de.lmu.ifi.dbs.elki.distance.distancefunction.correlation

Class WeightedPearsonCorrelationDistanceFunction

java.lang.Object

de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractPrimitiveDistanceFunction<NumberVector<?>,DoubleDistance>

de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractVectorDoubleDistanceFunction

de.lmu.ifi.dbs.elki.distance.distancefunction.correlation.WeightedPearsonCorrelationDistanceFunction

All Implemented Interfaces:

DistanceFunction<NumberVector<?>,DoubleDistance>, NumberVectorDistanceFunction<DoubleDistance>, PrimitiveDistanceFunction<NumberVector<?>,DoubleDistance>, PrimitiveDoubleDistanceFunction<NumberVector<?>>, InspectionUtilFrequentlyScanned, Parameterizable

public class WeightedPearsonCorrelationDistanceFunction
extends AbstractVectorDoubleDistanceFunction

Pearson correlation distance function for feature vectors. The Pearson correlation distance is computed from the Pearson correlation coefficient r as: 1-r. Hence, possible values of this distance are between 0 and 2. The distance between two vectors will be low (near 0), if their attribute values are dimension-wise strictly positively correlated, it will be high (near 2), if their attribute values are dimension-wise strictly negatively correlated. For Features with uncorrelated attributes, the distance value will be intermediate (around 1). This variation is for weighted dimensions.

Field Summary

Fields

Modifier and Type	Field and Description
private double[]	weights Weights

Constructor Summary

Constructors

Constructor and Description
WeightedPearsonCorrelationDistanceFunction(double[] weights) Provides a PearsonCorrelationDistanceFunction.

Method Summary

Methods

Modifier and Type	Method and Description
double	doubleDistance(NumberVector<?> v1, NumberVector<?> v2) Computes the Pearson correlation distance for two given feature vectors.
boolean	equals(Object obj)

Methods inherited from class de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractVectorDoubleDistanceFunction

distance, getDistanceFactory, getInputTypeRestriction

Methods inherited from class de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractPrimitiveDistanceFunction

instantiate, isMetric, isSymmetric

Methods inherited from class java.lang.Object

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

Methods inherited from interface de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction

instantiate, isMetric, isSymmetric

Field Detail

weights

private double[] weights

Weights

Constructor Detail

WeightedPearsonCorrelationDistanceFunction

public WeightedPearsonCorrelationDistanceFunction(double[] weights)

Provides a PearsonCorrelationDistanceFunction.

Parameters:

weights - Weights

Method Detail

doubleDistance

public double doubleDistance(NumberVector<?> v1,
                    NumberVector<?> v2)

Computes the Pearson correlation distance for two given feature vectors. The Pearson correlation distance is computed from the Pearson correlation coefficient r as: 1-r. Hence, possible values of this distance are between 0 and 2.

Parameters:

v1 - first feature vector

v2 - second feature vector

Returns:

the Pearson correlation distance for two given feature vectors v1 and v2

equals

public boolean equals(Object obj)

Overrides:

equals in class Object