Modifier and Type | Class and Description |
---|---|
class |
BayesNetGenerator
Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier. |
class |
BIFReader
Builds a description of a Bayes Net classifier stored in XML BIF 0.3 format.
For more details on XML BIF see: Fabio Cozman, Marek Druzdzel, Daniel Garcia (1998). |
class |
EditableBayesNet
Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier. |
Modifier and Type | Method and Description |
---|---|
void |
MarginCalculator.calcFullMargins(BayesNet bayesNet) |
void |
MarginCalculator.calcMargins(BayesNet bayesNet)
Calc marginal distributions of nodes in Bayesian network
Note that a connected network is assumed.
|
double |
BIFReader.divergence(BayesNet other)
calculates the divergence between the probability distribution
represented by this network and that of another, that is,
\sum_{x\in X} P(x)log P(x)/Q(x)
where X is the set of values the nodes in the network can take,
P(x) the probability of this network for configuration x
Q(x) the probability of the other network for configuration x
|
int |
BIFReader.extraArcs(BayesNet other)
Count nr of exta arcs from other network compared to current network
Note that an arc is not 'extra' if it is reversed.
|
int |
BIFReader.missingArcs(BayesNet other)
Count nr of arcs missing from other network compared to current network
Note that an arc is not 'missing' if it is reversed.
|
boolean[][] |
MarginCalculator.moralize(BayesNet bayesNet)
moralize DAG and calculate
adjacency matrix representation for a Bayes Network, effecively
converting the directed acyclic graph to an undirected graph.
|
void |
MarginCalculator.process(boolean[][] bAdjacencyMatrix,
BayesNet bayesNet) |
int |
BIFReader.reversedArcs(BayesNet other)
Count nr of reversed arcs from other network compared to current network
|
void |
BIFReader.Sync(BayesNet other)
synchronizes the node ordering of this Bayes network with
those in the other network (if possible).
|
Modifier and Type | Method and Description |
---|---|
double[] |
SimpleEstimator.distributionForInstance(BayesNet bayesNet,
Instance instance)
Calculates the class membership probabilities for the given test
instance.
|
double[] |
MultiNomialBMAEstimator.distributionForInstance(BayesNet bayesNet,
Instance instance)
Calculates the class membership probabilities for the given test
instance.
|
double[] |
BayesNetEstimator.distributionForInstance(BayesNet bayesNet,
Instance instance)
Calculates the class membership probabilities for the given test
instance.
|
void |
SimpleEstimator.estimateCPTs(BayesNet bayesNet)
estimateCPTs estimates the conditional probability tables for the Bayes
Net using the network structure.
|
void |
MultiNomialBMAEstimator.estimateCPTs(BayesNet bayesNet)
estimateCPTs estimates the conditional probability tables for the Bayes
Net using the network structure.
|
void |
BMAEstimator.estimateCPTs(BayesNet bayesNet)
estimateCPTs estimates the conditional probability tables for the Bayes
Net using the network structure.
|
void |
BayesNetEstimator.estimateCPTs(BayesNet bayesNet)
estimateCPTs estimates the conditional probability tables for the Bayes
Net using the network structure.
|
void |
SimpleEstimator.initCPTs(BayesNet bayesNet)
initCPTs reserves space for CPTs and set all counts to zero
|
void |
MultiNomialBMAEstimator.initCPTs(BayesNet bayesNet)
initCPTs reserves space for CPTs and set all counts to zero
|
void |
BMAEstimator.initCPTs(BayesNet bayesNet)
initCPTs reserves space for CPTs and set all counts to zero
|
void |
BayesNetEstimator.initCPTs(BayesNet bayesNet)
initCPTs reserves space for CPTs and set all counts to zero
|
void |
SimpleEstimator.updateClassifier(BayesNet bayesNet,
Instance instance)
Updates the classifier with the given instance.
|
void |
MultiNomialBMAEstimator.updateClassifier(BayesNet bayesNet,
Instance instance)
Updates the classifier with the given instance.
|
void |
BMAEstimator.updateClassifier(BayesNet bayesNet,
Instance instance)
Updates the classifier with the given instance.
|
void |
BayesNetEstimator.updateClassifier(BayesNet bayesNet,
Instance instance)
Updates the classifier with the given instance.
|
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.isArc(BayesNet bayesNet,
int iAttributeHead,
int iAttributeTail)
IsArc checks whether the arc from iAttributeTail to iAttributeHead already exists
|
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
|
double |
GlobalScoreSearchAlgorithm.calcScore(BayesNet bayesNet)
performCV returns the accuracy calculated using cross validation.
|
double |
GlobalScoreSearchAlgorithm.cumulativeCV(BayesNet bayesNet)
CumulativeCV returns the accuracy calculated using cumulative
cross validation.
|
double |
GlobalScoreSearchAlgorithm.kFoldCV(BayesNet bayesNet,
int nNrOfFolds)
kFoldCV uses k-fold cross validation to measure the accuracy of a Bayes
network classifier.
|
double |
GlobalScoreSearchAlgorithm.leaveOneOutCV(BayesNet bayesNet)
LeaveOneOutCV returns the accuracy calculated using Leave One Out
cross validation.
|
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 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
|
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