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

Packages that use DistanceFunction

Package	Description
de.lmu.ifi.dbs.elki.algorithm	Algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.benchmark	Benchmarking pseudo algorithms.
de.lmu.ifi.dbs.elki.algorithm.classification	Classification algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering	Clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation	Affinity Propagation (AP) clustering.
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan	Generalized DBSCAN.
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.optics	OPTICS family of clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.outlier	Outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.clustering	Clustering based outlier detection.
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.distance.parallel	Parallel implementations of distance-based outlier detectors.
de.lmu.ifi.dbs.elki.algorithm.outlier.lof	LOF family of outlier detection algorithms.
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel	Parallelized variants of LOF.
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial	Spatial outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood	Spatial outlier neighborhood classes
de.lmu.ifi.dbs.elki.algorithm.statistics	Statistical analysis algorithms The algorithms in this package perform statistical analysis of the data (e.g. compute distributions, distance distributions etc.)
de.lmu.ifi.dbs.elki.application.cache	Utility applications for the persistence layer such as distance cache builders.
de.lmu.ifi.dbs.elki.application.greedyensemble	Greedy ensembles for outlier detection.
de.lmu.ifi.dbs.elki.database	ELKI database layer - loading, storing, indexing and accessing data
de.lmu.ifi.dbs.elki.database.query.distance	Prepared queries for distances.
de.lmu.ifi.dbs.elki.database.relation	Relations, materialized and virtual (views).
de.lmu.ifi.dbs.elki.distance.distancefunction	Distance functions for use within ELKI.
de.lmu.ifi.dbs.elki.distance.distancefunction.adapter	Distance functions deriving distances from e.g. similarity measures
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.external	Distance functions using external data sources.
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.cluster	Similarity measures for comparing clusters.
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel	Kernel functions.
de.lmu.ifi.dbs.elki.evaluation.clustering.internal	Internal evaluation measures for clusterings.
de.lmu.ifi.dbs.elki.evaluation.similaritymatrix	Render a distance matrix to visualize a clustering-distance-combination.
de.lmu.ifi.dbs.elki.index	Index structure implementations
de.lmu.ifi.dbs.elki.index.distancematrix	Precomputed distance matrix.
de.lmu.ifi.dbs.elki.index.idistance	iDistance is a distance based indexing technique, using a reference points embedding.
de.lmu.ifi.dbs.elki.index.lsh.hashfamilies	Hash function families for LSH.
de.lmu.ifi.dbs.elki.index.preprocessed.knn	Indexes providing KNN and rKNN data.
de.lmu.ifi.dbs.elki.index.preprocessed.localpca	Index using a preprocessed local PCA.
de.lmu.ifi.dbs.elki.index.preprocessed.snn	Indexes providing nearest neighbor sets
de.lmu.ifi.dbs.elki.index.tree.metrical	Tree-based index structures for metrical vector spaces.
de.lmu.ifi.dbs.elki.index.tree.metrical.covertree	Cover-tree variations.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants	M-Tree and variants.
de.lmu.ifi.dbs.elki.utilities	Utility and helper classes - commonly used data structures, output formatting, exceptions, ...
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation.
tutorial.distancefunction	Classes from the tutorial on implementing distance functions.
tutorial.outlier

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

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

Modifier and Type	Field and Description
private DistanceFunction<? super O>	AbstractDistanceBasedAlgorithm.distanceFunction Holds the instance of the distance function specified by DistanceBasedAlgorithm.DISTANCE_FUNCTION_ID.
protected DistanceFunction<O>	AbstractDistanceBasedAlgorithm.Parameterizer.distanceFunction The distance function to use.

Methods in de.lmu.ifi.dbs.elki.algorithm with type parameters of type DistanceFunction

Modifier and Type	Method and Description
static <F extends DistanceFunction<?>> ObjectParameter<F>	AbstractAlgorithm.makeParameterDistanceFunction(Class<?> defaultDistanceFunction, Class<?> restriction) Make a default distance function configuration option.

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

Modifier and Type	Method and Description
DistanceFunction<? super O>	DistanceBasedAlgorithm.getDistanceFunction() Returns the distanceFunction.
DistanceFunction<? super O>	AbstractDistanceBasedAlgorithm.getDistanceFunction() Returns the distanceFunction.

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

Constructor and Description
AbstractDistanceBasedAlgorithm(DistanceFunction<? super O> distanceFunction) Constructor.
KNNDistancesSampler(DistanceFunction<O> distanceFunction, int k, double sample, RandomFactory rnd) Constructor.
KNNJoin(DistanceFunction<? super V> distanceFunction, int k) Constructor.
MaterializeDistances(DistanceFunction<? super O> distanceFunction) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.algorithm.benchmark

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

Constructor and Description
KNNBenchmarkAlgorithm(DistanceFunction<? super O> distanceFunction, int k, DatabaseConnection queries, double sampling, RandomFactory random) Constructor.
RangeQueryBenchmarkAlgorithm(DistanceFunction<? super O> distanceFunction, DatabaseConnection queries, double sampling, RandomFactory random) Constructor.
ValidateApproximativeKNNIndex(DistanceFunction<? super O> distanceFunction, int k, DatabaseConnection queries, double sampling, boolean forcelinear, RandomFactory random, Pattern pattern) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.algorithm.classification

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

Constructor and Description
KNNClassifier(DistanceFunction<? super O> distanceFunction, int k) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.algorithm.clustering

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering declared as DistanceFunction

Modifier and Type	Field and Description
private DistanceFunction<? super V>	AbstractProjectedClustering.distanceFunction The Euclidean distance function.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering that return DistanceFunction

Modifier and Type	Method and Description
protected DistanceFunction<? super V>	AbstractProjectedClustering.getDistanceFunction() Returns the distance function.

