Modifier and Type | Field and Description |
---|---|
protected Instances |
FilteredAssociator.m_FilteredInstances
The instance structure of the filtered instances
|
protected Instances |
Apriori.m_instances
The instances (transactions) to be used for generating
the association rules.
|
protected Instances |
PredictiveApriori.m_instances
The instances (transactions) to be used for generating
the association rules.
|
protected Instances |
PriorEstimation.m_instances
The instances for which association rules are mined.
|
protected Instances |
RuleGeneration.m_instances
The instances.
|
protected Instances |
Apriori.m_onlyClass
Only the class attribute of all Instances.
|
protected Instances |
GeneralizedSequentialPatterns.m_OriginalDataSet
original sequential data set to be used for sequential patterns extraction
|
Modifier and Type | Method and Description |
---|---|
static Instances |
LabeledItemSet.divide(Instances instances,
boolean invert)
Splits the class attribute away.
|
Instances |
Apriori.getInstancesNoClass()
Gets the instances without the class atrribute.
|
Instances |
PredictiveApriori.getInstancesNoClass()
Gets the instances without the class attribute
|
Instances |
CARuleMiner.getInstancesNoClass()
Gets the instances without the class attribute
|
Instances |
Apriori.getInstancesOnlyClass()
Gets only the class attribute of the instances.
|
Instances |
PredictiveApriori.getInstancesOnlyClass()
Gets the class attribute of all instances
|
Instances |
CARuleMiner.getInstancesOnlyClass()
Gets the class attribute and its values for all instances
|
protected Instances |
CheckAssociator.makeTestDataset(int seed,
int numInstances,
int numNominal,
int numNumeric,
int numString,
int numDate,
int numRelational,
int numClasses,
int classType,
boolean multiInstance)
Make a simple set of instances, which can later be modified
for use in specific tests.
|
protected Instances |
CheckAssociator.makeTestDataset(int seed,
int numInstances,
int numNominal,
int numNumeric,
int numString,
int numDate,
int numRelational,
int numClasses,
int classType,
int classIndex,
boolean multiInstance)
Make a simple set of instances with variable position of the class
attribute, which can later be modified for use in specific tests.
|
protected Instances |
Apriori.removeMissingColumns(Instances instances)
Removes columns that are all missing from the data
|
Modifier and Type | Method and Description |
---|---|
void |
Apriori.buildAssociations(Instances instances)
Method that generates all large itemsets with a minimum support, and from
these all association rules with a minimum confidence.
|
void |
Tertius.buildAssociations(Instances instances)
Method that launches the search to find the rules with the highest
confirmation.
|
void |
PredictiveApriori.buildAssociations(Instances instances)
Method that generates all large itemsets with a minimum support, and from
these all association rules.
|
void |
GeneralizedSequentialPatterns.buildAssociations(Instances data)
Extracts all sequential patterns out of a given sequential data set and
prints out the results.
|
void |
FilteredAssociator.buildAssociations(Instances data)
Build the associator on the filtered data.
|
void |
Associator.buildAssociations(Instances data)
Generates an associator.
|
void |
FPGrowth.buildAssociations(Instances data)
Method that generates all large item sets with a minimum support, and from
these all association rules with a minimum metric (i.e.
|
protected weka.associations.FPGrowth.FPTreeRoot |
FPGrowth.buildFPTree(ArrayList<FPGrowth.BinaryItem> singletons,
Instances data,
int minSupport)
Construct the frequent pattern tree by inserting each transaction
in the data into the tree.
|
static Instances |
LabeledItemSet.divide(Instances instances,
boolean invert)
Splits the class attribute away.
|
String |
AssociatorEvaluation.evaluate(Associator associator,
Instances data)
Evaluates the associator with the given commandline options and returns
the evaluation string.
|
protected FastVector |
GeneralizedSequentialPatterns.extractDataSequences(Instances originalDataSet,
int dataSeqID)
Extracts the data sequences out of the original data set according to
their sequence id attribute, which is removed after extraction.
|
RuleItem |
RuleItem.generateRuleItem(ItemSet premise,
ItemSet consequence,
Instances instances,
int genTime,
int minRuleCount,
double[] m_midPoints,
Hashtable m_priors)
Constructs a new RuleItem if the support of the given rule is above the support threshold.
|
TreeSet |
CaRuleGeneration.generateRules(int numRules,
double[] midPoints,
Hashtable priors,
double expectation,
Instances instances,
TreeSet best,
int genTime)
Generates all rules for an item set.
|
TreeSet |
RuleGeneration.generateRules(int numRules,
double[] midPoints,
Hashtable priors,
double expectation,
Instances instances,
TreeSet best,
int genTime)
Generates all rules for an item set.
|
protected ArrayList<FPGrowth.BinaryItem> |
FPGrowth.getSingletons(Instances data)
Get the singleton items in the data
|
FastVector[] |
Apriori.mineCARs(Instances data)
Method that mines all class association rules with minimum support and
with a minimum confidence.
|
FastVector[] |
PredictiveApriori.mineCARs(Instances data)
Method that mines the n best class association rules.
|
FastVector[] |
CARuleMiner.mineCARs(Instances data)
Method for mining class association rules.
|
protected Instances |
Apriori.removeMissingColumns(Instances instances)
Removes columns that are all missing from the data
|
static FastVector |
CaRuleGeneration.singleConsequence(Instances instances)
generates a consequence of length 1 for a class association rule.
|
static FastVector |
RuleGeneration.singleConsequence(Instances instances,
int attNum,
FastVector consequences)
generates a consequence of length 1 for an association rule.
|
static FastVector |
CaRuleGeneration.singletons(Instances instances)
Converts the header info of the given set of instances into a set
of item sets (singletons).
|
static FastVector |
ItemSet.singletons(Instances instances)
Converts the header info of the given set of instances into a set
of item sets (singletons).
|
static FastVector |
AprioriItemSet.singletons(Instances instances)
Converts the header info of the given set of instances into a set
of item sets (singletons).
|
static FastVector |
LabeledItemSet.singletons(Instances instancesNoClass,
Instances classes)
Converts the header info of the given set of instances into a set
of item sets (singletons).
|
String |
ItemSet.toString(Instances instances)
Returns the contents of an item set as a string.
|
String |
AprioriItemSet.toString(Instances instances)
Returns the contents of an item set as a string.
|
static void |
ItemSet.upDateCounters(FastVector itemSets,
Instances instances)
Updates counters for a set of item sets and a set of instances.
|
static void |
LabeledItemSet.upDateCounters(FastVector itemSets,
Instances instancesNoClass,
Instances instancesClass)
Updates counter of a specific item set
|
Constructor and Description |
---|
PriorEstimation(Instances instances,
int numRules,
int numIntervals,
boolean car)
Constructor
|
Modifier and Type | Method and Description |
---|---|
static FastVector |
Element.getOneElements(Instances instances)
Returns all events of the given data set as Elements containing a single
event.
|
protected boolean |
Sequence.isSubsequenceOf(Instances dataSequence)
Checks if the Sequence is subsequence of a given data sequence.
|
static String |
Sequence.setOfSequencesToString(FastVector setOfSequences,
Instances dataSet,
FastVector filterAttributes)
Returns a String representation of a set of Sequences where the numeric
value of each event/item is represented by its respective nominal value.
|
String |
Sequence.toNominalString(Instances dataSet)
Returns a String representation of a Sequences where the numeric value
of each event/item is represented by its respective nominal value.
|
String |
Element.toNominalString(Instances dataSet)
Returns a String representation of an Element where the numeric value
of each event/item is represented by its respective nominal value.
|
Modifier and Type | Class and Description |
---|---|
class |
IndividualInstances |
Modifier and Type | Method and Description |
---|---|
Instances |
IndividualInstance.getParts() |
Modifier and Type | Method and Description |
---|---|
void |
LiteralSet.upDate(Instances instances)
Update the number of counter-instances of this set in the dataset.
|
void |
Rule.upDate(Instances instances)
Update the number of counter-instances of this rule in the dataset.
|
Constructor and Description |
---|
Body(Instances instances)
Constructor storing the counter-instances.
|
Head(Instances instances)
Constructor storing the counter-instances.
|
IndividualInstance(Instance individual,
Instances parts) |
IndividualInstances(Instances individuals,
Instances parts) |
LiteralSet(Instances instances)
Constructor initializing the set of counter-instances to all the instances.
|
Rule(Instances instances,
boolean repeatPredicate,
int maxLiterals,
boolean negBody,
boolean negHead,
boolean classRule,
boolean horn)
Constructor for a rule when the counter-instances are stored,
giving all the constraints applied to this rule.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
FilteredSubsetEval.m_filteredInstances
Filtered instances structure
|
protected Instances |
FilteredAttributeEval.m_filteredInstances
Filtered instances structure
|
protected Instances |
GreedyStepwise.m_Instances |
Modifier and Type | Method and Description |
---|---|
protected Instances |
CheckAttributeSelection.makeTestDataset(int seed,
int numInstances,
int numNominal,
int numNumeric,
int numString,
int numDate,
int numRelational,
int numClasses,
int classType,
boolean multiInstance)
Make a simple set of instances, which can later be modified
for use in specific tests.
|
protected Instances |
CheckAttributeSelection.makeTestDataset(int seed,
int numInstances,
int numNominal,
int numNumeric,
int numString,
int numDate,
int numRelational,
int numClasses,
int classType,
int classIndex,
boolean multiInstance)
Make a simple set of instances with variable position of the class
attribute, which can later be modified for use in specific tests.
|
Instances |
AttributeSelection.reduceDimensionality(Instances in)
reduce the dimensionality of a set of instances to include only those
attributes chosen by the last run of attribute selection.
|
Instances |
AttributeTransformer.transformedData(Instances data)
Transform the supplied data set (assumed to be the same format
as the training data)
|
Instances |
LatentSemanticAnalysis.transformedData(Instances data)
Transform the supplied data set (assumed to be the same format
as the training data)
|
Instances |
PrincipalComponents.transformedData(Instances data)
Gets the transformed training data.
|
Instances |
AttributeTransformer.transformedHeader()
Returns just the header for the transformed data (ie.
|
Instances |
LatentSemanticAnalysis.transformedHeader()
Returns just the header for the transformed data (ie.
|
Instances |
PrincipalComponents.transformedHeader()
Returns just the header for the transformed data (ie.
|
Modifier and Type | Method and Description |
---|---|
void |
CfsSubsetEval.buildEvaluator(Instances data)
Generates a attribute evaluator.
|
void |
CostSensitiveASEvaluation.buildEvaluator(Instances data)
Generates a attribute evaluator.
|
void |
FilteredSubsetEval.buildEvaluator(Instances data)
Initializes a filtered attribute evaluator.
|
void |
SymmetricalUncertAttributeEval.buildEvaluator(Instances data)
Initializes a symmetrical uncertainty attribute evaluator.
|
void |
ChiSquaredAttributeEval.buildEvaluator(Instances data)
Initializes a chi-squared attribute evaluator.
|
void |
FilteredAttributeEval.buildEvaluator(Instances data)
Initializes a filtered attribute evaluator.
|
void |
LatentSemanticAnalysis.buildEvaluator(Instances data)
Initializes the singular values/vectors and performs the analysis
|
void |
OneRAttributeEval.buildEvaluator(Instances data)
Initializes a OneRAttribute attribute evaluator.
|
void |
PrincipalComponents.buildEvaluator(Instances data)
Initializes principal components and performs the analysis
|
void |
ConsistencySubsetEval.buildEvaluator(Instances data)
Generates a attribute evaluator.
|
abstract void |
ASEvaluation.buildEvaluator(Instances data)
Generates a attribute evaluator.
|
void |
ClassifierSubsetEval.buildEvaluator(Instances data)
Generates a attribute evaluator.
|
void |
ReliefFAttributeEval.buildEvaluator(Instances data)
Initializes a ReliefF attribute evaluator.
|
void |
GainRatioAttributeEval.buildEvaluator(Instances data)
Initializes a gain ratio attribute evaluator.
|
void |
SVMAttributeEval.buildEvaluator(Instances data)
Initializes the evaluator.
|
void |
WrapperSubsetEval.buildEvaluator(Instances data)
Generates a attribute evaluator.
|
void |
InfoGainAttributeEval.buildEvaluator(Instances data)
Initializes an information gain attribute evaluator.
|
abstract double |
HoldOutSubsetEvaluator.evaluateSubset(BitSet subset,
Instances holdOut)
Evaluates a subset of attributes with respect to a set of instances.
|
double |
ClassifierSubsetEval.evaluateSubset(BitSet subset,
Instances holdOut)
Evaluates a subset of attributes with respect to a set of instances.
|
BitSet |
LFSMethods.floatingForwardSearch(int cacheSize,
BitSet startGroup,
int[] ranking,
int k,
boolean incrementK,
int maxStale,
Instances data,
SubsetEvaluator evaluator,
boolean verbose)
Performs linear floating forward selection
( the stopping criteria cannot be changed to a specific size value )
|
BitSet |
LFSMethods.forwardSearch(int cacheSize,
BitSet startGroup,
int[] ranking,
int k,
boolean incrementK,
int maxStale,
int forceResultSize,
Instances data,
SubsetEvaluator evaluator,
boolean verbose)
Performs linear forward selection
|
int[] |
LFSMethods.rankAttributes(Instances data,
SubsetEvaluator evaluator,
boolean verbose) |
Instances |
AttributeSelection.reduceDimensionality(Instances in)
reduce the dimensionality of a set of instances to include only those
attributes chosen by the last run of attribute selection.
|
int[] |
RaceSearch.search(ASEvaluation ASEval,
Instances data)
Searches the attribute subset space by racing cross validation
errors of competing subsets
|
int[] |
ExhaustiveSearch.search(ASEvaluation ASEval,
Instances data)
Searches the attribute subset space using an exhaustive search.
|
int[] |
RankSearch.search(ASEvaluation ASEval,
Instances data)
Ranks attributes using the specified attribute evaluator and then
searches the ranking using the supplied subset evaluator.
|
int[] |
ScatterSearchV1.search(ASEvaluation ASEval,
Instances data)
Searches the attribute subset space using Scatter Search.
|
int[] |
Ranker.search(ASEvaluation ASEval,
Instances data)
Kind of a dummy search algorithm.
|
int[] |
BestFirst.search(ASEvaluation ASEval,
Instances data)
Searches the attribute subset space by best first search
|
int[] |
GreedyStepwise.search(ASEvaluation ASEval,
Instances data)
Searches the attribute subset space by forward selection.
|
abstract int[] |
ASSearch.search(ASEvaluation ASEvaluator,
Instances data)
Searches the attribute subset/ranking space.
|
int[] |
RandomSearch.search(ASEvaluation ASEval,
Instances data)
Searches the attribute subset space randomly.
|
int[] |
GeneticSearch.search(ASEvaluation ASEval,
Instances data)
Searches the attribute subset space using a genetic algorithm.
|
int[] |
SubsetSizeForwardSelection.search(ASEvaluation ASEval,
Instances data)
Searches the attribute subset space by subset size forward selection
|
int[] |
LinearForwardSelection.search(ASEvaluation ASEval,
Instances data)
Searches the attribute subset space by linear forward selection
|
protected AttributeSelection |
CheckAttributeSelection.search(ASSearch search,
ASEvaluation eval,
Instances data)
Performs a attribute selection with the given search and evaluation scheme
on the provided data.
|
static String |
AttributeSelection.SelectAttributes(ASEvaluation ASEvaluator,
String[] options,
Instances train)
Perform attribute selection with a particular evaluator and
a set of options specifying search method and options for the
search method and evaluator.
|
void |
AttributeSelection.SelectAttributes(Instances data)
Perform attribute selection on the supplied training instances.
|
void |
AttributeSelection.selectAttributesCVSplit(Instances split)
Select attributes for a split of the data.
|
String |
ConsistencySubsetEval.hashKey.toString(Instances t,
int maxColWidth)
Convert a hash entry to a string
|
Instances |
AttributeTransformer.transformedData(Instances data)
Transform the supplied data set (assumed to be the same format
as the training data)
|
Instances |
LatentSemanticAnalysis.transformedData(Instances data)
Transform the supplied data set (assumed to be the same format
as the training data)
|
Instances |
PrincipalComponents.transformedData(Instances data)
Gets the transformed training data.
|
Modifier and Type | Method and Description |
---|---|
Instances |
CostMatrix.applyCostMatrix(Instances data,
Random random)
Applies the cost matrix to a set of instances.
|
protected Instances |
CheckClassifier.makeTestDataset(int seed,
int numInstances,
int numNominal,
int numNumeric,
int numString,
int numDate,
int numRelational,
int numClasses,
int classType,
boolean multiInstance)
Make a simple set of instances, which can later be modified
for use in specific tests.
|
protected Instances |
CheckClassifier.makeTestDataset(int seed,
int numInstances,
int numNominal,
int numNumeric,
int numString,
int numDate,
int numRelational,
int numClasses,
int classType,
int classIndex,
boolean multiInstance)
Make a simple set of instances with variable position of the class
attribute, which can later be modified for use in specific tests.
|
Modifier and Type | Method and Description |
---|---|
Instances |
CostMatrix.applyCostMatrix(Instances data,
Random random)
Applies the cost matrix to a set of instances.
|
void |
IteratedSingleClassifierEnhancer.buildClassifier(Instances data)
Stump method for building the classifiers.
|
abstract void |
Classifier.buildClassifier(Instances data)
Generates a classifier.
|
void |
Evaluation.crossValidateModel(Classifier classifier,
Instances data,
int numFolds,
Random random,
Object... forPredictionsPrinting)
Performs a (stratified if class is nominal) cross-validation
for a classifier on a set of instances.
|
void |
Evaluation.crossValidateModel(String classifierString,
Instances data,
int numFolds,
String[] options,
Random random)
Performs a (stratified if class is nominal) cross-validation
for a classifier on a set of instances.
|
double[] |
Evaluation.evaluateModel(Classifier classifier,
Instances data,
Object... forPredictionsPrinting)
Evaluates the classifier on a given set of instances.
|
void |
IterativeClassifier.initClassifier(Instances instances)
Inits an iterative classifier.
|
static void |
Evaluation.printClassifications(Classifier classifier,
Instances train,
ConverterUtils.DataSource testSource,
int classIndex,
Range attributesToOutput,
boolean printDistribution,
StringBuffer text)
Prints the predictions for the given dataset into a supplied StringBuffer
|
static void |
Evaluation.printClassifications(Classifier classifier,
Instances train,
ConverterUtils.DataSource testSource,
int classIndex,
Range attributesToOutput,
StringBuffer predsText)
Prints the predictions for the given dataset into a String variable.
|
protected static void |
Evaluation.printClassificationsHeader(Instances test,
Range attributesToOutput,
boolean printDistribution,
StringBuffer text)
Prints the header for the predictions output into a supplied StringBuffer
|
void |
Evaluation.setPriors(Instances train)
Sets the class prior probabilities
|
protected boolean[] |
CheckClassifier.testWRTZeroR(Classifier classifier,
Evaluation evaluation,
Instances train,
Instances test)
Determine whether the scheme performs worse than ZeroR during testing
|
Constructor and Description |
---|
Evaluation(Instances data)
Initializes all the counters for the evaluation.
|
Evaluation(Instances data,
CostMatrix costMatrix)
Initializes all the counters for the evaluation and also takes a
cost matrix as parameter.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
NaiveBayesMultinomial.m_headerInfo
copy of header information for use in toString method
|
protected Instances |
DMNBtext.m_headerInfo |
protected Instances |
NaiveBayesSimple.m_Instances
The instances used for training.
|
protected Instances |
NaiveBayes.m_Instances
The dataset header for the purposes of printing out a semi-intelligible
model
|
Instances |
BayesNet.m_Instances
The dataset header for the purposes of printing out a semi-intelligible
model
|
protected Instances |
BayesianLogisticRegression.m_Instances
Dataset provided to do Training/Test set.
|
Modifier and Type | Method and Description |
---|---|
protected Instances |
BayesNet.normalizeDataSet(Instances instances)
ensure that all variables are nominal and that there are no missing values
|
Modifier and Type | Method and Description |
---|---|
void |
NaiveBayesSimple.buildClassifier(Instances instances)
Generates the classifier.
|
void |
WAODE.buildClassifier(Instances instances)
Generates the classifier.
|
void |
AODE.buildClassifier(Instances instances)
Generates the classifier.
|
void |
NaiveBayesMultinomial.buildClassifier(Instances instances)
Generates the classifier.
|
void |
NaiveBayes.buildClassifier(Instances instances)
Generates the classifier.
|
void |
ComplementNaiveBayes.buildClassifier(Instances instances)
Generates the classifier.
|
void |
NaiveBayesMultinomialUpdateable.buildClassifier(Instances instances)
Generates the classifier.
|
void |
BayesNet.buildClassifier(Instances instances)
Generates the classifier.
|
void |
AODEsr.buildClassifier(Instances instances)
Generates the classifier.
|
void |
DMNBtext.buildClassifier(Instances data)
Generates the classifier.
|
void |
BayesianLogisticRegression.buildClassifier(Instances data)
(1) Set the data to the class attribute m_Instances.
