moa.classifiers.trees

Class FIMTDD

java.lang.Object

moa.AbstractMOAObject

moa.options.AbstractOptionHandler

moa.classifiers.AbstractClassifier

moa.classifiers.trees.FIMTDD

All Implemented Interfaces:

Serializable, Classifier, Regressor, AWTRenderable, MOAObject, OptionHandler

public class FIMTDD
extends AbstractClassifier
implements Regressor

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
class	FIMTDD.FIMTDDPerceptron
static class	FIMTDD.LeafNode
static class	FIMTDD.Node
static class	FIMTDD.SplitNode

Field Summary

Fields

Modifier and Type	Field and Description
FloatOption	alternateTreeFadingFactorOption
IntOption	alternateTreeTimeOption
IntOption	alternateTreeTMinOption
IntOption	gracePeriodOption
FloatOption	learningRateDecayFactorOption
FlagOption	learningRatioConstOption
FloatOption	learningRatioOption
int	maxID
FloatOption	PageHinckleyAlphaOption
IntOption	PageHinckleyThresholdOption
FlagOption	regressionTreeOption
FloatOption	splitConfidenceOption
ClassOption	splitCriterionOption
FloatOption	tieThresholdOption
protected FIMTDD.Node	treeRoot

Fields inherited from class moa.classifiers.AbstractClassifier

classifierRandom, modelContext, randomSeed, randomSeedOption, trainingWeightSeenByModel

Fields inherited from class moa.options.AbstractOptionHandler

classOptionNamesToPreparedObjects, options

Constructor Summary

Constructors

Constructor and Description
FIMTDD()

Method Summary

Methods

Modifier and Type	Method and Description
protected void	attemptToSplit(FIMTDD.LeafNode node, FIMTDD.SplitNode parent, int parentIndex)
boolean	buildingModelTree()
int	calcByteSize()
protected void	checkRoot()
static double	computeHoeffdingBound(double range, double confidence, double n)
double	computeSD(double squaredVal, double val, double size)
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	getNormalizedError(weka.core.Instance inst)
String	getPurposeString() Gets the purpose of this object
double[]	getVotesForInstance(weka.core.Instance inst) Predicts the class memberships for a given instance.
boolean	isRandomizable() Gets whether this classifier needs a random seed.
protected FIMTDD.FIMTDDPerceptron	newLeafModel()
protected FIMTDD.LeafNode	newLeafNode()
protected FIMTDDNumericAttributeClassObserver	newNumericClassObserver()
protected FIMTDD.SplitNode	newSplitNode(InstanceConditionalTest splitTest)
double	normalizeTargetValue(double value)
void	processInstance(weka.core.Instance inst, FIMTDD.Node node, double prediction, double normalError, boolean growthAllowed, boolean inAlternate)
void	resetLearningImpl() Resets this classifier.
double	scalarProduct(DoubleVector u, DoubleVector v)
void	trainOnInstanceImpl(weka.core.Instance inst) Method for updating (training) the model using a new instance

Methods inherited from class moa.classifiers.AbstractClassifier

contextIsCompatible, copy, correctlyClassifies, getAttributeNameString, getAWTRenderer, getClassLabelString, getClassNameString, getDescription, getModelContext, getModelMeasurements, getNominalValueString, getSubClassifiers, modelAttIndexToInstanceAttIndex, modelAttIndexToInstanceAttIndex, prepareForUseImpl, resetLearning, setModelContext, setRandomSeed, trainingHasStarted, trainingWeightSeenByModel, trainOnInstance

Methods inherited from class moa.options.AbstractOptionHandler

discoverOptionsViaReflection, getCLICreationString, getOptions, getPreparedClassOption, getPreparedClassOption, prepareClassOptions, prepareForUse, prepareForUse