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

Constructor and Description
CanopyPreClustering(DistanceFunction<? super O> distanceFunction, double t1, double t2) Constructor.
DBSCAN(DistanceFunction<? super O> distanceFunction, double epsilon, int minpts) Constructor with parameters.
NaiveMeanShiftClustering(DistanceFunction<? super V> distanceFunction, KernelDensityFunction kernel, double range) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation declared as DistanceFunction

Modifier and Type	Field and Description
(package private) DistanceFunction<? super O>	DistanceBasedInitializationWithMedian.distance Distance function.
(package private) DistanceFunction<? super O>	DistanceBasedInitializationWithMedian.Parameterizer.distance istance function.

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation with parameters of type DistanceFunction

Constructor and Description
DistanceBasedInitializationWithMedian(DistanceFunction<? super O> distance, double quantile) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan declared as DistanceFunction

Modifier and Type	Field and Description
protected DistanceFunction<O>	EpsilonNeighborPredicate.Parameterizer.distfun Distance function to use
(package private) DistanceFunction<O>	AbstractRangeQueryNeighborPredicate.Parameterizer.distfun Distance function to use
protected DistanceFunction<? super O>	EpsilonNeighborPredicate.distFunc Distance function to use
protected DistanceFunction<? super O>	AbstractRangeQueryNeighborPredicate.distFunc Distance function to use.

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan with parameters of type DistanceFunction

Constructor and Description
AbstractRangeQueryNeighborPredicate(double epsilon, DistanceFunction<? super O> distFunc) Full constructor.
EpsilonNeighborPredicate(double epsilon, DistanceFunction<? super O> distFunc) Full constructor.
LSDBC(DistanceFunction<? super O> distanceFunction, int k, double alpha) Constructor.

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

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical with parameters of type DistanceFunction

Constructor and Description
AbstractHDBSCAN(DistanceFunction<? super O> distanceFunction, int minPts) Constructor.
AGNES(DistanceFunction<? super O> distanceFunction, LinkageMethod linkage) Constructor.
AnderbergHierarchicalClustering(DistanceFunction<? super O> distanceFunction, LinkageMethod linkage) Constructor.
CLINK(DistanceFunction<? super O> distanceFunction) Constructor.
HDBSCANLinearMemory(DistanceFunction<? super O> distanceFunction, int minPts) Constructor.
SLINK(DistanceFunction<? super O> distanceFunction) Constructor.
SLINKHDBSCANLinearMemory(DistanceFunction<? super O> distanceFunction, int minPts) Constructor.

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

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans that return DistanceFunction

Modifier and Type	Method and Description
DistanceFunction<? super V>	KMeansBisecting.getDistanceFunction()
DistanceFunction<? super V>	BestOfMultipleKMeans.getDistanceFunction()

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

Constructor and Description
CLARA(DistanceFunction<? super V> distanceFunction, int k, int maxiter, KMedoidsInitialization<V> initializer, int numsamples, double sampling, RandomFactory random) Constructor.
KMedoidsEM(DistanceFunction<? super V> distanceFunction, int k, int maxiter, KMedoidsInitialization<V> initializer) Constructor.
KMedoidsPAM(DistanceFunction<? super V> distanceFunction, int k, int maxiter, KMedoidsInitialization<V> initializer) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.algorithm.clustering.optics

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.optics with parameters of type DistanceFunction

Constructor and Description
AbstractOPTICS(DistanceFunction<? super O> distanceFunction, double epsilon, int minpts) Constructor.
DeLiClu(DistanceFunction<? super NV> distanceFunction, int minpts) Constructor.
OPTICSHeap(DistanceFunction<? super O> distanceFunction, double epsilon, int minpts) Constructor.
OPTICSList(DistanceFunction<? super O> distanceFunction, double epsilon, int minpts) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.algorithm.outlier

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

Constructor and Description
COP(DistanceFunction<? super V> distanceFunction, int k, PCARunner pca, double expect, COP.DistanceDist dist, boolean models) Constructor.
DWOF(DistanceFunction<? super O> distanceFunction, int k, double delta) Constructor.
OPTICSOF(DistanceFunction<? super O> distanceFunction, int minpts) Constructor with parameters.
SimpleCOP(DistanceFunction<? super V> distanceFunction, int k, PCAFilteredRunner pca) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering

Constructors in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering with parameters of type DistanceFunction

Constructor and Description
SilhouetteOutlierDetection(DistanceFunction<? super O> distanceFunction, ClusteringAlgorithm<?> clusterer, NoiseHandling noiseOption) Constructor.

