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

Interface NumberVectorDistanceFunction<O>

Type Parameters:

O - vector type, usually NumberVector or a parent type

All Superinterfaces:

DistanceFunction<O>, PrimitiveDistanceFunction<O>

All Known Subinterfaces:

WeightedNumberVectorDistanceFunction<V>

All Known Implementing Classes:

AbsolutePearsonCorrelationDistanceFunction, AbsoluteUncenteredCorrelationDistanceFunction, AbstractEditDistanceFunction, AbstractNumberVectorDistanceFunction, ArcCosineDistanceFunction, ArcCosineUnitlengthDistanceFunction, BrayCurtisDistanceFunction, CanberraDistanceFunction, ChiDistanceFunction, ChiSquaredDistanceFunction, ClarkDistanceFunction, CosineDistanceFunction, CosineUnitlengthDistanceFunction, DerivativeDTWDistanceFunction, DimensionSelectingLatLngDistanceFunction, DTWDistanceFunction, EDRDistanceFunction, ERPDistanceFunction, EuclideanDistanceFunction, FisherRaoDistanceFunction, HammingDistanceFunction, HellingerDistanceFunction, HistogramIntersectionDistanceFunction, HistogramMatchDistanceFunction, HSBHistogramQuadraticDistanceFunction, JaccardSimilarityDistanceFunction, JeffreyDivergenceDistanceFunction, JensenShannonDivergenceDistanceFunction, KolmogorovSmirnovDistanceFunction, Kulczynski1SimilarityFunction, KullbackLeiblerDivergenceAsymmetricDistanceFunction, KullbackLeiblerDivergenceReverseAsymmetricDistanceFunction, LatLngDistanceFunction, LCSSDistanceFunction, LngLatDistanceFunction, LPIntegerNormDistanceFunction, LPNormDistanceFunction, MahalanobisDistanceFunction, ManhattanDistanceFunction, MatrixWeightedQuadraticDistanceFunction, MaximumDistanceFunction, MinimumDistanceFunction, MultiLPNorm, OnedimensionalDistanceFunction, PearsonCorrelationDistanceFunction, RGBHistogramQuadraticDistanceFunction, SqrtJensenShannonDivergenceDistanceFunction, SquaredEuclideanDistanceFunction, SquaredPearsonCorrelationDistanceFunction, SquaredUncenteredCorrelationDistanceFunction, SubspaceEuclideanDistanceFunction, SubspaceLPNormDistanceFunction, SubspaceManhattanDistanceFunction, SubspaceMaximumDistanceFunction, TriangularDiscriminationDistanceFunction, TriangularDistanceFunction, TutorialDistanceFunction, UncenteredCorrelationDistanceFunction, WeightedCanberraDistanceFunction, WeightedEuclideanDistanceFunction, WeightedLPNormDistanceFunction, WeightedManhattanDistanceFunction, WeightedMaximumDistanceFunction, WeightedPearsonCorrelationDistanceFunction, WeightedSquaredEuclideanDistanceFunction, WeightedSquaredPearsonCorrelationDistanceFunction

public interface NumberVectorDistanceFunction<O>
extends PrimitiveDistanceFunction<O>

Base interface for the common case of distance functions defined on numerical vectors.

Since:

0.5.0

Author:

Erich Schubert

Method Summary

Modifier and Type	Method and Description
double	distance(NumberVector o1, NumberVector o2) Computes the distance between two given vectors according to this distance function.

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

distance, getInputTypeRestriction, instantiate

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

isMetric, isSquared, isSymmetric

Method Detail

distance

double distance(NumberVector o1,
                NumberVector o2)

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

Parameters:

o1 - first vector

o2 - second vector

Returns:

the distance between two given vectors according to this distance function