Package moa.classifiers.rules
Class AMRulesRegressorOld
- java.lang.Object
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- moa.AbstractMOAObject
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- moa.options.AbstractOptionHandler
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- moa.classifiers.AbstractClassifier
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- moa.classifiers.rules.AbstractAMRules
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- moa.classifiers.rules.AMRulesRegressorOld
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- All Implemented Interfaces:
Configurable
,Serializable
,CapabilitiesHandler
,Classifier
,Regressor
,AWTRenderable
,Learner<Example<Instance>>
,MOAObject
,OptionHandler
public class AMRulesRegressorOld extends AbstractAMRules implements Regressor
- See Also:
- Serialized Form
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Field Summary
Fields Modifier and Type Field Description FlagOption
constantLearningRatioDecayOption
FloatOption
learningRatioOption
MultiChoiceOption
predictionFunctionOption
ClassOption
splitCriterionOption
ClassOption
votingTypeOption
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Fields inherited from class moa.classifiers.rules.AbstractAMRules
anomalyNumInstThresholdOption, attributesPercentage, defaultRule, DriftDetectionOption, gracePeriodOption, multivariateAnomalyProbabilityThresholdOption, noAnomalyDetectionOption, NORMAL_CONSTANT, numericObserverOption, pageHinckleyAlphaOption, pageHinckleyThresholdOption, ruleNumberID, ruleSet, splitConfidenceOption, statistics, tieThresholdOption, univariateAnomalyprobabilityThresholdOption, unorderedRulesOption, VerbosityOption
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Fields inherited from class moa.classifiers.AbstractClassifier
classifierRandom, modelContext, randomSeed, randomSeedOption, trainingWeightSeenByModel
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Fields inherited from class moa.options.AbstractOptionHandler
config
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Constructor Summary
Constructors Constructor Description AMRulesRegressorOld()
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description void
getModelDescription(StringBuilder out, int indent)
print GUI learn modelboolean
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, getAttributesPercentage, getModelAttIndexToInstanceAttIndex, getModelMeasurementsImpl, getVotes, getVotesForInstance, modelAttIndexToInstanceAttIndex, PrintRuleSet, setAttributesPercentage, setRandomSeed, trainOnInstanceImpl, VerboseToConsole
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Methods inherited from class moa.classifiers.AbstractClassifier
contextIsCompatible, copy, correctlyClassifies, defineImmutableCapabilities, getAttributeNameString, getAWTRenderer, getClassLabelString, getClassNameString, getDescription, getModel, getModelContext, getModelMeasurements, getNominalValueString, getPredictionForInstance, getPredictionForInstance, getPurposeString, getSubClassifiers, getSublearners, getVotesForInstance, modelAttIndexToInstanceAttIndex, prepareForUseImpl, resetLearning, setModelContext, trainingHasStarted, trainingWeightSeenByModel, trainOnInstance, trainOnInstance
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Methods inherited from class moa.options.AbstractOptionHandler
getCLICreationString, getOptions, getPreparedClassOption, prepareClassOptions, prepareForUse, prepareForUse
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Methods inherited from class moa.AbstractMOAObject
copy, measureByteSize, measureByteSize, toString
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Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
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Methods inherited from interface moa.capabilities.CapabilitiesHandler
getCapabilities
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Methods inherited from interface moa.MOAObject
measureByteSize
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Methods inherited from interface moa.options.OptionHandler
getCLICreationString, getOptions, prepareForUse, prepareForUse
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Field Detail
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constantLearningRatioDecayOption
public FlagOption constantLearningRatioDecayOption
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learningRatioOption
public FloatOption learningRatioOption
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predictionFunctionOption
public MultiChoiceOption predictionFunctionOption
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votingTypeOption
public ClassOption votingTypeOption
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splitCriterionOption
public ClassOption splitCriterionOption
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Method Detail
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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 classAbstractAMRules
- Returns:
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newRuleActiveLearningNode
public RuleActiveLearningNode newRuleActiveLearningNode(Rule.Builder builder)
- Specified by:
newRuleActiveLearningNode
in classAbstractAMRules
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newRuleActiveLearningNode
public RuleActiveLearningNode newRuleActiveLearningNode(double[] initialClassObservations)
- Specified by:
newRuleActiveLearningNode
in classAbstractAMRules
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getModelDescription
public void getModelDescription(StringBuilder out, int indent)
Description copied from class:AbstractAMRules
print GUI learn model- Overrides:
getModelDescription
in classAbstractAMRules
- Parameters:
out
- the stringbuilder to add the descriptionindent
- the number of characters to indent
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resetLearningImpl
public void resetLearningImpl()
This method initializes and resets the algorithm.- Overrides:
resetLearningImpl
in classAbstractAMRules
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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 interfaceLearner<Example<Instance>>
- Specified by:
isRandomizable
in classAbstractAMRules
- Returns:
- true if the learner needs a random seed.
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newErrorWeightedVote
public ErrorWeightedVote newErrorWeightedVote()
- Specified by:
newErrorWeightedVote
in classAbstractAMRules
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