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

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.distance with parameters of type DistanceFunction

Modifier and Type	Method and Description
protected void	AbstractDBOutlier.Parameterizer.configD(Parameterization config, DistanceFunction<?> distanceFunction) Grab the 'd' configuration option.

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

Constructor and Description
AbstractDBOutlier(DistanceFunction<? super O> distanceFunction, double d) Constructor with actual parameters.
DBOutlierDetection(DistanceFunction<O> distanceFunction, double d, double p) Constructor with actual parameters.
DBOutlierScore(DistanceFunction<O> distanceFunction, double d) Constructor with parameters.
KNNOutlier(DistanceFunction<? super O> distanceFunction, int k) Constructor for a single kNN query.
KNNWeightOutlier(DistanceFunction<? super O> distanceFunction, int k) Constructor with parameters.
ODIN(DistanceFunction<? super O> distanceFunction, int k) Constructor.

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

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

Constructor and Description
ParallelKNNOutlier(DistanceFunction<? super O> distanceFunction, int k) Constructor.
ParallelKNNWeightOutlier(DistanceFunction<? super O> distanceFunction, int k) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.algorithm.outlier.lof

Fields in de.lmu.ifi.dbs.elki.algorithm.outlier.lof declared as DistanceFunction

Modifier and Type	Field and Description
protected DistanceFunction<? super O>	LoOP.comparisonDistanceFunction Distance function for comparison set.
protected DistanceFunction<O>	LoOP.Parameterizer.comparisonDistanceFunction Preprocessor Step 2.
protected DistanceFunction<O>	FlexibleLOF.Parameterizer.neighborhoodDistanceFunction Neighborhood distance function.
protected DistanceFunction<? super O>	FlexibleLOF.reachabilityDistanceFunction Reachability distance function.
protected DistanceFunction<O>	FlexibleLOF.Parameterizer.reachabilityDistanceFunction Reachability distance function.
protected DistanceFunction<? super O>	LoOP.reachabilityDistanceFunction Distance function for reachability.
protected DistanceFunction<O>	LoOP.Parameterizer.reachabilityDistanceFunction Preprocessor Step 1.
protected DistanceFunction<? super O>	FlexibleLOF.referenceDistanceFunction Neighborhood distance function.

Constructors in de.lmu.ifi.dbs.elki.algorithm.outlier.lof with parameters of type DistanceFunction

Constructor and Description
COF(int k, DistanceFunction<? super O> distanceFunction) Constructor.
FlexibleLOF(int krefer, int kreach, DistanceFunction<? super O> neighborhoodDistanceFunction, DistanceFunction<? super O> reachabilityDistanceFunction) Constructor.
FlexibleLOF(int krefer, int kreach, DistanceFunction<? super O> neighborhoodDistanceFunction, DistanceFunction<? super O> reachabilityDistanceFunction) Constructor.
INFLO(DistanceFunction<? super O> distanceFunction, double m, int k) Constructor with parameters.
KDEOS(DistanceFunction<? super O> distanceFunction, int kmin, int kmax, KernelDensityFunction kernel, double minBandwidth, double scale, int idim) Constructor.
LDF(int k, DistanceFunction<? super O> distance, KernelDensityFunction kernel, double h, double c) Constructor.
LDOF(DistanceFunction<? super O> distanceFunction, int k) Constructor.
LOCI(DistanceFunction<? super O> distanceFunction, double rmax, int nmin, double alpha) Constructor.
LOF(int k, DistanceFunction<? super O> distanceFunction) Constructor.
LoOP(int kreach, int kcomp, DistanceFunction<? super O> reachabilityDistanceFunction, DistanceFunction<? super O> comparisonDistanceFunction, double lambda) Constructor with parameters.
LoOP(int kreach, int kcomp, DistanceFunction<? super O> reachabilityDistanceFunction, DistanceFunction<? super O> comparisonDistanceFunction, double lambda) Constructor with parameters.
OnlineLOF(int krefer, int kreach, DistanceFunction<? super O> neighborhoodDistanceFunction, DistanceFunction<? super O> reachabilityDistanceFunction) Constructor.
OnlineLOF(int krefer, int kreach, DistanceFunction<? super O> neighborhoodDistanceFunction, DistanceFunction<? super O> reachabilityDistanceFunction) Constructor.
SimpleKernelDensityLOF(int k, DistanceFunction<? super O> distance, KernelDensityFunction kernel) Constructor.
SimplifiedLOF(int k, DistanceFunction<? super O> distance) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel

Constructors in de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel with parameters of type DistanceFunction

Constructor and Description
ParallelLOF(DistanceFunction<? super O> distanceFunction, int k) Constructor.
ParallelSimplifiedLOF(DistanceFunction<? super O> distanceFunction, int k) Constructor.

