weka.classifiers.trees.j48
Class NBTreeSplit

java.lang.Object
  weka.classifiers.trees.j48.ClassifierSplitModel
      weka.classifiers.trees.j48.NBTreeSplit

All Implemented Interfaces:

java.lang.Cloneable, java.io.Serializable

public class NBTreeSplit

extends ClassifierSplitModel

Class implementing a NBTree split on an attribute.

Version:

$Revision: 1.1 $

Author:

Mark Hall (mhall@cs.waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

NBTreeSplit(int attIndex, int minNoObj, double sumOfWeights)
Initializes the split model.

Method Summary

int	attIndex() Returns index of attribute for which split was generated.
void	buildClassifier(Instances trainInstances) Creates a NBTree-type split on the given data.
double	classProb(int classIndex, Instance instance, int theSubset) Return the probability for a class value
double	getErrors() Return the errors made by the naive bayes models arising from this split.
NBTreeNoSplit	getGlobalModel() Return the global naive bayes model for this node
java.lang.String	leftSide(Instances data) Prints left side of condition..
java.lang.String	rightSide(int index, Instances data) Prints the condition satisfied by instances in a subset.
void	setGlobalModel(NBTreeNoSplit global) Set the global naive bayes model for this node
java.lang.String	sourceExpression(int index, Instances data) Returns a string containing java source code equivalent to the test made at this node.
double[]	weights(Instance instance) Returns weights if instance is assigned to more than one subset.
int	whichSubset(Instance instance) Returns index of subset instance is assigned to.

Methods inherited from class weka.classifiers.trees.j48.ClassifierSplitModel

checkModel, classifyInstance, classProbLaplace, clone, codingCost, distribution, dumpLabel, dumpModel, numSubsets, resetDistribution, sourceClass, split

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

NBTreeSplit

public NBTreeSplit(int attIndex,
                   int minNoObj,
                   double sumOfWeights)

Initializes the split model.

Method Detail

buildClassifier

public void buildClassifier(Instances trainInstances)
                     throws java.lang.Exception

Creates a NBTree-type split on the given data. Assumes that none of the class values is missing.

Specified by:

buildClassifier in class ClassifierSplitModel

Throws:

java.lang.Exception - if something goes wrong

attIndex

public final int attIndex()

Returns index of attribute for which split was generated.

whichSubset

public final int whichSubset(Instance instance)
                      throws java.lang.Exception

Returns index of subset instance is assigned to. Returns -1 if instance is assigned to more than one subset.

Specified by:

whichSubset in class ClassifierSplitModel

Throws:

java.lang.Exception - if something goes wrong

weights

public final double[] weights(Instance instance)

Returns weights if instance is assigned to more than one subset. Returns null if instance is only assigned to one subset.

Specified by:

weights in class ClassifierSplitModel

sourceExpression

public final java.lang.String sourceExpression(int index,
                                               Instances data)

Returns a string containing java source code equivalent to the test made at this node. The instance being tested is called "i".

Specified by:

sourceExpression in class ClassifierSplitModel

Parameters:

index - index of the nominal value tested

data - the data containing instance structure info

Returns:

a value of type 'String'

rightSide

public final java.lang.String rightSide(int index,
                                        Instances data)

Prints the condition satisfied by instances in a subset.

Specified by:

rightSide in class ClassifierSplitModel

Parameters:

index - of subset

data - training set.

leftSide

public final java.lang.String leftSide(Instances data)

Prints left side of condition..

Specified by:

leftSide in class ClassifierSplitModel

Parameters:

data - training set.

classProb

public double classProb(int classIndex,
                        Instance instance,
                        int theSubset)
                 throws java.lang.Exception

Return the probability for a class value

Overrides:

classProb in class ClassifierSplitModel

Parameters:

classIndex - the index of the class value

instance - the instance to generate a probability for

theSubset - the subset to consider

Returns:

a probability

Throws:

java.lang.Exception - if an error occurs

getGlobalModel

public NBTreeNoSplit getGlobalModel()

Return the global naive bayes model for this node

Returns:

a NBTreeNoSplit value

setGlobalModel

public void setGlobalModel(NBTreeNoSplit global)

Set the global naive bayes model for this node

Parameters:

global - a NBTreeNoSplit value

getErrors

public double getErrors()

Return the errors made by the naive bayes models arising from this split.

Returns:

a double value