(2)Call the method initialize() to initialize the values.
|
void |
HNB.buildClassifier(Instances instances)
Generates the classifier.
|
double |
BayesianLogisticRegression.getLoglikeliHood(double[] betas,
Instances instances) |
void |
DMNBtext.DNBBinary.initClassifier(Instances instances) |
protected Instances |
BayesNet.normalizeDataSet(Instances instances)
ensure that all variables are nominal and that there are no missing values
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
Prior.m_Instances |
Modifier and Type | Method and Description |
---|---|
void |
Prior.computelogLikelihood(double[] betas,
Instances instances)
Function computes the log-likelihood value:
-sum{1 to n}{ln(1+exp(-Beta*x(i)*y(i))}
|
void |
GaussianPriorImpl.computeLoglikelihood(double[] betas,
Instances instances)
This method calls the log-likelihood implemented in the Prior
abstract class.
|
void |
LaplacePriorImpl.computeLogLikelihood(double[] betas,
Instances instances)
Computes the log-likelihood values using the implementation in the Prior class.
|
double |
Prior.update(int j,
Instances instances,
double beta,
double hyperparameter,
double[] r,
double deltaV)
Interface for the update functions for different types of
priors.
|
double |
LaplacePriorImpl.update(int j,
Instances instances,
double beta,
double hyperparameter,
double[] r,
double deltaV)
Update function specific to Laplace Prior.
|
double |
GaussianPriorImpl.update(int j,
Instances instances,
double beta,
double hyperparameter,
double[] r,
double deltaV)
Update function specific to Laplace Prior.
|
Modifier and Type | Method and Description |
---|---|
void |
ParentSet.addParent(int nParent,
Instances _Instances)
Add parent to parent set and update internals (specifically the cardinality of the parent set)
|
void |
ParentSet.addParent(int nParent,
int iParent,
Instances _Instances)
Add parent to parent set at specific location
and update internals (specifically the cardinality of the parent set)
|
void |
ParentSet.deleteLastParent(Instances _Instances)
Delete last added parent from parent set and update internals (specifically the cardinality of the parent set)
|
int |
ParentSet.deleteParent(int nParent,
Instances _Instances)
delete node from parent set
|
int |
ParentSet.getFreshCardinalityOfParents(Instances _Instances)
returns cardinality of parents after recalculation
|
static ADNode |
ADNode.makeADTree(Instances instances)
create AD tree from set of instances
|
static ADNode |
ADNode.makeADTree(int iNode,
FastVector nRecords,
Instances instances)
create sub tree
|
static VaryNode |
ADNode.makeVaryNode(int iNode,
FastVector nRecords,
Instances instances)
create sub tree
|
void |
EditableBayesNet.setData(Instances instances)
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.
|
Constructor and Description |
---|
EditableBayesNet(Instances instances)
constructor, creates empty network with nodes based on the attributes in a data set
|
Modifier and Type | Method and Description |
---|---|
protected boolean |
SearchAlgorithm.addArcMakesSense(BayesNet bayesNet,
Instances instances,
int iAttributeHead,
int iAttributeTail)
AddArcMakesSense checks whether adding the arc from iAttributeTail to iAttributeHead
does not already exists and does not introduce a cycle
|
void |
SearchAlgorithm.buildStructure(BayesNet bayesNet,
Instances instances)
buildStructure determines the network structure/graph of the network.
|
protected void |
SearchAlgorithm.doMarkovBlanketCorrection(BayesNet bayesNet,
Instances instances)
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.
|
protected boolean |
SearchAlgorithm.reverseArcMakesSense(BayesNet bayesNet,
Instances instances,
int iAttributeHead,
int iAttributeTail)
reverseArcMakesSense checks whether the arc from iAttributeTail to
iAttributeHead exists and reversing does not introduce a cycle
|
protected void |
SearchAlgorithm.search(BayesNet bayesNet,
Instances instances) |
Modifier and Type | Method and Description |
---|---|
protected void |
ICSSearchAlgorithm.search(BayesNet bayesNet,
Instances instances)
Search for Bayes network structure using ICS algorithm
|
Modifier and Type | Method and Description |
---|---|
void |
NaiveBayes.buildStructure(BayesNet bayesNet,
Instances instances) |
void |
FromFile.buildStructure(BayesNet bayesNet,
Instances instances) |
Modifier and Type | Method and Description |
---|---|
void |
TAN.buildStructure(BayesNet bayesNet,
Instances instances)
buildStructure determines the network structure/graph of the network
using the maximimum weight spanning tree algorithm of Chow and Liu
|
void |
SimulatedAnnealing.search(BayesNet bayesNet,
Instances instances) |
protected void |
RepeatedHillClimber.search(BayesNet bayesNet,
Instances instances)
search determines the network structure/graph of the network
with the repeated hill climbing.
|
protected void |
HillClimber.search(BayesNet bayesNet,
Instances instances)
search determines the network structure/graph of the network
with the Taby algorithm.
|
void |
K2.search(BayesNet bayesNet,
Instances instances)
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.
|
protected void |
GeneticSearch.search(BayesNet bayesNet,
Instances instances)
search determines the network structure/graph of the network
with a genetic search algorithm.
|
protected void |
TabuSearch.search(BayesNet bayesNet,
Instances instances)
search determines the network structure/graph of the network
with the Tabu search algorithm.
|
Modifier and Type | Method and Description |
---|---|
void |
LocalScoreSearchAlgorithm.buildStructure(BayesNet bayesNet,
Instances instances)
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.
|
void |
TAN.buildStructure(BayesNet bayesNet,
Instances instances)
buildStructure determines the network structure/graph of the network
using the maximimum weight spanning tree algorithm of Chow and Liu
|
protected double |
LocalScoreSearchAlgorithm.calcScoreOfCounts(int[] nCounts,
int nCardinality,
int numValues,
Instances instances)
utility function used by CalcScore and CalcNodeScore to determine the score
based on observed frequencies.
|
protected double |
LocalScoreSearchAlgorithm.calcScoreOfCounts2(int[][] nCounts,
int nCardinality,
int numValues,
Instances instances) |
protected weka.classifiers.bayes.net.search.local.HillClimber.Operation[] |
LAGDHillClimber.getGoodOperations(BayesNet bayesNet,
Instances instances,
int nrOfGoodOperations)
getGoodOperations determines the nrOfGoodOperations best Operations, which are considered for
the calculation of an optimal operationsequence
|
protected weka.classifiers.bayes.net.search.local.HillClimber.Operation[] |
LAGDHillClimber.getOptimalOperations(BayesNet bayesNet,
Instances instances,
int nrOfLookAheadSteps,
int nrOfGoodOperations)
getOptimalOperations determines an optimal operationsequence in respect of the parameters
nrOfLookAheadSteps and nrOfGoodOperations
|
protected void |
LAGDHillClimber.lookAheadInGoodDirectionsSearch(BayesNet bayesNet,
Instances instances,
int nrOfLookAheadSteps,
int nrOfGoodOperations)
lookAheadInGoodDirectionsSearch determines the network structure/graph of the network
with best score according to LAGD Hill Climbing
|
void |
SimulatedAnnealing.search(BayesNet bayesNet,
Instances instances) |
protected void |
RepeatedHillClimber.search(BayesNet bayesNet,
Instances instances)
search determines the network structure/graph of the network
with the repeated hill climbing.
|
protected void |
HillClimber.search(BayesNet bayesNet,
Instances instances)
search determines the network structure/graph of the network
with the Taby algorithm.
|
void |
K2.search(BayesNet bayesNet,
Instances instances)
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.
|
protected void |
GeneticSearch.search(BayesNet bayesNet,
Instances instances)
search determines the network structure/graph of the network
with a genetic search algorithm.
|
protected void |
TabuSearch.search(BayesNet bayesNet,
Instances instances)
search determines the network structure/graph of the network
with the Tabu search algorithm.
|
protected void |
LAGDHillClimber.search(BayesNet bayesNet,
Instances instances)
search determines the network structure/graph of the network
|
Constructor and Description |
---|
LocalScoreSearchAlgorithm(BayesNet bayesNet,
Instances instances)
constructor
|
Modifier and Type | Method and Description |
---|---|
Instances |
ThresholdCurve.getCurve(FastVector predictions)
Calculates the performance stats for the default class and return
results as a set of Instances.
|
Instances |
MarginCurve.getCurve(FastVector predictions)
Calculates the cumulative margin distribution for the set of
predictions, returning the result as a set of Instances.
|
Instances |
CostCurve.getCurve(FastVector predictions)
Calculates the performance stats for the default class and return
results as a set of Instances.
|
Instances |
ThresholdCurve.getCurve(FastVector predictions,
int classIndex)
Calculates the performance stats for the desired class and return
results as a set of Instances.
|
Instances |
CostCurve.getCurve(FastVector predictions,
int classIndex)
Calculates the performance stats for the desired class and return
results as a set of Instances.
|
Modifier and Type | Method and Description |
---|---|
FastVector |
EvaluationUtils.getCVPredictions(Classifier classifier,
Instances data,
int numFolds)
Generate a bunch of predictions ready for processing, by performing a
cross-validation on the supplied dataset.
|
static double |
ThresholdCurve.getNPointPrecision(Instances tcurve,
int n)
Calculates the n point precision result, which is the precision averaged
over n evenly spaced (w.r.t recall) samples of the curve.
|
static double |
ThresholdCurve.getROCArea(Instances tcurve)
Calculates the area under the ROC curve as the Wilcoxon-Mann-Whitney statistic.
|
FastVector |
EvaluationUtils.getTestPredictions(Classifier classifier,
Instances test)
Generate a bunch of predictions ready for processing, by performing a
evaluation on a test set assuming the classifier is already trained.
|
static int |
ThresholdCurve.getThresholdInstance(Instances tcurve,
double threshold)
Gets the index of the instance with the closest threshold value to the
desired target
|
FastVector |
EvaluationUtils.getTrainTestPredictions(Classifier classifier,
Instances train,
Instances test)
Generate a bunch of predictions ready for processing, by performing a
evaluation on a test set after training on the given training set.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
SMO.BinarySMO.m_data
The training data.
|
protected Instances |
SPegasos.m_data
Holds the header of the training data
|
Modifier and Type | Method and Description |
---|---|
void |
Logistic.buildClassifier(Instances train)
Builds the classifier
|
void |
SMO.buildClassifier(Instances insts)
Method for building the classifier.
|
void |
IsotonicRegression.buildClassifier(Instances insts)
Builds an isotonic regression model given the supplied training data.
|
void |
MultilayerPerceptron.buildClassifier(Instances i)
Call this function to build and train a neural network for the training
data provided.
|
void |
LeastMedSq.buildClassifier(Instances data)
Build lms regression
|
void |
Winnow.buildClassifier(Instances insts)
Builds the classifier
|
void |
GaussianProcesses.buildClassifier(Instances insts)
Method for building the classifier.
|
void |
SimpleLogistic.buildClassifier(Instances data)
Builds the logistic regression using LogitBoost.
|
void |
SimpleLinearRegression.buildClassifier(Instances insts)
Builds a simple linear regression model given the supplied training data.
|
void |
PaceRegression.buildClassifier(Instances data)
Builds a pace regression model for the given data.
|
void |
LinearRegression.buildClassifier(Instances data)
Builds a regression model for the given data.
|
void |
SPegasos.buildClassifier(Instances data)
Method for building the classifier.
|
void |
SMOreg.buildClassifier(Instances instances)
Method for building the classifier.
|
void |
VotedPerceptron.buildClassifier(Instances insts)
Builds the ensemble of perceptrons.
|
void |
LibLINEAR.buildClassifier(Instances insts)
builds the classifier
|
void |
RBFNetwork.buildClassifier(Instances instances)
Builds the classifier
|
void |
LibSVM.buildClassifier(Instances insts)
builds the classifier
|
void |
PLSClassifier.buildClassifier(Instances data)
builds the classifier
|
protected void |
SMO.BinarySMO.buildClassifier(Instances insts,
int cl1,
int cl2,
boolean fitLogistic,
int numFolds,
int randomSeed)
Method for building the binary classifier.
|
boolean |
PaceRegression.checkForMissing(Instance instance,
Instances model)
Checks if an instance has a missing value.
|
protected void |
SMO.BinarySMO.fitLogistic(Instances insts,
int cl1,
int cl2,
int numFolds,
Random random)
Fits logistic regression model to SVM outputs analogue
to John Platt's method.
|
protected void |
IsotonicRegression.regress(Attribute attribute,
Instances insts,
boolean ascending)
Does the actual regression.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
Kernel.m_data
The dataset
|
protected Instances |
RegOptimizer.m_data
points to data set
|
Modifier and Type | Method and Description |
---|---|
protected Instances |
CheckKernel.makeTestDataset(int seed,
int numInstances,
int numNominal,
int numNumeric,
int numString,
int numDate,
int numRelational,
int numClasses,
int classType,
boolean multiInstance)
Make a simple set of instances, which can later be modified
for use in specific tests.
|
protected Instances |
CheckKernel.makeTestDataset(int seed,
int numInstances,
int numNominal,
int numNumeric,
int numString,
int numDate,
int numRelational,
int numClasses,
int classType,
int classIndex,
boolean multiInstance)
Make a simple set of instances with variable position of the class
attribute, which can later be modified for use in specific tests.
|
Modifier and Type | Method and Description |
---|---|
void |
RegSMO.buildClassifier(Instances instances)
learn SVM parameters from data using Smola's SMO algorithm.
|
void |
RegSMOImproved.buildClassifier(Instances instances)
learn SVM parameters from data using Keerthi's SMO algorithm.
|
void |
RegOptimizer.buildClassifier(Instances data)
learn SVM parameters from data.
|
void |
Kernel.buildKernel(Instances data)
builds the kernel with the given data
|
void |
StringKernel.buildKernel(Instances data)
builds the kernel with the given data.
|
void |
Puk.buildKernel(Instances data)
builds the kernel with the given data.
|
void |
RBFKernel.buildKernel(Instances data)
builds the kernel with the given data.
|
void |
CachedKernel.buildKernel(Instances data)
builds the kernel with the given data.
|
String |
KernelEvaluation.evaluate(Kernel kernel,
Instances data)
Evaluates the Kernel with the given commandline options and returns
the evaluation string.
|
protected void |
RegSMO.init(Instances data)
initialize various variables before starting the actual optimizer
|
protected void |
RegSMOImproved.init(Instances data)
initialize various variables before starting the actual optimizer
|
protected void |
RegOptimizer.init(Instances data)
initializes the algorithm
|
protected void |
Kernel.initVars(Instances data)
initializes variables etc.
|
protected void |
StringKernel.initVars(Instances data)
initializes variables etc.
|
protected void |
Puk.initVars(Instances data)
initializes variables etc.
|
protected void |
PrecomputedKernelMatrixKernel.initVars(Instances data)
initializes variables etc.
|
protected void |
RBFKernel.initVars(Instances data)
initializes variables etc.
|
protected void |
CachedKernel.initVars(Instances data)
initializes variables etc.
|
Constructor and Description |
---|
CachedKernel(Instances data,
int cacheSize)
Initializes the kernel cache.
|
NormalizedPolyKernel(Instances dataset,
int cacheSize,
double exponent,
boolean lowerOrder)
Creates a new
NormalizedPolyKernel instance. |
PolyKernel(Instances data,
int cacheSize,
double exponent,
boolean lowerOrder)
Creates a new
PolyKernel instance. |
Puk(Instances data,
int cacheSize,
double omega,
double sigma)
Constructor.
|
RBFKernel(Instances data,
int cacheSize,
double gamma)
Constructor.
|
StringKernel(Instances data,
int cacheSize,
int subsequenceLength,
double lambda,
boolean debug)
creates a new StringKernel object.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
LBR.m_Instances
The set of instances used for current training.
|
protected Instances |
LWL.m_Train
The training instances used for classification.
|
protected Instances |
IBk.m_Train
The training instances used for classification.
|
protected Instances |
KStar.m_Train
The training instances used for classification.
|
Modifier and Type | Method and Description |
---|---|
Instances |
IBk.pruneToK(Instances neighbours,
double[] distances,
int k)
Prunes the list to contain the k nearest neighbors.
|
Modifier and Type | Method and Description |
---|---|
void |
LWL.buildClassifier(Instances instances)
Generates the classifier.
|
void |
IBk.buildClassifier(Instances instances)
Generates the classifier.
|
void |
LBR.buildClassifier(Instances instances)
For lazy learning, building classifier is only to prepare their inputs
until classification time.
|
void |
IB1.buildClassifier(Instances instances)
Generates the classifier.
|
void |
KStar.buildClassifier(Instances instances)
Generates the classifier.
|
protected double[] |
IBk.makeDistribution(Instances neighbours,
double[] distances)
Turn the list of nearest neighbors into a probability distribution.
|
Instances |
IBk.pruneToK(Instances neighbours,
double[] distances,
int k)
Prunes the list to contain the k nearest neighbors.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
KStarNumericAttribute.m_TrainSet
The training instances used for classification.
|
protected Instances |
KStarNominalAttribute.m_TrainSet
The training instances used for classification.
|
Constructor and Description |
---|
KStarNominalAttribute(Instance test,
Instance train,
int attrIndex,
Instances trainSet,
int[][] randClassCol,
KStarCache cache)
Constructor
|
KStarNumericAttribute(Instance test,
Instance train,
int attrIndex,
Instances trainSet,
int[][] randClassCols,
KStarCache cache)
Constructor
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
Stacking.m_BaseFormat
Format for base data
|
protected Instances |
ClassificationViaClustering.m_ClusteringHeader
the modified training data header
|
protected Instances |
RacedIncrementalLogitBoost.m_currentSet
The instances currently in memory for training
|
protected Instances |
GridSearch.m_Data
the training data
|
protected Instances |
RegressionByDiscretization.m_DiscretizedHeader
Header of discretized data.