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

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

Modifier and Type	Field and Description
private DistanceFunction<O>	AbstractDistanceBasedSpatialOutlier.nonSpatialDistanceFunction The distance function to use

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial that return DistanceFunction

Modifier and Type	Method and Description
protected DistanceFunction<O>	AbstractDistanceBasedSpatialOutlier.getNonSpatialDistanceFunction() Get the non-spatial relation

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

Constructor and Description
AbstractDistanceBasedSpatialOutlier(NeighborSetPredicate.Factory<N> npredf, DistanceFunction<O> nonSpatialDistanceFunction) Constructor.
CTLuGLSBackwardSearchAlgorithm(DistanceFunction<V> distanceFunction, int k, double alpha) Constructor.
CTLuRandomWalkEC(DistanceFunction<N> distanceFunction, double alpha, double c, int k) Constructor.

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

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

Modifier and Type	Field and Description
private DistanceFunction<? super O>	PrecomputedKNearestNeighborNeighborhood.Factory.distFunc distance function to use
(package private) DistanceFunction<? super O>	PrecomputedKNearestNeighborNeighborhood.Factory.Parameterizer.distFunc Distance function

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

Constructor and Description
Factory(int k, DistanceFunction<? super O> distFunc) Factory Constructor

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.algorithm.statistics

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

Constructor and Description
AveragePrecisionAtK(DistanceFunction<? super O> distanceFunction, int k, double sampling, RandomFactory random, boolean includeSelf) Constructor.
DistanceStatisticsWithClasses(DistanceFunction<? super O> distanceFunction, int numbins, boolean exact, boolean sampling) Constructor.
EstimateIntrinsicDimensionality(DistanceFunction<? super O> distanceFunction, IntrinsicDimensionalityEstimator estimator, double krate, double samples) Constructor.
EvaluateRankingQuality(DistanceFunction<? super V> distanceFunction, int numbins) Constructor.
EvaluateRetrievalPerformance(DistanceFunction<? super O> distanceFunction, double sampling, RandomFactory random, boolean includeSelf, int maxk) Constructor.
RankingQualityHistogram(DistanceFunction<? super O> distanceFunction, int numbins) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.application.cache

Fields in de.lmu.ifi.dbs.elki.application.cache declared as DistanceFunction

Modifier and Type	Field and Description
private DistanceFunction<O>	CacheFloatDistanceInOnDiskMatrix.distance Distance function that is to be cached.
private DistanceFunction<O>	CacheFloatDistanceInOnDiskMatrix.Parameterizer.distance Distance function that is to be cached.
private DistanceFunction<O>	CacheDoubleDistanceKNNLists.distance Distance function that is to be cached.
private DistanceFunction<O>	CacheDoubleDistanceKNNLists.Parameterizer.distance Distance function that is to be cached.
private DistanceFunction<O>	CacheDoubleDistanceRangeQueries.distance Distance function that is to be cached.
private DistanceFunction<O>	CacheDoubleDistanceRangeQueries.Parameterizer.distance Distance function that is to be cached.
private DistanceFunction<O>	CacheDoubleDistanceInOnDiskMatrix.distance Distance function that is to be cached.
private DistanceFunction<O>	CacheDoubleDistanceInOnDiskMatrix.Parameterizer.distance Distance function that is to be cached.

Constructors in de.lmu.ifi.dbs.elki.application.cache with parameters of type DistanceFunction

Constructor and Description
CacheDoubleDistanceInOnDiskMatrix(InputStep input, DistanceFunction<O> distance, File out) Constructor.
CacheDoubleDistanceKNNLists(InputStep input, DistanceFunction<O> distance, int k, File out) Constructor.
CacheDoubleDistanceRangeQueries(InputStep input, DistanceFunction<O> distance, double radius, File out) Constructor.
CacheFloatDistanceInOnDiskMatrix(InputStep input, DistanceFunction<O> distance, File out) Constructor.

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

Fields in de.lmu.ifi.dbs.elki.application.greedyensemble declared as DistanceFunction

Modifier and Type	Field and Description
(package private) DistanceFunction<? super O>	ComputeKNNOutlierScores.distf Distance function to use
(package private) DistanceFunction<? super O>	ComputeKNNOutlierScores.Parameterizer.distf Distance function to use

Constructors in de.lmu.ifi.dbs.elki.application.greedyensemble with parameters of type DistanceFunction

Constructor and Description
ComputeKNNOutlierScores(InputStep inputstep, DistanceFunction<? super O> distf, int startk, int stepk, int maxk, ByLabelOutlier bylabel, File outfile, ScalingFunction scaling, Pattern disable) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.database

Methods in de.lmu.ifi.dbs.elki.database with parameters of type DistanceFunction

Modifier and Type	Method and Description
static <O> DistanceQuery<O>	QueryUtil.getDistanceQuery(Database database, DistanceFunction<? super O> distanceFunction, Object... hints) Get a distance query for a given distance function, automatically choosing a relation.
<O> DistanceQuery<O>	Database.getDistanceQuery(Relation<O> relation, DistanceFunction<? super O> distanceFunction, Object... hints) Get the distance query for a particular distance function.
<O> DistanceQuery<O>	AbstractDatabase.getDistanceQuery(Relation<O> objQuery, DistanceFunction<? super O> distanceFunction, Object... hints)
static <O> KNNQuery<O>	QueryUtil.getKNNQuery(Database database, DistanceFunction<? super O> distanceFunction, Object... hints) Get a KNN query object for the given distance function.
static <O> KNNQuery<O>	QueryUtil.getKNNQuery(Relation<O> relation, DistanceFunction<? super O> distanceFunction, Object... hints) Get a KNN query object for the given distance function.
static <O> RangeQuery<O>	QueryUtil.getRangeQuery(Database database, DistanceFunction<? super O> distanceFunction, Object... hints) Get a range query object for the given distance function for radius-based neighbor search.
static <O> RangeQuery<O>	QueryUtil.getRangeQuery(Relation<O> relation, DistanceFunction<? super O> distanceFunction, Object... hints) Get a range query object for the given distance function for radius-based neighbor search.
static <O> RKNNQuery<O>	QueryUtil.getRKNNQuery(Relation<O> relation, DistanceFunction<? super O> distanceFunction, Object... hints) Get a rKNN query object for the given distance function.

