wGmdh
Class Msc

java.lang.Object
  weka.classifiers.Classifier
      wGmdh.Msc

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, weka.classifiers.IterativeClassifier, weka.core.CapabilitiesHandler, weka.core.Drawable, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler

Direct Known Subclasses:

AdditiveMsc

public class Msc
extends weka.classifiers.Classifier
implements weka.core.OptionHandler, weka.classifiers.IterativeClassifier, weka.core.Drawable, weka.core.TechnicalInformationHandler

It will randomize the provided dataset

See Also:

Serialized Form

Field Summary

MultiSelectCombi.ModelAndLayer	currentlyBestStructure
MultiSelectCombi	gmdhNet
int	iterations
TwoInputModel	modelToOutput
protected StructureLearningPerformance	structureLearningPerformance
protected StructureValidationPerformance	structureValidationPerformance

Fields inherited from class weka.classifiers.Classifier

m_Debug

Fields inherited from interface weka.core.Drawable

BayesNet, NOT_DRAWABLE, TREE

Constructor Summary

Msc()

Method Summary

void	buildClassifier(weka.core.Instances instances)
double	classifyInstance(weka.core.Instance instance) Classifies the given test instance.
java.lang.Object	clone()
java.lang.String	dataProviderTipText()
double[]	distributionForInstance(weka.core.Instance instance)
void	done()
protected TwoInputModel	getBest(int layerIndex)
weka.core.Capabilities	getCapabilities()
DatasetSupervised	getDataProvider()
int	getMaxLayers()
int	getNumberOfLayers() Attribute layer included in the count.
java.lang.String[]	getOptions() Gets the current settings of our Classifier.
long	getRandomSeed()
java.lang.String	getRevision() Returns the revision string.
NodeFilter	getSelector()
Measure	getStructureLearningPerformanceMeasure()
Measure	getStructureValidationPerformanceMeasure()
weka.core.TechnicalInformation	getTechnicalInformation()
boolean	getVisualize()
java.lang.String	globalInfo()
java.lang.String	graph()
int	graphType()
TwoInputModel	handleOutput(weka.core.Instances instances)
void	initClassifier(weka.core.Instances instances)
boolean	isRelearn()
java.util.Enumeration	listOptions() Returns an enumeration describing the available options.
java.lang.String	maxLayersTipText()
void	next(int nrIteration)
java.lang.String	randomSeedTipText()
java.lang.String	relearnTipText()
java.lang.String	selectorTipText()
void	setDataProvider(DatasetSupervised dataProvider)
void	setMaxLayers(int nrLayers)
void	setOptions(java.lang.String[] options) Parses a given list of options
void	setOutput(int layer, weka.core.Instances insts) Chooses the best model structure on layer, trains it on insts and prepares the classifier for evaluating it.
void	setRandomSeed(long randomSeed)
void	setRelearn(boolean relearn)
void	setSelector(NodeFilter selector)
void	setStructureLearningPerformanceMeasure(Measure m)
void	setStructureValidationPerformanceMeasure(Measure m)
void	setVisualize(boolean visualizeNet)
java.lang.String	structureLearningPerformanceMeasureTipText()
java.lang.String	structureValidationPerformanceMeasureTipText()
java.lang.String	toString()
java.lang.String	visualizeTipText()

Methods inherited from class weka.classifiers.Classifier

debugTipText, forName, getDebug, makeCopies, makeCopy, runClassifier, setDebug

Methods inherited from class java.lang.Object

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

Field Detail

gmdhNet

public MultiSelectCombi gmdhNet

currentlyBestStructure

public MultiSelectCombi.ModelAndLayer currentlyBestStructure

modelToOutput

public TwoInputModel modelToOutput

structureLearningPerformance

protected StructureLearningPerformance structureLearningPerformance

structureValidationPerformance

protected StructureValidationPerformance structureValidationPerformance

iterations

public int iterations

Constructor Detail

Msc

public Msc()

Method Detail

setStructureLearningPerformanceMeasure

public void setStructureLearningPerformanceMeasure(Measure m)

getStructureLearningPerformanceMeasure

public Measure getStructureLearningPerformanceMeasure()

setStructureValidationPerformanceMeasure

public void setStructureValidationPerformanceMeasure(Measure m)

getStructureValidationPerformanceMeasure

public Measure getStructureValidationPerformanceMeasure()

getNumberOfLayers

public int getNumberOfLayers()

Attribute layer included in the count.

