Uses of Interface
de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction

Packages that use PrimitiveDistanceFunction

Package	Description
de.lmu.ifi.dbs.elki.algorithm	Algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.clustering.em	Expectation-Maximization clustering algorithm.
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical	Hierarchical agglomerative clustering (HAC).
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization	Initialization strategies for k-means.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel	Parallelized implementations of k-means.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality	Quality measures for k-Means results.
de.lmu.ifi.dbs.elki.algorithm.outlier.distance	Distance-based outlier detection algorithms, such as DBOutlier and kNN.
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial	Spatial outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.subspace	Subspace outlier detection methods.
de.lmu.ifi.dbs.elki.application.greedyensemble	Greedy ensembles for outlier detection.
de.lmu.ifi.dbs.elki.database.query.distance	Prepared queries for distances.
de.lmu.ifi.dbs.elki.database.query.knn	Prepared queries for k nearest neighbor (kNN) queries.
de.lmu.ifi.dbs.elki.database.query.range	Prepared queries for ε-range queries.
de.lmu.ifi.dbs.elki.datasource.filter.transform	Data space transformations.
de.lmu.ifi.dbs.elki.distance.distancefunction	Distance functions for use within ELKI.
de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram	Distance functions using correlations.
de.lmu.ifi.dbs.elki.distance.distancefunction.correlation	Distance functions using correlations.
de.lmu.ifi.dbs.elki.distance.distancefunction.geo	Geographic (earth) distance functions.
de.lmu.ifi.dbs.elki.distance.distancefunction.histogram	Distance functions for one-dimensional histograms.
de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski	Minkowski space L_p norms such as the popular Euclidean and Manhattan distances.
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic	Distance from probability theory, mostly divergences such as K-L-divergence, J-divergence.
de.lmu.ifi.dbs.elki.distance.distancefunction.set	Distance functions for binary and set type data.
de.lmu.ifi.dbs.elki.distance.distancefunction.strings	Distance functions for strings.
de.lmu.ifi.dbs.elki.distance.distancefunction.subspace	Distance functions based on subspaces.
de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries	Distance functions designed for time series.
de.lmu.ifi.dbs.elki.distance.similarityfunction	Similarity functions.
de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster	Similarity measures for comparing clusters.
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel	Kernel functions.
de.lmu.ifi.dbs.elki.math.linearalgebra.pca	Principal Component Analysis (PCA) and Eigenvector processing.
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation.
tutorial.distancefunction	Classes from the tutorial on implementing distance functions.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.algorithm

Fields in de.lmu.ifi.dbs.elki.algorithm declared as PrimitiveDistanceFunction

Modifier and Type	Field and Description
protected PrimitiveDistanceFunction<? super O>	AbstractPrimitiveDistanceBasedAlgorithm.distanceFunction Holds the instance of the distance function specified by DistanceBasedAlgorithm.DISTANCE_FUNCTION_ID.
protected PrimitiveDistanceFunction<O>	AbstractPrimitiveDistanceBasedAlgorithm.Parameterizer.distanceFunction Distance function to use.

Methods in de.lmu.ifi.dbs.elki.algorithm that return PrimitiveDistanceFunction

Modifier and Type	Method and Description
PrimitiveDistanceFunction<? super O>	AbstractPrimitiveDistanceBasedAlgorithm.getDistanceFunction() Returns the distanceFunction.

Constructors in de.lmu.ifi.dbs.elki.algorithm with parameters of type PrimitiveDistanceFunction

Constructor and Description
AbstractPrimitiveDistanceBasedAlgorithm(PrimitiveDistanceFunction<? super O> distanceFunction) Constructor.
DependencyDerivator(PrimitiveDistanceFunction<V> distanceFunction, NumberFormat nf, PCAFilteredRunner pca, int sampleSize, boolean randomsample) Constructor.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.algorithm.clustering.em

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.em with parameters of type PrimitiveDistanceFunction

