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.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
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations.
de.lmu.ifi.dbs.elki.algorithm.outlier	Outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.lof	LOF family of outlier detection algorithms.
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.distance	Distance values, distance functions and similarity functions.
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.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.kernel	Kernel functions.
de.lmu.ifi.dbs.elki.evaluation.similaritymatrix	Render a distance matrix to visualize a clustering-distance-combination.
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.preprocessed.subspaceproj	Index using a preprocessed local subspaces.
de.lmu.ifi.dbs.elki.index.tree.metrical	Tree-based index structures for metrical vector spaces.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants	M-Tree and variants.
de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters	Classes for various typed parameters.
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,D>	AbstractDistanceBasedAlgorithm.distanceFunction Holds the instance of the distance function specified by DistanceBasedAlgorithm.DISTANCE_FUNCTION_ID.
protected DistanceFunction<O,D>	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,D>	DistanceBasedAlgorithm.getDistanceFunction() Returns the distanceFunction.
DistanceFunction<? super O,D>	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,D> distanceFunction) Constructor.
KNNDistanceOrder(DistanceFunction<O,D> distanceFunction, int k, double percentage) Constructor.
KNNJoin(DistanceFunction<? super V,D> distanceFunction, int k) Constructor.
MaterializeDistances(DistanceFunction<? super O,D> 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,D> distanceFunction, int k, DatabaseConnection queries, double sampling, RandomFactory random) Constructor.
RangeQueryBenchmarkAlgorithm(DistanceFunction<? super O,D> distanceFunction, DatabaseConnection queries, double sampling, RandomFactory random) Constructor.
ValidateApproximativeKNNIndex(DistanceFunction<? super O,D> distanceFunction, int k, DatabaseConnection queries, double sampling, boolean forcelinear, RandomFactory random, Pattern pattern) 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,DoubleDistance>	AbstractProjectedClustering.distanceFunction The euclidean distance function.
protected DistanceFunction<V,D>	AbstractProjectedDBSCAN.Parameterizer.innerdist

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

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

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

Modifier and Type	Method and Description
protected void	AbstractProjectedDBSCAN.Parameterizer.configEpsilon(Parameterization config, DistanceFunction<V,D> innerdist)
protected void	AbstractProjectedDBSCAN.Parameterizer.configOuterDistance(Parameterization config, D epsilon, int minpts, Class<?> preprocessorClass, DistanceFunction<V,D> innerdist)

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

Constructor and Description
CanopyPreClustering(DistanceFunction<? super O,D> distanceFunction, D t1, D t2) Constructor.
DBSCAN(DistanceFunction<? super O,D> distanceFunction, D epsilon, int minpts) Constructor with parameters.
DeLiClu(DistanceFunction<? super NV,D> distanceFunction, int minpts) Constructor.
NaiveMeanShiftClustering(DistanceFunction<? super V,D> distanceFunction, KernelDensityFunction kernel, D range) Constructor.
OPTICS(DistanceFunction<? super O,D> distanceFunction, D epsilon, int minpts) 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,D>	DistanceBasedInitializationWithMedian.distance Distance function.
(package private) DistanceFunction<? super O,D>	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,D> 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
(package private) DistanceFunction<O,D>	EpsilonNeighborPredicate.Parameterizer.distfun Distance function to use
protected DistanceFunction<O,D>	EpsilonNeighborPredicate.distFunc Distance function to use

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

Constructor and Description
EpsilonNeighborPredicate(D epsilon, DistanceFunction<O,D> distFunc) Full 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
NaiveAgglomerativeHierarchicalClustering(DistanceFunction<? super O,D> distanceFunction, LinkageMethod linkage) Constructor.
SLINK(DistanceFunction<? super O,D> distanceFunction) 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,D>	KMeansBisecting.getDistanceFunction()
DistanceFunction<? super V,D>	BestOfMultipleKMeans.getDistanceFunction()

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

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

Modifier and Type	Field and Description
private DistanceFunction<V,D>	ReferenceBasedOutlierDetection.distanceFunction Distance function to use.