|
protected Instances |
FilteredClassifier.m_FilteredInstances
The instance structure of the filtered instances
|
protected Instances[] |
RotationForest.m_Headers
Headers of the transformed dataset
|
protected Instances |
Stacking.m_MetaFormat
Format for meta data
|
protected Instances |
LogitBoost.m_NumericClassData
Dummy dataset with a numeric class
|
protected Instances |
RacedIncrementalLogitBoost.m_NumericClassData
Dummy dataset with a numeric class
|
protected Instances |
ClassificationViaClustering.m_OriginalHeader
the original training data header
|
protected Instances |
AttributeSelectedClassifier.m_ReducedHeader
The header of the dimensionally reduced data
|
protected Instances[][] |
RotationForest.m_ReducedHeaders
Headers of the reduced datasets
|
protected Instances |
RacedIncrementalLogitBoost.m_validationSet
The instances used for validation
|
Modifier and Type | Method and Description |
---|---|
protected Instances |
Decorate.generateArtificialData(int artSize,
Instances data)
Generate artificial training examples.
|
protected Instances |
Grading.metaFormat(Instances instances)
Makes the format for the level-1 data.
|
protected Instances |
Stacking.metaFormat(Instances instances)
Makes the format for the level-1 data.
|
Instances |
Bagging.resampleWithWeights(Instances data,
Random random,
boolean[] sampled)
Creates a new dataset of the same size using random sampling
with replacement according to the given weight vector.
|
protected Instances |
AdaBoostM1.selectWeightQuantile(Instances data,
double quantile)
Select only instances with weights that contribute to
the specified quantile of the weight distribution
|
protected Instances |
LogitBoost.selectWeightQuantile(Instances data,
double quantile)
Select only instances with weights that contribute to
the specified quantile of the weight distribution
|
Modifier and Type | Method and Description |
---|---|
protected void |
Decorate.addInstances(Instances data,
Instances newData)
Add new instances to the given set of instances.
|
protected void |
RotationForest.addRandomInstances(Instances dataset,
int numInstances,
Random random)
Adds random instances to the dataset.
|
protected Classifier[] |
RacedIncrementalLogitBoost.Committee.boost(Instances data)
performs a boosting iteration, returning a new model for the committee
|
void |
MetaCost.buildClassifier(Instances data)
Builds the model of the base learner.
|
void |
ClassificationViaRegression.buildClassifier(Instances insts)
Builds the classifiers.
|
void |
CostSensitiveClassifier.buildClassifier(Instances data)
Builds the model of the base learner.
|
void |
END.buildClassifier(Instances data)
Builds the committee of randomizable classifiers.
|
void |
AdaBoostM1.buildClassifier(Instances data)
Boosting method.
|
void |
Vote.buildClassifier(Instances data)
Buildclassifier selects a classifier from the set of classifiers
by minimising error on the training data.
|
void |
ClassificationViaClustering.buildClassifier(Instances data)
builds the classifier
|
void |
RandomCommittee.buildClassifier(Instances data)
Builds the committee of randomizable classifiers.
|
void |
Stacking.buildClassifier(Instances data)
Buildclassifier selects a classifier from the set of classifiers
by minimising error on the training data.
|
void |
MultiClassClassifier.buildClassifier(Instances insts)
Builds the classifiers.
|
void |
LogitBoost.buildClassifier(Instances data)
Builds the boosted classifier
|
void |
RacedIncrementalLogitBoost.buildClassifier(Instances data)
Builds the classifier.
|
void |
RotationForest.buildClassifier(Instances data)
builds the classifier.
|
void |
Decorate.buildClassifier(Instances data)
Build Decorate classifier
|
void |
RegressionByDiscretization.buildClassifier(Instances instances)
Generates the classifier.
|
void |
FilteredClassifier.buildClassifier(Instances data)
Build the classifier on the filtered data.
|
void |
ThresholdSelector.buildClassifier(Instances instances)
Generates the classifier.
|
void |
Bagging.buildClassifier(Instances data)
Bagging method.
|
void |
RandomSubSpace.buildClassifier(Instances data)
builds the classifier.
|
void |
MultiBoostAB.buildClassifier(Instances training)
Method for building this classifier.
|
void |
OrdinalClassClassifier.buildClassifier(Instances insts)
Builds the classifiers.
|
void |
AdditiveRegression.buildClassifier(Instances data)
Build the classifier on the supplied data
|
void |
MultiScheme.buildClassifier(Instances data)
Buildclassifier selects a classifier from the set of classifiers
by minimising error on the training data.
|
void |
Dagging.buildClassifier(Instances data)
Bagging method.
|
void |
CVParameterSelection.buildClassifier(Instances instances)
Generates the classifier.
|
void |
GridSearch.buildClassifier(Instances data)
builds the classifier
|
void |
AttributeSelectedClassifier.buildClassifier(Instances data)
Build the classifier on the dimensionally reduced data.
|
protected void |
AdaBoostM1.buildClassifierUsingResampling(Instances data)
Boosting method.
|
protected void |
AdaBoostM1.buildClassifierWithWeights(Instances data)
Boosting method.
|
protected void |
RotationForest.checkMinMax(Instances data)
Checks m_MinGroup and m_MaxGroup
|
protected double |
Decorate.computeError(Instances data)
Computes the error in classification on the given data.
|
protected void |
Decorate.computeStats(Instances data)
Compute and store statistics required for generating artificial data.
|
protected GridSearch.PointDouble |
GridSearch.determineBestInGrid(GridSearch.Grid grid,
Instances inst,
int cv)
determines the best values-pair for the given grid, using CV with
specified number of folds.
|
protected void |
CVParameterSelection.findParamsByCrossValidation(int depth,
Instances trainData,
Random random)
Finds the best parameter combination.
|
protected Instances |
Decorate.generateArtificialData(int artSize,
Instances data)
Generate artificial training examples.
|
protected void |
RotationForest.generateGroupsFromNumbers(Instances data,
Random random)
generates the groups of attributes, given their minimum and maximum
numbers.
|
protected void |
RotationForest.generateGroupsFromSizes(Instances data,
Random random)
generates the groups of attributes, given their minimum and maximum
sizes.
|
protected void |
Grading.generateMetaLevel(Instances newData,
Random random)
Generates the meta data
|
protected void |
Stacking.generateMetaLevel(Instances newData,
Random random)
Generates the meta data
|
protected void |
StackingC.generateMetaLevel(Instances newData,
Random random)
Method that builds meta level.
|
protected FastVector |
ThresholdSelector.getPredictions(Instances instances,
int mode,
int numFolds)
Collects the classifier predictions using the specified evaluation method.
|
protected void |
Decorate.labelData(Instances artData)
Labels the artificially generated data.
|
protected Instances |
Grading.metaFormat(Instances instances)
Makes the format for the level-1 data.
|
protected Instances |
Stacking.metaFormat(Instances instances)
Makes the format for the level-1 data.
|
protected void |
Decorate.removeInstances(Instances data,
int numRemove)
Removes a specified number of instances from the given set of instances.
|
Instances |
Bagging.resampleWithWeights(Instances data,
Random random,
boolean[] sampled)
Creates a new dataset of the same size using random sampling
with replacement according to the given weight vector.
|
protected Instances |
AdaBoostM1.selectWeightQuantile(Instances data,
double quantile)
Select only instances with weights that contribute to
the specified quantile of the weight distribution
|
protected Instances |
LogitBoost.selectWeightQuantile(Instances data,
double quantile)
Select only instances with weights that contribute to
the specified quantile of the weight distribution
|
protected void |
AdaBoostM1.setWeights(Instances training,
double reweight)
Sets the weights for the next iteration.
|
protected void |
MultiBoostAB.setWeights(Instances training,
double reweight)
Sets the weights for the next iteration.
|
Modifier and Type | Method and Description |
---|---|
void |
ClassBalancedND.buildClassifier(Instances data)
Builds tree recursively.
|
void |
DataNearBalancedND.buildClassifier(Instances data)
Builds tree recursively.
|
void |
ND.buildClassifier(Instances data)
Builds the classifier.
|
void |
ND.buildClassifierForNode(ND.NDTree node,
Instances data)
Builds the classifier for one node.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
CitationKNN.m_Attributes
attribute name structure of the relational attribute
|
protected Instances |
MINND.m_Attributes
header info of the data
|
protected Instances |
MDD.m_Attributes
All attribute names
|
protected Instances |
MIEMDD.m_Attributes
All attribute names
|
protected Instances |
MIBoost.m_Attributes
attributes name for the new dataset used to build the model
|
protected Instances |
MIDD.m_Attributes
All attribute names
|
protected Instances |
MILR.m_Attributes
All attribute names
|
protected Instances |
MISMO.BinaryMISMO.m_data
The training data.
|
protected Instances |
CitationKNN.m_TrainBags
Training bags
|
Modifier and Type | Method and Description |
---|---|
Instances |
SimpleMI.transform(Instances train)
Implements MITransform (3 type of transformation) 1.arithmatic average;
2.geometric centor; 3.merge minima and maxima attribute value together
|
Modifier and Type | Method and Description |
---|---|
void |
CitationKNN.buildClassifier(Instances train)
Builds the classifier
|
void |
MINND.buildClassifier(Instances exs)
As normal Nearest Neighbour algorithm does, it's lazy and simply
records the exemplar information (i.e.
|
void |
MDD.buildClassifier(Instances train)
Builds the classifier
|
void |
MIWrapper.buildClassifier(Instances data)
Builds the classifier
|
void |
SimpleMI.buildClassifier(Instances train)
Builds the classifier
|
void |
MIEMDD.buildClassifier(Instances train)
Builds the classifier
|
void |
MIOptimalBall.buildClassifier(Instances data)
Builds the classifier
|
void |
MIBoost.buildClassifier(Instances exps)
Builds the classifier
|
void |
MISMO.buildClassifier(Instances insts)
Method for building the classifier.
|
void |
MISVM.buildClassifier(Instances train)
Builds the classifier
|
void |
MIDD.buildClassifier(Instances train)
Builds the classifier
|
void |
MILR.buildClassifier(Instances train)
Builds the classifier
|
protected void |
MISMO.BinaryMISMO.buildClassifier(Instances insts,
int cl1,
int cl2,
boolean fitLogistic,
int numFolds,
int randomSeed)
Method for building the binary classifier.
|
void |
MIOptimalBall.calculateDistance(Instances train)
calculate the distances from each instance in a positive bag to each bag.
|
protected weka.classifiers.mi.CitationKNN.NeighborList |
CitationKNN.findNeighbors(Instance bag,
int kNN,
Instances bags)
Build the list of nearest k neighbors to the given test instance.
|
void |
MIOptimalBall.findRadius(Instances train)
Find the maximum radius for the optimal ball.
|
protected void |
MISMO.BinaryMISMO.fitLogistic(Instances insts,
int cl1,
int cl2,
int numFolds,
Random random)
Fits logistic regression model to SVM outputs analogue
to John Platt's method.
|
static double[] |
SimpleMI.minimax(Instances data,
int attIndex)
Get the minimal and maximal value of a certain attribute in a certain data
|
Instance |
MINND.preprocess(Instances data,
int pos)
Pre-process the given exemplar according to the other exemplars
in the given exemplars.
|
Instances |
SimpleMI.transform(Instances train)
Implements MITransform (3 type of transformation) 1.arithmatic average;
2.geometric centor; 3.merge minima and maxima attribute value together
|
Modifier and Type | Method and Description |
---|---|
void |
MIRBFKernel.buildKernel(Instances data)
builds the kernel with the given data.
|
protected void |
MIRBFKernel.initVars(Instances data)
initializes variables etc.
|
Constructor and Description |
---|
MIPolyKernel(Instances data,
int cacheSize,
double exponent,
boolean lowerOrder)
Creates a new
MIPolyKernel instance. |
MIRBFKernel(Instances data,
int cacheSize,
double gamma)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
VFI.m_Instances
The training data
|
protected Instances |
HyperPipes.m_Instances
The structure of the training data
|
Modifier and Type | Method and Description |
---|---|
void |
SerializedClassifier.buildClassifier(Instances data)
loads only the serialized classifier
|
void |
VFI.buildClassifier(Instances instances)
Generates the classifier.
|
void |
HyperPipes.buildClassifier(Instances instances)
Generates the classifier.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
PMMLClassifier.m_dataDictionary
The data dictionary
|
Modifier and Type | Method and Description |
---|---|
Instances |
PMMLClassifier.getDataDictionary()
Get the data dictionary.
|
Modifier and Type | Method and Description |
---|---|
void |
PMMLClassifier.buildClassifier(Instances data)
Throw an exception - PMML models are pre-built.
|
void |
PMMLClassifier.mapToMiningSchema(Instances dataSet)
Map mining schema to incoming instances.
|
Constructor and Description |
---|
GeneralRegression(Element model,
Instances dataDictionary,
MiningSchema miningSchema)
Constructs a GeneralRegression classifier.
|
NeuralNetwork(Element model,
Instances dataDictionary,
MiningSchema miningSchema) |
Regression(Element model,
Instances dataDictionary,
MiningSchema miningSchema)
Constructs a new PMML Regression.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
DecisionTable.m_dtInstances
Holds the final feature selected set of instances
|
protected Instances |
DecisionTable.m_theInstances
Holds the original training instances
|
Modifier and Type | Method and Description |
---|---|
Instances |
RuleStats.getData()
Get the data of the stats
|
Instances[] |
RuleStats.getFiltered(int index)
Get the data after filtering the given rule
|
static Instances[] |
RuleStats.partition(Instances data,
int numFolds)
Patition the data into 2, first of which has (numFolds-1)/numFolds of
the data and the second has 1/numFolds of the data
|
static Instances |
RuleStats.rmCoveredBySuccessives(Instances data,
FastVector rules,
int index)
Static utility function to count the data covered by the
rules after the given index in the given rules, and then
remove them.
|
protected Instances |
JRip.rulesetForOneClass(double expFPRate,
Instances data,
double classIndex,
double defDL)
Build a ruleset for the given class according to the given data
|
abstract Instances[] |
JRip.Antd.splitData(Instances data,
double defAcRt,
double cla) |
Instances[] |
JRip.NumericAntd.splitData(Instances insts,
double defAcRt,
double cl)
Implements the splitData function.
|
Instances[] |
JRip.NominalAntd.splitData(Instances data,
double defAcRt,
double cl)
Implements the splitData function.
|
static Instances |
RuleStats.stratify(Instances data,
int folds,
Random rand)
Stratify the given data into the given number of bags based on the class
values.
|
Modifier and Type | Method and Description |
---|---|
void |
NNge.buildClassifier(Instances data)
Generates a classifier.
|
void |
DecisionTable.buildClassifier(Instances data)
Generates the classifier.
|
void |
Prism.buildClassifier(Instances data)
Generates the classifier.
|
void |
ZeroR.buildClassifier(Instances instances)
Generates the classifier.
|
void |
PART.buildClassifier(Instances instances)
Generates the classifier.
|
void |
DTNB.buildClassifier(Instances data)
Generates the classifier.
|
void |
OneR.buildClassifier(Instances instances)
Generates the classifier.
|
void |
Ridor.buildClassifier(Instances instances)
Builds a ripple-down manner rule learner.
|
void |
JRip.buildClassifier(Instances instances)
Builds Ripper in the order of class frequencies.
|
void |
ConjunctiveRule.buildClassifier(Instances instances)
Builds a single rule learner with REP dealing with nominal classes or
numeric classes.
|
void |
DTNB.EvalWithDelete.buildEvaluator(Instances data) |
void |
RuleStats.countData(int index,
Instances uncovered,
double[][] prevRuleStats)
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. |
abstract void |
Rule.grow(Instances data)
Build this rule
|
void |
JRip.RipperRule.grow(Instances data)
Build one rule using the growing data
|
weka.classifiers.rules.OneR.OneRRule |
OneR.newNominalRule(Attribute attr,
Instances data,
int[] missingValueCounts)
Create a rule branching on this nominal attribute.
|
weka.classifiers.rules.OneR.OneRRule |
OneR.newNumericRule(Attribute attr,
Instances data,
int[] missingValueCounts)
Create a rule branching on this numeric attribute
|
weka.classifiers.rules.OneR.OneRRule |
OneR.newRule(Attribute attr,
Instances data)
Create a rule branching on this attribute.
|
static double |
RuleStats.numAllConditions(Instances data)
Compute the number of all possible conditions that could
appear in a rule of a given data.
|
static Instances[] |
RuleStats.partition(Instances data,
int numFolds)
Patition the data into 2, first of which has (numFolds-1)/numFolds of
the data and the second has 1/numFolds of the data
|
void |
JRip.RipperRule.prune(Instances pruneData,
boolean useWhole)
Prune all the possible final sequences of the rule using the
pruning data.
|
static Instances |
RuleStats.rmCoveredBySuccessives(Instances data,
FastVector rules,
int index)
Static utility function to count the data covered by the
rules after the given index in the given rules, and then
remove them.
|
protected Instances |
JRip.rulesetForOneClass(double expFPRate,
Instances data,
double classIndex,
double defDL)
Build a ruleset for the given class according to the given data
|
int[] |
DTNB.BackwardsWithDelete.search(ASEvaluation eval,
Instances data) |
void |
RuleStats.setData(Instances data)
Set the data of the stats, overwriting the old one if any
|
abstract Instances[] |
JRip.Antd.splitData(Instances data,
double defAcRt,
double cla) |
Instances[] |
JRip.NumericAntd.splitData(Instances insts,
double defAcRt,
double cl)
Implements the splitData function.
|
Instances[] |
JRip.NominalAntd.splitData(Instances data,
double defAcRt,
double cl)
Implements the splitData function.
|
static Instances |
RuleStats.stratify(Instances data,
int folds,
Random rand)
Stratify the given data into the given number of bags based on the class
values.
|
String |
DecisionTableHashKey.toString(Instances t,
int maxColWidth)
Convert a hash entry to a string
|
Constructor and Description |
---|
RuleStats(Instances data,
FastVector rules)
Constructor that provides ruleset and data
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
ClassifierDecList.m_train
The training instances.
|
Modifier and Type | Method and Description |
---|---|
void |
MakeDecList.buildClassifier(Instances data)
Builds dec list.
|
void |
ClassifierDecList.buildDecList(Instances data,
boolean leaf)
Builds the partial tree without hold out set.
|
void |
C45PruneableDecList.buildDecList(Instances data,
boolean leaf)
Builds the partial tree without hold out set.
|
void |
PruneableDecList.buildDecList(Instances train,
Instances test,
boolean leaf)
Builds the partial tree with hold out set
|
void |
ClassifierDecList.buildRule(Instances data)
Method for building a pruned partial tree.
|
void |
PruneableDecList.buildRule(Instances train,
Instances test)
Method for building a pruned partial tree.
|
void |
ClassifierDecList.cleanup(Instances justHeaderInfo)
Cleanup in order to save memory.
|
protected ClassifierDecList |
ClassifierDecList.getNewDecList(Instances train,
boolean leaf)
Returns a newly created tree.
|
protected ClassifierDecList |
C45PruneableDecList.getNewDecList(Instances data,
boolean leaf)
Returns a newly created tree.
|
protected ClassifierDecList |
PruneableDecList.getNewDecList(Instances train,
Instances test,
boolean leaf)
Returns a newly created tree.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
RandomTree.m_Info
The header information.
|
protected Instances |
REPTree.Tree.m_Info
The header information (for printing the tree).
|
protected Instances |
LADTree.m_search_bestPathInstances |
protected Instances |
ADTree.m_search_bestPathNegInstances
The negative instances that apply to the best path found so far
|
protected Instances |
ADTree.m_search_bestPathPosInstances
The positive instances that apply to the best path found so far
|
protected Instances |
SimpleCart.m_train
Training data.
