weka.classifiers.trees
Class RandomRegressionForest

java.lang.Object
  weka.classifiers.AbstractClassifier
      weka.classifiers.RandomizableClassifier
          weka.classifiers.trees.RandomRegressionForest

All Implemented Interfaces:

Serializable, Cloneable, weka.classifiers.Classifier, weka.core.CapabilitiesHandler, weka.core.OptionHandler, weka.core.Randomizable, weka.core.RevisionHandler, weka.core.WeightedInstancesHandler

public class RandomRegressionForest
extends weka.classifiers.RandomizableClassifier
implements weka.core.WeightedInstancesHandler

RandomRegressionForest: subtract mean and pls, then grow completely random trees (leaf: min .. 2min).
plus local regression models (-S 1 -C), min >> numPLScomps

Valid options are:

 -N <num>
  Number of trees.
  (default 100)

 -M <num>
  Leaf threshold.
  (default 100)

 -C <num>
  Number of PLS components.
  (default 20)

 -S <num>
  Random number seed.
  (default 1)

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

Version:

$Revision: 5087 $

Author:

Bernhard Pfahringer (bernhard at cs dot waikato dot ac dot nz)

See Also:

Serialized Form

Nested Class Summary

class

RandomRegressionForest.Node TODO: description of class

Field Summary

protected weka.core.Instances	m_Data the original header
protected double	m_Mean the mean
protected int	m_Min the minimum number of instances in subsets
protected RandomRegressionForest.Node[]	m_Node the generated nodes
protected int	m_NumIterations The number of iterations.
protected int	m_PLS the number of components to use in PLS
protected weka.filters.supervised.attribute.PLSFilter	m_PLSFilter the PLS filter used internally

Fields inherited from class weka.classifiers.RandomizableClassifier

m_Seed

Fields inherited from class weka.classifiers.AbstractClassifier

m_Debug

Constructor Summary

RandomRegressionForest()

Method Summary

void	buildClassifier(weka.core.Instances data) builds the classifier
protected weka.core.Instances	centerClass(weka.core.Instances data) Centers the class value in the data.
double	classifyInstance(weka.core.Instance instance) Calculates the class membership probabilities for the given test instance.
weka.core.Capabilities	getCapabilities() Returns default capabilities of the classifier.
int	getMin() Gets the current leaf threshold.
int	getNumIterations() Gets the number of iterations
String[]	getOptions() Gets the current settings of the Classifier.
int	getPLS() Gets the current number of PLS components to generate.
String	getRevision()
String	globalInfo() Returns a string describing this classifier.
Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(String[] args) Main method for testing this class.
String	minTipText() Returns the tip text for this property
String	numIterationsTipText() Returns the tip text for this property
String	PLSTipText() Returns the tip text for this property
void	setMin(int value) Sets the leaf threshold.
void	setNumIterations(int value) Sets the number of iterations
void	setOptions(String[] options) Parses a given list of options.
void	setPLS(int value) Sets the number of PLS components to generate.
String	toString() Returns description of the classifier.

Methods inherited from class weka.classifiers.RandomizableClassifier

getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.AbstractClassifier

debugTipText, distributionForInstance, forName, getDebug, makeCopies, makeCopy, runClassifier, setDebug

Methods inherited from class java.lang.Object

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

Field Detail

m_Node

protected RandomRegressionForest.Node[] m_Node

the generated nodes

m_NumIterations

protected int m_NumIterations

The number of iterations.

m_PLS

protected int m_PLS

the number of components to use in PLS

m_Min

protected int m_Min

the minimum number of instances in subsets

m_Data

protected weka.core.Instances m_Data

the original header

m_PLSFilter

protected weka.filters.supervised.attribute.PLSFilter m_PLSFilter

the PLS filter used internally

m_Mean

protected double m_Mean

the mean

Constructor Detail

RandomRegressionForest

public RandomRegressionForest()

Method Detail

globalInfo

public String globalInfo()

Returns a string describing this classifier.

Returns:

a description of the classifier suitable for displaying in the explorer/experimenter gui

listOptions

public Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface weka.core.OptionHandler

Overrides:

listOptions in class weka.classifiers.RandomizableClassifier

Returns:

an enumeration of all the available options.

setOptions

public void setOptions(String[] options)
                throws Exception

Parses a given list of options.

Valid options are:

 -N <num>
  Number of trees.
  (default 100)

 -M <num>
  Leaf threshold.
  (default 100)

 -C <num>
  Number of PLS components.
  (default 20)

 -S <num>
  Random number seed.
  (default 1)

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

Specified by:

setOptions in interface weka.core.OptionHandler

Overrides:

setOptions in class weka.classifiers.RandomizableClassifier

Parameters:

options - the list of options as an array of strings

Throws:

Exception - if an option is not supported

getOptions

public String[] getOptions()

Gets the current settings of the Classifier.

Specified by:

getOptions in interface weka.core.OptionHandler

Overrides:

getOptions in class weka.classifiers.RandomizableClassifier

Returns:

an array of strings suitable for passing to setOptions

numIterationsTipText

public String numIterationsTipText()

Returns the tip text for this property

Returns:

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

setNumIterations

public void setNumIterations(int value)

Sets the number of iterations

Parameters:

value - the number of iterations to use

getNumIterations

public int getNumIterations()

Gets the number of iterations

Returns:

the maximum number of iterations

minTipText

public String minTipText()

Returns the tip text for this property

Returns:

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

setMin

public void setMin(int value)

Sets the leaf threshold.

Parameters:

value - the new leaf threshold

getMin

public int getMin()

Gets the current leaf threshold.

Returns:

the current leaf threshold

PLSTipText

public String PLSTipText()

Returns the tip text for this property

Returns:

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

setPLS

public void setPLS(int value)

Sets the number of PLS components to generate.

Parameters:

value - the number of PLS components

getPLS

public int getPLS()

Gets the current number of PLS components to generate.

Returns:

the current number of PLS components

centerClass

protected weka.core.Instances centerClass(weka.core.Instances data)

Centers the class value in the data.

Parameters:

data - the data to work on

Returns:

the modified data

getCapabilities

public weka.core.Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface weka.classifiers.Classifier

Specified by:

getCapabilities in interface weka.core.CapabilitiesHandler

Overrides:

getCapabilities in class weka.classifiers.AbstractClassifier

Returns:

the capabilities of this classifier

buildClassifier

public void buildClassifier(weka.core.Instances data)
                     throws Exception

builds the classifier

Specified by:

buildClassifier in interface weka.classifiers.Classifier

Parameters:

data - the training data to be used for generating the

Throws:

Exception - if the classifier could not be built successfully

classifyInstance

public double classifyInstance(weka.core.Instance instance)
                        throws Exception

Calculates the class membership probabilities for the given test instance.

Specified by:

classifyInstance in interface weka.classifiers.Classifier

Overrides:

classifyInstance in class weka.classifiers.AbstractClassifier

Parameters:

instance - the instance to be classified

Returns:

preedicted class probability distribution

Throws:

Exception - if distribution can't be computed successfully

toString

public String toString()

Returns description of the classifier.

Overrides:

toString in class Object

Returns:

description of the classifier as a string

main

public static void main(String[] args)

Main method for testing this class.

Parameters:

args - the options

getRevision

public String getRevision()

Specified by:

getRevision in interface weka.core.RevisionHandler

Overrides:

getRevision in class weka.classifiers.AbstractClassifier