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

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

Modifier and Type	Method and Description
DistanceFunction<? super O>	DistanceQuery.getDistanceFunction() Get the inner distance function.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.database.relation

Methods in de.lmu.ifi.dbs.elki.database.relation with parameters of type DistanceFunction

Modifier and Type	Method and Description
DistanceQuery<O>	AbstractRelation.getDistanceQuery(DistanceFunction<? super O> distanceFunction, Object... hints)
DistanceQuery<O>	Relation.getDistanceQuery(DistanceFunction<? super O> distanceFunction, Object... hints) Get the distance query for a particular distance function.
KNNQuery<O>	AbstractRelation.getKNNQuery(DistanceFunction<? super O> distanceFunction, Object... hints)
KNNQuery<O>	Relation.getKNNQuery(DistanceFunction<? super O> distanceFunction, Object... hints) Get a KNN query object for the given distance query.
RangeQuery<O>	AbstractRelation.getRangeQuery(DistanceFunction<? super O> distanceFunction, Object... hints)
RangeQuery<O>	Relation.getRangeQuery(DistanceFunction<? super O> distanceFunction, Object... hints) Get a range query object for the given distance query.
RKNNQuery<O>	AbstractRelation.getRKNNQuery(DistanceFunction<? super O> distanceFunction, Object... hints)
RKNNQuery<O>	Relation.getRKNNQuery(DistanceFunction<? super O> distanceFunction, Object... hints) Get a rKNN query object for the given distance query.

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

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction with type parameters of type DistanceFunction

Modifier and Type	Class and Description
static class	AbstractIndexBasedDistanceFunction.Instance<O,I extends Index,F extends DistanceFunction<? super O>> The actual instance bound to a particular database.

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

Modifier and Type	Interface and Description
interface	DBIDDistanceFunction Distance functions valid in a database context only (i.e. for DBIDs) For any "distance" that cannot be computed for arbitrary objects, only those that exist in the database and referenced by their ID.
interface	DBIDRangeDistanceFunction Distance functions valid in a static database context only (i.e. for DBIDRanges) For any "distance" that cannot be computed for arbitrary objects, only those that exist in the database and referenced by their ID.
interface	IndexBasedDistanceFunction<O> Distance function relying on an index (such as preprocessed neighborhoods).
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	PrimitiveDistanceFunction<O> Primitive distance function that is defined on some kind of object.
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 DistanceFunction

Modifier and Type	Class and Description
class	AbstractDatabaseDistanceFunction<O> Abstract super class for distance functions needing a database context.
class	AbstractDBIDRangeDistanceFunction Abstract base class for distance functions that rely on integer offsets within a consecutive range.
class	AbstractIndexBasedDistanceFunction<O,I extends Index> Abstract super class for distance functions needing a database index.
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	RandomStableDistanceFunction This is a dummy distance providing random values (obviously not metrical), useful mostly for unit tests and baseline evaluations: obviously this distance provides no benefit whatsoever.
class	SharedNearestNeighborJaccardDistanceFunction<O> SharedNearestNeighborJaccardDistanceFunction computes the Jaccard coefficient, which is a proper distance metric.
class	WeightedCanberraDistanceFunction Weighted Canberra distance function, a variation of Manhattan distance.

Fields in de.lmu.ifi.dbs.elki.distance.distancefunction declared as DistanceFunction

Modifier and Type	Field and Description
(package private) DistanceFunction<? super O>	AbstractDatabaseDistanceFunction.Instance.parent Parent distance
protected F	AbstractIndexBasedDistanceFunction.Instance.parent Our parent distance function

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction that return DistanceFunction

Modifier and Type	Method and Description
DistanceFunction<? super O>	AbstractDatabaseDistanceFunction.Instance.getDistanceFunction()

Constructors in de.lmu.ifi.dbs.elki.distance.distancefunction with parameters of type DistanceFunction

Constructor and Description
Instance(Relation<O> database, DistanceFunction<? super O> parent) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.adapter

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

Modifier and Type	Class and Description
class	AbstractSimilarityAdapter<O> Adapter from a similarity function to a distance function.
class	ArccosSimilarityAdapter<O> Adapter from a normalized similarity function to a distance function using arccos(sim).
class	LinearAdapterLinear<O> Adapter from a normalized similarity function to a distance function using 1 - sim.
class	LnSimilarityAdapter<O> Adapter from a normalized similarity function to a distance function using -log(sim).