|
protected Instances |
ADTree.m_trainInstances
The instances used to train the tree
|
Modifier and Type | Method and Description |
---|---|
abstract Instances |
LADTree.Splitter.instancesDownBranch(int branch,
Instances sourceInstances) |
Instances |
LADTree.TwoWayNominalSplit.instancesDownBranch(int branch,
Instances instances) |
Instances |
LADTree.TwoWayNumericSplit.instancesDownBranch(int branch,
Instances instances) |
protected Instances[] |
RandomTree.splitData(Instances data)
Splits instances into subsets based on the given split.
|
Modifier and Type | Method and Description |
---|---|
void |
RandomTree.backfitData(Instances data)
Backfits the given data into the tree.
|
protected void |
RandomTree.backfitData(Instances data,
double[] classProbs)
Recursively backfits data into the tree.
|
void |
RandomTree.buildClassifier(Instances data)
Builds classifier.
|
void |
J48.buildClassifier(Instances instances)
Generates the classifier.
|
void |
FT.buildClassifier(Instances data)
Builds the classifier.
|
void |
DecisionStump.buildClassifier(Instances instances)
Generates the classifier.
|
void |
RandomForest.buildClassifier(Instances data)
Builds a classifier for a set of instances.
|
void |
REPTree.buildClassifier(Instances data)
Builds classifier.
|
void |
SimpleCart.buildClassifier(Instances data)
Build the classifier.
|
void |
J48graft.buildClassifier(Instances instances)
Generates the classifier.
|
void |
UserClassifier.buildClassifier(Instances i)
Call this function to build a decision tree for the training
data provided.
|
void |
LMT.buildClassifier(Instances data)
Builds the classifier.
|
void |
NBTree.buildClassifier(Instances instances)
Generates the classifier.
|
void |
BFTree.buildClassifier(Instances data)
Method for building a BestFirst decision tree classifier.
|
void |
LADTree.buildClassifier(Instances instances)
Builds a classifier for a set of instances.
|
void |
Id3.buildClassifier(Instances data)
Builds Id3 decision tree classifier.
|
void |
ADTree.buildClassifier(Instances instances)
Builds a classifier for a set of instances.
|
protected void |
RandomTree.buildTree(Instances data,
double[] classProbs,
Instances header,
double minNum,
boolean debug,
int[] attIndicesWindow,
Random random,
int depth,
boolean allow)
Recursively generates a tree.
|
protected void |
REPTree.Tree.buildTree(int[][][] sortedIndices,
double[][][] weights,
Instances data,
double totalWeight,
double[] classProbs,
Instances header,
double minNum,
double minVariance,
int depth,
int maxDepth)
Recursively generates a tree.
|
protected double |
SimpleCart.computeSortedInfo(Instances data,
int[][] sortedIndices,
double[][] weights,
double[] classProbs)
Compute sorted indices, weights and class probabilities for a given
dataset.
|
protected double |
BFTree.computeSortedInfo(Instances data,
int[][] sortedIndices,
double[][] weights,
double[] classProbs)
Compute sorted indices, weights and class probabilities for a given
dataset.
|
protected FastVector |
BFTree.computeSplitInfo(BFTree node,
Instances data,
int[][] sortedIndices,
double[][] weights,
double[][][] dists,
double[][] props,
double[][] totalSubsetWeights,
boolean useHeuristic,
boolean useGini)
Compute the best splitting attribute, split point or subset and the best
gini gain or iformation gain for a given dataset.
|
protected double |
RandomTree.distribution(double[][] props,
double[][][] dists,
int att,
Instances data)
Computes class distribution for an attribute.
|
protected double |
REPTree.Tree.distribution(double[][] props,
double[][][] dists,
int att,
int[] sortedIndices,
double[] weights,
double[][] subsetWeights,
Instances data)
Computes class distribution for an attribute.
|
protected void |
ADTree.graphTraverse(PredictionNode currentNode,
StringBuffer text,
int splitOrder,
int predOrder,
Instances instances)
Traverses the tree, graphing each node.
|
void |
LADTree.initClassifier(Instances instances)
Sets up the tree ready to be trained.
|
void |
ADTree.initClassifier(Instances instances)
Sets up the tree ready to be trained, using two-class optimized method.
|
protected void |
REPTree.Tree.insertHoldOutSet(Instances data)
Inserts hold-out set into tree.
|
abstract Instances |
LADTree.Splitter.instancesDownBranch(int branch,
Instances sourceInstances) |
Instances |
LADTree.TwoWayNominalSplit.instancesDownBranch(int branch,
Instances instances) |
Instances |
LADTree.TwoWayNumericSplit.instancesDownBranch(int branch,
Instances instances) |
protected void |
SimpleCart.makeLeaf(Instances data)
Make the node leaf node.
|
protected void |
BFTree.makeLeaf(Instances data)
Make the node leaf node.
|
protected void |
BFTree.makeSuccessors(FastVector BestFirstElements,
Instances data,
int[][][] subsetSortedIndices,
double[][][] subsetWeights,
double[][][] dists,
Attribute att,
boolean useHeuristic,
boolean useGini)
Generate successor nodes for a node and put them into BestFirstElements
according to gini gain or information gain in a descending order.
|
protected void |
BFTree.makeTree(FastVector BestFirstElements,
BFTree root,
Instances train,
Instances test,
FastVector modelError,
int[][] sortedIndices,
double[][] weights,
double[][][] dists,
double[] classProbs,
double totalWeight,
double[] branchProps,
int minNumObj,
boolean useHeuristic,
boolean useGini,
boolean useErrorRate)
This method is to find the number of expansions based on internal
cross-validation for just post-pruning.
|
protected boolean |
BFTree.makeTree(FastVector BestFirstElements,
BFTree root,
Instances train,
int[][] sortedIndices,
double[][] weights,
double[][][] dists,
double[] classProbs,
double totalWeight,
double[] branchProps,
int minNumObj,
boolean useHeuristic,
boolean useGini)
This method is to find the number of expansions based on internal
cross-validation for just pre-pruning.
|
protected void |
BFTree.makeTree(FastVector BestFirstElements,
Instances data,
int[][] sortedIndices,
double[][] weights,
double[][][] dists,
double[] classProbs,
double totalWeight,
double[] branchProps,
int minNumObj,
boolean useHeuristic,
boolean useGini,
int preExpansion)
Recursively build a best-first decision tree.
|
protected void |
SimpleCart.makeTree(Instances data,
int totalInstances,
int[][] sortedIndices,
double[][] weights,
double[] classProbs,
double totalWeight,
double minNumObj,
boolean useHeuristic)
Make binary decision tree recursively.
|
protected String |
SimpleCart.nominalDistribution(double[][] props,
double[][][] dists,
Attribute att,
int[] sortedIndices,
double[] weights,
double[][] subsetWeights,
double[] giniGains,
Instances data,
boolean useHeuristic)
Compute distributions, proportions and total weights of two successor
nodes for a given nominal attribute.
|
protected String |
BFTree.nominalDistribution(double[][] props,
double[][][] dists,
Attribute att,
int[] sortedIndices,
double[] weights,
double[][] subsetWeights,
double[] gains,
Instances data,
boolean useHeuristic,
boolean useGini)
Compute distributions, proportions and total weights of two successor
nodes for a given nominal attribute.
|
protected double |
SimpleCart.numericDistribution(double[][] props,
double[][][] dists,
Attribute att,
int[] sortedIndices,
double[] weights,
double[][] subsetWeights,
double[] giniGains,
Instances data)
Compute distributions, proportions and total weights of two successor
nodes for a given numeric attribute.
|
protected double |
BFTree.numericDistribution(double[][] props,
double[][][] dists,
Attribute att,
int[] sortedIndices,
double[] weights,
double[][] subsetWeights,
double[] gains,
Instances data,
boolean useGini)
Compute distributions, proportions and total weights of two successor nodes for
a given numeric attribute.
|
protected double |
REPTree.Tree.numericDistribution(double[][] props,
double[][][] dists,
int att,
int[] sortedIndices,
double[] weights,
double[][] subsetWeights,
Instances data,
double[] vals)
Computes class distribution for an attribute.
|
int |
LADTree.predictiveError(Instances test) |
int |
SimpleCart.prune(double[] alphas,
double[] errors,
Instances test)
Method for performing one fold in the cross-validation of minimal
cost-complexity pruning.
|
protected Instances[] |
RandomTree.splitData(Instances data)
Splits instances into subsets based on the given split.
|
protected void |
REPTree.Tree.splitData(int[][][][] subsetIndices,
double[][][][] subsetWeights,
int att,
double splitPoint,
int[][] sortedIndices,
double[][] weights,
Instances data)
Splits instances into subsets.
|
protected void |
SimpleCart.splitData(int[][][] subsetIndices,
double[][][] subsetWeights,
Attribute att,
double splitPoint,
String splitStr,
int[][] sortedIndices,
double[][] weights,
Instances data)
Split data into two subsets and store sorted indices and weights for two
successor nodes.
|
protected void |
BFTree.splitData(int[][][] subsetIndices,
double[][][] subsetWeights,
Attribute att,
double splitPoint,
String splitStr,
int[][] sortedIndices,
double[][] weights,
Instances data)
Split data into two subsets and store sorted indices and weights for two
successor nodes.
|
Constructor and Description |
---|
TwoWayNumericSplit(int _attIndex,
Instances instances) |
Modifier and Type | Class and Description |
---|---|
class |
ReferenceInstances
Simple class that extends the Instances class making it possible to create
subsets of instances that reference their source set.
|
Modifier and Type | Method and Description |
---|---|
String |
TwoWayNumericSplit.attributeString(Instances dataset)
Gets the string describing the attributes the split depends on.
|
abstract String |
Splitter.attributeString(Instances dataset)
Gets the string describing the attributes the split depends on.
|
String |
TwoWayNominalSplit.attributeString(Instances dataset)
Gets the string describing the attributes the split depends on.
|
String |
TwoWayNumericSplit.comparisonString(int branchNum,
Instances dataset)
Gets the string describing the comparision the split depends on for a particular
branch.
|
abstract String |
Splitter.comparisonString(int branchNum,
Instances dataset)
Gets the string describing the comparision the split depends on for a particular
branch.
|
String |
TwoWayNominalSplit.comparisonString(int branchNum,
Instances dataset)
Gets the string describing the comparision the split depends on for a particular
branch.
|
ReferenceInstances |
TwoWayNumericSplit.instancesDownBranch(int branch,
Instances instances)
Gets the subset of instances that apply to a particluar branch of the split.
|
abstract ReferenceInstances |
Splitter.instancesDownBranch(int branch,
Instances sourceInstances)
Gets the subset of instances that apply to a particluar branch of the split.
|
ReferenceInstances |
TwoWayNominalSplit.instancesDownBranch(int branch,
Instances instances)
Gets the subset of instances that apply to a particluar branch of the split.
|
Constructor and Description |
---|
ReferenceInstances(Instances dataset,
int capacity)
Creates an empty set of instances.
|
Modifier and Type | Method and Description |
---|---|
protected Instances |
FTtree.getNumericData(Instances train)
Returns a numeric version of a set of instances.
|
protected Instances |
FTtree.insertNewAttr(Instances data)
Inserts new attributes in current dataset or instance
|
protected Instances |
FTtree.removeExtAttributes(Instances data)
Removes extended attributes in current dataset or instance
|
Modifier and Type | Method and Description |
---|---|
abstract void |
FTtree.buildClassifier(Instances data)
Method for building a Functional Tree (only called for the root node).
|
void |
FTNode.buildClassifier(Instances data)
Method for building a Functional tree (only called for the root node).
|
void |
FTInnerNode.buildClassifier(Instances data)
Method for building a Functional Inner tree (only called for the root node).
|
void |
FTLeavesNode.buildClassifier(Instances data)
Method for building a Functional Leaves tree (only called for the root node).
|
abstract void |
FTtree.buildTree(Instances data,
SimpleLinearRegression[][] higherRegressions,
double totalInstanceWeight,
double higherNumParameters)
Abstract method for building the tree structure.
|
void |
FTNode.buildTree(Instances data,
SimpleLinearRegression[][] higherRegressions,
double totalInstanceWeight,
double higherNumParameters)
Method for building the tree structure.
|
void |
FTInnerNode.buildTree(Instances data,
SimpleLinearRegression[][] higherRegressions,
double totalInstanceWeight,
double higherNumParameters)
Method for building the tree structure.
|
void |
FTLeavesNode.buildTree(Instances data,
SimpleLinearRegression[][] higherRegressions,
double totalInstanceWeight,
double higherNumParameters)
Method for building the tree structure.
|
protected double |
FTtree.getEstimatedErrorsForBranch(Instances data)
Computes estimated errors for one branch.
|
protected Instances |
FTtree.getNumericData(Instances train)
Returns a numeric version of a set of instances.
|
protected Instances |
FTtree.insertNewAttr(Instances data)
Inserts new attributes in current dataset or instance
|
protected Instances |
FTtree.removeExtAttributes(Instances data)
Removes extended attributes in current dataset or instance
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
ClassifierTree.m_train
The training instances.
|
Modifier and Type | Method and Description |
---|---|
Instances[] |
ClassifierSplitModel.split(Instances data)
Splits the given set of instances into subsets.
|
Modifier and Type | Method and Description |
---|---|
void |
Distribution.addInstWithUnknown(Instances source,
int attIndex)
Adds all instances with unknown values for given attribute, weighted
according to frequency of instances in each bag.
|
void |
Distribution.addRange(int bagIndex,
Instances source,
int startIndex,
int lastPlusOne)
Adds all instances in given range to given bag.
|
void |
PruneableClassifierTree.buildClassifier(Instances data)
Method for building a pruneable classifier tree.
|
void |
NBTreeNoSplit.buildClassifier(Instances instances)
Build the no-split node
|
void |
NoSplit.buildClassifier(Instances instances)
Creates a "no-split"-split for a given set of instances.
|
void |
NBTreeClassifierTree.buildClassifier(Instances data)
Method for building a naive bayes classifier tree
|
void |
C45PruneableClassifierTree.buildClassifier(Instances data)
Method for building a pruneable classifier tree.
|
abstract void |
ClassifierSplitModel.buildClassifier(Instances instances)
Builds the classifier split model for the given set of instances.
|
void |
C45PruneableClassifierTreeG.buildClassifier(Instances data)
Method for building a pruneable classifier tree.
|
void |
ClassifierTree.buildClassifier(Instances data)
Method for building a classifier tree.
|
void |
NBTreeSplit.buildClassifier(Instances trainInstances)
Creates a NBTree-type split on the given data.
|
void |
C45Split.buildClassifier(Instances trainInstances)
Creates a C4.5-type split on the given data.
|
void |
GraftSplit.buildClassifier(Instances data)
builds m_graftdistro using the passed data
|
void |
BinC45Split.buildClassifier(Instances trainInstances)
Creates a C4.5-type split on the given data.
|
void |
ClassifierTree.buildTree(Instances data,
boolean keepData)
Builds the tree structure.
|
void |
ClassifierTree.buildTree(Instances train,
Instances test,
boolean keepData)
Builds the tree structure with hold out set
|
void |
ClassifierTree.cleanup(Instances justHeaderInfo)
Cleanup in order to save memory.
|
static double |
NBTreeNoSplit.crossValidate(NaiveBayesUpdateable fullModel,
Instances trainingSet,
Random r)
Utility method for fast 5-fold cross validation of a naive bayes
model
|
void |
GraftSplit.deleteGraftedCases(Instances data)
deletes the cases in data that belong to leaf pointed to by
the test (i.e.
|
void |
Distribution.delRange(int bagIndex,
Instances source,
int startIndex,
int lastPlusOne)
Deletes all instances in given range from given bag.
|
void |
C45PruneableClassifierTreeG.doGrafting(Instances data)
Initializes variables for grafting.
|
String |
ClassifierSplitModel.dumpLabel(int index,
Instances data)
Prints label for subset index of instances (eg class).
|
String |
GraftSplit.dumpLabelG(int index,
Instances data)
Prints label for subset index of instances (eg class).
|
String |
ClassifierSplitModel.dumpModel(Instances data)
Prints the split model.
|
protected ClassifierTree |
NBTreeClassifierTree.getNewTree(Instances data)
Returns a newly created tree.
|
protected ClassifierTree |
C45PruneableClassifierTree.getNewTree(Instances data)
Returns a newly created tree.
|
protected ClassifierTree |
C45PruneableClassifierTreeG.getNewTree(Instances data)
Returns a newly created tree.
|
protected ClassifierTree |
ClassifierTree.getNewTree(Instances data)
Returns a newly created tree.
|
protected ClassifierTree |
PruneableClassifierTree.getNewTree(Instances train,
Instances test)
Returns a newly created tree.
|
protected ClassifierTree |
NBTreeClassifierTree.getNewTree(Instances train,
Instances test)
Returns a newly created tree.
|
protected ClassifierTree |
ClassifierTree.getNewTree(Instances train,
Instances test)
Returns a newly created tree.
|
String |
NBTreeNoSplit.leftSide(Instances instances)
Does nothing because no condition has to be satisfied.
|
String |
NoSplit.leftSide(Instances instances)
Does nothing because no condition has to be satisfied.
|
abstract String |
ClassifierSplitModel.leftSide(Instances data)
Prints left side of condition satisfied by instances.
|
String |
NBTreeSplit.leftSide(Instances data)
Prints left side of condition..
|
String |
C45Split.leftSide(Instances data)
Prints left side of condition..
|
String |
GraftSplit.leftSide(Instances data)
Prints left side of condition satisfied by instances.
|
String |
BinC45Split.leftSide(Instances data)
Prints left side of condition.
|
double[][] |
C45Split.minsAndMaxs(Instances data,
double[][] minsAndMaxs,
int index)
Returns the minsAndMaxs of the index.th subset.
|
void |
ClassifierSplitModel.resetDistribution(Instances data)
Sets distribution associated with model.
|
void |
C45Split.resetDistribution(Instances data)
Sets distribution associated with model.
|
void |
BinC45Split.resetDistribution(Instances data)
Sets distribution associated with model.
|
String |
NBTreeNoSplit.rightSide(int index,
Instances instances)
Does nothing because no condition has to be satisfied.
|
String |
NoSplit.rightSide(int index,
Instances instances)
Does nothing because no condition has to be satisfied.
|
abstract String |
ClassifierSplitModel.rightSide(int index,
Instances data)
Prints left side of condition satisfied by instances in subset index.
|
String |
NBTreeSplit.rightSide(int index,
Instances data)
Prints the condition satisfied by instances in a subset.
|
String |
C45Split.rightSide(int index,
Instances data)
Prints the condition satisfied by instances in a subset.
|
String |
GraftSplit.rightSide(int index,
Instances data)
Prints condition satisfied by instances in subset index.
|
String |
BinC45Split.rightSide(int index,
Instances data)
Prints the condition satisfied by instances in a subset.
|
ClassifierSplitModel |
NBTreeModelSelection.selectModel(Instances data)
Selects NBTree-type split for the given dataset.
|
ClassifierSplitModel |
BinC45ModelSelection.selectModel(Instances data)
Selects C4.5-type split for the given dataset.
|
ClassifierSplitModel |
C45ModelSelection.selectModel(Instances data)
Selects C4.5-type split for the given dataset.
|
abstract ClassifierSplitModel |
ModelSelection.selectModel(Instances data)
Selects a model for the given dataset.
|
ClassifierSplitModel |
NBTreeModelSelection.selectModel(Instances train,
Instances test)
Selects NBTree-type split for the given dataset.
|
ClassifierSplitModel |
BinC45ModelSelection.selectModel(Instances train,
Instances test)
Selects C4.5-type split for the given dataset.
|
ClassifierSplitModel |
C45ModelSelection.selectModel(Instances train,
Instances test)
Selects C4.5-type split for the given dataset.
