weka.classifiers.trees
Class RandomTree

java.lang.Object
  weka.classifiers.Classifier
      weka.classifiers.trees.RandomTree

All Implemented Interfaces:

java.lang.Cloneable, OptionHandler, Randomizable, java.io.Serializable, WeightedInstancesHandler

public class RandomTree

extends Classifier

implements OptionHandler, WeightedInstancesHandler, Randomizable

Class for constructing a tree that considers K random features at each node. Performs no pruning.

Version:

$Revision: 1.7 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz), Richard Kirkby (rkirkby@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

protected int	m_Attribute The attribute to split on.
protected double[]	m_ClassProbs Class probabilities from the training data.
protected boolean	m_Debug Debug info
protected double[][]	m_Distribution The class distribution from the training data.
protected Instances	m_Info The header information.
protected int	m_KValue The number of attributes considered for a split.
protected double	m_MinNum Minimum number of instances for leaf.
protected double[]	m_Prop The proportions of training instances going down each branch.
protected int	m_randomSeed The random seed to use.
protected double	m_SplitPoint The split point.
protected RandomTree[]	m_Successors The subtrees appended to this tree.

Constructor Summary

RandomTree()

Method Summary

void	buildClassifier(Instances data) Builds classifier.
protected void	buildTree(int[][] sortedIndices, double[][] weights, Instances data, double[] classProbs, Instances header, double minNum, boolean debug, int[] attIndicesWindow, java.util.Random random) Recursively generates a tree.
java.lang.String	debugTipText() Returns the tip text for this property
protected double	distribution(double[][] props, double[][][] dists, int att, int[] sortedIndices, double[] weights, Instances data) Computes class distribution for an attribute.
double[]	distributionForInstance(Instance instance) Computes class distribution of an instance using the decision tree.
protected double	gain(double[][] dist, double priorVal) Computes value of splitting criterion after split.
boolean	getDebug() Get the value of Debug.
int	getKValue() Get the value of K.
double	getMinNum() Get the value of MinNum.
java.lang.String[]	getOptions() Gets options from this classifier.
int	getSeed() Gets the seed for the random number generations
java.lang.String	globalInfo() Returns a string describing classifier
java.lang.String	KValueTipText() Returns the tip text for this property
protected java.lang.String	leafString() Outputs a leaf.
java.util.Enumeration	listOptions() Lists the command-line options for this classifier.
static void	main(java.lang.String[] argv) Main method for this class.
java.lang.String	minNumTipText() Returns the tip text for this property
int	numNodes() Computes size of the tree.
protected double	priorVal(double[][] dist) Computes value of splitting criterion before split.
java.lang.String	seedTipText() Returns the tip text for this property
void	setDebug(boolean newDebug) Set the value of Debug.
void	setKValue(int k) Set the value of K.
void	setMinNum(double newMinNum) Set the value of MinNum.
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setSeed(int seed) Set the seed for random number generation.
protected void	splitData(int[][][] subsetIndices, double[][][] subsetWeights, int att, double splitPoint, int[][] sortedIndices, double[][] weights, double[][] dist, Instances data) Splits instances into subsets.
java.lang.String	toGraph() Outputs the decision tree as a graph
int	toGraph(java.lang.StringBuffer text, int num) Outputs one node for graph.
java.lang.String	toString() Outputs the decision tree.
protected java.lang.String	toString(int level) Recursively outputs the tree.

Methods inherited from class weka.classifiers.Classifier

classifyInstance, forName, makeCopies

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

m_Successors

protected RandomTree[] m_Successors

The subtrees appended to this tree.

m_Attribute

protected int m_Attribute

The attribute to split on.

m_SplitPoint

protected double m_SplitPoint

The split point.

m_Distribution

protected double[][] m_Distribution

The class distribution from the training data.

m_Info

protected Instances m_Info

The header information.

m_Prop

protected double[] m_Prop

The proportions of training instances going down each branch.

m_ClassProbs

protected double[] m_ClassProbs

Class probabilities from the training data.

m_MinNum

protected double m_MinNum

Minimum number of instances for leaf.

m_Debug

protected boolean m_Debug

Debug info

m_KValue

protected int m_KValue

The number of attributes considered for a split.

m_randomSeed

protected int m_randomSeed

The random seed to use.