Constructors in de.lmu.ifi.dbs.elki.distance.distancefunction.adapter with parameters of type DistanceFunction

Constructor and Description
Instance(Relation<O> database, DistanceFunction<? super O> parent, SimilarityQuery<? super O> similarityQuery) Constructor.
Instance(Relation<O> database, DistanceFunction<? super O> parent, SimilarityQuery<? super O> similarityQuery) Constructor.
Instance(Relation<O> database, DistanceFunction<? super O> parent, SimilarityQuery<O> similarityQuery) Constructor.
Instance(Relation<O> database, DistanceFunction<? super O> parent, SimilarityQuery<O> similarityQuery) Constructor.

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

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

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 DistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.correlation

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

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 DistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.external

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

Modifier and Type	Class and Description
class	DiskCacheBasedDoubleDistanceFunction Distance function that is based on double distances given by a distance matrix of an external binary matrix file.
class	DiskCacheBasedFloatDistanceFunction Distance function that is based on float distances given by a distance matrix of an external binary matrix file.
class	FileBasedDoubleDistanceFunction Distance function that is based on double distances given by a distance matrix of an external ASCII file.
class	FileBasedFloatDistanceFunction Distance function that is based on float distances given by a distance matrix of an external ASCII file.

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

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

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 DistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.histogram

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

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 DistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski

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

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

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

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 DistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.set

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

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 DistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.strings

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

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 DistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.subspace

Subinterfaces of DistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.subspace

Modifier and Type	Interface and Description
interface	DimensionSelectingSubspaceDistanceFunction<O> Interface for dimension selecting subspace distance functions.

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

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 DistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries

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

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 DistanceFunction in de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster

Subinterfaces of DistanceFunction in de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster

Modifier and Type	Interface and Description
interface	ClusteringDistanceSimilarityFunction Distance and similarity measure for clusterings.

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

Modifier and Type	Class and Description
class	ClusteringAdjustedRandIndexSimilarityFunction Measure the similarity of clusters via the Adjusted Rand Index.
class	ClusteringBCubedF1SimilarityFunction Measure the similarity of clusters via the BCubed F1 Index.
class	ClusteringFowlkesMallowsSimilarityFunction Measure the similarity of clusters via the Fowlkes-Mallows Index.
class	ClusteringRandIndexSimilarityFunction Measure the similarity of clusters via the 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 DistanceFunction in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel

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

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 DistanceFunction in de.lmu.ifi.dbs.elki.evaluation.clustering.internal

Fields in de.lmu.ifi.dbs.elki.evaluation.clustering.internal declared as DistanceFunction

Modifier and Type	Field and Description
private DistanceFunction<? super O>	EvaluateCIndex.distance Distance function to use.
private DistanceFunction<? super O>	EvaluateCIndex.Parameterizer.distance Distance function to use.
private DistanceFunction<? super O>	EvaluateSilhouette.distance Distance function to use.
private DistanceFunction<? super O>	EvaluateSilhouette.Parameterizer.distance Distance function to use.

Constructors in de.lmu.ifi.dbs.elki.evaluation.clustering.internal with parameters of type DistanceFunction

Constructor and Description
EvaluateCIndex(DistanceFunction<? super O> distance, NoiseHandling noiseOpt) Constructor.
EvaluateSilhouette(DistanceFunction<? super O> distance, boolean mergenoise) Constructor.
EvaluateSilhouette(DistanceFunction<? super O> distance, NoiseHandling noiseOption, boolean penalize) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.evaluation.similaritymatrix

Fields in de.lmu.ifi.dbs.elki.evaluation.similaritymatrix declared as DistanceFunction

Modifier and Type	Field and Description
private DistanceFunction<? super O>	ComputeSimilarityMatrixImage.distanceFunction The distance function to use
private DistanceFunction<O>	ComputeSimilarityMatrixImage.Parameterizer.distanceFunction The distance function to use

Constructors in de.lmu.ifi.dbs.elki.evaluation.similaritymatrix with parameters of type DistanceFunction

Constructor and Description
ComputeSimilarityMatrixImage(DistanceFunction<? super O> distanceFunction, ScalingFunction scaling, boolean skipzero) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.index

Methods in de.lmu.ifi.dbs.elki.index with parameters of type DistanceFunction

Modifier and Type	Method and Description
DistanceQuery<O>	DistanceIndex.getDistanceQuery(DistanceFunction<? super O> distanceFunction, Object... hints) Get a KNN query object for the given distance query and k.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.index.distancematrix

Fields in de.lmu.ifi.dbs.elki.index.distancematrix declared as DistanceFunction

Modifier and Type	Field and Description
protected DistanceFunction<? super O>	PrecomputedDistanceMatrix.distanceFunction Nested distance function.
protected DistanceFunction<? super O>	PrecomputedDistanceMatrix.Factory.distanceFunction Nested distance function.
protected DistanceFunction<? super O>	PrecomputedDistanceMatrix.Factory.Parameterizer.distanceFunction Nested distance function.

