resets the Loader object and sets the source of the data set to be the supplied InputStream.
setSource(URL) - Method in class weka.core.converters.SVMLightLoader
Resets the Loader object and sets the source of the data set to be the supplied url.
setSource(InputStream) - Method in class weka.core.converters.SVMLightLoader
Resets the Loader object and sets the source of the data set to be the supplied InputStream.
setSource(File) - Method in class weka.core.converters.TextDirectoryLoader
Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(File) - Method in class weka.core.converters.XRFFLoader
Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(URL) - Method in class weka.core.converters.XRFFLoader
Resets the Loader object and sets the source of the data set to be the supplied url.
setSource(InputStream) - Method in class weka.core.converters.XRFFLoader
Resets the Loader object and sets the source of the data set to be the supplied InputStream.
setSource(Node) - Method in class weka.gui.treevisualizer.Edge
Set the value of source.
setSourceCode(Classifier) - Method in class weka.classifiers.CheckSource
Sets the class to test.
setSourceCode(Filter) - Method in class weka.filters.CheckSource
Sets the class to test.
setSparseData(boolean) - Method in class weka.experiment.InstanceQuery
Sets whether data should be encoded as sparse instances
setSplitByDataSet(boolean) - Method in class weka.experiment.RemoteExperiment
Set whether sub experiments are to be created on the basis of data set.
setSplitEvaluator(SplitEvaluator) - Method in class weka.experiment.CrossValidationResultProducer
Set the SplitEvaluator.
setSplitEvaluator(SplitEvaluator) - Method in class weka.experiment.RandomSplitResultProducer
Set the SplitEvaluator.
setSplitOnResiduals(boolean) - Method in class weka.classifiers.trees.LMT
Set the value of splitOnResiduals.
setSplitPoint(Instances) - Method in class weka.classifiers.trees.j48.BinC45Split
Sets split point to greatest value in given data smaller or equal to old split point.
setSplitPoint(Instances) - Method in class weka.classifiers.trees.j48.C45Split
Sets split point to greatest value in given data smaller or equal to old split point.
setSplitPoint(double) - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Split point to be used for selection on numeric attribute.
setStartEndIndices(int, int) - Method in class weka.core.neighboursearch.balltrees.BallNode
Sets the the start and end index of the portion of the master index array that is assigned to this node.
setStartPoint(int) - Method in class weka.attributeSelection.RankSearch
Set the point at which to start evaluating the ranking
setStartSequentially(boolean) - Method in class weka.gui.beans.FlowRunner
Set whether to launch Startable beans one after the other or all in parallel.
setStartSet(String) - Method in class weka.attributeSelection.BestFirst
Sets a starting set of attributes for the search.
setStartSet(String) - Method in class weka.attributeSelection.GeneticSearch
Sets a starting set of attributes for the search.
setStartSet(String) - Method in class weka.attributeSelection.GreedyStepwise
Sets a starting set of attributes for the search.
setStartSet(String) - Method in class weka.attributeSelection.LinearForwardSelection
Sets a starting set of attributes for the search.
setStartSet(String) - Method in class weka.attributeSelection.RandomSearch
Sets a starting set of attributes for the search.
setStartSet(String) - Method in class weka.attributeSelection.Ranker
Sets a starting set of attributes for the search.
setStartSet(String) - Method in interface weka.attributeSelection.StartSetHandler
Sets a starting set of attributes for the search.
setStatic() - Method in class weka.gui.beans.BeanVisual
Set the static version of the icon
setStatus(int) - Method in class weka.gui.beans.IncrementalClassifierEvent
Set the status
setStatus(int) - Method in class weka.gui.beans.InstanceEvent
Set the status
setStatusFrequency(int) - Method in class weka.gui.beans.IncrementalClassifierEvaluator
Set how often progress is reported to the status bar.
setStatusMessage(String) - Method in class weka.experiment.TaskStatusInfo
Set the status message.
setStdDev(int, int, double) - Method in class weka.experiment.ResultMatrix
sets the std deviation at the given position (if the position is valid)
setStdDevPrec(int) - Method in class weka.experiment.ResultMatrix
sets the precision for the standard deviation
setStdDevPrec(int) - Method in class weka.gui.experiment.OutputFormatDialog
Sets the precision of the std.
setStdDevWidth(int) - Method in class weka.experiment.ResultMatrix
sets the width for the std dev (0 = optimal)
setStemmer(String) - Method in class weka.core.stemmers.SnowballStemmer
sets the stemmer with the given name, e.g., "porter".
setStemmer(Stemmer) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
the stemming algorithm to use, null means no stemming at all (i.e., the NullStemmer is used).
setStepSize(int) - Method in class weka.attributeSelection.RankSearch
Set the number of attributes to add from the rankining in each iteration
setStepSize(int) - Method in class weka.experiment.LearningRateResultProducer
Set the value of StepSize.
setStopwords(File) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
sets the file containing the stopwords, null or a directory unset the stopwords.
setStringAttributes(String) - Method in class weka.core.converters.CSVLoader
Sets the attribute range to be forced to type string.
setStroke(Stroke) - Method in class weka.gui.visualize.PostscriptGraphics
 
setStructure(Instances) - Method in class weka.core.converters.AbstractSaver
Sets the strcuture of the instances for the first step of incremental saving.
setStructure(Instances) - Method in class weka.gui.beans.IncrementalClassifierEvent
Set the instances structure
setStructure(Instances) - Method in class weka.gui.beans.InstanceEvent
Set the instances structure
setSubFlow(Vector) - Method in class weka.gui.beans.MetaBean
 
setSubFlowPreview(ImageIcon) - Method in class weka.gui.beans.MetaBean
 
setSubsequenceLength(int) - Method in class weka.classifiers.functions.supportVector.StringKernel
Sets the length of the subsequence.
setSubsetEvaluator(ASEvaluation) - Method in class weka.attributeSelection.FilteredSubsetEval
Set the subset evaluator to use
setSubsetSizeEvaluator(ASEvaluation) - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Set the subset evaluator to use for subset size determination.
setSubSpaceSize(double) - Method in class weka.classifiers.meta.RandomSubSpace
Sets the size of each subSpace, as a percentage of the training set size.
setSubtreeRaising(boolean) - Method in class weka.classifiers.trees.J48
Set the value of subtreeRaising.
setSubtreeRaising(boolean) - Method in class weka.classifiers.trees.J48graft
Set the value of subtreeRaising.
setSummary(int[][], int[][]) - Method in class weka.experiment.ResultMatrix
sets the non-significant and significant wins of the resultsets
setSupport(int) - Method in class weka.associations.FPGrowth.FrequentBinaryItemSet
Set the support for this item set.
setSupportCount(int) - Method in class weka.associations.gsp.Sequence
Sets the support count of the Sequence.
setSuppressErrorMessage(boolean) - Method in class weka.classifiers.functions.SimpleLinearRegression
Turn off the error message that is reported when no useful attribute is found.
setSVMType(SelectedTag) - Method in class weka.classifiers.functions.LibLINEAR
Sets type of SVM (default SVMTYPE_L2)
setSVMType(SelectedTag) - Method in class weka.classifiers.functions.LibSVM
Sets type of SVM (default SVMTYPE_C_SVC)
setSymbols(HashMap) - Method in class weka.core.mathematicalexpression.Parser
Sets the variable - value relation to use.
setSymbols(HashMap) - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
Sets the variable - value relation to use.
setSyntax(StreamTokenizer) - Method in class weka.gui.graphvisualizer.DotParser
This method sets the syntax of the StreamTokenizer.
setTable(AttributeStats, int) - Method in class weka.gui.AttributeSummaryPanel
Creates a tablemodel for the attribute being displayed
setTableName(String) - Method in class weka.core.converters.DatabaseSaver
Sets the table's name.
setTabTitle(JComponent) - Method in class weka.gui.arffviewer.ArffViewerMainPanel
sets the title of the tab that contains the given component
setTabuList(int) - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
Sets the Tabu List length.
setTabuList(int) - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
Sets the Tabu List length.
setTarget(Object) - Method in class weka.gui.PropertySheetPanel
Sets a new target object for customisation.
setTarget(Node) - Method in class weka.gui.treevisualizer.Edge
Set the value of target.
setTargetClass(int) - Method in class weka.classifiers.bayes.DMNBtext.DNBBinary
Sets the Target Class
setTaskResult(Object) - Method in class weka.experiment.TaskStatusInfo
Set the returnable result for this task..
setter(CoverTree.MyHeap, double, int) - Method in class weka.core.neighboursearch.CoverTree
Initializes a heap with k values of the the given upper_bound.
setTestBaseFromDialog() - Method in class weka.gui.experiment.ResultsPanel
 
setTester() - Method in class weka.gui.experiment.ResultsPanel
sets the currently selected Tester-Class.
setTestEvaluator(boolean) - Method in class weka.attributeSelection.CheckAttributeSelection
Sets whether the evaluator or the search method is being tested.
setTestSet(DataSetEvent) - Method in class weka.gui.beans.BatchClassifierEvent
Set the test set
setTestSet() - Method in class weka.gui.explorer.ClassifierPanel
Sets the user test set.
setTestSet() - Method in class weka.gui.explorer.ClustererPanel
Sets the user test set.
setText(String) - Method in class weka.gui.beans.BeanVisual
Set the label for the visual.
setTFTransform(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Sets whether if the word frequencies should be transformed into log(1+fij) where fij is the frequency of word i in document(instance) j.
setThreshold(double) - Method in class weka.attributeSelection.GreedyStepwise
Set the threshold by which the AttributeSelection module can discard attributes.
setThreshold(double) - Method in class weka.attributeSelection.RaceSearch
Sets the threshold for comparisons
setThreshold(double) - Method in interface weka.attributeSelection.RankedOutputSearch
Sets a threshold by which attributes can be discarded from the ranking.
setThreshold(double) - Method in class weka.attributeSelection.Ranker
Set the threshold by which the AttributeSelection module can discard attributes.
setThreshold(double) - Method in class weka.attributeSelection.ScatterSearchV1
Set the treshold
setThreshold(double) - Method in class weka.attributeSelection.WrapperSubsetEval
Set the value of the threshold for repeating cross validation
setThreshold(double) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Set the threshold to use.
setThreshold(double) - Method in class weka.classifiers.functions.PaceRegression
Set threshold for the olsc estimator
setThreshold(double) - Method in class weka.classifiers.functions.Winnow
Set the value of Threshold.
setThreshold(double) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Sets the threshold for the max error when predicting a numeric class.
setTimes(int, int, double) - Method in class weka.classifiers.functions.pace.PaceMatrix
Multiply a value with an element and reset the element
setTimes(int, double) - Method in class weka.core.matrix.DoubleVector
Multiplies a value to an element
setTokenizer(Tokenizer) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
the tokenizer algorithm to use.
setTolerance(double) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Set the tolerance value
setTolerance(double) - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
sets the tolerance
setToleranceParameter(double) - Method in class weka.attributeSelection.SVMAttributeEval
Set the value of T for SMO
setToleranceParameter(double) - Method in class weka.classifiers.functions.SMO
Set the value of tolerance parameter.
setToleranceParameter(double) - Method in class weka.classifiers.mi.MISMO
Set the value of tolerance parameter.
setTop(double) - Method in class weka.gui.treevisualizer.Node
Set the value of top.
setTrainingData(Instances) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Set the training data to use
setTrainingTime(int) - Method in class weka.classifiers.functions.MultilayerPerceptron
Set the number of training epochs to perform.
setTrainIterations(int) - Method in class weka.classifiers.BVDecompose
Sets the maximum number of boost iterations
setTrainPercent(double) - Method in class weka.experiment.RandomSplitResultProducer
Set the value of TrainPercent.
setTrainPercent(double) - Method in class weka.gui.beans.TrainTestSplitMaker
Set the percentage of data to be in the training portion of the split
setTrainPoolSize(int) - Method in class weka.classifiers.BVDecompose
Set the number of instances in the training pool.
setTrainSet(DataSetEvent) - Method in class weka.gui.beans.BatchClassifierEvent
Set the training set
setTrainSize(int) - Method in class weka.classifiers.BVDecomposeSegCVSub
Set the training size.
setTransactionsMustContain(String) - Method in class weka.associations.FPGrowth
Set the comma separated list of items that transactions must contain in order to be considered for large item sets and rules.
setTransform(AffineTransform) - Method in class weka.gui.visualize.PostscriptGraphics
 
setTransformAllValues(boolean) - Method in class weka.filters.supervised.attribute.NominalToBinary
Sets whether all nominal values are transformed into new attributes, not just if there are more than 2.
setTransformAllValues(boolean) - Method in class weka.filters.unsupervised.attribute.NominalToBinary
Sets whether all nominal values are transformed into new attributes, not just if there are more than 2.
setTransformBackToOriginal(boolean) - Method in class weka.attributeSelection.PrincipalComponents
Sets whether the data should be transformed back to the original space
setTransformMethod(SelectedTag) - Method in class weka.classifiers.mi.SimpleMI
Set the method used in transformation.
setTranslation(double) - Method in class weka.filters.unsupervised.attribute.Normalize
Sets the translation.
setTraversal(SelectedTag) - Method in class weka.classifiers.meta.GridSearch
Sets the type of traversal for the grid.
setTrimingThreshold(double) - Method in class weka.classifiers.functions.pace.ChisqMixture
Sets the triming thresholding value.
setTrimingThreshold(double) - Method in class weka.classifiers.functions.pace.NormalMixture
Sets the triming thresholding value.
setTrueNegative(double) - Method in class weka.classifiers.evaluation.TwoClassStats
Sets the number of negative instances predicted as negative
setTruePositive(double) - Method in class weka.classifiers.evaluation.TwoClassStats
Sets the number of positive instances predicted as positive
setTStart(double) - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
Sets the m_fTStart.
setTStart(double) - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
Sets the m_fTStart.
setTTester() - Method in class weka.gui.experiment.ResultsPanel
Updates the test chooser with possible tests.
setType(SelectedTag) - Method in class weka.attributeSelection.LinearForwardSelection
Set the type
setType(SelectedTag) - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Set the type
setType(int) - Method in class weka.classifiers.functions.neural.NeuralConnection
 
setUndoEnabled(boolean) - Method in interface weka.core.Undoable
sets whether undo support is enabled
setUndoEnabled(boolean) - Method in class weka.gui.arffviewer.ArffPanel
sets whether undo support is enabled
setUndoEnabled(boolean) - Method in class weka.gui.arffviewer.ArffSortedTableModel
sets whether undo support is enabled
setUndoEnabled(boolean) - Method in class weka.gui.arffviewer.ArffTableModel
sets whether undo support is enabled
setUnpruned(boolean) - Method in class weka.classifiers.rules.PART
Set the value of unpruned.
setUnpruned(boolean) - Method in class weka.classifiers.trees.J48
Set the value of unpruned.
setUnpruned(boolean) - Method in class weka.classifiers.trees.J48graft
Set the value of unpruned.
setUnpruned(boolean) - Method in class weka.classifiers.trees.m5.M5Base
Use unpruned tree/rules
setUnpruned(boolean) - Method in class weka.classifiers.trees.m5.Rule
Use unpruned tree/rules
setup(Object, double, double) - Method in class weka.classifiers.meta.GridSearch
returns a fully configures object (a copy of the provided one)
setup(Instances) - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
Initializes the filter with the given input data.
setupAttribLists() - Method in class weka.gui.visualize.MatrixPanel
Sets up the UI's attributes lists
setUpBoundaryPanel() - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
Sets up the BoundaryPanel object so that it is ready for plotting.
setUpComboBoxes(Instances) - Method in class weka.gui.visualize.ThresholdVisualizePanel
This overloads VisualizePanel's setUpComboBoxes to add ActionListeners to watch for when the X/Y Axis comboboxes are changed.
setUpComboBoxes(Instances) - Method in class weka.gui.visualize.VisualizePanel
initializes the comboboxes based on the data
setUpdateIncrementalClassifier(boolean) - Method in class weka.gui.beans.Classifier
Set whether an incremental classifier will be updated on the incoming instance stream.
setUpEvaluator() - Method in class weka.classifiers.rules.DecisionTable
Sets up a dummy subset evaluator that basically just delegates evaluation to the estimatePerformance method in DecisionTable
setUpEvaluator() - Method in class weka.classifiers.rules.DTNB
Sets up a dummy subset evaluator that basically just delegates evaluation to the estimatePerformance method in DecisionTable
setUpFile() - Method in class weka.gui.beans.LoaderCustomizer
 
setUpFile() - Method in class weka.gui.beans.SaverCustomizer
Sets up dialog for saving instances in a file
setUpFile() - Method in class weka.gui.beans.SerializedModelSaverCustomizer
Sets up dialog for saving models to a file
setupFileChooser() - Method in class weka.gui.beans.Classifier
 
setUpFinal() - Method in class weka.gui.beans.AttributeSummarizer
 
setUpFinal() - Method in class weka.gui.beans.CostBenefitAnalysis
 
setUpFinal() - Method in class weka.gui.beans.DataVisualizer
 
setUpFinal() - Method in class weka.gui.beans.GraphViewer
 
setUpFinal() - Method in class weka.gui.beans.ModelPerformanceChart
 
setUpFinal() - Method in class weka.gui.beans.ScatterPlotMatrix
 
setUpFinal() - Method in class weka.gui.beans.TextViewer
 
SetupModePanel - Class in weka.gui.experiment
This panel switches between simple and advanced experiment setup panels.
SetupModePanel() - Constructor for class weka.gui.experiment.SetupModePanel
Creates the setup panel with no initial experiment.
SetupPanel - Class in weka.gui.experiment
This panel controls the configuration of an experiment.
SetupPanel(Experiment) - Constructor for class weka.gui.experiment.SetupPanel
Creates the setup panel with the supplied initial experiment.
SetupPanel() - Constructor for class weka.gui.experiment.SetupPanel
Creates the setup panel with no initial experiment.
setUpper(int) - Method in class weka.core.Range
Sets the value of "last".
setUpper(int) - Method in class weka.core.SingleIndex
Sets the value of "last".
setUpperBoundMinSupport(double) - Method in class weka.associations.Apriori
Set the value of upperBoundMinSupport.
setUpperBoundMinSupport(double) - Method in class weka.associations.FPGrowth
Set the value of upperBoundMinSupport.
setUpperSize(int) - Method in class weka.experiment.LearningRateResultProducer
Set the value of UpperSize.
setUpVisualizableInstances(Instances) - Static method in class weka.gui.explorer.ClassifierPanel
Sets up the structure for the visualizable instances.
setUpVisualizableInstances(Instances, ClusterEvaluation) - Static method in class weka.gui.explorer.ClustererPanel
Sets up the structure for the visualizable instances.
setURL(String) - Method in class weka.core.converters.ArffLoader
Set the url to load from
setUrl(String) - Method in interface weka.core.converters.DatabaseConverter
 
setUrl(String) - Method in class weka.core.converters.DatabaseLoader
Sets the database URL
setUrl(String) - Method in class weka.core.converters.DatabaseSaver
Sets the database URL.
setURL(String) - Method in class weka.core.converters.LibSVMLoader
Set the url to load from.
setURL(String) - Method in class weka.core.converters.SVMLightLoader
Set the url to load from.
setURL(String) - Method in interface weka.core.converters.URLSourcedLoader
Set the url to load from
setURL(String) - Method in class weka.core.converters.XRFFLoader
Set the url to load from
setURL(String) - Method in class weka.gui.sql.ConnectionPanel
sets the URL.
setUseADTree(boolean) - Method in class weka.classifiers.bayes.BayesNet
Set whether ADTree structure is used or not
setUseAIC(boolean) - Method in class weka.classifiers.functions.SimpleLogistic
Set the value of useAIC.
setUseAIC(boolean) - Method in class weka.classifiers.trees.FT
Set the value of useAIC.
setUseAIC(boolean) - Method in class weka.classifiers.trees.lmt.LogisticBase
Set the value of useAIC.
setUseAIC(boolean) - Method in class weka.classifiers.trees.LMT
Set the value of useAIC.
setUseArcReversal(boolean) - Method in class weka.classifiers.bayes.net.search.global.HillClimber
set use the arc reversal operation
setUseArcReversal(boolean) - Method in class weka.classifiers.bayes.net.search.local.HillClimber
set use the arc reversal operation
setUseBetterEncoding(boolean) - Method in class weka.filters.supervised.attribute.Discretize
Sets whether better encoding is to be used for MDL.
setUseCpuTime(boolean) - Method in class weka.core.Debug.Clock
enables/disables the use of CPU time (if measurement of CPU time is available).
setUseCrossOver(boolean) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
 
setUseCrossOver(boolean) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
 
setUseCrossValidation(boolean) - Method in class weka.classifiers.functions.SimpleLogistic
Set the value of useCrossValidation.
setUseCustomDimensions(boolean) - Method in class weka.gui.visualize.JComponentWriter
sets whether to use custom dimensions for the image
setUseEqualFrequency(boolean) - Method in class weka.classifiers.meta.RegressionByDiscretization
Set the value of UseEqualFrequency.
setUseEqualFrequency(boolean) - Method in class weka.filters.unsupervised.attribute.Discretize
Set the value of UseEqualFrequency.
setUseEqualFrequency(boolean) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Set the value of UseEqualFrequency.
setUseErrorRate(boolean) - Method in class weka.classifiers.trees.BFTree
Set if use error rate in internal cross-validation.
setUseGini(boolean) - Method in class weka.classifiers.trees.BFTree
Set if use Gini index as splitting criterion.
setUseIBk(boolean) - Method in class weka.classifiers.rules.DecisionTable
Sets whether IBk should be used instead of the majority class
setUseK2Prior(boolean) - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator
Sets the UseK2Prior.
setUseK2Prior(boolean) - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
Sets the UseK2Prior.
setUseKDTree(boolean) - Method in class weka.clusterers.XMeans
Sets whether to use the KDTree or not.
setUseKernelEstimator(boolean) - Method in class weka.classifiers.bayes.NaiveBayes
Sets if kernel estimator is to be used.
setUseKononenko(boolean) - Method in class weka.filters.supervised.attribute.Discretize
Sets whether Kononenko's MDL criterion is to be used.
setUseLaplace(boolean) - Method in class weka.classifiers.bayes.AODEsr
Sets if laplace correction is to be used.
setUseLaplace(boolean) - Method in class weka.classifiers.trees.J48
Set the value of useLaplace.
setUseLaplace(boolean) - Method in class weka.classifiers.trees.J48graft
Set the value of useLaplace.
setUseLeastValues(boolean) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Sets whether to use values with least or most instances
setUseLowerOrder(boolean) - Method in class weka.classifiers.functions.supportVector.PolyKernel
Sets whether to use lower-order terms.
setUseMEstimates(boolean) - Method in class weka.classifiers.bayes.AODE
Sets if m-estimates is to be used.
setUseMissing(boolean) - Method in class weka.filters.unsupervised.attribute.AddNoise
Sets the flag if missing values are treated as extra values.
setUseMutation(boolean) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
 
setUseMutation(boolean) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
 
setUseNormalization(boolean) - Method in class weka.classifiers.functions.supportVector.StringKernel
Sets whether to use normalization.
setUseOneSE(boolean) - Method in class weka.classifiers.trees.BFTree
Set if use the 1SE rule to choose final model.
setUseOneSE(boolean) - Method in class weka.classifiers.trees.SimpleCart
Set if use the 1SE rule to choose final model.
setUseORForMustContainList(boolean) - Method in class weka.associations.FPGrowth
Set whether to use OR rather than AND when considering must contain lists.
setUsePairwiseCoupling(boolean) - Method in class weka.classifiers.meta.MultiClassClassifier
Set whether to use pairwise coupling with 1-vs-1 classification to improve probability estimates.
setUseProb(boolean) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
 
setUsePropertyIterator(boolean) - Method in class weka.experiment.Experiment
Sets whether the custom property iterator should be used.
setUsePropertyIterator(boolean) - Method in class weka.experiment.RemoteExperiment
Sets whether the custom property iterator should be used.
setUsePrune(boolean) - Method in class weka.classifiers.trees.SimpleCart
Set if use minimal cost-complexity pruning.
setUsePruning(boolean) - Method in class weka.classifiers.rules.JRip
Sets whether pruning is performed
setUser(String) - Method in interface weka.core.converters.DatabaseConverter
 
setUser(String) - Method in class weka.core.converters.DatabaseLoader
Sets the database user
setUser(String) - Method in class weka.core.converters.DatabaseSaver
Sets the database user.
setUser(String) - Method in class weka.gui.sql.ConnectionPanel
sets the User.
setUseRelativePath(boolean) - Method in class weka.core.converters.AbstractFileLoader
Set whether to use relative rather than absolute paths
setUseRelativePath(boolean) - Method in class weka.core.converters.AbstractFileSaver
Set whether to use relative rather than absolute paths
setUseRelativePath(boolean) - Method in interface weka.core.converters.FileSourcedConverter
Set whether to use relative rather than absolute paths
setUseRelativePath(boolean) - Method in class weka.gui.beans.SerializedModelSaver
Set whether to use relative paths for the directory.
setUseResampling(boolean) - Method in class weka.classifiers.meta.AdaBoostM1
Set resampling mode
setUseResampling(boolean) - Method in class weka.classifiers.meta.LogitBoost
Set resampling mode
setUseResampling(boolean) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Set resampling mode
setUsername(String) - Method in class weka.experiment.DatabaseUtils
Set the database username.
setUserOptions(String[]) - Method in class weka.classifiers.functions.supportVector.KernelEvaluation
sets the option the user supplied for the kernel
setUserOptions(String[]) - Method in class weka.core.CheckOptionHandler
Sets the user-supplied options (creates a copy)
setUseStars(boolean) - Method in class weka.core.AllJavadoc
sets whether to prefix the Javadoc with "*"
setUseStars(boolean) - Method in class weka.core.Javadoc
sets whether to prefix the Javadoc with "*"
setUseStoplist(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Sets whether if the words that are on a stoplist are to be ignored (The stop list is in weka.core.StopWords).
setUseSupervisedDiscretization(boolean) - Method in class weka.classifiers.bayes.NaiveBayes
Set whether supervised discretization is to be used.
setUseSupervisedDiscretization(boolean) - Method in class weka.classifiers.bayes.NaiveBayesUpdateable
Set whether supervised discretization is to be used.
setUseTournamentSelection(boolean) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
 
setUseTournamentSelection(boolean) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
 
setUseTraining(boolean) - Method in class weka.attributeSelection.ClassifierSubsetEval
Set if training data is to be used instead of hold out/test data
setUseTree(boolean) - Method in class weka.classifiers.trees.m5.Rule
Use an m5 tree rather than generate rules
setUseUnsmoothed(boolean) - Method in class weka.classifiers.trees.m5.M5Base
Use unsmoothed predictions
setUseVariant1(boolean) - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
Sets whether to use variant 1
setValidating(boolean) - Method in class weka.core.xml.XMLDocument
sets whether to use a validating parser or not.
Note: this does clear the current DOM document!
setValidating(boolean) - Method in class weka.core.xml.XMLOptions
sets whether to use a validating parser or not.
setValidationChunkSize(int) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Set the validation chunk size
setValidationSetSize(int) - Method in class weka.classifiers.functions.MultilayerPerceptron
This will set the size of the validation set.
setValidationThreshold(int) - Method in class weka.classifiers.functions.MultilayerPerceptron
This sets the threshold to use for when validation testing is being done.
setValue(Object, String, double) - Method in class weka.classifiers.meta.GridSearch
tries to set the value as double, integer (just casts it to int!) or boolean (false if 0, otherwise true) in the object according to the specified path.
setValue(double) - Method in class weka.classifiers.trees.adtree.PredictionNode
Sets the prediction value of the node.
setValue(int, double) - Method in class weka.core.BinarySparseInstance
Sets a specific value in the instance to the given value (internal floating-point format).
setValue(int, double) - Method in class weka.core.Instance
Sets a specific value in the instance to the given value (internal floating-point format).
setValue(int, String) - Method in class weka.core.Instance
Sets a value of a nominal or string attribute to the given value.
setValue(Attribute, double) - Method in class weka.core.Instance
Sets a specific value in the instance to the given value (internal floating-point format).
setValue(Attribute, String) - Method in class weka.core.Instance
Sets a value of an nominal or string attribute to the given value.
setValue(Object, PropertyPath.Path, Object) - Static method in class weka.core.PropertyPath
set the given value specified by the given path in the object
setValue(Object, String, Object) - Static method in class weka.core.PropertyPath
set the given value specified by the given path in the object
setValue() - Method in class weka.core.SingleIndex
Translates a single string selection into it's internal 0-based equivalent
setValue(int, double) - Method in class weka.core.SparseInstance
Sets a specific value in the instance to the given value (internal floating-point format).
setValue(TechnicalInformation.Field, String) - Method in class weka.core.TechnicalInformation
sets the value for the given field, overwrites any previously existing one.
setValue(Object) - Method in class weka.gui.CostMatrixEditor
Sets the value of the CostMatrix to be edited.
setValue(Object) - Method in class weka.gui.GenericArrayEditor
Sets the current object array.
setValue(Object) - Method in class weka.gui.GenericObjectEditor
Sets the current Object.
setValue(Object) - Method in class weka.gui.SimpleDateFormatEditor
Sets the value of the date format to be edited.
setValueAt(Object, int, int) - Method in class weka.gui.arffviewer.ArffTableModel
sets the value in the cell at columnIndex and rowIndex to aValue.
setValueAt(Object, int, int, boolean) - Method in class weka.gui.arffviewer.ArffTableModel
sets the value in the cell at columnIndex and rowIndex to aValue.
setValueAt(Object, int, int) - Method in class weka.gui.SortedTableModel
Sets the value in the cell at columnIndex and rowIndex to aValue.
setValueAt(Object, int, int) - Method in class weka.gui.sql.ResultSetTableModel
sets the value in the cell at columnIndex and rowIndex to aValue.
setValueIndex(int) - Method in class weka.filters.unsupervised.attribute.MakeIndicator
Sets index of the indicator value.
setValueIndices(String) - Method in class weka.filters.unsupervised.attribute.MakeIndicator
Sets indices of the indicator values.
setValueIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.MakeIndicator
Set which attributes are to be deleted (or kept if invert is true)
setValues(double[]) - Method in class weka.classifiers.trees.LADTree.PredictionNode
 
setValuesList(String) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Sets the ranges for each attribute.
setValuesList(String, double[], double[], String) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Sets the ranges for each attribute.
setValuesOutput(SelectedTag) - Method in class weka.associations.Tertius
Set the value of valuesOutput.
setValueSparse(int, double) - Method in class weka.core.BinarySparseInstance
Sets a specific value in the instance to the given value (internal floating-point format).
setValueSparse(int, double) - Method in class weka.core.Instance
Sets a specific value in the instance to the given value (internal floating-point format).
setValueSparse(int, double) - Method in class weka.core.SparseInstance
Sets a specific value in the instance to the given value (internal floating-point format).
setVarianceCovered(double) - Method in class weka.attributeSelection.PrincipalComponents
Sets the amount of variance to account for when retaining principal components
setVarianceCovered(double) - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
Sets the amount of variance to account for when retaining principal components.
setVector(Matrix, Matrix, int) - Method in class weka.filters.supervised.attribute.PLSFilter
stores the data from the (column) vector in the matrix at the specified index
setVerbose(boolean) - Method in class weka.associations.Apriori
Sets verbose mode
setVerbose(boolean) - Method in class weka.attributeSelection.ExhaustiveSearch
set whether or not to output new best subsets as the search proceeds
setVerbose(boolean) - Method in class weka.attributeSelection.LinearForwardSelection
Set whether verbose output should be generated.
setVerbose(boolean) - Method in class weka.attributeSelection.RandomSearch
set whether or not to output new best subsets as the search proceeds
setVerbose(boolean) - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Set whether verbose output should be generated.
setVerbose(boolean) - Method in class weka.classifiers.meta.Dagging
Set the verbose state.
setVerboseOn() - Method in class weka.core.Debug.DBO
Set the verbose on flag on
setVerticalAdjustment(int) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel
Sets a new value for the vertical verticalAdjustment
setVisible(boolean) - Method in class weka.gui.Main
Shows or hides this component depending on the value of parameter b.
setVisible(boolean) - Method in class weka.gui.sql.SqlViewerDialog
displays the dialog if TRUE
setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractDataSink
Set the visual for this data source
setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractDataSource
Set the visual for this data source
setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractEvaluator
Set the visual
setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractTestSetProducer
Set the visual for this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
Set the visual for this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractTrainingSetProducer
Set the visual for this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.Associator
Sets the visual appearance of this wrapper bean
setVisual(BeanVisual) - Method in class weka.gui.beans.ClassAssigner
 
setVisual(BeanVisual) - Method in class weka.gui.beans.Classifier
Sets the visual appearance of this wrapper bean
setVisual(BeanVisual) - Method in class weka.gui.beans.ClassValuePicker
 
setVisual(BeanVisual) - Method in class weka.gui.beans.Clusterer
Sets the visual appearance of this wrapper bean
setVisual(BeanVisual) - Method in class weka.gui.beans.CostBenefitAnalysis
 
