moa.classifiers.rules.functions

Class Perceptron

java.lang.Object

moa.AbstractMOAObject

moa.options.AbstractOptionHandler

moa.classifiers.AbstractClassifier

moa.classifiers.rules.functions.Perceptron

All Implemented Interfaces:

Serializable, Classifier, Regressor, AWTRenderable, MOAObject, OptionHandler

public class Perceptron
extends AbstractClassifier
implements Regressor

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected double	accumulatedError
FlagOption	constantLearningRatioDecayOption
protected double	fadingFactor
FloatOption	fadingFactorOption
protected boolean	initialisePerceptron
protected double	learningRateDecay
FloatOption	learningRateDecayOption
protected double	learningRatio
FloatOption	learningRatioOption
DoubleVector	perceptronattributeStatistics
protected int	perceptronInstancesSeen
protected double	perceptronsumY
protected int	perceptronYSeen
DoubleVector	squaredperceptronattributeStatistics
protected double	squaredperceptronsumY
protected double[]	weightAttribute

Fields inherited from class moa.classifiers.AbstractClassifier

classifierRandom, modelContext, randomSeed, randomSeedOption, trainingWeightSeenByModel

Fields inherited from class moa.options.AbstractOptionHandler

classOptionNamesToPreparedObjects, options

Constructor Summary

Constructors

Constructor and Description
Perceptron()
Perceptron(Perceptron p)

Method Summary

Methods

Modifier and Type	Method and Description
double	computeSD(double squaredVal, double val, int size)
double	getCurrentError()
int	getInstancesSeen()
void	getModelDescription(StringBuilder out, int indent) Returns a string representation of the model.
protected Measurement[]	getModelMeasurementsImpl() Gets the current measurements of this classifier. The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods.
double[]	getVotesForInstance(weka.core.Instance inst) Predicts the class memberships for a given instance.
double[]	getWeights()
boolean	isRandomizable() Gets whether this classifier needs a random seed.
double[]	normalizedInstance(weka.core.Instance inst)
double	normalizedPrediction(weka.core.Instance inst)
void	normalizeWeights()
double	prediction(double[] instanceValues)
void	reset()
void	resetError()
void	resetLearningImpl() A method to reset the model
void	setInstancesSeen(int pInstancesSeen)
void	setLearningRatio(double learningRatio)
void	setWeights(double[] w)
void	trainOnInstanceImpl(weka.core.Instance inst) Update the model using the provided instance
double	updateWeights(weka.core.Instance inst, double learningRatio)

Methods inherited from class moa.classifiers.AbstractClassifier

contextIsCompatible, copy, correctlyClassifies, getAttributeNameString, getAWTRenderer, getClassLabelString, getClassNameString, getDescription, getModelContext, getModelMeasurements, getNominalValueString, getPurposeString, getSubClassifiers, modelAttIndexToInstanceAttIndex, modelAttIndexToInstanceAttIndex, prepareForUseImpl, resetLearning, setModelContext, setRandomSeed, trainingHasStarted, trainingWeightSeenByModel, trainOnInstance

Methods inherited from class moa.options.AbstractOptionHandler

discoverOptionsViaReflection, getCLICreationString, getOptions, getPreparedClassOption, getPreparedClassOption, prepareClassOptions, prepareForUse, prepareForUse

Methods inherited from class moa.AbstractMOAObject

copy, measureByteSize, measureByteSize, toString

Methods inherited from class java.lang.Object

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

Methods inherited from interface moa.options.OptionHandler

getCLICreationString, getOptions, prepareForUse, prepareForUse

Methods inherited from interface moa.MOAObject

measureByteSize

Field Detail

constantLearningRatioDecayOption

public FlagOption constantLearningRatioDecayOption

learningRatioOption

public FloatOption learningRatioOption

learningRateDecayOption

public FloatOption learningRateDecayOption

fadingFactorOption

public FloatOption fadingFactorOption

fadingFactor

protected double fadingFactor

learningRatio

protected double learningRatio

learningRateDecay

protected double learningRateDecay

weightAttribute

protected double[] weightAttribute

perceptronattributeStatistics

public DoubleVector perceptronattributeStatistics

squaredperceptronattributeStatistics

public DoubleVector squaredperceptronattributeStatistics

perceptronInstancesSeen

protected int perceptronInstancesSeen

perceptronYSeen

protected int perceptronYSeen

accumulatedError

protected double accumulatedError

initialisePerceptron

protected boolean initialisePerceptron

perceptronsumY

protected double perceptronsumY

squaredperceptronsumY

protected double squaredperceptronsumY

Constructor Detail

Perceptron

public Perceptron()

Perceptron

public Perceptron(Perceptron p)

Method Detail

setWeights

public void setWeights(double[] w)

getWeights

public double[] getWeights()

getInstancesSeen

public int getInstancesSeen()

setInstancesSeen

public void setInstancesSeen(int pInstancesSeen)

resetLearningImpl

public void resetLearningImpl()

A method to reset the model

Specified by:

resetLearningImpl in class AbstractClassifier

reset

public void reset()

resetError

public void resetError()

trainOnInstanceImpl

public void trainOnInstanceImpl(weka.core.Instance inst)

Update the model using the provided instance

Specified by:

trainOnInstanceImpl in class AbstractClassifier

Parameters:

inst - the instance to be used for training

normalizedPrediction

public double normalizedPrediction(weka.core.Instance inst)

prediction

public double prediction(double[] instanceValues)

normalizedInstance

public double[] normalizedInstance(weka.core.Instance inst)

computeSD

public double computeSD(double squaredVal,
               double val,
               int size)

updateWeights

public double updateWeights(weka.core.Instance inst,
                   double learningRatio)

normalizeWeights

public void normalizeWeights()

isRandomizable

public boolean isRandomizable()

Description copied from interface: Classifier

Gets whether this classifier needs a random seed. Examples of methods that needs a random seed are bagging and boosting.

Specified by:

isRandomizable in interface Classifier

Returns:

true if the classifier needs a random seed.

getVotesForInstance

public double[] getVotesForInstance(weka.core.Instance inst)

Description copied from interface: Classifier

Predicts the class memberships for a given instance. If an instance is unclassified, the returned array elements must be all zero.

Specified by:

getVotesForInstance in interface Classifier

Parameters:

inst - the instance to be classified

Returns:

an array containing the estimated membership probabilities of the test instance in each class

getModelMeasurementsImpl

protected Measurement[] getModelMeasurementsImpl()

Description copied from class: AbstractClassifier

Gets the current measurements of this classifier.

The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods. Note that this will produce compiler errors if not overridden.

Specified by:

getModelMeasurementsImpl in class AbstractClassifier

Returns:

an array of measurements to be used in evaluation tasks

getModelDescription

public void getModelDescription(StringBuilder out,
                       int indent)

Description copied from class: AbstractClassifier

Returns a string representation of the model.

Specified by:

getModelDescription in class AbstractClassifier

Parameters:

out - the stringbuilder to add the description

indent - the number of characters to indent

setLearningRatio

public void setLearningRatio(double learningRatio)

getCurrentError

public double getCurrentError()