Methods in de.lmu.ifi.dbs.elki.index.distancematrix that return DistanceFunction

Modifier and Type	Method and Description
DistanceFunction<? super O>	PrecomputedDistanceMatrix.PrecomputedDistanceQuery.getDistanceFunction()

Methods in de.lmu.ifi.dbs.elki.index.distancematrix with parameters of type DistanceFunction

Modifier and Type	Method and Description
DistanceQuery<O>	PrecomputedDistanceMatrix.getDistanceQuery(DistanceFunction<? super O> distanceFunction, Object... hints)

Constructors in de.lmu.ifi.dbs.elki.index.distancematrix with parameters of type DistanceFunction

Constructor and Description
Factory(DistanceFunction<? super O> distanceFunction) Constructor.
PrecomputedDistanceMatrix(Relation<O> relation, DistanceFunction<? super O> distanceFunction) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.index.idistance

Fields in de.lmu.ifi.dbs.elki.index.idistance declared as DistanceFunction

Modifier and Type	Field and Description
(package private) DistanceFunction<? super V>	InMemoryIDistanceIndex.Factory.distance Distance function to use.
(package private) DistanceFunction<? super V>	InMemoryIDistanceIndex.Factory.Parameterizer.distance Distance function to use.

Methods in de.lmu.ifi.dbs.elki.index.idistance that return DistanceFunction

Modifier and Type	Method and Description
private DistanceFunction<? super O>	InMemoryIDistanceIndex.getDistanceFunction() Distance function.

Constructors in de.lmu.ifi.dbs.elki.index.idistance with parameters of type DistanceFunction

Constructor and Description
Factory(DistanceFunction<? super V> distance, KMedoidsInitialization<V> initialization, int k) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.index.lsh.hashfamilies

Methods in de.lmu.ifi.dbs.elki.index.lsh.hashfamilies with parameters of type DistanceFunction

Modifier and Type	Method and Description
boolean	LocalitySensitiveHashFunctionFamily.isCompatible(DistanceFunction<?> df) Check whether the given distance function can be accelerated using this hash family.
boolean	ManhattanHashFunctionFamily.isCompatible(DistanceFunction<?> df)
boolean	CosineHashFunctionFamily.isCompatible(DistanceFunction<?> df)
boolean	EuclideanHashFunctionFamily.isCompatible(DistanceFunction<?> df)

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.index.preprocessed.knn

Fields in de.lmu.ifi.dbs.elki.index.preprocessed.knn declared as DistanceFunction

Modifier and Type	Field and Description
protected DistanceFunction<? super O>	AbstractMaterializeKNNPreprocessor.distanceFunction The distance function to be used.
protected DistanceFunction<? super O>	AbstractMaterializeKNNPreprocessor.Factory.distanceFunction Hold the distance function to be used.
protected DistanceFunction<? super O>	AbstractMaterializeKNNPreprocessor.Factory.Parameterizer.distanceFunction Hold the distance function to be used.

Methods in de.lmu.ifi.dbs.elki.index.preprocessed.knn that return DistanceFunction

Modifier and Type	Method and Description
DistanceFunction<? super O>	AbstractMaterializeKNNPreprocessor.Factory.getDistanceFunction() Get the distance function.

Constructors in de.lmu.ifi.dbs.elki.index.preprocessed.knn with parameters of type DistanceFunction

Constructor and Description
AbstractMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k) Constructor.
CachedDoubleDistanceKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k, File file) Constructor.
Factory(int k, DistanceFunction<? super NumberVector> distanceFunction) Constructor.
Factory(int k, DistanceFunction<? super O> distanceFunction) Constructor.
Factory(int k, DistanceFunction<? super O> distanceFunction) Index factory.
Factory(int k, DistanceFunction<? super O> distanceFunction) Index factory.
Factory(int k, DistanceFunction<? super O> distanceFunction) Constructor.
Factory(int k, DistanceFunction<? super O> distanceFunction) Constructor.
Factory(int k, DistanceFunction<? super O> distanceFunction, double share, RandomFactory rnd) Constructor.
Factory(int k, DistanceFunction<? super O> distanceFunction, File filename) Index factory.
Factory(int k, DistanceFunction<? super O> distanceFunction, int partitions, RandomFactory rnd) Constructor.
Factory(int k, DistanceFunction<? super V> distanceFunction, List<SpatialSorter> curvegen, double window, int variants, RandomFactory random) Constructor.
KNNJoinMaterializeKNNPreprocessor(Relation<V> relation, DistanceFunction<? super V> distanceFunction, int k) Constructor.
MaterializeKNNAndRKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k) Constructor.
MaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k) Constructor with preprocessing step.
MetricalIndexApproximationMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k) Constructor
PartitionApproximationMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k, int partitions, RandomFactory rnd) Constructor
RandomSampleKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k, double share, RandomFactory rnd) Constructor.
SpacefillingMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k, List<SpatialSorter> curvegen, double window, int variants, Random random) Constructor.
SpatialApproximationMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k) Constructor

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.index.preprocessed.localpca

Fields in de.lmu.ifi.dbs.elki.index.preprocessed.localpca declared as DistanceFunction

Modifier and Type	Field and Description
protected DistanceFunction<NV>	AbstractFilteredPCAIndex.Factory.pcaDistanceFunction Holds the instance of the distance function specified by AbstractFilteredPCAIndex.Factory.PCA_DISTANCE_ID.
protected DistanceFunction<NV>	AbstractFilteredPCAIndex.Factory.Parameterizer.pcaDistanceFunction Holds the instance of the distance function specified by AbstractFilteredPCAIndex.Factory.PCA_DISTANCE_ID.

