moa.classifiers.rules.core

Class RuleActiveLearningNode

java.lang.Object

moa.AbstractMOAObject

moa.classifiers.trees.HoeffdingTree.Node

moa.classifiers.trees.HoeffdingTree.LearningNode

moa.classifiers.trees.HoeffdingTree.ActiveLearningNode

moa.classifiers.rules.core.RuleActiveLearningNode

All Implemented Interfaces:

Serializable, MOAObject

Direct Known Subclasses:

RuleActiveRegressionNode

public abstract class RuleActiveLearningNode
extends HoeffdingTree.ActiveLearningNode

A modified ActiveLearningNode that uses a Perceptron as the leaf node model, and ensures that the class values sent to the attribute observers are not truncated to ints if regression is being performed

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected AbstractAMRules	amRules
protected AttributeSplitSuggestion	bestSuggestion
protected boolean	changeDetection
protected DoubleVector	nodeStatistics
protected Rule	owner
protected PageHinkleyTest	pageHinckleyTest
protected int	predictionFunction
protected int	splitIndex
protected double[]	statisticsBranchSplit
protected double[]	statisticsNewRuleActiveLearningNode
protected double[]	statisticsOtherBranchSplit

Fields inherited from class moa.classifiers.trees.HoeffdingTree.ActiveLearningNode

attributeObservers, isInitialized, weightSeenAtLastSplitEvaluation

Fields inherited from class moa.classifiers.trees.HoeffdingTree.Node

observedClassDistribution

Constructor Summary

Constructors

Constructor and Description
RuleActiveLearningNode()
RuleActiveLearningNode(double[] initialClassObservations) Create a new RuleActiveLearningNode
RuleActiveLearningNode(Rule.Builder builder)

Method Summary

Methods

Modifier and Type	Method and Description
abstract double	computeError(weka.core.Instance instance)
static double	computeHoeffdingBound(double range, double confidence, double n)
double	computeProbability(double mean, double sd, double value)
protected void	debug(String string, int level)
AutoExpandVector<AttributeClassObserver>	getAttributeObservers()
AttributeSplitSuggestion	getBestSuggestion()
abstract double	getCurrentError()
long	getInstancesSeen()
abstract int	getLearnerToUse(weka.core.Instance instance, int predictionMode)
DoubleVector	getNodeStatistics()
double[]	getPrediction(weka.core.Instance instance)
abstract double[]	getPrediction(weka.core.Instance instance, int predictionMode)
abstract double[]	getSimplePrediction()
int	getSplitIndex()
double[]	getStatisticsBranchSplit()
double[]	getStatisticsNewRuleActiveLearningNode()
double[]	getStatisticsOtherBranchSplit()
abstract void	initialize(RuleActiveLearningNode oldLearningNode)
abstract boolean	isAnomaly(weka.core.Instance instance, double uniVariateAnomalyProbabilityThreshold, double multiVariateAnomalyProbabilityThreshold, int numberOfInstanceesForAnomaly)
abstract void	learnFromInstance(weka.core.Instance inst)
void	learnFromInstance(weka.core.Instance inst, HoeffdingTree ht)
protected AttributeClassObserver	newNumericClassObserver()
void	setBestSuggestion(AttributeSplitSuggestion bestSuggestion)
void	setSplitIndex(int splitIndex)
void	setStatisticsBranchSplit(double[] statisticsBranchSplit)
void	setStatisticsNewRuleActiveLearningNode(double[] statisticsNewRuleActiveLearningNode)
void	setStatisticsOtherBranchSplit(double[] statisticsOtherBranchSplit)
abstract boolean	tryToExpand(double splitConfidence, double tieThreshold)
boolean	updateChangeDetection(double error)
boolean	updatePageHinckleyTest(double error)
void	updateStatistics(weka.core.Instance instance)

Methods inherited from class moa.classifiers.trees.HoeffdingTree.ActiveLearningNode

calcByteSize, disableAttribute, getBestSplitSuggestions, getWeightSeen, getWeightSeenAtLastSplitEvaluation, setWeightSeenAtLastSplitEvaluation

