Uses of Class
org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree.Distribution

Packages that use Distribution

Package	Description
org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree

Uses of Distribution in org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree

Fields in org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree declared as Distribution

Modifier and Type	Field	Description
protected Distribution	ClassifierSplitModel.m_distribution	Distribution of class values.
protected Distribution	ClassifierTree.m_test	The pruning instances.

Methods in org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree that return Distribution

Modifier and Type	Method	Description
Distribution	ClassifierSplitModel.distribution()	Returns the distribution of class values induced by the model.
Distribution	Distribution.subtract(Distribution toSubstract)	Subtracts the given distribution from this one.

Methods in org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree with parameters of type Distribution

Modifier and Type	Method	Description
double	EntropyBasedSplitCrit.newEnt(Distribution bags)	Computes entropy of distribution after splitting.
double	EntropyBasedSplitCrit.oldEnt(Distribution bags)	Computes entropy of distribution before splitting.
double	GainRatioSplitCrit.splitCritValue(Distribution bags)	This method is a straightforward implementation of the gain ratio criterion for the given distribution.
double	GainRatioSplitCrit.splitCritValue(Distribution bags, double totalnoInst, double numerator)	This method computes the gain ratio in the same way C4.5 does.
double	InfoGainSplitCrit.splitCritValue(Distribution bags)	This method is a straightforward implementation of the information gain criterion for the given distribution.
double	InfoGainSplitCrit.splitCritValue(Distribution bags, double totalNoInst)	This method computes the information gain in the same way C4.5 does.
double	InfoGainSplitCrit.splitCritValue(Distribution bags, double totalNoInst, double oldEnt)	This method computes the information gain in the same way C4.5 does.
double	SplitCriterion.splitCritValue(Distribution bags)	Computes result of splitting criterion for given distribution.
double	SplitCriterion.splitCritValue(Distribution train, Distribution test)	Computes result of splitting criterion for given training and test distributions.
double	SplitCriterion.splitCritValue(Distribution train, Distribution test, int noClassesDefault)	Computes result of splitting criterion for given training and test distributions and given number of classes.
double	SplitCriterion.splitCritValue(Distribution train, Distribution test, Distribution defC)	Computes result of splitting criterion for given training and test distributions and given default distribution.
double	EntropyBasedSplitCrit.splitEnt(Distribution bags)	Computes entropy after splitting without considering the class values.
Distribution	Distribution.subtract(Distribution toSubstract)	Subtracts the given distribution from this one.

Constructors in org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree with parameters of type Distribution

Constructor	Description
Distribution(Distribution toMerge)	Creates distribution with only one bag by merging all bags of given distribution.
Distribution(Distribution toMerge, int index)	Creates distribution with two bags by merging all bags apart of the indicated one.
NoSplit(Distribution distribution)	Creates "no-split"-split for given distribution.