Modifier and Type	Method and Description
List<SphericalGaussianModel>	SphericalGaussianModelFactory.buildInitialModels(Database database, Relation<V> relation, int k, PrimitiveDistanceFunction<? super NumberVector> df)
List<DiagonalGaussianModel>	DiagonalGaussianModelFactory.buildInitialModels(Database database, Relation<V> relation, int k, PrimitiveDistanceFunction<? super NumberVector> df)
List<MultivariateGaussianModel>	MultivariateGaussianModelFactory.buildInitialModels(Database database, Relation<V> relation, int k, PrimitiveDistanceFunction<? super NumberVector> df)
List<? extends EMClusterModel<M>>	EMClusterModelFactory.buildInitialModels(Database database, Relation<V> relation, int k, PrimitiveDistanceFunction<? super NumberVector> df) Build the initial models

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical with parameters of type PrimitiveDistanceFunction

Modifier and Type	Method and Description
private void	SLINK.step2primitive(DBIDRef id, DBIDs processedIDs, Relation<? extends O> relation, PrimitiveDistanceFunction<? super O> distFunc, WritableDoubleDataStore m) Second step: Determine the pairwise distances from all objects in the pointer representation to the new object with the specified id.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans with parameters of type PrimitiveDistanceFunction

Modifier and Type	Method and Description
void	KMeansBisecting.setDistanceFunction(PrimitiveDistanceFunction<? super NumberVector> distanceFunction)
void	KMeans.setDistanceFunction(PrimitiveDistanceFunction<? super NumberVector> distanceFunction) Set the distance function to use.
void	BestOfMultipleKMeans.setDistanceFunction(PrimitiveDistanceFunction<? super NumberVector> distanceFunction)
void	AbstractKMeans.setDistanceFunction(PrimitiveDistanceFunction<? super NumberVector> distanceFunction)

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans with parameters of type PrimitiveDistanceFunction

Constructor and Description
AbstractKMeans(PrimitiveDistanceFunction<? super NumberVector> distanceFunction, int k, int maxiter, KMeansInitialization<? super V> initializer) Constructor.
KMeansBatchedLloyd(PrimitiveDistanceFunction<NumberVector> distanceFunction, int k, int maxiter, KMeansInitialization<? super V> initializer, int blocks, RandomFactory random) Constructor.
KMeansHybridLloydMacQueen(PrimitiveDistanceFunction<NumberVector> distanceFunction, int k, int maxiter, KMeansInitialization<? super V> initializer) Constructor.
KMeansLloyd(PrimitiveDistanceFunction<NumberVector> distanceFunction, int k, int maxiter, KMeansInitialization<? super V> initializer) Constructor.
KMeansMacQueen(PrimitiveDistanceFunction<NumberVector> distanceFunction, int k, int maxiter, KMeansInitialization<? super V> initializer) Constructor.
KMediansLloyd(PrimitiveDistanceFunction<? super NumberVector> distanceFunction, int k, int maxiter, KMeansInitialization<? super V> initializer) Constructor.
SingleAssignmentKMeans(PrimitiveDistanceFunction<NumberVector> distanceFunction, int k, KMeansInitialization<? super V> initializer) Constructor.
XMeans(PrimitiveDistanceFunction<? super NumberVector> distanceFunction, int k_min, int k_max, int maxiter, KMeans<V,M> innerKMeans, KMeansInitialization<? super V> initializer, PredefinedInitialMeans splitInitializer, KMeansQualityMeasure<V> informationCriterion, RandomFactory random) Constructor.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization with parameters of type PrimitiveDistanceFunction

