Class StackedPredictor
- 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.AbstractMultiLabelLearner
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- moa.classifiers.rules.multilabel.functions.StackedPredictor
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- All Implemented Interfaces:
Configurable
,Serializable
,CapabilitiesHandler
,Classifier
,MultiLabelLearner
,MultiTargetRegressor
,AMRulesFunction
,AWTRenderable
,Learner<Example<Instance>>
,MOAObject
,OptionHandler
public class StackedPredictor extends AbstractMultiLabelLearner implements MultiTargetRegressor, AMRulesFunction
- See Also:
- Serialized Form
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Field Summary
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Fields inherited from class moa.classifiers.AbstractClassifier
classifierRandom, modelContext, randomSeed, trainingWeightSeenByModel
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Fields inherited from class moa.options.AbstractOptionHandler
config
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Constructor Summary
Constructors Constructor Description StackedPredictor()
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description protected double[]
getDenormalizedOutput(double[] normOutputs)
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.protected double[]
getNormalizedInput(MultiLabelInstance instance)
protected double[]
getNormalizedOutput(MultiLabelInstance instance)
Prediction
getPredictionForInstance(MultiLabelInstance inst)
boolean
isRandomizable()
Gets whether this learner needs a random seed.void
resetLearningImpl()
Resets this classifier.void
resetWithMemory()
void
selectOutputsToLearn(int[] outputAtributtes)
void
trainOnInstanceImpl(MultiLabelInstance instance)
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Methods inherited from class moa.classifiers.AbstractMultiLabelLearner
getPredictionForInstance, getPredictionForInstance, getVotesForInstance, trainOnInstanceImpl
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Methods inherited from class moa.classifiers.AbstractClassifier
contextIsCompatible, copy, correctlyClassifies, defineImmutableCapabilities, getAttributeNameString, getAWTRenderer, getClassLabelString, getClassNameString, getDescription, getModel, getModelContext, getModelMeasurements, getNominalValueString, getPurposeString, getSubClassifiers, getSublearners, getVotesForInstance, modelAttIndexToInstanceAttIndex, modelAttIndexToInstanceAttIndex, prepareForUseImpl, resetLearning, setModelContext, setRandomSeed, 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.gui.AWTRenderable
getAWTRenderer
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Methods inherited from interface moa.capabilities.CapabilitiesHandler
getCapabilities
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Methods inherited from interface moa.classifiers.Classifier
copy, correctlyClassifies, getPredictionForInstance, getSubClassifiers, getVotesForInstance, trainOnInstance
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Methods inherited from interface moa.learners.Learner
getModel, getModelContext, getModelMeasurements, getPredictionForInstance, getSublearners, getVotesForInstance, resetLearning, setModelContext, setRandomSeed, trainingHasStarted, trainingWeightSeenByModel, trainOnInstance
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Methods inherited from interface moa.MOAObject
getDescription, measureByteSize
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Methods inherited from interface moa.options.OptionHandler
getCLICreationString, getOptions, getPurposeString, 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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learningRatio2ndLayerOption
public FloatOption learningRatio2ndLayerOption
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learningRateDecayOption
public FloatOption learningRateDecayOption
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skipStackingOption
public FlagOption skipStackingOption
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randomSeedOption
public IntOption randomSeedOption
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printWeightsOption
public FlagOption printWeightsOption
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Method Detail
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isRandomizable
public boolean isRandomizable()
Description copied from interface:Learner
Gets whether this learner needs a random seed. Examples of methods that needs a random seed are bagging and boosting.- Specified by:
isRandomizable
in interfaceLearner<Example<Instance>>
- Returns:
- true if the learner needs a random seed.
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resetWithMemory
public void resetWithMemory()
- Specified by:
resetWithMemory
in interfaceAMRulesFunction
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trainOnInstanceImpl
public void trainOnInstanceImpl(MultiLabelInstance instance)
- Specified by:
trainOnInstanceImpl
in interfaceMultiLabelLearner
- Specified by:
trainOnInstanceImpl
in classAbstractMultiLabelLearner
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getPredictionForInstance
public Prediction getPredictionForInstance(MultiLabelInstance inst)
- Specified by:
getPredictionForInstance
in interfaceMultiLabelLearner
- Specified by:
getPredictionForInstance
in classAbstractMultiLabelLearner
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resetLearningImpl
public void resetLearningImpl()
Description copied from class:AbstractClassifier
Resets this classifier. It must be similar to starting a new classifier from scratch.
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:
resetLearningImpl
in classAbstractClassifier
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getNormalizedInput
protected double[] getNormalizedInput(MultiLabelInstance instance)
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getNormalizedOutput
protected double[] getNormalizedOutput(MultiLabelInstance instance)
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getDenormalizedOutput
protected double[] getDenormalizedOutput(double[] normOutputs)
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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 classAbstractClassifier
- Returns:
- an array of measurements to be used in evaluation tasks
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getModelDescription
public void getModelDescription(StringBuilder out, int indent)
Description copied from class:AbstractClassifier
Returns a string representation of the model.- Specified by:
getModelDescription
in classAbstractClassifier
- Parameters:
out
- the stringbuilder to add the descriptionindent
- the number of characters to indent
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selectOutputsToLearn
public void selectOutputsToLearn(int[] outputAtributtes)
- Specified by:
selectOutputsToLearn
in interfaceAMRulesFunction
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