setVisual(BeanVisual) - Method in class weka.gui.beans.DataVisualizer
Set the visual appearance of this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.Filter
Set the visual appearance of this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.GraphViewer
Set the visual appearance of this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.InstanceStreamToBatchMaker
Sets the visual appearance of this wrapper bean
setVisual(BeanVisual) - Method in class weka.gui.beans.MetaBean
Sets the visual appearance of this wrapper bean
setVisual(BeanVisual) - Method in class weka.gui.beans.ModelPerformanceChart
Set the visual appearance of this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.PredictionAppender
Set the visual for this data source
setVisual(BeanVisual) - Method in class weka.gui.beans.SerializedModelSaver
Set the visual for this data source.
setVisual(BeanVisual) - Method in class weka.gui.beans.StripChart
Set the visual appearance of this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.TextViewer
Describe setVisual method here.
setVisual(BeanVisual) - Method in interface weka.gui.beans.Visible
Set a new visual representation
setVoteFlag(boolean) - Method in class weka.datagenerators.classifiers.classification.RDG1
Sets the vote flag.
setWeight(int) - Method in class weka.classifiers.bayes.AODE
Sets the weight for m-estimate
setWeight(double) - Method in class weka.core.Attribute
Sets the new attribute's weight
setWeight(double) - Method in class weka.core.Instance
Sets the weight of an instance.
setWeightByConfidence(boolean) - Method in class weka.classifiers.misc.VFI
Set weighting by confidence
setWeightByDistance(boolean) - Method in class weka.attributeSelection.ReliefFAttributeEval
Set the nearest neighbour weighting method
setWeightingDimensions(boolean[]) - Method in interface weka.gui.boundaryvisualizer.DataGenerator
Set the dimensions to be used in computing a weight for each instance generated
setWeightingDimensions(boolean[]) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator
Set which dimensions to use when computing a weight for the next instance to generate
setWeightingKernel(int) - Method in class weka.classifiers.lazy.LWL
Sets the kernel weighting method to use.
setWeightingValues(double[]) - Method in interface weka.gui.boundaryvisualizer.DataGenerator
Set the values of the dimensions (chosen via setWeightingDimensions) to be used when computing instance weights
setWeightingValues(double[]) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator
Set the values for the weighting dimensions to be used when computing the weight for the next instance to be generated
setWeightMethod(SelectedTag) - Method in class weka.classifiers.mi.MIWrapper
The new method for weighting the instances.
setWeightMethod(SelectedTag) - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
The new method for weighting the instances.
setWeights(String) - Method in class weka.classifiers.functions.LibLINEAR
Sets the parameters C of class i to weight[i]*C (default 1).
setWeights(String) - Method in class weka.classifiers.functions.LibSVM
Sets the parameters C of class i to weight[i]*C, for C-SVC (default 1).
setWeights(Instances, double) - Method in class weka.classifiers.meta.AdaBoostM1
Sets the weights for the next iteration.
setWeights(Instances, double) - Method in class weka.classifiers.meta.MultiBoostAB
Sets the weights for the next iteration.
setWeightThreshold(int) - Method in class weka.classifiers.meta.AdaBoostM1
Set weight threshold
setWeightThreshold(int) - Method in class weka.classifiers.meta.LogitBoost
Set weight thresholding
setWeightTrimBeta(double) - Method in class weka.classifiers.functions.SimpleLogistic
Set the value of weightTrimBeta.
setWeightTrimBeta(double) - Method in class weka.classifiers.trees.FT
Set the value of weightTrimBeta.
setWeightTrimBeta(double) - Method in class weka.classifiers.trees.lmt.LogisticBase
Sets the option "weightTrimBeta".
setWeightTrimBeta(double) - Method in class weka.classifiers.trees.LMT
Set the value of weightTrimBeta.
setWholeDataErr(boolean) - Method in class weka.classifiers.rules.Ridor
 
setWindowSize(int) - Method in class weka.classifiers.lazy.IBk
Sets the maximum number of instances allowed in the training pool.
setWords(String) - Method in class weka.core.CheckScheme
Sets the comma-separated list of words to use for generating strings.
setWords(String) - Method in class weka.core.TestInstances
Sets the comma-separated list of words to use for generating strings.
setWordSeparators(String) - Method in class weka.core.CheckScheme
sets the word separators (chars) to use for assembling strings.
setWordSeparators(String) - Method in class weka.core.TestInstances
sets the word separators (chars) to use for assembling strings.
setWordsToKeep(int) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Sets the number of words (per class if there is a class attribute assigned) to attempt to keep.
setWordwrap(boolean) - Method in class weka.gui.LogWindow
toggles the wordwrap
override wordwrap from: http://forum.java.sun.com/thread.jspa?threadID=498535&messageID=2356174
setWrappedAlgorithm(Object) - Method in class weka.gui.beans.Associator
Sets the algorithm (associator) for this bean
setWrappedAlgorithm(Object) - Method in class weka.gui.beans.Classifier
Sets the algorithm (classifier) for this bean
setWrappedAlgorithm(Object) - Method in class weka.gui.beans.Clusterer
Sets the algorithm (clusterer) for this bean
setWrappedAlgorithm(Object) - Method in class weka.gui.beans.Filter
Set the filter to be wrapped by this bean
setWrappedAlgorithm(Object) - Method in class weka.gui.beans.Loader
Set the loader
setWrappedAlgorithm(Object) - Method in class weka.gui.beans.Saver
Set the saver
setWrappedAlgorithm(Object) - Method in interface weka.gui.beans.WekaWrapper
Set the algorithm.
setWriteMode(int) - Method in class weka.core.converters.AbstractSaver
Sets the write mode.
setWriteOPTICSresults(boolean) - Method in class weka.clusterers.OPTICS
Sets the flag for writing actions
setX(double) - Method in class weka.classifiers.functions.neural.NeuralConnection
 
setX(int) - Method in class weka.gui.beans.BeanInstance
Sets the x coordinate of this bean
setX(int) - Method in class weka.gui.visualize.AttributePanel
shows which bar is the current x attribute.
setXAttribute(int) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Set the x attribute index
setXAttribute(int) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
Set the x axis fixed dimension
setXBase(double) - Method in class weka.classifiers.meta.GridSearch
Set the value of the base for X.
setXExpression(String) - Method in class weka.classifiers.meta.GridSearch
Set the expression for the X value.
setXindex(int) - Method in class weka.gui.visualize.Plot2D
Set the index of the attribute to go on the x axis
setXindex(int) - Method in class weka.gui.visualize.PlotData2D
Set the x index of the data.
setXindex(int) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
Set the index of the attribute to go on the x axis
setXIndex(int) - Method in class weka.gui.visualize.VisualizePanel
Set the index of the attribute for the x axis
setXLabelFreq(int) - Method in class weka.gui.beans.StripChart
Set the frequency for printing x label values
setXMax(double) - Method in class weka.classifiers.meta.GridSearch
Set the value of the Maximum of X.
setXMin(double) - Method in class weka.classifiers.meta.GridSearch
Set the value of the minimum of X.
setXML(Reader) - Method in class weka.core.xml.XMLInstances
reads the XML structure from the given reader
setXORMode(Color) - Method in class weka.gui.visualize.PostscriptGraphics
Not implemented
setXProperty(String) - Method in class weka.classifiers.meta.GridSearch
Set the X property.
setXStep(double) - Method in class weka.classifiers.meta.GridSearch
Set the value of the step size for X.
setXval(boolean) - Method in class weka.attributeSelection.AttributeSelection
do a cross validation
setXY(int, int) - Method in class weka.gui.beans.BeanInstance
Set the x and y coordinates of this bean
setY(double) - Method in class weka.classifiers.functions.neural.NeuralConnection
 
setY(int) - Method in class weka.gui.beans.BeanInstance
Sets the y coordinate of this bean
setY(int) - Method in class weka.gui.visualize.AttributePanel
shows which bar is the current y attribute.
setYAttribute(int) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Set the y attribute index
setYAttribute(int) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
Set the y axis fixed dimension
setYBase(double) - Method in class weka.classifiers.meta.GridSearch
Set the value of the base for Y.
setYExpression(String) - Method in class weka.classifiers.meta.GridSearch
Set the expression for the Y value.
setYindex(int) - Method in class weka.gui.visualize.Plot2D
Set the index of the attribute to go on the y axis
setYindex(int) - Method in class weka.gui.visualize.PlotData2D
Set the y index of the data
setYindex(int) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
Set the index of the attribute to go on the y axis
setYIndex(int) - Method in class weka.gui.visualize.VisualizePanel
Set the index of the attribute for the y axis
setYMax(double) - Method in class weka.classifiers.meta.GridSearch
Set the value of the Maximum of Y.
setYMin(double) - Method in class weka.classifiers.meta.GridSearch
Set the value of the minimum of Y.
setYProperty(String) - Method in class weka.classifiers.meta.GridSearch
Set the Y property (normally the classifier).
setYStep(double) - Method in class weka.classifiers.meta.GridSearch
Set the value of the step size for Y.
SEVERE - Static variable in class weka.core.Debug
the log level Severe
sf - Variable in class weka.core.mathematicalexpression.Scanner
 
SFEntropyGain() - Method in class weka.classifiers.Evaluation
Returns the total SF, which is the null model entropy minus the scheme entropy.
SFMeanEntropyGain() - Method in class weka.classifiers.Evaluation
Returns the SF per instance, which is the null model entropy minus the scheme entropy, per instance.
SFMeanPriorEntropy() - Method in class weka.classifiers.Evaluation
Returns the entropy per instance for the null model
SFMeanSchemeEntropy() - Method in class weka.classifiers.Evaluation
Returns the entropy per instance for the scheme
SFPriorEntropy() - Method in class weka.classifiers.Evaluation
Returns the total entropy for the null model
SFSchemeEntropy() - Method in class weka.classifiers.Evaluation
Returns the total entropy for the scheme
sgn(double) - Static method in class weka.classifiers.bayes.BayesianLogisticRegression
Sign for a given value.
ShadowCounts() - Constructor for class weka.associations.FPGrowth.ShadowCounts
 
shear(double, double) - Method in class weka.gui.visualize.PostscriptGraphics
 
shell(double, double, double) - Method in class weka.core.neighboursearch.CoverTree
Function to check if a child node can be inside a query ball, without calculating the child node's distance to the query.
shift(int, int, Instance) - Method in class weka.classifiers.trees.j48.Distribution
Shifts given instance from one bag to another one.
shift(int, int) - Method in class weka.core.matrix.IntVector
Shifts an element to another position.
shiftBeans(BeanInstance, boolean) - Method in class weka.gui.beans.MetaBean
Move coords of all inputs and outputs of this meta bean to the coords of the supplied BeanInstance.
shiftRange(int, int, Instances, int, int) - Method in class weka.classifiers.trees.j48.Distribution
Shifts all instances in given range from one bag to another one.
shiftToEnd(int) - Method in class weka.core.matrix.IntVector
Shifts an element to the end of the vector.
SHORT - Static variable in class weka.experiment.DatabaseUtils
Type mapping for SHORT used for reading experiment results.
show(Component, int, int) - Method in class weka.gui.GenericObjectEditor.JTreePopupMenu
Displays the menu, making sure it will fit on the screen.
showAttributes() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
displays all the attributes, returns the selected item or NULL if canceled
showChart() - Method in class weka.gui.beans.StripChart
Popup the chart panel
showDialog(Component, String) - Method in class weka.gui.ConverterFileChooser
Pops a custom file chooser dialog with a custom approve button.
showDialog() - Method in class weka.gui.experiment.OutputFormatDialog
Pops up the modal dialog and waits for cancel or a selection.
showDialog() - Method in class weka.gui.ListSelectorDialog
Pops up the modal dialog and waits for cancel or a selection.
showDialog() - Method in class weka.gui.PropertySelectorDialog
Pops up the modal dialog and waits for cancel or a selection.
showDialog() - Method in class weka.gui.sql.ConnectionPanel
displays the database dialog.
showDialog() - Method in class weka.gui.ViewerDialog
Pops up the modal dialog and waits for Cancel or OK.
showDialog(Instances) - Method in class weka.gui.ViewerDialog
Pops up the modal dialog and waits for Cancel or OK.
showExplorer(String) - Method in class weka.gui.GUIChooser
 
showGeneratedInstances(String) - Method in class weka.gui.explorer.PreprocessPanel
displays a dialog with the generated instances from the DataGenerator
showGUITipText() - Method in class weka.clusterers.OPTICS
Returns the tip text for this property.
showHistory() - Method in class weka.gui.sql.ConnectionPanel
displays the query history.
showHistory() - Method in class weka.gui.sql.QueryPanel
displays the query history.
showInputBox(Component, String, String, Object) - Static method in class weka.gui.ComponentHelper
pops up an input dialog
showKnowledgeFlow(String) - Method in class weka.gui.GUIChooser
 
showMessageBox(Component, String, String, int, int) - Static method in class weka.gui.ComponentHelper
displays a message box with the given title, message, buttons and icon ant the dimension.
showOpenDialog(Component) - Method in class weka.gui.ConverterFileChooser
Pops up an "Open File" file chooser dialog.
showOutOfMemory() - Method in class weka.core.Memory
prints an error message if OutOfMemory (and if GUI is present a dialog), otherwise nothing happens.
showPopup() - Method in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
if a JPopupMenu is set, it is displayed again.
showProperties() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
displays some properties of the instances
showPropertyDialog() - Method in class weka.gui.PropertyPanel
Displays the property edit dialog for the panel.
showResults() - Method in class weka.gui.beans.GraphViewer
Popup a result list from which the user can select a graph to view
showResults() - Method in class weka.gui.beans.TextViewer
Popup a component to display the selected text
showSaveDialog(Component) - Method in class weka.gui.ConverterFileChooser
Pops up an "Save File" file chooser dialog.
showTree() - Method in class weka.gui.HierarchyPropertyParser
Show the whole tree in text format
showValues() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
displays all the distinct values for an attribute
showWindow(Container) - Method in class weka.gui.Main
brings child frame to the top.
showWindow(Class) - Method in class weka.gui.Main
brings the first frame to the top that is of the specified window class.
shrinkageTipText() - Method in class weka.classifiers.meta.AdditiveRegression
Returns the tip text for this property
shrinkageTipText() - Method in class weka.classifiers.meta.LogitBoost
Returns the tip text for this property
shrinkingTipText() - Method in class weka.classifiers.functions.LibSVM
Returns the tip text for this property
shuffleTipText() - Method in class weka.classifiers.rules.Ridor
Returns the tip text for this property
sIB - Class in weka.clusterers
Cluster data using the sequential information bottleneck algorithm.

Note: only hard clustering scheme is supported.
sIB() - Constructor for class weka.clusterers.sIB
 
sigLevel - Variable in class weka.experiment.PairedStats
The significance level for comparisons
sigmaTipText() - Method in class weka.attributeSelection.ReliefFAttributeEval
Returns the tip text for this property
sigmaTipText() - Method in class weka.classifiers.functions.supportVector.Puk
Returns the tip text for this property
SigmoidUnit - Class in weka.classifiers.functions.neural
This can be used by the neuralnode to perform all it's computations (as a sigmoid unit).
SigmoidUnit() - Constructor for class weka.classifiers.functions.neural.SigmoidUnit
 
sign() - Method in class weka.core.matrix.DoubleVector
Returns the signs of all elements in terms of -1, 0 and +1.
sign - Variable in class weka.core.matrix.ExponentialFormat
 
SIGNIFICANCE_LOSS - Static variable in class weka.experiment.ResultMatrix
loss
SIGNIFICANCE_TIE - Static variable in class weka.experiment.ResultMatrix
tie
SIGNIFICANCE_WIN - Static variable in class weka.experiment.ResultMatrix
win
significanceLevelTipText() - Method in class weka.associations.Apriori
Returns the tip text for this property
significanceLevelTipText() - Method in class weka.attributeSelection.RaceSearch
Returns the tip text for this property
SIGNIFICANT - Static variable in class weka.associations.Tertius
Way of handling missing values: missing as a particular value
SIGNLOWER - Static variable in class weka.classifiers.lazy.LBR
significantly lower
simetricDif(ScatterSearchV1.Subset, ScatterSearchV1.Subset, int) - Method in class weka.attributeSelection.ScatterSearchV1
 
SimetricDiference(ScatterSearchV1.Subset, BitSet) - Method in class weka.attributeSelection.ScatterSearchV1
Calculate the Simetric Diference of two subsets
SimpleBatchFilter - Class in weka.filters
This filter is a superclass for simple batch filters.
SimpleBatchFilter() - Constructor for class weka.filters.SimpleBatchFilter
 
SimpleCart - Class in weka.classifiers.trees
Class implementing minimal cost-complexity pruning.
Note when dealing with missing values, use "fractional instances" method instead of surrogate split method.

For more information, see:

Leo Breiman, Jerome H.
SimpleCart() - Constructor for class weka.classifiers.trees.SimpleCart
 
SimpleCLI - Class in weka.gui
Creates a very simple command line for invoking the main method of classes.
SimpleCLI() - Constructor for class weka.gui.SimpleCLI
Constructor
SimpleCLIPanel - Class in weka.gui
Creates a very simple command line for invoking the main method of classes.
SimpleCLIPanel() - Constructor for class weka.gui.SimpleCLIPanel
Constructor.
SimpleCLIPanel.CommandlineCompletion - Class in weka.gui
A class for commandline completion of classnames.
SimpleDateFormatEditor - Class in weka.gui
Class for editing SimpleDateFormat strings.
SimpleDateFormatEditor() - Constructor for class weka.gui.SimpleDateFormatEditor
Constructs a new SimpleDateFormatEditor.
SimpleEstimator - Class in weka.classifiers.bayes.net.estimate
SimpleEstimator is used for estimating the conditional probability tables of a Bayes network once the structure has been learned.
SimpleEstimator() - Constructor for class weka.classifiers.bayes.net.estimate.SimpleEstimator
 
SimpleFilter - Class in weka.filters
This filter contains common behavior of the SimpleBatchFilter and the SimpleStreamFilter.
SimpleFilter() - Constructor for class weka.filters.SimpleFilter
 
SimpleKMeans - Class in weka.clusterers
Cluster data using the k means algorithm

Valid options are:

SimpleKMeans() - Constructor for class weka.clusterers.SimpleKMeans
the default constructor
SimpleLinearRegression - Class in weka.classifiers.functions
Learns a simple linear regression model.
SimpleLinearRegression() - Constructor for class weka.classifiers.functions.SimpleLinearRegression
 
SimpleLinkedList - Class in weka.associations.tertius
 
SimpleLinkedList() - Constructor for class weka.associations.tertius.SimpleLinkedList
 
SimpleLinkedList.LinkedListInverseIterator - Class in weka.associations.tertius
 
SimpleLinkedList.LinkedListIterator - Class in weka.associations.tertius
 
SimpleLog() - Constructor for class weka.core.Debug.SimpleLog
default constructor, uses only stdout
SimpleLog(String) - Constructor for class weka.core.Debug.SimpleLog
Creates a logger that writes into the specified file.
SimpleLog(String, boolean) - Constructor for class weka.core.Debug.SimpleLog
Creates a logger that writes into the specified file.
SimpleLogger() - Constructor for class weka.gui.beans.FlowRunner.SimpleLogger
 
SimpleLogistic - Class in weka.classifiers.functions
Classifier for building linear logistic regression models.
SimpleLogistic() - Constructor for class weka.classifiers.functions.SimpleLogistic
Constructor for creating SimpleLogistic object with standard options.
SimpleLogistic(int, boolean, boolean) - Constructor for class weka.classifiers.functions.SimpleLogistic
Constructor for creating SimpleLogistic object.
SimpleMI - Class in weka.classifiers.mi
Reduces MI data into mono-instance data.
SimpleMI() - Constructor for class weka.classifiers.mi.SimpleMI
 
SimpleSetupPanel - Class in weka.gui.experiment
This panel controls the configuration of an experiment.
SimpleSetupPanel(Experiment) - Constructor for class weka.gui.experiment.SimpleSetupPanel
Creates the setup panel with the supplied initial experiment.
SimpleSetupPanel() - Constructor for class weka.gui.experiment.SimpleSetupPanel
Creates the setup panel with no initial experiment.
SimpleStreamFilter - Class in weka.filters
This filter is a superclass for simple stream filters.
SimpleStreamFilter() - Constructor for class weka.filters.SimpleStreamFilter
 
SimulatedAnnealing - Class in weka.classifiers.bayes.net.search.global
This Bayes Network learning algorithm uses the general purpose search method of simulated annealing to find a well scoring network structure.

For more information see:

R.R.
SimulatedAnnealing() - Constructor for class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
 
SimulatedAnnealing - Class in weka.classifiers.bayes.net.search.local
This Bayes Network learning algorithm uses the general purpose search method of simulated annealing to find a well scoring network structure.

For more information see:

R.R.
SimulatedAnnealing() - Constructor for class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
 
SIN - Static variable in interface weka.core.mathematicalexpression.sym
 
SIN - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
SINE - Static variable in class weka.datagenerators.clusterers.BIRCHCluster
Constant set for choice of pattern.
SingleAssociatorEnhancer - Class in weka.associations
Abstract utility class for handling settings common to meta associators that use a single base associator.
SingleAssociatorEnhancer() - Constructor for class weka.associations.SingleAssociatorEnhancer
 
SingleClassifierEnhancer - Class in weka.classifiers
Abstract utility class for handling settings common to meta classifiers that use a single base learner.
SingleClassifierEnhancer() - Constructor for class weka.classifiers.SingleClassifierEnhancer
 
SingleClustererEnhancer - Class in weka.clusterers
Meta-clusterer for enhancing a base clusterer.
SingleClustererEnhancer() - Constructor for class weka.clusterers.SingleClustererEnhancer
 
singleConsequence(Instances) - Static method in class weka.associations.CaRuleGeneration
generates a consequence of length 1 for a class association rule.
singleConsequence(Instances, int, FastVector) - Static method in class weka.associations.RuleGeneration
generates a consequence of length 1 for an association rule.
SingleIndex - Class in weka.core
Class representing a single cardinal number.
SingleIndex() - Constructor for class weka.core.SingleIndex
Default constructor.
SingleIndex(String) - Constructor for class weka.core.SingleIndex
Constructor to set initial index.
singletons(Instances) - Static method in class weka.associations.AprioriItemSet
Converts the header info of the given set of instances into a set of item sets (singletons).
singletons(Instances) - Static method in class weka.associations.CaRuleGeneration
Converts the header info of the given set of instances into a set of item sets (singletons).
singletons(Instances) - Static method in class weka.associations.ItemSet
Converts the header info of the given set of instances into a set of item sets (singletons).
singletons(Instances, Instances) - Static method in class weka.associations.LabeledItemSet
Converts the header info of the given set of instances into a set of item sets (singletons).
singleVariance(double, double, double) - Method in class weka.classifiers.trees.REPTree.Tree
Computes the variance for a single set
SINGULAR_DUMMY - Static variable in interface weka.gui.graphvisualizer.GraphConstants
SINGULAR_DUMMY node - node with only one outgoing edge i.e.
SingularValueDecomposition - Class in weka.core.matrix
Singular Value Decomposition.
SingularValueDecomposition(Matrix) - Constructor for class weka.core.matrix.SingularValueDecomposition
Construct the singular value decomposition
size() - Method in class weka.associations.FPGrowth.FrequentItemSets
Get the number of item sets.
size() - Method in class weka.associations.tertius.SimpleLinkedList
 
size() - Method in class weka.classifiers.CostMatrix
The number of rows (and columns)
size() - Method in class weka.classifiers.evaluation.ConfusionMatrix
Gets the number of classes.
size() - Method in class weka.classifiers.functions.pace.DiscreteFunction
Returns the size of the point set.
size() - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable
Returns the number of keys in this hashtable.
size() - Method in class weka.classifiers.rules.JRip.RipperRule
the number of antecedents of the rule
size() - Method in class weka.classifiers.rules.Rule
The size of the rule.
size() - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database
Returns the size of the database (the number of dataObjects in the database)
size() - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase
Returns the size of the database (the number of dataObjects in the database)
size() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueue
Returns the queue's size
size() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueue
Returns the queue's size
size() - Method in class weka.core.FastVector
Returns the vector's current size.
size() - Method in class weka.core.matrix.DoubleVector
Gets the size of the vector.
size() - Method in class weka.core.matrix.IntVector
Gets the size of the vector.
size() - Method in class weka.core.neighboursearch.CoverTree.MyHeap
returns the size of the heap.
size() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.MyHeap
returns the size of the heap.
size() - Method in class weka.core.PropertyPath.Path
returns the number of path elements of this structure
size() - Method in class weka.core.Queue
Gets queue's size.
size() - Method in class weka.core.Tee
returns the number of streams currently in the list.
size() - Method in class weka.core.Trie
Returns the number of elements in this collection.
size() - Method in class weka.core.Trie.TrieNode
returns the number of stored strings, i.e., leaves
size() - Method in class weka.core.xml.MethodHandler
returns the number of currently stored Methods
sizePerTipText() - Method in class weka.classifiers.trees.BFTree
Returns the tip text for this property
sizePerTipText() - Method in class weka.classifiers.trees.SimpleCart
Returns the tip text for this property
skipIdenticalTipText() - Method in class weka.core.neighboursearch.LinearNNSearch
Returns the tip text for this property.
SlidingMidPointOfWidestSide - Class in weka.core.neighboursearch.kdtrees
The class that splits a node into two based on the midpoint value of the dimension in which the node's rectangle is widest.
SlidingMidPointOfWidestSide() - Constructor for class weka.core.neighboursearch.kdtrees.SlidingMidPointOfWidestSide
 
sm(double, double) - Static method in class weka.core.Utils
Tests if a is smaller than b.
SMALL - Static variable in class weka.core.Utils
The small deviation allowed in double comparisons.
SMO - Class in weka.classifiers.functions
Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.

This implementation globally replaces all missing values and transforms nominal attributes into binary ones.
SMO() - Constructor for class weka.classifiers.functions.SMO
 
SMO.BinarySMO - Class in weka.classifiers.functions
Class for building a binary support vector machine.
smoothingOriginal(double, double, double) - Static method in class weka.classifiers.trees.m5.RuleNode
Applies the m5 smoothing procedure to a prediction
smoothingParameterTipText() - Method in class weka.classifiers.bayes.ComplementNaiveBayes
Returns the tip text for this property
SMOreg - Class in weka.classifiers.functions
SMOreg implements the support vector machine for regression.
SMOreg() - Constructor for class weka.classifiers.functions.SMOreg
 
smOrEq(double, double) - Static method in class weka.core.Utils
Tests if a is smaller or equal to b.
SMOset - Class in weka.classifiers.functions.supportVector
Stores a set of integer of a given size.
SMOset(int) - Constructor for class weka.classifiers.functions.supportVector.SMOset
Creates a new set of the given size.
SMOTE - Class in weka.filters.supervised.instance
Resamples a dataset by applying the Synthetic Minority Oversampling TEchnique (SMOTE).
SMOTE() - Constructor for class weka.filters.supervised.instance.SMOTE
 
SNOWBALL_PROGRAM - Static variable in class weka.core.stemmers.SnowballStemmer
the snowball program, all stemmers are derived from.
SnowballStemmer - Class in weka.core.stemmers
A wrapper class for the Snowball stemmers.
SnowballStemmer() - Constructor for class weka.core.stemmers.SnowballStemmer
initializes the stemmer ("porter").
SnowballStemmer(String) - Constructor for class weka.core.stemmers.SnowballStemmer
initializes the stemmer with the given stemmer.
solve(Matrix) - Method in class weka.core.matrix.CholeskyDecomposition
Solve A*X = B
solve(Matrix) - Method in class weka.core.matrix.LUDecomposition
Solve A*X = B
solve(Matrix) - Method in class weka.core.matrix.Matrix
Solve A*X = B
solve(Matrix) - Method in class weka.core.matrix.QRDecomposition
Least squares solution of A*X = B
solve(double[]) - Method in class weka.core.Matrix
Deprecated.
Solve A*X = B using backward substitution.
solveTranspose(Matrix) - Method in class weka.core.matrix.Matrix
Solve X*A = B, which is also A'*X' = B'
solveTriangle(Matrix, double[], boolean, boolean[]) - Static method in class weka.core.Optimization
Solve the linear equation of TX=B where T is a triangle matrix It can be solved using back/forward substitution, with O(N^2) complexity
SOME_OTHER_FAILURE - Static variable in class weka.experiment.RemoteExperiment
status of the remote host: some other failure
SOME_OTHER_FAILURE - Static variable in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
 
son(int) - Method in class weka.classifiers.rules.part.ClassifierDecList
Method just exists to make program easier to read.
sort(Comparator<FPGrowth.FrequentBinaryItemSet>) - Method in class weka.associations.FPGrowth.FrequentItemSets
Sort the item sets according to the supplied comparator.
sort() - Method in class weka.associations.FPGrowth.FrequentItemSets
Sort the item sets.
sort(Comparator) - Method in class weka.associations.tertius.SimpleLinkedList
 
sort() - Method in class weka.classifiers.functions.pace.DiscreteFunction
Sorts the point values of the discrete function.
sort(int) - Method in class weka.core.Instances
Sorts the instances based on an attribute.
sort(Attribute) - Method in class weka.core.Instances
Sorts the instances based on an attribute.
sort() - Method in class weka.core.matrix.DoubleVector
Sorts the array in place
sort() - Method in class weka.core.matrix.IntVector
Sorts the elements in place
sort(int[]) - Static method in class weka.core.Utils
Sorts a given array of integers in ascending order and returns an array of integers with the positions of the elements of the original array in the sorted array.
sort(double[]) - Static method in class weka.core.Utils
Sorts a given array of doubles in ascending order and returns an array of integers with the positions of the elements of the original array in the sorted array.
sort(int) - Method in class weka.experiment.PairedTTester.Dataset
Sorts the instances in the dataset by the run number.
sort(int) - Method in class weka.experiment.PairedTTester.Resultset
Sorts the instances in each dataset by the run number.
sort(int) - Method in class weka.gui.SortedTableModel
sorts the table over the given column (ascending)
sort(int, boolean) - Method in class weka.gui.SortedTableModel
sorts the table over the given column, either ascending or descending
sortArray(double[]) - Method in class weka.classifiers.mi.MIOptimalBall
Sort the array.
sortClassesByRoot(String) - Static method in class weka.gui.GenericObjectEditor
parses the given string of classes separated by ", " and returns the a hashtable with as many entries as there are different root elements in the class names (the key is the root element).
SortContainer(Comparable, int) - Constructor for class weka.gui.SortedTableModel.SortContainer
Initializes the container.
SortedTableModel - Class in weka.gui
Represents a TableModel with sorting functionality.
SortedTableModel() - Constructor for class weka.gui.SortedTableModel
initializes with no model
SortedTableModel(TableModel) - Constructor for class weka.gui.SortedTableModel
initializes with the given model
SortedTableModel.SortContainer - Class in weka.gui
Helper class for sorting the columns.
sortInstances() - Method in class weka.gui.arffviewer.ArffPanel
sorts the instances via the currently selected column
sortInstances(int) - Method in class weka.gui.arffviewer.ArffSortedTableModel
sorts the instances via the given attribute
sortInstances(int) - Method in class weka.gui.arffviewer.ArffTableModel
sorts the instances via the given attribute
sortInstances() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
sorts the current selected attribute
sortTipText() - Method in class weka.filters.unsupervised.attribute.AddValues
Returns the tip text for this property
sortWithIndex() - Method in class weka.core.matrix.DoubleVector
Sorts the array in place with index returned
sortWithIndex(int, int, IntVector) - Method in class weka.core.matrix.DoubleVector
Sorts the array in place with index changed
Sourcable - Interface in weka.classifiers
Interface for classifiers that can be converted to Java source.
Sourcable - Interface in weka.filters
Interface for filters that can be converted to Java source.
sourceClass(int, Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
 
sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.BinC45Split
Returns a string containing java source code equivalent to the test made at this node.
sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.C45Split
Returns a string containing java source code equivalent to the test made at this node.
sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
 
sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.GraftSplit
Returns a string containing java source code equivalent to the test made at this node.
sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.NBTreeNoSplit
Returns a string containing java source code equivalent to the test made at this node.
sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.NBTreeSplit
Returns a string containing java source code equivalent to the test made at this node.
sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.NoSplit
Returns a string containing java source code equivalent to the test made at this node.
sourceExpression(int, Instances) - Method in class weka.classifiers.trees.lmt.ResidualSplit
Method not in use
sourceExpression(int) - Method in class weka.classifiers.trees.REPTree.Tree
Returns a string containing java source code equivalent to the test made at this node.
SOUTH_CONNECTOR - Static variable in class weka.gui.beans.BeanVisual
 
spaceHorizontal(FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet
space out set of nodes evenly between left and right most node in the list
spaceVertical(FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet
space out set of nodes evenly between top and bottom most node in the list
SPARSE1 - Static variable in class weka.filters.unsupervised.attribute.RandomProjection
distribution type: sparse 1
SPARSE2 - Static variable in class weka.filters.unsupervised.attribute.RandomProjection
distribution type: sparse 2
sparseDataTipText() - Method in class weka.experiment.InstanceQuery
Returns the tip text for this property
sparseIndices() - Method in class weka.classifiers.functions.SMO
Returns the indices in sparse format.
sparseIndices() - Method in class weka.classifiers.mi.MISMO
Returns the indices in sparse format.
SparseInstance - Class in weka.core
Class for storing an instance as a sparse vector.
SparseInstance() - Constructor for class weka.core.SparseInstance
 
SparseInstance(Instance) - Constructor for class weka.core.SparseInstance
Constructor that generates a sparse instance from the given instance.
SparseInstance(SparseInstance) - Constructor for class weka.core.SparseInstance
Constructor that copies the info from the given instance.
SparseInstance(double, double[]) - Constructor for class weka.core.SparseInstance
Constructor that generates a sparse instance from the given parameters.
SparseInstance(double, double[], int[], int) - Constructor for class weka.core.SparseInstance
Constructor that inititalizes instance variable with given values.
SparseInstance(int) - Constructor for class weka.core.SparseInstance
Constructor of an instance that sets weight to one, all values to be missing, and the reference to the dataset to null.
SparseToNonSparse - Class in weka.filters.unsupervised.instance
An instance filter that converts all incoming sparse instances into non-sparse format.
SparseToNonSparse() - Constructor for class weka.filters.unsupervised.instance.SparseToNonSparse
 
sparseWeights() - Method in class weka.classifiers.functions.SMO
Returns the weights in sparse format.
sparseWeights() - Method in class weka.classifiers.mi.MISMO
Returns the weights in sparse format.
SpecialFunctions - Class in weka.core
Class implementing some mathematical functions.
SpecialFunctions() - Constructor for class weka.core.SpecialFunctions
 