|
ClassifierSplitModel |
ModelSelection.selectModel(Instances train,
Instances test)
Selects a model for the given train data using the given test data
|
void |
C45Split.setSplitPoint(Instances allInstances)
Sets split point to greatest value in given data smaller or equal to
old split point.
|
void |
BinC45Split.setSplitPoint(Instances allInstances)
Sets split point to greatest value in given data smaller or equal to
old split point.
|
void |
Distribution.shiftRange(int from,
int to,
Instances source,
int startIndex,
int lastPlusOne)
Shifts all instances in given range from one bag to another one.
|
String |
ClassifierSplitModel.sourceClass(int index,
Instances data) |
String |
NBTreeNoSplit.sourceExpression(int index,
Instances data)
Returns a string containing java source code equivalent to the test
made at this node.
|
String |
NoSplit.sourceExpression(int index,
Instances data)
Returns a string containing java source code equivalent to the test
made at this node.
|
abstract String |
ClassifierSplitModel.sourceExpression(int index,
Instances data) |
String |
NBTreeSplit.sourceExpression(int index,
Instances data)
Returns a string containing java source code equivalent to the test
made at this node.
|
String |
C45Split.sourceExpression(int index,
Instances data)
Returns a string containing java source code equivalent to the test
made at this node.
|
String |
GraftSplit.sourceExpression(int index,
Instances data)
Returns a string containing java source code equivalent to the test
made at this node.
|
String |
BinC45Split.sourceExpression(int index,
Instances data)
Returns a string containing java source code equivalent to the test
made at this node.
|
Instances[] |
ClassifierSplitModel.split(Instances data)
Splits the given set of instances into subsets.
|
String |
GraftSplit.toString(Instances data)
method for returning information about this GraftSplit
|
Constructor and Description |
---|
BinC45ModelSelection(int minNoObj,
Instances allData)
Initializes the split selection method with the given parameters.
|
C45ModelSelection(int minNoObj,
Instances allData)
Initializes the split selection method with the given parameters.
|
C45PruneableClassifierTreeG(ModelSelection toSelectLocModel,
Instances data,
ClassifierSplitModel gs,
boolean prune,
float cf,
boolean raise,
boolean isLeaf,
boolean relabel,
boolean cleanup)
Constructor for pruneable tree structure.
|
Distribution(Instances source)
Creates a distribution with only one bag according
to instances in source.
|
Distribution(Instances source,
ClassifierSplitModel modelToUse)
Creates a distribution according to given instances and
split model.
|
NBTreeModelSelection(int minNoObj,
Instances allData)
Initializes the split selection method with the given parameters.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
ResidualSplit.m_data
The set of instances
|
protected Instances |
LogisticBase.m_numericData
Numeric version of the training data.
|
protected Instances |
LogisticBase.m_numericDataHeader
Header-only version of the numeric version of the training data
|
protected Instances |
LogisticBase.m_train
Training data
|
Modifier and Type | Method and Description |
---|---|
protected Instances |
LogisticBase.getNumericData(Instances data)
Converts training data to numeric version.
|
protected Instances |
LMTNode.getNumericData(Instances train)
Returns a numeric version of a set of instances.
|
Modifier and Type | Method and Description |
---|---|
void |
ResidualSplit.buildClassifier(Instances data)
Method not in use
|
void |
LogisticBase.buildClassifier(Instances data)
Builds the logistic regression model usiing LogitBoost.
|
void |
LMTNode.buildClassifier(Instances data)
Method for building a logistic model tree (only called for the root node).
|
void |
ResidualSplit.buildClassifier(Instances data,
double[][] dataZs,
double[][] dataWs)
Builds the split.
|
void |
LMTNode.buildTree(Instances data,
SimpleLinearRegression[][] higherRegressions,
double totalInstanceWeight,
double higherNumParameters)
Method for building the tree structure.
|
protected double |
LogisticBase.getErrorRate(Instances data)
Returns the misclassification error of the current model on a set of instances.
|
protected double[][] |
LogisticBase.getFs(Instances data)
Computes the F-values for a set of instances.
|
protected double |
LogisticBase.getMeanAbsoluteError(Instances data)
Returns the error of the probability estimates for the current model on a set of instances.
|
protected Instances |
LogisticBase.getNumericData(Instances data)
Converts training data to numeric version.
|
protected Instances |
LMTNode.getNumericData(Instances train)
Returns a numeric version of a set of instances.
|
protected double[][] |
LogisticBase.getYs(Instances data)
Computes the Y-values (actual class probabilities) for a set of instances.
|
String |
ResidualSplit.leftSide(Instances data)
Returns name of splitting attribute (left side of condition).
|
protected int |
LogisticBase.performBoosting(Instances train,
Instances test,
double[] error,
int maxIterations)
Runs LogitBoost on a training set and monitors the error on a test set.
|
protected boolean |
LogisticBase.performIteration(int iteration,
double[][] trainYs,
double[][] trainFs,
double[][] probs,
Instances trainNumeric)
Performs a single iteration of LogitBoost, and updates the model accordingly.
|
int |
LMTNode.prune(double[] alphas,
double[] errors,
Instances test)
Method for performing one fold in the cross-validation of the cost-complexity parameter.
|
String |
ResidualSplit.rightSide(int index,
Instances data)
Prints the condition satisfied by instances in a subset.
|
ClassifierSplitModel |
ResidualModelSelection.selectModel(Instances train)
Method not in use
|
ClassifierSplitModel |
ResidualModelSelection.selectModel(Instances data,
double[][] dataZs,
double[][] dataWs)
Selects split based on residuals for the given dataset.
|
ClassifierSplitModel |
ResidualModelSelection.selectModel(Instances train,
Instances test)
Method not in use
|
String |
ResidualSplit.sourceExpression(int index,
Instances data)
Method not in use
|
protected int |
LMTNode.tryLogistic(Instances data)
Determines the optimum number of LogitBoost iterations to perform by building a standalone logistic
regression function on the training data.
|
Modifier and Type | Method and Description |
---|---|
Instances |
Rule.notCoveredInstances()
Get the instances not covered by this rule
|
Modifier and Type | Method and Description |
---|---|
protected static double |
Rule.absDev(int attr,
Instances inst)
Returns the absolute deviation value of the supplied attribute index.
|
void |
SplitEvaluate.attrSplit(int attr,
Instances inst)
Finds the best splitting point for an attribute in the instances
|
void |
YongSplitInfo.attrSplit(int attr,
Instances inst)
Finds the best splitting point for an attribute in the instances
|
void |
CorrelationSplitInfo.attrSplit(int attr,
Instances inst)
Finds the best splitting point for an attribute in the instances
|
void |
M5Base.buildClassifier(Instances data)
Generates the classifier.
|
void |
RuleNode.buildClassifier(Instances data)
Build this node (find an attribute and split point)
|
void |
Rule.buildClassifier(Instances data)
Generates a single rule or m5 model tree.
|
void |
PreConstructedLinearModel.buildClassifier(Instances instances)
Builds the classifier.
|
protected static double |
Rule.stdDev(int attr,
Instances inst)
Returns the standard deviation value of the supplied attribute index.
|
String |
YongSplitInfo.toString(Instances inst)
Converts the spliting information to string
|
Constructor and Description |
---|
Impurity(int partition,
int attribute,
Instances inst,
int k)
Constructs an Impurity object containing the impurity values of partitioning the instances using an attribute
|
Values(int low,
int high,
int attribute,
Instances inst)
Constructs an object which stores some statistics of the instances such
as sum, squared sum, variance, standard deviation
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
XMeans.m_ClusterCenters
cluster centers.
|
protected Instances |
FarthestFirst.m_ClusterCentroids
holds the cluster centroids
|
protected Instances |
XMeans.m_DebugVectors
all the debug vectors.
|
protected Instances |
FilteredClusterer.m_FilteredInstances
The instance structure of the filtered instances.
|
protected Instances |
FarthestFirst.m_instances
training instances, not necessary to keep,
could be replaced by m_ClusterCentroids where needed for header info
|
protected Instances |
XMeans.m_Instances
training instances.
|
protected Instances |
XMeans.m_Model
model information, should increase readability.
|
Modifier and Type | Method and Description |
---|---|
Instances |
XMeans.getClusterCenters()
Return the centers of the clusters as an Instances object
|
Instances |
SimpleKMeans.getClusterCentroids()
Gets the the cluster centroids
|
Instances |
SimpleKMeans.getClusterStandardDevs()
Gets the standard deviations of the numeric attributes in each cluster
|
protected Instances |
XMeans.makeCentersRandomly(Random random0,
Instances model,
int numClusters)
Generates new centers randomly.
|
protected Instances |
CheckClusterer.makeTestDataset(int seed,
int numInstances,
int numNominal,
int numNumeric,
int numString,
int numDate,
int numRelational,
boolean multiInstance)
Make a simple set of instances with variable position of the class
attribute, which can later be modified for use in specific tests.
|
protected Instances |
XMeans.newCentersAfterSplit(boolean[] splitWon,
Instances splitCenters)
Returns new centers.
|
protected Instances |
XMeans.newCentersAfterSplit(double[] pbic,
double[] cbic,
double cutoffFactor,
Instances splitCenters)
Returns new center list.
|
protected Instances |
XMeans.splitCenter(Random random,
Instance center,
double variance,
Instances model)
Split centers in their region.
|
protected Instances |
XMeans.splitCenters(Random random,
Instances instances,
Instances model)
Split centers in their region.
|
Modifier and Type | Method and Description |
---|---|
protected void |
CheckClusterer.addMissing(Instances data,
int level,
boolean predictorMissing)
Add missing values to a dataset.
|
protected boolean |
XMeans.assignToCenters(Instances centers,
int[][] instOfCent,
int[] allInstList,
int[] assignments)
Assign instances to centers.
|
protected boolean |
XMeans.assignToCenters(KDTree kdtree,
Instances centers,
int[][] instOfCent,
int[] assignments,
int iterationCount)
Assign instances to centers using KDtree.
|
protected boolean |
XMeans.assignToCenters(KDTree tree,
Instances centers,
int[][] instOfCent,
int[] allInstList,
int[] assignments,
int iterationCount)
Assigns instances to centers.
|
void |
XMeans.buildClusterer(Instances data)
Generates the X-Means clusterer.
|
void |
CLOPE.buildClusterer(Instances data)
Generate Clustering via CLOPE
|
void |
FarthestFirst.buildClusterer(Instances data)
Generates a clusterer.
|
void |
OPTICS.buildClusterer(Instances instances)
Generate Clustering via OPTICS
|
void |
FilteredClusterer.buildClusterer(Instances data)
Build the clusterer on the filtered data.
|
void |
Cobweb.buildClusterer(Instances data)
Builds the clusterer.
|
void |
MakeDensityBasedClusterer.buildClusterer(Instances data)
Builds a clusterer for a set of instances.
|
void |
DBScan.buildClusterer(Instances instances)
Generate Clustering via DBScan
|
void |
Clusterer.buildClusterer(Instances data)
Generates a clusterer.
|
void |
SimpleKMeans.buildClusterer(Instances data)
Generates a clusterer.
|
void |
sIB.buildClusterer(Instances data)
Generates a clusterer.
|
abstract void |
AbstractClusterer.buildClusterer(Instances data)
Generates a clusterer.
|
void |
HierarchicalClusterer.buildClusterer(Instances data) |
void |
EM.buildClusterer(Instances data)
Generates a clusterer.
|
protected double |
XMeans.calculateBIC(int[][] instOfCent,
Instances centers,
double[] mle)
Calculates the BIC for the given set of centers and instances.
|
protected double |
XMeans.calculateBIC(int[] instList,
Instance center,
double mle,
Instances model)
Returns the BIC-value for the given center and instances.
|
boolean |
XMeans.checkForNominalAttributes(Instances data)
Checks for nominal attributes in the dataset.
|
protected int |
XMeans.clusterProcessedInstance(Instance instance,
Instances centers)
Clusters an instance.
|
static double |
ClusterEvaluation.crossValidateModel(DensityBasedClusterer clusterer,
Instances data,
int numFolds,
Random random)
Perform a cross-validation for DensityBasedClusterer on a set of instances.
|
static String |
ClusterEvaluation.crossValidateModel(String clustererString,
Instances data,
int numFolds,
String[] options,
Random random)
Performs a cross-validation
for a DensityBasedClusterer clusterer on a set of instances.
|
Database |
OPTICS.databaseForName(String database_Type,
Instances instances)
Returns a new Class-Instance of the specified database
|
Database |
DBScan.databaseForName(String database_Type,
Instances instances)
Returns a new Class-Instance of the specified database
|
protected double[] |
XMeans.distortion(int[][] instOfCent,
Instances centers)
Calculates the maximum likelihood estimate for the variance.
|
void |
ClusterEvaluation.evaluateClusterer(Instances test)
Evaluate the clusterer on a set of instances.
|
void |
ClusterEvaluation.evaluateClusterer(Instances test,
String testFileName)
Evaluate the clusterer on a set of instances.
|
void |
ClusterEvaluation.evaluateClusterer(Instances test,
String testFileName,
boolean outputModel)
Evaluate the clusterer on a set of instances.
|
Instance |
XMeans.getNextDebugVectorsInstance(Instances model)
Read an instance from debug vectors file.
|
protected void |
FarthestFirst.initMinMax(Instances data) |
protected Instances |
XMeans.makeCentersRandomly(Random random0,
Instances model,
int numClusters)
Generates new centers randomly.
|
protected double |
XMeans.meanOrMode(Instances instances,
int[] instList,
int attIndex)
Computes Mean Or Mode of one attribute on a subset of m_Instances.
|
protected double[] |
SimpleKMeans.moveCentroid(int centroidIndex,
Instances members,
boolean updateClusterInfo)
Move the centroid to it's new coordinates.
|
protected Instances |
XMeans.newCentersAfterSplit(boolean[] splitWon,
Instances splitCenters)
Returns new centers.
|
protected Instances |
XMeans.newCentersAfterSplit(double[] pbic,
double[] cbic,
double cutoffFactor,
Instances splitCenters)
Returns new center list.
|
protected boolean |
XMeans.recomputeCenters(Instances centers,
int[][] instOfCent,
Instances model)
Recompute the new centers.
|
protected void |
XMeans.recomputeCentersFast(Instances centers,
int[][] instOfCentIndexes,
Instances model)
Recompute the new centers - 2nd version
Same as recomputeCenters, but does not check if center stays the same.
|
protected Instances |
XMeans.splitCenter(Random random,
Instance center,
double variance,
Instances model)
Split centers in their region.
|
protected Instances |
XMeans.splitCenters(Random random,
Instances instances,
Instances model)
Split centers in their region.
|
protected void |
FarthestFirst.updateMinDistance(double[] minDistance,
boolean[] selected,
Instances data,
Instance center) |
Modifier and Type | Method and Description |
---|---|
Instances |
SequentialDatabase.getInstances()
Returns the original instances delivered from WEKA
|
Instances |
Database.getInstances()
Returns the original instances delivered from WEKA
|
Constructor and Description |
---|
SequentialDatabase(Instances instances)
Constructs a new sequential database and holds the original instances
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
TestInstances.m_Data
the generated data
|
protected Instances |
NormalizableDistance.m_Data
the instances used internally.
|
protected Instances |
AttributeLocator.m_Data
the referenced data
|
protected Instances |
Instance.m_Dataset
The dataset the instance has access to.
|
protected Instances |
TestInstances.m_RelationalClassFormat
the format of the multi-instance data of the class
|
protected Instances[] |
TestInstances.m_RelationalFormat
the format of the multi-instance data
|
Modifier and Type | Method and Description |
---|---|
Instances |
Instance.dataset()
Returns the dataset this instance has access to.
|
Instances |
TestInstances.generate()
Generates a new dataset
|
Instances |
TestInstances.generate(String namePrefix)
generates a new dataset.
|
Instances |
TestInstances.getData()
returns the current dataset, can be null
|
Instances |
AttributeLocator.getData()
returns the underlying data
|
Instances |
NormalizableDistance.getInstances()
returns the instances currently set.
|
Instances |
DistanceFunction.getInstances()
returns the instances currently set.
|
Instances |
TestInstances.getRelationalClassFormat()
returns the current strcuture of the relational class attribute, can
be null
|
Instances |
TestInstances.getRelationalFormat(int index)
returns the format for the specified relational attribute, can be null
|
static Instances |
Instances.mergeInstances(Instances first,
Instances second)
Merges two sets of Instances together.
|
protected Instances |
CheckScheme.process(Instances data)
Provides a hook for derived classes to further modify the data.
|
Instances |
CheckScheme.PostProcessor.process(Instances data)
Provides a hook for derived classes to further modify the data.
|
Instances |
Attribute.relation()
Returns the header info for a relation-valued attribute,
null if the attribute is not relation-valued.
|
Instances |
Attribute.relation(int valIndex)
Returns a value of a relation-valued attribute.
|
Instances |
Instance.relationalValue(Attribute att)
Returns the relational value of a relational attribute.
|
Instances |
Instance.relationalValue(int attIndex)
Returns the relational value of a relational attribute.
|
Instances |
Instances.resample(Random random)
Creates a new dataset of the same size using random sampling
with replacement.
|
Instances |
Instances.resampleWithWeights(Random random)
Creates a new dataset of the same size using random sampling
with replacement according to the current instance weights.
|
Instances |
Instances.resampleWithWeights(Random random,
double[] weights)
Creates a new dataset of the same size using random sampling
with replacement according to the given weight vector.
|
Instances |
Instances.stringFreeStructure()
Create a copy of the structure if the data has string or
relational attributes, "cleanses" string types (i.e.
|
Instances |
Instances.testCV(int numFolds,
int numFold)
Creates the test set for one fold of a cross-validation on
the dataset.
|
Instances |
Instances.trainCV(int numFolds,
int numFold)
Creates the training set for one fold of a cross-validation
on the dataset.
|
Instances |
Instances.trainCV(int numFolds,
int numFold,
Random random)
Creates the training set for one fold of a cross-validation
on the dataset.
|
Modifier and Type | Method and Description |
---|---|
protected void |
CheckScheme.addMissing(Instances data,
int level,
boolean predictorMissing,
boolean classMissing)
Add missing values to a dataset.
|
int |
Attribute.addRelation(Instances value)
Adds a relation to a relation-valued attribute.
|
int |
EuclideanDistance.closestPoint(Instance instance,
Instances allPoints,
int[] pointList)
Returns the index of the closest point to the current instance.
|
protected void |
CheckScheme.compareDatasets(Instances data1,
Instances data2)
Compare two datasets to see if they differ.
|
protected void |
Instances.copyInstances(int from,
Instances dest,
int num)
Copies instances from one set to the end of another
one.
|
static void |
RelationalLocator.copyRelationalValues(Instance instance,
boolean instSrcCompat,
Instances srcDataset,
AttributeLocator srcLoc,
Instances destDataset,
AttributeLocator destLoc)
Takes relational values referenced by an Instance and copies them from a
source dataset to a destination dataset.
|
static void |
RelationalLocator.copyRelationalValues(Instance inst,
Instances destDataset,
AttributeLocator strAtts)
Copies relational values contained in the instance copied to a new
dataset.
|
static void |
StringLocator.copyStringValues(Instance instance,
boolean instSrcCompat,
Instances srcDataset,
AttributeLocator srcLoc,
Instances destDataset,
AttributeLocator destLoc)
Takes string values referenced by an Instance and copies them from a
source dataset to a destination dataset.
|
static void |
StringLocator.copyStringValues(Instance inst,
Instances destDataset,
AttributeLocator strAtts)
Copies string values contained in the instance copied to a new
dataset.
|
boolean |
Instances.equalHeaders(Instances dataset)
Checks if two headers are equivalent.