Methods inherited from class moa.classifiers.trees.HoeffdingTree.Node

calcByteSizeIncludingSubtree, calculatePromise, describeSubtree, filterInstanceToLeaf, getClassVotes, getDescription, getObservedClassDistribution, isLeaf, observedClassDistributionIsPure, subtreeDepth

Methods inherited from class moa.AbstractMOAObject

copy, copy, measureByteSize, measureByteSize, toString

Methods inherited from class java.lang.Object

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

Field Detail

pageHinckleyTest

protected PageHinkleyTest pageHinckleyTest

predictionFunction

protected int predictionFunction

changeDetection

protected boolean changeDetection

owner

protected Rule owner

nodeStatistics

protected DoubleVector nodeStatistics

amRules

protected AbstractAMRules amRules

bestSuggestion

protected AttributeSplitSuggestion bestSuggestion

splitIndex

protected int splitIndex

statisticsNewRuleActiveLearningNode

protected double[] statisticsNewRuleActiveLearningNode

statisticsBranchSplit

protected double[] statisticsBranchSplit

statisticsOtherBranchSplit

protected double[] statisticsOtherBranchSplit

Constructor Detail

RuleActiveLearningNode

public RuleActiveLearningNode(double[] initialClassObservations)

Create a new RuleActiveLearningNode

RuleActiveLearningNode

public RuleActiveLearningNode()

RuleActiveLearningNode

public RuleActiveLearningNode(Rule.Builder builder)

Method Detail

learnFromInstance

public abstract void learnFromInstance(weka.core.Instance inst)

learnFromInstance

public void learnFromInstance(weka.core.Instance inst,
                     HoeffdingTree ht)

Overrides:

learnFromInstance in class HoeffdingTree.ActiveLearningNode

newNumericClassObserver

protected AttributeClassObserver newNumericClassObserver()

updateStatistics

public void updateStatistics(weka.core.Instance instance)

getAttributeObservers

public AutoExpandVector<AttributeClassObserver> getAttributeObservers()

debug

protected void debug(String string,
         int level)

getPrediction

public double[] getPrediction(weka.core.Instance instance)

getPrediction

public abstract double[] getPrediction(weka.core.Instance instance,
                     int predictionMode)

getLearnerToUse

public abstract int getLearnerToUse(weka.core.Instance instance,
                  int predictionMode)

computeError

public abstract double computeError(weka.core.Instance instance)

updatePageHinckleyTest

public boolean updatePageHinckleyTest(double error)

getInstancesSeen

public long getInstancesSeen()

isAnomaly

public abstract boolean isAnomaly(weka.core.Instance instance,
                double uniVariateAnomalyProbabilityThreshold,
                double multiVariateAnomalyProbabilityThreshold,
                int numberOfInstanceesForAnomaly)

computeProbability

public double computeProbability(double mean,
                        double sd,
                        double value)

getSplitIndex

public int getSplitIndex()

setSplitIndex

public void setSplitIndex(int splitIndex)

getBestSuggestion

public AttributeSplitSuggestion getBestSuggestion()

setBestSuggestion

public void setBestSuggestion(AttributeSplitSuggestion bestSuggestion)

getStatisticsBranchSplit

public double[] getStatisticsBranchSplit()

setStatisticsBranchSplit

public void setStatisticsBranchSplit(double[] statisticsBranchSplit)

getStatisticsNewRuleActiveLearningNode

public double[] getStatisticsNewRuleActiveLearningNode()

setStatisticsNewRuleActiveLearningNode

public void setStatisticsNewRuleActiveLearningNode(double[] statisticsNewRuleActiveLearningNode)

getStatisticsOtherBranchSplit

public double[] getStatisticsOtherBranchSplit()

setStatisticsOtherBranchSplit

public void setStatisticsOtherBranchSplit(double[] statisticsOtherBranchSplit)

tryToExpand

public abstract boolean tryToExpand(double splitConfidence,
                  double tieThreshold)

computeHoeffdingBound

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

initialize

public abstract void initialize(RuleActiveLearningNode oldLearningNode)

getSimplePrediction

public abstract double[] getSimplePrediction()

getNodeStatistics

public DoubleVector getNodeStatistics()

updateChangeDetection

public boolean updateChangeDetection(double error)

getCurrentError

public abstract double getCurrentError()