SPECIFIC_VALUE - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
 
specifier(int) - Method in class weka.experiment.PairedTTester.DatasetSpecifiers
Get the template at the given position.
SPegasos - Class in weka.classifiers.functions
Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al.
SPegasos() - Constructor for class weka.classifiers.functions.SPegasos
 
sphere - Variable in class weka.classifiers.lazy.kstar.KStarWrapper
used/reused to hold the sphere size
splash(Image) - Static method in class weka.gui.SplashWindow
Open's a splash window using the specified image.
splash(URL) - Static method in class weka.gui.SplashWindow
Open's a splash window using the specified image.
SplashWindow - Class in weka.gui
A Splash window.
split(Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Splits the given set of instances into subsets.
split() - Method in class weka.classifiers.trees.m5.RuleNode
Finds an attribute and split point for this node
split(Stack<CoverTree.DistanceNode>, Stack<CoverTree.DistanceNode>, int) - Method in class weka.core.neighboursearch.CoverTree
Splits a given point_set into near and far based on the given scale/level.
splitAtt() - Method in class weka.classifiers.trees.m5.RuleNode
Get the index of the splitting attribute for this node
splitAttr() - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo
Returns the attribute used in this split
splitAttr() - Method in interface weka.classifiers.trees.m5.SplitEvaluate
Returns the attribute used in this split
splitAttr() - Method in class weka.classifiers.trees.m5.YongSplitInfo
Returns the attribute used in this split
splitCenter(Random, Instance, double, Instances) - Method in class weka.clusterers.XMeans
Split centers in their region.
splitCenters(Random, Instances, Instances) - Method in class weka.clusterers.XMeans
Split centers in their region.
SplitCriterion - Class in weka.classifiers.trees.j48
Abstract class for computing splitting criteria with respect to distributions of class values.
SplitCriterion() - Constructor for class weka.classifiers.trees.j48.SplitCriterion
 
splitCritValue(Distribution) - Method in class weka.classifiers.trees.j48.EntropySplitCrit
Computes entropy for given distribution.
splitCritValue(Distribution, Distribution) - Method in class weka.classifiers.trees.j48.EntropySplitCrit
Computes entropy of test distribution with respect to training distribution.
splitCritValue(Distribution) - Method in class weka.classifiers.trees.j48.GainRatioSplitCrit
This method is a straightforward implementation of the gain ratio criterion for the given distribution.
splitCritValue(Distribution, double, double) - Method in class weka.classifiers.trees.j48.GainRatioSplitCrit
This method computes the gain ratio in the same way C4.5 does.
splitCritValue(Distribution) - Method in class weka.classifiers.trees.j48.InfoGainSplitCrit
This method is a straightforward implementation of the information gain criterion for the given distribution.
splitCritValue(Distribution, double) - Method in class weka.classifiers.trees.j48.InfoGainSplitCrit
This method computes the information gain in the same way C4.5 does.
splitCritValue(Distribution, double, double) - Method in class weka.classifiers.trees.j48.InfoGainSplitCrit
This method computes the information gain in the same way C4.5 does.
splitCritValue(Distribution) - Method in class weka.classifiers.trees.j48.SplitCriterion
Computes result of splitting criterion for given distribution.
splitCritValue(Distribution, Distribution) - Method in class weka.classifiers.trees.j48.SplitCriterion
Computes result of splitting criterion for given training and test distributions.
splitCritValue(Distribution, Distribution, int) - Method in class weka.classifiers.trees.j48.SplitCriterion
Computes result of splitting criterion for given training and test distributions and given number of classes.
splitCritValue(Distribution, Distribution, Distribution) - Method in class weka.classifiers.trees.j48.SplitCriterion
Computes result of splitting criterion for given training and test distributions and given default distribution.
splitData(Instances, double, double) - Method in class weka.classifiers.rules.JRip.Antd
 
splitData(Instances, double, double) - Method in class weka.classifiers.rules.JRip.NominalAntd
Implements the splitData function.
splitData(Instances, double, double) - Method in class weka.classifiers.rules.JRip.NumericAntd
Implements the splitData function.
splitData(int[][][], double[][][], Attribute, double, String, int[][], double[][], Instances) - Method in class weka.classifiers.trees.BFTree
Split data into two subsets and store sorted indices and weights for two successor nodes.
splitData(Instances) - Method in class weka.classifiers.trees.RandomTree
Splits instances into subsets based on the given split.
splitData(int[][][][], double[][][][], int, double, int[][], double[][], Instances) - Method in class weka.classifiers.trees.REPTree.Tree
Splits instances into subsets.
splitData(int[][][], double[][][], Attribute, double, String, int[][], double[][], Instances) - Method in class weka.classifiers.trees.SimpleCart
Split data into two subsets and store sorted indices and weights for two successor nodes.
splitEnt(Distribution) - Method in class weka.classifiers.trees.j48.EntropyBasedSplitCrit
Computes entropy after splitting without considering the class values.
SplitEvaluate - Interface in weka.classifiers.trees.m5
Interface for objects that determine a split point on an attribute
SplitEvaluator - Interface in weka.experiment
Interface to objects able to generate a fixed set of results for a particular split of a dataset.
splitEvaluatorTipText() - Method in class weka.experiment.CrossValidationResultProducer
Returns the tip text for this property
splitEvaluatorTipText() - Method in class weka.experiment.RandomSplitResultProducer
Returns the tip text for this property
splitItemSet(int, int[]) - Method in class weka.associations.PriorEstimation
splits an item set into premise and consequence and constructs therefore an association rule.
splitNode(BallNode, int) - Method in class weka.core.neighboursearch.balltrees.BallSplitter
Splits a node into two.
splitNode(BallNode, int) - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
Splits a ball into two.
splitNode(BallNode, int) - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
Splits a ball into two.
splitNode(BallNode, int) - Method in class weka.core.neighboursearch.balltrees.PointsClosestToFurthestChildren
Splits a ball into two.
splitNode(KDTreeNode, int, double[][], double[][]) - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
Splits a node into two.
splitNode(KDTreeNode, int, double[][], double[][]) - Method in class weka.core.neighboursearch.kdtrees.KMeansInpiredMethod
Splits a node into two such that the overall sum of squared distances of points to their centres on both sides of the (axis-parallel) splitting plane is minimum.
splitNode(KDTreeNode, int, double[][], double[][]) - Method in class weka.core.neighboursearch.kdtrees.MedianOfWidestDimension
Splits a node into two based on the median value of the dimension in which the points have the widest spread.
splitNode(KDTreeNode, int, double[][], double[][]) - Method in class weka.core.neighboursearch.kdtrees.MidPointOfWidestDimension
Splits a node into two based on the midpoint value of the dimension in which the points have the widest spread.
splitNode(KDTreeNode, int, double[][], double[][]) - Method in class weka.core.neighboursearch.kdtrees.SlidingMidPointOfWidestSide
Splits a node into two based on the midpoint value of the dimension in which the node's rectangle is widest.
splitNodes(BallNode, int, double) - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor
Recursively splits nodes of a ball tree until <=m_MaxInstancesInLeaf instances remain in a node.
splitNodes(KDTreeNode, double[][], int) - Method in class weka.core.neighboursearch.KDTree
Recursively splits nodes of a tree starting from the supplied node.
splitOnResidualsTipText() - Method in class weka.classifiers.trees.LMT
Returns the tip text for this property
splitOptions(String) - Static method in class weka.core.Utils
Split up a string containing options into an array of strings, one for each option.
splitPoint() - Method in class weka.classifiers.trees.j48.GraftSplit
 
splitPointTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Returns the tip text for this property
Splitter - Class in weka.classifiers.trees.adtree
Abstract class representing a splitter node in an alternating tree.
Splitter() - Constructor for class weka.classifiers.trees.adtree.Splitter
 
Splitter() - Constructor for class weka.classifiers.trees.LADTree.Splitter
 
splitVal() - Method in class weka.classifiers.trees.m5.RuleNode
Get the split point for this node
splitValue() - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo
Returns the split value
splitValue() - Method in interface weka.classifiers.trees.m5.SplitEvaluate
Returns the split value
splitValue() - Method in class weka.classifiers.trees.m5.YongSplitInfo
Returns the split value
SpreadSubsample - Class in weka.filters.supervised.instance
Produces a random subsample of a dataset.
SpreadSubsample() - Constructor for class weka.filters.supervised.instance.SpreadSubsample
 
sqDifference(int, double, double) - Method in class weka.core.EuclideanDistance
Returns the squared difference of two values of an attribute.
SqlViewer - Class in weka.gui.sql
Represents a little tool for querying SQL databases.
SqlViewer(JFrame) - Constructor for class weka.gui.sql.SqlViewer
initializes the SqlViewer.
SqlViewerDialog - Class in weka.gui.sql
A little dialog containing the SqlViewer.
SqlViewerDialog(JFrame) - Constructor for class weka.gui.sql.SqlViewerDialog
initializes the dialog
SQRT - Static variable in interface weka.core.mathematicalexpression.sym
 
sqrt() - Method in class weka.core.matrix.DoubleVector
Returns the square-root of all the elements in the vector
sqrt() - Method in class weka.core.matrix.Matrix
returns the square root of the matrix, i.e., X from the equation X*X = A.
Steps in the Calculation (see sqrtm in Matlab):
perform eigenvalue decomposition
[V,D]=eig(A) take the square root of all elements in D (only the ones with positive sign are considered for further computation)
S=sqrt(D) calculate the root
X=V*S/V, which can be also written as X=(V'\(V*S)')'

Note: since this method uses other high-level methods, it generates several instances of matrices.

SQRT - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
SQRTH - Static variable in class weka.core.Statistics
 
SQTPI - Static variable in class weka.core.Statistics
 
square() - Method in class weka.core.matrix.DoubleVector
Returns the squared vector
square(double) - Static method in class weka.core.matrix.Maths
Returns the square of a value
src - Variable in class weka.gui.graphvisualizer.GraphEdge
The index of source node in Nodes vector
srcLbl - Variable in class weka.gui.graphvisualizer.GraphEdge
Label of source node
stableSort(double[]) - Static method in class weka.core.Utils
Sorts a given array of doubles in ascending order and returns an array of integers with the positions of the elements of the original array in the sorted array.
Stack<T> - Class in weka.core.neighboursearch.covertrees
Class implementing a stack.
Stack() - Constructor for class weka.core.neighboursearch.covertrees.Stack
Constructor.
Stack(int) - Constructor for class weka.core.neighboursearch.covertrees.Stack
Constructor.
Stacking - Class in weka.classifiers.meta
Combines several classifiers using the stacking method.
Stacking() - Constructor for class weka.classifiers.meta.Stacking
 
StackingC - Class in weka.classifiers.meta
Implements StackingC (more efficient version of stacking).

For more information, see

A.K.
StackingC() - Constructor for class weka.classifiers.meta.StackingC
The constructor.
Standardize - Class in weka.filters.unsupervised.attribute
Standardizes all numeric attributes in the given dataset to have zero mean and unit variance (apart from the class attribute, if set).
Standardize() - Constructor for class weka.filters.unsupervised.attribute.Standardize
 
start() - Method in class weka.core.Debug.Clock
saves the current system time (or CPU time) in msec as start time
start() - Method in class weka.gui.beans.Loader
Start loading
start() - Method in interface weka.gui.beans.Startable
Start the flow running
start() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Start the plotting thread
start() - Method in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
Start processing
start_production() - Method in class weka.core.mathematicalexpression.Parser
Indicates start production.
start_production() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
Indicates start production.
start_state() - Method in class weka.core.mathematicalexpression.Parser
Indicates start state.
start_state() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
Indicates start state.
Startable - Interface in weka.gui.beans
Interface to something that is a start point for a flow and can be launched programatically.
startApp() - Static method in class weka.gui.beans.KnowledgeFlow
Static method that can be called from a running program to launch the KnowledgeFlow
startAssociator() - Method in class weka.gui.explorer.AssociationsPanel
Starts running the currently configured associator with the current settings.
startAttributeSelection() - Method in class weka.gui.explorer.AttributeSelectionPanel
Starts running the currently configured attribute evaluator and search method.
startClassifier() - Method in class weka.gui.explorer.ClassifierPanel
Starts running the currently configured classifier with the current settings.
startClock() - Method in class weka.core.Debug
starts the clock
startClusterer() - Method in class weka.gui.explorer.ClustererPanel
Starts running the currently configured clusterer with the current settings.
startLoading() - Method in class weka.gui.beans.Loader
Start loading data
startPlotThread() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Starts the plotting thread.
startPointTipText() - Method in class weka.attributeSelection.RankSearch
Returns the tip text for this property
StartSetHandler - Interface in weka.attributeSelection
Interface for search methods capable of doing something sensible given a starting set of attributes.
startSetTipText() - Method in class weka.attributeSelection.BestFirst
Returns the tip text for this property
startSetTipText() - Method in class weka.attributeSelection.GeneticSearch
Returns the tip text for this property
startSetTipText() - Method in class weka.attributeSelection.GreedyStepwise
Returns the tip text for this property
startSetTipText() - Method in class weka.attributeSelection.LinearForwardSelection
Returns the tip text for this property
startSetTipText() - Method in class weka.attributeSelection.RandomSearch
Returns the tip text for this property
startSetTipText() - Method in class weka.attributeSelection.Ranker
Returns the tip text for this property
startSetToString() - Method in class weka.attributeSelection.GreedyStepwise
converts the array of starting attributes to a string.
startUpComplete() - Method in interface weka.gui.beans.StartUpListener
 
StartUpListener - Interface in weka.gui.beans
Interface to something that can be notified of a successful startup
stateChanged(ChangeEvent) - Method in class weka.gui.arffviewer.ArffPanel
Invoked when the target of the listener has changed its state.
stateChanged(ChangeEvent) - Method in class weka.gui.arffviewer.ArffViewerMainPanel
Invoked when the target of the listener has changed its state.
stateChanged(ChangeEvent) - Method in class weka.gui.LogWindow
Invoked when the target of the listener has changed its state.
stateChanged(ChangeEvent) - Method in class weka.gui.sql.ResultPanel
Invoked when the target of the listener has changed its state.
stateChanged(ChangeEvent) - Method in class weka.gui.ViewerDialog
Invoked when the target of the listener has changed its state.
Statistics - Class in weka.core
Class implementing some distributions, tests, etc.
Statistics() - Constructor for class weka.core.Statistics
 
Stats - Class in weka.classifiers.trees.j48
Class implementing a statistical routine needed by J48 to compute its error estimate.
Stats() - Constructor for class weka.classifiers.trees.j48.Stats
 
Stats - Class in weka.experiment
A class to store simple statistics
Stats() - Constructor for class weka.experiment.Stats
 
statusFrequencyTipText() - Method in class weka.gui.beans.IncrementalClassifierEvaluator
Return a tip text string for this property
statusMessage(String) - Method in class weka.gui.beans.FlowRunner.SimpleLogger
 
statusMessage(String) - Method in class weka.gui.beans.LogPanel
Sends the supplied message to the status area.
statusMessage(String) - Method in class weka.gui.experiment.RunPanel
Sends the supplied message to the log panel status line.
statusMessage(String) - Method in interface weka.gui.Logger
Sends the supplied message to the status line.
statusMessage(String) - Method in class weka.gui.LogPanel
Sends the supplied message to the status line.
statusMessage(String) - Method in class weka.gui.SysErrLog
Sends the supplied message to the status line.
stdDev(int, Instances) - Static method in class weka.classifiers.trees.m5.Rule
Returns the standard deviation value of the supplied attribute index.
stdDev - Variable in class weka.experiment.Stats
The std deviation of values at the last calculateDerived() call
stealPoints(MiddleOutConstructor.TempNode, Vector, Vector) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
Removes points from old anchors that are nearer to the given new anchor and adds them to the list of points of the new anchor.
stem(String) - Method in class weka.core.stemmers.IteratedLovinsStemmer
Iterated stemming of the given word.
stem(String) - Method in class weka.core.stemmers.LovinsStemmer
Returns the stemmed version of the given word.
stem(String) - Method in class weka.core.stemmers.NullStemmer
Returns the word as it is.
stem(String) - Method in class weka.core.stemmers.SnowballStemmer
Returns the word in its stemmed form.
stem(String) - Method in interface weka.core.stemmers.Stemmer
Stems the given word and returns the stemmed version
Stemmer - Interface in weka.core.stemmers
Interface for all stemming algorithms.
stemmerTipText() - Method in class weka.core.stemmers.SnowballStemmer
Returns the tip text for this property.
stemmerTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Returns the tip text for this property.
Stemming - Class in weka.core.stemmers
A helper class for using the stemmers.
Stemming() - Constructor for class weka.core.stemmers.Stemming
 
stemString(String) - Method in class weka.core.stemmers.LovinsStemmer
Stems everything in the given string.
STEP_FIELD_NAME - Static variable in class weka.experiment.LearningRateResultProducer
The name of the key field containing the learning rate step number
steplsqr(PaceMatrix, IntVector, int, int, boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix
Stepwise least squares QR-decomposition of the problem A x = b
stepSizeTipText() - Method in class weka.attributeSelection.RankSearch
Returns the tip text for this property
stepSizeTipText() - Method in class weka.experiment.LearningRateResultProducer
Returns the tip text for this property
stmt(StreamTokenizer) - Method in class weka.gui.graphvisualizer.DotParser
 
stmtList(StreamTokenizer) - Method in class weka.gui.graphvisualizer.DotParser
 
stop() - Method in class weka.core.Debug.Clock
saves the current system (or CPU time) in msec as stop time
STOP - Static variable in class weka.core.Trie.TrieNode
the stop character
stop() - Method in class weka.gui.beans.AbstractDataSink
Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.AbstractEvaluator
Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.AbstractTestSetProducer
Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.AbstractTrainingSetProducer
Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.Associator
Stop any associator action
stop() - Method in interface weka.gui.beans.BeanCommon
Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.ClassAssigner
 
stop() - Method in class weka.gui.beans.Classifier
Stop any classifier action
stop() - Method in class weka.gui.beans.ClassifierPerformanceEvaluator
Try and stop any action
stop() - Method in class weka.gui.beans.ClassValuePicker
 
stop() - Method in class weka.gui.beans.Clusterer
Stop any clusterer action
stop() - Method in class weka.gui.beans.ClustererPerformanceEvaluator
Try and stop any action
stop() - Method in class weka.gui.beans.CostBenefitAnalysis
Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.CrossValidationFoldMaker
Stop any action
stop() - Method in class weka.gui.beans.Filter
Stop all action if possible
stop() - Method in class weka.gui.beans.IncrementalClassifierEvaluator
Stop all action
stop() - Method in class weka.gui.beans.InstanceStreamToBatchMaker
Stop any action (if possible).
stop() - Method in class weka.gui.beans.Loader
Stop any loading action.
stop() - Method in class weka.gui.beans.MetaBean
Stop all encapsulated beans
stop() - Method in class weka.gui.beans.PredictionAppender
 
stop() - Method in class weka.gui.beans.Saver
Stops the bean
stop() - Method in class weka.gui.beans.SerializedModelSaver
Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.StripChart
Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.TestSetMaker
 
stop() - Method in class weka.gui.beans.TextViewer
Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.TrainingSetMaker
Stop any action
stop() - Method in class weka.gui.beans.TrainTestSplitMaker
Stop processing
stopAllFlows() - Method in class weka.gui.beans.FlowRunner
 
stopAssociator() - Method in class weka.gui.explorer.AssociationsPanel
Stops the currently running Associator (if any).
stopAttributeSelection() - Method in class weka.gui.explorer.AttributeSelectionPanel
Stops the currently running attribute selection (if any).
stopClassifier() - Method in class weka.gui.explorer.ClassifierPanel
Stops the currently running classifier (if any).
stopClock(String) - Method in class weka.core.Debug
stops the clock and prints the message associated with the time, but only if the logging is enabled.
stopClusterer() - Method in class weka.gui.explorer.ClustererPanel
Stops the currently running clusterer (if any).
stopIteration(int, int) - Method in class weka.clusterers.XMeans
Checks if iterationCount has to be checked and if yes (this means max is > 0) compares it with max.
stopKMeansIteration(int, int) - Method in class weka.clusterers.XMeans
Controls that counter does not exceed max iteration value.
stopMonitoring() - Method in class weka.gui.MemoryUsagePanel.MemoryMonitor
stops the monitoring thread.
stopMonitoring() - Method in class weka.gui.MemoryUsagePanel
stops the monitoring thread.
stoppingCriterion() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
This method implements the stopping criterion function.
stopPlotting() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Stop the plotting thread
stopPlotting() - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
Stops the plotting thread.
stopThreads() - Method in class weka.core.Memory
stops all the current threads, to make a restart possible
Stopwords - Class in weka.core
Class that can test whether a given string is a stop word.
Stopwords() - Constructor for class weka.core.Stopwords
initializes the stopwords (based on Rainbow).
stopwordsTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Returns the tip text for this property.
store(double, double, double) - Method in class weka.classifiers.lazy.kstar.KStarCache
Stores the specified values in the cahce table for easy retrieval.
storeOutputProperties() - Method in class weka.gui.GenericPropertiesCreator
stores the generated output properties file
StratifiedRemoveFolds - Class in weka.filters.supervised.instance
This filter takes a dataset and outputs a specified fold for cross validation.
StratifiedRemoveFolds() - Constructor for class weka.filters.supervised.instance.StratifiedRemoveFolds
 
stratify(Instances, int, Random) - Static method in class weka.classifiers.rules.RuleStats
Stratify the given data into the given number of bags based on the class values.
stratify(int) - Method in class weka.core.Instances
Stratifies a set of instances according to its class values if the class attribute is nominal (so that afterwards a stratified cross-validation can be performed).
stratStep(int) - Method in class weka.core.Instances
Help function needed for stratification of set.
StreamableFilter - Interface in weka.filters
Interface for filters can work with a stream of instances.
STRING - Static variable in class weka.core.Attribute
Constant set for attributes with string values.
STRING - Static variable in class weka.experiment.DatabaseUtils
Type mapping for STRING used for reading experiment results.
STRING - Static variable in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner
lexical states
STRING - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
stringAttributesTipText() - Method in class weka.core.converters.CSVLoader
Returns the tip text for this property.
StringCompare() - Constructor for class weka.core.ClassDiscovery.StringCompare
 
stringFreeStructure() - Method in class weka.core.Instances
Create a copy of the structure if the data has string or relational attributes, "cleanses" string types (i.e.
StringKernel - Class in weka.classifiers.functions.supportVector
Implementation of the subsequence kernel (SSK) as described in [1] and of the subsequence kernel with lambda pruning (SSK-LP) as described in [2].

For more information, see

Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Christopher J.
StringKernel() - Constructor for class weka.classifiers.functions.supportVector.StringKernel
default constructor
StringKernel(Instances, int, int, double, boolean) - Constructor for class weka.classifiers.functions.supportVector.StringKernel
creates a new StringKernel object.
StringLocator - Class in weka.core
This class locates and records the indices of String attributes, recursively in case of Relational attributes.
StringLocator(Instances) - Constructor for class weka.core.StringLocator
initializes the StringLocator with the given data
StringLocator(Instances, int, int) - Constructor for class weka.core.StringLocator
Initializes the StringLocator with the given data.
StringLocator(Instances, int[]) - Constructor for class weka.core.StringLocator
Initializes the AttributeLocator with the given data.
stringSize(FontMetrics) - Method in class weka.gui.treevisualizer.Edge
This will calculate how large a rectangle using the FontMetrics passed that the lines of the label will take up
stringSize(FontMetrics) - Method in class weka.gui.treevisualizer.Node
This will return the width and height of the rectangle that the text will fit into.
stringToBoolean(String) - Method in class weka.core.xml.XMLSerialization
turns the given string into a boolean, if a positive number is given, then zero is considered FALSE, every other number TRUE; the empty string is also considered being FALSE
stringToLevel(String) - Static method in class weka.core.Debug.Log
turns the string representing a level, e.g., "FINE" or "ALL" into the corresponding level (case-insensitive).
stringToLevel(String) - Static method in class weka.core.Debug
turns the string representing a level, e.g., "FINE" or "ALL" into the corresponding level (case-insensitive).
stringToModel(String) - Method in class weka.gui.sql.SqlViewer
transforms the given, comma-separated string into a DefaultListModel.
StringToNominal - Class in weka.filters.unsupervised.attribute
Converts a string attribute (i.e.
StringToNominal() - Constructor for class weka.filters.unsupervised.attribute.StringToNominal
 
StringToWordVector - Class in weka.filters.unsupervised.attribute
Converts String attributes into a set of attributes representing word occurrence (depending on the tokenizer) information from the text contained in the strings.
StringToWordVector() - Constructor for class weka.filters.unsupervised.attribute.StringToWordVector
Default constructor.
StringToWordVector(int) - Constructor for class weka.filters.unsupervised.attribute.StringToWordVector
Constructor that allows specification of the target number of words in the output.
stringValue(int) - Method in class weka.core.Instance
Returns the value of a nominal, string, date, or relational attribute for the instance as a string.
stringValue(Attribute) - Method in class weka.core.Instance
Returns the value of a nominal, string, date, or relational attribute for the instance as a string.
stringWithoutHeader() - Method in class weka.core.Instances
Returns the instances in the dataset as a string in ARFF format.
StripChart - Class in weka.gui.beans
Bean that can display a horizontally scrolling strip chart.
StripChart() - Constructor for class weka.gui.beans.StripChart
 
StripChartBeanInfo - Class in weka.gui.beans
Bean info class for the strip chart bean
StripChartBeanInfo() - Constructor for class weka.gui.beans.StripChartBeanInfo
 
StripChartCustomizer - Class in weka.gui.beans
GUI Customizer for the strip chart bean
StripChartCustomizer() - Constructor for class weka.gui.beans.StripChartCustomizer
 
STRUCTURE_READY - Static variable in class weka.core.converters.AbstractSaver
 
StructureProducer - Interface in weka.gui.beans
Interface for something that can describe the structure of what is encapsulated in a named event type as an empty set of Instances (i.e.
STYLE_STDERR - Static variable in class weka.gui.LogWindow
the name of the style for stderr
STYLE_STDOUT - Static variable in class weka.gui.LogWindow
the name of the style for stdout
sub(int, Instance) - Method in class weka.classifiers.trees.j48.Distribution
Subtracts given instance from given bag.
subFlowContains(BeanInstance) - Method in class weka.gui.beans.MetaBean
 
subgrid(int, int, int, int) - Method in class weka.classifiers.meta.GridSearch.Grid
returns a subgrid with the same step sizes, but different borders
subList(int, int) - Method in class weka.core.neighboursearch.covertrees.Stack
Returns a sublist of the elements in the stack.
subpath(int) - Method in class weka.core.PropertyPath.Path
returns a subpath of the current structure, starting with the specified element index up to the end
subpath(int, int) - Method in class weka.core.PropertyPath.Path
returns a subpath of the current structure, starting with the specified element index up.
subsequenceLengthTipText() - Method in class weka.classifiers.functions.supportVector.StringKernel
Returns the tip text for this property
Subset(BitSet, double) - Constructor for class weka.attributeSelection.ScatterSearchV1.Subset
 
SubsetByExpression - Class in weka.filters.unsupervised.instance
Filters instances according to a user-specified expression.

Grammar:

boolexpr_list ::= boolexpr_list boolexpr_part | boolexpr_part;

boolexpr_part ::= boolexpr:e {: parser.setResult(e); :} ;

boolexpr ::= BOOLEAN
| true
| false
| expr < expr
| expr <= expr
| expr > expr
| expr >= expr
| expr = expr
| ( boolexpr )
| not boolexpr
| boolexpr and boolexpr
| boolexpr or boolexpr
| ATTRIBUTE is STRING
;

expr ::= NUMBER
| ATTRIBUTE
| ( expr )
| opexpr
| funcexpr
;

opexpr ::= expr + expr
| expr - expr
| expr * expr
| expr / expr
;

funcexpr ::= abs ( expr )
| sqrt ( expr )
| log ( expr )
| exp ( expr )
| sin ( expr )
| cos ( expr )
| tan ( expr )
| rint ( expr )
| floor ( expr )
| pow ( expr for base , expr for exponent )
| ceil ( expr )
;

Notes:
- NUMBER
any integer or floating point number
(but not in scientific notation!)
- STRING
any string surrounded by single quotes;
the string may not contain a single quote though.
- ATTRIBUTE
the following placeholders are recognized for
attribute values:
- CLASS for the class value in case a class attribute is set.
- ATTxyz with xyz a number from 1 to # of attributes in the
dataset, representing the value of indexed attribute.

Examples:
- extracting only mammals and birds from the 'zoo' UCI dataset:
(CLASS is 'mammal') or (CLASS is 'bird')
- extracting only animals with at least 2 legs from the 'zoo' UCI dataset:
(ATT14 >= 2)
- extracting only instances with non-missing 'wage-increase-second-year'
from the 'labor' UCI dataset:
not ismissing(ATT3)

Valid options are:

SubsetByExpression() - Constructor for class weka.filters.unsupervised.instance.SubsetByExpression
 
subsetDL(double, double, double) - Static method in class weka.classifiers.rules.RuleStats
Subset description length:
S(t,k,p) = -k*log2(p)-(n-k)log2(1-p) Details see Quilan: "MDL and categorical theories (Continued)",ML95
subsetEstimate(DoubleVector) - Method in class weka.classifiers.functions.pace.NormalMixture
Returns the estimate of optimal subset selection.
SubsetEvaluator - Interface in weka.attributeSelection
Interface for attribute subset evaluators.
subsetEvaluatorTipText() - Method in class weka.attributeSelection.FilteredSubsetEval
Returns the tip text for this property
subsetOfInterest() - Method in class weka.classifiers.trees.j48.GraftSplit
 
subsetSizeEvaluatorTipText() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Returns the tip text for this property
SubsetSizeForwardSelection - Class in weka.attributeSelection
SubsetSizeForwardSelection:

Extension of LinearForwardSelection.
SubsetSizeForwardSelection() - Constructor for class weka.attributeSelection.SubsetSizeForwardSelection
Constructor
SubspaceCluster - Class in weka.datagenerators.clusterers
A data generator that produces data points in hyperrectangular subspace clusters.
SubspaceCluster() - Constructor for class weka.datagenerators.clusterers.SubspaceCluster
initializes the generator, sets the number of clusters to 0, since user has to specify them explicitly
SubspaceClusterDefinition - Class in weka.datagenerators.clusterers
A single cluster for the SubspaceCluster datagenerator

Valid options are:

SubspaceClusterDefinition() - Constructor for class weka.datagenerators.clusterers.SubspaceClusterDefinition
initializes the cluster, without a parent cluster (necessary for GOE)
SubspaceClusterDefinition(ClusterGenerator) - Constructor for class weka.datagenerators.clusterers.SubspaceClusterDefinition
initializes the cluster with default values
subSpaceSizeTipText() - Method in class weka.classifiers.meta.RandomSubSpace
Returns the tip text for this property
substitute(String) - Method in class weka.core.Environment
Substitute a variable names for their values in the given string.
substract(AlgVector) - Method in class weka.core.AlgVector
Returns the difference of this vector minus another.
subsumes(Rule) - Method in class weka.associations.tertius.Rule
Test if this rule subsumes another rule.
subsumptionTipText() - Method in class weka.associations.Tertius
Returns the tip text for this property.
subtract(AprioriItemSet) - Method in class weka.associations.AprioriItemSet
Subtracts an item set from another one.
subtract(Distribution) - Method in class weka.classifiers.trees.j48.Distribution
Subtracts the given distribution from this one.
subtract(double, double) - Method in class weka.experiment.PairedStats
Removes an observed pair of values.
subtract(double[], double[]) - Method in class weka.experiment.PairedStats
Removes an array of observed pair of values.
subtract(double) - Method in class weka.experiment.Stats
Removes a value to the observed values (no checking is done that the value being removed was actually added).
subtract(double, double) - Method in class weka.experiment.Stats
Subtracts a value that has been seen n times from the observed values
subtreeRaisingTipText() - Method in class weka.classifiers.trees.J48
Returns the tip text for this property
subtreeRaisingTipText() - Method in class weka.classifiers.trees.J48graft
Returns the tip text for this property
subvector(int, int) - Method in class weka.core.matrix.DoubleVector
Returns a subvector.
subvector(IntVector) - Method in class weka.core.matrix.DoubleVector
Returns a subvector.
subvector(int, int) - Method in class weka.core.matrix.IntVector
Returns a subvector.
subvector(IntVector) - Method in class weka.core.matrix.IntVector
Returns a subvector as indexed by an IntVector.
sum() - Method in class weka.core.matrix.DoubleVector
Returns the sum of all elements in the vector.
sum(double[]) - Static method in class weka.core.Utils
Computes the sum of the elements of an array of doubles.
sum(int[]) - Static method in class weka.core.Utils
Computes the sum of the elements of an array of integers.
sum - Variable in class weka.experiment.Stats
The sum of values seen
sum2(int, int, int, boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix
Squared sum of a column or row in a matrix
sum2(boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix
Squared sum of columns or rows of a matrix
sum2() - Method in class weka.core.matrix.DoubleVector
Returns the squared sum of all elements in the vector.
sum2(DoubleVector) - Method in class weka.core.matrix.DoubleVector
Returns ||u-v||^2
Summarizable - Interface in weka.core
Interface to something that provides a short textual summary (as opposed to toString() which is usually a fairly complete description) of itself.
sumOfWeights() - Method in class weka.core.Instances
Computes the sum of all the instances' weights.
sumSq - Variable in class weka.experiment.Stats
The sum of values squared seen
supervisedEstimator() - Method in class weka.estimators.CheckEstimator
Checks whether the estimator is supervised.
SupervisedFilter - Interface in weka.filters
Interface for filters that make use of a class attribute.
support() - Method in class weka.associations.ItemSet
Outputs the support for an item set.
support() - Method in class weka.associations.LabeledItemSet
Outputs the support for an item set.
supportPoints(DoubleVector, int) - Method in class weka.classifiers.functions.pace.ChisqMixture
Contructs the set of support points for mixture estimation.
supportPoints(DoubleVector, int) - Method in class weka.classifiers.functions.pace.MixtureDistribution
Contructs the set of support points for mixture estimation.
supportPoints(DoubleVector, int) - Method in class weka.classifiers.functions.pace.NormalMixture
Contructs the set of support points for mixture estimation.
supports(Capabilities) - Method in class weka.core.Capabilities
Returns true if the currently set capabilities support at least all of the capabiliites of the given Capabilities object (checks only the enum!)
supportsCustomEditor() - Method in class weka.gui.CostMatrixEditor
Indicates whether the cost matrix can be edited in a GUI, which it can.
supportsCustomEditor() - Method in class weka.gui.FileEditor
Returns true because we do support a custom editor.
supportsCustomEditor() - Method in class weka.gui.GenericArrayEditor
Returns true because we do support a custom editor.
supportsCustomEditor() - Method in class weka.gui.GenericObjectEditor
Returns true because we do support a custom editor.
supportsCustomEditor() - Method in class weka.gui.SimpleDateFormatEditor
Indicates whether the date format can be edited in a GUI, which it can.
supportsMaybe(Capabilities) - Method in class weka.core.Capabilities
Returns true if the currently set capabilities support (or have a dependency) at least all of the capabilities of the given Capabilities object (checks only the enum!)
supportThreshold - Variable in class weka.classifiers.functions.pace.ChisqMixture
 
svd() - Method in class weka.core.matrix.Matrix
Singular Value Decomposition
SVMAttributeEval - Class in weka.attributeSelection
SVMAttributeEval :

Evaluates the worth of an attribute by using an SVM classifier.
SVMAttributeEval() - Constructor for class weka.attributeSelection.SVMAttributeEval
Constructor
SVMLightLoader - Class in weka.core.converters
Reads a source that is in svm light format.

For more information about svm light see:

http://svmlight.joachims.org/

SVMLightLoader() - Constructor for class weka.core.converters.SVMLightLoader
 
SVMLightSaver - Class in weka.core.converters
Writes to a destination that is in svm light format.

