weka.classifiers
Class RegressionByDiscretization

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.RegressionByDiscretization

All Implemented Interfaces:

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

public class RegressionByDiscretization

extends Classifier

implements OptionHandler

Class for a regression scheme that employs any distribution classifier on a copy of the data that has the class attribute discretized. The predicted value is the expected value of the mean class value for each discretized interval (based on the predicted probabilities for each interval).

Valid options are:

-D
Produce debugging output.

-W classname
Specify the full class name of a classifier as the basis for regression (required).

-B num
The number of bins the class attribute will be discretized into. (default 10)

-O
Optimize number of bins (values up to and including the -B option will be considered). (default no debugging output)

Any options after -- will be passed to the sub-classifier.

Author:

Len Trigg (trigg@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

protected DistributionClassifier	m_Classifier The subclassifier.
protected double[]	m_ClassMeans The mean values for each Discretized class interval.
protected boolean	m_Debug Whether debugging output will be printed
protected DiscretizeFilter	m_Discretizer The discretization filter.
protected int	m_NumBins The number of classes in the Discretized training data.
protected boolean	m_OptimizeBins Whether the Discretizer will optimise the number of bins

Constructor Summary

RegressionByDiscretization()

Method Summary

void	buildClassifier(Instances instances) Generates the classifier.
double	classifyInstance(Instance instance) Returns a predicted class for the test instance.
Classifier	getClassifier() Get the classifier used as the classifier
boolean	getDebug() Gets whether debugging output will be printed
int	getNumBins() Gets the number of bins the class attribute will be discretized into.
boolean	getOptimizeBins() Gets whether the discretizer optimizes the number of bins
java.lang.String[]	getOptions() Gets the current settings of the 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.
void	setClassifier(Classifier newClassifier) Set the classifier for boosting.
void	setDebug(boolean debug) Sets whether debugging output will be printed
void	setNumBins(int numBins) Sets the number of bins the class attribute will be discretized into.
void	setOptimizeBins(boolean optimize) Sets whether the discretizer optimizes the number of bins
void	setOptions(java.lang.String[] options) Parses a given list of options.
java.lang.String	toString() Returns a description of the classifier.

Methods inherited from class weka.classifiers.Classifier

forName, makeCopies

Methods inherited from class java.lang.Object

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

Field Detail

m_Classifier

protected DistributionClassifier m_Classifier

The subclassifier.

m_Discretizer

protected DiscretizeFilter m_Discretizer

The discretization filter.

m_NumBins

protected int m_NumBins

The number of classes in the Discretized training data.

m_ClassMeans

protected double[] m_ClassMeans

The mean values for each Discretized class interval.

m_Debug

protected boolean m_Debug

Whether debugging output will be printed

m_OptimizeBins

protected boolean m_OptimizeBins

Whether the Discretizer will optimise the number of bins

Constructor Detail

RegressionByDiscretization

public RegressionByDiscretization()

Method Detail

buildClassifier

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

Generates the classifier.

Overrides:

buildClassifier in class Classifier

Parameters:

instances - set of instances serving as training data

Throws:

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

classifyInstance

public double classifyInstance(Instance instance)
                        throws java.lang.Exception

Returns a predicted class for the test instance.

Overrides:

classifyInstance in class Classifier

Parameters:

instance - the instance to be classified

Returns:

predicted class value

Throws:

java.lang.Exception - if the prediction couldn't be made

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options

Specified by:

listOptions in interface 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:

-D
Produce debugging output.

-W classname
Specify the full class name of a classifier as the basis for regression (required).

-B num
The number of bins the class attribute will be discretized into. (default 10)

-O
Optimize number of bins (values up to and including the -B option will be considered). (default no debugging output)

Any options after -- will be passed to the sub-classifier.

Specified by:

setOptions in interface 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 OptionHandler

Returns:

an array of strings suitable for passing to setOptions

setClassifier

public void setClassifier(Classifier newClassifier)

Set the classifier for boosting.

Parameters:

newClassifier - the Classifier to use.

getClassifier

public Classifier getClassifier()

Get the classifier used as the classifier

Returns:

the classifier used as the classifier

setOptimizeBins

public void setOptimizeBins(boolean optimize)

Sets whether the discretizer optimizes the number of bins

Parameters:

optimize - true if the discretizer should optimize the number of bins

getOptimizeBins

public boolean getOptimizeBins()

Gets whether the discretizer optimizes the number of bins

Returns:

true if the discretizer should optimize the number of bins

setDebug

public void setDebug(boolean debug)

Sets whether debugging output will be printed

Parameters:

debug - true if debug output should be printed

getDebug

public boolean getDebug()

Gets whether debugging output will be printed

Returns:

true if debug output should be printed

setNumBins

public void setNumBins(int numBins)

Sets the number of bins the class attribute will be discretized into.

Parameters:

numBins - the number of bins to use

getNumBins

public int getNumBins()

Gets the number of bins the class attribute will be discretized into.

Returns:

the number of bins to use

toString

public java.lang.String toString()

Returns a description of the classifier.

Overrides:

toString in class java.lang.Object

Returns:

a description of the classifier as a string.

main

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

Main method for testing this class.

Parameters:

argv - the options