weka.classifiers.neural.lvq
Class LvqAlgorithmAncestor

java.lang.Object
  weka.classifiers.Classifier
      weka.classifiers.neural.lvq.AlgorithmAncestor
          weka.classifiers.neural.lvq.LvqAlgorithmAncestor

All Implemented Interfaces:

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

Direct Known Subclasses:

Lvq1, Lvq2_1, Lvq3, Olvq1

public abstract class LvqAlgorithmAncestor
extends AlgorithmAncestor

Description: Represents a common ancestor for specific LVQ algorithm implementations. Provides common functionality shared between all LVQ implementations. Provides a framwork to be implemented by specific LVQ implementations for consistent validation, model construction and instance classification.
Copyright (c) Jason Brownlee 2004

Author:

Jason Brownlee

See Also:

Serialized Form

Constructor Summary

LvqAlgorithmAncestor()

Method Summary

weka.core.SelectedTag	getLearningFunction() Return the learning function
double	getLearningRate() Return the learning rate
java.lang.String[]	getOptions() Returns a list of all common and specific algorithm options
int	getTotalCodebookVectors()
int	getTotalTrainingIterations() Return total training iterations
java.lang.String	initialisationModeTipText() Initialisation mode tip
java.lang.String	learningFunctionTipText() Learning function tip
java.lang.String	learningRateTipText() Learning rate tip
java.util.Enumeration	listOptions() Provides a list of common algorithm options, as well as specific options
java.lang.String	randomSeedTipText() Random number seed
void	setLearningFunction(weka.core.SelectedTag l) Set learning functiom
void	setLearningRate(double r) Set the learning rate
void	setOptions(java.lang.String[] options) Set algorithm options, common and specific
void	setTotalCodebookVectors(int i)
void	setTotalTrainingIterations(int t) Set total training iterations
java.lang.String	totalCodebookVectorsTipText() Codebook vectors tip
java.lang.String	totalTrainingIterationsTipText() Training iterations tip
java.lang.String	useVotingTipText()

Methods inherited from class weka.classifiers.neural.lvq.AlgorithmAncestor

addEpochEventListener, buildClassifier, calculateQuantisationError, distributionForInstance, getInitialisationMode, getModel, getSeed, getTotalTrainingBmuHits, getTrainingBmuUsage, getUseVoting, globalInfo, prepareBuildTimeReport, prepareClassDistributionReport, prepareCodebookVectorReport, prepareIndividualClassDistributionReport, prepareTrainingBMUReport, quantisationErrorReport, setInitialisationMode, setPreInitialisedModel, setPrepareBmuStatistis, setSeed, setUseVoting, toString

Methods inherited from class weka.classifiers.Classifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug

Methods inherited from class java.lang.Object

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

Constructor Detail

LvqAlgorithmAncestor

public LvqAlgorithmAncestor()

Method Detail

listOptions

public java.util.Enumeration listOptions()

Provides a list of common algorithm options, as well as specific options

Specified by:

listOptions in interface weka.core.OptionHandler

Overrides:

listOptions in class weka.classifiers.Classifier

Returns:

Enumeration

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Set algorithm options, common and specific

Specified by:

setOptions in interface weka.core.OptionHandler

Overrides:

setOptions in class weka.classifiers.Classifier

Parameters:

options - - list of options

Throws:

java.lang.Exception

getOptions

public java.lang.String[] getOptions()

Returns a list of all common and specific algorithm options

Specified by:

getOptions in interface weka.core.OptionHandler

Overrides:

getOptions in class weka.classifiers.Classifier

initialisationModeTipText

public java.lang.String initialisationModeTipText()

Initialisation mode tip

Returns:

totalCodebookVectorsTipText

public java.lang.String totalCodebookVectorsTipText()

Codebook vectors tip

Returns:

totalTrainingIterationsTipText

public java.lang.String totalTrainingIterationsTipText()

Training iterations tip

Returns:

learningFunctionTipText

public java.lang.String learningFunctionTipText()

Learning function tip

Returns:

learningRateTipText

public java.lang.String learningRateTipText()

Learning rate tip

Returns:

randomSeedTipText

public java.lang.String randomSeedTipText()

Random number seed

Returns:

useVotingTipText

public java.lang.String useVotingTipText()

setTotalTrainingIterations

public void setTotalTrainingIterations(int t)

Set total training iterations

Parameters:

t -

getTotalTrainingIterations

public int getTotalTrainingIterations()

Return total training iterations

Returns:

setLearningFunction

public void setLearningFunction(weka.core.SelectedTag l)

Set learning functiom

Parameters:

l -

getLearningFunction

public weka.core.SelectedTag getLearningFunction()

Return the learning function

Returns:

setLearningRate

public void setLearningRate(double r)

Set the learning rate

Parameters:

r -

getLearningRate

public double getLearningRate()

Return the learning rate

Returns:

getTotalCodebookVectors

public int getTotalCodebookVectors()

Specified by:

getTotalCodebookVectors in class AlgorithmAncestor

Returns:

setTotalCodebookVectors

public void setTotalCodebookVectors(int i)

Parameters:

i -