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

Class AbsolutePearsonCorrelationDistanceFunction

java.lang.Object

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

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

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

All Implemented Interfaces:

DistanceFunction<NumberVector>, NumberVectorDistanceFunction<NumberVector>, PrimitiveDistanceFunction<NumberVector>

public class AbsolutePearsonCorrelationDistanceFunction
extends AbstractNumberVectorDistanceFunction

Absolute Pearson correlation distance function for feature vectors. The absolute Pearson correlation distance is computed from the Pearson correlation coefficient r as: 1-abs(r). The distance between two vectors will be low (near 0), if their attribute values are dimension-wise strictly positively or negatively correlated, it will be high (near 1), if their attribute values are dimension-wise uncorrelated.

Since:

0.3

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	AbsolutePearsonCorrelationDistanceFunction.Parameterizer Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
static AbsolutePearsonCorrelationDistanceFunction	STATIC Static instance.

Constructor Summary

Constructors

Constructor and Description
AbsolutePearsonCorrelationDistanceFunction() Deprecated. Use static instance!

Method Summary

Methods

Modifier and Type	Method and Description
double	distance(NumberVector v1, NumberVector v2) Computes the absolute 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.AbstractNumberVectorDistanceFunction

dimensionality, dimensionality, dimensionality, dimensionality, 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 AbsolutePearsonCorrelationDistanceFunction STATIC

Static instance.

Constructor Detail

AbsolutePearsonCorrelationDistanceFunction

@Deprecated
public AbsolutePearsonCorrelationDistanceFunction()

Deprecated. Use static instance!

Constructor - use STATIC instead.

Method Detail

distance

public double distance(NumberVector v1,
              NumberVector v2)

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

Specified by:

distance in interface NumberVectorDistanceFunction<NumberVector>

Specified by:

distance in interface PrimitiveDistanceFunction<NumberVector>

Specified by:

distance in class AbstractPrimitiveDistanceFunction<NumberVector>

Parameters:

v1 - first feature vector

v2 - second feature vector

Returns:

the absolute 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