For more information about svm light see:

http://svmlight.joachims.org/

Valid options are:

SVMLightSaver() - Constructor for class weka.core.converters.SVMLightSaver
Constructor.
svmlightToArray(String) - Method in class weka.core.converters.SVMLightLoader
turns a svm light row into a double array with the class as the last entry.
SVMOutput(int, Instance) - Method in class weka.classifiers.functions.SMO.BinarySMO
Computes SVM output for given instance.
SVMOutput(int) - Method in class weka.classifiers.functions.supportVector.RegOptimizer
SVMOutput of an instance in the training set, m_data This uses the cache, unlike SVMOutput(Instance)
SVMOutput(Instance) - Method in class weka.classifiers.functions.supportVector.RegOptimizer
 
SVMOutput(int, Instance) - Method in class weka.classifiers.mi.MISMO.BinaryMISMO
Computes SVM output for given instance.
SVMTYPE_C_SVC - Static variable in class weka.classifiers.functions.LibSVM
SVM type C-SVC (classification)
SVMTYPE_EPSILON_SVR - Static variable in class weka.classifiers.functions.LibSVM
SVM type epsilon-SVR (regression)
SVMTYPE_L1LOSS_SVM_DUAL - Static variable in class weka.classifiers.functions.LibLINEAR
SVM solver type L1-loss support vector machines (dual)
SVMTYPE_L2_LR - Static variable in class weka.classifiers.functions.LibLINEAR
SVM solver type L2-regularized logistic regression
SVMTYPE_L2LOSS_SVM - Static variable in class weka.classifiers.functions.LibLINEAR
SVM solver type L2-loss support vector machines (primal)
SVMTYPE_L2LOSS_SVM_DUAL - Static variable in class weka.classifiers.functions.LibLINEAR
SVM solver type L2-loss support vector machines (dual)
SVMTYPE_MCSVM_CS - Static variable in class weka.classifiers.functions.LibLINEAR
SVM solver type multi-class support vector machines by Crammer and Singer
SVMTYPE_NU_SVC - Static variable in class weka.classifiers.functions.LibSVM
SVM type nu-SVC (classification)
SVMTYPE_NU_SVR - Static variable in class weka.classifiers.functions.LibSVM
SVM type nu-SVR (regression)
SVMTYPE_ONE_CLASS_SVM - Static variable in class weka.classifiers.functions.LibSVM
SVM type one-class SVM (classification)
SVMTypeTipText() - Method in class weka.classifiers.functions.LibLINEAR
Returns the tip text for this property
SVMTypeTipText() - Method in class weka.classifiers.functions.LibSVM
Returns the tip text for this property
swap(int, int) - Method in class weka.core.FastVector
Swaps two elements in the vector.
swap(int, int) - Method in class weka.core.Instances
Swaps two instances in the set.
swap(int, int) - Method in class weka.core.matrix.DoubleVector
Swaps the values stored at i and j
swap(int, int) - Method in class weka.core.matrix.IntVector
Swaps the values stored at i and j
SWAP(int, int, Stack<CoverTree.d_node>) - Method in class weka.core.neighboursearch.CoverTree
Swap two nodes in a cover set.
SwapValues - Class in weka.filters.unsupervised.attribute
Swaps two values of a nominal attribute.
SwapValues() - Constructor for class weka.filters.unsupervised.attribute.SwapValues
 
switchToAdvanced(Experiment) - Method in class weka.gui.experiment.SetupModePanel
Switches to the advanced setup mode.
switchToBars() - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
 
switchToLegend() - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
Remove the attibute panel and replace it with the legend panel
switchToSimple(Experiment) - Method in class weka.gui.experiment.SetupModePanel
Switches to the simple setup mode only if allowed to.
sym - Interface in weka.core.mathematicalexpression
CUP generated interface containing symbol constants.
sym - Interface in weka.filters.unsupervised.instance.subsetbyexpression
CUP generated interface containing symbol constants.
symmetricalUncertainty(double[][]) - Static method in class weka.core.ContingencyTables
Calculates the symmetrical uncertainty for base 2.
SymmetricalUncertAttributeEval - Class in weka.attributeSelection
SymmetricalUncertAttributeEval :

Evaluates the worth of an attribute by measuring the symmetrical uncertainty with respect to the class.
SymmetricalUncertAttributeEval() - Constructor for class weka.attributeSelection.SymmetricalUncertAttributeEval
Constructor
Sync(BayesNet) - Method in class weka.classifiers.bayes.net.BIFReader
synchronizes the node ordering of this Bayes network with those in the other network (if possible).
synopsis() - Method in class weka.core.Option
Returns the option's synopsis.
SysErrLog - Class in weka.gui
This Logger just sends messages to System.err.
SysErrLog() - Constructor for class weka.gui.SysErrLog
 
SystemInfo - Class in weka.core
This class prints some information about the system setup, like Java version, JVM settings etc.
SystemInfo() - Constructor for class weka.core.SystemInfo
initializes the object and reads the system information

T

TAB_INSTANCES - Static variable in class weka.gui.arffviewer.ArffPanel
the name of the tab for instances that were set directly
tabbedPane - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
tableChanged(TableModelEvent) - Method in class weka.gui.arffviewer.ArffTable
This fine grain notification tells listeners the exact range of cells, rows, or columns that changed.
tableChanged(TableModelEvent) - Method in class weka.gui.SortedTableModel
This fine grain notification tells listeners the exact range of cells, rows, or columns that changed.
TableEntry(int, double, double, double, KStarCache.TableEntry) - Constructor for class weka.classifiers.lazy.kstar.KStarCache.TableEntry
Constructor
tableExists(String) - Method in class weka.experiment.DatabaseUtils
Checks that a given table exists.
tableNameTipText() - Method in class weka.core.converters.DatabaseSaver
Returns the tip text for this property.
tabuListTipText() - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
 
tabuListTipText() - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
 
TabuSearch - Class in weka.classifiers.bayes.net.search.global
This Bayes Network learning algorithm uses tabu search for finding a well scoring Bayes network structure.
TabuSearch() - Constructor for class weka.classifiers.bayes.net.search.global.TabuSearch
 
TabuSearch - Class in weka.classifiers.bayes.net.search.local
This Bayes Network learning algorithm uses tabu search for finding a well scoring Bayes network structure.
TabuSearch() - Constructor for class weka.classifiers.bayes.net.search.local.TabuSearch
 
Tag - Class in weka.core
A Tag simply associates a numeric ID with a String description.
Tag() - Constructor for class weka.core.Tag
Creates a new default Tag
Tag(int, String) - Constructor for class weka.core.Tag
Creates a new Tag instance.
Tag(int, String, String) - Constructor for class weka.core.Tag
Creates a new Tag instance.
Tag(int, String, String, boolean) - Constructor for class weka.core.Tag
 
TAG_ATTRIBUTE - Static variable in class weka.core.xml.XMLInstances
the attribute element
TAG_ATTRIBUTES - Static variable in class weka.core.xml.XMLInstances
the attributes element
TAG_BODY - Static variable in class weka.core.xml.XMLInstances
the body element
TAG_DATASET - Static variable in class weka.core.xml.XMLInstances
the root element
TAG_HEADER - Static variable in class weka.core.xml.XMLInstances
the header element
TAG_INSTANCE - Static variable in class weka.core.xml.XMLInstances
the instance element
TAG_INSTANCES - Static variable in class weka.core.xml.XMLInstances
the data element
TAG_LABEL - Static variable in class weka.core.xml.XMLInstances
the label element
TAG_LABELS - Static variable in class weka.core.xml.XMLInstances
the labels element
TAG_METADATA - Static variable in class weka.core.xml.XMLInstances
the meta-data element
TAG_NOTES - Static variable in class weka.core.xml.XMLInstances
the notes element
TAG_OBJECT - Static variable in class weka.core.xml.XMLSerialization
the tag for an object
TAG_OPTION - Static variable in class weka.core.xml.XMLOptions
tag for a single option.
TAG_OPTIONS - Static variable in class weka.core.xml.XMLOptions
tag for a list of options.
TAG_PROPERTY - Static variable in class weka.core.xml.XMLInstances
the property element
TAG_VALUE - Static variable in class weka.core.xml.XMLInstances
the value element
TAGS_ALGORITHM - Static variable in class weka.filters.supervised.attribute.PLSFilter
the types of algorithm
TAGS_ALGORITHM - Static variable in class weka.filters.unsupervised.attribute.Wavelet
the types of algorithm
TAGS_ALGORITHMTYPE - Static variable in class weka.classifiers.mi.MILR
the types of algorithms
TAGS_ATTRIBUTETYPE - Static variable in class weka.filters.unsupervised.attribute.RemoveType
Tag allowing selection of attribute type to delete
TAGS_CLUSTERSUBTYPE - Static variable in class weka.datagenerators.clusterers.SubspaceCluster
the tags for the cluster types
TAGS_CLUSTERTYPE - Static variable in class weka.datagenerators.clusterers.SubspaceCluster
the tags for the cluster types
TAGS_CV_TYPE - Static variable in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
the score types
TAGS_DSTRS_TYPE - Static variable in class weka.filters.unsupervised.attribute.RandomProjection
The types of distributions that can be used for calculating the random matrix
TAGS_ESTIMATOR - Static variable in class weka.classifiers.functions.PaceRegression
estimator types
TAGS_EVAL - Static variable in class weka.classifiers.meta.ThresholdSelector
The evaluation modes
TAGS_EVALUATION - Static variable in class weka.classifiers.meta.GridSearch
evaluation
TAGS_EVALUATION - Static variable in class weka.classifiers.rules.DecisionTable
 
TAGS_FILTER - Static variable in class weka.classifiers.functions.GaussianProcesses
The filter to apply to the training data
TAGS_FILTER - Static variable in class weka.classifiers.functions.SMO
The filter to apply to the training data
TAGS_FILTER - Static variable in class weka.classifiers.functions.SMOreg
The filter to apply to the training data
TAGS_FILTER - Static variable in class weka.classifiers.mi.MDD
The filter to apply to the training data
TAGS_FILTER - Static variable in class weka.classifiers.mi.MIDD
The filter to apply to the training data
TAGS_FILTER - Static variable in class weka.classifiers.mi.MIEMDD
The filter to apply to the training data
TAGS_FILTER - Static variable in class weka.classifiers.mi.MIOptimalBall
The filter to apply to the training data
TAGS_FILTER - Static variable in class weka.classifiers.mi.MISMO
The filter to apply to the training data
TAGS_FILTER - Static variable in class weka.classifiers.mi.MISVM
The filter to apply to the training data
TAGS_FILTER - Static variable in class weka.filters.unsupervised.attribute.StringToWordVector
Specifies whether document's (instance's) word frequencies are to be normalized.
TAGS_FORMAT - Static variable in class weka.core.Debug.Clock
the output formats
TAGS_GUI - Static variable in class weka.gui.Main
GUI tags.
TAGS_HYPER_METHOD - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
 
TAGS_INPUTORDER - Static variable in class weka.datagenerators.clusterers.BIRCHCluster
the input order tags
TAGS_KERNELTYPE - Static variable in class weka.classifiers.functions.LibSVM
the different kernel types
TAGS_LINK_TYPE - Static variable in class weka.clusterers.HierarchicalClusterer
 
TAGS_MATRIX_SOURCE - Static variable in class weka.attributeSelection.CostSensitiveASEvaluation
Specify possible sources of the cost matrix
TAGS_MATRIX_SOURCE - Static variable in class weka.classifiers.meta.CostSensitiveClassifier
Specify possible sources of the cost matrix
TAGS_MATRIX_SOURCE - Static variable in class weka.classifiers.meta.MetaCost
Specify possible sources of the cost matrix
TAGS_MEASURE - Static variable in class weka.classifiers.meta.ThresholdSelector
the measure to use
TAGS_METHOD - Static variable in class weka.classifiers.meta.MultiClassClassifier
The error correction modes
TAGS_MISSING - Static variable in class weka.classifiers.lazy.KStar
Define possible missing value handling methods
TAGS_MODEL - Static variable in class weka.classifiers.trees.FT
possible model types.
TAGS_OPTIMIZE - Static variable in class weka.classifiers.meta.ThresholdSelector
How to determine which class value to optimize for
TAGS_PADDING - Static variable in class weka.filters.unsupervised.attribute.Wavelet
the types of padding
TAGS_PATTERN - Static variable in class weka.datagenerators.clusterers.BIRCHCluster
the pattern tags
TAGS_PREPROCESSING - Static variable in class weka.filters.supervised.attribute.PLSFilter
the types of preprocessing
TAGS_PRIOR - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
 
TAGS_PRUNETYPE - Static variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The pruning types
TAGS_PRUNING - Static variable in class weka.classifiers.functions.supportVector.StringKernel
Pruning methods
TAGS_PRUNING - Static variable in class weka.classifiers.trees.BFTree
pruning strategy
TAGS_RANGE - Static variable in class weka.classifiers.meta.ThresholdSelector
Type of correction applied to threshold range
TAGS_RULES - Static variable in class weka.classifiers.meta.Vote
combination rules
TAGS_SCORE_TYPE - Static variable in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
the score types
TAGS_SEARCH_METHOD - Static variable in class weka.attributeSelection.LinearForwardSelection
 
TAGS_SEARCHPATH - Static variable in class weka.classifiers.trees.ADTree
The search modes
TAGS_SELECTION - Static variable in class weka.associations.Apriori
Metric types.
TAGS_SELECTION - Static variable in class weka.associations.FPGrowth.AssociationRule
Tags for display in the GUI
TAGS_SELECTION - Static variable in class weka.attributeSelection.BestFirst
search directions
TAGS_SELECTION - Static variable in class weka.attributeSelection.RaceSearch
 
TAGS_SELECTION - Static variable in class weka.attributeSelection.ScatterSearchV1
 
TAGS_SELECTION - Static variable in class weka.classifiers.functions.LinearRegression
Attribute selection methods
TAGS_SELECTION - Static variable in class weka.classifiers.functions.SPegasos
Loss functions to choose from
TAGS_SVMTYPE - Static variable in class weka.classifiers.functions.LibLINEAR
SVM solver types
TAGS_SVMTYPE - Static variable in class weka.classifiers.functions.LibSVM
SVM types
TAGS_TESTMETHOD - Static variable in class weka.classifiers.mi.MIWrapper
the test methods
TAGS_TRANSFORMMETHOD - Static variable in class weka.classifiers.mi.SimpleMI
the transformation methods
TAGS_TRAVERSAL - Static variable in class weka.classifiers.meta.GridSearch
traversal
TAGS_TYPE - Static variable in class weka.attributeSelection.LinearForwardSelection
 
TAGS_TYPE - Static variable in class weka.attributeSelection.SubsetSizeForwardSelection
 
TAGS_TYPE - Static variable in class weka.filters.unsupervised.attribute.Add
the attribute type.
TAGS_WEIGHTING - Static variable in class weka.classifiers.lazy.IBk
possible instance weighting methods.
TAGS_WEIGHTMETHOD - Static variable in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
weight methods
takeStep(int, int, double) - Method in class weka.classifiers.functions.SMO.BinarySMO
Method solving for the Lagrange multipliers for two instances.
takeStep(int, int, double, double, double) - Method in class weka.classifiers.functions.supportVector.RegSMO
takeStep method from pseudocode.
takeStep(int, int, double, double, double) - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
takeStep method from Shevade et al.s paper.
takeStep(int, int, double) - Method in class weka.classifiers.mi.MISMO.BinaryMISMO
Method solving for the Lagrange multipliers for two instances.
TAN - Class in weka.classifiers.bayes.net.search.global
This Bayes Network learning algorithm determines the maximum weight spanning tree and returns a Naive Bayes network augmented with a tree.

For more information see:

N.
TAN() - Constructor for class weka.classifiers.bayes.net.search.global.TAN
 
TAN - Class in weka.classifiers.bayes.net.search.local
This Bayes Network learning algorithm determines the maximum weight spanning tree and returns a Naive Bayes network augmented with a tree.

For more information see:

N.
TAN() - Constructor for class weka.classifiers.bayes.net.search.local.TAN
 
TAN - Static variable in interface weka.core.mathematicalexpression.sym
 
TAN - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
target(double[], double[][], int, double[]) - Method in class weka.classifiers.mi.MINND
Compute the target function to minimize in gradient descent The formula is:
1/2*sum[i=1..p](f(X, Xi)-var(Y, Yi))^2

where p is the number of exemplars and Y is the class label.

TargetMetaInfo - Class in weka.core.pmml
Class to encapsulate information about a Target.
TargetMetaInfo(Element) - Constructor for class weka.core.pmml.TargetMetaInfo
Constructor.
Task - Interface in weka.experiment
Interface to something that can be remotely executed as a task.
taskFinished() - Method in class weka.gui.LogPanel
Record a task ending
taskFinished() - Method in interface weka.gui.TaskLogger
Tells the task logger that a task has completed
taskFinished() - Method in class weka.gui.WekaTaskMonitor
Tells the panel that a task has completed
TaskLogger - Interface in weka.gui
Interface for objects that display log and display information on running tasks.
taskStarted() - Method in class weka.gui.LogPanel
Record the starting of a new task
taskStarted() - Method in interface weka.gui.TaskLogger
Tells the task logger that a new task has been started
taskStarted() - Method in class weka.gui.WekaTaskMonitor
Tells the panel that a new task has been started
TaskStatusInfo - Class in weka.experiment
A class holding information for tasks being executed on RemoteEngines.
TaskStatusInfo() - Constructor for class weka.experiment.TaskStatusInfo
 
tauVal(double[][]) - Static method in class weka.core.ContingencyTables
Computes Goodman and Kruskal's tau-value for a contingency table.
TechnicalInformation - Class in weka.core
Used for paper references in the Javadoc and for BibTex generation.
TechnicalInformation(TechnicalInformation.Type) - Constructor for class weka.core.TechnicalInformation
Initializes the information with the given type
TechnicalInformation(TechnicalInformation.Type, String) - Constructor for class weka.core.TechnicalInformation
Initializes the information with the given type
TechnicalInformation.Field - Enum in weka.core
the possible fields
TechnicalInformation.Type - Enum in weka.core
the different types of information
TechnicalInformationHandler - Interface in weka.core
For classes that are based on some kind of publications.
TechnicalInformationHandlerJavadoc - Class in weka.core
Generates Javadoc comments from the TechnicalInformationHandler's data.
TechnicalInformationHandlerJavadoc() - Constructor for class weka.core.TechnicalInformationHandlerJavadoc
default constructor
Tee - Class in weka.core
This class pipelines print/println's to several PrintStreams.
Tee() - Constructor for class weka.core.Tee
initializes the object, with a default printstream.
Tee(PrintStream) - Constructor for class weka.core.Tee
initializes the object with the given default printstream, e.g., System.out.
tempCnt - Variable in class weka.classifiers.lazy.LBR
 
templateString() - Method in class weka.experiment.PairedTTester.Resultset
Returns a string descriptive of the resultset key column values for this resultset
templateString(Instance) - Method in class weka.experiment.PairedTTester
Returns a string descriptive of the key column values for the "datasets
TempNode() - Constructor for class weka.core.neighboursearch.balltrees.BottomUpConstructor.TempNode
 
TempNode() - Constructor for class weka.core.neighboursearch.balltrees.MiddleOutConstructor.TempNode
 
tempSubInstances - Variable in class weka.classifiers.lazy.LBR
index of instances and attributes for the given dataset
Tertius - Class in weka.associations
Finds rules according to confirmation measure (Tertius-type algorithm).

For more information see:

P.
Tertius() - Constructor for class weka.associations.Tertius
Constructor that sets the options to the default values.
test(Attribute) - Method in class weka.core.Capabilities
Test the given attribute, whether it can be processed by the handler, given its capabilities.
test(Attribute, boolean) - Method in class weka.core.Capabilities
Test the given attribute, whether it can be processed by the handler, given its capabilities.
test(Instances) - Method in class weka.core.Capabilities
Tests the given data, whether it can be processed by the handler, given its capabilities.
test(Instances, int, int) - Method in class weka.core.Capabilities
Tests a certain range of attributes of the given data, whether it can be processed by the handler, given its capabilities.
test(String[]) - Static method in class weka.core.Instances
Method for testing this class.
Test - Class in weka.datagenerators
Class to represent a test.
Test(int, double, Instances) - Constructor for class weka.datagenerators.Test
Constructor
Test(int, double, Instances, boolean) - Constructor for class weka.datagenerators.Test
Constructor
TEST - Static variable in class weka.gui.beans.BatchClustererEvent
 
testCapabilities(Instances, int) - Method in class weka.estimators.Estimator
Test if the estimator can handle the data.
testCV(int, int) - Method in class weka.core.Instances
Creates the test set for one fold of a cross-validation on the dataset.
Tester - Interface in weka.experiment
Interface for different kinds of Testers in the Experimenter.
testInputFormat(Instances) - Method in class weka.filters.Filter
tests the data whether the filter can actually handle it
testInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AddCluster
tests the data whether the filter can actually handle it
testInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.ClusterMembership
tests the data whether the filter can actually handle it
testInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
tests the data whether the filter can actually handle it.
TestInstances - Class in weka.core
Generates artificial datasets for testing.
TestInstances() - Constructor for class weka.core.TestInstances
the default constructor
TESTMETHOD_ARITHMETIC - Static variable in class weka.classifiers.mi.MIWrapper
arithmetic average
TESTMETHOD_GEOMETRIC - Static variable in class weka.classifiers.mi.MIWrapper
geometric average
TESTMETHOD_MAXPROB - Static variable in class weka.classifiers.mi.MIWrapper
max probability of positive bag
TestSetEvent - Class in weka.gui.beans
Event encapsulating a test set
TestSetEvent(Object, Instances) - Constructor for class weka.gui.beans.TestSetEvent
Creates a new TestSetEvent
TestSetEvent(Object, Instances, int, int) - Constructor for class weka.gui.beans.TestSetEvent
Creates a new TestSetEvent
TestSetEvent(Object, Instances, int, int, int, int) - Constructor for class weka.gui.beans.TestSetEvent
Creates a new TestSetEvent
TestSetListener - Interface in weka.gui.beans
Interface to something that can accpet test set events
TestSetMaker - Class in weka.gui.beans
Bean that accepts data sets and produces test sets
TestSetMaker() - Constructor for class weka.gui.beans.TestSetMaker
 
TestSetMakerBeanInfo - Class in weka.gui.beans
Bean info class for the test set maker bean.
TestSetMakerBeanInfo() - Constructor for class weka.gui.beans.TestSetMakerBeanInfo
 
TestSetProducer - Interface in weka.gui.beans
Interface to something that can produce test sets
testsPerClassType(int, boolean, boolean) - Method in class weka.associations.CheckAssociator
Run a battery of tests for a given class attribute type
testsPerClassType(int, boolean, boolean) - Method in class weka.attributeSelection.CheckAttributeSelection
Run a battery of tests for a given class attribute type
testsPerClassType(int, boolean, boolean, boolean) - Method in class weka.classifiers.CheckClassifier
Run a battery of tests for a given class attribute type
testsPerClassType(int, boolean, boolean) - Method in class weka.classifiers.functions.supportVector.CheckKernel
Run a battery of tests for a given class attribute type
testsPerClassType(int, CheckEstimator.EstTypes) - Method in class weka.estimators.CheckEstimator
Run a battery of tests for a given class attribute type
testsWithoutClass(boolean, boolean) - Method in class weka.associations.CheckAssociator
Run a battery of tests without a class
testToString() - Method in class weka.classifiers.CheckClassifier
Checks whether the scheme's toString() method works even though the classifies hasn't been built yet.
testType() - Method in class weka.classifiers.trees.j48.GraftSplit
returns the test type
testWithFail(Attribute) - Method in class weka.core.Capabilities
tests the given attribute by calling the test(Attribute,boolean) method and throws an exception if the test fails.
testWithFail(Attribute, boolean) - Method in class weka.core.Capabilities
tests the given attribute by calling the test(Attribute,boolean) method and throws an exception if the test fails.
testWithFail(Instances, int, int) - Method in class weka.core.Capabilities
tests the given data by calling the test(Instances,int,int) method and throws an exception if the test fails.
testWithFail(Instances) - Method in class weka.core.Capabilities
tests the given data by calling the test(Instances) method and throws an exception if the test fails.
testWithTestValues(Estimator, Vector) - Method in class weka.estimators.CheckEstimator
Test with test values.
testWRTZeroR(Classifier, Evaluation, Instances, Instances) - Method in class weka.classifiers.CheckClassifier
Determine whether the scheme performs worse than ZeroR during testing
TEXT - Static variable in class weka.experiment.DatabaseUtils
Type mapping for TEXT used for reading, e.g., text blobs.
TextDirectoryLoader - Class in weka.core.converters
Loads all text files in a directory and uses the subdirectory names as class labels.
TextDirectoryLoader() - Constructor for class weka.core.converters.TextDirectoryLoader
default constructor
TextEvent - Class in weka.gui.beans
Event that encapsulates some textual information
TextEvent(Object, String, String) - Constructor for class weka.gui.beans.TextEvent
Creates a new TextEvent instance.
TextListener - Interface in weka.gui.beans
Interface to something that can process a TextEvent
TextViewer - Class in weka.gui.beans
Bean that collects and displays pieces of text
TextViewer() - Constructor for class weka.gui.beans.TextViewer
 
TextViewerBeanInfo - Class in weka.gui.beans
Bean info class for the text viewer
TextViewerBeanInfo() - Constructor for class weka.gui.beans.TextViewerBeanInfo
 
TFD(int) - Method in class weka.clusterers.XMeans
Tests on debug status.
TFTransformTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Returns the tip text for this property.
theoryDL(int) - Method in class weka.classifiers.rules.RuleStats
The description length of the theory for a given rule.
Threshold - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Threshold for binary classification of probabilisitic estimate
THRESHOLD_NAME - Static variable in class weka.classifiers.evaluation.CostCurve
attribute name: Threshold
THRESHOLD_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
attribute name: Threshold
ThresholdCurve - Class in weka.classifiers.evaluation
Generates points illustrating prediction tradeoffs that can be obtained by varying the threshold value between classes.
ThresholdCurve() - Constructor for class weka.classifiers.evaluation.ThresholdCurve
 
ThresholdDataEvent - Class in weka.gui.beans
Event encapsulating classifier performance data based on varying a threshold over the classifier's predicted probabilities
ThresholdDataEvent(Object, PlotData2D) - Constructor for class weka.gui.beans.ThresholdDataEvent
 
ThresholdDataEvent(Object, PlotData2D, Attribute) - Constructor for class weka.gui.beans.ThresholdDataEvent
 
ThresholdDataListener - Interface in weka.gui.beans
Interface to something that can accept ThresholdDataEvents
ThresholdSelector - Class in weka.classifiers.meta
A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier.
ThresholdSelector() - Constructor for class weka.classifiers.meta.ThresholdSelector
Constructor.
thresholdTipText() - Method in class weka.attributeSelection.GreedyStepwise
Returns the tip text for this property
thresholdTipText() - Method in class weka.attributeSelection.RaceSearch
Returns the tip text for this property
thresholdTipText() - Method in class weka.attributeSelection.Ranker
Returns the tip text for this property
thresholdTipText() - Method in class weka.attributeSelection.ScatterSearchV1
Returns the tip text for this property
thresholdTipText() - Method in class weka.attributeSelection.WrapperSubsetEval
Returns the tip text for this property
thresholdTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns the tip text for this property
thresholdTipText() - Method in class weka.classifiers.functions.PaceRegression
Returns the tip text for this property
thresholdTipText() - Method in class weka.classifiers.functions.Winnow
Returns the tip text for this property
thresholdTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Returns the tip text for this property
ThresholdVisualizePanel - Class in weka.gui.visualize
This panel is a VisualizePanel, with the added ablility to display the area under the ROC curve if an ROC curve is chosen.
ThresholdVisualizePanel() - Constructor for class weka.gui.visualize.ThresholdVisualizePanel
default constructor
TIE_STRING - Variable in class weka.experiment.ResultMatrix
tie string
TIME - Static variable in class weka.experiment.DatabaseUtils
Type mapping for TIME used for reading TIME columns.
times(int, int, int, PaceMatrix, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Multiplication between a row (or part of a row) of the first matrix and a column (or part or a column) of the second matrix.
TIMES - Static variable in interface weka.core.mathematicalexpression.sym
 
times(double) - Method in class weka.core.matrix.DoubleVector
Multiplies a scalar
times(DoubleVector) - Method in class weka.core.matrix.DoubleVector
Multiplies another DoubleVector element by element
times(double) - Method in class weka.core.matrix.Matrix
Multiply a matrix by a scalar, C = s*A
times(Matrix) - Method in class weka.core.matrix.Matrix
Linear algebraic matrix multiplication, A * B
TIMES - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
timesEquals(double) - Method in class weka.classifiers.functions.pace.DiscreteFunction
All function values are multiplied by a double
timesEquals(double) - Method in class weka.core.matrix.DoubleVector
Multiply a vector by a scalar in place, u = s * u
timesEquals(DoubleVector) - Method in class weka.core.matrix.DoubleVector
Multiplies another DoubleVector element by element in place
timesEquals(double) - Method in class weka.core.matrix.Matrix
Multiply a matrix by a scalar in place, A = s*A
TimeSeriesDelta - Class in weka.filters.unsupervised.attribute
An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the difference between the current value and the equivalent attribute attribute value of some previous (or future) instance.
TimeSeriesDelta() - Constructor for class weka.filters.unsupervised.attribute.TimeSeriesDelta
 
TimeSeriesTranslate - Class in weka.filters.unsupervised.attribute
An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the equivalent attribute values of some previous (or future) instance.
TimeSeriesTranslate() - Constructor for class weka.filters.unsupervised.attribute.TimeSeriesTranslate
 
Timestamp() - Constructor for class weka.core.Debug.Timestamp
creates a timestamp with the current date and time and the default format.
Timestamp(String) - Constructor for class weka.core.Debug.Timestamp
creates a timestamp with the current date and time and the specified format.
Timestamp(Date) - Constructor for class weka.core.Debug.Timestamp
creates a timestamp with the given date and the default format.
Timestamp(Date, String) - Constructor for class weka.core.Debug.Timestamp
creates a timestamp with the given date and format.
TIMESTAMP_FIELD_NAME - Static variable in class weka.experiment.CrossValidationResultProducer
The name of the result field containing the timestamp
TIMESTAMP_FIELD_NAME - Static variable in class weka.experiment.RandomSplitResultProducer
The name of the result field containing the timestamp
TO_BE_RUN - Static variable in class weka.experiment.TaskStatusInfo
 
toArray() - Method in class weka.core.FastVector
Returns all the elements of this vector as an array
toArray() - Method in class weka.core.Trie
Returns an array containing all of the elements in this collection.
toArray(T[]) - Method in class weka.core.Trie
Returns an array containing all of the elements in this collection; the runtime type of the returned array is that of the specified array.
toArray() - Method in class weka.core.xml.XMLOptions
returns the current DOM document as string array.
toArray() - Method in class weka.experiment.ResultMatrix
returns a 2-dimensional array with the prepared data.
toArray() - Method in class weka.gui.CheckBoxList.CheckBoxListModel
Returns an array containing all of the elements in this list in the correct order.
toBibTex() - Method in class weka.core.TechnicalInformation
Returns a BibTex string representing this technical information.
toClassDetailsString() - Method in class weka.classifiers.Evaluation
Generates a breakdown of the accuracy for each class (with default title), incorporating various information-retrieval statistics, such as true/false positive rate, precision/recall/F-Measure.
toClassDetailsString(String) - Method in class weka.classifiers.Evaluation
Generates a breakdown of the accuracy for each class, incorporating various information-retrieval statistics, such as true/false positive rate, precision/recall/F-Measure.
toCommandLine(Element) - Method in class weka.core.xml.XMLOptions
converts the given node into a command line representation and returns it.
toCommandLine() - Method in class weka.core.xml.XMLOptions
returns the given DOM document as command line.
toCumulativeMarginDistributionString() - Method in class weka.classifiers.Evaluation
Output the cumulative margin distribution as a string suitable for input for gnuplot or similar package.
toDoubleArray() - Method in class weka.core.BinarySparseInstance
Returns the values of each attribute as an array of doubles.
toDoubleArray() - Method in class weka.core.Instance
Returns the values of each attribute as an array of doubles.
toDoubleArray() - Method in class weka.core.SparseInstance
Returns the values of each attribute as an array of doubles.
toGnuplot(int) - Method in class weka.classifiers.meta.GridSearch.Performance
returns a Gnuplot string of this performance object
toGnuplot() - Method in class weka.classifiers.meta.GridSearch.PerformanceTable
returns a string containing a gnuplot script+data file
toGraph() - Method in class weka.classifiers.trees.RandomTree
Outputs the decision tree as a graph
toGraph(StringBuffer, int) - Method in class weka.classifiers.trees.RandomTree
Outputs one node for graph.
toGraph(StringBuffer, int, RandomTree) - Method in class weka.classifiers.trees.RandomTree
Outputs one node for graph.
toGraph(StringBuffer, int, REPTree.Tree) - Method in class weka.classifiers.trees.REPTree.Tree
Outputs one node for graph.
toHTML(String) - Method in class weka.core.Javadoc
converts the given String into HTML, i.e., replacing some char entities with HTML entities.
toInstances(Instances, Matrix, Matrix) - Method in class weka.filters.supervised.attribute.PLSFilter
returns the X and Y matrix again as Instances object, based on the given header (must have a class attribute set).
tokenize(String) - Method in class weka.core.tokenizers.AlphabeticTokenizer
Sets the string to tokenize.
tokenize(String) - Method in class weka.core.tokenizers.NGramTokenizer
Sets the string to tokenize.
tokenize(String) - Method in class weka.core.tokenizers.Tokenizer
Sets the string to tokenize.
tokenize(Tokenizer, String[]) - Static method in class weka.core.tokenizers.Tokenizer
initializes the given tokenizer with the given options and runs the tokenizer over all the remaining strings in the options array.
tokenize(String) - Method in class weka.core.tokenizers.WordTokenizer
Sets the string to tokenize.
tokenize(String) - Method in class weka.gui.HierarchyPropertyParser
Tokenize the given string based on the seperator and put the tokens into an array of strings
Tokenizer - Class in weka.core.tokenizers
A superclass for all tokenizer algorithms.
Tokenizer() - Constructor for class weka.core.tokenizers.Tokenizer
 
tokenizerTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Returns the tip text for this property.
Tolerance - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Tolerance criteria for the stopping criterion.
toleranceParameterTipText() - Method in class weka.attributeSelection.SVMAttributeEval
Returns a tip text for this property suitable for display in the GUI
toleranceParameterTipText() - Method in class weka.classifiers.functions.SMO
Returns the tip text for this property
toleranceParameterTipText() - Method in class weka.classifiers.mi.MISMO
Returns the tip text for this property
toleranceTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns the tip text for this property
toleranceTipText() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
Returns the tip text for this property
toMatlab() - Method in class weka.classifiers.CostMatrix
converts the Matrix into a single line Matlab string: matrix is enclosed by parentheses, rows are separated by semicolon and single cells by blanks, e.g., [1 2; 3 4].
toMatlab() - Method in class weka.core.matrix.Matrix
converts the Matrix into a single line Matlab string: matrix is enclosed by parentheses, rows are separated by semicolon and single cells by blanks, e.g., [1 2; 3 4].
toMatlab() - Method in class weka.core.Matrix
Deprecated.
converts the Matrix into a single line Matlab string: matrix is enclosed by parentheses, rows are separated by semicolon and single cells by blanks, e.g., [1 2; 3 4].
toMatrixString() - Method in class weka.classifiers.Evaluation
Calls toMatrixString() with a default title.
toMatrixString(String) - Method in class weka.classifiers.Evaluation
Outputs the performance statistics as a classification confusion matrix.
toMegaByte(long) - Static method in class weka.core.Memory
returns the amount of bytes as MB
toNominalString(Instances) - Method in class weka.associations.gsp.Element
Returns a String representation of an Element where the numeric value of each event/item is represented by its respective nominal value.
toNominalString(Instances) - Method in class weka.associations.gsp.Sequence
Returns a String representation of a Sequences where the numeric value of each event/item is represented by its respective nominal value.
toOptionList(Tag[]) - Static method in class weka.core.Tag
returns a list that can be used in the listOption methods to list all the available ID strings, e.g.: <0|1|2> or <what|ever>
toOptionSynopsis(Tag[]) - Static method in class weka.core.Tag
returns a string that can be used in the listOption methods to list all the available options, i.e., "\t\tID = Text\n" for each option
toOutput() - Method in class weka.gui.visualize.JComponentWriter
saves the current component to the currently set file.
toOutput(JComponentWriter, JComponent, File) - Static method in class weka.gui.visualize.JComponentWriter
outputs the given component with the given writer in the specified file
toOutput(JComponentWriter, JComponent, File, int, int) - Static method in class weka.gui.visualize.JComponentWriter
outputs the given component with the given writer in the specified file.
top - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
TopDownConstructor - Class in weka.core.neighboursearch.balltrees
The class implementing the TopDown construction method of ball trees.
TopDownConstructor() - Constructor for class weka.core.neighboursearch.balltrees.TopDownConstructor
Creates a new instance of TopDownConstructor.
topOfTree() - Method in class weka.classifiers.trees.m5.Rule
Returns the top of the tree.
toPrologString() - Method in class weka.datagenerators.Test
Returns the test represented by a string in Prolog notation.
toResultsString() - Method in class weka.attributeSelection.AttributeSelection
get a description of the attribute selection
toSource(String) - Method in class weka.classifiers.meta.AdaBoostM1
Returns the boosted model as Java source code.
toSource(String) - Method in class weka.classifiers.meta.LogitBoost
Returns the boosted model as Java source code.
toSource(String) - Method in class weka.classifiers.rules.OneR
Returns a string that describes the classifier as source.
toSource(String) - Method in class weka.classifiers.rules.ZeroR
Returns a string that describes the classifier as source.
toSource(String) - Method in interface weka.classifiers.Sourcable
Returns a string that describes the classifier as source.
toSource(String) - Method in class weka.classifiers.trees.DecisionStump
Returns the decision tree as Java source code.
toSource(int, StringBuffer) - Method in class weka.classifiers.trees.Id3
Adds this tree recursively to the buffer.
toSource(String) - Method in class weka.classifiers.trees.Id3
Returns a string that describes the classifier as source.
toSource(String) - Method in class weka.classifiers.trees.j48.ClassifierTree
Returns source code for the tree as an if-then statement.
toSource(String) - Method in class weka.classifiers.trees.J48
Returns tree as an if-then statement.
toSource(String) - Method in class weka.classifiers.trees.J48graft
Returns tree as an if-then statement.
toSource(String) - Method in class weka.classifiers.trees.REPTree
Returns the tree as if-then statements.
toSource(String, REPTree.Tree) - Method in class weka.classifiers.trees.REPTree.Tree
Returns source code for the tree as if-then statements.
toSource(String) - Method in class weka.core.Capabilities
turns the capabilities object into source code.
toSource(String, int) - Method in class weka.core.Capabilities
turns the capabilities object into source code.
toSource(String, Instances) - Method in class weka.filters.AllFilter
Returns a string that describes the filter as source.
toSource(String, Instances) - Method in interface weka.filters.Sourcable
Returns a string that describes the filter as source.
toSource(String, Instances) - Method in class weka.filters.unsupervised.attribute.Center
Returns a string that describes the filter as source.
toSource(String, Instances) - Method in class weka.filters.unsupervised.attribute.Normalize
Returns a string that describes the filter as source.
toSource(String, Instances) - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues
Returns a string that describes the filter as source.
toSource(String, Instances) - Method in class weka.filters.unsupervised.attribute.Standardize
Returns a string that describes the filter as source.
toString() - Method in class weka.associations.Apriori
Outputs the size of all the generated sets of itemsets and the rules.
toString(Instances) - Method in class weka.associations.AprioriItemSet
Returns the contents of an item set as a string.
toString() - Method in class weka.associations.AssociatorEvaluation
returns the current result
toString() - Method in class weka.associations.FilteredAssociator
Output a representation of this associator
toString() - Method in enum weka.associations.FPGrowth.AssociationRule.METRIC_TYPE
 
toString() - Method in class weka.associations.FPGrowth.AssociationRule
Get a textual description of this rule.
toString() - Method in class weka.associations.FPGrowth.BinaryItem
A string representation of this item.
toString(boolean) - Method in class weka.associations.FPGrowth.BinaryItem
A string representation of this item.
toString(int) - Method in class weka.associations.FPGrowth.FPTreeNode
Return a textual description of this node for a given recursion level.
toString(String, int) - Method in class weka.associations.FPGrowth.FPTreeNode
Return a textual description of this node for a given recursion level.
toString() - Method in class weka.associations.FPGrowth.FrequentBinaryItemSet
Get a textual description of this item set.
toString(int) - Method in class weka.associations.FPGrowth.FrequentItemSets
Get a textual description of this list of item sets.
toString() - Method in class weka.associations.FPGrowth
Output the association rules.
toString() - Method in class weka.associations.GeneralizedSequentialPatterns
Returns a String containing the result information of the algorithm.
toString() - Method in class weka.associations.gsp.Element
Returns a String representation of an Element.
toString() - Method in class weka.associations.gsp.Sequence
Returns a String representation of a Sequence.
toString(Instances) - Method in class weka.associations.ItemSet
Returns the contents of an item set as a string.
toString() - Method in class weka.associations.PredictiveApriori
Outputs the association rules.
toString() - Method in class weka.associations.tertius.AttributeValueLiteral
 
toString() - Method in class weka.associations.tertius.Body
Gives a String representation of this set of literals as a conjunction.
toString() - Method in class weka.associations.tertius.Head
Gives a String representation of this set of literals as a disjunction.
toString() - Method in class weka.associations.tertius.Literal
 
toString() - Method in class weka.associations.tertius.LiteralSet
Gives a String representation for this set of literals.
toString() - Method in class weka.associations.tertius.Predicate
 
toString() - Method in class weka.associations.tertius.Rule
Retrun a String for this rule.
toString() - Method in class weka.associations.tertius.SimpleLinkedList
 
toString() - Method in class weka.associations.Tertius
Outputs the best rules found with their confirmation value and number of counter-instances.
toString() - Method in class weka.attributeSelection.BestFirst.Link2
 
toString() - Method in class weka.attributeSelection.BestFirst
returns a description of the search as a String
toString() - Method in class weka.attributeSelection.CfsSubsetEval
returns a string describing CFS
toString() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
Describe the attribute evaluator
toString() - Method in class weka.attributeSelection.ClassifierSubsetEval
Returns a string describing classifierSubsetEval
toString(Instances, int) - Method in class weka.attributeSelection.ConsistencySubsetEval.hashKey
Convert a hash entry to a string
toString() - Method in class weka.attributeSelection.ConsistencySubsetEval
returns a description of the evaluator
toString() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
Output a representation of this evaluator
toString() - Method in class weka.attributeSelection.ExhaustiveSearch
prints a description of the search
toString() - Method in class weka.attributeSelection.FilteredAttributeEval
Describe the attribute evaluator
toString() - Method in class weka.attributeSelection.FilteredSubsetEval
Describe the attribute evaluator
toString() - Method in class weka.attributeSelection.GainRatioAttributeEval
Return a description of the evaluator
toString() - Method in class weka.attributeSelection.GeneticSearch
returns a description of the search
toString() - Method in class weka.attributeSelection.GreedyStepwise
returns a description of the search.
toString() - Method in class weka.attributeSelection.InfoGainAttributeEval
Describe the attribute evaluator
toString() - Method in class weka.attributeSelection.LatentSemanticAnalysis
Returns a description of this attribute transformer
toString() - Method in class weka.attributeSelection.LFSMethods.Link2
 
toString() - Method in class weka.attributeSelection.LinearForwardSelection
returns a description of the search as a String
toString() - Method in class weka.attributeSelection.OneRAttributeEval
Return a description of the evaluator
toString() - Method in class weka.attributeSelection.PrincipalComponents
Returns a description of this attribute transformer
toString() - Method in class weka.attributeSelection.RaceSearch
Returns a string represenation
toString() - Method in class weka.attributeSelection.RandomSearch
prints a description of the search
toString() - Method in class weka.attributeSelection.Ranker
returns a description of the search as a String
toString() - Method in class weka.attributeSelection.RankSearch
returns a description of the search as a String
toString() - Method in class weka.attributeSelection.ReliefFAttributeEval
Return a description of the ReliefF attribute evaluator.
toString() - Method in class weka.attributeSelection.ScatterSearchV1
returns a description of the search.
toString() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
returns a description of the search as a String
toString() - Method in class weka.attributeSelection.SVMAttributeEval
Return a description of the evaluator
toString() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Return a description of the evaluator
toString() - Method in class weka.attributeSelection.WrapperSubsetEval
Returns a string describing the wrapper
toString() - Method in class weka.classifiers.bayes.AODE
Returns a description of the classifier.
toString() - Method in class weka.classifiers.bayes.AODEsr
Returns a description of the classifier.
toString() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Outputs the linear regression model as a string.
toString() - Method in class weka.classifiers.bayes.BayesNet
Returns a description of the classifier.
toString() - Method in class weka.classifiers.bayes.ComplementNaiveBayes
Prints out the internal model built by the classifier.
toString() - Method in class weka.classifiers.bayes.DMNBtext.DNBBinary
Returns a string representation of the classifier.
toString() - Method in class weka.classifiers.bayes.DMNBtext
Returns a string representation of the classifier.
toString() - Method in class weka.classifiers.bayes.HNB
returns a string representation of the classifier
toString() - Method in class weka.classifiers.bayes.NaiveBayes
Returns a description of the classifier.
toString() - Method in class weka.classifiers.bayes.NaiveBayesMultinomial
Returns a string representation of the classifier.
toString() - Method in class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable
Returns a string representation of the classifier.
toString() - Method in class weka.classifiers.bayes.NaiveBayesSimple
Returns a description of the classifier.
toString() - Method in class weka.classifiers.bayes.net.BayesNetGenerator
Returns either the net (if BIF format) or the generated instances
toString() - Method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes
Display a representation of this estimator
toString() - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode
 
toString() - Method in class weka.classifiers.bayes.net.MarginCalculator
 
toString() - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
a string representation of the algorithm
toString() - Method in class weka.classifiers.bayes.WAODE
returns a string representation of the classifier
toString() - Method in class weka.classifiers.BVDecompose
Returns description of the bias-variance decomposition results.
toString() - Method in class weka.classifiers.BVDecomposeSegCVSub
Returns description of the bias-variance decomposition results.
toString() - Method in class weka.classifiers.CostMatrix
Converts a matrix to a string.
toString() - Method in class weka.classifiers.evaluation.ConfusionMatrix
Calls toString() with a default title.
toString(String) - Method in class weka.classifiers.evaluation.ConfusionMatrix
Outputs the performance statistics as a classification confusion matrix.
toString() - Method in class weka.classifiers.evaluation.NominalPrediction
Gets a human readable representation of this prediction.
toString() - Method in class weka.classifiers.evaluation.NumericPrediction
Gets a human readable representation of this prediction.
toString() - Method in class weka.classifiers.evaluation.TwoClassStats
Returns a string containing the various performance measures for the current object
toString() - Method in class weka.classifiers.functions.GaussianProcesses
Prints out the classifier.
toString() - Method in class weka.classifiers.functions.IsotonicRegression
Returns a description of this classifier as a string
toString() - Method in class weka.classifiers.functions.LeastMedSq
Returns a string representing the best LinearRegression classifier found.
toString() - Method in class weka.classifiers.functions.LibLINEAR
returns a string representation
toString() - Method in class weka.classifiers.functions.LibSVM
returns a string representation
toString() - Method in class weka.classifiers.functions.LinearRegression
Outputs the linear regression model as a string.
toString() - Method in class weka.classifiers.functions.Logistic
Gets a string describing the classifier.
toString() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
toString() - Method in class weka.classifiers.functions.pace.ChisqMixture
Converts to a string
toString() - Method in class weka.classifiers.functions.pace.DiscreteFunction
Converts the discrete function to string.
toString() - Method in class weka.classifiers.functions.pace.MixtureDistribution
Converts to a string
toString() - Method in class weka.classifiers.functions.pace.NormalMixture
Converts to a string
toString() - Method in class weka.classifiers.functions.pace.PaceMatrix
Converts matrix to string
toString(int, boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix
Converts matrix to string
toString() - Method in class weka.classifiers.functions.PaceRegression
Outputs the linear regression model as a string.
toString() - Method in class weka.classifiers.functions.PLSClassifier
returns a string representation of the classifier
toString() - Method in class weka.classifiers.functions.RBFNetwork
Returns a description of this classifier as a String
toString() - Method in class weka.classifiers.functions.SimpleLinearRegression
Returns a description of this classifier as a string
toString() - Method in class weka.classifiers.functions.SimpleLogistic
Returns a description of the logistic model (attributes/coefficients).
toString() - Method in class weka.classifiers.functions.SMO.BinarySMO
Prints out the classifier.
toString() - Method in class weka.classifiers.functions.SMO
Prints out the classifier.
toString() - Method in class weka.classifiers.functions.SMOreg
Prints out the classifier.
toString() - Method in class weka.classifiers.functions.SPegasos
Prints out the classifier.
toString() - Method in class weka.classifiers.functions.supportVector.KernelEvaluation
returns the current result
toString() - Method in class weka.classifiers.functions.supportVector.NormalizedPolyKernel
returns a string representation for the Kernel
toString() - Method in class weka.classifiers.functions.supportVector.PolyKernel
returns a string representation for the Kernel
toString() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
returns a string representation for the Kernel
toString() - Method in class weka.classifiers.functions.supportVector.Puk
returns a string representation for the Kernel
toString() - Method in class weka.classifiers.functions.supportVector.RBFKernel
returns a string representation for the Kernel
toString() - Method in class weka.classifiers.functions.supportVector.RegOptimizer
Prints out the classifier.
toString() - Method in class weka.classifiers.functions.VotedPerceptron
Returns textual description of classifier.
toString() - Method in class weka.classifiers.functions.Winnow
Returns textual description of the classifier.
toString() - Method in class weka.classifiers.lazy.IB1
Returns a description of this classifier.
toString() - Method in class weka.classifiers.lazy.IBk
Returns a description of this classifier.
toString() - Method in class weka.classifiers.lazy.KStar
Returns a description of this classifier.
toString() - Method in class weka.classifiers.lazy.LBR
Returns a description of the classifier.
toString() - Method in class weka.classifiers.lazy.LWL
Returns a description of this classifier.
toString() - Method in class weka.classifiers.meta.AdaBoostM1
Returns description of the boosted classifier.
toString() - Method in class weka.classifiers.meta.AdditiveRegression
Returns textual description of the classifier.
toString() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.Bagging
Returns description of the bagged classifier.
toString() - Method in class weka.classifiers.meta.ClassificationViaClustering
Returns a string representation of the classifier.
toString() - Method in class weka.classifiers.meta.ClassificationViaRegression
Prints the classifiers.
toString() - Method in class weka.classifiers.meta.CostSensitiveClassifier
Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.CVParameterSelection.CVParameter
Returns a CVParameter as a string.
toString() - Method in class weka.classifiers.meta.CVParameterSelection
Returns description of the cross-validated classifier.
toString() - Method in class weka.classifiers.meta.Dagging
Returns description of the classifier.
toString() - Method in class weka.classifiers.meta.Decorate
Returns description of the Decorate classifier.
toString() - Method in class weka.classifiers.meta.END
Returns description of the committee.
toString() - Method in class weka.classifiers.meta.FilteredClassifier
Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.Grading
Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.GridSearch.Grid
returns a string representation of the grid
toString(int) - Method in class weka.classifiers.meta.GridSearch.Performance
returns a string representation of this performance object
toString() - Method in class weka.classifiers.meta.GridSearch.Performance
returns a string representation of this performance object
toString() - Method in class weka.classifiers.meta.GridSearch.PerformanceCache
returns a string representation of the cache
toString() - Method in class weka.classifiers.meta.GridSearch.PerformanceTable
returns the table as string
toString() - Method in class weka.classifiers.meta.GridSearch.PointDouble
returns a string representation of the Point
toString() - Method in class weka.classifiers.meta.GridSearch.PointInt
returns a string representation of the Point
toString() - Method in class weka.classifiers.meta.GridSearch
returns a string representation of the classifier
toString() - Method in class weka.classifiers.meta.LogitBoost
Returns description of the boosted classifier.
toString() - Method in class weka.classifiers.meta.MetaCost
Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.MultiBoostAB
Returns description of the boosted classifier.
toString() - Method in class weka.classifiers.meta.MultiClassClassifier
Prints the classifiers.
toString() - Method in class weka.classifiers.meta.MultiScheme
Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
Outputs the classifier as a string.
toString() - Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
Outputs the classifier as a string.
toString(StringBuffer, int[], int) - Method in class weka.classifiers.meta.nestedDichotomies.ND.NDTree
Returns a description of the tree rooted at this node.
toString() - Method in class weka.classifiers.meta.nestedDichotomies.ND
Outputs the classifier as a string.
toString() - Method in class weka.classifiers.meta.OrdinalClassClassifier
Prints the classifiers.
toString() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
outputs description of the committee
toString() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Returns description of the boosted classifier.
toString() - Method in class weka.classifiers.meta.RandomCommittee
Returns description of the committee.
toString() - Method in class weka.classifiers.meta.RandomSubSpace
Returns description of the bagged classifier.
toString() - Method in class weka.classifiers.meta.RegressionByDiscretization
Returns a description of the classifier.
toString() - Method in class weka.classifiers.meta.RotationForest
Returns description of the Rotation Forest classifier.
toString() - Method in class weka.classifiers.meta.Stacking
Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.StackingC
Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.ThresholdSelector
Returns description of the cross-validated classifier.
toString() - Method in class weka.classifiers.meta.Vote
Output a representation of this classifier
toString() - Method in class weka.classifiers.mi.CitationKNN
returns a string representation of the classifier
toString() - Method in class weka.classifiers.mi.MDD
Gets a string describing the classifier.
toString() - Method in class weka.classifiers.mi.MIBoost
Gets a string describing the classifier.
toString() - Method in class weka.classifiers.mi.MIDD
Gets a string describing the classifier.
toString() - Method in class weka.classifiers.mi.MIEMDD
Gets a string describing the classifier.
toString() - Method in class weka.classifiers.mi.MILR
Gets a string describing the classifier.
toString() - Method in class weka.classifiers.mi.MISMO.BinaryMISMO
Prints out the classifier.
toString() - Method in class weka.classifiers.mi.MISMO
Prints out the classifier.
toString() - Method in class weka.classifiers.mi.MIWrapper
Gets a string describing the classifier.
toString() - Method in class weka.classifiers.mi.SimpleMI
Gets a string describing the classifier.
toString() - Method in class weka.classifiers.misc.HyperPipes
Returns a description of this classifier.
toString() - Method in class weka.classifiers.misc.SerializedClassifier
Returns a string representation of the classifier
toString() - Method in class weka.classifiers.misc.VFI
Returns a description of this classifier.
toString() - Method in class weka.classifiers.pmml.consumer.GeneralRegression
Return a textual description of this general regression.
toString() - Method in class weka.classifiers.pmml.consumer.NeuralNetwork
 
toString() - Method in class weka.classifiers.pmml.consumer.Regression
Return a textual description of this Regression model.
toString(String, String) - Method in class weka.classifiers.rules.ConjunctiveRule
Prints this rule with the specified class label
toString() - Method in class weka.classifiers.rules.ConjunctiveRule
Prints this rule
toString() - Method in class weka.classifiers.rules.DecisionTable
Returns a description of the classifier.
toString(Instances, int) - Method in class weka.classifiers.rules.DecisionTableHashKey
Convert a hash entry to a string
toString() - Method in class weka.classifiers.rules.DTNB.BackwardsWithDelete
 
toString() - Method in class weka.classifiers.rules.DTNB
 
toString() - Method in class weka.classifiers.rules.JRip.Antd
 
toString() - Method in class weka.classifiers.rules.JRip.NominalAntd
Prints this antecedent
toString() - Method in class weka.classifiers.rules.JRip.NumericAntd
Prints this antecedent
toString(Attribute) - Method in class weka.classifiers.rules.JRip.RipperRule
Prints this rule
toString() - Method in class weka.classifiers.rules.JRip
Prints the all the rules of the rule learner.
toString() - Method in class weka.classifiers.rules.NNge
Returns a description of this classifier.
toString() - Method in class weka.classifiers.rules.OneR
Returns a description of the classifier
toString() - Method in class weka.classifiers.rules.part.ClassifierDecList
Prints rules.
toString() - Method in class weka.classifiers.rules.part.MakeDecList
Outputs the classifier into a string.
toString() - Method in class weka.classifiers.rules.PART
Returns a description of the classifier
toString() - Method in class weka.classifiers.rules.Prism
Prints a description of the classifier.
toString() - Method in class weka.classifiers.rules.Ridor
Prints the all the rules of the rule learner.
toString() - Method in class weka.classifiers.rules.ZeroR
Returns a description of the classifier.
toString() - Method in class weka.classifiers.trees.ADTree
Returns a description of the classifier.
toString(PredictionNode, int) - Method in class weka.classifiers.trees.ADTree
Traverses the tree, forming a string that describes it.
toString() - Method in class weka.classifiers.trees.BFTree
Prints the decision tree using the protected toString method from below.
toString(int) - Method in class weka.classifiers.trees.BFTree
Outputs a tree at a certain level.
toString() - Method in class weka.classifiers.trees.DecisionStump
Returns a description of the classifier.
toString() - Method in class weka.classifiers.trees.ft.FTtree
Returns a description of the Functional tree (tree structure and logistic models)
toString() - Method in class weka.classifiers.trees.FT
Returns a description of the classifier.
toString() - Method in class weka.classifiers.trees.Id3
Prints the decision tree using the private toString method from below.
toString() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG
Prints tree structure.
toString() - Method in class weka.classifiers.trees.j48.ClassifierTree
Prints tree structure.
toString(Instances) - Method in class weka.classifiers.trees.j48.GraftSplit
method for returning information about this GraftSplit
toString() - Method in class weka.classifiers.trees.j48.NBTreeClassifierTree
Prints tree structure.
toString() - Method in class weka.classifiers.trees.j48.NBTreeNoSplit
Return a textual description of the node
toString() - Method in class weka.classifiers.trees.J48
Returns a description of the classifier.
toString() - Method in class weka.classifiers.trees.J48graft
Returns a description of the classifier.
toString() - Method in class weka.classifiers.trees.LADTree.LADInstance
 
toString() - Method in class weka.classifiers.trees.LADTree
Returns a description of the classifier.
toString() - Method in class weka.classifiers.trees.lmt.LMTNode
Returns a description of the logistic model tree (tree structure and logistic models)
toString() - Method in class weka.classifiers.trees.lmt.LogisticBase
Returns a description of the logistic model (i.e., attributes and coefficients).
toString() - Method in class weka.classifiers.trees.LMT
Returns a description of the classifier.
toString() - Method in class weka.classifiers.trees.m5.Impurity
Converts an Impurity object to a string
toString() - Method in class weka.classifiers.trees.m5.M5Base
Returns a description of the classifier
toString() - Method in class weka.classifiers.trees.m5.PreConstructedLinearModel
Returns a textual description of this linear model
toString() - Method in class weka.classifiers.trees.m5.Rule
Return a description of the m5 tree or rule
toString() - Method in class weka.classifiers.trees.m5.RuleNode
print the linear model at this node
toString() - Method in class weka.classifiers.trees.m5.Values
Converts the stats to a string
toString(Instances) - Method in class weka.classifiers.trees.m5.YongSplitInfo
Converts the spliting information to string
toString() - Method in class weka.classifiers.trees.NBTree
Returns a description of the classifier.
toString() - Method in class weka.classifiers.trees.RandomForest
Outputs a description of this classifier.
toString() - Method in class weka.classifiers.trees.RandomTree
Outputs the decision tree.
toString(int) - Method in class weka.classifiers.trees.RandomTree
Recursively outputs the tree.
toString() - Method in class weka.classifiers.trees.REPTree
Outputs the decision tree.
toString(int, REPTree.Tree) - Method in class weka.classifiers.trees.REPTree.Tree
Recursively outputs the tree.
toString() - Method in class weka.classifiers.trees.SimpleCart
Prints the decision tree using the protected toString method from below.
toString(int) - Method in class weka.classifiers.trees.SimpleCart
Outputs a tree at a certain level.
toString() - Method in class weka.classifiers.trees.UserClassifier
 
toString() - Method in class weka.clusterers.CLOPE
return a string describing this clusterer
toString() - Method in class weka.clusterers.Cobweb
Returns a description of the clusterer as a string.
toString() - Method in class weka.clusterers.DBScan
Returns a description of the clusterer
toString() - Method in class weka.clusterers.EM
Outputs the generated clusters into a string.
toString() - Method in class weka.clusterers.FarthestFirst
return a string describing this clusterer
toString() - Method in class weka.clusterers.FilteredClusterer
Output a representation of this clusterer.
toString() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
 
toString() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
 
toString() - Method in class weka.clusterers.HierarchicalClusterer
 
toString() - Method in class weka.clusterers.MakeDensityBasedClusterer
Returns a description of the clusterer.
toString() - Method in class weka.clusterers.OPTICS
Returns a description of the clusterer
toString() - Method in class weka.clusterers.sIB
 
toString() - Method in class weka.clusterers.SimpleKMeans
return a string describing this clusterer
toString() - Method in class weka.clusterers.XMeans
Return a string describing this clusterer.
toString() - Method in class weka.core.AlgVector
Converts a vector to a string
toString() - Method in class weka.core.Attribute
Returns a description of this attribute in ARFF format.
toString() - Method in class weka.core.AttributeExpression
 
toString() - Method in class weka.core.AttributeLocator
returns a string representation of this object
toString() - Method in class weka.core.AttributeStats
Returns a human readable representation of this AttributeStats instance.
toString() - Method in class weka.core.BinarySparseInstance
Returns the description of one instance in sparse format.
toString() - Method in enum weka.core.Capabilities.Capability
returns the display string of the capability
toString() - Method in class weka.core.Capabilities
returns a string representation of the capabilities
toString() - Method in class weka.core.Debug.Clock
returns the elapsed time, getStop() - getStart(), as string
toString() - Method in class weka.core.Debug.Log
returns a string representation of the logger
toString() - Method in class weka.core.Debug.Random
returns a string representation of this number generator
toString() - Method in class weka.core.Debug.SimpleLog
returns a string representation of the logger
toString() - Method in class weka.core.Debug.Timestamp
returns the timestamp as string in the specified format
toString() - Method in class weka.core.Instance
Returns the description of one instance.
toString(int) - Method in class weka.core.Instance
Returns the description of one value of the instance as a string.
toString(Attribute) - Method in class weka.core.Instance
Returns the description of one value of the instance as a string.
toString() - Method in class weka.core.Instances
Returns the dataset as a string in ARFF format.
toString() - Method in class weka.core.matrix.DoubleVector
Convert the DoubleVecor to a string
toString(int, boolean) - Method in class weka.core.matrix.DoubleVector
Convert the DoubleVecor to a string
toString() - Method in class weka.core.matrix.IntVector
Converts the IntVecor to a string
toString(int, boolean) - Method in class weka.core.matrix.IntVector
Convert the IntVecor to a string
toString() - Method in class weka.core.matrix.LinearRegression
returns the coefficients in a string representation
toString() - Method in class weka.core.matrix.Matrix
Converts a matrix to a string.
toString() - Method in class weka.core.Matrix
Deprecated.
Converts a matrix to a string
toString() - Method in class weka.core.neighboursearch.balltrees.BottomUpConstructor.TempNode
Prints the node.
toString() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor.TempNode
Returns a string represention of the node.
toString() - Method in class weka.core.NormalizableDistance
Returns an empty string.
toString() - Method in class weka.core.pmml.BuiltInArithmetic
 
toString(String) - Method in class weka.core.pmml.BuiltInArithmetic
 
toString() - Method in class weka.core.pmml.BuiltInMath
 
toString() - Method in class weka.core.pmml.BuiltInString
 
toString(String) - Method in class weka.core.pmml.Constant
 
toString() - Method in class weka.core.pmml.DefineFunction
 
toString(String) - Method in class weka.core.pmml.DefineFunction
 
toString() - Method in class weka.core.pmml.DerivedFieldMetaInfo
 
toString() - Method in class weka.core.pmml.Discretize.DiscretizeBin
 
toString(String) - Method in class weka.core.pmml.Discretize
 
toString() - Method in class weka.core.pmml.Expression
 
toString(String) - Method in class weka.core.pmml.Expression
 
toString() - Method in enum weka.core.pmml.FieldMetaInfo.Interval.Closure
 
toString(double, double) - Method in enum weka.core.pmml.FieldMetaInfo.Interval.Closure
 
toString() - Method in class weka.core.pmml.FieldMetaInfo.Interval
 
toString() - Method in enum weka.core.pmml.FieldMetaInfo.Optype
 
toString() - Method in enum weka.core.pmml.FieldMetaInfo.Value.Property
 
toString() - Method in class weka.core.pmml.FieldMetaInfo.Value
 
toString(String) - Method in class weka.core.pmml.FieldRef
 
toString() - Method in class weka.core.pmml.Function
 
toString(String) - Method in class weka.core.pmml.Function
 
toString() - Method in class weka.core.pmml.MiningFieldMetaInfo
Return a textual representation of this MiningField.
toString() - Method in class weka.core.pmml.MiningSchema
Get a textual description of the mining schema.
toString(String) - Method in class weka.core.pmml.NormContinuous
 
toString(String) - Method in class weka.core.pmml.NormDiscrete
 
toString() - Method in enum weka.core.pmml.PMMLFactory.ModelType
 
toString() - Method in class weka.core.PropertyPath.Path
returns the structure again as a dot-path
toString() - Method in class weka.core.PropertyPath.PathElement
returns the element once again as string
toString() - Method in class weka.core.Queue
Produces textual description of queue.
toString() - Method in class weka.core.Range
Constructs a representation of the current range.
toString() - Method in class weka.core.SelectedTag
returns the selected tag in string representation
toString() - Method in class weka.core.SingleIndex
Constructs a representation of the current range.
toString() - Method in class weka.core.SparseInstance
Returns the description of one instance in sparse format.
toString() - Method in class weka.core.stemmers.LovinsStemmer
returns a string representation of the stemmer
toString() - Method in class weka.core.stemmers.NullStemmer
returns a string representation of the stemmer
toString() - Method in class weka.core.stemmers.SnowballStemmer
returns a string representation of the stemmer.
toString() - Method in class weka.core.Stopwords
returns the current stopwords in a string
toString() - Method in class weka.core.SystemInfo
returns a string representation of all the system properties
toString() - Method in class weka.core.Tag
returns the IDStr
toString() - Method in enum weka.core.TechnicalInformation.Field
returns the display string of the Type
toString() - Method in class weka.core.TechnicalInformation
Returns a plain-text string representing this technical information.
toString() - Method in enum weka.core.TechnicalInformation.Type
returns the display string of the Type
toString() - Method in class weka.core.Tee
returns only the classname and the number of streams.
toString() - Method in class weka.core.TestInstances
returns a string representation of the object
toString(Trie.TrieNode) - Method in class weka.core.Trie
returns the node as String
toString() - Method in class weka.core.Trie
returns the trie in string representation
toString() - Method in class weka.core.Trie.TrieNode
returns the node in a string representation
toString() - Method in class weka.core.Version
returns the current version as string
toString() - Method in class weka.core.xml.MethodHandler
returns the internal Hashtable (propety/class - method relationship) in a string representation
toString(StringBuffer, Node, int) - Method in class weka.core.xml.XMLDocument
turns the given node into a XML-stringbuffer according to the depth.
toString() - Method in class weka.core.xml.XMLDocument
returns the current DOM document as XML-string.
toString() - Method in class weka.core.xml.XMLOptions
returns the object in a string representation (as indented XML output).
toString() - Method in class weka.core.xml.XMLSerializationMethodHandler
returns the read and write method handlers as string
toString() - Method in class weka.datagenerators.ClusterDefinition
returns a string representation of the cluster
toString() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Make a string from the cluster features.
toString() - Method in class weka.datagenerators.Test
Returns the test represented by a string.
toString() - Method in class weka.estimators.DDConditionalEstimator
Display a representation of this estimator
toString() - Method in class weka.estimators.DiscreteEstimator
Display a representation of this estimator
toString() - Method in class weka.estimators.DKConditionalEstimator
Display a representation of this estimator
toString() - Method in class weka.estimators.DNConditionalEstimator
Display a representation of this estimator
toString() - Method in class weka.estimators.KDConditionalEstimator
Display a representation of this estimator
toString() - Method in class weka.estimators.KernelEstimator
Display a representation of this estimator
toString() - Method in class weka.estimators.KKConditionalEstimator
Display a representation of this estimator
toString() - Method in class weka.estimators.MahalanobisEstimator
Display a representation of this estimator
toString() - Method in class weka.estimators.NDConditionalEstimator
Display a representation of this estimator
toString() - Method in class weka.estimators.NNConditionalEstimator
Display a representation of this estimator
toString() - Method in class weka.estimators.NormalEstimator
Display a representation of this estimator
toString() - Method in class weka.estimators.PoissonEstimator
Display a representation of this estimator
toString() - Method in class weka.experiment.AveragingResultProducer
Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.ClassifierSplitEvaluator
Returns a text description of the split evaluator.
toString() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
Returns a text description of the split evaluator.
toString() - Method in class weka.experiment.CrossValidationResultProducer
Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.DatabaseResultProducer
Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
Returns a text description of the split evaluator.
toString() - Method in class weka.experiment.Experiment
Gets a string representation of the experiment configuration.
toString() - Method in class weka.experiment.LearningRateResultProducer
Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.PairedStats
Returns statistics on the paired comparison.
toString() - Method in class weka.experiment.PropertyNode
Returns a string description of this property.
toString() - Method in class weka.experiment.RandomSplitResultProducer
Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.RegressionSplitEvaluator
Returns a text description of the split evaluator.
toString() - Method in class weka.experiment.RemoteExperiment
Overides toString in Experiment
toString() - Method in class weka.experiment.ResultMatrix
returns the matrix as a string
toString() - Method in class weka.experiment.Stats
Returns a string summarising the stats so far.
toString() - Method in class weka.filters.Filter
Returns a description of the filter, by default only the classname.
toString() - Method in class weka.gui.arffviewer.ArffViewer
returns only the classname
toString() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
returns only the classname
toString() - Method in class weka.gui.CheckBoxList.CheckBoxListItem
returns the string representation of the content object
toString() - Method in class weka.gui.GenericObjectEditor.GOETreeNode
returns a string representation of this treenode.
toString() - Method in class weka.gui.graphvisualizer.GraphEdge
 
