Package moa.classifiers.meta
Class OzaBoostAdwin
- 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.meta.OzaBoostAdwin
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- All Implemented Interfaces:
Configurable
,Serializable
,CapabilitiesHandler
,Classifier
,MultiClassClassifier
,AWTRenderable
,Learner<Example<Instance>>
,MOAObject
,OptionHandler
public class OzaBoostAdwin extends AbstractClassifier implements MultiClassClassifier
Boosting for evolving data streams using ADWIN.- Version:
- $Revision: 7 $
- Author:
- Albert Bifet (abifet at cs dot waikato dot ac dot nz)
- See Also:
- Serialized Form
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Field Summary
Fields Modifier and Type Field Description protected ADWIN[]
ADError
ClassOption
baseLearnerOption
FloatOption
deltaAdwinOption
protected Classifier[]
ensemble
IntOption
ensembleSizeOption
protected boolean
initKm1
protected boolean
initMatrixCodes
protected int
Km1
protected double
logKm1
protected int[][]
matrixCodes
protected int
numberOfChangesDetected
FlagOption
outputCodesOption
FlagOption
pureBoostOption
FlagOption
sammeOption
protected double[]
scms
protected double[]
swms
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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 OzaBoostAdwin()
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description protected double
getEnsembleMemberWeight(int i)
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.String
getPurposeString()
Dictionary with option texts and objectsClassifier[]
getSubClassifiers()
Gets the classifiers of this ensemble.double[]
getVotesForInstance(Instance inst)
Predicts the class memberships for a given instance.double[]
getVotesForInstanceBinary(Instance inst)
boolean
isRandomizable()
Gets whether this learner needs a random seed.void
resetLearningImpl()
Resets this classifier.void
trainOnInstanceImpl(Instance inst)
Trains this classifier incrementally using the given instance.
The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods.-
Methods inherited from class moa.classifiers.AbstractClassifier
contextIsCompatible, copy, correctlyClassifies, defineImmutableCapabilities, getAttributeNameString, getAWTRenderer, getClassLabelString, getClassNameString, getDescription, getModel, getModelContext, getModelMeasurements, getNominalValueString, getPredictionForInstance, getPredictionForInstance, 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.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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baseLearnerOption
public ClassOption baseLearnerOption
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ensembleSizeOption
public IntOption ensembleSizeOption
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pureBoostOption
public FlagOption pureBoostOption
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deltaAdwinOption
public FloatOption deltaAdwinOption
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outputCodesOption
public FlagOption outputCodesOption
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sammeOption
public FlagOption sammeOption
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ensemble
protected Classifier[] ensemble
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scms
protected double[] scms
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swms
protected double[] swms
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ADError
protected ADWIN[] ADError
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numberOfChangesDetected
protected int numberOfChangesDetected
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matrixCodes
protected int[][] matrixCodes
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initMatrixCodes
protected boolean initMatrixCodes
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logKm1
protected double logKm1
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Km1
protected int Km1
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initKm1
protected boolean initKm1
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Method Detail
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getPurposeString
public String getPurposeString()
Description copied from class:AbstractOptionHandler
Dictionary with option texts and objects- Specified by:
getPurposeString
in interfaceOptionHandler
- Overrides:
getPurposeString
in classAbstractClassifier
- Returns:
- the string with the purpose of this object
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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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trainOnInstanceImpl
public void trainOnInstanceImpl(Instance inst)
Description copied from class:AbstractClassifier
Trains this classifier incrementally using the given instance.
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:
trainOnInstanceImpl
in classAbstractClassifier
- Parameters:
inst
- the instance to be used for training
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getEnsembleMemberWeight
protected double getEnsembleMemberWeight(int i)
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getVotesForInstance
public double[] getVotesForInstance(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 interfaceClassifier
- Specified by:
getVotesForInstance
in classAbstractClassifier
- Parameters:
inst
- the instance to be classified- Returns:
- an array containing the estimated membership probabilities of the test instance in each class
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getVotesForInstanceBinary
public double[] getVotesForInstanceBinary(Instance inst)
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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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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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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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getSubClassifiers
public Classifier[] getSubClassifiers()
Description copied from interface:Classifier
Gets the classifiers of this ensemble. Returns null if this learner is a single learner.- Specified by:
getSubClassifiers
in interfaceClassifier
- Overrides:
getSubClassifiers
in classAbstractClassifier
- Returns:
- an array of the learners of the ensemble
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