Modifier and Type	Method and Description
<T extends NumberVector,O extends NumberVector> List<O>	PredefinedInitialMeans.chooseInitialMeans(Database database, Relation<T> relation, int k, PrimitiveDistanceFunction<? super T> distanceFunction, NumberVector.Factory<O> factory)
<T extends V,O extends NumberVector> List<O>	KMeansInitialization.chooseInitialMeans(Database database, Relation<T> relation, int k, PrimitiveDistanceFunction<? super T> distanceFunction, NumberVector.Factory<O> factory) Choose initial means
<T extends V,O extends NumberVector> List<O>	SampleKMeansInitialization.chooseInitialMeans(Database database, Relation<T> relation, int k, PrimitiveDistanceFunction<? super T> distanceFunction, NumberVector.Factory<O> factory)
<T extends NumberVector,V extends NumberVector> List<V>	FarthestSumPointsInitialMeans.chooseInitialMeans(Database database, Relation<T> relation, int k, PrimitiveDistanceFunction<? super T> distanceFunction, NumberVector.Factory<V> factory)
<T extends NumberVector,V extends NumberVector> List<V>	RandomlyChosenInitialMeans.chooseInitialMeans(Database database, Relation<T> relation, int k, PrimitiveDistanceFunction<? super T> distanceFunction, NumberVector.Factory<V> factory)
<T extends NumberVector,V extends NumberVector> List<V>	RandomlyGeneratedInitialMeans.chooseInitialMeans(Database database, Relation<T> relation, int k, PrimitiveDistanceFunction<? super T> distanceFunction, NumberVector.Factory<V> factory)
<T extends NumberVector,V extends NumberVector> List<V>	FirstKInitialMeans.chooseInitialMeans(Database database, Relation<T> relation, int k, PrimitiveDistanceFunction<? super T> distanceFunction, NumberVector.Factory<V> factory)
<T extends NumberVector,V extends NumberVector> List<V>	KMeansPlusPlusInitialMeans.chooseInitialMeans(Database database, Relation<T> relation, int k, PrimitiveDistanceFunction<? super T> distanceFunction, NumberVector.Factory<V> factory)
<T extends NumberVector,V extends NumberVector> List<V>	PAMInitialMeans.chooseInitialMeans(Database database, Relation<T> relation, int k, PrimitiveDistanceFunction<? super T> distanceFunction, NumberVector.Factory<V> factory)
<T extends NumberVector,V extends NumberVector> List<V>	FarthestPointsInitialMeans.chooseInitialMeans(Database database, Relation<T> relation, int k, PrimitiveDistanceFunction<? super T> distanceFunction, NumberVector.Factory<V> factory)

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel declared as PrimitiveDistanceFunction

Modifier and Type	Field and Description
(package private) PrimitiveDistanceFunction<? super NumberVector>	KMeansProcessor.distance Distance function.
private PrimitiveDistanceFunction<? super NumberVector>	KMeansProcessor.Instance.distance Distance function.

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel with parameters of type PrimitiveDistanceFunction

Constructor and Description
Instance(Relation<V> relation, PrimitiveDistanceFunction<? super NumberVector> distance, WritableIntegerDataStore assignment, List<? extends NumberVector> means) Constructor.
KMeansProcessor(Relation<V> relation, PrimitiveDistanceFunction<? super NumberVector> distance, WritableIntegerDataStore assignment, double[] varsum) Constructor.
ParallelLloydKMeans(PrimitiveDistanceFunction<? super NumberVector> distanceFunction, int k, int maxiter, KMeansInitialization<? super V> initializer) Constructor.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality with parameters of type PrimitiveDistanceFunction

Modifier and Type	Method and Description
static double	AbstractKMeansQualityMeasure.logLikelihood(Relation<? extends NumberVector> relation, Clustering<? extends MeanModel> clustering, PrimitiveDistanceFunction<? super NumberVector> distanceFunction) Computes log likelihood of an entire clustering.
static double	AbstractKMeansQualityMeasure.logLikelihoodAlternate(Relation<? extends NumberVector> relation, Clustering<? extends MeanModel> clustering, PrimitiveDistanceFunction<? super NumberVector> distanceFunction) Computes log likelihood of an entire clustering.
<V extends NumberVector> double	WithinClusterVarianceQualityMeasure.quality(Clustering<? extends MeanModel> clustering, PrimitiveDistanceFunction<? super NumberVector> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	BayesianInformationCriterion.quality(Clustering<? extends MeanModel> clustering, PrimitiveDistanceFunction<? super NumberVector> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	BayesianInformationCriterionZhao.quality(Clustering<? extends MeanModel> clustering, PrimitiveDistanceFunction<? super NumberVector> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	AkaikeInformationCriterion.quality(Clustering<? extends MeanModel> clustering, PrimitiveDistanceFunction<? super NumberVector> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	WithinClusterMeanDistanceQualityMeasure.quality(Clustering<? extends MeanModel> clustering, PrimitiveDistanceFunction<? super NumberVector> distanceFunction, Relation<V> relation)
<V extends O> double	KMeansQualityMeasure.quality(Clustering<? extends MeanModel> clustering, PrimitiveDistanceFunction<? super NumberVector> distanceFunction, Relation<V> relation) Calculates and returns the quality measure.
static double	AbstractKMeansQualityMeasure.varianceOfCluster(Cluster<? extends MeanModel> cluster, PrimitiveDistanceFunction<? super NumberVector> distanceFunction, Relation<? extends NumberVector> relation) Variance contribution of a single cluster.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.algorithm.outlier.distance