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

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

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

Constructor and Description
AbstractDBOutlier(DistanceFunction<? super O,D> distanceFunction, D d) Constructor with actual parameters.
COP(DistanceFunction<? super V,D> distanceFunction, int k, PCARunner<V> pca, double expect, COP.DistanceDist dist, boolean models) Constructor.
DBOutlierDetection(DistanceFunction<O,D> distanceFunction, D d, double p) Constructor with actual parameters.
DBOutlierScore(DistanceFunction<O,D> distanceFunction, D d) Constructor with parameters.
DWOF(DistanceFunction<? super O,D> distanceFunction, int k, double delta) Constructor.
KNNOutlier(DistanceFunction<? super O,D> distanceFunction, int k) Constructor for a single kNN query.
KNNWeightOutlier(DistanceFunction<? super O,D> distanceFunction, int k) Constructor with parameters.
ODIN(DistanceFunction<? super O,D> distanceFunction, int k) Constructor.
OPTICSOF(DistanceFunction<? super O,D> distanceFunction, int minpts) Constructor with parameters.
ReferenceBasedOutlierDetection(int k, DistanceFunction<V,D> distanceFunction, ReferencePointsHeuristic<V> refp) Constructor with parameters.
SimpleCOP(DistanceFunction<? super V,D> distanceFunction, int k, PCAFilteredRunner<V> pca) 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,D>	LoOP.comparisonDistanceFunction Preprocessor Step 2.
protected DistanceFunction<O,D>	LoOP.Parameterizer.comparisonDistanceFunction Preprocessor Step 2.
protected DistanceFunction<O,D>	FlexibleLOF.Parameterizer.neighborhoodDistanceFunction Neighborhood distance function.
protected DistanceFunction<? super O,D>	FlexibleLOF.reachabilityDistanceFunction Reachability distance function.
protected DistanceFunction<O,D>	FlexibleLOF.Parameterizer.reachabilityDistanceFunction Reachability distance function.
protected DistanceFunction<? super O,D>	LoOP.reachabilityDistanceFunction Preprocessor Step 1.
protected DistanceFunction<O,D>	LoOP.Parameterizer.reachabilityDistanceFunction Preprocessor Step 1.
protected DistanceFunction<? super O,D>	FlexibleLOF.referenceDistanceFunction Neighborhood distance function.

