Package elki.distance

Interface PrimitiveDistance<O>

Type Parameters:

O - input object type

All Superinterfaces:

Distance<O>

All Known Subinterfaces:

ClusteringDistanceSimilarity, Norm<O>, NumberVectorDistance<O>, SpatialPrimitiveDistance<V>, WeightedNumberVectorDistance<V>

All Known Implementing Classes:

AbsolutePearsonCorrelationDistance, AbsoluteUncenteredCorrelationDistance, AbstractDimensionsSelectingDistance, AbstractEditDistance, AbstractNumberVectorDistance, AbstractSetDistance, ArcCosineDistance, ArcCosineUnitlengthDistance, BrayCurtisDistance, CanberraDistance, ChiDistance, ChiSquaredDistance, ClarkDistance, ClusteringAdjustedRandIndexSimilarity, ClusteringBCubedF1Similarity, ClusteringFowlkesMallowsSimilarity, ClusteringRandIndexSimilarity, ClusterIntersectionSimilarity, ClusterJaccardSimilarity, CosineDistance, CosineUnitlengthDistance, DerivativeDTWDistance, DimensionSelectingLatLngDistance, DTWDistance, EDRDistance, ERPDistance, EuclideanDistance, FisherRaoDistance, HammingDistance, HellingerDistance, HistogramIntersectionDistance, HistogramMatchDistance, HSBHistogramQuadraticDistance, JaccardSimilarityDistance, JeffreyDivergenceDistance, JensenShannonDivergenceDistance, KolmogorovSmirnovDistance, Kulczynski1Similarity, KullbackLeiblerDivergenceAsymmetricDistance, KullbackLeiblerDivergenceReverseAsymmetricDistance, LatLngDistance, LCSSDistance, LevenshteinDistance, LinearKernel, LngLatDistance, LPIntegerNormDistance, LPNormDistance, MahalanobisDistance, ManhattanDistance, MatrixWeightedQuadraticDistance, MaximumDistance, MinimumDistance, MultiLPNorm, NormalizedLevenshteinDistance, OnedimensionalDistance, PearsonCorrelationDistance, PolynomialKernel, RGBHistogramQuadraticDistance, SparseEuclideanDistance, SparseLPNormDistance, SparseManhattanDistance, SparseMaximumDistance, SparseSquaredEuclideanDistance, SqrtCosineDistance, SqrtCosineUnitlengthDistance, SqrtJensenShannonDivergenceDistance, SquaredEuclideanDistance, SquaredPearsonCorrelationDistance, SquaredUncenteredCorrelationDistance, SubspaceEuclideanDistance, SubspaceLPNormDistance, SubspaceManhattanDistance, SubspaceMaximumDistance, TriangularDiscriminationDistance, TriangularDistance, TutorialDistance, UncenteredCorrelationDistance, WeightedCanberraDistance, WeightedEuclideanDistance, WeightedLPNormDistance, WeightedManhattanDistance, WeightedMaximumDistance, WeightedPearsonCorrelationDistance, WeightedSquaredEuclideanDistance, WeightedSquaredPearsonCorrelationDistance

public interface PrimitiveDistance<O>
extends Distance<O>

Primitive distance function that is defined on some kind of object.

Since:

0.4.0

Author:

Erich Schubert

Method Summary

Modifier and Type	Method	Description
double	distance(O o1, O o2)	Computes the distance between two given DatabaseObjects according to this distance function.
SimpleTypeInformation<? super O>	getInputTypeRestriction()	Get the input data type of the function.
default <T extends O> DistanceQuery<T>	instantiate(Relation<T> relation)	Instantiate with a database to get the actual distance query.

Methods inherited from interface elki.distance.Distance

isMetric, isSquared, isSymmetric

Method Detail

distance

double distance(O o1,
                O o2)

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

Parameters:

o1 - first DatabaseObject

o2 - second DatabaseObject

Returns:

the distance between two given DatabaseObjects according to this distance function

getInputTypeRestriction

SimpleTypeInformation<? super O> getInputTypeRestriction()

Description copied from interface: Distance

Get the input data type of the function.

Specified by:

getInputTypeRestriction in interface Distance<O>

Returns:

Type restriction

instantiate

default <T extends O> DistanceQuery<T> instantiate(Relation<T> relation)

Description copied from interface: Distance

Instantiate with a database to get the actual distance query.

Specified by:

instantiate in interface Distance<O>

Parameters:

relation - The representation to use

Returns:

Actual distance query.