weka.classifiers.neural.lvq
Class Som

java.lang.Object
  extended by weka.classifiers.Classifier
      extended by weka.classifiers.neural.lvq.AlgorithmAncestor
          extended by weka.classifiers.neural.lvq.Som
All Implemented Interfaces:
java.io.Serializable, java.lang.Cloneable, weka.core.OptionHandler, weka.core.WeightedInstancesHandler

public class Som
extends AlgorithmAncestor

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

Author:
Jason Brownlee
See Also:
Serialized Form

Constructor Summary
Som()
           
 
Method Summary
 double[] distributionForInstance(weka.core.Instance instance)
          Overriden
 weka.core.SelectedTag getLearningFunction()
          Return the learning function
 double getLearningRate()
           
 int getMapHeight()
           
 int getMapWidth()
           
 weka.core.SelectedTag getNeighbourhoodFunction()
           
 int getNeighbourhoodSize()
           
 java.lang.String[] getOptions()
           
 weka.core.SelectedTag getTopology()
           
 int getTotalCodebookVectors()
           
 int getTrainingIterations()
           
 java.lang.String globalInfo()
           
 java.lang.String initialisationModeTipText()
           
 boolean isSupervised()
           
 java.lang.String learningFunctionTipText()
           
 java.util.Enumeration listOptions()
           
static void main(java.lang.String[] args)
          Entry point into the algorithm for direct usage
 java.lang.String mapHeightTipText()
           
 java.lang.String mapWidthTipText()
           
 java.lang.String neighbourhoodFunctionTipText()
           
 java.lang.String neighbourhoodSizeTipText()
           
 java.lang.String seedTipText()
           
 void setLearningFunction(weka.core.SelectedTag l)
          Set learning functiom
 void setLearningRate(double d)
           
 void setMapHeight(int i)
           
 void setMapWidth(int i)
           
 void setNeighbourhoodFunction(weka.core.SelectedTag s)
           
 void setNeighbourhoodSize(int i)
           
 void setOptions(java.lang.String[] options)
           
 void setSupervised(boolean b)
           
 void setTopology(weka.core.SelectedTag s)
           
 void setTrainingIterations(int i)
           
 java.lang.String supervisedTipText()
           
 java.lang.String topologyTipText()
           
 java.lang.String trainingIterationsTipText()
           
 java.lang.String useVotingTipText()
           
 
Methods inherited from class weka.classifiers.neural.lvq.AlgorithmAncestor
addEpochEventListener, buildClassifier, calculateQuantisationError, getInitialisationMode, getModel, getSeed, getTotalTrainingBmuHits, getTrainingBmuUsage, getUseVoting, 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

Som

public Som()
Method Detail

distributionForInstance

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

Overrides:
distributionForInstance in class AlgorithmAncestor
Parameters:
instance - - an instance to calculate the class distribution for
Returns:
double [] - class distribution for instance
Throws:
java.lang.Exception

getTotalCodebookVectors

public int getTotalCodebookVectors()
Specified by:
getTotalCodebookVectors in class AlgorithmAncestor

listOptions

public java.util.Enumeration listOptions()
Specified by:
listOptions in interface weka.core.OptionHandler
Overrides:
listOptions in class weka.classifiers.Classifier

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception
Specified by:
setOptions in interface weka.core.OptionHandler
Overrides:
setOptions in class weka.classifiers.Classifier
Throws:
java.lang.Exception

getOptions

public java.lang.String[] getOptions()
Specified by:
getOptions in interface weka.core.OptionHandler
Overrides:
getOptions in class weka.classifiers.Classifier

globalInfo

public java.lang.String globalInfo()
Specified by:
globalInfo in class AlgorithmAncestor

main

public static void main(java.lang.String[] args)
Entry point into the algorithm for direct usage

Parameters:
args -

mapWidthTipText

public java.lang.String mapWidthTipText()

mapHeightTipText

public java.lang.String mapHeightTipText()

topologyTipText

public java.lang.String topologyTipText()

initialisationModeTipText

public java.lang.String initialisationModeTipText()

neighbourhoodFunctionTipText

public java.lang.String neighbourhoodFunctionTipText()

learningFunctionTipText

public java.lang.String learningFunctionTipText()

neighbourhoodSizeTipText

public java.lang.String neighbourhoodSizeTipText()

trainingIterationsTipText

public java.lang.String trainingIterationsTipText()

supervisedTipText

public java.lang.String supervisedTipText()

seedTipText

public java.lang.String seedTipText()

useVotingTipText

public java.lang.String useVotingTipText()

getLearningRate

public double getLearningRate()
Returns:

getMapHeight

public int getMapHeight()
Returns:

getMapWidth

public int getMapWidth()
Returns:

getNeighbourhoodSize

public int getNeighbourhoodSize()
Returns:

isSupervised

public boolean isSupervised()
Returns:

getTrainingIterations

public int getTrainingIterations()
Returns:

setLearningRate

public void setLearningRate(double d)
Parameters:
d -

setMapHeight

public void setMapHeight(int i)
Parameters:
i -

setMapWidth

public void setMapWidth(int i)
Parameters:
i -

setNeighbourhoodSize

public void setNeighbourhoodSize(int i)
Parameters:
i -

setSupervised

public void setSupervised(boolean b)
Parameters:
b -

setTrainingIterations

public void setTrainingIterations(int i)
Parameters:
i -

setLearningFunction

public void setLearningFunction(weka.core.SelectedTag l)
Set learning functiom

Parameters:
l -

getLearningFunction

public weka.core.SelectedTag getLearningFunction()
Return the learning function

Returns:

setTopology

public void setTopology(weka.core.SelectedTag s)

getTopology

public weka.core.SelectedTag getTopology()

setNeighbourhoodFunction

public void setNeighbourhoodFunction(weka.core.SelectedTag s)

getNeighbourhoodFunction

public weka.core.SelectedTag getNeighbourhoodFunction()