toString() - Method in class weka.gui.SortedTableModel.SortContainer
Returns a string representation of the sort container.
toString() - Method in class weka.gui.sql.event.ConnectionEvent
returns the event in a string representation
toString() - Method in class weka.gui.sql.event.HistoryChangedEvent
returns the event in a string representation
toString() - Method in class weka.gui.sql.event.QueryExecuteEvent
returns the event in a string representation
toString() - Method in class weka.gui.sql.event.ResultChangedEvent
returns the event in a string representation
toStringFormat() - Method in class weka.datagenerators.DataGenerator
Returns a string representing the dataset in the instance queue.
toStringHeader() - Method in class weka.experiment.ResultMatrix
returns the header of the matrix as a string
toStringHeader() - Method in class weka.experiment.ResultMatrixCSV
returns the header of the matrix as a string
toStringHeader() - Method in class weka.experiment.ResultMatrixGnuPlot
returns the header of the matrix as a string
toStringHeader() - Method in class weka.experiment.ResultMatrixHTML
returns the header of the matrix as a string
toStringHeader() - Method in class weka.experiment.ResultMatrixLatex
returns the header of the matrix as a string
toStringHeader() - Method in class weka.experiment.ResultMatrixPlainText
returns the header of the matrix as a string
toStringHeader() - Method in class weka.experiment.ResultMatrixSignificance
returns the header of the matrix as a string
toStringKey() - Method in class weka.experiment.ResultMatrix
returns returns a key for all the col names, for better readability if the names got cut off
toStringKey() - Method in class weka.experiment.ResultMatrixCSV
returns returns a key for all the col names, for better readability if the names got cut off
toStringKey() - Method in class weka.experiment.ResultMatrixGnuPlot
returns returns a key for all the col names, for better readability if the names got cut off
toStringKey() - Method in class weka.experiment.ResultMatrixHTML
returns returns a key for all the col names, for better readability if the names got cut off
toStringKey() - Method in class weka.experiment.ResultMatrixLatex
returns returns a key for all the col names, for better readability if the names got cut off
toStringKey() - Method in class weka.experiment.ResultMatrixPlainText
returns returns a key for all the col names, for better readability if the names got cut off
toStringKey() - Method in class weka.experiment.ResultMatrixSignificance
returns returns a key for all the col names, for better readability if the names got cut off
toStringMatrix() - Method in class weka.experiment.ResultMatrix
returns the matrix as a string
toStringMatrix() - Method in class weka.experiment.ResultMatrixCSV
returns the matrix in CSV format
toStringMatrix() - Method in class weka.experiment.ResultMatrixGnuPlot
returns the matrix in CSV format
toStringMatrix() - Method in class weka.experiment.ResultMatrixHTML
returns the matrix in an HTML table
toStringMatrix() - Method in class weka.experiment.ResultMatrixLatex
returns the matrix as latex table
toStringMatrix() - Method in class weka.experiment.ResultMatrixPlainText
returns the matrix as plain text
toStringMatrix() - Method in class weka.experiment.ResultMatrixSignificance
returns the matrix as plain text
toStringMetric(int, int, int, int) - Method in enum weka.associations.FPGrowth.AssociationRule.METRIC_TYPE
 
toStringNoWeight() - Method in class weka.core.Instance
Returns the description of one instance (without weight appended).
toStringOriginal() - Method in class weka.classifiers.bayes.NaiveBayes
Returns a description of the classifier in the old format.
toStringOriginal() - Method in class weka.clusterers.EM
Outputs the generated clusters into a string.
toStringRanking() - Method in class weka.experiment.ResultMatrix
returns the ranking in a string representation
toStringRanking() - Method in class weka.experiment.ResultMatrixCSV
returns the ranking in a string representation
toStringRanking() - Method in class weka.experiment.ResultMatrixGnuPlot
returns the ranking in a string representation
toStringRanking() - Method in class weka.experiment.ResultMatrixHTML
returns the ranking in a string representation
toStringRanking() - Method in class weka.experiment.ResultMatrixLatex
returns the ranking in a string representation
toStringRanking() - Method in class weka.experiment.ResultMatrixPlainText
returns the ranking in a string representation
toStringRanking() - Method in class weka.experiment.ResultMatrixSignificance
returns the ranking in a string representation
toStringSummary() - Method in class weka.experiment.ResultMatrix
returns the summary as string
toStringSummary() - Method in class weka.experiment.ResultMatrixCSV
returns the summary as string
toStringSummary() - Method in class weka.experiment.ResultMatrixGnuPlot
returns the summary as string
toStringSummary() - Method in class weka.experiment.ResultMatrixHTML
returns the summary as string
toStringSummary() - Method in class weka.experiment.ResultMatrixLatex
returns the summary as string
toStringSummary() - Method in class weka.experiment.ResultMatrixPlainText
returns the summary as string
toStringSummary() - Method in class weka.experiment.ResultMatrixSignificance
returns the summary as string
toSummaryString() - Method in class weka.associations.AssociatorEvaluation
returns a summary string of the evaluation with a no title
toSummaryString(String) - Method in class weka.associations.AssociatorEvaluation
returns a summary string of the evaluation with a default title
toSummaryString() - Method in class weka.classifiers.Evaluation
Calls toSummaryString() with no title and no complexity stats
toSummaryString(boolean) - Method in class weka.classifiers.Evaluation
Calls toSummaryString() with a default title.
toSummaryString(String, boolean) - Method in class weka.classifiers.Evaluation
Outputs the performance statistics in summary form.
toSummaryString() - Method in class weka.classifiers.functions.supportVector.KernelEvaluation
returns a summary string of the evaluation with a no title
toSummaryString(String) - Method in class weka.classifiers.functions.supportVector.KernelEvaluation
returns a summary string of the evaluation with a default title
toSummaryString() - Method in class weka.classifiers.meta.CVParameterSelection
A concise description of the model.
toSummaryString() - Method in class weka.classifiers.meta.GridSearch
Returns a string that summarizes the object.
toSummaryString() - Method in class weka.classifiers.rules.PART
Returns a superconcise version of the model
toSummaryString() - Method in class weka.classifiers.trees.J48
Returns a superconcise version of the model
toSummaryString() - Method in class weka.classifiers.trees.J48graft
Returns a superconcise version of the model
toSummaryString() - Method in class weka.classifiers.trees.NBTree
Returns a superconcise version of the model
toSummaryString() - Method in class weka.core.Instances
Generates a string summarizing the set of instances.
toSummaryString() - Method in interface weka.core.Summarizable
Returns a string that summarizes the object.
total() - Method in class weka.classifiers.evaluation.ConfusionMatrix
Gets the number of predictions that were made (actually the sum of the weights of predictions where the class value was known).
total() - Method in class weka.classifiers.trees.j48.Distribution
Returns total number of (possibly fractional) instances.
TOTAL_UNIFORM - Static variable in class weka.datagenerators.clusterers.SubspaceCluster
cluster type: total uniform
totalCost() - Method in class weka.classifiers.Evaluation
Gets the total cost, that is, the cost of each prediction times the weight of the instance, summed over all instances.
totalCount - Variable in class weka.core.AttributeStats
The total number of values (i.e.
totalForSubset(int) - Method in class weka.classifiers.trees.j48.GraftSplit
 
totalForSubsetOfInterest() - Method in class weka.classifiers.trees.j48.GraftSplit
 
totalSize() - Method in class weka.core.neighboursearch.CoverTree.MyHeap
returns the total size.
totalSize() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.MyHeap
returns the total size.
toXML(int, int, int, int) - Method in enum weka.associations.FPGrowth.AssociationRule.METRIC_TYPE
 
toXML() - Method in class weka.associations.FPGrowth.AssociationRule
 
toXML() - Method in class weka.associations.FPGrowth.BinaryItem
 
toXML(Object) - Method in class weka.core.xml.XMLSerialization
extracts all accesible properties from the given object
toXMLBIF03() - Method in class weka.classifiers.bayes.BayesNet
Returns a description of the classifier in XML BIF 0.3 format.
toXMLBIF03() - Method in class weka.classifiers.bayes.net.EditableBayesNet
returns network in XMLBIF format
toXMLBIF03(FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet
return fragment of network in XMLBIF format
toXMLBIF03() - Method in class weka.classifiers.bayes.net.MarginCalculator
 
TP_RATE - Static variable in class weka.classifiers.meta.ThresholdSelector
true-positive rate
TP_RATE_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
attribute name: True Positive Rate
trace() - Method in class weka.core.matrix.Matrix
Matrix trace.
trace(Throwable, String) - Method in class weka.core.xml.XMLSerialization
used for debugging purposes, i.e.
trailing - Variable in class weka.core.matrix.ExponentialFormat
 
trailing - Variable in class weka.core.matrix.FloatingPointFormat
 
trainCV(int, int) - Method in class weka.core.Instances
Creates the training set for one fold of a cross-validation on the dataset.
trainCV(int, int, Random) - Method in class weka.core.Instances
Creates the training set for one fold of a cross-validation on the dataset.
TRAINING - Static variable in class weka.gui.beans.BatchClustererEvent
 
TrainingSetEvent - Class in weka.gui.beans
Event encapsulating a training set
TrainingSetEvent(Object, Instances) - Constructor for class weka.gui.beans.TrainingSetEvent
Creates a new TrainingSetEvent
TrainingSetEvent(Object, Instances, int, int) - Constructor for class weka.gui.beans.TrainingSetEvent
Creates a new TrainingSetEvent
TrainingSetEvent(Object, Instances, int, int, int, int) - Constructor for class weka.gui.beans.TrainingSetEvent
Creates a new TrainingSetEvent
TrainingSetListener - Interface in weka.gui.beans
Interface to something that can accept and process training set events
TrainingSetMaker - Class in weka.gui.beans
Bean that accepts a data sets and produces a training set
TrainingSetMaker() - Constructor for class weka.gui.beans.TrainingSetMaker
 
TrainingSetMakerBeanInfo - Class in weka.gui.beans
Bean info class for the training set maker bean
TrainingSetMakerBeanInfo() - Constructor for class weka.gui.beans.TrainingSetMakerBeanInfo
 
TrainingSetProducer - Interface in weka.gui.beans
Interface to something that can produce a training set
TrainingTask(int, int, int, int, Instances) - Constructor for class weka.gui.beans.Classifier.TrainingTask
 
trainingTimeTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
trainPercentTipText() - Method in class weka.experiment.RandomSplitResultProducer
Returns the tip text for this property
trainPercentTipText() - Method in class weka.gui.beans.TrainTestSplitMaker
Tip text info for this property
TrainTestSplitMaker - Class in weka.gui.beans
Bean that accepts data sets, training sets, test sets and produces both a training and test set by randomly spliting the data
TrainTestSplitMaker() - Constructor for class weka.gui.beans.TrainTestSplitMaker
 
TrainTestSplitMakerBeanInfo - Class in weka.gui.beans
Bean info class for the train test split maker bean
TrainTestSplitMakerBeanInfo() - Constructor for class weka.gui.beans.TrainTestSplitMakerBeanInfo
 
TrainTestSplitMakerCustomizer - Class in weka.gui.beans
GUI customizer for the train test split maker bean
TrainTestSplitMakerCustomizer() - Constructor for class weka.gui.beans.TrainTestSplitMakerCustomizer
 
transactionsMustContainTipText() - Method in class weka.associations.FPGrowth
Returns the tip text for this property
transform(Instances) - Method in class weka.classifiers.mi.SimpleMI
Implements MITransform (3 type of transformation) 1.arithmatic average; 2.geometric centor; 3.merge minima and maxima attribute value together
transform(AffineTransform) - Method in class weka.gui.visualize.PostscriptGraphics
 
transformAllValuesTipText() - Method in class weka.filters.supervised.attribute.NominalToBinary
Returns the tip text for this property
transformAllValuesTipText() - Method in class weka.filters.unsupervised.attribute.NominalToBinary
Returns the tip text for this property
transformBackToOriginalTipText() - Method in class weka.attributeSelection.PrincipalComponents
Returns the tip text for this property
transformedData(Instances) - Method in interface weka.attributeSelection.AttributeTransformer
Transform the supplied data set (assumed to be the same format as the training data)
transformedData(Instances) - Method in class weka.attributeSelection.LatentSemanticAnalysis
Transform the supplied data set (assumed to be the same format as the training data)
transformedData(Instances) - Method in class weka.attributeSelection.PrincipalComponents
Gets the transformed training data.
transformedHeader() - Method in interface weka.attributeSelection.AttributeTransformer
Returns just the header for the transformed data (ie.
transformedHeader() - Method in class weka.attributeSelection.LatentSemanticAnalysis
Returns just the header for the transformed data (ie.
transformedHeader() - Method in class weka.attributeSelection.PrincipalComponents
Returns just the header for the transformed data (ie.
TRANSFORMMETHOD_ARITHMETIC - Static variable in class weka.classifiers.mi.SimpleMI
arithmetic average
TRANSFORMMETHOD_GEOMETRIC - Static variable in class weka.classifiers.mi.SimpleMI
geometric average
TRANSFORMMETHOD_MINIMAX - Static variable in class weka.classifiers.mi.SimpleMI
using minimax combined features of a bag
transformMethodTipText() - Method in class weka.classifiers.mi.SimpleMI
Returns the tip text for this property
translate(int, int) - Method in class weka.gui.visualize.PostscriptGraphics
Translates the origin of the graphics context to the point (x, y) in the current coordinate system.
translate(double, double) - Method in class weka.gui.visualize.PostscriptGraphics
 
translateDBColumnType(String) - Method in class weka.experiment.DatabaseUtils
translates the column data type string to an integer value that indicates which data type / get()-Method to use in order to retrieve values from the database (see DatabaseUtils.Properties, InstanceQuery()).
translationTipText() - Method in class weka.filters.unsupervised.attribute.Normalize
Returns the tip text for this property.
transpose() - Method in class weka.core.matrix.Matrix
Matrix transpose.
transpose() - Method in class weka.core.Matrix
Deprecated.
Returns the transpose of a matrix.
transProb() - Method in class weka.classifiers.lazy.kstar.KStarNominalAttribute
Calculates the probability of the indexed nominal attribute of the test instance transforming into the indexed nominal attribute of the training instance.
transProb() - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute
Calculates the transformation probability of the attribute indexed "m_AttrIndex" in test instance "m_Test" to the same attribute in the train instance "m_Train".
TRAVERSAL_BY_COLUMN - Static variable in class weka.classifiers.meta.GridSearch
column-wise grid traversal
TRAVERSAL_BY_ROW - Static variable in class weka.classifiers.meta.GridSearch
row-wise grid traversal
traversalTipText() - Method in class weka.classifiers.meta.GridSearch
Returns the tip text for this property
Tree() - Constructor for class weka.classifiers.trees.REPTree.Tree
 
TREE - Static variable in interface weka.core.Drawable
 
TreeBuild - Class in weka.gui.treevisualizer
This class will parse a dotty file and construct a tree structure from it with Edge's and Node's
TreeBuild() - Constructor for class weka.gui.treevisualizer.TreeBuild
Upon construction this will only setup the color table for quick reference of a color.
TreeDisplayEvent - Class in weka.gui.treevisualizer
An event containing the user selection from the tree display
TreeDisplayEvent(int, String) - Constructor for class weka.gui.treevisualizer.TreeDisplayEvent
Constructs an event with the specified command and what the command is applied to.
TreeDisplayListener - Interface in weka.gui.treevisualizer
Interface implemented by classes that wish to recieve user selection events from a tree displayer.
treeErrors() - Method in class weka.classifiers.trees.lmt.LMTNode
Updates the numIncorrectTree field for all nodes.
treeErrors() - Method in class weka.classifiers.trees.SimpleCart
Updates the numIncorrectTree field for all nodes.
TreePerformanceStats - Class in weka.core.neighboursearch
The class that measures the performance of a tree based nearest neighbour search algorithm.
TreePerformanceStats() - Constructor for class weka.core.neighboursearch.TreePerformanceStats
Default constructor.
treeToString(int) - Method in class weka.classifiers.trees.m5.RuleNode
Recursively builds a textual description of the tree
TreeVisualizePlugin - Interface in weka.gui.visualize.plugins
Interface implemented by classes loaded dynamically to visualize classifier results in the explorer.
TreeVisualizer - Class in weka.gui.treevisualizer
Class for displaying a Node structure in Swing.
TreeVisualizer(TreeDisplayListener, String, NodePlace) - Constructor for class weka.gui.treevisualizer.TreeVisualizer
Constructs Displayer to display a tree provided in a dot format.
TreeVisualizer(TreeDisplayListener, Node, NodePlace) - Constructor for class weka.gui.treevisualizer.TreeVisualizer
Constructs Displayer with the specified Node as the top of the tree, and uses the NodePlacer to place the Nodes.
TRIANGLEDOWN_SHAPE - Static variable in class weka.gui.visualize.Plot2D
 
TRIANGLEUP_SHAPE - Static variable in class weka.gui.visualize.Plot2D
 
TRICUBE - Static variable in class weka.classifiers.lazy.LWL
 
Trie - Class in weka.core
A class representing a Trie data structure for strings.
Trie() - Constructor for class weka.core.Trie
initializes the data structure
Trie.TrieIterator - Class in weka.core
Represents an iterator over a trie
Trie.TrieNode - Class in weka.core
Represents a node in the trie.
TrieIterator(Trie.TrieNode) - Constructor for class weka.core.Trie.TrieIterator
initializes the iterator
TrieNode(char) - Constructor for class weka.core.Trie.TrieNode
initializes the node
TrieNode(Character) - Constructor for class weka.core.Trie.TrieNode
initializes the node
trim(DoubleVector) - Method in class weka.classifiers.functions.pace.ChisqMixture
Trims the small values of the estaimte
trim(DoubleVector) - Method in class weka.classifiers.functions.pace.NormalMixture
Trims the small values of the estaimte
trim() - Method in class weka.gui.LogWindow
trims the JTextPane, if too big
trimingThreshold - Variable in class weka.classifiers.functions.pace.ChisqMixture
the triming thresholding
trimingThreshold - Variable in class weka.classifiers.functions.pace.NormalMixture
the triming thresholding
trimString(String, int) - Method in class weka.experiment.ResultMatrix
trims the given string down to the given length if longer, otherwise leaves it unchanged.
trimToSize() - Method in class weka.core.FastVector
Sets the vector's capacity to its size.
TRUE - Static variable in interface weka.core.mathematicalexpression.sym
 
TRUE - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
 
TRUE_NEG - Static variable in class weka.classifiers.meta.ThresholdSelector
true-negative
TRUE_NEG_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
attribute name: True Negatives
TRUE_POS - Static variable in class weka.classifiers.meta.ThresholdSelector
true-positive
TRUE_POS_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
attribute name: True Positives
trueNegativeRate(int) - Method in class weka.classifiers.Evaluation
Calculate the true negative rate with respect to a particular class.
truePositiveRate(int) - Method in class weka.classifiers.Evaluation
Calculate the true positive rate with respect to a particular class.
tryLogistic(Instances) - Method in class weka.classifiers.trees.lmt.LMTNode
Determines the optimum number of LogitBoost iterations to perform by building a standalone logistic regression function on the training data.
TStartTipText() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
 
TStartTipText() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
 
turnChecksOff() - Method in class weka.classifiers.functions.LinearRegression
Turns off checks for missing values, etc.
turnChecksOff() - Method in class weka.classifiers.functions.SMO
Turns off checks for missing values, etc.
turnChecksOff() - Method in class weka.classifiers.mi.MISMO
Turns off checks for missing values, etc.
turnChecksOn() - Method in class weka.classifiers.functions.LinearRegression
Turns on checks for missing values, etc.
turnChecksOn() - Method in class weka.classifiers.functions.SMO
Turns on checks for missing values, etc.
turnChecksOn() - Method in class weka.classifiers.mi.MISMO
Turns on checks for missing values, etc.
TwoClassStats - Class in weka.classifiers.evaluation
Encapsulates performance functions for two-class problems.
TwoClassStats(double, double, double, double) - Constructor for class weka.classifiers.evaluation.TwoClassStats
Creates the TwoClassStats with the given initial performance values.
TwoWayNominalSplit - Class in weka.classifiers.trees.adtree
Class representing a two-way split on a nominal attribute, of the form: either 'is some_value' or 'is not some_value'.
TwoWayNominalSplit(int, int) - Constructor for class weka.classifiers.trees.adtree.TwoWayNominalSplit
Creates a new two-way nominal splitter.
TwoWayNominalSplit(int, int) - Constructor for class weka.classifiers.trees.LADTree.TwoWayNominalSplit
 
TwoWayNumericSplit - Class in weka.classifiers.trees.adtree
Class representing a two-way split on a numeric attribute, of the form: either 'is < some_value' or 'is >= some_value'.
TwoWayNumericSplit(int, double) - Constructor for class weka.classifiers.trees.adtree.TwoWayNumericSplit
Creates a new two-way numeric splitter.
TwoWayNumericSplit(int, double) - Constructor for class weka.classifiers.trees.LADTree.TwoWayNumericSplit
 
TwoWayNumericSplit(int, Instances) - Constructor for class weka.classifiers.trees.LADTree.TwoWayNumericSplit
 
type() - Method in class weka.core.Attribute
Returns the attribute's type as an integer.
type - Variable in class weka.gui.graphvisualizer.GraphEdge
The type of Edge
TYPE_CROSSVALIDATION_TEXT - Static variable in class weka.gui.experiment.SimpleSetupPanel
 
TYPE_FIXED_SET - Static variable in class weka.attributeSelection.LinearForwardSelection
search directions
TYPE_FIXED_SET - Static variable in class weka.attributeSelection.SubsetSizeForwardSelection
search directions
TYPE_FIXED_WIDTH - Static variable in class weka.attributeSelection.LinearForwardSelection
 
TYPE_FIXED_WIDTH - Static variable in class weka.attributeSelection.SubsetSizeForwardSelection
 
TYPE_FIXEDSPLIT_TEXT - Static variable in class weka.gui.experiment.SimpleSetupPanel
 
TYPE_RANDOMSPLIT_TEXT - Static variable in class weka.gui.experiment.SimpleSetupPanel
 
typeIsNumeric(int) - Static method in class weka.gui.sql.ResultSetHelper
returns whether the SQL type is numeric (and therefore the justification should be right).
typeName(int) - Static method in class weka.experiment.DatabaseUtils
Returns the name associated with a SQL type.
typeTipText() - Method in class weka.attributeSelection.LinearForwardSelection
Returns the tip text for this property
typeTipText() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Returns the tip text for this property
typeToClass(int) - Static method in class weka.gui.sql.ResultSetHelper
Returns the class associated with a SQL type.

U

uBCenter(int, int, int[]) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
 
uConnectivity(int, int) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
 
uminus() - Method in class weka.core.matrix.Matrix
Unary minus
UnassignedClassException - Exception in weka.core
Exception that is raised when trying to use some data that has no class assigned to it, but a class is needed to perform the operation.
UnassignedClassException() - Constructor for exception weka.core.UnassignedClassException
Creates a new UnassignedClassException with no message.
UnassignedClassException(String) - Constructor for exception weka.core.UnassignedClassException
Creates a new UnassignedClassException.
UnassignedDatasetException - Exception in weka.core
Exception that is raised when trying to use something that has no reference to a dataset, when one is required.
UnassignedDatasetException() - Constructor for exception weka.core.UnassignedDatasetException
Creates a new UnassignedDatasetException with no message.
UnassignedDatasetException(String) - Constructor for exception weka.core.UnassignedDatasetException
Creates a new UnassignedDatasetException.
unbackQuoteChars(String) - Static method in class weka.core.Utils
The inverse operation of backQuoteChars().
unclassified() - Method in class weka.classifiers.Evaluation
Gets the number of instances not classified (that is, for which no prediction was made by the classifier).
UNCLASSIFIED - Static variable in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
 
UNCONNECTED - Static variable in class weka.classifiers.functions.neural.NeuralConnection
This unit is not connected to any others.
UNDEFINED - Static variable in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
 
undefinedDistribution - Static variable in class weka.core.matrix.Maths
Distribution type: undefined
undo() - Method in class weka.classifiers.bayes.net.EditableBayesNet
undo the last edit action performed on the network.
undo() - Method in interface weka.core.Undoable
undoes the last action
undo() - Method in class weka.gui.arffviewer.ArffPanel
performs an undo action
undo() - Method in class weka.gui.arffviewer.ArffSortedTableModel
undoes the last action
undo() - Method in class weka.gui.arffviewer.ArffTableModel
undoes the last action
undo() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
undoes the last action
undo() - Method in class weka.gui.explorer.PreprocessPanel
Reverts to the last backed up version of the dataset.
Undoable - Interface in weka.core
Interface implemented by classes that support undo.
UNHANDLED_DIALOG - Static variable in class weka.gui.ConverterFileChooser
unhandled type of dialog
UNIFORM_RANDOM - Static variable in class weka.datagenerators.clusterers.SubspaceCluster
cluster type: uniform/random
unifyTree() - Method in class weka.classifiers.meta.nestedDichotomies.ND.NDTree
Unifies tree for improve hashing.
unique() - Method in class weka.classifiers.functions.pace.DiscreteFunction
Makes each individual point value unique
uniqueCount - Variable in class weka.core.AttributeStats
The number of values that only appear once
UNKNOWN_NOMINAL_VALUE - Static variable in class weka.core.pmml.MappingInfo
Index for incoming nominal values that are not defined in the mining schema.
unnormalizedKernel(char[], char[]) - Method in class weka.classifiers.functions.supportVector.StringKernel
evaluates the unnormalized kernel between s and t.
unpivoting(IntVector, int) - Method in class weka.core.matrix.DoubleVector
Returns a vector from the pivoting indices.
unprune() - Method in class weka.classifiers.trees.lmt.LMTNode
Method to "unprune" a logistic model tree.
unprune() - Method in class weka.classifiers.trees.SimpleCart
Method to "unprune" the CART tree.
unprunedTipText() - Method in class weka.classifiers.rules.PART
Returns the tip text for this property
unprunedTipText() - Method in class weka.classifiers.trees.J48
Returns the tip text for this property
unprunedTipText() - Method in class weka.classifiers.trees.J48graft
Returns the tip text for this property
unprunedTipText() - Method in class weka.classifiers.trees.m5.M5Base
Returns the tip text for this property
unquote(String) - Static method in class weka.core.Utils
unquotes are previously quoted string (but only if necessary), i.e., it removes the single quotes around it.
UNSET - Static variable in class weka.filters.unsupervised.attribute.ClassAssigner
unset the class attribute.
unsorted() - Method in class weka.core.matrix.DoubleVector
Returns true if vector not sorted
UnsupervisedAttributeEvaluator - Class in weka.attributeSelection
Abstract unsupervised attribute evaluator.
UnsupervisedAttributeEvaluator() - Constructor for class weka.attributeSelection.UnsupervisedAttributeEvaluator
 
UnsupervisedFilter - Interface in weka.filters
Interface for filters that do not need a class attribute.
UnsupervisedSubsetEvaluator - Class in weka.attributeSelection
Abstract unsupervised attribute subset evaluator.
UnsupervisedSubsetEvaluator() - Constructor for class weka.attributeSelection.UnsupervisedSubsetEvaluator
 
UnsupportedAttributeTypeException - Exception in weka.core
Exception that is raised by an object that is unable to process some of the attribute types it has been passed.
UnsupportedAttributeTypeException() - Constructor for exception weka.core.UnsupportedAttributeTypeException
Creates a new UnsupportedAttributeTypeException with no message.
UnsupportedAttributeTypeException(String) - Constructor for exception weka.core.UnsupportedAttributeTypeException
Creates a new UnsupportedAttributeTypeException.
UnsupportedClassTypeException - Exception in weka.core
Exception that is raised by an object that is unable to process the class type of the data it has been passed.
UnsupportedClassTypeException() - Constructor for exception weka.core.UnsupportedClassTypeException
Creates a new UnsupportedClassTypeException with no message.
UnsupportedClassTypeException(String) - Constructor for exception weka.core.UnsupportedClassTypeException
Creates a new UnsupportedClassTypeException.
upDate(Instances) - Method in class weka.associations.tertius.LiteralSet
Update the number of counter-instances of this set in the dataset.
upDate(Instances) - Method in class weka.associations.tertius.Rule
Update the number of counter-instances of this rule in the dataset.
update(int, Instances, double, double, double[], double) - Method in class weka.classifiers.bayes.blr.GaussianPriorImpl
Update function specific to Laplace Prior.
update(int, Instances, double, double, double[], double) - Method in class weka.classifiers.bayes.blr.LaplacePriorImpl
Update function specific to Laplace Prior.
update(int, Instances, double, double, double[], double) - Method in class weka.classifiers.bayes.blr.Prior
Interface for the update functions for different types of priors.
update(MarginCalculator.JunctionTreeNode) - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeSeparator
marginalize junciontTreeNode node over all nodes outside the separator set
update() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
update the committee
update(Instance) - Method in interface weka.core.DistanceFunction
Update the distance function (if necessary) for the newly added instance.
update(double) - Method in class weka.core.matrix.FlexibleDecimalFormat
 
update(Instance) - Method in class weka.core.neighboursearch.BallTree
Adds one instance to the BallTree.
update(CoverTree.MyHeap, double) - Method in class weka.core.neighboursearch.CoverTree
Replaces the current top/max value in the heap with the new one.
update(Instance) - Method in class weka.core.neighboursearch.CoverTree
Adds an instance to the cover tree.
update(Instance) - Method in class weka.core.neighboursearch.KDTree
Adds one instance to the KDTree.
update(Instance) - Method in class weka.core.neighboursearch.LinearNNSearch
Updates the LinearNNSearch to cater for the new added instance.
update(Instance) - Method in class weka.core.neighboursearch.NearestNeighbourSearch
Updates the NearNeighbourSearch algorithm for the new added instance.
update(Instance) - Method in class weka.core.NormalizableDistance
Update the distance function (if necessary) for the newly added instance.
update(String) - Method in class weka.experiment.DatabaseUtils
Executes a SQL DDL query or an INSERT, DELETE or UPDATE.
update() - Method in class weka.gui.MemoryUsagePanel.MemoryMonitor
Updates the GUI.
update(Graphics) - Method in class weka.gui.SplashWindow
Updates the display area of the window.
updateableClassifier() - Method in class weka.classifiers.CheckClassifier
Checks whether the scheme can build models incrementally.
UpdateableClassifier - Interface in weka.classifiers
Interface to incremental classification models that can learn using one instance at a time.
updateableClusterer() - Method in class weka.clusterers.CheckClusterer
Checks whether the scheme can build models incrementally.
UpdateableClusterer - Interface in weka.clusterers
Interface to incremental cluster models that can learn using one instance at a time.
updateBoundaries(int, double) - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
updates boundaries bLow and bHi and corresponding indexes
updateCapabilities() - Method in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
transfers the selected Capabilities from the JList to the Capabilities object.
updateCapabilitiesFilter(Capabilities) - Method in class weka.gui.explorer.AssociationsPanel
updates the capabilities filter of the GOE
updateCapabilitiesFilter(Capabilities) - Method in class weka.gui.explorer.AttributeSelectionPanel
updates the capabilities filter of the GOE
updateCapabilitiesFilter(Capabilities) - Method in class weka.gui.explorer.ClassifierPanel
updates the capabilities filter of the GOE
updateCapabilitiesFilter(Capabilities) - Method in class weka.gui.explorer.ClustererPanel
updates the capabilities filter of the GOE
updateCapabilitiesFilter(Capabilities) - Method in class weka.gui.explorer.PreprocessPanel
updates the capabilities filter of the GOE
updateChart(double[]) - Method in class weka.gui.beans.StripChart
Update the plot
updateChildPropertySheet() - Method in class weka.gui.GenericObjectEditor.GOEPanel
Updates the child property sheet, and creates if needed.
updateCholeskyFactor(Matrix, double[], double[], double, boolean[]) - Method in class weka.core.Optimization
One rank update of the Cholesky factorization of B matrix in BFGS updates, i.e.
updateClassifier(Instance) - Method in class weka.classifiers.bayes.AODE
Updates the classifier with the given instance.
updateClassifier(Instance) - Method in class weka.classifiers.bayes.AODEsr
Updates the classifier with the given instance.
updateClassifier(Instance) - Method in class weka.classifiers.bayes.BayesNet
Updates the classifier with the given instance.
updateClassifier(Instance) - Method in class weka.classifiers.bayes.DMNBtext.DNBBinary
 
updateClassifier(Instance) - Method in class weka.classifiers.bayes.DMNBtext
Updates the classifier with the given instance.
updateClassifier(Instance) - Method in class weka.classifiers.bayes.NaiveBayes
Updates the classifier with the given instance.
updateClassifier(Instance) - Method in class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable
Updates the classifier with the given instance.
updateClassifier(BayesNet, Instance) - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator
Updates the classifier with the given instance.
updateClassifier(BayesNet, Instance) - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator
Updates the classifier with the given instance.
updateClassifier(BayesNet, Instance) - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
Updates the classifier with the given instance.
updateClassifier(BayesNet, Instance) - Method in class weka.classifiers.bayes.net.estimate.SimpleEstimator
Updates the classifier with the given instance.
updateClassifier(Instance) - Method in class weka.classifiers.functions.SPegasos
Updates the classifier with the given instance.
updateClassifier(Instance) - Method in class weka.classifiers.functions.Winnow
Updates the classifier with a new learning example
updateClassifier(Instance) - Method in class weka.classifiers.lazy.IB1
Updates the classifier.
updateClassifier(Instance) - Method in class weka.classifiers.lazy.IBk
Adds the supplied instance to the training set.
updateClassifier(Instance) - Method in class weka.classifiers.lazy.KStar
Adds the supplied instance to the training set
updateClassifier(Instance) - Method in class weka.classifiers.lazy.LWL
Adds the supplied instance to the training set.
updateClassifier(Instance) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Updates the classifier.
updateClassifier(Instance) - Method in class weka.classifiers.misc.HyperPipes
Updates the classifier.
updateClassifier(Instance) - Method in class weka.classifiers.rules.NNge
Updates the classifier using the given instance.
updateClassifier(Instance) - Method in interface weka.classifiers.UpdateableClassifier
Updates a classifier using the given instance.
updateClusterer(Instance) - Method in class weka.clusterers.Cobweb
Adds an instance to the clusterer.
updateClusterer(Instance) - Method in interface weka.clusterers.UpdateableClusterer
Adds an instance to the clusterer.
upDateCounter(Instance) - Method in class weka.associations.ItemSet
Updates counter of item set with respect to given transaction.
upDateCounter(Instance, Instance) - Method in class weka.associations.LabeledItemSet
Updates counter of item set with respect to given transaction.
upDateCounters(FastVector, Instances) - Static method in class weka.associations.ItemSet
Updates counters for a set of item sets and a set of instances.
upDateCounters(FastVector, Instances, Instances) - Static method in class weka.associations.LabeledItemSet
Updates counter of a specific item set
updateCounters(ItemSet) - Method in class weka.associations.PriorEstimation
updates the support count of an item set
updateCurrentConverter() - Method in class weka.gui.ConverterFileChooser
sets the current converter according to the current filefilter
updateDimensions(JTextField) - Method in class weka.gui.visualize.PrintableComponent
updates the dimensions if necessary (i.e., if aspect ratio is to be kept).
updateDistance(double, double) - Method in class weka.core.AbstractStringDistanceFunction
Updates the current distance calculated so far with the new difference between two attributes.
updateDistance(double, double) - Method in class weka.core.ChebyshevDistance
Updates the current distance calculated so far with the new difference between two attributes.
updateDistance(double, double) - Method in class weka.core.EuclideanDistance
Updates the current distance calculated so far with the new difference between two attributes.
updateDistance(double, double) - Method in class weka.core.ManhattanDistance
Updates the current distance calculated so far with the new difference between two attributes.
updateDistance(double, double) - Method in class weka.core.NormalizableDistance
Updates the current distance calculated so far with the new difference between two attributes.
updateFinished() - Method in class weka.clusterers.Cobweb
Singals the end of the updating.
updateFinished() - Method in interface weka.clusterers.UpdateableClusterer
Singals the end of the updating.
updateFrameTitle() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
sets the title of the parent frame, if one was provided
updateFromChild() - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeSeparator
marginalize junciontTreeNode node over all nodes outside the separator set of the child clique
updateFromParent() - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeSeparator
marginalize junciontTreeNode node over all nodes outside the separator set of the parent clique
updateFS(Instance, Classifier[], double[]) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
updates the Fs values given a new model in the committee
updateIndexSetFor(int, double) - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
updates the index sets I0a, IOb, I1, I2 and I3 for vector i
updateJavadoc(String) - Method in class weka.core.AllJavadoc
updates the Javadoc in the given source code, using all the found Javadoc updaters.
updateJavadoc(String, int) - Method in class weka.core.Javadoc
generates and returns the Javadoc for the specified start/end tag pair
updateJavadoc(String) - Method in class weka.core.Javadoc
updates the Javadoc in the given source code.
updateJavadoc() - Method in class weka.core.Javadoc
generates the Javadoc and returns it applied to the source file if one was provided, otherwise an empty string.
updateList() - Method in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
transfers the Capabilities object to the JList.
updateMargins(double[], int, double) - Method in class weka.classifiers.Evaluation
Update the cumulative record of classification margins
updateMenu() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
sets the enabled/disabled state of the menu
updateMinDistance(double[], boolean[], Instances, Instance) - Method in class weka.clusterers.FarthestFirst
 
updateNormalization(Instance) - Method in class weka.classifiers.mi.CitationKNN
Updates the normalization of each attribute.
updateNumericScores(double[], double[], double) - Method in class weka.classifiers.Evaluation
Update the numeric accuracy measures.
updateObjectNames() - Method in class weka.gui.GenericObjectEditor
Updates the list of selectable object names, adding any new names to the list.
updateOption(String[], String, String) - Method in class weka.classifiers.meta.GridSearch
replaces the current option in the options array with a new value
updateOptions() - Method in class weka.experiment.ClassifierSplitEvaluator
Updates the options that the current classifier is using.
updateOptions() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
 
updateOptions() - Method in class weka.experiment.RegressionSplitEvaluator
Updates the options that the current classifier is using.
updatePointCount(int) - Method in class weka.core.neighboursearch.PerformanceStats
adds the given number to the point count.
updatePriors(Instance) - Method in class weka.classifiers.Evaluation
Updates the class prior probabilities (when incrementally training)
updatePVector(double[]) - Method in class weka.classifiers.trees.LADTree.LADInstance
 
