weka.classifiers.functions
Class RBFNetwork

java.lang.Object
  weka.classifiers.Classifier
      weka.classifiers.functions.RBFNetwork

All Implemented Interfaces:

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

public class RBFNetwork

extends Classifier

implements OptionHandler

Class that implements a normalized Gaussian radial basis function network. It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that. Symmetric multivariate Gaussians are fit to the data from each cluster. If the class is nominal it uses the given number of clusters per class. It standardizes all numeric attributes to zero mean and unit variance. Valid options are:

-B num
Set the number of clusters (basis functions) to use.

-R ridge
Set the ridge parameter for the logistic regression or linear regression.

-M num
Set the maximum number of iterations for logistic regression. (default -1, until convergence)

-S seed
Set the random seed used by K-means when generating clusters. (default 1).

-W num
Set the minimum standard deviation for the clusters. (default 0.1).

Version:

$Revision: 1.4 $

Author:

Mark Hall, Eibe Frank

See Also:

Serialized Form

Constructor Summary

RBFNetwork()

Method Summary

void	buildClassifier(Instances instances) Builds the classifier
java.lang.String	clusteringSeedTipText() Returns the tip text for this property
double[]	distributionForInstance(Instance instance) Computes the distribution for a given instance
int	getClusteringSeed() Get the random seed used by K-means.
int	getMaxIts() Get the value of MaxIts.
double	getMinStdDev() Get the MinStdDev value.
int	getNumClusters() Return the number of clusters to generate.
java.lang.String[]	getOptions() Gets the current settings of the classifier.
double	getRidge() Gets the ridge value.
java.lang.String	globalInfo() Returns a string describing this classifier
java.util.Enumeration	listOptions() Returns an enumeration describing the available options
static void	main(java.lang.String[] argv) Main method for testing this class.
java.lang.String	maxItsTipText() Returns the tip text for this property
java.lang.String	minStdDevTipText() Returns the tip text for this property
java.lang.String	numClustersTipText() Returns the tip text for this property
java.lang.String	ridgeTipText() Returns the tip text for this property
void	setClusteringSeed(int seed) Set the random seed to be passed on to K-means.
void	setMaxIts(int newMaxIts) Set the value of MaxIts.
void	setMinStdDev(double newMinStdDev) Set the MinStdDev value.
void	setNumClusters(int numClusters) Set the number of clusters for K-means to generate.
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setRidge(double ridge) Sets the ridge value for logistic or linear regression.
java.lang.String	toString() Returns a description of this classifier as a String

Methods inherited from class weka.classifiers.Classifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug

Methods inherited from class java.lang.Object

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

Constructor Detail

RBFNetwork

public RBFNetwork()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing this classifier

Returns:

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

buildClassifier

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

Builds the classifier

Specified by:

buildClassifier in class Classifier

Parameters:

instances - the training data

Throws:

java.lang.Exception - if the classifier could not be built successfully

distributionForInstance

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

Computes the distribution for a given instance

Overrides:

distributionForInstance in class Classifier

Parameters:

instance - the instance for which distribution is computed

Returns:

the distribution

Throws:

java.lang.Exception - if the distribution can't be computed successfully

toString

public java.lang.String toString()

Returns a description of this classifier as a String

Returns:

a description of this classifier

maxItsTipText

public java.lang.String maxItsTipText()

Returns the tip text for this property

Returns:

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

getMaxIts

public int getMaxIts()

Get the value of MaxIts.

Returns:

Value of MaxIts.

setMaxIts

public void setMaxIts(int newMaxIts)

Set the value of MaxIts.

Parameters:

newMaxIts - Value to assign to MaxIts.

ridgeTipText

public java.lang.String ridgeTipText()

Returns the tip text for this property

Returns:

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

setRidge

public void setRidge(double ridge)

Sets the ridge value for logistic or linear regression.

Parameters:

ridge - the ridge

getRidge

public double getRidge()

Gets the ridge value.

Returns:

the ridge

numClustersTipText

public java.lang.String numClustersTipText()

Returns the tip text for this property

Returns:

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

setNumClusters

public void setNumClusters(int numClusters)

Set the number of clusters for K-means to generate.

Parameters:

numClusters - the number of clusters to generate.

getNumClusters

public int getNumClusters()

Return the number of clusters to generate.

Returns:

the number of clusters to generate.

clusteringSeedTipText

public java.lang.String clusteringSeedTipText()

Returns the tip text for this property

Returns:

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

setClusteringSeed

public void setClusteringSeed(int seed)

Set the random seed to be passed on to K-means.

Parameters:

seed - a seed value.

getClusteringSeed

public int getClusteringSeed()

Get the random seed used by K-means.

Returns:

the seed value.

minStdDevTipText

public java.lang.String minStdDevTipText()

Returns the tip text for this property

Returns:

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

getMinStdDev

public double getMinStdDev()

Get the MinStdDev value.

Returns:

the MinStdDev value.

setMinStdDev

public void setMinStdDev(double newMinStdDev)

Set the MinStdDev value.

Parameters:

newMinStdDev - The new MinStdDev value.

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class Classifier

Returns:

an enumeration of all the available options

setOptions

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

Parses a given list of options. Valid options are:

-B num
Set the number of clusters (basis functions) to use.

-R ridge
Set the ridge parameter for the logistic regression or linear regression.

-M num
Set the maximum number of iterations for logistic regression. (default -1, until convergence)

-S seed
Set the random seed used by K-means when generating clusters. (default 1).

-W num
Set the minimum standard deviation for the clusters. (default 0.1).

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

getOptions

public java.lang.String[] getOptions()

Gets the current settings of the classifier.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class Classifier

Returns:

an array of strings suitable for passing to setOptions

main

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

Main method for testing this class.

Parameters:

argv - should contain the command line arguments to the scheme (see Evaluation)