Constructors in de.lmu.ifi.dbs.elki.algorithm.outlier.distance with parameters of type PrimitiveDistanceFunction

Constructor and Description
ReferenceBasedOutlierDetection(int k, PrimitiveDistanceFunction<? super NumberVector> distanceFunction, ReferencePointsHeuristic refp) Constructor with parameters.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial

Fields in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial declared as PrimitiveDistanceFunction

Modifier and Type	Field and Description
protected PrimitiveDistanceFunction<O>	AbstractDistanceBasedSpatialOutlier.Parameterizer.distanceFunction The distance function to use on the non-spatial attributes.

Constructors in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial with parameters of type PrimitiveDistanceFunction

Constructor and Description
SLOM(NeighborSetPredicate.Factory<N> npred, PrimitiveDistanceFunction<O> nonSpatialDistanceFunction) Constructor.
SOF(NeighborSetPredicate.Factory<N> npred, PrimitiveDistanceFunction<O> nonSpatialDistanceFunction) Constructor.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.algorithm.outlier.subspace

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.subspace with parameters of type PrimitiveDistanceFunction

Modifier and Type	Method and Description
private DoubleDBIDList	OUTRES.subsetNeighborhoodQuery(DoubleDBIDList neighc, DBIDRef dbid, PrimitiveDistanceFunction<? super V> df, double adjustedEps, OUTRES.KernelDensityEstimator kernel) Refine neighbors within a subset.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.application.greedyensemble

Methods in de.lmu.ifi.dbs.elki.application.greedyensemble that return PrimitiveDistanceFunction

Modifier and Type	Method and Description
private PrimitiveDistanceFunction<NumberVector>	GreedyEnsembleExperiment.getDistanceFunction(double[] estimated_weights)

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.database.query.distance

Fields in de.lmu.ifi.dbs.elki.database.query.distance declared as PrimitiveDistanceFunction

Modifier and Type	Field and Description
protected PrimitiveDistanceFunction<? super O>	PrimitiveDistanceQuery.distanceFunction The distance function we use.

Methods in de.lmu.ifi.dbs.elki.database.query.distance that return PrimitiveDistanceFunction

Modifier and Type	Method and Description
PrimitiveDistanceFunction<? super O>	PrimitiveDistanceQuery.getDistanceFunction()

Constructors in de.lmu.ifi.dbs.elki.database.query.distance with parameters of type PrimitiveDistanceFunction

Constructor and Description
PrimitiveDistanceQuery(Relation<? extends O> relation, PrimitiveDistanceFunction<? super O> distanceFunction) Constructor.
PrimitiveDistanceSimilarityQuery(Relation<? extends O> relation, PrimitiveDistanceFunction<? super O> distanceFunction, PrimitiveSimilarityFunction<? super O> similarityFunction) Constructor.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.database.query.knn

Fields in de.lmu.ifi.dbs.elki.database.query.knn declared as PrimitiveDistanceFunction

Modifier and Type	Field and Description
private PrimitiveDistanceFunction<? super O>	LinearScanPrimitiveDistanceKNNQuery.rawdist Unboxed distance function.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.database.query.range

Fields in de.lmu.ifi.dbs.elki.database.query.range declared as PrimitiveDistanceFunction

Modifier and Type	Field and Description
private PrimitiveDistanceFunction<? super O>	LinearScanPrimitiveDistanceRangeQuery.rawdist Unboxed distance function.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.datasource.filter.transform

Fields in de.lmu.ifi.dbs.elki.datasource.filter.transform declared as PrimitiveDistanceFunction

Modifier and Type	Field and Description
(package private) PrimitiveDistanceFunction<? super O>	ClassicMultidimensionalScalingTransform.dist Distance function to use.
(package private) PrimitiveDistanceFunction<? super O>	ClassicMultidimensionalScalingTransform.Parameterizer.dist Distance function to use.

