weka.classifiers.functions.supportVector

Class RegOptimizer

java.lang.Object

weka.classifiers.functions.supportVector.RegOptimizer

All Implemented Interfaces:

java.io.Serializable, OptionHandler, RevisionHandler

Direct Known Subclasses:

RegSMO

public class RegOptimizer
extends java.lang.Object
implements OptionHandler, java.io.Serializable, RevisionHandler

Base class implementation for learning algorithm of SMOreg Valid options are:

 -L <double>
  The epsilon parameter in epsilon-insensitive loss function.
  (default 1.0e-3)

 -W <double>
  The random number seed.
  (default 1)

Version:

$Revision: 11614 $

Author:

Remco Bouckaert (remco@cs.waikato.ac.nz,rrb@xm.co.nz)

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
double[]	m_alpha alpha and alpha* arrays containing weights for solving dual problem
double[]	m_alphaStar

Constructor Summary

Constructors

Constructor and Description
RegOptimizer() the default constructor

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(Instances data) learn SVM parameters from data.
java.lang.String	epsilonParameterTipText() Returns the tip text for this property
int	getCacheHits() return the number of kernel cache hits
double	getEpsilonParameter() Get the value of epsilon parameter of the epsilon insensitive loss function.
long	getKernelEvaluations() returns the number of kernel evaluations
java.lang.String[]	getOptions() Gets the current settings of the classifier.
java.lang.String	getRevision() Returns the revision string.
int	getSeed() Gets the current seed value for the random number generator
java.util.Enumeration	listOptions() Gets an enumeration describing the available options.
boolean	modelBuilt() flag to indicate whether the model was built yet
java.lang.String	seedTipText() Returns the tip text for this property
void	setEpsilonParameter(double v) Set the value of epsilon parameter of the epsilon insensitive loss function.
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setSeed(int value) Sets the seed value for the random number generator
void	setSMOReg(SMOreg value) sets the parent SVM
double	SVMOutput(Instance inst)
java.lang.String	toString() Prints out the classifier.

Methods inherited from class java.lang.Object

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

Field Detail

m_alpha

public double[] m_alpha

alpha and alpha* arrays containing weights for solving dual problem

m_alphaStar

public double[] m_alphaStar

Constructor Detail

RegOptimizer

public RegOptimizer()

the default constructor

Method Detail

listOptions

public java.util.Enumeration listOptions()

Gets 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:

 -L <double>
  The epsilon parameter in epsilon-insensitive loss function.
  (default 1.0e-3)

 -W <double>
  The random number seed.
  (default 1)

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

modelBuilt

public boolean modelBuilt()

flag to indicate whether the model was built yet

Returns:

true if the model was built

setSMOReg

public void setSMOReg(SMOreg value)

sets the parent SVM

Parameters:

value - the parent SVM

getKernelEvaluations

public long getKernelEvaluations()

returns the number of kernel evaluations

Returns:

the number of kernel evaluations

getCacheHits

public int getCacheHits()

return the number of kernel cache hits

Returns:

the number of hits

buildClassifier

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

learn SVM parameters from data. Subclasses should implement something more interesting.

Parameters:

data - the data to work with

Throws:

java.lang.Exception - always an Exceoption since subclasses must override it

SVMOutput

public double SVMOutput(Instance inst)
                 throws java.lang.Exception

Parameters:

inst -

Returns:

Throws:

java.lang.Exception

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()

Gets the current seed value for the random number generator

Returns:

the seed value

setSeed

public void setSeed(int value)

Sets the seed value for the random number generator

Parameters:

value - the seed value

epsilonParameterTipText

public java.lang.String epsilonParameterTipText()

Returns the tip text for this property

Returns:

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

getEpsilonParameter

public double getEpsilonParameter()

Get the value of epsilon parameter of the epsilon insensitive loss function.

Returns:

Value of epsilon parameter.

setEpsilonParameter

public void setEpsilonParameter(double v)

Set the value of epsilon parameter of the epsilon insensitive loss function.

Parameters:

v - Value to assign to epsilon parameter.

toString

public java.lang.String toString()

Prints out the classifier.

Overrides:

toString in class java.lang.Object

Returns:

a description of the classifier as a string

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Returns:

the revision