moa.classifiers.rules

Class AMRulesRegressor

java.lang.Object

moa.AbstractMOAObject

moa.options.AbstractOptionHandler

moa.classifiers.AbstractClassifier

moa.classifiers.rules.AbstractAMRules

moa.classifiers.rules.AMRulesRegressor

All Implemented Interfaces:

Serializable, Classifier, Regressor, AWTRenderable, MOAObject, OptionHandler

public class AMRulesRegressor
extends AbstractAMRules
implements Regressor

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
FlagOption	constantLearningRatioDecayOption
FloatOption	learningRatioOption
MultiChoiceOption	predictionFunctionOption
ClassOption	votingTypeOption

Fields inherited from class moa.classifiers.rules.AbstractAMRules

anomalyNumInstThresholdOption, defaultRule, DriftDetectionOption, gracePeriodOption, multivariateAnomalyProbabilityThresholdOption, noAnomalyDetectionOption, NORMAL_CONSTANT, numericObserverOption, pageHinckleyAlphaOption, pageHinckleyThresholdOption, ruleNumberID, ruleSet, splitConfidenceOption, statistics, tieThresholdOption, univariateAnomalyprobabilityThresholdOption, unorderedRulesOption, VerbosityOption

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
AMRulesRegressor()

Method Summary

Methods

Modifier and Type	Method and Description
void	getModelDescription(StringBuilder out, int indent) print GUI learn model
boolean	isRandomizable() description of the Methods used.
ErrorWeightedVote	newErrorWeightedVote()
protected Rule	newRule(int ID, RuleActiveLearningNode node, double[] statistics) Rule.Builder() to build an object with the parameters.
RuleActiveLearningNode	newRuleActiveLearningNode(double[] initialClassObservations)
RuleActiveLearningNode	newRuleActiveLearningNode(Rule.Builder builder)
void	resetLearningImpl() This method initializes and resets the algorithm.

Methods inherited from class moa.classifiers.rules.AbstractAMRules

debug, getModelAttIndexToInstanceAttIndex, getModelMeasurementsImpl, getVotesForInstance, modelAttIndexToInstanceAttIndex, PrintRuleSet, trainOnInstanceImpl, VerboseToConsole

Methods inherited from class moa.classifiers.AbstractClassifier

contextIsCompatible, copy, correctlyClassifies, getAttributeNameString, getAWTRenderer, getClassLabelString, getClassNameString, getDescription, getModelContext, getModelMeasurements, getNominalValueString, getPurposeString, getSubClassifiers, 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

predictionFunctionOption

public MultiChoiceOption predictionFunctionOption

votingTypeOption

public ClassOption votingTypeOption

Constructor Detail

AMRulesRegressor

public AMRulesRegressor()

Method Detail

newRule

protected Rule newRule(int ID,
           RuleActiveLearningNode node,
           double[] statistics)

Description copied from class: AbstractAMRules

Rule.Builder() to build an object with the parameters. If you have an algorithm with many parameters, especially if some of them are optional, it can be beneficial to define an object that represents all of the parameters.

Specified by:

newRule in class AbstractAMRules

Returns:

newRuleActiveLearningNode

public RuleActiveLearningNode newRuleActiveLearningNode(Rule.Builder builder)

Specified by:

newRuleActiveLearningNode in class AbstractAMRules

newRuleActiveLearningNode

public RuleActiveLearningNode newRuleActiveLearningNode(double[] initialClassObservations)

Specified by:

newRuleActiveLearningNode in class AbstractAMRules

getModelDescription

public void getModelDescription(StringBuilder out,
                       int indent)

Description copied from class: AbstractAMRules

print GUI learn model

Overrides:

getModelDescription in class AbstractAMRules

Parameters:

out - the stringbuilder to add the description

indent - the number of characters to indent

resetLearningImpl

public void resetLearningImpl()

This method initializes and resets the algorithm.

Overrides:

resetLearningImpl in class AbstractAMRules

isRandomizable

public boolean isRandomizable()

Description copied from class: AbstractAMRules

description of the Methods used. isRandomizable resetLearningImpl newRule // to build an object with the parameters. trainOnInstanceImpl isAnomaly getVotesForInstance getModelMeasurementsImpl getModelDescription // to printout to MOA GUI debug // use debug('string') to printout to console

Specified by:

isRandomizable in interface Classifier

Specified by:

isRandomizable in class AbstractAMRules

Returns:

true if the classifier needs a random seed.

newErrorWeightedVote

public ErrorWeightedVote newErrorWeightedVote()

Specified by:

newErrorWeightedVote in class AbstractAMRules