UpdateQueue - Class in weka.clusterers.forOPTICSAndDBScan.Utils
UpdateQueue.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 27, 2004
Time: 5:36:35 PM
$ Revision 1.4 $
UpdateQueue() - Constructor for class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueue
Creates a new PriorityQueue (backed on a binary heap) with the ability to efficiently update the priority of the stored objects in the heap.
UpdateQueueElement - Class in weka.clusterers.forOPTICSAndDBScan.Utils
UpdateQueueElement.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 31, 2004
Time: 6:43:18 PM
$ Revision 1.4 $
UpdateQueueElement(double, Object, String) - Constructor for class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueueElement
 
updateRadioLinks() - Method in class weka.gui.explorer.AttributeSelectionPanel
Updates the enabled status of the input fields and labels.
updateRadioLinks() - Method in class weka.gui.explorer.ClassifierPanel
Updates the enabled status of the input fields and labels.
updateRadioLinks() - Method in class weka.gui.explorer.ClustererPanel
Updates the enabled status of the input fields and labels.
updateRanges(Instance, int, double[][]) - Method in class weka.core.NormalizableDistance
Updates the minimum and maximum and width values for all the attributes based on a new instance.
updateRanges(Instance, double[][]) - Method in class weka.core.NormalizableDistance
Updates the ranges given a new instance.
updateRanges(Instance) - Method in class weka.core.NormalizableDistance
Update the ranges if a new instance comes.
updateRangesFirst(Instance, int, double[][]) - Method in class weka.core.NormalizableDistance
Used to initialize the ranges.
UpdateReferenceSet(int, int) - Method in class weka.attributeSelection.ScatterSearchV1
Update the ReferenceSet putting the new obtained Solutions there
updateResult(String) - Method in class weka.gui.ResultHistoryPanel
Tells any component currently displaying the named result that the contents of the result text in the StringBuffer have been updated.
updateResultsTableName(ResultProducer) - Method in class weka.experiment.DatabaseResultListener
Determines the table name that results will be inserted into.
updateStatsForClassifier(double[], Instance) - Method in class weka.classifiers.Evaluation
Updates all the statistics about a classifiers performance for the current test instance.
updateStatsForPredictor(double, Instance) - Method in class weka.classifiers.Evaluation
Updates all the statistics about a predictors performance for the current test instance.
updateSupportCount(FastVector, FastVector) - Static method in class weka.associations.gsp.Sequence
Updates the support count of a set of Sequence candidates according to a given set of data sequences.
updateText() - Method in class weka.gui.PropertySheetPanel.CapabilitiesHelpDialog
updates the content of the capabilities help dialog.
updateVectors(double[]) - Method in class weka.classifiers.trees.LADTree.LADInstance
 
updateWeights(NeuralNode, double, double) - Method in class weka.classifiers.functions.neural.LinearUnit
This function will calculate what the change in weights should be and also update them.
updateWeights(double, double) - Method in class weka.classifiers.functions.neural.NeuralConnection
Call this function to update the weight values at this unit.
updateWeights(NeuralNode, double, double) - Method in interface weka.classifiers.functions.neural.NeuralMethod
This function will calculate what the change in weights should be and also update them.
updateWeights(double, double) - Method in class weka.classifiers.functions.neural.NeuralNode
Call this function to update the weight values at this unit.
updateWeights(NeuralNode, double, double) - Method in class weka.classifiers.functions.neural.SigmoidUnit
This function will calculate what the change in weights should be and also update them.
updateWeights(double[]) - Method in class weka.classifiers.trees.LADTree.LADInstance
 
updateWVector() - Method in class weka.classifiers.trees.LADTree.LADInstance
 
updateZVector() - Method in class weka.classifiers.trees.LADTree.LADInstance
 
updatingEquality(boolean, boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.classifiers.CheckClassifier
Checks whether an updateable scheme produces the same model when trained incrementally as when batch trained.
updatingEquality(boolean, boolean, boolean, boolean, boolean, boolean) - Method in class weka.clusterers.CheckClusterer
Checks whether an updateable scheme produces the same model when trained incrementally as when batch trained.
upheap() - Method in class weka.core.neighboursearch.CoverTree.MyHeap
performs upheap operation for the heap to maintian its properties.
upheap() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.MyHeap
performs upheap operation for the heap to maintian its properties.
upperBoundMinSupportTipText() - Method in class weka.associations.Apriori
Returns the tip text for this property
upperBoundMinSupportTipText() - Method in class weka.associations.FPGrowth
Returns the tip text for this property
upperNumericBoundIsOpen() - Method in class weka.core.Attribute
Returns whether the upper numeric bound of the attribute is open.
upperSizeTipText() - Method in class weka.experiment.LearningRateResultProducer
Returns the tip text for this property
URLSourcedLoader - Interface in weka.core.converters
Interface to a loader that can load from a http url
urlTipText() - Method in class weka.core.converters.DatabaseLoader
the tip text for this property
urlTipText() - Method in class weka.core.converters.DatabaseSaver
Returns the tip text for this property.
USE_DYNAMIC - Static variable in class weka.gui.GenericPropertiesCreator
name of property whether to use the dynamic approach or the old GenericObjectEditor.props file
useADTreeTipText() - Method in class weka.classifiers.bayes.BayesNet
 
useAICTipText() - Method in class weka.classifiers.functions.SimpleLogistic
Returns the tip text for this property
useAICTipText() - Method in class weka.classifiers.trees.FT
Returns the tip text for this property
useAICTipText() - Method in class weka.classifiers.trees.LMT
Returns the tip text for this property
useArcReversalTipText() - Method in class weka.classifiers.bayes.net.search.global.HillClimber
 
useArcReversalTipText() - Method in class weka.classifiers.bayes.net.search.local.HillClimber
 
useBetterEncodingTipText() - Method in class weka.filters.supervised.attribute.Discretize
Returns the tip text for this property
useCrossOverTipText() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
 
useCrossOverTipText() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
 
useCrossValidationTipText() - Method in class weka.classifiers.functions.SimpleLogistic
Returns the tip text for this property
useDefaultVisual() - Method in class weka.gui.beans.AbstractDataSink
Use the default images for a data source
useDefaultVisual() - Method in class weka.gui.beans.AbstractDataSource
Use the default images for a data source
useDefaultVisual() - Method in class weka.gui.beans.AbstractEvaluator
Use the default images for an evaluator
useDefaultVisual() - Method in class weka.gui.beans.AbstractTestSetProducer
Use the default visual for this bean
useDefaultVisual() - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
Use the default visual for this bean
useDefaultVisual() - Method in class weka.gui.beans.AbstractTrainingSetProducer
Use the default visual for this bean
useDefaultVisual() - Method in class weka.gui.beans.Associator
Use the default visual appearance for this bean
useDefaultVisual() - Method in class weka.gui.beans.AttributeSummarizer
Use the default appearance for this bean
useDefaultVisual() - Method in class weka.gui.beans.ClassAssigner
 
useDefaultVisual() - Method in class weka.gui.beans.Classifier
Use the default visual appearance for this bean
useDefaultVisual() - Method in class weka.gui.beans.ClassValuePicker
 
useDefaultVisual() - Method in class weka.gui.beans.Clusterer
Use the default visual appearance for this bean
useDefaultVisual() - Method in class weka.gui.beans.CostBenefitAnalysis
 
useDefaultVisual() - Method in class weka.gui.beans.DataVisualizer
Use the default appearance for this bean
useDefaultVisual() - Method in class weka.gui.beans.Filter
Use the default visual appearance
useDefaultVisual() - Method in class weka.gui.beans.GraphViewer
Use the default visual appearance
useDefaultVisual() - Method in class weka.gui.beans.InstanceStreamToBatchMaker
Use the default visual appearance for this bean
useDefaultVisual() - Method in class weka.gui.beans.MetaBean
Use the default visual appearance for this bean
useDefaultVisual() - Method in class weka.gui.beans.ModelPerformanceChart
Use the default appearance for this bean
useDefaultVisual() - Method in class weka.gui.beans.PredictionAppender
Use the default images for a data source
useDefaultVisual() - Method in class weka.gui.beans.SerializedModelSaver
Use the default images for this bean.
useDefaultVisual() - Method in class weka.gui.beans.StripChart
Use the default visual appearance for this bean
useDefaultVisual() - Method in class weka.gui.beans.TextViewer
Use the default visual appearance for this bean
useDefaultVisual() - Method in interface weka.gui.beans.Visible
Use the default visual representation
useDynamic() - Method in class weka.gui.GenericPropertiesCreator
gets whether the dynamic approach should be used or not
useEqualFrequencyTipText() - Method in class weka.classifiers.meta.RegressionByDiscretization
Returns the tip text for this property
useEqualFrequencyTipText() - Method in class weka.filters.unsupervised.attribute.Discretize
Returns the tip text for this property
useEqualFrequencyTipText() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Returns the tip text for this property
useErrorRateTipText() - Method in class weka.classifiers.trees.BFTree
Returns the tip text for this property
useFilter(Instances, Filter) - Static method in class weka.filters.Filter
Filters an entire set of instances through a filter and returns the new set.
useGiniTipText() - Method in class weka.classifiers.trees.BFTree
Returns the tip text for this property
useIBkTipText() - Method in class weka.classifiers.rules.DecisionTable
Returns the tip text for this property
useKDTreeTipText() - Method in class weka.clusterers.XMeans
Returns the tip text for this property.
useKernelEstimatorTipText() - Method in class weka.classifiers.bayes.NaiveBayes
Returns the tip text for this property
useKononenkoTipText() - Method in class weka.filters.supervised.attribute.Discretize
Returns the tip text for this property
useLaplaceTipText() - Method in class weka.classifiers.bayes.AODEsr
Returns the tip text for this property
useLaplaceTipText() - Method in class weka.classifiers.trees.J48
Returns the tip text for this property
useLaplaceTipText() - Method in class weka.classifiers.trees.J48graft
Returns the tip text for this property
useLeastValuesTipText() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
Returns the tip text for this property
useLowerOrderTipText() - Method in class weka.classifiers.functions.supportVector.PolyKernel
Returns the tip text for this property
useMEstimatesTipText() - Method in class weka.classifiers.bayes.AODE
Returns the tip text for this property
useMissingTipText() - Method in class weka.filters.unsupervised.attribute.AddNoise
Returns the tip text for this property
useMutationTipText() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
 
useMutationTipText() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
 
useNoPriors() - Method in class weka.classifiers.Evaluation
disables the use of priors, e.g., in case of de-serialized schemes that have no access to the original training set, but are evaluated on a set set.
useNormalizationTipText() - Method in class weka.classifiers.functions.supportVector.StringKernel
Returns the tip text for this property
useOneSETipText() - Method in class weka.classifiers.trees.BFTree
Returns the tip text for this property
useOneSETipText() - Method in class weka.classifiers.trees.SimpleCart
Returns the tip text for this property
useORForMustContainListTipText() - Method in class weka.associations.FPGrowth
Returns the tip text for this property
usePairwiseCouplingTipText() - Method in class weka.classifiers.meta.MultiClassClassifier
 
useProbTipText() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
 
usePruneTipText() - Method in class weka.classifiers.trees.SimpleCart
Return the tip text for this property
usePruningTipText() - Method in class weka.classifiers.rules.JRip
Returns the tip text for this property
UserClassifier - Class in weka.classifiers.trees
Interactively classify through visual means.
UserClassifier() - Constructor for class weka.classifiers.trees.UserClassifier
Constructor
userCommand(TreeDisplayEvent) - Method in class weka.classifiers.trees.UserClassifier
Receives user choices from the tree view, and then deals with these choices.
userCommand(TreeDisplayEvent) - Method in interface weka.gui.treevisualizer.TreeDisplayListener
Gets called when the user selects something, in the tree display.
USERCOMPONENTS_XML_EXTENSION - Static variable in class weka.gui.beans.KnowledgeFlowApp
the extension for the user components, when serialized to XML
userDataEvent(VisualizePanelEvent) - Method in class weka.classifiers.trees.UserClassifier
This receives shapes from the data view.
userDataEvent(VisualizePanelEvent) - Method in interface weka.gui.visualize.VisualizePanelListener
This method receives an object containing the shapes, instances inside and outside these shapes and the attributes these shapes were created in.
useRelativePathTipText() - Method in class weka.core.converters.AbstractFileLoader
Tip text suitable for displaying int the GUI
useRelativePathTipText() - Method in class weka.core.converters.AbstractFileSaver
Tip text suitable for displaying int the GUI
useResamplingTipText() - Method in class weka.classifiers.meta.AdaBoostM1
Returns the tip text for this property
useResamplingTipText() - Method in class weka.classifiers.meta.LogitBoost
Returns the tip text for this property
useResamplingTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
 
usernameTipText() - Method in class weka.experiment.DatabaseUtils
Returns the tip text for this property.
UserRequestAcceptor - Interface in weka.gui.beans
Interface to something that can accept requests from a user to perform some action
userTipText() - Method in class weka.core.converters.DatabaseLoader
the tip text for this property
userTipText() - Method in class weka.core.converters.DatabaseSaver
Returns the tip text for this property.
useStemmer(Stemmer, String[]) - Static method in class weka.core.stemmers.Stemming
Applies the given stemmer according to the given options.
useStoplistTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Returns the tip text for this property.
useSupervisedDiscretizationTipText() - Method in class weka.classifiers.bayes.NaiveBayes
Returns the tip text for this property
useTournamentSelectionTipText() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
 
useTournamentSelectionTipText() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
 
useTrainingTipText() - Method in class weka.attributeSelection.ClassifierSubsetEval
Returns the tip text for this property
useUnsmoothedTipText() - Method in class weka.classifiers.trees.m5.M5Base
Returns the tip text for this property
useVariant1TipText() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
Returns the tip text for this property
Utils - Class in weka.core
Class implementing some simple utility methods.
Utils() - Constructor for class weka.core.Utils
 

V

VAL_ANIMATEDICONPATH - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the animatedIconPath property
VAL_ASSOCIATEDCONNECTIONS - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the associatedConnections property
VAL_BEAN - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the bean property
VAL_BEANCONTEXT - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the beanContext property
VAL_BLUE - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the blue property
VAL_CELLS - Static variable in class weka.core.xml.XMLBasicSerialization
the matrix cells
VAL_COLOR - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the color property
VAL_CUSTOM_NAME - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the customName property
VAL_DATE - Static variable in class weka.core.xml.XMLInstances
the value for date
VAL_DIR - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the dir property
VAL_EVENTNAME - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the eventname property
VAL_FILE - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the file property
VAL_FONT - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the font property
VAL_GREEN - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the green property
VAL_HEIGHT - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the height property
VAL_HIDDEN - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the hidden property
VAL_ICONPATH - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the iconpath property
VAL_ID - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the id property
VAL_INPUTS - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the input property
VAL_INPUTSID - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the input id property
VAL_KEY - Static variable in class weka.core.xml.XMLBasicSerialization
the value for a mapping-key, e.g., Maps
VAL_LOADER - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the loader property
VAL_LOCATION - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the location property
VAL_MAPPING - Static variable in class weka.core.xml.XMLBasicSerialization
the value for mapping, e.g., Maps
VAL_NAME - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the value property
VAL_NO - Static variable in class weka.core.xml.XMLDocument
the value "no".
VAL_NO - Static variable in class weka.core.xml.XMLSerialization
the value "no" for the primitive and array attribute
VAL_NOMINAL - Static variable in class weka.core.xml.XMLInstances
the value for nominal
VAL_NORMAL - Static variable in class weka.core.xml.XMLInstances
the value for normal
VAL_NUMERIC - Static variable in class weka.core.xml.XMLInstances
the value for numeric
VAL_OPTIONS - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the options property
VAL_ORIGINALCOORDS - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the originalCoords property
VAL_OUTPUTS - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the outputs id property
VAL_OUTPUTSID - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the outputs property
VAL_PREFIX - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the prefix property
VAL_RED - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the red property
VAL_RELATIONAL - Static variable in class weka.core.xml.XMLInstances
the value for relational
VAL_RELATIONNAMEFORFILENAME - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the relationNameForFilename property (Saver)
VAL_RELATIVE_PATH - Static variable in class weka.gui.beans.xml.XMLBeans
 
VAL_ROOT - Static variable in class weka.core.xml.XMLSerialization
the value of the name for the root node
VAL_SAVER - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the saver property
VAL_SIZE - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the size property
VAL_SOURCEID - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the source property
VAL_SPARSE - Static variable in class weka.core.xml.XMLInstances
the value for sparse
VAL_STRING - Static variable in class weka.core.xml.XMLInstances
the value for string
VAL_STYLE - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the style property
VAL_SUBFLOW - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the subFlow property
VAL_TARGETID - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the target property
VAL_TEXT - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the text property
VAL_TYPE_CLASSIFIER - Static variable in class weka.core.xml.XMLOptions
a value of the type attribute.
VAL_TYPE_FLAG - Static variable in class weka.core.xml.XMLOptions
a value of the type attribute.
VAL_TYPE_HYPHENS - Static variable in class weka.core.xml.XMLOptions
a value of the type attribute.
VAL_TYPE_OPTIONHANDLER - Static variable in class weka.core.xml.XMLOptions
a value of the type attribute.
VAL_TYPE_QUOTES - Static variable in class weka.core.xml.XMLOptions
a value of the type attribute.
VAL_TYPE_SINGLE - Static variable in class weka.core.xml.XMLOptions
a value of the type attribute.
VAL_VALUE - Static variable in class weka.core.xml.XMLBasicSerialization
the value for mapping-value, e.g., Maps
VAL_WIDTH - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the width property
VAL_X - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the x property
VAL_Y - Static variable in class weka.gui.beans.xml.XMLBeans
the value of the y property
VAL_YES - Static variable in class weka.core.xml.XMLDocument
the value "yes".
VAL_YES - Static variable in class weka.core.xml.XMLSerialization
the value "yes" for the primitive and array attribute
validate() - Method in class weka.core.NormalizableDistance
performs the initializations if necessary.
validateField() - Method in class weka.core.pmml.FieldRef
 
validateFileFormat(Tag) - Method in class weka.gui.beans.SerializedModelSaver
Validate the file format.
validationChunkSizeTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
 
validationError() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
calculates the validation error of the committee
validationSetSizeTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
validationThresholdTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
validContent(String) - Method in class weka.core.xml.XMLInstances
turns all <, > and &into character entities and returns that string.
value - Variable in class weka.classifiers.lazy.kstar.KStarCache.TableEntry
scale factor or stop parameter
value - Variable in class weka.classifiers.rules.JRip.Antd
The attribute value of the antecedent.
value(int) - Method in class weka.core.Attribute
Returns a value of a nominal or string attribute.
value(int) - Method in class weka.core.BinarySparseInstance
Returns an instance's attribute value in internal format.
value(int) - Method in class weka.core.Instance
Returns an instance's attribute value in internal format.
value(Attribute) - Method in class weka.core.Instance
Returns an instance's attribute value in internal format.
Value(Element) - Constructor for class weka.core.pmml.FieldMetaInfo.Value
Construct a value.
value(int) - Method in class weka.core.SparseInstance
Returns an instance's attribute value in internal format.
value - Variable in class weka.experiment.PropertyNode
The current property value
valueIndicesTipText() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
 
valueIsSmallerEqual(Instance, int, double) - Method in class weka.core.EuclideanDistance
Returns true if the value of the given dimension is smaller or equal the value to be compared with.
valueOf(String) - Static method in enum weka.associations.FPGrowth.AssociationRule.METRIC_TYPE
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum weka.core.Capabilities.Capability
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum weka.core.logging.Logger.Level
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum weka.core.pmml.FieldMetaInfo.Interval.Closure
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum weka.core.pmml.FieldMetaInfo.Optype
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum weka.core.pmml.FieldMetaInfo.Value.Property
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum weka.core.pmml.PMMLFactory.ModelType
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum weka.core.RevisionUtils.Type
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum weka.core.TechnicalInformation.Field
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum weka.core.TechnicalInformation.Type
Returns the enum constant of this type with the specified name.
values() - Static method in enum weka.associations.FPGrowth.AssociationRule.METRIC_TYPE
Returns an array containing the constants of this enum type, in the order they are declared.
values - Variable in class weka.classifiers.functions.pace.DiscreteFunction
 
Values - Class in weka.classifiers.trees.m5
Stores some statistics.
Values(int, int, int, Instances) - Constructor for class weka.classifiers.trees.m5.Values
Constructs an object which stores some statistics of the instances such as sum, squared sum, variance, standard deviation
values() - Static method in enum weka.core.Capabilities.Capability
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum weka.core.logging.Logger.Level
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum weka.core.pmml.FieldMetaInfo.Interval.Closure
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum weka.core.pmml.FieldMetaInfo.Optype
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum weka.core.pmml.FieldMetaInfo.Value.Property
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum weka.core.pmml.PMMLFactory.ModelType
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum weka.core.RevisionUtils.Type
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum weka.core.TechnicalInformation.Field
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum weka.core.TechnicalInformation.Type
Returns an array containing the constants of this enum type, in the order they are declared.
valuesListTipText() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
Returns the tip text for this property
valuesOutputTipText() - Method in class weka.associations.Tertius
Returns the tip text for this property.
valueSparse(int) - Method in class weka.core.BinarySparseInstance
Returns an instance's attribute value in internal format.
valueSparse(int) - Method in class weka.core.Instance
Returns an instance's attribute value in internal format.
valuesToString() - Method in class weka.associations.tertius.Rule
Return a String giving the confirmation and optimistic estimate of this rule.
VARIABLE - Static variable in interface weka.core.mathematicalexpression.sym
 
variance(double[], double[], double[]) - Method in class weka.classifiers.trees.REPTree.Tree
Computes variance for subsets.
variance(int) - Method in class weka.core.Instances
Computes the variance for a numeric attribute.
variance(Attribute) - Method in class weka.core.Instances
Computes the variance for a numeric attribute.
variance(double[]) - Static method in class weka.core.Utils
Computes the variance for an array of doubles.
varianceCoveredTipText() - Method in class weka.attributeSelection.PrincipalComponents
Returns the tip text for this property
varianceCoveredTipText() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
Returns the tip text for this property.
VaryNode - Class in weka.classifiers.bayes.net
Part of ADTree implementation.
VaryNode(int) - Constructor for class weka.classifiers.bayes.net.VaryNode
Creates new VaryNode
VERBOSE - Static variable in class weka.core.ClassDiscovery
whether to output some debug information.
VERBOSE - Static variable in class weka.gui.GenericPropertiesCreator
whether to output some debug information
verboseTipText() - Method in class weka.associations.Apriori
Returns the tip text for this property
verboseTipText() - Method in class weka.attributeSelection.ExhaustiveSearch
Returns the tip text for this property
verboseTipText() - Method in class weka.attributeSelection.LinearForwardSelection
Returns the tip text for this property
verboseTipText() - Method in class weka.attributeSelection.RandomSearch
Returns the tip text for this property
verboseTipText() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
Returns the tip text for this property
verboseTipText() - Method in class weka.classifiers.meta.Dagging
Returns the tip text for this property
Version - Class in weka.core
This class contains the version number of the current WEKA release and some methods for comparing another version string.
Version() - Constructor for class weka.core.Version
 
VERSION - Static variable in class weka.core.Version
the complete version
VERSION_FILE - Static variable in class weka.core.Version
the version file
VFI - Class in weka.classifiers.misc
Classification by voting feature intervals.
VFI() - Constructor for class weka.classifiers.misc.VFI
 
ViewerDialog - Class in weka.gui
A downsized version of the ArffViewer, displaying only one Instances-Object.
ViewerDialog(Frame) - Constructor for class weka.gui.ViewerDialog
initializes the dialog with the given parent
Visible - Interface in weka.gui.beans
Interface to something that has a visible (via BeanVisual) reprentation
VisualizableErrorEvent - Class in weka.gui.beans
Event encapsulating error information for a learning scheme that can be visualized in the DataVisualizer
VisualizableErrorEvent(Object, PlotData2D) - Constructor for class weka.gui.beans.VisualizableErrorEvent
 
VisualizableErrorListener - Interface in weka.gui.beans
Interface to something that can accept VisualizableErrorEvents
visualize(String, int, int) - Method in class weka.gui.beans.TextViewer
Handles constructing a popup menu with visualization options.
visualize(String, int, int) - Method in class weka.gui.explorer.AttributeSelectionPanel
Handles constructing a popup menu with visualization options
visualize(String, int, int) - Method in class weka.gui.explorer.ClassifierPanel
Handles constructing a popup menu with visualization options.
VISUALIZE_PROPERTIES - Static variable in class weka.gui.visualize.VisualizeUtils
Contains the visualization properties
visualizeBayesNet(String, String) - Method in class weka.gui.explorer.ClassifierPanel
Pops up a GraphVisualizer for the BayesNet classifier from the currently selected item in the results list
visualizeClassifierErrors(VisualizePanel) - Method in class weka.gui.explorer.ClassifierPanel
Pops up a VisualizePanel for visualizing the data and errors for the classifier from the currently selected item in the results list
visualizeClusterAssignments(VisualizePanel) - Method in class weka.gui.explorer.ClustererPanel
Pops up a visualize panel to display cluster assignments
visualizeClusterer(String, int, int) - Method in class weka.gui.explorer.ClustererPanel
Handles constructing a popup menu with visualization options
visualizeCostBenefitAnalysis(CostBenefitAnalysis, String) - Method in class weka.gui.explorer.ClassifierPanel
Pops up the Cost/Benefit analysis panel.
VisualizePanel - Class in weka.gui.explorer
A slightly extended MatrixPanel for better support in the Explorer.
VisualizePanel() - Constructor for class weka.gui.explorer.VisualizePanel
 
VisualizePanel - Class in weka.gui.visualize
This panel allows the user to visualize a dataset (and if provided) a classifier's/clusterer's predictions in two dimensions.
VisualizePanel(VisualizePanelListener) - Constructor for class weka.gui.visualize.VisualizePanel
This constructor allows a VisualizePanelListener to be set.
VisualizePanel() - Constructor for class weka.gui.visualize.VisualizePanel
Constructor
VisualizePanel.PlotPanel - Class in weka.gui.visualize
Inner class to handle plotting
VisualizePanelEvent - Class in weka.gui.visualize
This event Is fired to a listeners 'userDataEvent' function when The user on the VisualizePanel clicks submit.
VisualizePanelEvent(FastVector, Instances, Instances, int, int) - Constructor for class weka.gui.visualize.VisualizePanelEvent
This constructor creates the event with all the parameters set.
VisualizePanelListener - Interface in weka.gui.visualize
Interface implemented by a class that is interested in receiving submited shapes from a visualize panel.
VisualizePlugin - Interface in weka.gui.visualize.plugins
Interface implemented by classes loaded dynamically to visualize classifier results in the explorer.
visualizeTransformedData(Instances) - Method in class weka.gui.explorer.AttributeSelectionPanel
Popup a visualize panel for viewing transformed data
visualizeTree(String, String) - Method in class weka.gui.explorer.ClassifierPanel
Pops up a TreeVisualizer for the classifier from the currently selected item in the results list
visualizeTree(String, String) - Method in class weka.gui.explorer.ClustererPanel
Pops up a TreeVisualizer for the clusterer from the currently selected item in the results list
VisualizeUtils - Class in weka.gui.visualize
This class contains utility routines for visualization
VisualizeUtils() - Constructor for class weka.gui.visualize.VisualizeUtils
 
VLINE - Static variable in class weka.gui.visualize.VisualizePanelEvent
 
Vote - Class in weka.classifiers.meta
Class for combining classifiers.
Vote() - Constructor for class weka.classifiers.meta.Vote
 
VotedPerceptron - Class in weka.classifiers.functions
Implementation of the voted perceptron algorithm by Freund and Schapire.
VotedPerceptron() - Constructor for class weka.classifiers.functions.VotedPerceptron
 
voteFlagTipText() - Method in class weka.datagenerators.classifiers.classification.RDG1
Returns the tip text for this property

W

WAIT - Static variable in class weka.core.converters.AbstractSaver
 
waitingExperiment(int) - Method in class weka.experiment.RemoteExperiment
Push an experiment back on the queue of waiting experiments
waitingTask(int) - Method in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
Push an experiment back on the queue of waiting experiments
waitUntilFinished() - Method in class weka.gui.beans.FlowRunner
Waits until all flows have finished executing before returning
WAODE - Class in weka.classifiers.bayes
WAODE contructs the model called Weightily Averaged One-Dependence Estimators.

For more information, see

L.
WAODE() - Constructor for class weka.classifiers.bayes.WAODE
 
WARNING - Static variable in class weka.core.Debug
the log level Warning
Wavelet - Class in weka.filters.unsupervised.attribute
A filter for wavelet transformation.