|
static Capabilities |
Capabilities.forInstances(Instances data)
returns a Capabilities object specific for this data.
|
static Capabilities |
Capabilities.forInstances(Instances data,
boolean multi)
returns a Capabilities object specific for this data.
|
protected double |
TestInstances.generateAttributeValue(Instances data,
int index,
double classVal)
Generates a new value for the specified attribute.
|
protected double |
TestInstances.generateClassValue(Instances data)
Generates the class value
|
Instance |
AlgVector.getAsInstance(Instances model,
Random random)
Gets the elements of the vector as an instance.
|
protected void |
Instances.initialize(Instances dataset,
int capacity)
initializes with the header information of the given dataset and sets
the capacity of the set of instances.
|
protected void |
AttributeLocator.initialize(Instances data,
int type,
int[] indices)
initializes the AttributeLocator
|
static Instances |
Instances.mergeInstances(Instances first,
Instances second)
Merges two sets of Instances together.
|
protected Instances |
CheckScheme.process(Instances data)
Provides a hook for derived classes to further modify the data.
|
Instances |
CheckScheme.PostProcessor.process(Instances data)
Provides a hook for derived classes to further modify the data.
|
void |
Instance.setDataset(Instances instances)
Sets the reference to the dataset.
|
void |
NormalizableDistance.setInstances(Instances insts)
Sets the instances.
|
void |
DistanceFunction.setInstances(Instances insts)
Sets the instances.
|
void |
TestInstances.setRelationalClassFormat(Instances value)
sets the structure for the relational class attribute
|
void |
TestInstances.setRelationalFormat(int index,
Instances value)
sets the structure for the bags for the relational attribute
|
boolean |
Capabilities.test(Instances data)
Tests the given data, whether it can be processed by the handler,
given its capabilities.
|
boolean |
Capabilities.test(Instances data,
int fromIndex,
int toIndex)
Tests a certain range of attributes of the given data, whether it can be
processed by the handler, given its capabilities.
|
void |
Capabilities.testWithFail(Instances data)
tests the given data by calling the test(Instances) method and throws
an exception if the test fails.
|
void |
Capabilities.testWithFail(Instances data,
int fromIndex,
int toIndex)
tests the given data by calling the test(Instances,int,int) method and
throws an exception if the test fails.
|
Constructor and Description |
---|
AbstractStringDistanceFunction(Instances data)
Constructor that sets the data
|
AlgVector(Instances format,
Random random)
Constructs a vector using a given data format.
|
Attribute(String attributeName,
Instances header)
Constructor for relation-valued attributes.
|
Attribute(String attributeName,
Instances header,
int index)
Constructor for a relation-valued attribute with a particular index.
|
Attribute(String attributeName,
Instances header,
ProtectedProperties metadata)
Constructor for relation-valued attributes.
|
AttributeLocator(Instances data,
int type)
Initializes the AttributeLocator with the given data for the specified
type of attribute.
|
AttributeLocator(Instances data,
int type,
int[] indices)
initializes the AttributeLocator with the given data for the specified
type of attribute.
|
AttributeLocator(Instances data,
int type,
int fromIndex,
int toIndex)
Initializes the AttributeLocator with the given data for the specified
type of attribute.
|
ChebyshevDistance(Instances data)
Constructs an Chebyshev Distance object and automatically initializes the
ranges.
|
EditDistance(Instances data) |
EuclideanDistance(Instances data)
Constructs an Euclidean Distance object and automatically initializes the
ranges.
|
Instances(Instances dataset)
Constructor copying all instances and references to
the header information from the given set of instances.
|
Instances(Instances dataset,
int capacity)
Constructor creating an empty set of instances.
|
Instances(Instances source,
int first,
int toCopy)
Creates a new set of instances by copying a
subset of another set.
|
ManhattanDistance(Instances data)
Constructs an Manhattan Distance object and automatically initializes the
ranges.
|
NormalizableDistance(Instances data)
Initializes the distance function and automatically initializes the
ranges.
|
RelationalLocator(Instances data)
Initializes the RelationalLocator with the given data.
|
RelationalLocator(Instances data,
int[] indices)
Initializes the RelationalLocator with the given data.
|
RelationalLocator(Instances data,
int fromIndex,
int toIndex)
Initializes the RelationalLocator with the given data.
|
StringLocator(Instances data)
initializes the StringLocator with the given data
|
StringLocator(Instances data,
int[] indices)
Initializes the AttributeLocator with the given data.
|
StringLocator(Instances data,
int fromIndex,
int toIndex)
Initializes the StringLocator with the given data.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
ConverterUtils.DataSource.m_BatchBuffer
the batch buffer.
|
protected Instances |
ArffLoader.ArffReader.m_Data
the actual data
|
protected Instances |
SerializedInstancesLoader.m_Dataset
Holds the structure (header) of the data set.
|
protected Instances |
TextDirectoryLoader.m_structure
Holds the determined structure (header) of the data set.
|
protected Instances |
AbstractFileLoader.m_structure
Holds the determined structure (header) of the data set.
|
protected Instances |
DatabaseLoader.m_structure
The header information that is retrieved in the beginning of incremental loading
|
Modifier and Type | Method and Description |
---|---|
Instances |
ArffLoader.ArffReader.getData()
Returns the data that was read
|
Instances |
TextDirectoryLoader.getDataSet()
Return the full data set.
|
Instances |
ConverterUtils.DataSource.getDataSet()
returns the full dataset, can be null in case of an error.
|
Instances |
C45Loader.getDataSet()
Return the full data set.
|
Instances |
ArffLoader.getDataSet()
Return the full data set.
|
Instances |
XRFFLoader.getDataSet()
Return the full data set.
|
abstract Instances |
AbstractLoader.getDataSet() |
Instances |
CSVLoader.getDataSet()
Return the full data set.
|
Instances |
SerializedInstancesLoader.getDataSet()
Return the full data set.
|
Instances |
SVMLightLoader.getDataSet()
Return the full data set.
|
Instances |
Loader.getDataSet()
Return the full data set.
|
Instances |
LibSVMLoader.getDataSet()
Return the full data set.
|
Instances |
DatabaseLoader.getDataSet()
Return the full data set in batch mode (header and all intances at once).
|
Instances |
ConverterUtils.DataSource.getDataSet(int classIndex)
returns the full dataset with the specified class index set,
can be null in case of an error.
|
Instances |
AbstractSaver.getInstances()
Gets instances that should be stored.
|
Instances |
TextDirectoryLoader.getStructure()
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
|
Instances |
ConverterUtils.DataSource.getStructure()
returns the structure of the data.
|
Instances |
C45Loader.getStructure()
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
|
Instances |
ArffLoader.getStructure()
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
|
Instances |
ArffLoader.ArffReader.getStructure()
Returns the header format
|
Instances |
XRFFLoader.getStructure()
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
|
abstract Instances |
AbstractLoader.getStructure() |
Instances |
CSVLoader.getStructure()
Determines and returns (if possible) the structure (internally the header)
of the data set as an empty set of instances.
|
Instances |
SerializedInstancesLoader.getStructure()
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
|
Instances |
SVMLightLoader.getStructure()
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
|
Instances |
Loader.getStructure()
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
|
Instances |
LibSVMLoader.getStructure()
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
|
Instances |
DatabaseLoader.getStructure()
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
|
Instances |
ConverterUtils.DataSource.getStructure(int classIndex)
returns the structure of the data, with the defined class index.
|
static Instances |
ConverterUtils.DataSource.read(InputStream stream)
convencience method for loading a dataset in batch mode from a stream.
|
static Instances |
ConverterUtils.DataSource.read(Loader loader)
convencience method for loading a dataset in batch mode.
|
static Instances |
ConverterUtils.DataSource.read(String location)
convencience method for loading a dataset in batch mode.
|
Modifier and Type | Method and Description |
---|---|
protected Instance |
ArffLoader.ArffReader.getInstance(Instances structure,
boolean flag)
Reads a single instance using the tokenizer and returns it.
|
Instance |
TextDirectoryLoader.getNextInstance(Instances structure)
TextDirectoryLoader is unable to process a data set incrementally.
|
Instance |
C45Loader.getNextInstance(Instances structure)
Read the data set incrementally---get the next instance in the data
set or returns null if there are no
more instances to get.
|
Instance |
ArffLoader.getNextInstance(Instances structure)
Read the data set incrementally---get the next instance in the data
set or returns null if there are no
more instances to get.
|
Instance |
XRFFLoader.getNextInstance(Instances structure)
XRFFLoader is unable to process a data set incrementally.
|
abstract Instance |
AbstractLoader.getNextInstance(Instances structure) |
Instance |
CSVLoader.getNextInstance(Instances structure)
CSVLoader is unable to process a data set incrementally.
|
Instance |
SerializedInstancesLoader.getNextInstance(Instances structure)
Read the data set incrementally---get the next instance in the data
set or returns null if there are no
more instances to get.
|
Instance |
SVMLightLoader.getNextInstance(Instances structure)
SVMLightLoader is unable to process a data set incrementally.
|
Instance |
Loader.getNextInstance(Instances structure)
Read the data set incrementally---get the next instance in the data
set or returns null if there are no
more instances to get.
|
Instance |
LibSVMLoader.getNextInstance(Instances structure)
LibSVmLoader is unable to process a data set incrementally.
|
Instance |
DatabaseLoader.getNextInstance(Instances structure)
Read the data set incrementally---get the next instance in the data
set or returns null if there are no
more instances to get.
|
boolean |
ConverterUtils.DataSource.hasMoreElements(Instances structure)
returns whether there are more Instance objects in the data.
|
Instance |
ConverterUtils.DataSource.nextElement(Instances dataset)
returns the next element and sets the specified dataset, null if
none available.
|
Instance |
ArffLoader.ArffReader.readInstance(Instances structure)
Reads a single instance using the tokenizer and returns it.
|
Instance |
ArffLoader.ArffReader.readInstance(Instances structure,
boolean flag)
Reads a single instance using the tokenizer and returns it.
|
void |
XRFFSaver.setInstances(Instances instances)
Sets instances that should be stored.
|
void |
AbstractSaver.setInstances(Instances instances)
Sets instances that should be stored.
|
void |
LibSVMSaver.setInstances(Instances instances)
Sets instances that should be stored.
|
void |
Saver.setInstances(Instances instances)
Sets the instances to be saved
|
void |
SVMLightSaver.setInstances(Instances instances)
Sets instances that should be stored.
|
int |
AbstractSaver.setStructure(Instances headerInfo)
Sets the strcuture of the instances for the first step of incremental saving.
|
void |
ConverterUtils.DataSink.write(Instances data)
writes the given data either via the saver or to the defined
output stream (depending on the constructor).
|
static void |
ConverterUtils.DataSink.write(OutputStream stream,
Instances data)
writes the data to the given stream (always in ARFF format).
|
static void |
ConverterUtils.DataSink.write(Saver saver,
Instances data)
writes the data via the given saver.
|
static void |
ConverterUtils.DataSink.write(String filename,
Instances data)
writes the data to the given file.
|
Constructor and Description |
---|
ArffReader(Reader reader,
Instances template,
int lines)
Reads the data without header according to the specified template.
|
ArffReader(Reader reader,
Instances template,
int lines,
int capacity)
Initializes the reader without reading the header according to the
specified template.
|
DataSource(Instances inst)
Initializes the datasource with the given dataset.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
NearestNeighbourSearch.m_Instances
The neighbourhood of instances to find neighbours in.
|
Modifier and Type | Method and Description |
---|---|
Instances |
NearestNeighbourSearch.getInstances()
returns the instances currently set.
|
Instances |
KDTree.kNearestNeighbours(Instance target,
int k)
Returns the k nearest neighbours of the supplied instance.
|
Instances |
BallTree.kNearestNeighbours(Instance target,
int k)
Returns k nearest instances in the current neighbourhood to the supplied
instance.
|
abstract Instances |
NearestNeighbourSearch.kNearestNeighbours(Instance target,
int k)
Returns k nearest instances in the current neighbourhood to the supplied
instance.
|
Instances |
LinearNNSearch.kNearestNeighbours(Instance target,
int kNN)
Returns k nearest instances in the current neighbourhood to the supplied
instance.
|
Instances |
CoverTree.kNearestNeighbours(Instance target,
int k)
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.
|
Modifier and Type | Method and Description |
---|---|
void |
KDTree.assignSubToCenters(KDTreeNode node,
Instances centers,
int[] centList,
int[] assignments)
Assigns instances of this node to center.
|
protected void |
CoverTree.buildCoverTree(Instances insts)
Builds the tree on the given set of instances.
|
protected void |
KDTree.buildKDTree(Instances instances)
Builds the KDTree on the supplied set of instances/points.
|
void |
KDTree.centerInstances(Instances centers,
int[] assignments,
double pc)
Assigns instances to centers using KDTree.
|
protected void |
KDTree.checkMissing(Instances instances)
Checks if there is any instance with missing values.
|
protected void |
CoverTree.checkMissing(Instances instances)
Checks if there is any instance with missing values.
|
protected void |
KDTree.determineAssignments(KDTreeNode node,
Instances centers,
int[] candidates,
int[] assignments,
double pc)
Assigns instances to the current centers called candidates.
|
protected int[] |
KDTree.refineOwners(KDTreeNode node,
Instances centers,
int[] candidates)
Refines the ownerlist.
|
void |
KDTree.setInstances(Instances instances)
Builds the KDTree on the given set of instances.
|
void |
BallTree.setInstances(Instances insts)
Builds the BallTree based on the given set of instances.
|
void |
NearestNeighbourSearch.setInstances(Instances insts)
Sets the instances.
|
void |
LinearNNSearch.setInstances(Instances insts)
Sets the instances comprising the current neighbourhood.
|
void |
CoverTree.setInstances(Instances instances)
Builds the Cover Tree on the given set of instances.
|
Constructor and Description |
---|
BallTree(Instances insts)
Creates a new instance of BallTree.
|
KDTree(Instances insts)
Creates a new instance of KDTree.
|
LinearNNSearch(Instances insts)
Constructor that uses the supplied set of
instances.
|
NearestNeighbourSearch(Instances insts)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
BallTreeConstructor.m_Instances
The instances on which to build the tree.
|
protected Instances |
BallSplitter.m_Instances
The instance on which the tree is built.
|
Modifier and Type | Method and Description |
---|---|
static Instance |
BallNode.calcCentroidPivot(int[] instList,
Instances insts)
Calculates the centroid pivot of a node.
|
static Instance |
BallNode.calcCentroidPivot(int start,
int end,
int[] instList,
Instances insts)
Calculates the centroid pivot of a node.
|
static Instance |
BallNode.calcPivot(BallNode child1,
BallNode child2,
Instances insts)
Calculates the centroid pivot of a node based on its
two child nodes (if merging two nodes).
|
Instance |
BottomUpConstructor.calcPivot(BottomUpConstructor.TempNode node1,
BottomUpConstructor.TempNode node2,
Instances insts)
Calculates the centroid pivot of a node based on its
two child nodes.
|
Instance |
MiddleOutConstructor.calcPivot(MiddleOutConstructor.MyIdxList list1,
MiddleOutConstructor.MyIdxList list2,
Instances insts)
Calculates the centroid pivot of a node based on
the list of points that it contains (tbe two
lists of its children are provided).
|
Instance |
MiddleOutConstructor.calcPivot(MiddleOutConstructor.TempNode node1,
MiddleOutConstructor.TempNode node2,
Instances insts)
/**
Calculates the centroid pivot of a node based on its
two child nodes (if merging two nodes).
|
static double |
BallNode.calcRadius(int[] instList,
Instances insts,
Instance pivot,
DistanceFunction distanceFunction)
Calculates the radius of node.
|
static double |
BallNode.calcRadius(int start,
int end,
int[] instList,
Instances insts,
Instance pivot,
DistanceFunction distanceFunction)
Calculates the radius of a node.
|
double |
MiddleOutConstructor.calcRadius(MiddleOutConstructor.MyIdxList list1,
MiddleOutConstructor.MyIdxList list2,
Instance pivot,
Instances insts)
Calculates the radius of a node based on the
list of points that it contains (the two lists of
its children are provided).
|
void |
BallTreeConstructor.setInstances(Instances inst)
Sets the instances on which the tree is to be built.
|
void |
BallSplitter.setInstances(Instances inst)
Sets the training instances on which the tree is
(or is to be) built.
|
void |
MiddleOutConstructor.setInstances(Instances insts)
Sets the instances on which the tree is to be built.
|
Constructor and Description |
---|
BallSplitter(int[] instList,
Instances insts,
EuclideanDistance e)
Creates a new instance of BallSplitter.
|
MedianDistanceFromArbitraryPoint(int[] instList,
Instances insts,
EuclideanDistance e)
Constructor.
|
MedianOfWidestDimension(int[] instList,
Instances insts,
EuclideanDistance e)
Constructor.
|
PointsClosestToFurthestChildren(int[] instList,
Instances insts,
EuclideanDistance e)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
KDTreeNodeSplitter.m_Instances
The instances that'll be used for tree construction.
|
Modifier and Type | Method and Description |
---|---|
protected static int |
KMeansInpiredMethod.partition(Instances insts,
int[] index,
int attidx,
int l,
int r)
Partitions the instances around a pivot.
|
protected static void |
KMeansInpiredMethod.quickSort(Instances insts,
int[] indices,
int attidx,
int left,
int right)
Sorts the instances according to the given attribute/dimension.
|
void |
KDTreeNodeSplitter.setInstances(Instances inst)
Sets the training instances on which the tree is (or is
to be) built.
|
Constructor and Description |
---|
KDTreeNodeSplitter(int[] instList,
Instances insts,
EuclideanDistance e)
Creates a new instance of KDTreeNodeSplitter.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
MiningSchema.m_fieldInstancesStructure
The structure of all the fields (both mining schema and derived) as Instances
|
protected Instances |
MiningSchema.m_miningSchemaInstancesStructure
Just the mining schema fields as Instances
|
Modifier and Type | Method and Description |
---|---|
protected static Instances |
PMMLFactory.getDataDictionaryAsInstances(Document doc)
Get the data dictionary as an Instances object
|
Instances |
MiningSchema.getFieldsAsInstances()
Get the all the fields (both mining schema and derived) as Instances.
|
Instances |
MiningSchema.getMiningSchemaAsInstances()
Get the mining schema fields as an Instances object.
|
protected static Instances |
PMMLFactory.getMiningSchemaAsInstances(Element model,
Instances dataDictionary)
Deprecated.
Use the MiningSchema class instead
|
Modifier and Type | Method and Description |
---|---|
static String |
PMMLFactory.applyClassifier(PMMLModel model,
Instances test) |
protected static Instances |
PMMLFactory.getMiningSchemaAsInstances(Element model,
Instances dataDictionary)
Deprecated.
