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>, 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

private double[]

weights Weights

Constructor Summary

WeightedPearsonCorrelationDistanceFunction(double[] weights)
Provides a PearsonCorrelationDistanceFunction.

Method Summary

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