Constructor Detail

RandomTree

public RandomTree()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

minNumTipText

public java.lang.String minNumTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getMinNum

public double getMinNum()

Get the value of MinNum.

Returns:

Value of MinNum.

setMinNum

public void setMinNum(double newMinNum)

Set the value of MinNum.

Parameters:

newMinNum - Value to assign to MinNum.

KValueTipText

public java.lang.String KValueTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getKValue

public int getKValue()

Get the value of K.

Returns:

Value of K.

setKValue

public void setKValue(int k)

Set the value of K.

Parameters:

k - Value to assign to K.

debugTipText

public java.lang.String debugTipText()

Returns the tip text for this property

Overrides:

debugTipText in class Classifier

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getDebug

public boolean getDebug()

Get the value of Debug.

Overrides:

getDebug in class Classifier

Returns:

Value of Debug.

setDebug

public void setDebug(boolean newDebug)

Set the value of Debug.

Overrides:

setDebug in class Classifier

Parameters:

newDebug - Value to assign to Debug.

seedTipText

public java.lang.String seedTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setSeed

public void setSeed(int seed)

Set the seed for random number generation.

Specified by:

setSeed in interface Randomizable

Parameters:

seed - the seed

getSeed

public int getSeed()

Gets the seed for the random number generations

Specified by:

getSeed in interface Randomizable

Returns:

the seed for the random number generation

listOptions

public java.util.Enumeration listOptions()

Lists the command-line options for this classifier.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class Classifier

Returns:

an enumeration of all the available options.

getOptions

public java.lang.String[] getOptions()

Gets options from this classifier.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class Classifier

Returns:

an array of strings suitable for passing to setOptions

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Parses a given list of options.

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class Classifier

Parameters:

options - the list of options as an array of strings

Throws:

java.lang.Exception - if an option is not supported

buildClassifier

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

Builds classifier.

Specified by:

buildClassifier in class Classifier

Parameters:

data - set of instances serving as training data

Throws:

java.lang.Exception - if the classifier has not been generated successfully

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception

Computes class distribution of an instance using the decision tree.

Overrides:

distributionForInstance in class Classifier

Parameters:

instance - the instance to be classified

Returns:

an array containing the estimated membership probabilities of the test instance in each class or the numeric prediction

Throws:

java.lang.Exception - if distribution could not be computed successfully

toGraph

public java.lang.String toGraph()

Outputs the decision tree as a graph

toGraph

public int toGraph(java.lang.StringBuffer text,
                   int num)
            throws java.lang.Exception

Outputs one node for graph.

Throws:

java.lang.Exception

toString

public java.lang.String toString()

Outputs the decision tree.

leafString

protected java.lang.String leafString()
                               throws java.lang.Exception

Outputs a leaf.

Throws:

java.lang.Exception

toString

protected java.lang.String toString(int level)

Recursively outputs the tree.

buildTree

protected void buildTree(int[][] sortedIndices,
                         double[][] weights,
                         Instances data,
                         double[] classProbs,
                         Instances header,
                         double minNum,
                         boolean debug,
                         int[] attIndicesWindow,
                         java.util.Random random)
                  throws java.lang.Exception

Recursively generates a tree.

Throws:

java.lang.Exception

numNodes

public int numNodes()

Computes size of the tree.

splitData

protected void splitData(int[][][] subsetIndices,
                         double[][][] subsetWeights,
                         int att,
                         double splitPoint,
                         int[][] sortedIndices,
                         double[][] weights,
                         double[][] dist,
                         Instances data)
                  throws java.lang.Exception

Splits instances into subsets.

Throws:

java.lang.Exception

distribution

protected double distribution(double[][] props,
                              double[][][] dists,
                              int att,
                              int[] sortedIndices,
                              double[] weights,
                              Instances data)
                       throws java.lang.Exception

Computes class distribution for an attribute.

Throws:

java.lang.Exception

priorVal

protected double priorVal(double[][] dist)

Computes value of splitting criterion before split.

gain

protected double gain(double[][] dist,
                      double priorVal)

Computes value of splitting criterion after split.

main

public static void main(java.lang.String[] argv)

Main method for this class.