Constructors in de.lmu.ifi.dbs.elki.datasource.filter.transform with parameters of type PrimitiveDistanceFunction

Constructor and Description
ClassicMultidimensionalScalingTransform(int tdim, PrimitiveDistanceFunction<? super O> dist) Constructor.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction

Subinterfaces of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction

Modifier and Type	Interface and Description
interface	Norm<O> Abstract interface for a mathematical norm.
interface	NumberVectorDistanceFunction<O> Base interface for the common case of distance functions defined on numerical vectors.
interface	SpatialPrimitiveDistanceFunction<V extends SpatialComparable> API for a spatial primitive distance function.
interface	WeightedNumberVectorDistanceFunction<V> Distance functions where each dimension is assigned a weight.

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction that implement PrimitiveDistanceFunction

Modifier and Type	Class and Description
class	AbstractNumberVectorDistanceFunction Abstract base class for the most common family of distance functions: defined on number vectors and returning double values.
class	AbstractNumberVectorNorm Abstract base class for double-valued number-vector-based distances based on norms.
class	AbstractPrimitiveDistanceFunction<O> AbstractDistanceFunction provides some methods valid for any extending class.
class	AbstractSpatialDistanceFunction Abstract base class for typical distance functions that allow rectangle-to-rectangle lower bounds.
class	AbstractSpatialNorm Abstract base class for typical distance functions that allow rectangle-to-rectangle lower bounds.
class	ArcCosineDistanceFunction Cosine distance function for feature vectors.
class	BrayCurtisDistanceFunction Bray-Curtis distance function / Sørensen–Dice coefficient for continuous spaces.
class	CanberraDistanceFunction Canberra distance function, a variation of Manhattan distance.
class	ClarkDistanceFunction Clark distance function for vector spaces.
class	CosineDistanceFunction Cosine distance function for feature vectors.
class	Kulczynski1DistanceFunction Kulczynski similarity 1, in distance form.
class	LorentzianDistanceFunction Lorentzian distance function for vector spaces.
class	MatrixWeightedDistanceFunction Weighted distance for feature vectors.
class	WeightedCanberraDistanceFunction Weighted Canberra distance function, a variation of Manhattan distance.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram that implement PrimitiveDistanceFunction

Modifier and Type	Class and Description
class	HistogramIntersectionDistanceFunction Intersection distance for color histograms.
class	HSBHistogramQuadraticDistanceFunction Distance function for HSB color histograms based on a quadratic form and color similarity.
class	RGBHistogramQuadraticDistanceFunction Distance function for RGB color histograms based on a quadratic form and color similarity.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.correlation

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.correlation that implement PrimitiveDistanceFunction

Modifier and Type	Class and Description
class	AbsolutePearsonCorrelationDistanceFunction Absolute Pearson correlation distance function for feature vectors.
class	AbsoluteUncenteredCorrelationDistanceFunction Absolute uncentered correlation distance function for feature vectors.
class	PearsonCorrelationDistanceFunction Pearson correlation distance function for feature vectors.
class	SquaredPearsonCorrelationDistanceFunction Squared Pearson correlation distance function for feature vectors.
class	SquaredUncenteredCorrelationDistanceFunction Squared uncentered correlation distance function for feature vectors.
class	UncenteredCorrelationDistanceFunction Uncentered correlation distance.
class	WeightedPearsonCorrelationDistanceFunction Pearson correlation distance function for feature vectors.
class	WeightedSquaredPearsonCorrelationDistanceFunction Squared Pearson correlation distance function for feature vectors.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.geo

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.geo that implement PrimitiveDistanceFunction

