org.apache.samoa.learners.classifiers.rules.common

Class RuleRegressionNode

java.lang.Object

org.apache.samoa.learners.classifiers.rules.common.RuleRegressionNode

All Implemented Interfaces:

Serializable

Direct Known Subclasses:

RuleActiveRegressionNode, RulePassiveRegressionNode

public abstract class RuleRegressionNode
extends Object
implements Serializable

The base class for LearningNode for regression rule.

Author:

Anh Thu Vu

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
RuleRegressionNode()
RuleRegressionNode(double[] initialClassObservations)

Method Summary

Modifier and Type	Method and Description
double	computeError(Instance instance)
static double	computeHoeffdingBound(double range, double confidence, double n)
static double	computeProbability(double mean, double sd, double value)
double	computeSD(double squaredVal, double val, long size)
double	getCurrentError()
long	getInstancesSeen()
int	getLearnerToUse(int predMode)
DoubleVector	getNodeStatistics()
double	getNormalizedPrediction(Instance instance)
Perceptron	getPerceptron()
double[]	getPrediction(Instance instance)
double[]	getPrediction(Instance instance, int predictionMode)
double[]	getSimplePrediction()
TargetMean	getTargetMean()
boolean	isAnomaly(Instance instance, double uniVariateAnomalyProbabilityThreshold, double multiVariateAnomalyProbabilityThreshold, int numberOfInstanceesForAnomaly)
void	setPerceptron(Perceptron perceptron)
void	setTargetMean(TargetMean targetMean)
abstract void	updateStatistics(Instance instance)

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

RuleRegressionNode

public RuleRegressionNode(double[] initialClassObservations)

RuleRegressionNode

public RuleRegressionNode()

Method Detail

getPerceptron

public Perceptron getPerceptron()

setPerceptron

public void setPerceptron(Perceptron perceptron)

getTargetMean

public TargetMean getTargetMean()

setTargetMean

public void setTargetMean(TargetMean targetMean)

updateStatistics

public abstract void updateStatistics(Instance instance)

getPrediction

public double[] getPrediction(Instance instance)

getSimplePrediction

public double[] getSimplePrediction()

getPrediction

public double[] getPrediction(Instance instance,
                              int predictionMode)

getNormalizedPrediction

public double getNormalizedPrediction(Instance instance)

getLearnerToUse

public int getLearnerToUse(int predMode)

computeError

public double computeError(Instance instance)

getCurrentError

public double getCurrentError()

getInstancesSeen

public long getInstancesSeen()

getNodeStatistics

public DoubleVector getNodeStatistics()

isAnomaly

public boolean isAnomaly(Instance instance,
                         double uniVariateAnomalyProbabilityThreshold,
                         double multiVariateAnomalyProbabilityThreshold,
                         int numberOfInstanceesForAnomaly)

computeProbability

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

computeHoeffdingBound

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

computeSD

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