weka.classifiers.neural.lvq
Class AlgorithmAncestor

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

All Implemented Interfaces:

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

Direct Known Subclasses:

LvqAlgorithmAncestor, Som

public abstract class AlgorithmAncestor
extends weka.classifiers.Classifier
implements weka.core.OptionHandler, weka.core.WeightedInstancesHandler

Date: 25/05/2004 File: AlgorithmAncestor.java

Author:

Jason Brownlee

See Also:

Serialized Form

Constructor Summary

AlgorithmAncestor()

Method Summary

void	addEpochEventListener(EpochEventListener aListener)
void	buildClassifier(weka.core.Instances instances) Build a model of the provided training dataset using the specific LVQ algorithm implementation.
double	calculateQuantisationError(weka.core.Instances instances)
double[]	distributionForInstance(weka.core.Instance instance) Calcualte the class distribution for the provided instance
weka.core.SelectedTag	getInitialisationMode() Return the initialisation mode
CommonModel	getModel()
long	getSeed()
abstract int	getTotalCodebookVectors()
int	getTotalTrainingBmuHits()
int[][]	getTrainingBmuUsage()
boolean	getUseVoting()
abstract java.lang.String	globalInfo()
java.lang.String	prepareBuildTimeReport()
java.lang.String	prepareClassDistributionReport(java.lang.String aHeader) Responsible for calculating the class distribution of nodes in the provided model
java.lang.String	prepareCodebookVectorReport()
java.lang.String	prepareIndividualClassDistributionReport()
java.lang.String	prepareTrainingBMUReport()
java.lang.String	quantisationErrorReport()
void	setInitialisationMode(weka.core.SelectedTag s) Set the initialisation mode
void	setPreInitialisedModel(CommonModel aModel)
void	setPrepareBmuStatistis(boolean b)
void	setSeed(long l)
void	setUseVoting(boolean b)
java.lang.String	toString()

Methods inherited from class weka.classifiers.Classifier

classifyInstance, debugTipText, forName, getDebug, getOptions, listOptions, makeCopies, makeCopy, setDebug, setOptions

Methods inherited from class java.lang.Object

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

Methods inherited from interface weka.core.OptionHandler

getOptions, listOptions, setOptions

Constructor Detail

AlgorithmAncestor

public AlgorithmAncestor()

Method Detail

globalInfo

public abstract java.lang.String globalInfo()

getTotalCodebookVectors

public abstract int getTotalCodebookVectors()

addEpochEventListener

public void addEpochEventListener(EpochEventListener aListener)

buildClassifier

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

Build a model of the provided training dataset using the specific LVQ algorithm implementation. The model is constructed (if not already provided), it is initialised, then the model is trained (constructed) using the specific implementation of the LVQ algorithm by calling prepareLVQClassifier()

Specified by:

buildClassifier in class weka.classifiers.Classifier

Parameters:

instances - - training dataset.

Throws:

java.lang.Exception

setPreInitialisedModel

public void setPreInitialisedModel(CommonModel aModel)

getModel

public CommonModel getModel()

distributionForInstance

public double[] distributionForInstance(weka.core.Instance instance)
                                 throws java.lang.Exception

Calcualte the class distribution for the provided instance

Overrides:

distributionForInstance in class weka.classifiers.Classifier

Parameters:

instance - - an instance to calculate the class distribution for

Returns:

double [] - class distribution for instance

Throws:

java.lang.Exception

prepareClassDistributionReport

public java.lang.String prepareClassDistributionReport(java.lang.String aHeader)

Responsible for calculating the class distribution of nodes in the provided model

Parameters:

context - - context of the distribution (descriptiuon)

distribution - - a calculated distribution of each class

aModel - - model to evaluate

Returns:

prepareIndividualClassDistributionReport

public java.lang.String prepareIndividualClassDistributionReport()

prepareTrainingBMUReport

public java.lang.String prepareTrainingBMUReport()

prepareCodebookVectorReport

public java.lang.String prepareCodebookVectorReport()

prepareBuildTimeReport

public java.lang.String prepareBuildTimeReport()

quantisationErrorReport

public java.lang.String quantisationErrorReport()

calculateQuantisationError

public double calculateQuantisationError(weka.core.Instances instances)

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object

setInitialisationMode

public void setInitialisationMode(weka.core.SelectedTag s)

Set the initialisation mode

Parameters:

s -

getInitialisationMode

public weka.core.SelectedTag getInitialisationMode()

Return the initialisation mode

Returns:

getTotalTrainingBmuHits

public int getTotalTrainingBmuHits()

Returns:

getTrainingBmuUsage

public int[][] getTrainingBmuUsage()

Returns:

setSeed

public void setSeed(long l)

Parameters:

l -

getSeed

public long getSeed()

Returns:

getUseVoting

public boolean getUseVoting()

Returns:

setUseVoting

public void setUseVoting(boolean b)

Parameters:

b -

setPrepareBmuStatistis

public void setPrepareBmuStatistis(boolean b)

Parameters:

b -