Modifier and Type	Class and Description
class	DimensionSelectingLatLngDistanceFunction Distance function for 2D vectors in Latitude, Longitude form.
class	LatLngDistanceFunction Distance function for 2D vectors in Latitude, Longitude form.
class	LngLatDistanceFunction Distance function for 2D vectors in Longitude, Latitude form.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.histogram

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.histogram that implement PrimitiveDistanceFunction

Modifier and Type	Class and Description
class	HistogramMatchDistanceFunction Distance function based on histogram matching, i.e.
class	KolmogorovSmirnovDistanceFunction Distance function based on the Kolmogorov-Smirnov goodness of fit test.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski that implement PrimitiveDistanceFunction

Modifier and Type	Class and Description
class	EuclideanDistanceFunction Euclidean distance for NumberVectors.
class	LPIntegerNormDistanceFunction LP-Norm for NumberVectors, optimized version for integer values of p.
class	LPNormDistanceFunction LP-Norm for NumberVectors.
class	ManhattanDistanceFunction Manhattan distance for NumberVectors.
class	MaximumDistanceFunction Maximum distance for NumberVectors.
class	MinimumDistanceFunction Maximum distance for NumberVectors.
class	SparseEuclideanDistanceFunction Euclidean distance function, optimized for SparseNumberVectors.
class	SparseLPNormDistanceFunction LP-Norm, optimized for SparseNumberVectors.
class	SparseManhattanDistanceFunction Manhattan distance, optimized for SparseNumberVectors.
class	SparseMaximumDistanceFunction Maximum distance, optimized for SparseNumberVectors.
class	SquaredEuclideanDistanceFunction Squared Euclidean distance, optimized for SparseNumberVectors.
class	WeightedEuclideanDistanceFunction Weighted Euclidean distance for NumberVectors.
class	WeightedLPNormDistanceFunction Weighted version of the Minkowski L_p norm distance for NumberVector.
class	WeightedManhattanDistanceFunction Weighted version of the Minkowski L_p metrics distance function for NumberVectors.
class	WeightedMaximumDistanceFunction Weighted version of the Minkowski L_p metrics distance function for NumberVectors.
class	WeightedSquaredEuclideanDistanceFunction Squared Euclidean distance for NumberVectors.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic that implement PrimitiveDistanceFunction

Modifier and Type	Class and Description
class	ChiSquaredDistanceFunction Chi-Squared distance function, symmetric version.
class	HellingerDistanceFunction Hellinger kernel / Hellinger distance are used with SIFT vectors, and also known as Bhattacharyya distance / coefficient.
class	JeffreyDivergenceDistanceFunction Jeffrey Divergence Distance for NumberVectors.
class	JensenShannonDivergenceDistanceFunction Jensen-Shannon Divergence is essentially the same as Jeffrey divergence, only scaled by half.
class	KullbackLeiblerDivergenceAsymmetricDistanceFunction Kullback-Leibler (asymmetric!)
class	KullbackLeiblerDivergenceReverseAsymmetricDistanceFunction Kullback-Leibler (asymmetric!)
class	SqrtJensenShannonDivergenceDistanceFunction The square root of Jensen-Shannon divergence is metric.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.set

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.set that implement PrimitiveDistanceFunction

Modifier and Type	Class and Description
class	AbstractSetDistanceFunction<O> Abstract base class for set distance functions.
class	HammingDistanceFunction Computes the Hamming distance of arbitrary vectors - i.e. counting, on how many places they differ.
class	JaccardSimilarityDistanceFunction<O extends FeatureVector<?>> A flexible extension of Jaccard similarity to non-binary vectors.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.strings

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.strings that implement PrimitiveDistanceFunction

Modifier and Type	Class and Description
class	LevenshteinDistanceFunction Classic Levenshtein distance on strings.
class	NormalizedLevenshteinDistanceFunction Levenshtein distance on strings, normalized by string length.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.subspace

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.subspace that implement PrimitiveDistanceFunction

