public class TargetMean extends AbstractClassifier implements AMRulesRegressorFunction
Modifier and Type | Field and Description |
---|---|
protected double |
errorSum |
FloatOption |
fadingErrorFactorOption |
protected double |
n |
protected double |
nError |
protected double |
sum |
classifierRandom, downSampleRatio, modelContext, randomSeed, randomSeedOption, trainingWeightSeenByModel
config
Constructor and Description |
---|
TargetMean() |
TargetMean(TargetMean t) |
Modifier and Type | Method and Description |
---|---|
double |
getCurrentError() |
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. |
double[] |
getVotesForInstance(Instance inst)
Predicts the class memberships for a given instance.
|
boolean |
isRandomizable()
Gets whether this learner needs a random seed.
|
void |
reset(double currentMean,
double numberOfInstances) |
void |
resetError() |
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. |
protected void |
updateAccumulatedError(Instance inst) |
contextIsCompatible, copy, correctlyClassifies, getAttributeNameString, getAWTRenderer, getClassLabelString, getClassNameString, getDescription, getDownSampleRatio, getModel, getModelContext, getModelMeasurements, getNominalValueString, getPredictionForInstance, getPredictionForInstance, getPurposeString, getSubClassifiers, getSublearners, getVotesForInstance, modelAttIndexToInstanceAttIndex, modelAttIndexToInstanceAttIndex, prepareForUseImpl, resetLearning, setModelContext, setRandomSeed, trainingHasStarted, trainingWeightSeenByModel, trainOnInstance, trainOnInstance, updateDownSampleRatio
getCLICreationString, getOptions, getPreparedClassOption, prepareClassOptions, prepareForUse, prepareForUse
copy, measureByteSize, measureByteSize, toString
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
getModel, getModelContext, getModelMeasurements, getPredictionForInstance, getSublearners, getVotesForInstance, resetLearning, setModelContext, setRandomSeed, trainingHasStarted, trainingWeightSeenByModel, trainOnInstance
copy, getCLICreationString, getOptions, getPurposeString, prepareForUse, prepareForUse
getDescription, measureByteSize
getAWTRenderer
protected double n
protected double sum
protected double errorSum
protected double nError
public FloatOption fadingErrorFactorOption
public TargetMean(TargetMean t)
public TargetMean()
public boolean isRandomizable()
Learner
isRandomizable
in interface Learner<Example<Instance>>
public double[] getVotesForInstance(Instance inst)
Classifier
getVotesForInstance
in interface Classifier
getVotesForInstance
in class AbstractClassifier
inst
- the instance to be classifiedpublic void resetLearningImpl()
AbstractClassifier
resetLearningImpl
in class AbstractClassifier
public void trainOnInstanceImpl(Instance inst)
AbstractClassifier
trainOnInstanceImpl
in class AbstractClassifier
inst
- the instance to be used for trainingprotected void updateAccumulatedError(Instance inst)
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 reset(double currentMean, double numberOfInstances)
public double getCurrentError()
getCurrentError
in interface AMRulesLearner
public void resetError()
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