weka.classifiers.functions
Class VotedPerceptron

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

All Implemented Interfaces:

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

public class VotedPerceptron

extends Classifier

implements OptionHandler

Implements the voted perceptron algorithm by Freund and Schapire. Globally replaces all missing values, and transforms nominal attributes into binary ones. For more information, see

Y. Freund and R. E. Schapire (1998). Large margin classification using the perceptron algorithm. Proc. 11th Annu. Conf. on Comput. Learning Theory, pp. 209-217, ACM Press, New York, NY.

Valid options are:

-I num
The number of iterations to be performed. (default 1)

-E num
The exponent for the polynomial kernel. (default 1)

-S num
The seed for the random number generator. (default 1)

-M num
The maximum number of alterations allowed. (default 10000)

Version:

$Revision: 1.17 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

private int[]	m_Additions The training instances added to the perceptron
private double	m_Exponent The exponent
private boolean[]	m_IsAddition Addition or subtraction?
private int	m_K The actual number of alterations
private int	m_MaxK The maximum number of alterations to the perceptron
private NominalToBinary	m_NominalToBinary The filter used to make attributes numeric.
private int	m_NumIterations The number of iterations
private ReplaceMissingValues	m_ReplaceMissingValues The filter used to get rid of missing values.
private int	m_Seed Seed used for shuffling the dataset
private Instances	m_Train The training instances
private int[]	m_Weights The weights for each perceptron

Fields inherited from class weka.classifiers.Classifier

m_Debug

Constructor Summary

VotedPerceptron()

Method Summary

void	buildClassifier(Instances insts) Builds the ensemble of perceptrons.
double[]	distributionForInstance(Instance inst) Outputs the distribution for the given output.
java.lang.String	exponentTipText() Returns the tip text for this property
double	getExponent() Get the value of exponent.
int	getMaxK() Get the value of maxK.
int	getNumIterations() Get the value of NumIterations.
java.lang.String[]	getOptions() Gets the current settings of the classifier.
int	getSeed() Get the value of Seed.
java.lang.String	globalInfo() Returns a string describing this classifier
private double	innerProduct(Instance i1, Instance i2) Computes the inner product of two instances
java.util.Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] argv) Main method.
private int	makePrediction(int k, Instance inst) Compute a prediction from a perceptron
java.lang.String	maxKTipText() Returns the tip text for this property
java.lang.String	numIterationsTipText() Returns the tip text for this property
java.lang.String	seedTipText() Returns the tip text for this property
void	setExponent(double v) Set the value of exponent.
void	setMaxK(int v) Set the value of maxK.
void	setNumIterations(int v) Set the value of NumIterations.
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setSeed(int v) Set the value of Seed.
java.lang.String	toString() Returns textual description of classifier.

Methods inherited from class weka.classifiers.Classifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, setDebug

Methods inherited from class java.lang.Object

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

Field Detail

m_MaxK

private int m_MaxK

The maximum number of alterations to the perceptron

m_NumIterations

private int m_NumIterations

The number of iterations

m_Exponent

private double m_Exponent

The exponent

m_K

private int m_K

The actual number of alterations

m_Additions

private int[] m_Additions

The training instances added to the perceptron

m_IsAddition

private boolean[] m_IsAddition

Addition or subtraction?

m_Weights

private int[] m_Weights

The weights for each perceptron

m_Train

private Instances m_Train

The training instances

m_Seed

private int m_Seed

Seed used for shuffling the dataset

m_NominalToBinary

private NominalToBinary m_NominalToBinary

The filter used to make attributes numeric.

m_ReplaceMissingValues

private ReplaceMissingValues m_ReplaceMissingValues

The filter used to get rid of missing values.

Constructor Detail

VotedPerceptron

public VotedPerceptron()

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

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:

-I num
The number of iterations to be performed. (default 1)

-E num
The exponent for the polynomial kernel. (default 1)

-S num
The seed for the random number generator. (default 1)

-M num
The maximum number of alterations allowed. (default 10000)

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

buildClassifier

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

Builds the ensemble of perceptrons.

Specified by:

buildClassifier in class Classifier

Parameters:

insts - set of instances serving as training data

Throws:

java.lang.Exception - if something goes wrong during building

distributionForInstance

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

Outputs the distribution for the given output. Pipes output of SVM through sigmoid function.

Overrides:

distributionForInstance in class Classifier

Parameters:

inst - the instance for which distribution is to be computed

Returns:

the distribution

Throws:

java.lang.Exception - if something goes wrong

toString

public java.lang.String toString()

Returns textual description of classifier.

maxKTipText

public java.lang.String maxKTipText()

Returns the tip text for this property

Returns:

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

getMaxK

public int getMaxK()

Get the value of maxK.

Returns:

Value of maxK.

setMaxK

public void setMaxK(int v)

Set the value of maxK.

Parameters:

v - Value to assign to maxK.

numIterationsTipText

public java.lang.String numIterationsTipText()

Returns the tip text for this property

Returns:

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

getNumIterations

public int getNumIterations()

Get the value of NumIterations.

Returns:

Value of NumIterations.

setNumIterations

public void setNumIterations(int v)

Set the value of NumIterations.

Parameters:

v - Value to assign to NumIterations.

exponentTipText

public java.lang.String exponentTipText()

Returns the tip text for this property

Returns:

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

getExponent

public double getExponent()

Get the value of exponent.

Returns:

Value of exponent.

setExponent

public void setExponent(double v)

Set the value of exponent.

Parameters:

v - Value to assign to exponent.

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

getSeed

public int getSeed()

Get the value of Seed.

Returns:

Value of Seed.

setSeed

public void setSeed(int v)

Set the value of Seed.

Parameters:

v - Value to assign to Seed.

innerProduct

private double innerProduct(Instance i1,
                            Instance i2)
                     throws java.lang.Exception

Computes the inner product of two instances

Throws:

java.lang.Exception

makePrediction

private int makePrediction(int k,
                           Instance inst)
                    throws java.lang.Exception

Compute a prediction from a perceptron

Throws:

java.lang.Exception

main

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

Main method.