weka.classifiers.meta
Class AbstainLeastMedianSq

java.lang.Object
  weka.classifiers.AbstractClassifier
      weka.classifiers.SingleClassifierEnhancer
          weka.classifiers.meta.AbstainLeastMedianSq

All Implemented Interfaces:

Serializable, Cloneable, weka.classifiers.Classifier, weka.core.CapabilitiesHandler, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.WeightedInstancesHandler

public class AbstainLeastMedianSq
extends weka.classifiers.SingleClassifierEnhancer
implements weka.core.WeightedInstancesHandler

Finds the base classifier with the best least median squared error.

Valid options are:

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

 -W
  Full name of base classifier.
  (default: weka.classifiers.rules.ZeroR)

 Options specific to classifier weka.classifiers.rules.ZeroR:
 

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

 -S <sint>
  Number of regressions
  (default: 10)
 

 -P <int>
  Percent sample size
  (default: 60)
 

 -E <int>
  Percent Error at
  (default: 50)
 

 -G <seed>
  Set the seed used to generate samples
  (default: 0)
 

Version:

$Revision: 5690 $

Author:

Dale

See Also:

Serialized Form

Field Summary

protected weka.classifiers.Classifier	m_bestClassifier
protected double	m_bestMedian
protected weka.classifiers.Classifier[]	m_cs
protected weka.classifiers.Classifier	m_currentClassifier
protected weka.core.Instances	m_Data
protected boolean	m_debug
protected int	m_errPct
protected int	m_instancepct
protected double	m_max
protected double	m_min
protected int	m_numreg
protected boolean	m_ok
protected Random	m_random
protected long	m_randomseed
protected double[]	m_Residuals
protected double	m_Ridge
protected double	m_scalefactor
protected double	m_SSR
protected weka.core.Instances	m_SubSample
protected double	m_variance
protected double[]	m_weight

Fields inherited from class weka.classifiers.SingleClassifierEnhancer

m_Classifier

Fields inherited from class weka.classifiers.AbstractClassifier

m_Debug

Constructor Summary

AbstainLeastMedianSq()

Method Summary

void	buildClassifier(weka.core.Instances data) Builds the classifier.
double	classifyInstance(weka.core.Instance inst) Returns the prediction.
String	errorTipText()
protected void	findBestRegression() Finds the best regression generated from m_samples random samples from the training data.
protected void	findResiduals() Finds residuals (squared) for the current regression.
protected void	genRegression(Random r) Generates a LinearRegression classifier from the current m_SubSample.
weka.core.Capabilities	getCapabilities() Returns default capabilities of the base classifier.
boolean	getDebug() Returns whether or not debugging output shouild be printed
int	getError()
protected void	getErrorAtPct(int pct) finds the median residual squared for the current regression.
int	getNumRegressions() gets number of samples
String[]	getOptions() Gets the current option settings for the OptionHandler.
int	getPct()
long	getRandomSeed() get the seed for the random number generator
String	getRevision()
double	getVariance()
String	globalInfo() Returns a string describing classifier.
Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(String[] argv) Main method for running this class.
String	numRegressionsTipText() Returns the tip text for this property
String	pctTipText()
String	randomSeedTipText() Returns the tip text for this property
protected void	selectSubSample(weka.core.Instances data, Random r) Produces a random sample from m_Data in m_SubSample.
void	setDebug(boolean debug) sets whether or not debugging output shouild be printed
void	setError(int pct)
void	setNumRegressions(int samplesize) sets number of samples
void	setOptions(String[] options) Sets the OptionHandler's options using the given list.
void	setPct(int pct)
void	setRandomSeed(long randomseed) Set the seed for the random number generator
void	setVariance(double var)
String	toString() Returns description of classifier.
String	varianceTipText()

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, defaultClassifierString, getClassifier, getClassifierSpec, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

debugTipText, distributionForInstance, forName, makeCopies, makeCopy, runClassifier

Methods inherited from class java.lang.Object

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

Field Detail

m_Residuals

protected double[] m_Residuals

m_weight

protected double[] m_weight

m_SSR

protected double m_SSR

m_scalefactor

protected double m_scalefactor

m_bestMedian

protected double m_bestMedian

m_Ridge

protected double m_Ridge

m_currentClassifier

protected weka.classifiers.Classifier m_currentClassifier

m_bestClassifier

protected weka.classifiers.Classifier m_bestClassifier

m_Data

protected weka.core.Instances m_Data

m_SubSample

protected weka.core.Instances m_SubSample

m_numreg

protected int m_numreg

m_instancepct

protected int m_instancepct

m_errPct

protected int m_errPct

m_debug

protected boolean m_debug

m_random

protected Random m_random

m_randomseed

protected long m_randomseed

m_variance

protected double m_variance

m_ok

protected boolean m_ok

m_min

protected double m_min

m_max

protected double m_max

m_cs

protected weka.classifiers.Classifier[] m_cs

Constructor Detail

AbstainLeastMedianSq

public AbstainLeastMedianSq()

Method Detail

globalInfo

public String globalInfo()

Returns a string describing classifier.

