milk.classifiers
Class MIRBFNetwork

java.lang.Object
  |
  +--milk.classifiers.MIClassifier
        |
        +--milk.classifiers.MIRBFNetwork

All Implemented Interfaces:

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

public class MIRBFNetwork

extends MIClassifier

implements weka.core.OptionHandler

Multi-instance RBF network. Uses k-means with distributions fit in post-processing step plus MILR at the second level.

See Also:

Serialized Form

Field Summary

protected weka.filters.unsupervised.attribute.ClusterMembership	m_clm The RBF filter
protected MIClassifier	m_logistic The logistic regression model
protected int	m_num_clusters The number of clusters to use
protected double	m_ridge The ridge regression coefficient for logistic regression

Constructor Summary

MIRBFNetwork()

Method Summary

void	buildClassifier(Exemplars train) Builds the classifier.
double[]	distributionForExemplar(Exemplar exmp) Computes the distribution for a given exemplar
int	getNumClusters() Get the Num_clusters value.
java.lang.String[]	getOptions() Gets the current settings of the classifier.
double	getRidge() Get the Ridge value.
java.util.Enumeration	listOptions() Returns an enumeration describing the available options
static void	main(java.lang.String[] argv) Main method for testing this class.
void	setNumClusters(int newNum_clusters) Set the Num_clusters value.
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setRidge(double newRidge) Set the Ridge value.
java.lang.String	toString() Gets a string describing the classifier.
Exemplar	transform(Exemplar test)
Exemplars	transform(Exemplars ex)

Methods inherited from class milk.classifiers.MIClassifier

classifyExemplar, forName, makeCopies

Methods inherited from class java.lang.Object

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

Field Detail

m_logistic

protected MIClassifier m_logistic

The logistic regression model

m_clm

protected weka.filters.unsupervised.attribute.ClusterMembership m_clm

The RBF filter

m_num_clusters

protected int m_num_clusters

The number of clusters to use

m_ridge

protected double m_ridge

The ridge regression coefficient for logistic regression

Constructor Detail

MIRBFNetwork

public MIRBFNetwork()

Method Detail

getNumClusters

public int getNumClusters()

Get the Num_clusters value.

Returns:

the Num_clusters value.

setNumClusters

public void setNumClusters(int newNum_clusters)

Set the Num_clusters value.

Parameters:

newNum_clusters - The new Num_clusters value.

getRidge

public double getRidge()

Get the Ridge value.

Returns:

the Ridge value.

setRidge

public void setRidge(double newRidge)

Set the Ridge value.

Parameters:

newRidge - The new Ridge value.

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options

Specified by:

listOptions in interface weka.core.OptionHandler

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:

-N number
The number of clusters to use.

-R ridge
Set the ridge parameter for the log-likelihood.

Specified by:

setOptions in interface weka.core.OptionHandler

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 weka.core.OptionHandler

Returns:

an array of strings suitable for passing to setOptions

transform

public Exemplars transform(Exemplars ex)
                    throws java.lang.Exception

java.lang.Exception

transform

public Exemplar transform(Exemplar test)
                   throws java.lang.Exception

java.lang.Exception

buildClassifier

public void buildClassifier(Exemplars train)
                     throws java.lang.Exception

Builds the classifier.

Specified by:

buildClassifier in class MIClassifier

Parameters:

train - the training data to be used for generating the boosted classifier.

Throws:

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

distributionForExemplar

public double[] distributionForExemplar(Exemplar exmp)
                                 throws java.lang.Exception

Computes the distribution for a given exemplar

Overrides:

distributionForExemplar in class MIClassifier

Parameters:

exmp - the exemplar 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()

Gets a string describing the classifier.

Overrides:

toString in class java.lang.Object

Returns:

a string describing the classifer built.

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)