Uses of Class
weka.classifiers.trees.j48.Distribution

Packages that use Distribution

Package	Description
weka.classifiers.trees.j48

Uses of Distribution in weka.classifiers.trees.j48

Methods in weka.classifiers.trees.j48 that return Distribution

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

Methods in weka.classifiers.trees.j48 with parameters of type Distribution

Modifier and Type	Method	Description
final double	EntropyBasedSplitCrit.newEnt(Distribution bags)	Computes entropy of distribution after splitting.
final double	EntropyBasedSplitCrit.oldEnt(Distribution bags)	Computes entropy of distribution before splitting.
void	ClassifierSplitModel.setDistribution(Distribution dist)	Sets the distribution associated with model.
final double	EntropySplitCrit.splitCritValue(Distribution bags)	Computes entropy for given distribution.
final double	EntropySplitCrit.splitCritValue(Distribution train, Distribution test)	Computes entropy of test distribution with respect to training distribution.
final double	GainRatioSplitCrit.splitCritValue(Distribution bags)	This method is a straightforward implementation of the gain ratio criterion for the given distribution.
final double	GainRatioSplitCrit.splitCritValue(Distribution bags, double totalnoInst, double numerator)	This method computes the gain ratio in the same way C4.5 does.
final double	InfoGainSplitCrit.splitCritValue(Distribution bags)	This method is a straightforward implementation of the information gain criterion for the given distribution.
final double	InfoGainSplitCrit.splitCritValue(Distribution bags, double totalNoInst)	This method computes the information gain in the same way C4.5 does.
final 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.
final double	EntropyBasedSplitCrit.splitEnt(Distribution bags)	Computes entropy after splitting without considering the class values.
final Distribution	Distribution.subtract(Distribution toSubstract)	Subtracts the given distribution from this one.

Constructors in weka.classifiers.trees.j48 with parameters of type Distribution

Modifier	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.