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

Class SqrtJensenShannonDivergenceDistanceFunction

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.probabilistic.SqrtJensenShannonDivergenceDistanceFunction

All Implemented Interfaces:

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

@Reference(authors="D. M. Endres, J. E. Schindelin",
           title="A new metric for probability distributions",
           booktitle="IEEE Transactions on Information Theory, 49(7)",
           url="http://dx.doi.org/10.1109/TIT.2003.813506")
public class SqrtJensenShannonDivergenceDistanceFunction
extends AbstractNumberVectorDistanceFunction

The square root of Jensen-Shannon divergence is metric. Reference (proof of triangle inequality, distance called D_PQ):

D. M. Endres, J. E. Schindelin
A new metric for probability distributions
IEEE Transactions on Information Theory, 49(7).

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	SqrtJensenShannonDivergenceDistanceFunction.Parameterizer Parameterization class, using the static instance.

Field Summary

Fields

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

Constructor Summary

Constructors

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

Method Summary

Methods

Modifier and Type	Method and Description
double	distance(NumberVector v1, NumberVector v2) Computes the distance between two given DatabaseObjects according to this distance function.
boolean	equals(Object obj)
boolean	isMetric() Is this distance function metric (in particular, does it satisfy the triangle equation?)
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, 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, isSymmetric

Field Detail

STATIC

public static final SqrtJensenShannonDivergenceDistanceFunction STATIC

Static instance. Use this!

Constructor Detail

SqrtJensenShannonDivergenceDistanceFunction

@Deprecated
public SqrtJensenShannonDivergenceDistanceFunction()

Deprecated. Use static instance!

Constructor for sqrt Jensen Shannon divergence.

Method Detail

distance

public double distance(NumberVector v1,
              NumberVector v2)

Description copied from interface: PrimitiveDistanceFunction

Computes the distance between two given DatabaseObjects according to this distance function.

Specified by:

distance in interface NumberVectorDistanceFunction<NumberVector>

Specified by:

distance in interface PrimitiveDistanceFunction<NumberVector>

Specified by:

distance in class AbstractPrimitiveDistanceFunction<NumberVector>

Parameters:

v1 - first DatabaseObject

v2 - second DatabaseObject

Returns:

the distance between two given DatabaseObjects according to this distance function

isMetric

public boolean isMetric()

Description copied from interface: DistanceFunction

Is this distance function metric (in particular, does it satisfy the triangle equation?)

Specified by:

isMetric in interface DistanceFunction<NumberVector>

Overrides:

isMetric in class AbstractPrimitiveDistanceFunction<NumberVector>

Returns:

true when metric.

toString

public String toString()

Overrides:

toString in class Object

equals

public boolean equals(Object obj)

Overrides:

equals in class Object