For more information see:

Wikipedia (2004).
Wavelet() - Constructor for class weka.filters.unsupervised.attribute.Wavelet
default constructor
weight() - Method in class weka.classifiers.evaluation.NominalPrediction
Gets the weight assigned to this prediction.
weight() - Method in class weka.classifiers.evaluation.NumericPrediction
Gets the weight assigned to this prediction.
weight() - Method in interface weka.classifiers.evaluation.Prediction
Gets the weight assigned to this prediction.
weight(Instance) - Method in class weka.classifiers.rules.part.ClassifierDecList
Returns the weight a rule assigns to an instance.
weight() - Method in class weka.core.Attribute
Returns the attribute's weight.
weight() - Method in class weka.core.Instance
Returns the instance's weight.
WEIGHT_INVERSE - Static variable in class weka.classifiers.lazy.IBk
weight by 1/distance.
WEIGHT_NONE - Static variable in class weka.classifiers.lazy.IBk
no weighting.
WEIGHT_SIMILARITY - Static variable in class weka.classifiers.lazy.IBk
weight by 1-distance.
weightByConfidenceTipText() - Method in class weka.classifiers.misc.VFI
Returns the tip text for this property
weightByDistanceTipText() - Method in class weka.attributeSelection.ReliefFAttributeEval
Returns the tip text for this property
weightedAreaUnderROC() - Method in class weka.classifiers.Evaluation
Calculates the weighted (by class size) AUC.
weightedDistribution(int[]) - Method in class weka.filters.unsupervised.attribute.RandomProjection
Calculates a weighted distribution
weightedFalseNegativeRate() - Method in class weka.classifiers.Evaluation
Calculates the weighted (by class size) false negative rate.
weightedFalsePositiveRate() - Method in class weka.classifiers.Evaluation
Calculates the weighted (by class size) false positive rate.
weightedFMeasure() - Method in class weka.classifiers.Evaluation
Calculates the weighted (by class size) F-Measure.
weightedInstancesHandler() - Method in class weka.associations.CheckAssociator
Checks whether the scheme says it can handle instance weights.
weightedInstancesHandler() - Method in class weka.attributeSelection.CheckAttributeSelection
Checks whether the scheme says it can handle instance weights.
weightedInstancesHandler() - Method in class weka.classifiers.CheckClassifier
Checks whether the scheme says it can handle instance weights.
weightedInstancesHandler() - Method in class weka.classifiers.functions.supportVector.CheckKernel
Checks whether the scheme says it can handle instance weights.
weightedInstancesHandler() - Method in class weka.clusterers.CheckClusterer
Checks whether the scheme says it can handle instance weights.
WeightedInstancesHandler - Interface in weka.core
Interface to something that makes use of the information provided by instance weights.
weightedInstancesHandler() - Method in class weka.estimators.CheckEstimator
Checks whether the scheme says it can handle instance weights.
weightedPrecision() - Method in class weka.classifiers.Evaluation
Calculates the weighted (by class size) false precision.
weightedRecall() - Method in class weka.classifiers.Evaluation
Calculates the weighted (by class size) recall.
weightedTrueNegativeRate() - Method in class weka.classifiers.Evaluation
Calculates the weighted (by class size) true negative rate.
weightedTruePositiveRate() - Method in class weka.classifiers.Evaluation
Calculates the weighted (by class size) true positive rate.
weightingKernelTipText() - Method in class weka.classifiers.lazy.LWL
Returns the tip text for this property.
WEIGHTMETHOD_1 - Static variable in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
weight method: 1.0
WEIGHTMETHOD_INVERSE1 - Static variable in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
weight method: 1.0 / Total # of prop.
WEIGHTMETHOD_INVERSE2 - Static variable in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
weight method: Total # of prop.
WEIGHTMETHOD_ORIGINAL - Static variable in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
weight method: keep the weight to be the same as the original value
weightMethodTipText() - Method in class weka.classifiers.mi.MIWrapper
Returns the tip text for this property
weightMethodTipText() - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
Returns the tip text for this property
weights(Instance) - Method in class weka.classifiers.trees.j48.BinC45Split
Returns weights if instance is assigned to more than one subset.
weights(Instance) - Method in class weka.classifiers.trees.j48.C45Split
Returns weights if instance is assigned to more than one subset.
weights(Instance) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Returns weights if instance is assigned to more than one subset.
weights(Instance) - Method in class weka.classifiers.trees.j48.GraftSplit
 
weights(Instance) - Method in class weka.classifiers.trees.j48.NBTreeNoSplit
Always returns null because there is only one subset.
weights(Instance) - Method in class weka.classifiers.trees.j48.NBTreeSplit
Returns weights if instance is assigned to more than one subset.
weights(Instance) - Method in class weka.classifiers.trees.j48.NoSplit
Always returns null because there is only one subset.
weights(Instance) - Method in class weka.classifiers.trees.lmt.ResidualSplit
Method not in use
weightsTipText() - Method in class weka.classifiers.functions.LibLINEAR
Returns the tip text for this property
weightsTipText() - Method in class weka.classifiers.functions.LibSVM
Returns the tip text for this property
weightThresholdTipText() - Method in class weka.classifiers.meta.AdaBoostM1
Returns the tip text for this property
weightThresholdTipText() - Method in class weka.classifiers.meta.LogitBoost
Returns the tip text for this property
weightTipText() - Method in class weka.classifiers.bayes.AODE
Returns the tip text for this property
weightTrimBetaTipText() - Method in class weka.classifiers.functions.SimpleLogistic
Returns the tip text for this property
weightTrimBetaTipText() - Method in class weka.classifiers.trees.FT
Returns the tip text for this property
weightTrimBetaTipText() - Method in class weka.classifiers.trees.LMT
Returns the tip text for this property
weightValue(int) - Method in class weka.classifiers.functions.neural.NeuralConnection
Call this to get the weight value on a particular connection.
weightValue(int) - Method in class weka.classifiers.functions.neural.NeuralNode
Call this to get the weight value on a particular connection.
weka.associations - package weka.associations
 
weka.associations.gsp - package weka.associations.gsp
 
weka.associations.tertius - package weka.associations.tertius
 
weka.attributeSelection - package weka.attributeSelection
 
weka.classifiers - package weka.classifiers
 
weka.classifiers.bayes - package weka.classifiers.bayes
 
weka.classifiers.bayes.blr - package weka.classifiers.bayes.blr
 
weka.classifiers.bayes.net - package weka.classifiers.bayes.net
 
weka.classifiers.bayes.net.estimate - package weka.classifiers.bayes.net.estimate
 
weka.classifiers.bayes.net.search - package weka.classifiers.bayes.net.search
 
weka.classifiers.bayes.net.search.ci - package weka.classifiers.bayes.net.search.ci
 
weka.classifiers.bayes.net.search.fixed - package weka.classifiers.bayes.net.search.fixed
 
weka.classifiers.bayes.net.search.global - package weka.classifiers.bayes.net.search.global
 
weka.classifiers.bayes.net.search.local - package weka.classifiers.bayes.net.search.local
 
weka.classifiers.evaluation - package weka.classifiers.evaluation
 
weka.classifiers.functions - package weka.classifiers.functions
 
weka.classifiers.functions.neural - package weka.classifiers.functions.neural
 
weka.classifiers.functions.pace - package weka.classifiers.functions.pace
 
weka.classifiers.functions.supportVector - package weka.classifiers.functions.supportVector
 
weka.classifiers.lazy - package weka.classifiers.lazy
 
weka.classifiers.lazy.kstar - package weka.classifiers.lazy.kstar
 
weka.classifiers.meta - package weka.classifiers.meta
 
weka.classifiers.meta.nestedDichotomies - package weka.classifiers.meta.nestedDichotomies
 
weka.classifiers.mi - package weka.classifiers.mi
 
weka.classifiers.mi.supportVector - package weka.classifiers.mi.supportVector
 
weka.classifiers.misc - package weka.classifiers.misc
 
weka.classifiers.pmml.consumer - package weka.classifiers.pmml.consumer
 
weka.classifiers.rules - package weka.classifiers.rules
 
weka.classifiers.rules.part - package weka.classifiers.rules.part
 
weka.classifiers.trees - package weka.classifiers.trees
 
weka.classifiers.trees.adtree - package weka.classifiers.trees.adtree
 
weka.classifiers.trees.ft - package weka.classifiers.trees.ft
 
weka.classifiers.trees.j48 - package weka.classifiers.trees.j48
 
weka.classifiers.trees.lmt - package weka.classifiers.trees.lmt
 
weka.classifiers.trees.m5 - package weka.classifiers.trees.m5
 
weka.classifiers.xml - package weka.classifiers.xml
 
weka.clusterers - package weka.clusterers
 
weka.clusterers.forOPTICSAndDBScan.Databases - package weka.clusterers.forOPTICSAndDBScan.Databases
 
weka.clusterers.forOPTICSAndDBScan.DataObjects - package weka.clusterers.forOPTICSAndDBScan.DataObjects
 
weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI - package weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI
 
weka.clusterers.forOPTICSAndDBScan.Utils - package weka.clusterers.forOPTICSAndDBScan.Utils
 
weka.core - package weka.core
 
weka.core.converters - package weka.core.converters
 
weka.core.logging - package weka.core.logging
 
weka.core.mathematicalexpression - package weka.core.mathematicalexpression
 
weka.core.matrix - package weka.core.matrix
 
weka.core.neighboursearch - package weka.core.neighboursearch
 
weka.core.neighboursearch.balltrees - package weka.core.neighboursearch.balltrees
 
weka.core.neighboursearch.covertrees - package weka.core.neighboursearch.covertrees
 
weka.core.neighboursearch.kdtrees - package weka.core.neighboursearch.kdtrees
 
weka.core.pmml - package weka.core.pmml
 
weka.core.stemmers - package weka.core.stemmers
 
weka.core.tokenizers - package weka.core.tokenizers
 
weka.core.xml - package weka.core.xml
 
weka.datagenerators - package weka.datagenerators
 
weka.datagenerators.classifiers.classification - package weka.datagenerators.classifiers.classification
 
weka.datagenerators.classifiers.regression - package weka.datagenerators.classifiers.regression
 
weka.datagenerators.clusterers - package weka.datagenerators.clusterers
 
weka.estimators - package weka.estimators
 
weka.experiment - package weka.experiment
 
weka.experiment.xml - package weka.experiment.xml
 
weka.filters - package weka.filters
 
weka.filters.supervised.attribute - package weka.filters.supervised.attribute
 
weka.filters.supervised.instance - package weka.filters.supervised.instance
 
weka.filters.unsupervised.attribute - package weka.filters.unsupervised.attribute
 
weka.filters.unsupervised.instance - package weka.filters.unsupervised.instance
 
weka.filters.unsupervised.instance.subsetbyexpression - package weka.filters.unsupervised.instance.subsetbyexpression
 
weka.gui - package weka.gui
 
weka.gui.arffviewer - package weka.gui.arffviewer
 
weka.gui.beans - package weka.gui.beans
 
weka.gui.beans.xml - package weka.gui.beans.xml
 
weka.gui.boundaryvisualizer - package weka.gui.boundaryvisualizer
 
weka.gui.experiment - package weka.gui.experiment
 
weka.gui.explorer - package weka.gui.explorer
 
weka.gui.graphvisualizer - package weka.gui.graphvisualizer
 
weka.gui.hierarchyvisualizer - package weka.gui.hierarchyvisualizer
 
weka.gui.sql - package weka.gui.sql
 
weka.gui.sql.event - package weka.gui.sql.event
 
weka.gui.streams - package weka.gui.streams
 
weka.gui.treevisualizer - package weka.gui.treevisualizer
 
weka.gui.visualize - package weka.gui.visualize
 
weka.gui.visualize.plugins - package weka.gui.visualize.plugins
 
WekaException - Exception in weka.core
Class for Weka-specific exceptions.
WekaException() - Constructor for exception weka.core.WekaException
Creates a new WekaException with no message.
WekaException(String) - Constructor for exception weka.core.WekaException
Creates a new WekaException.
wekaStaticWrapper(Sourcable, String) - Static method in class weka.classifiers.Evaluation
Wraps a static classifier in enough source to test using the weka class libraries.
wekaStaticWrapper(Sourcable, String, Instances, Instances) - Static method in class weka.filters.Filter
generates source code from the filter
WekaTaskMonitor - Class in weka.gui
This panel records the number of weka tasks running and displays a simple bird animation while their are active tasks
WekaTaskMonitor() - Constructor for class weka.gui.WekaTaskMonitor
Constructor
WekaWrapper - Interface in weka.gui.beans
Interface to something that can wrap around a class of Weka algorithms (classifiers, filters etc).
WEST_CONNECTOR - Static variable in class weka.gui.beans.BeanVisual
 
whichSubset(Instance) - Method in class weka.classifiers.trees.j48.BinC45Split
Returns index of subset instance is assigned to.
whichSubset(Instance) - Method in class weka.classifiers.trees.j48.C45Split
Returns index of subset instance is assigned to.
whichSubset(Instance) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Returns index of subset instance is assigned to.
whichSubset(Instance) - Method in class weka.classifiers.trees.j48.GraftSplit
 
whichSubset(Instance) - Method in class weka.classifiers.trees.j48.NBTreeNoSplit
Always returns 0 because only there is only one subset.
whichSubset(Instance) - Method in class weka.classifiers.trees.j48.NBTreeSplit
Returns index of subset instance is assigned to.
whichSubset(Instance) - Method in class weka.classifiers.trees.j48.NoSplit
Always returns 0 because only there is only one subset.
whichSubset(Instance) - Method in class weka.classifiers.trees.lmt.ResidualSplit
 
whileCnt - Variable in class weka.classifiers.lazy.LBR
 
wholeDataErrTipText() - Method in class weka.classifiers.rules.Ridor
Returns the tip text for this property
widestDim(double[][], double[][]) - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
Returns the widest dimension.
widestDim(double[][], double[][]) - Method in class weka.core.neighboursearch.KDTree
Returns the widest dimension/attribute in a KDTreeNode (widest after normalizing).
widestDim(double[][], double[][]) - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
Returns the widest dimension.
width() - Method in class weka.classifiers.meta.GridSearch.Grid
returns the number of points in the grid on the X axis (incl.
width() - Method in class weka.core.matrix.ExponentialFormat
 
width() - Method in class weka.core.matrix.FlexibleDecimalFormat
 
width - Variable in class weka.core.matrix.FloatingPointFormat
 
width() - Method in class weka.core.matrix.FloatingPointFormat
 
WIDTH - Static variable in class weka.core.neighboursearch.KDTree
The index of WIDTH (MAX-MIN) value in attributes' range array.
WIDTH - Static variable in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
Index of width value (max-min) in an array of attributes' range.
WIDTH - Static variable in class weka.gui.arffviewer.ArffViewerMainPanel
default width
width - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
 
WIDTH - Static variable in class weka.gui.sql.SqlViewer
the width property in the history file.
WIN_STRING - Variable in class weka.experiment.ResultMatrix
win string
windowActivated(WindowEvent) - Method in class weka.gui.arffviewer.ArffViewer
invoked when a window is activated
windowClosed(WindowEvent) - Method in class weka.gui.arffviewer.ArffViewer
invoked when a window is closed
windowClosing(WindowEvent) - Method in class weka.gui.arffviewer.ArffViewer
invoked when a window is in the process of closing
windowDeactivated(WindowEvent) - Method in class weka.gui.arffviewer.ArffViewer
invoked when a window is deactivated
windowDeiconified(WindowEvent) - Method in class weka.gui.arffviewer.ArffViewer
invoked when a window is deiconified
windowIconified(WindowEvent) - Method in class weka.gui.arffviewer.ArffViewer
invoked when a window is iconified
windowListChanged() - Method in class weka.gui.Main
is called when window list changed somehow (add or remove).
windowOpened(WindowEvent) - Method in class weka.gui.arffviewer.ArffViewer
invoked when a window is has been opened
windowSizeTipText() - Method in class weka.classifiers.lazy.IBk
Returns the tip text for this property.
Winnow - Class in weka.classifiers.functions
Implements Winnow and Balanced Winnow algorithms by Littlestone.

For more information, see

N.
Winnow() - Constructor for class weka.classifiers.functions.Winnow
 
WITHIN_BATCH - Static variable in class weka.gui.beans.IncrementalClassifierEvent
 
wordsToKeepTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Returns the tip text for this property.
WordTokenizer - Class in weka.core.tokenizers
A simple tokenizer that is using the java.util.StringTokenizer class to tokenize the strings.
WordTokenizer() - Constructor for class weka.core.tokenizers.WordTokenizer
 
WrapperSubsetEval - Class in weka.attributeSelection
WrapperSubsetEval:

Evaluates attribute sets by using a learning scheme.
WrapperSubsetEval() - Constructor for class weka.attributeSelection.WrapperSubsetEval
Constructor.
wrapUp() - Method in class weka.classifiers.functions.supportVector.RegOptimizer
wrap up various variables to save memeory and do some housekeeping after optimization has finished.
wrapUp() - Method in class weka.classifiers.functions.supportVector.RegSMO
wrap up various variables to save memeory and do some housekeeping after optimization has finished.
wrapUp() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
wrap up various variables to save memeory and do some housekeeping after optimization has finished.
write(Writer) - Method in class weka.classifiers.CostMatrix
Writes out a matrix.
WRITE - Static variable in class weka.core.converters.AbstractSaver
The write modes
write(Instances) - Method in class weka.core.converters.ConverterUtils.DataSink
writes the given data either via the saver or to the defined output stream (depending on the constructor).
write(String, Instances) - Static method in class weka.core.converters.ConverterUtils.DataSink
writes the data to the given file.
write(Saver, Instances) - Static method in class weka.core.converters.ConverterUtils.DataSink
writes the data via the given saver.
write(OutputStream, Instances) - Static method in class weka.core.converters.ConverterUtils.DataSink
writes the data to the given stream (always in ARFF format).
write(Writer) - Method in class weka.core.matrix.Matrix
Writes out a matrix.
write(Writer) - Method in class weka.core.Matrix
Deprecated.
Writes out a matrix.
write(String, Object) - Static method in class weka.core.SerializationHelper
serializes the given object to the specified file.
write(OutputStream, Object) - Static method in class weka.core.SerializationHelper
serializes the given object to the specified stream.
write(String) - Method in class weka.core.Stopwords
Writes the current stopwords to the given file
write(File) - Method in class weka.core.Stopwords
Writes the current stopwords to the given file
write(BufferedWriter) - Method in class weka.core.Stopwords
Writes the current stopwords to the given writer.
write(byte[], int, int) - Method in class weka.core.Tee
Writes len bytes from the specified byte array starting at offset off to this stream.
write(int) - Method in class weka.core.Tee
Writes the specified byte to this stream.
write(String, Object) - Static method in class weka.core.xml.KOML
writes the XML-serialized object to the given file
write(File, Object) - Static method in class weka.core.xml.KOML
write the XML-serialized object to the given file
write(OutputStream, Object) - Static method in class weka.core.xml.KOML
writes the XML-serialized object to a stream
write(String) - Method in class weka.core.xml.XMLDocument
writes the current DOM document into the given file.
write(File) - Method in class weka.core.xml.XMLDocument
writes the current DOM document into the given file.
write(OutputStream) - Method in class weka.core.xml.XMLDocument
writes the current DOM document into the given stream.
write(Writer) - Method in class weka.core.xml.XMLDocument
writes the current DOM document into the given writer.
write(String, Object) - Method in class weka.core.xml.XMLSerialization
writes the given object into the file
write(File, Object) - Method in class weka.core.xml.XMLSerialization
writes the given object into the file
write(OutputStream, Object) - Method in class weka.core.xml.XMLSerialization
writes the given object into the stream
write(Writer, Object) - Method in class weka.core.xml.XMLSerialization
writes the given object into the writer
write() - Method in class weka.core.xml.XMLSerializationMethodHandler
returns the handler for write methods
write(String, Object) - Static method in class weka.core.xml.XStream
writes the XML-serialized object to the given file
write(File, Object) - Static method in class weka.core.xml.XStream
write the XML-serialized object to the given file
write(OutputStream, Object) - Static method in class weka.core.xml.XStream
writes the XML-serialized object to the given output stream
write(Writer, Object) - Static method in class weka.core.xml.XStream
writes the XML-serialized object to the given Writer
write(String, Experiment) - Static method in class weka.experiment.Experiment
Writes the experiment to disk.
writeAll(String, Object[]) - Static method in class weka.core.SerializationHelper
serializes the given objects to the specified file.
writeAll(OutputStream, Object[]) - Static method in class weka.core.SerializationHelper
serializes the given objects to the specified stream.
writeBatch() - Method in class weka.core.converters.AbstractSaver
Writes to a file in batch mode To be overridden.
writeBatch() - Method in class weka.core.converters.ArffSaver
Writes a Batch of instances
writeBatch() - Method in class weka.core.converters.C45Saver
Writes a Batch of instances
writeBatch() - Method in class weka.core.converters.CSVSaver
Writes a Batch of instances
writeBatch() - Method in class weka.core.converters.DatabaseSaver
Writes a Batch of instances.
writeBatch() - Method in class weka.core.converters.LibSVMSaver
Writes a Batch of instances
writeBatch() - Method in interface weka.core.converters.Saver
Writes to a destination in batch mode
writeBatch() - Method in class weka.core.converters.SerializedInstancesSaver
Writes a Batch of instances.
writeBatch() - Method in class weka.core.converters.SVMLightSaver
Writes a Batch of instances.
writeBatch() - Method in class weka.core.converters.XRFFSaver
Writes a Batch of instances
writeBeanConnection(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans
adds the given BeanConncetion to a DOM structure.
writeBeanInstance(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans
adds the given BeanInstance to a DOM structure.
writeBeanLoader(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans
adds the given Loader (a bean) to a DOM structure.
writeBeanSaver(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans
adds the given Saver (a bean) to a DOM structure.
writeBeanVisual(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans
adds the given BeanVisual to a DOM structure.
writeBinary(String, Object) - Static method in class weka.core.xml.SerialUIDChanger
serializes the given object into the given file
writeBooleanToXML(Element, boolean, String) - Method in class weka.core.xml.XMLSerialization
adds the given primitive to the DOM structure.
writeByteToXML(Element, byte, String) - Method in class weka.core.xml.XMLSerialization
adds the given primitive to the DOM structure.
writeCharToXML(Element, char, String) - Method in class weka.core.xml.XMLSerialization
adds the given primitive to the DOM structure.
writeCollection(Element, Object, String) - Method in class weka.core.xml.XMLBasicSerialization
adds the given Collection to a DOM structure.
writeColor(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans
adds the given Color to a DOM structure.
writeColorUIResource(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans
adds the given ColorUIResource to a DOM structure.
writeCostMatrixOld(Element, Object, String) - Method in class weka.core.xml.XMLBasicSerialization
adds the given CostMatrix (old) to a DOM structure.
writeCurve(String, Estimator, double, double, int) - Static method in class weka.estimators.EstimatorUtils
Output of an n points of a density curve.
writeCurve(String, Estimator, Estimator, double, double, double, int) - Static method in class weka.estimators.EstimatorUtils
Output of an n points of a density curve.
writeDefaultListModel(Element, Object, String) - Method in class weka.core.xml.XMLBasicSerialization
adds the given DefaultListModel to a DOM structure.
writeDimension(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans
adds the given Dimension to a DOM structure.
writeDOT(String, String, FastVector, FastVector) - Static method in class weka.gui.graphvisualizer.DotParser
This method saves a graph in a file in DOT format.
writeDoubleToXML(Element, double, String) - Method in class weka.core.xml.XMLSerialization
adds the given primitive to the DOM structure.
writeFloatToXML(Element, float, String) - Method in class weka.core.xml.XMLSerialization
adds the given primitive to the DOM structure.
writeFont(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans
adds the given Font to a DOM structure.
writeFontUIResource(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans
adds the given FontUIResource to a DOM structure.
writeIncremental(Instance) - Method in class weka.core.converters.AbstractSaver
Method for incremental saving.
writeIncremental(Instance) - Method in class weka.core.converters.ArffSaver
Saves an instances incrementally.
writeIncremental(Instance) - Method in class weka.core.converters.C45Saver
Saves an instances incrementally.
writeIncremental(Instance) - Method in class weka.core.converters.CSVSaver
Saves an instances incrementally.
writeIncremental(Instance) - Method in class weka.core.converters.DatabaseSaver
Saves an instances incrementally.
writeIncremental(Instance) - Method in class weka.core.converters.LibSVMSaver
Saves an instances incrementally.
writeIncremental(Instance) - Method in interface weka.core.converters.Saver
Writes to a destination in incremental mode.
writeIncremental(Instance) - Method in class weka.core.converters.SVMLightSaver
Saves an instances incrementally.
writeIntToXML(Element, int, String) - Method in class weka.core.xml.XMLSerialization
adds the given primitive to the DOM structure.
writeLoader(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans
adds the given Loader to a DOM structure.
writeLongToXML(Element, long, String) - Method in class weka.core.xml.XMLSerialization
adds the given primitive to the DOM structure.
writeMap(Element, Object, String) - Method in class weka.core.xml.XMLBasicSerialization
adds the given Map to a DOM structure.
writeMatrix(Element, Object, String) - Method in class weka.core.xml.XMLBasicSerialization
adds the given Matrix to a DOM structure.
writeMatrixOld(Element, Object, String) - Method in class weka.core.xml.XMLBasicSerialization
adds the given Matrix (old) to a DOM structure.
writeMetaBean(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans
adds the given MetaBean to a DOM structure.
writeOPTICSresultsTipText() - Method in class weka.clusterers.OPTICS
Returns the tip text for this property
writePoint(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans
adds the given Point to a DOM structure.
writePostProcess(Object) - Method in class weka.core.xml.XMLSerialization
enables derived classes to add other properties to the DOM tree, e.g.
writePostProcess(Object) - Method in class weka.experiment.xml.XMLExperiment
enables derived classes to add other properties to the DOM tree, e.g.
writePostProcess(Object) - Method in class weka.gui.beans.xml.XMLBeans
enables derived classes to add other properties to the DOM tree, e.g.
writePreProcess(Object) - Method in class weka.core.xml.XMLSerialization
enables derived classes to due some pre-processing on the objects, that's about to be serialized.
writePreProcess(Object) - Method in class weka.gui.beans.xml.XMLBeans
enables derived classes to due some pre-processing on the objects, that's about to be serialized.
writePropertyNode(Element, Object, String) - Method in class weka.experiment.xml.XMLExperiment
adds the given PropertyNode to a DOM structure.
writeSaver(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans
adds the given Saver to a DOM structure.
writeShortToXML(Element, short, String) - Method in class weka.core.xml.XMLSerialization
adds the given primitive to the DOM structure.
writeToFile(String, Object) - Static method in class weka.core.Debug
Writes the given object to the specified file.
writeToFile(String, String) - Static method in class weka.core.Debug
Writes the given message to the specified file.
writeToFile(String, Object, boolean) - Static method in class weka.core.Debug
Writes the given object to the specified file.
writeToFile(String, String, boolean) - Static method in class weka.core.Debug
Writes the given message to the specified file.
writeToXML(Element, Object, String) - Method in class weka.core.xml.XMLSerialization
adds the given Object to a DOM structure.
writeXMLBIF03(String, String, FastVector, FastVector) - Static method in class weka.gui.graphvisualizer.BIFParser
This method writes a graph in XMLBIF ver.
wVector - Variable in class weka.classifiers.trees.LADTree.LADInstance
 

X

x - Variable in class weka.gui.graphvisualizer.GraphNode
The x and y position of the node
X_SHAPE - Static variable in class weka.gui.visualize.Plot2D
 
XBaseTipText() - Method in class weka.classifiers.meta.GridSearch
Returns the tip text for this property
XExpressionTipText() - Method in class weka.classifiers.meta.GridSearch
Returns the tip text for this property
xLabelFreqTipText() - Method in class weka.gui.beans.StripChart
GUI Tip text
xlogx(int) - Static method in class weka.core.Utils
Returns c*log2(c) for a given integer value c.
XMaxTipText() - Method in class weka.classifiers.meta.GridSearch
Returns the tip text for this property
xMean - Variable in class weka.classifiers.mi.MILR
 
XMeans - Class in weka.clusterers
Cluster data using the X-means algorithm.

X-Means is K-Means extended by an Improve-Structure part In this part of the algorithm the centers are attempted to be split in its region.
XMeans() - Constructor for class weka.clusterers.XMeans
the default constructor.
XMinTipText() - Method in class weka.classifiers.meta.GridSearch
Returns the tip text for this property
XMLBasicSerialization - Class in weka.core.xml
This serializer contains some read/write methods for common classes that are not beans-conform.
XMLBasicSerialization() - Constructor for class weka.core.xml.XMLBasicSerialization
initializes the serialization
XMLBeans - Class in weka.gui.beans.xml
This class serializes and deserializes a KnowledgeFlow setup to and fro XML.
XMLBeans(JComponent, BeanContextSupport) - Constructor for class weka.gui.beans.xml.XMLBeans
initializes the serialization for layouts
XMLBeans(JComponent, BeanContextSupport, int) - Constructor for class weka.gui.beans.xml.XMLBeans
initializes the serialization for different types of data
XMLClassifier - Class in weka.classifiers.xml
This class serializes and deserializes a Classifier instance to and fro XML.
XMLClassifier() - Constructor for class weka.classifiers.xml.XMLClassifier
initializes the serialization
XMLDocument - Class in weka.core.xml
This class offers some methods for generating, reading and writing XML documents.
It can only handle UTF-8.
XMLDocument() - Constructor for class weka.core.xml.XMLDocument
initializes the factory with non-validating parser.
XMLDocument(String) - Constructor for class weka.core.xml.XMLDocument
Creates a new instance of XMLDocument.
XMLDocument(File) - Constructor for class weka.core.xml.XMLDocument
Creates a new instance of XMLDocument.
XMLDocument(InputStream) - Constructor for class weka.core.xml.XMLDocument
Creates a new instance of XMLDocument.
XMLDocument(Reader) - Constructor for class weka.core.xml.XMLDocument
Creates a new instance of XMLDocument.
XMLExperiment - Class in weka.experiment.xml
This class serializes and deserializes an Experiment instance to and fro XML.
It omits the options from the Experiment, since these are handled by the get/set-methods.
XMLExperiment() - Constructor for class weka.experiment.xml.XMLExperiment
initializes the serialization
XMLInstances - Class in weka.core.xml
XML representation of the Instances class.
XMLInstances() - Constructor for class weka.core.xml.XMLInstances
the default constructor
XMLInstances(Instances) - Constructor for class weka.core.xml.XMLInstances
generates the XML structure based on the given data
XMLInstances(Reader) - Constructor for class weka.core.xml.XMLInstances
generates the Instances directly from the reader containing the XML data.
XMLNormalize(String) - Method in class weka.classifiers.bayes.BayesNet
XMLNormalize converts the five standard XML entities in a string g.e.
XMLOptions - Class in weka.core.xml
A class for transforming options listed in XML to a regular WEKA command line string.
XMLOptions() - Constructor for class weka.core.xml.XMLOptions
Creates a new instance of XMLOptions.
XMLOptions(String) - Constructor for class weka.core.xml.XMLOptions
Creates a new instance of XMLOptions.
XMLOptions(File) - Constructor for class weka.core.xml.XMLOptions
Creates a new instance of XMLOptions.
XMLOptions(InputStream) - Constructor for class weka.core.xml.XMLOptions
Creates a new instance of XMLOptions.
XMLOptions(Reader) - Constructor for class weka.core.xml.XMLOptions
Creates a new instance of XMLOptions.
xmlRules() - Method in class weka.associations.FPGrowth
 
XMLSerialization - Class in weka.core.xml
With this class objects can be serialized to XML instead into a binary format.
XMLSerialization() - Constructor for class weka.core.xml.XMLSerialization
initializes the serialization
XMLSerializationMethodHandler - Class in weka.core.xml
This class handles relationships between display names of properties (or classes) and Methods that are associated with them.
XMLSerializationMethodHandler(Object) - Constructor for class weka.core.xml.XMLSerializationMethodHandler
initializes the method handling, executes also clear(), which adds initial methods automatically.
XPropertyTipText() - Method in class weka.classifiers.meta.GridSearch
Returns the tip text for this property
XRFFLoader - Class in weka.core.converters
Reads a source that is in the XML version of the ARFF format.
XRFFLoader() - Constructor for class weka.core.converters.XRFFLoader
 
XRFFSaver - Class in weka.core.converters
Writes to a destination that is in the XML version of the ARFF format.
XRFFSaver() - Constructor for class weka.core.converters.XRFFSaver
Constructor
xSD - Variable in class weka.classifiers.mi.MILR
 
xStats - Variable in class weka.experiment.PairedStats
The stats associated with the data in column 1
XStepTipText() - Method in class weka.classifiers.meta.GridSearch
Returns the tip text for this property
XStream - Class in weka.core.xml
This class is a helper class for XML serialization using XStream .
XStream() - Constructor for class weka.core.xml.XStream
 
XSTREAM - Static variable in class weka.gui.beans.SerializedModelSaver
 
XVALTAGS_SELECTION - Static variable in class weka.attributeSelection.RaceSearch
 
xySum - Variable in class weka.experiment.PairedStats
The sum of the products

Y

y - Variable in class weka.gui.graphvisualizer.GraphNode
The x and y position of the node
YBaseTipText() - Method in class weka.classifiers.meta.GridSearch
Returns the tip text for this property
YExpressionTipText() - Method in class weka.classifiers.meta.GridSearch
Returns the tip text for this property
YMaxTipText() - Method in class weka.classifiers.meta.GridSearch
Returns the tip text for this property
YMinTipText() - Method in class weka.classifiers.meta.GridSearch
Returns the tip text for this property
YongSplitInfo - Class in weka.classifiers.trees.m5
Stores split information.
YongSplitInfo(int, int, int) - Constructor for class weka.classifiers.trees.m5.YongSplitInfo
Constructs an object which contains the split information
YPropertyTipText() - Method in class weka.classifiers.meta.GridSearch
Returns the tip text for this property
yStats - Variable in class weka.experiment.PairedStats
The stats associated with the data in column 2
YStepTipText() - Method in class weka.classifiers.meta.GridSearch
Returns the tip text for this property
yVector(int) - Method in class weka.classifiers.trees.LADTree.LADInstance
 
yybegin(int) - Method in class weka.core.mathematicalexpression.Scanner
Enters a new lexical state
yybegin(int) - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner
Enters a new lexical state
yycharat(int) - Method in class weka.core.mathematicalexpression.Scanner
Returns the character at position pos from the matched text.
yycharat(int) - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner
Returns the character at position pos from the matched text.
yyclose() - Method in class weka.core.mathematicalexpression.Scanner
Closes the input stream.
yyclose() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner
Closes the input stream.
YYEOF - Static variable in class weka.core.mathematicalexpression.Scanner
This character denotes the end of file
YYEOF - Static variable in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner
This character denotes the end of file
YYINITIAL - Static variable in class weka.core.mathematicalexpression.Scanner
lexical states
YYINITIAL - Static variable in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner
 
yylength() - Method in class weka.core.mathematicalexpression.Scanner
Returns the length of the matched text region.
yylength() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner
Returns the length of the matched text region.
yypushback(int) - Method in class weka.core.mathematicalexpression.Scanner
Pushes the specified amount of characters back into the input stream.
yypushback(int) - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner
Pushes the specified amount of characters back into the input stream.
yyreset(Reader) - Method in class weka.core.mathematicalexpression.Scanner
Resets the scanner to read from a new input stream.
yyreset(Reader) - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner
Resets the scanner to read from a new input stream.
yystate() - Method in class weka.core.mathematicalexpression.Scanner
Returns the current lexical state.
yystate() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner
Returns the current lexical state.
yytext() - Method in class weka.core.mathematicalexpression.Scanner
Returns the text matched by the current regular expression.
yytext() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner
Returns the text matched by the current regular expression.

Z

Z_MAX - Static variable in class weka.classifiers.meta.LogitBoost
A threshold for responses (Friedman suggests between 2 and 4)
Z_MAX - Static variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
A threshold for responses (Friedman suggests between 2 and 4)
Z_MAX - Variable in class weka.classifiers.trees.LADTree
 
Z_MAX - Static variable in class weka.classifiers.trees.lmt.LogisticBase
Threshold on the Z-value for LogitBoost
ZeroR - Class in weka.classifiers.rules
Class for building and using a 0-R classifier.
ZeroR() - Constructor for class weka.classifiers.rules.ZeroR
 
zipit(String, String) - Method in class weka.experiment.OutputZipper
Saves a string to either an individual gzipped file or as an entry in a zip file.
zVector - Variable in class weka.classifiers.trees.LADTree.LADInstance
 

_

_action_table - Static variable in class weka.core.mathematicalexpression.Parser
Parse-action table.
_action_table - Static variable in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
Parse-action table.
_production_table - Static variable in class weka.core.mathematicalexpression.Parser
Production table.
_production_table - Static variable in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
Production table.
_reduce_table - Static variable in class weka.core.mathematicalexpression.Parser
reduce_goto table.
_reduce_table - Static variable in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
reduce_goto table.
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