Methods inherited from class moa.AbstractMOAObject

copy, measureByteSize, measureByteSize, toString

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface moa.options.OptionHandler

getCLICreationString, getOptions, prepareForUse, prepareForUse

Methods inherited from interface moa.MOAObject

measureByteSize

Field Detail

treeRoot

protected FIMTDD.Node treeRoot

maxID

public int maxID

splitCriterionOption

public ClassOption splitCriterionOption

gracePeriodOption

public IntOption gracePeriodOption

splitConfidenceOption

public FloatOption splitConfidenceOption

tieThresholdOption

public FloatOption tieThresholdOption

PageHinckleyAlphaOption

public FloatOption PageHinckleyAlphaOption

PageHinckleyThresholdOption

public IntOption PageHinckleyThresholdOption

alternateTreeFadingFactorOption

public FloatOption alternateTreeFadingFactorOption

alternateTreeTMinOption

public IntOption alternateTreeTMinOption

alternateTreeTimeOption

public IntOption alternateTreeTimeOption

regressionTreeOption

public FlagOption regressionTreeOption

learningRatioOption

public FloatOption learningRatioOption

learningRateDecayFactorOption

public FloatOption learningRateDecayFactorOption

learningRatioConstOption

public FlagOption learningRatioConstOption

Constructor Detail

FIMTDD

public FIMTDD()

Method Detail

getPurposeString

public String getPurposeString()

Description copied from interface: OptionHandler

Gets the purpose of this object

Specified by:

getPurposeString in interface OptionHandler

Overrides:

getPurposeString in class AbstractClassifier

Returns:

the string with the purpose of this object

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 class AbstractClassifier

isRandomizable

public boolean isRandomizable()

Description copied from interface: Classifier

Gets whether this classifier needs a random seed. Examples of methods that needs a random seed are bagging and boosting.

Specified by:

isRandomizable in interface Classifier

Returns:

true if the classifier needs a random seed.

getModelDescription

public void getModelDescription(StringBuilder out,
                       int indent)

Description copied from class: AbstractClassifier

Returns a string representation of the model.

Specified by:

getModelDescription in class AbstractClassifier

Parameters:

out - the stringbuilder to add the description

indent - the number of characters to indent

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 class AbstractClassifier

Returns:

an array of measurements to be used in evaluation tasks

calcByteSize

public int calcByteSize()

getVotesForInstance

public double[] getVotesForInstance(weka.core.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 interface Classifier

Parameters:

inst - the instance to be classified

Returns:

an array containing the estimated membership probabilities of the test instance in each class

normalizeTargetValue

public double normalizeTargetValue(double value)

getNormalizedError

public double getNormalizedError(weka.core.Instance inst)

trainOnInstanceImpl

public void trainOnInstanceImpl(weka.core.Instance inst)

Method for updating (training) the model using a new instance

Specified by:

trainOnInstanceImpl in class AbstractClassifier

Parameters:

inst - the instance to be used for training

processInstance

public void processInstance(weka.core.Instance inst,
                   FIMTDD.Node node,
                   double prediction,
                   double normalError,
                   boolean growthAllowed,
                   boolean inAlternate)

newNumericClassObserver

protected FIMTDDNumericAttributeClassObserver newNumericClassObserver()

newSplitNode

protected FIMTDD.SplitNode newSplitNode(InstanceConditionalTest splitTest)

newLeafNode

protected FIMTDD.LeafNode newLeafNode()

newLeafModel

protected FIMTDD.FIMTDDPerceptron newLeafModel()

checkRoot

protected void checkRoot()

computeHoeffdingBound

public static double computeHoeffdingBound(double range,
                           double confidence,
                           double n)

buildingModelTree

public boolean buildingModelTree()

attemptToSplit

protected void attemptToSplit(FIMTDD.LeafNode node,
                  FIMTDD.SplitNode parent,
                  int parentIndex)

computeSD

public double computeSD(double squaredVal,
               double val,
               double size)

scalarProduct

public double scalarProduct(DoubleVector u,
                   DoubleVector v)