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

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.PearsonCorrelationDistanceFunction

All Implemented Interfaces:

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

public class PearsonCorrelationDistanceFunction
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).

Nested Class Summary

static class

PearsonCorrelationDistanceFunction.Parameterizer Parameterization class.

Field Summary

static PearsonCorrelationDistanceFunction

STATIC Static instance.

Constructor Summary

PearsonCorrelationDistanceFunction()
Deprecated. Use static instance!

Method Summary

double	doubleDistance(NumberVector<?,?> v1, NumberVector<?,?> v2) Computes the Pearson correlation distance for two given feature vectors.
boolean	equals(Object obj)
String	toString()

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, wait, wait, wait

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

instantiate, isMetric, isSymmetric

Field Detail

STATIC

public static final PearsonCorrelationDistanceFunction STATIC

Static instance.

Constructor Detail

PearsonCorrelationDistanceFunction

@Deprecated
public PearsonCorrelationDistanceFunction()

Deprecated. Use static instance!

Provides a PearsonCorrelationDistanceFunction.

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

toString

public String toString()

Overrides:

toString in class Object

equals

public boolean equals(Object obj)

Overrides:

equals in class Object