Returns:

getBest

protected TwoInputModel getBest(int layerIndex)

Parameters:

layerIndex -

Returns:

best model on the layerIndex-th layer

getMaxLayers

public int getMaxLayers()

setMaxLayers

public void setMaxLayers(int nrLayers)

getVisualize

public boolean getVisualize()

setVisualize

public void setVisualize(boolean visualizeNet)

initClassifier

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

Specified by:

initClassifier in interface weka.classifiers.IterativeClassifier

Throws:

java.lang.Exception

next

public void next(int nrIteration)
          throws ExpressionEqualToZero,
                 TooBig,
                 TooSmall

Specified by:

next in interface weka.classifiers.IterativeClassifier

Throws:

ExpressionEqualToZero

TooBig

TooSmall

done

public void done()
          throws java.lang.Exception

Specified by:

done in interface weka.classifiers.IterativeClassifier

Throws:

java.lang.Exception

clone

public java.lang.Object clone()

Specified by:

clone in interface weka.classifiers.IterativeClassifier

Overrides:

clone in class java.lang.Object

buildClassifier

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

Specified by:

buildClassifier in class weka.classifiers.Classifier

Throws:

java.lang.Exception

handleOutput

public TwoInputModel handleOutput(weka.core.Instances instances)
                           throws java.lang.Exception

Throws:

java.lang.Exception

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface weka.core.RevisionHandler

Returns:

the revision

getCapabilities

public weka.core.Capabilities getCapabilities()

Specified by:

getCapabilities in interface weka.core.CapabilitiesHandler

Overrides:

getCapabilities in class weka.classifiers.Classifier

classifyInstance

public double classifyInstance(weka.core.Instance instance)
                        throws java.lang.Exception

Classifies the given test instance. The instance has to belong to a dataset when it's being classified.

Overrides:

classifyInstance in class weka.classifiers.Classifier

Parameters:

instance - the instance to be classified

Returns:

the predicted most likely class for the instance or Instance.missingValue() if no prediction is made

Throws:

java.lang.Exception - if an error occurred during the prediction

distributionForInstance

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

Overrides:

distributionForInstance in class weka.classifiers.Classifier

Throws:

java.lang.Exception

globalInfo

public java.lang.String globalInfo()

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface weka.core.OptionHandler

Overrides:

listOptions in class weka.classifiers.Classifier

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. - take a peek in listOptions for valid options. Default parameters can be found here and are set if not provided or valid.

Specified by:

setOptions in interface weka.core.OptionHandler

Overrides:

setOptions in class weka.classifiers.Classifier

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 our Classifier.

Specified by:

getOptions in interface weka.core.OptionHandler

Overrides:

getOptions in class weka.classifiers.Classifier

Returns:

an array of strings suitable for passing to setOptions

structureLearningPerformanceMeasureTipText

public java.lang.String structureLearningPerformanceMeasureTipText()

structureValidationPerformanceMeasureTipText

public java.lang.String structureValidationPerformanceMeasureTipText()

maxLayersTipText

public java.lang.String maxLayersTipText()

visualizeTipText

public java.lang.String visualizeTipText()

randomSeedTipText

public java.lang.String randomSeedTipText()

relearnTipText

public java.lang.String relearnTipText()

selectorTipText

public java.lang.String selectorTipText()

dataProviderTipText

public java.lang.String dataProviderTipText()

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object

Returns:

string that describes the model.

graphType

public int graphType()

Specified by:

graphType in interface weka.core.Drawable

graph

public java.lang.String graph()
                       throws java.lang.Exception

Specified by:

graph in interface weka.core.Drawable

Throws:

java.lang.Exception

getTechnicalInformation

public weka.core.TechnicalInformation getTechnicalInformation()

Specified by:

getTechnicalInformation in interface weka.core.TechnicalInformationHandler

setOutput

public void setOutput(int layer,
                      weka.core.Instances insts)

Chooses the best model structure on layer, trains it on insts and prepares the classifier for evaluating it.

Parameters:

layer -

getSelector

public NodeFilter getSelector()

Returns:

the selector

setSelector

public void setSelector(NodeFilter selector)

Parameters:

selector - the selector to set

getRandomSeed

public long getRandomSeed()

Returns:

the randomSeed

setRandomSeed

public void setRandomSeed(long randomSeed)

Parameters:

randomSeed - the randomSeed to set

getDataProvider

public DatasetSupervised getDataProvider()

Returns:

the dataProvider

setDataProvider

public void setDataProvider(DatasetSupervised dataProvider)

Parameters:

dataProvider - the dataProvider to set

isRelearn

public boolean isRelearn()

Returns:

the relearn

setRelearn

public void setRelearn(boolean relearn)

Parameters:

relearn - the relearn to set