Constructors in de.lmu.ifi.dbs.elki.index.preprocessed.localpca with parameters of type DistanceFunction

Constructor and Description
Factory(DistanceFunction<NV> pcaDistanceFunction, PCAFilteredRunner pca) Constructor.
Factory(DistanceFunction<V> pcaDistanceFunction, PCAFilteredRunner pca, int k) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.index.preprocessed.snn

Fields in de.lmu.ifi.dbs.elki.index.preprocessed.snn declared as DistanceFunction

Modifier and Type	Field and Description
protected DistanceFunction<O>	SharedNearestNeighborPreprocessor.distanceFunction Hold the distance function to be used.
protected DistanceFunction<O>	SharedNearestNeighborPreprocessor.Factory.distanceFunction Hold the distance function to be used.
protected DistanceFunction<O>	SharedNearestNeighborPreprocessor.Factory.Parameterizer.distanceFunction Hold the distance function to be used.

Constructors in de.lmu.ifi.dbs.elki.index.preprocessed.snn with parameters of type DistanceFunction

Constructor and Description
Factory(int numberOfNeighbors, DistanceFunction<O> distanceFunction) Constructor.
SharedNearestNeighborPreprocessor(Relation<O> relation, int numberOfNeighbors, DistanceFunction<O> distanceFunction) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.index.tree.metrical

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical that return DistanceFunction

Modifier and Type	Method and Description
abstract DistanceFunction<? super O>	MetricalIndexTree.getDistanceFunction() Returns the distance function of this metrical index.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.index.tree.metrical.covertree

Fields in de.lmu.ifi.dbs.elki.index.tree.metrical.covertree declared as DistanceFunction

Modifier and Type	Field and Description
protected DistanceFunction<? super O>	AbstractCoverTree.distanceFunction Holds the instance of the trees distance function.
protected DistanceFunction<? super O>	AbstractCoverTree.Factory.distanceFunction Holds the instance of the trees distance function.
protected DistanceFunction<? super O>	AbstractCoverTree.Factory.Parameterizer.distanceFunction Holds the instance of the trees distance function.

Constructors in de.lmu.ifi.dbs.elki.index.tree.metrical.covertree with parameters of type DistanceFunction

Constructor and Description
AbstractCoverTree(Relation<O> relation, DistanceFunction<? super O> distanceFunction, double expansion, int truncate) Constructor.
CoverTree(Relation<O> relation, DistanceFunction<? super O> distanceFunction, double expansion, int truncate) Constructor.
Factory(DistanceFunction<? super O> distanceFunction, double expansion, int truncate) Constructor.
Factory(DistanceFunction<? super O> distanceFunction, double expansion, int truncate) Constructor.
Factory(DistanceFunction<? super O> distanceFunction, double expansion, int truncate) Constructor.
SimplifiedCoverTree(Relation<O> relation, DistanceFunction<? super O> distanceFunction, double expansion, int truncate) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants

Fields in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants declared as DistanceFunction

Modifier and Type	Field and Description
protected DistanceFunction<? super O>	MTreeSettings.distanceFunction Holds the instance of the trees distance function.

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants that return DistanceFunction

Modifier and Type	Method and Description
DistanceFunction<? super O>	AbstractMTree.getDistanceFunction()

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.utilities

Methods in de.lmu.ifi.dbs.elki.utilities with parameters of type DistanceFunction

Modifier and Type	Method and Description
static <O> KNNQuery<O>	DatabaseUtil.precomputedKNNQuery(Database database, Relation<O> relation, DistanceFunction<? super O> distf, int k) Get (or create) a precomputed kNN query for the database.

Uses of DistanceFunction in tutorial.clustering

Constructors in tutorial.clustering with parameters of type DistanceFunction

Constructor and Description
NaiveAgglomerativeHierarchicalClustering1(DistanceFunction<? super O> distanceFunction, int numclusters) Constructor.
NaiveAgglomerativeHierarchicalClustering2(DistanceFunction<? super O> distanceFunction, int numclusters) Constructor.
NaiveAgglomerativeHierarchicalClustering3(DistanceFunction<? super O> distanceFunction, int numclusters, NaiveAgglomerativeHierarchicalClustering3.Linkage linkage) Constructor.
NaiveAgglomerativeHierarchicalClustering4(DistanceFunction<? super O> distanceFunction, NaiveAgglomerativeHierarchicalClustering4.Linkage linkage) Constructor.

Uses of DistanceFunction in tutorial.distancefunction

Classes in tutorial.distancefunction that implement DistanceFunction

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

Uses of DistanceFunction in tutorial.outlier

Constructors in tutorial.outlier with parameters of type DistanceFunction

Constructor and Description
DistanceStddevOutlier(DistanceFunction<? super O> distanceFunction, int k) Constructor.
ODIN(DistanceFunction<? super O> distanceFunction, int k) Constructor.