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

Distance from probability theory, mostly divergences such as K-L-divergence, J-divergence, F-divergence, χ²-divergence, etc.

See: Description

Class Summary

Class	Description
ChiDistanceFunction	χ distance function, symmetric version.
ChiDistanceFunction.Parameterizer	Parameterization class, using the static instance.
ChiSquaredDistanceFunction	χ² distance function, symmetric version.
ChiSquaredDistanceFunction.Parameterizer	Parameterization class, using the static instance.
FisherRaoDistanceFunction	Fisher-Rao riemannian metric for (discrete) probability distributions.
FisherRaoDistanceFunction.Parameterizer	Parameterization class.
HellingerDistanceFunction	Hellinger metric / affinity / kernel, Bhattacharyya coefficient, fidelity similarity, Matusita distance, Hellinger-Kakutani metric on a probability distribution.
HellingerDistanceFunction.Parameterizer	Parameterization class.
JeffreyDivergenceDistanceFunction	Jeffrey Divergence for NumberVectors is a symmetric, smoothened version of the KullbackLeiblerDivergenceAsymmetricDistanceFunction.
JeffreyDivergenceDistanceFunction.Parameterizer	Parameterization class, using the static instance.
JensenShannonDivergenceDistanceFunction	Jensen-Shannon Divergence for NumberVectors is a symmetric, smoothened version of the KullbackLeiblerDivergenceAsymmetricDistanceFunction.
JensenShannonDivergenceDistanceFunction.Parameterizer	Parameterization class, using the static instance.
KullbackLeiblerDivergenceAsymmetricDistanceFunction	Kullback-Leibler divergence, also known as relative entropy, information deviation, or just KL-distance (albeit asymmetric).
KullbackLeiblerDivergenceAsymmetricDistanceFunction.Parameterizer	Parameterization class, using the static instance.
KullbackLeiblerDivergenceReverseAsymmetricDistanceFunction	Kullback-Leibler divergence, also known as relative entropy, information deviation or just KL-distance (albeit asymmetric).
KullbackLeiblerDivergenceReverseAsymmetricDistanceFunction.Parameterizer	Parameterization class, using the static instance.
SqrtJensenShannonDivergenceDistanceFunction	The square root of Jensen-Shannon divergence is a metric.
SqrtJensenShannonDivergenceDistanceFunction.Parameterizer	Parameterization class, using the static instance.
TriangularDiscriminationDistanceFunction	Triangular Discrimination has relatively tight upper and lower bounds to the Jensen-Shannon divergence, but is much less expensive.
TriangularDiscriminationDistanceFunction.Parameterizer	Parameterization class, using the static instance.
TriangularDistanceFunction	Triangular Distance has relatively tight upper and lower bounds to the (square root of the) Jensen-Shannon divergence, but is much less expensive.
TriangularDistanceFunction.Parameterizer	Parameterization class, using the static instance.

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

Distance from probability theory, mostly divergences such as K-L-divergence, J-divergence, F-divergence, χ²-divergence, etc.