Use the MiningSchema class instead
|
protected static PMMLModel |
PMMLFactory.getModelInstance(Document doc,
PMMLFactory.ModelType modelType,
Element model,
Instances dataDictionary,
MiningSchema miningSchema)
Get an instance of a PMMLModel from the supplied Document
|
protected static weka.core.pmml.TransformationDictionary |
PMMLFactory.getTransformationDictionary(Document doc,
Instances dataDictionary)
Get the transformation dictionary (if there is one).
|
protected void |
MiningFieldMetaInfo.setMiningSchemaInstances(Instances miningSchemaI)
Set the Instances that represent the mining schema.
|
Constructor and Description |
---|
MappingInfo(Instances dataSet,
MiningSchema miningSchema,
Logger log) |
MiningSchema(Element model,
Instances dataDictionary,
weka.core.pmml.TransformationDictionary transDict)
Constructor for MiningSchema.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
XMLInstances.m_Instances
the underlying Instances
|
Modifier and Type | Method and Description |
---|---|
protected Instances |
XMLInstances.createInstances(Instances header,
Element parent)
creates Instances from the given XML node
|
protected Instances |
XMLInstances.dataFromXML(Instances header)
generates the complete dataset from the XML document
|
Instances |
XMLInstances.getInstances()
returns the current instances, either the ones that were set or the ones
that were generated from the XML structure.
|
protected Instances |
XMLInstances.headerFromXML()
generates the header from the XML document
|
Modifier and Type | Method and Description |
---|---|
protected Instance |
XMLInstances.createInstance(Instances header,
Element parent)
creates an Instance from the given XML node
|
protected Instances |
XMLInstances.createInstances(Instances header,
Element parent)
creates Instances from the given XML node
|
protected Instances |
XMLInstances.dataFromXML(Instances header)
generates the complete dataset from the XML document
|
void |
XMLInstances.setInstances(Instances data)
builds up the XML structure based on the given data
|
Constructor and Description |
---|
XMLInstances(Instances data)
generates the XML structure based on the given data
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
DataGenerator.m_DatasetFormat
The format for the generated dataset
|
Modifier and Type | Method and Description |
---|---|
Instances |
DataGenerator.defineDataFormat()
Initializes the format for the dataset produced.
|
abstract Instances |
DataGenerator.generateExamples()
Generates all examples of the dataset.
|
Instances |
DataGenerator.getDatasetFormat()
Gets the format of the dataset that is to be generated.
|
Modifier and Type | Method and Description |
---|---|
void |
DataGenerator.setDatasetFormat(Instances newFormat)
Sets the format of the dataset that is to be generated.
|
Constructor and Description |
---|
Test(int i,
double s,
Instances dataset)
Constructor
|
Test(int i,
double s,
Instances dataset,
boolean n)
Constructor
|
Modifier and Type | Method and Description |
---|---|
Instances |
RDG1.defineDataFormat()
Initializes the format for the dataset produced.
|
Instances |
RandomRBF.defineDataFormat()
Initializes the format for the dataset produced.
|
Instances |
BayesNet.defineDataFormat()
Initializes the format for the dataset produced.
|
Instances |
LED24.defineDataFormat()
Initializes the format for the dataset produced.
|
Instances |
Agrawal.defineDataFormat()
Initializes the format for the dataset produced.
|
Instances |
RDG1.generateExamples()
Generate all examples of the dataset.
|
Instances |
RandomRBF.generateExamples()
Generates all examples of the dataset.
|
Instances |
BayesNet.generateExamples()
Generates all examples of the dataset.
|
Instances |
LED24.generateExamples()
Generates all examples of the dataset.
|
Instances |
Agrawal.generateExamples()
Generates all examples of the dataset.
|
Instances |
RDG1.generateExamples(int num,
Random random,
Instances format)
Generate all examples of the dataset.
|
Modifier and Type | Method and Description |
---|---|
Instances |
RDG1.generateExamples(int num,
Random random,
Instances format)
Generate all examples of the dataset.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
Expression.m_RawData
the input data structure for the filter
|
Modifier and Type | Method and Description |
---|---|
Instances |
Expression.defineDataFormat()
Initializes the format for the dataset produced.
|
Instances |
MexicanHat.defineDataFormat()
Initializes the format for the dataset produced.
|
Instances |
Expression.generateExamples()
Generates all examples of the dataset.
|
Instances |
MexicanHat.generateExamples()
Generates all examples of the dataset.
|
Modifier and Type | Method and Description |
---|---|
Instances |
SubspaceCluster.defineDataFormat()
Initializes the format for the dataset produced.
|
Instances |
BIRCHCluster.defineDataFormat()
Initializes the format for the dataset produced.
|
Instances |
SubspaceCluster.generateExamples()
Generate all examples of the dataset.
|
Instances |
BIRCHCluster.generateExamples()
Generate all examples of the dataset.
|
Instances |
BIRCHCluster.generateExamples(Random random,
Instances format)
Generate all examples of the dataset.
|
Modifier and Type | Method and Description |
---|---|
Instances |
BIRCHCluster.generateExamples(Random random,
Instances format)
Generate all examples of the dataset.
|
Modifier and Type | Method and Description |
---|---|
static Instances |
EstimatorUtils.getInstancesFromClass(Instances data,
int classIndex,
double classValue)
Returns a dataset that contains of all instances of a certain class value.
|
static Instances |
EstimatorUtils.getInstancesFromValue(Instances data,
int index,
double v)
Returns a dataset that contains of all instances of a certain value
for the given attribute.
|
protected Instances |
CheckEstimator.makeTestDataset(int seed,
int numInstances,
int numAttr,
CheckEstimator.AttrTypes attrTypes,
int numClasses,
int classType)
Make a simple set of instances, which can later be modified
for use in specific tests.
|
protected Instances |
CheckEstimator.makeTestDataset(int seed,
int numInstances,
int numAttr,
CheckEstimator.AttrTypes attrTypes,
int numClasses,
int classType,
int classIndex)
Make a simple set of instances with variable position of the class
attribute, which can later be modified for use in specific tests.
|
protected Instances |
CheckEstimator.process(Instances data)
Provides a hook for derived classes to further modify the data.
|
protected Instances |
CheckEstimator.PostProcessor.process(Instances data)
Provides a hook for derived classes to further modify the data.
|
Modifier and Type | Method and Description |
---|---|
protected void |
CheckEstimator.addMissing(Instances data,
int level,
boolean attributeMissing,
boolean classMissing,
int attrIndex)
Add missing values to a dataset.
|
void |
Estimator.addValues(Instances data,
int attrIndex)
Initialize the estimator with a new dataset.
|
void |
Estimator.addValues(Instances data,
int attrIndex,
double min,
double max,
double factor)
Initialize the estimator with all values of one attribute of a dataset.
|
void |
Estimator.addValues(Instances data,
int attrIndex,
int classIndex,
int classValue)
Initialize the estimator using only the instance of one class.
|
void |
Estimator.addValues(Instances data,
int attrIndex,
int classIndex,
int classValue,
double min,
double max)
Initialize the estimator using only the instance of one class.
|
static void |
Estimator.buildEstimator(Estimator est,
Instances instances,
int attrIndex,
int classIndex,
int classValueIndex,
boolean isIncremental) |
protected void |
CheckEstimator.compareDatasets(Instances data1,
Instances data2)
Compare two datasets to see if they differ.
|
static double |
EstimatorUtils.findMinDistance(Instances inst,
int attrIndex)
Find the minimum distance between values
|
static Instances |
EstimatorUtils.getInstancesFromClass(Instances data,
int classIndex,
double classValue)
Returns a dataset that contains of all instances of a certain class value.
|
static Vector |
EstimatorUtils.getInstancesFromClass(Instances data,
int attrIndex,
int classIndex,
double classValue,
Instances workData)
Returns a dataset that contains all instances of a certain class value.
|
static Instances |
EstimatorUtils.getInstancesFromValue(Instances data,
int index,
double v)
Returns a dataset that contains of all instances of a certain value
for the given attribute.
|
protected double[] |
CheckEstimator.getMinimumMaximum(Instances inst,
int attrIndex)
Gets the minimum and maximum of the values a the first attribute
of the given data set
|
static int |
CheckEstimator.getMinMax(Instances inst,
int attrIndex,
double[] minMax)
Find the minimum and the maximum of the attribute and return it in
the last parameter..
|
static int |
EstimatorUtils.getMinMax(Instances inst,
int attrIndex,
double[] minMax)
Find the minimum and the maximum of the attribute and return it in
the last parameter..
|
protected Vector |
CheckEstimator.makeTestValueList(int seed,
int numValues,
Instances data,
int attrIndex,
int attrType)
Make a simple set of values.
|
protected Instances |
CheckEstimator.process(Instances data)
Provides a hook for derived classes to further modify the data.
|
protected Instances |
CheckEstimator.PostProcessor.process(Instances data)
Provides a hook for derived classes to further modify the data.
|
void |
Estimator.testCapabilities(Instances data,
int attrIndex)
Test if the estimator can handle the data.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
Experiment.m_CurrentInstances
The dataset currently being used
|
protected Instances |
DatabaseResultProducer.m_Instances
The dataset of interest
|
protected Instances |
AveragingResultProducer.m_Instances
The dataset of interest
|
protected Instances |
CrossValidationResultProducer.m_Instances
The dataset of interest
|
protected Instances |
LearningRateResultProducer.m_Instances
The dataset of interest
|
protected Instances |
RandomSplitResultProducer.m_Instances
The dataset of interest
|
protected Instances |
PairedTTester.m_Instances
The set of instances we will analyse
|
Modifier and Type | Method and Description |
---|---|
Instances |
Tester.getInstances()
Get the value of Instances.
|
Instances |
PairedTTester.getInstances()
Get the value of Instances.
|
Instances |
InstanceQuery.retrieveInstances()
Makes a database query using the query set through the -Q option
to convert a table into a set of instances
|
Instances |
InstanceQuery.retrieveInstances(String query)
Makes a database query to convert a table into a set of instances
|
Modifier and Type | Method and Description |
---|---|
Object[] |
SplitEvaluator.getResult(Instances train,
Instances test)
Gets the results for the supplied train and test datasets.
|
Object[] |
DensityBasedClustererSplitEvaluator.getResult(Instances train,
Instances test)
Gets the results for the supplied train and test datasets.
|
Object[] |
ClassifierSplitEvaluator.getResult(Instances train,
Instances test)
Gets the results for the supplied train and test datasets.
|
Object[] |
RegressionSplitEvaluator.getResult(Instances train,
Instances test)
Gets the results for the supplied train and test datasets.
|
Object[] |
CostSensitiveClassifierSplitEvaluator.getResult(Instances train,
Instances test)
Gets the results for the supplied train and test datasets.
|
void |
DatabaseResultProducer.setInstances(Instances instances)
Sets the dataset that results will be obtained for.
|
void |
AveragingResultProducer.setInstances(Instances instances)
Sets the dataset that results will be obtained for.
|
void |
CrossValidationResultProducer.setInstances(Instances instances)
Sets the dataset that results will be obtained for.
|
void |
LearningRateResultProducer.setInstances(Instances instances)
Sets the dataset that results will be obtained for.
|
void |
Tester.setInstances(Instances newInstances)
Set the value of Instances.
|
void |
RandomSplitResultProducer.setInstances(Instances instances)
Sets the dataset that results will be obtained for.
|
void |
PairedTTester.setInstances(Instances newInstances)
Set the value of Instances.
|
void |
ResultProducer.setInstances(Instances instances)
Sets the dataset that results will be obtained for.
|
Modifier and Type | Method and Description |
---|---|
protected abstract Instances |
SimpleStreamFilter.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected Instances |
MultiFilter.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected abstract Instances |
SimpleFilter.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected Instances |
Filter.getInputFormat()
Gets the currently set inputformat instances.
|
Instances |
Filter.getOutputFormat()
Gets the format of the output instances.
|
protected Instances |
Filter.inputFormatPeek()
Returns a reference to the current input format without
copying it.
|
protected Instances |
Filter.outputFormatPeek()
Returns a reference to the current output format without
copying it.
|
protected Instances |
SimpleStreamFilter.process(Instances instances)
Processes the given data (may change the provided dataset) and returns
the modified version.
|
protected Instances |
MultiFilter.process(Instances instances)
Processes the given data (may change the provided dataset) and returns
the modified version.
|
protected abstract Instances |
SimpleFilter.process(Instances instances)
Processes the given data (may change the provided dataset) and returns
the modified version.
|
static Instances |
Filter.useFilter(Instances data,
Filter filter)
Filters an entire set of instances through a filter and returns
the new set.
|
Modifier and Type | Method and Description |
---|---|
protected boolean |
CheckSource.compare(Instances inst1,
Instances inst2)
compares the two Instances objects
|
protected void |
Filter.copyValues(Instance instance,
boolean instSrcCompat,
Instances srcDataset,
Instances destDataset)
Takes string/relational values referenced by an Instance and copies them
from a source dataset to a destination dataset.
|
protected abstract Instances |
SimpleStreamFilter.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected Instances |
MultiFilter.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected abstract Instances |
SimpleFilter.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
Capabilities |
Filter.getCapabilities(Instances data)
Returns the Capabilities of this filter, customized based on the data.
|
protected void |
Filter.initInputLocators(Instances data,
int[] indices)
Initializes the input attribute locators.
|
protected void |
Filter.initOutputLocators(Instances data,
int[] indices)
Initializes the output attribute locators.
|
protected void |
SimpleStreamFilter.preprocess(Instances instances)
In case the output format cannot be returned immediately, this method
is called before the actual processing of the instances.
|
protected Instances |
SimpleStreamFilter.process(Instances instances)
Processes the given data (may change the provided dataset) and returns
the modified version.
|
protected Instances |
MultiFilter.process(Instances instances)
Processes the given data (may change the provided dataset) and returns
the modified version.
|
protected abstract Instances |
SimpleFilter.process(Instances instances)
Processes the given data (may change the provided dataset) and returns
the modified version.
|
boolean |
AllFilter.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Filter.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
SimpleFilter.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
protected void |
Filter.setOutputFormat(Instances outputFormat)
Sets the format of output instances.
|
protected void |
Filter.testInputFormat(Instances instanceInfo)
tests the data whether the filter can actually handle it
|
String |
Sourcable.toSource(String className,
Instances data)
Returns a string that describes the filter as source.
|
String |
AllFilter.toSource(String className,
Instances data)
Returns a string that describes the filter as source.
|
static Instances |
Filter.useFilter(Instances data,
Filter filter)
Filters an entire set of instances through a filter and returns
the new set.
|
static String |
Filter.wekaStaticWrapper(Sourcable filter,
String className,
Instances input,
Instances output)
generates source code from the filter
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
AddClassification.m_SerializedHeader
the header of the file the serialized classifier was trained with.
|
Modifier and Type | Method and Description |
---|---|
protected Instances |
PLSFilter.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected Instances |
AddClassification.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected Instances |
PLSFilter.process(Instances instances)
Processes the given data (may change the provided dataset) and returns
the modified version.
|
protected Instances |
AddClassification.process(Instances instances)
Processes the given data (may change the provided dataset) and returns
the modified version.
|
protected Instances |
PLSFilter.processPLS1(Instances instances)
processes the instances using the PLS1 algorithm
|
protected Instances |
PLSFilter.processSIMPLS(Instances instances)
processes the instances using the SIMPLS algorithm
|
protected Instances |
PLSFilter.toInstances(Instances header,
Matrix x,
Matrix y)
returns the X and Y matrix again as Instances object, based on the given
header (must have a class attribute set).
|
Modifier and Type | Method and Description |
---|---|
protected void |
Discretize.calculateCutPointsByMDL(int index,
Instances data)
Set cutpoints for a single attribute using MDL.
|
protected Instances |
PLSFilter.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected Instances |
AddClassification.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected Matrix |
PLSFilter.getX(Instances instances)
returns the data minus the class column as matrix
|
protected Matrix |
PLSFilter.getY(Instances instances)
returns the data class column as matrix
|
protected Instances |
PLSFilter.process(Instances instances)
Processes the given data (may change the provided dataset) and returns
the modified version.
|
protected Instances |
AddClassification.process(Instances instances)
Processes the given data (may change the provided dataset) and returns
the modified version.
|
protected Instances |
PLSFilter.processPLS1(Instances instances)
processes the instances using the PLS1 algorithm
|
protected Instances |
PLSFilter.processSIMPLS(Instances instances)
processes the instances using the SIMPLS algorithm
|
boolean |
Discretize.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
ClassOrder.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
NominalToBinary.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
protected Instances |
PLSFilter.toInstances(Instances header,
Matrix x,
Matrix y)
returns the X and Y matrix again as Instances object, based on the given
header (must have a class attribute set).
|
Modifier and Type | Method and Description |
---|---|
boolean |
SpreadSubsample.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Resample.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
StratifiedRemoveFolds.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
SMOTE.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
PrincipalComponents.m_TrainCopy
Keep a copy for the class attribute (if set).
|
protected Instances |
PrincipalComponents.m_TrainInstances
The data to transform analyse/transform.
|
protected Instances |
PrincipalComponents.m_TransformedFormat
The header for the transformed data format.
|
Modifier and Type | Method and Description |
---|---|
protected Instances |
ClassAssigner.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected Instances |
Wavelet.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected Instances |
RandomSubset.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected Instances |
RELAGGS.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected Instances |
KernelFilter.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected Instances |
PrincipalComponents.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected Instances |
InterquartileRange.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected Instances |
NumericCleaner.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected Instances |
PartitionedMultiFilter.determineOutputFormat(Instances inputFormat)
Determines the output format based only on the full input dataset and
returns this otherwise null is returned.
|
protected Instances |
NumericToNominal.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected Instances |
PartitionedMultiFilter.generateSubset(Instances data,
Range range)
generates a subset of the dataset with only the attributes from the range
(class is always added if present).
|
Instances |
PotentialClassIgnorer.getOutputFormat()
Gets the format of the output instances.
|
protected Instances |
Wavelet.pad(Instances data)
pads the data to conform to the necessary number of attributes
|
protected Instances |
Wavelet.process(Instances instances)
Processes the given data (may change the provided dataset) and returns
the modified version.
|
protected Instances |
RELAGGS.process(Instances instances)
Processes the given data (may change the provided dataset) and returns
the modified version.
|
protected Instances |
KernelFilter.process(Instances instances)
Processes the given data (may change the provided dataset) and returns
the modified version.
|
protected Instances |
InterquartileRange.process(Instances instances)
Processes the given data (may change the provided dataset) and returns
the modified version.
|
protected Instances |
PartitionedMultiFilter.process(Instances instances)
Processes the given data (may change the provided dataset) and returns
the modified version.
|
protected Instances |
NumericToNominal.process(Instances instances)
Processes the given data (may change the provided dataset) and returns
the modified version.
|
protected Instances |
Wavelet.processHAAR(Instances instances)
processes the instances using the HAAR algorithm
|
protected Instances |
AddCluster.removeIgnored(Instances data)
filters all attributes that should be ignored
|
protected Instances |
ClusterMembership.removeIgnored(Instances data)
filters all attributes that should be ignored
|
protected Instances |
PartitionedMultiFilter.renameAttributes(Instances data,
String prefix)
renames all the attributes in the dataset (excluding the class if present)
by adding the prefix to the name.
|
Modifier and Type | Method and Description |
---|---|
protected void |
PropositionalToMultiInstance.addBag(Instances input,
Instances output,
Instances bagInsts,
int bagIndex,
double classValue,
double bagWeight)
adds a new bag out of the given data and adds it to the output
|
void |
AddNoise.addNoise(Instances instances,
int seed,
int percent,
int attIndex,
boolean useMissing)
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.
|
protected void |
InterquartileRange.computeThresholds(Instances instances)
computes the thresholds for outliers and extreme values
|
protected Instances |
ClassAssigner.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected Instances |
Wavelet.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected Instances |
RandomSubset.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected Instances |
RELAGGS.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected Instances |
KernelFilter.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected Instances |
PrincipalComponents.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected Instances |
InterquartileRange.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected Instances |
NumericCleaner.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected Instances |
PartitionedMultiFilter.determineOutputFormat(Instances inputFormat)
Determines the output format based only on the full input dataset and
returns this otherwise null is returned.
|
protected Instances |
NumericToNominal.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected void |
PartitionedMultiFilter.determineUnusedIndices(Instances data)
determines the indices of unused attributes (ones that are not covered
by any of the range).
