public class StackedPredictor extends AbstractMultiLabelLearner implements MultiTargetRegressor, AMRulesFunction
Modifier and Type | Field and Description |
---|---|
FlagOption |
constantLearningRatioDecayOption |
FloatOption |
learningRateDecayOption |
FloatOption |
learningRatio2ndLayerOption |
FloatOption |
learningRatioOption |
FlagOption |
printWeightsOption |
IntOption |
randomSeedOption |
FlagOption |
skipStackingOption |
classifierRandom, modelContext, randomSeed, trainingWeightSeenByModel
config
Constructor and Description |
---|
StackedPredictor() |
Modifier and Type | Method and 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) |
getPredictionForInstance, getPredictionForInstance, getVotesForInstance, trainOnInstanceImpl
contextIsCompatible, copy, correctlyClassifies, getAttributeNameString, getAWTRenderer, getClassLabelString, getClassNameString, getDescription, getModel, getModelContext, getModelMeasurements, getNominalValueString, getPurposeString, getSubClassifiers, getSublearners, getVotesForInstance, modelAttIndexToInstanceAttIndex, modelAttIndexToInstanceAttIndex, prepareForUseImpl, resetLearning, setModelContext, setRandomSeed, trainingHasStarted, trainingWeightSeenByModel, trainOnInstance, trainOnInstance
getCLICreationString, getOptions, getPreparedClassOption, prepareClassOptions, prepareForUse, prepareForUse
copy, measureByteSize, measureByteSize, toString
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
copy, correctlyClassifies, getPredictionForInstance, getSubClassifiers, getVotesForInstance, trainOnInstance
getModel, getModelContext, getModelMeasurements, getPredictionForInstance, getSublearners, getVotesForInstance, resetLearning, setModelContext, setRandomSeed, trainingHasStarted, trainingWeightSeenByModel, trainOnInstance
getCLICreationString, getOptions, getPurposeString, prepareForUse, prepareForUse
getDescription, measureByteSize
getAWTRenderer
public FlagOption constantLearningRatioDecayOption
public FloatOption learningRatioOption
public FloatOption learningRatio2ndLayerOption
public FloatOption learningRateDecayOption
public FlagOption skipStackingOption
public IntOption randomSeedOption
public FlagOption printWeightsOption
public boolean isRandomizable()
Learner
isRandomizable
in interface Learner<Example<Instance>>
public void resetWithMemory()
resetWithMemory
in interface AMRulesFunction
public void trainOnInstanceImpl(MultiLabelInstance instance)
trainOnInstanceImpl
in interface MultiLabelLearner
trainOnInstanceImpl
in class AbstractMultiLabelLearner
public Prediction getPredictionForInstance(MultiLabelInstance inst)
getPredictionForInstance
in interface MultiLabelLearner
getPredictionForInstance
in class AbstractMultiLabelLearner
public void resetLearningImpl()
AbstractClassifier
resetLearningImpl
in class AbstractClassifier
protected double[] getNormalizedInput(MultiLabelInstance instance)
protected double[] getNormalizedOutput(MultiLabelInstance instance)
protected double[] getDenormalizedOutput(double[] normOutputs)
protected Measurement[] getModelMeasurementsImpl()
AbstractClassifier
getModelMeasurementsImpl
in class AbstractClassifier
public void getModelDescription(StringBuilder out, int indent)
AbstractClassifier
getModelDescription
in class AbstractClassifier
out
- the stringbuilder to add the descriptionindent
- the number of characters to indentpublic void selectOutputsToLearn(int[] outputAtributtes)
selectOutputsToLearn
in interface AMRulesFunction
Copyright © 2019 University of Waikato, Hamilton, NZ. All Rights Reserved.