Returns:

a description suitable for displaying in the explorer/experimenter gui

listOptions

public Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface weka.core.OptionHandler

Overrides:

listOptions in class weka.classifiers.SingleClassifierEnhancer

Returns:

an enumeration of all the available options.

setOptions

public void setOptions(String[] options)
                throws Exception

Sets the OptionHandler's options using the given list. All options will be set (or reset) during this call (i.e. incremental setting of options is not possible). Valid options are:

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

 -W
  Full name of base classifier.
  (default: weka.classifiers.rules.ZeroR)

 Options specific to classifier weka.classifiers.rules.ZeroR:
 

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

 -S <sint>
  Number of regressions
  (default: 10)
 

 -P <int>
  Percent sample size
  (default: 60)
 

 -E <int>
  Percent Error at
  (default: 50)
 

 -G <seed>
  Set the seed used to generate samples
  (default: 0)
 

Specified by:

setOptions in interface weka.core.OptionHandler

Overrides:

setOptions in class weka.classifiers.SingleClassifierEnhancer

Parameters:

options - the list of options as an array of strings

Throws:

Exception - if an option is not supported

getOptions

public String[] getOptions()

Gets the current option settings for the OptionHandler.

Specified by:

getOptions in interface weka.core.OptionHandler

Overrides:

getOptions in class weka.classifiers.SingleClassifierEnhancer

Returns:

the list of current option settings as an array of strings

findResiduals

protected void findResiduals()
                      throws Exception

Finds residuals (squared) for the current regression.

Throws:

Exception - if an error occurs

getErrorAtPct

protected void getErrorAtPct(int pct)
                      throws Exception

finds the median residual squared for the current regression.

Throws:

Exception - if an error occurs

findBestRegression

protected void findBestRegression()
                           throws Exception

Finds the best regression generated from m_samples random samples from the training data.

Throws:

Exception - if an error occurs

selectSubSample

protected void selectSubSample(weka.core.Instances data,
                               Random r)
                        throws Exception

Produces a random sample from m_Data in m_SubSample.

Parameters:

data - data from which to take sample

Throws:

Exception - if an error occurs

genRegression

protected void genRegression(Random r)
                      throws Exception

Generates a LinearRegression classifier from the current m_SubSample.

Throws:

Exception - if an error occurs

getCapabilities

public weka.core.Capabilities getCapabilities()

Returns default capabilities of the base classifier.

Specified by:

getCapabilities in interface weka.classifiers.Classifier

Specified by:

getCapabilities in interface weka.core.CapabilitiesHandler

Overrides:

getCapabilities in class weka.classifiers.SingleClassifierEnhancer

Returns:

the capabilities of the base classifier

buildClassifier

public void buildClassifier(weka.core.Instances data)
                     throws Exception

Builds the classifier.

Specified by:

buildClassifier in interface weka.classifiers.Classifier

Parameters:

data - the training data

Throws:

Exception - if something goes wrong

classifyInstance

public double classifyInstance(weka.core.Instance inst)
                        throws Exception

Returns the prediction.

Specified by:

classifyInstance in interface weka.classifiers.Classifier

Overrides:

classifyInstance in class weka.classifiers.AbstractClassifier

Throws:

Exception

toString

public String toString()

Returns description of classifier.

Overrides:

toString in class Object

getRevision

public String getRevision()

Specified by:

getRevision in interface weka.core.RevisionHandler

Overrides:

getRevision in class weka.classifiers.AbstractClassifier

numRegressionsTipText

public String numRegressionsTipText()

Returns the tip text for this property

Returns:

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

setVariance

public void setVariance(double var)

getVariance

public double getVariance()

varianceTipText

public String varianceTipText()

setPct

public void setPct(int pct)

getPct

public int getPct()

pctTipText

public String pctTipText()

setError

public void setError(int pct)

getError

public int getError()

errorTipText

public String errorTipText()

setNumRegressions

public void setNumRegressions(int samplesize)

sets number of samples

Parameters:

samplesize - value

getNumRegressions

public int getNumRegressions()

gets number of samples

Returns:

value

randomSeedTipText

public String randomSeedTipText()

Returns the tip text for this property

Returns:

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

setRandomSeed

public void setRandomSeed(long randomseed)

Set the seed for the random number generator

Parameters:

randomseed - the seed

getRandomSeed

public long getRandomSeed()

get the seed for the random number generator

Returns:

the seed value

setDebug

public void setDebug(boolean debug)

sets whether or not debugging output shouild be printed

Overrides:

setDebug in class weka.classifiers.AbstractClassifier

Parameters:

debug - true if debugging output selected

getDebug

public boolean getDebug()

Returns whether or not debugging output shouild be printed

Overrides:

getDebug in class weka.classifiers.AbstractClassifier

Returns:

true if debuging output selected

main

public static void main(String[] argv)

Main method for running this class.

Parameters:

argv - the options