Modifier and Type	Class and Description
class	AbstractDimensionsSelectingDistanceFunction<V extends FeatureVector<?>> Abstract base class for distances computed only in subspaces.
class	OnedimensionalDistanceFunction Distance function that computes the distance between feature vectors as the absolute difference of their values in a specified dimension only.
class	SubspaceEuclideanDistanceFunction Euclidean distance function between NumberVectors only in specified dimensions.
class	SubspaceLPNormDistanceFunction LP-Norm distance function between NumberVectors only in specified dimensions.
class	SubspaceManhattanDistanceFunction Manhattan distance function between NumberVectors only in specified dimensions.
class	SubspaceMaximumDistanceFunction Maximum distance function between NumberVectors only in specified dimensions.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries that implement PrimitiveDistanceFunction

Modifier and Type	Class and Description
class	AbstractEditDistanceFunction Edit Distance for FeatureVectors.
class	DerivativeDTWDistanceFunction Derivative Dynamic Time Warping distance for numerical vectors.
class	DTWDistanceFunction Dynamic Time Warping distance (DTW) for numerical vectors.
class	EDRDistanceFunction Edit Distance on Real Sequence distance for numerical vectors.
class	ERPDistanceFunction Edit Distance With Real Penalty distance for numerical vectors.
class	LCSSDistanceFunction Longest Common Subsequence distance for numerical vectors.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.distance.similarityfunction

Fields in de.lmu.ifi.dbs.elki.distance.similarityfunction declared as PrimitiveDistanceFunction

Modifier and Type	Field and Description
protected PrimitiveDistanceFunction<? super O>	InvertedDistanceSimilarityFunction.distanceFunction Holds the similarity function.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster

Classes in de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster that implement PrimitiveDistanceFunction

Modifier and Type	Class and Description
class	ClusteringAdjustedRandIndexSimilarityFunction Measure the similarity of clusters via the Adjusted Rand Index.
class	ClusterIntersectionSimilarityFunction Measure the similarity of clusters via the intersection size.
class	ClusterJaccardSimilarityFunction Measure the similarity of clusters via the Jaccard coefficient.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel

Classes in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel that implement PrimitiveDistanceFunction

Modifier and Type	Class and Description
class	LinearKernelFunction Linear Kernel function that computes a similarity between the two feature vectors V1 and V2 defined by V1^T*V2.
class	PolynomialKernelFunction Polynomial Kernel function that computes a similarity between the two feature vectors V1 and V2 defined by (V1^T*V2)^degree.

Uses of PrimitiveDistanceFunction in de.lmu.ifi.dbs.elki.math.linearalgebra.pca

Fields in de.lmu.ifi.dbs.elki.math.linearalgebra.pca declared as PrimitiveDistanceFunction

Modifier and Type	Field and Description
private PrimitiveDistanceFunction<? super NumberVector>	WeightedCovarianceMatrixBuilder.weightDistance Holds the distance function used for weight calculation.

Uses of PrimitiveDistanceFunction in tutorial.clustering

Fields in tutorial.clustering declared as PrimitiveDistanceFunction

Modifier and Type	Field and Description
protected PrimitiveDistanceFunction<? super NumberVector>	SameSizeKMeansAlgorithm.Parameterizer.distanceFunction Distance function

Methods in tutorial.clustering with parameters of type PrimitiveDistanceFunction

Modifier and Type	Method and Description
protected void	SameSizeKMeansAlgorithm.updateDistances(Relation<V> relation, List<Vector> means, WritableDataStore<SameSizeKMeansAlgorithm.Meta> metas, PrimitiveDistanceFunction<? super NumberVector> df) Compute the distances of each object to all means.

Constructors in tutorial.clustering with parameters of type PrimitiveDistanceFunction

Constructor and Description
SameSizeKMeansAlgorithm(PrimitiveDistanceFunction<? super NumberVector> distanceFunction, int k, int maxiter, KMeansInitialization<? super V> initializer) Constructor.

Uses of PrimitiveDistanceFunction in tutorial.distancefunction

Classes in tutorial.distancefunction that implement PrimitiveDistanceFunction

Modifier and Type	Class and Description
class	MultiLPNorm Tutorial example for ELKI.
class	TutorialDistanceFunction Tutorial example for ELKI.