|
protected Instances |
PartitionedMultiFilter.generateSubset(Instances data,
Range range)
generates a subset of the dataset with only the attributes from the range
(class is always added if present).
|
Capabilities |
AddCluster.getCapabilities(Instances data)
Returns the Capabilities of this filter, makes sure that the class is
never set (for the clusterer).
|
Capabilities |
ClusterMembership.getCapabilities(Instances data)
Returns the Capabilities of this filter, makes sure that the class is
never set (for the clusterer).
|
void |
KernelFilter.initFilter(Instances instances)
initializes the filter with the given dataset, i.e., the kernel gets
built.
|
protected Instances |
Wavelet.pad(Instances data)
pads the data to conform to the necessary number of attributes
|
protected Instances |
Wavelet.process(Instances instances)
Processes the given data (may change the provided dataset) and returns
the modified version.
|
protected Instances |
RELAGGS.process(Instances instances)
Processes the given data (may change the provided dataset) and returns
the modified version.
|
protected Instances |
KernelFilter.process(Instances instances)
Processes the given data (may change the provided dataset) and returns
the modified version.
|
protected Instances |
InterquartileRange.process(Instances instances)
Processes the given data (may change the provided dataset) and returns
the modified version.
|
protected Instances |
PartitionedMultiFilter.process(Instances instances)
Processes the given data (may change the provided dataset) and returns
the modified version.
|
protected Instances |
NumericToNominal.process(Instances instances)
Processes the given data (may change the provided dataset) and returns
the modified version.
|
protected Instances |
Wavelet.processHAAR(Instances instances)
processes the instances using the HAAR algorithm
|
protected Instances |
AddCluster.removeIgnored(Instances data)
filters all attributes that should be ignored
|
protected Instances |
ClusterMembership.removeIgnored(Instances data)
filters all attributes that should be ignored
|
protected Instances |
PartitionedMultiFilter.renameAttributes(Instances data,
String prefix)
renames all the attributes in the dataset (excluding the class if present)
by adding the prefix to the name.
|
boolean |
MakeIndicator.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Discretize.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
MergeTwoValues.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Remove.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
AddID.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
MultiInstanceToPropositional.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
AbstractTimeSeries.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
NumericTransform.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Center.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
StringToNominal.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
AddCluster.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Obfuscate.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
StringToWordVector.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Reorder.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
RandomProjection.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
MathExpression.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
TimeSeriesDelta.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
PrincipalComponents.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
NominalToBinary.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
SwapValues.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Normalize.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
ClusterMembership.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
AddValues.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
PKIDiscretize.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
PotentialClassIgnorer.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
AddExpression.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
TimeSeriesTranslate.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
RemoveType.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
FirstOrder.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
AddNoise.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
NominalToString.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Add.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
RemoveUseless.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
PropositionalToMultiInstance.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
NumericToBinary.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Standardize.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
ReplaceMissingValues.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Copy.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
ChangeDateFormat.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
protected void |
PrincipalComponents.setup(Instances instances)
Initializes the filter with the given input data.
|
protected void |
AddCluster.testInputFormat(Instances instanceInfo)
tests the data whether the filter can actually handle it
|
protected void |
ClusterMembership.testInputFormat(Instances instanceInfo)
tests the data whether the filter can actually handle it
|
protected void |
PartitionedMultiFilter.testInputFormat(Instances instanceInfo)
tests the data whether the filter can actually handle it.
|
String |
Center.toSource(String className,
Instances data)
Returns a string that describes the filter as source.
|
String |
Normalize.toSource(String className,
Instances data)
Returns a string that describes the filter as source.
|
String |
Standardize.toSource(String className,
Instances data)
Returns a string that describes the filter as source.
|
String |
ReplaceMissingValues.toSource(String className,
Instances data)
Returns a string that describes the filter as source.
|
Modifier and Type | Method and Description |
---|---|
protected Instances |
SubsetByExpression.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
protected Instances |
RemoveFrequentValues.modifyHeader(Instances instanceInfo)
modifies the header of the Instances and returns the format w/o
any instances
|
protected Instances |
SubsetByExpression.process(Instances instances)
Processes the given data (may change the provided dataset) and returns
the modified version.
|
Modifier and Type | Method and Description |
---|---|
protected Instances |
SubsetByExpression.determineOutputFormat(Instances inputFormat)
Determines the output format based on the input format and returns
this.
|
void |
RemoveFrequentValues.determineValues(Instances inst)
determines the values to retain, it is always at least 1
and up to the maximum number of distinct values
|
protected Instances |
RemoveFrequentValues.modifyHeader(Instances instanceInfo)
modifies the header of the Instances and returns the format w/o
any instances
|
protected Instances |
SubsetByExpression.process(Instances instances)
Processes the given data (may change the provided dataset) and returns
the modified version.
|
boolean |
NonSparseToSparse.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
RemoveRange.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
RemoveFolds.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
RemoveWithValues.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
ReservoirSample.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
SparseToNonSparse.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
RemovePercentage.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Randomize.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Normalize.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
RemoveFrequentValues.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
RemoveMisclassified.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Resample.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
Modifier and Type | Method and Description |
---|---|
static Instances |
Parser.filter(String expression,
Instances input)
Filters the input dataset against the provided expression.
|
Modifier and Type | Method and Description |
---|---|
static Instances |
Parser.filter(String expression,
Instances input)
Filters the input dataset against the provided expression.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
AttributeVisualizationPanel.m_data
This holds the current set of instances
|
protected Instances |
InstancesSummaryPanel.m_Instances
The instances we're playing with
|
protected Instances |
AttributeSummaryPanel.m_Instances
The instances we're playing with
|
protected Instances |
SetInstancesPanel.m_Instances
The current set of instances loaded
|
Modifier and Type | Method and Description |
---|---|
Instances |
ViewerDialog.getInstances()
returns the currently displayed instances
|
Instances |
SetInstancesPanel.getInstances()
Gets the set of instances currently held by the panel
|
Modifier and Type | Method and Description |
---|---|
void |
InstancesSummaryPanel.setInstances(Instances inst)
Tells the panel to use a new set of instances.
|
void |
AttributeListPanel.setInstances(Instances newInstances)
Sets the instances who's attribute names will be displayed.
|
void |
AttributeSelectionPanel.setInstances(Instances newInstances)
Sets the instances who's attribute names will be displayed.
|
void |
ViewerDialog.setInstances(Instances inst)
sets the instances to display
|
void |
AttributeSummaryPanel.setInstances(Instances inst)
Tells the panel to use a new set of instances.
|
void |
AttributeVisualizationPanel.setInstances(Instances newins)
Sets the instances for use
|
void |
SetInstancesPanel.setInstances(Instances i)
Updates the set of instances that is currently held by the panel
|
int |
ViewerDialog.showDialog(Instances inst)
Pops up the modal dialog and waits for Cancel or OK.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
ArffTable.RelationalCellEditor.m_CurrentInst
the current instances
|
Modifier and Type | Method and Description |
---|---|
Instances |
ArffSortedTableModel.getInstances()
returns the data
|
Instances |
ArffTableModel.getInstances()
returns the data
|
Instances |
ArffPanel.getInstances()
returns the instances of the panel, if none then NULL
|
protected Instances |
ArffTable.RelationalCellEditor.getInstancesAt(int rowIndex,
int columnIndex)
returns the underlying instances at the given position
|
Modifier and Type | Method and Description |
---|---|
void |
ArffSortedTableModel.setInstances(Instances data)
sets the data
|
void |
ArffTableModel.setInstances(Instances data)
sets the data
|
void |
ArffPanel.setInstances(Instances data)
displays the given instances, i.e.
|
Constructor and Description |
---|
ArffPanel(Instances data)
initializes the panel with the given data
|
ArffSortedTableModel(Instances data)
initializes the sorter w/o a model, but uses the given data to create
a model from that
|
ArffTableModel(Instances data)
initializes the model with the given data
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
PredictionAppender.m_format
Format of instances to be produced.
|
protected Instances |
TestSetEvent.m_testSet
The test set instances
|
protected Instances |
TrainingSetEvent.m_trainingSet
The training instances
|
protected Instances |
DataVisualizer.m_visualizeDataSet |
Modifier and Type | Method and Description |
---|---|
Instances |
ClassAssigner.getConnectedFormat()
Returns the structure of the incoming instances (if any)
|
Instances |
ClassValuePicker.getConnectedFormat()
Returns the structure of the incoming instances (if any)
|
Instances |
DataSetEvent.getDataSet()
Return the instances of the data set
|
Instances |
InstanceEvent.getStructure()
Get the instances structure (may be null if this is not
a FORMAT_AVAILABLE event)
|
Instances |
IncrementalClassifierEvent.getStructure()
Get the instances structure (may be null if this is not
a NEW_BATCH event)
|
protected Instances |
ClassValuePicker.getStructure() |
Instances |
ClassAssigner.getStructure(String eventName)
Get the structure of the output encapsulated in the named
event.
|
Instances |
StructureProducer.getStructure(String eventName)
Get the structure of the output encapsulated in the named
event.
|
Instances |
Loader.getStructure(String eventName)
Get the structure of the output encapsulated in the named
event.
|
Instances |
ClassValuePicker.getStructure(String eventName) |
Instances |
TestSetEvent.getTestSet()
Get the test set instances
|
Instances |
TrainingSetEvent.getTrainingSet()
Get the training instances
|
Modifier and Type | Method and Description |
---|---|
protected void |
Loader.notifyStructureAvailable(Instances structure)
Notify all listeners that the structure of a data set
is available.
|
static void |
SerializedModelSaver.saveBinary(File saveTo,
Object model,
Instances header)
Save a model in binary form.
|
static void |
SerializedModelSaver.saveKOML(File saveTo,
Object model,
Instances header)
Save a model in KOML deep object serialized XML form.
|
static void |
SerializedModelSaver.saveXStream(File saveTo,
Object model,
Instances header)
Save a model in XStream deep object serialized XML form.
|
void |
ScatterPlotMatrix.setInstances(Instances inst)
Set instances for this bean.
|
void |
DataVisualizer.setInstances(Instances inst)
Set instances for this bean.
|
void |
AttributeSummarizer.setInstances(Instances inst)
Set instances for this bean.
|
void |
InstanceEvent.setStructure(Instances structure)
Set the instances structure
|
void |
IncrementalClassifierEvent.setStructure(Instances structure)
Set the instances structure
|
Constructor and Description |
---|
DataSetEvent(Object source,
Instances dataSet) |
IncrementalClassifierEvent(Object source,
Classifier scheme,
Instances structure)
Creates a new incremental classifier event that encapsulates
header information and classifier.
|
InstanceEvent(Object source,
Instances structure)
Creates a new
InstanceEvent instance which encapsulates
header information only. |
TestSetEvent(Object source,
Instances testSet)
Creates a new
TestSetEvent |
TestSetEvent(Object source,
Instances testSet,
int setNum,
int maxSetNum)
Creates a new
TestSetEvent |
TestSetEvent(Object source,
Instances testSet,
int runNum,
int maxRunNum,
int setNum,
int maxSetNum)
Creates a new
TestSetEvent |
TrainingSetEvent(Object source,
Instances trainSet)
Creates a new
TrainingSetEvent |
TrainingSetEvent(Object source,
Instances trainSet,
int setNum,
int maxSetNum)
Creates a new
TrainingSetEvent |
TrainingSetEvent(Object source,
Instances trainSet,
int runNum,
int maxRunNum,
int setNum,
int maxSetNum)
Creates a new
TrainingSetEvent |
TrainingTask(int runNum,
int maxRunNum,
int setNum,
int maxSetNum,
Instances train) |
Modifier and Type | Field and Description |
---|---|
protected Instances |
BoundaryPanel.m_trainingData
training data
|
Modifier and Type | Method and Description |
---|---|
Instances |
BoundaryVisualizer.getInstances()
Get the training instances
|
Modifier and Type | Method and Description |
---|---|
void |
DataGenerator.buildGenerator(Instances inputInstances)
Build the data generator
|
void |
KDDataGenerator.buildGenerator(Instances inputInstances)
Initialize the generator using the supplied instances
|
static void |
BoundaryVisualizer.createNewVisualizerWindow(Classifier classifier,
Instances instances)
Creates a new GUI window with all of the BoundaryVisualizer trappings,
|
void |
BoundaryVisualizer.setInstances(Instances inst)
Set the training instances
|
void |
RemoteBoundaryVisualizerSubTask.setInstances(Instances i)
Set the training data
|
void |
BoundaryPanel.setTrainingData(Instances trainingData)
Set the training data to use
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
ResultsPanel.m_Instances
The instances we're extracting results from.
|
Modifier and Type | Method and Description |
---|---|
protected Vector |
ResultsPanel.determineColumnNames(String list,
String defaultList,
Instances inst)
Returns a vector with column names of the dataset, listed in "list".
|
void |
ResultsPanel.setInstances(Instances newInstances)
Sets up the panel with a new set of instances, attempting
to guess the correct settings for various columns.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
PreprocessPanel.m_Instances
The working instances
|
protected Instances |
AttributeSelectionPanel.m_Instances
The main set of instances we're playing with
|
protected Instances |
ClustererPanel.m_Instances
The main set of instances we're playing with
|
protected Instances |
DataGeneratorPanel.m_Instances
the generated Instances
|
protected Instances |
ClassifierPanel.m_Instances
The main set of instances we're playing with
|
protected Instances |
AssociationsPanel.m_Instances
The main set of instances we're playing with
|
protected Instances |
ClustererPanel.m_TestInstances
The user-supplied test set (if any)
|
protected Instances |
AssociationsPanel.m_TestInstances
The user-supplied test set (if any)
|
Modifier and Type | Method and Description |
---|---|
Instances |
PreprocessPanel.getInstances()
Gets the working set of instances.
|
Instances |
DataGeneratorPanel.getInstances()
returns the generated instances, null if the process was cancelled.
|
static Instances |
ClassifierPanel.setUpVisualizableInstances(Instances trainInstances)
Sets up the structure for the visualizable instances.
|
Modifier and Type | Method and Description |
---|---|
protected void |
ClassifierPanel.printPredictionsHeader(StringBuffer outBuff,
Instances inst,
String title)
outputs the header for the predictions on the data
|
static void |
ClassifierPanel.processClassifierPrediction(Instance toPredict,
Classifier classifier,
Evaluation eval,
Instances plotInstances,
FastVector plotShape,
FastVector plotSize)
Process a classifier's prediction for an instance and update a set of
plotting instances and additional plotting info.
|
protected void |
ClassifierPanel.reevaluateModel(String name,
Classifier classifier,
Instances trainHeader)
Re-evaluates the named classifier with the current test set.
|
protected void |
ClustererPanel.reevaluateModel(String name,
Clusterer clusterer,
Instances trainHeader,
int[] ignoredAtts)
Re-evaluates the named clusterer with the current test set.
|
protected void |
ClassifierPanel.saveClassifier(String name,
Classifier classifier,
Instances trainHeader)
Saves the currently selected classifier
|
protected void |
ClustererPanel.saveClusterer(String name,
Clusterer clusterer,
Instances trainHeader,
int[] ignoredAtts)
Saves the currently selected clusterer
|
void |
PreprocessPanel.saveInstancesToFile(AbstractFileSaver saver,
Instances inst)
saves the data with the specified saver
|
protected void |
AttributeSelectionPanel.saveTransformedData(Instances ti)
Popup a SaveDialog for saving the transformed data
|
void |
PreprocessPanel.setInstances(Instances inst)
Tells the panel to use a new base set of instances.
|
void |
Explorer.ExplorerPanel.setInstances(Instances inst)
Tells the panel to use a new set of instances.
|
void |
AttributeSelectionPanel.setInstances(Instances inst)
Tells the panel to use a new set of instances.
|
void |
ClustererPanel.setInstances(Instances inst)
Tells the panel to use a new set of instances.
|
void |
ClassifierPanel.setInstances(Instances inst)
Tells the panel to use a new set of instances.
|
void |
AssociationsPanel.setInstances(Instances inst)
Tells the panel to use a new set of instances.
|
static Instances |
ClassifierPanel.setUpVisualizableInstances(Instances trainInstances)
Sets up the structure for the visualizable instances.
|
static PlotData2D |
ClustererPanel.setUpVisualizableInstances(Instances testInstances,
ClusterEvaluation eval)
Sets up the structure for the visualizable instances.
|
protected void |
AttributeSelectionPanel.visualizeTransformedData(Instances ti)
Popup a visualize panel for viewing transformed data
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
InstanceJoiner.m_InputFormat
The input format for instances
|
Modifier and Type | Method and Description |
---|---|
Instances |
InstanceJoiner.outputFormat()
Gets the format of the output instances.
|
Instances |
InstanceLoader.outputFormat() |
Instances |
InstanceProducer.outputFormat() |
Modifier and Type | Method and Description |
---|---|
boolean |
InstanceJoiner.inputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
void |
InstanceTable.inputFormat(Instances instanceInfo) |
void |
InstanceViewer.inputFormat(Instances instanceInfo) |
void |
InstanceSavePanel.inputFormat(Instances instanceInfo) |
void |
InstanceCounter.inputFormat(Instances instanceInfo) |
Modifier and Type | Method and Description |
---|---|
Instances |
Node.getInstances()
This will return the Instances object related to this node.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
MatrixPanel.m_data
The dataset for which this panel will display the plot matrix for
|
protected Instances |
VisualizePanel.PlotPanel.m_plotInstances
The instances from the master plot
|
protected Instances |
Plot2D.m_plotInstances
The instances to be plotted
|
protected Instances |
PlotData2D.m_plotInstances
The instances
|
protected Instances |
AttributePanel.m_plotInstances
The instances to be plotted
|
Modifier and Type | Method and Description |
---|---|
Instances |
VisualizePanel.getInstances()
Get the master plot's instances
|
Instances |
VisualizePanelEvent.getInstances1() |
Instances |
VisualizePanelEvent.getInstances2() |
Instances |
PlotData2D.getPlotInstances()
Returns the instances for this plot
|
Modifier and Type | Method and Description |
---|---|
protected void |
VisualizePanel.newColorAttribute(int a,
Instances i)
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.
|
protected void |
VisualizePanel.openVisibleInstances(Instances insts)
displays the previously saved instances
|
protected void |
ThresholdVisualizePanel.openVisibleInstances(Instances insts)
displays the previously saved instances
|
void |
VisualizePanel.setInstances(Instances inst)
Tells the panel to use a new set of instances.
|
void |
Plot2D.setInstances(Instances inst)
Sets the master plot from a set of instances
|
void |
MatrixPanel.setInstances(Instances newInst)
This method changes the Instances object of this class to a new one.
|
void |
ClassPanel.setInstances(Instances insts)
Set the instances.
|
void |
AttributePanel.setInstances(Instances ins)
This sets the instances to be drawn into the attribute panel
|
void |
VisualizePanel.setUpComboBoxes(Instances inst)
initializes the comboboxes based on the data
|
void |
ThresholdVisualizePanel.setUpComboBoxes(Instances inst)
This overloads VisualizePanel's setUpComboBoxes to add
ActionListeners to watch for when the X/Y Axis comboboxes
are changed.
|
Constructor and Description |
---|
PlotData2D(Instances insts)
Construct a new PlotData2D using the supplied instances
|
VisualizePanelEvent(FastVector ar,
Instances i,
Instances i2,
int at1,
int at2)
This constructor creates the event with all the parameters set.
|
Modifier and Type | Method and Description |
---|---|
JMenuItem |
ErrorVisualizePlugin.getVisualizeMenuItem(Instances predInst)
Get a JMenu or JMenuItem which contain action listeners
that perform the visualization of the classifier errors.
|
Copyright © 2015 University of Waikato, Hamilton, NZ. All rights reserved.