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

Constructor and Description
FlexibleLOF(int krefer, int kreach, DistanceFunction<? super O,D> neighborhoodDistanceFunction, DistanceFunction<? super O,D> reachabilityDistanceFunction) Constructor.
FlexibleLOF(int krefer, int kreach, DistanceFunction<? super O,D> neighborhoodDistanceFunction, DistanceFunction<? super O,D> reachabilityDistanceFunction) Constructor.
INFLO(DistanceFunction<? super O,D> distanceFunction, double m, int k) Constructor with parameters.
LDF(int k, DistanceFunction<? super O,D> distance, KernelDensityFunction kernel, double h, double c) Constructor.
LDOF(DistanceFunction<? super O,D> distanceFunction, int k) Constructor.
LOCI(DistanceFunction<? super O,D> distanceFunction, D rmax, int nmin, double alpha) Constructor.
LOF(int k, DistanceFunction<? super O,D> distanceFunction) Constructor.
LoOP(int kreach, int kcomp, DistanceFunction<? super O,D> reachabilityDistanceFunction, DistanceFunction<? super O,D> comparisonDistanceFunction, double lambda) Constructor with parameters.
LoOP(int kreach, int kcomp, DistanceFunction<? super O,D> reachabilityDistanceFunction, DistanceFunction<? super O,D> comparisonDistanceFunction, double lambda) Constructor with parameters.
OnlineLOF(int krefer, int kreach, DistanceFunction<? super O,D> neighborhoodDistanceFunction, DistanceFunction<? super O,D> reachabilityDistanceFunction) Constructor.
OnlineLOF(int krefer, int kreach, DistanceFunction<? super O,D> neighborhoodDistanceFunction, DistanceFunction<? super O,D> reachabilityDistanceFunction) Constructor.
SimpleKernelDensityLOF(int k, DistanceFunction<? super O,D> distance, KernelDensityFunction kernel) Constructor.
SimplifiedLOF(int k, DistanceFunction<? super O,D> distance) 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,D>	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,D>	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,D> nonSpatialDistanceFunction) Constructor.
CTLuGLSBackwardSearchAlgorithm(DistanceFunction<V,D> distanceFunction, int k, double alpha) Constructor.
CTLuRandomWalkEC(DistanceFunction<N,D> 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,D>	PrecomputedKNearestNeighborNeighborhood.Factory.distFunc distance function to use
(package private) DistanceFunction<? super O,D>	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
PrecomputedKNearestNeighborNeighborhood.Factory(int k, DistanceFunction<? super O,D> 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 V,D> distanceFunction, int k, double sampling, Long seed, boolean includeSelf) Constructor.
DistanceStatisticsWithClasses(DistanceFunction<? super O,D> distanceFunction, int numbins, boolean exact, boolean sampling) Constructor.
EvaluateRankingQuality(DistanceFunction<? super V,D> distanceFunction, int numbins) Constructor.
RankingQualityHistogram(DistanceFunction<? super O,D> 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,D>	CacheFloatDistanceInOnDiskMatrix.distance Distance function that is to be cached.
private DistanceFunction<O,D>	CacheFloatDistanceInOnDiskMatrix.Parameterizer.distance Distance function that is to be cached.
private DistanceFunction<O,D>	CacheDoubleDistanceKNNLists.distance Distance function that is to be cached.
private DistanceFunction<O,D>	CacheDoubleDistanceKNNLists.Parameterizer.distance Distance function that is to be cached.
private DistanceFunction<O,DoubleDistance>	CacheDoubleDistanceRangeQueries.distance Distance function that is to be cached.
private DistanceFunction<O,DoubleDistance>	CacheDoubleDistanceRangeQueries.Parameterizer.distance Distance function that is to be cached.
private DistanceFunction<O,D>	CacheDoubleDistanceInOnDiskMatrix.distance Distance function that is to be cached.
private DistanceFunction<O,D>	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,D> distance, File out) Constructor.
CacheDoubleDistanceKNNLists(InputStep input, DistanceFunction<O,D> distance, int k, File out) Constructor.
CacheDoubleDistanceRangeQueries(InputStep input, DistanceFunction<O,DoubleDistance> distance, double radius, File out) Constructor.
CacheFloatDistanceInOnDiskMatrix(InputStep input, DistanceFunction<O,D> 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,D>	ComputeKNNOutlierScores.distf Distance function to use
(package private) DistanceFunction<? super O,D>	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,D> distf, int startk, int stepk, int maxk, ByLabelOutlier bylabel, File outfile, ScalingFunction scaling) 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,D extends Distance<D>> DistanceQuery<O,D>	QueryUtil.getDistanceQuery(Database database, DistanceFunction<? super O,D> distanceFunction, Object... hints) Get a distance query for a given distance function, automatically choosing a relation.
<O,D extends Distance<D>> DistanceQuery<O,D>	Database.getDistanceQuery(Relation<O> relation, DistanceFunction<? super O,D> distanceFunction, Object... hints) Get the distance query for a particular distance function.
<O,D extends Distance<D>> DistanceQuery<O,D>	AbstractDatabase.getDistanceQuery(Relation<O> objQuery, DistanceFunction<? super O,D> distanceFunction, Object... hints)
static <O,D extends Distance<D>> KNNQuery<O,D>	QueryUtil.getKNNQuery(Database database, DistanceFunction<? super O,D> distanceFunction, Object... hints) Get a KNN query object for the given distance function.
static <O,D extends Distance<D>> KNNQuery<O,D>	QueryUtil.getKNNQuery(Relation<O> relation, DistanceFunction<? super O,D> distanceFunction, Object... hints) Get a KNN query object for the given distance function.
static <O,D extends Distance<D>> RangeQuery<O,D>	QueryUtil.getRangeQuery(Database database, DistanceFunction<? super O,D> distanceFunction, Object... hints) Get a range query object for the given distance function.
static <O,D extends Distance<D>> RangeQuery<O,D>	QueryUtil.getRangeQuery(Relation<O> relation, DistanceFunction<? super O,D> distanceFunction, Object... hints) Get a range query object for the given distance function.
static <O,D extends Distance<D>> RKNNQuery<O,D>	QueryUtil.getRKNNQuery(Relation<O> relation, DistanceFunction<? super O,D> 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,D>	DistanceQuery.getDistanceFunction() Get the inner distance function.

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

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

Modifier and Type	Method and Description
static boolean	DistanceUtil.isDoubleDistanceFunction(DistanceFunction<?,?> df) Test whether a distance function is double-valued.

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,D extends Distance<D>,F extends DistanceFunction<? super O,D>> 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<D extends Distance<?>> 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	DoubleNorm<O> Interface for norms in the double domain.
interface	FilteredLocalPCABasedDistanceFunction<O extends NumberVector<?>,P extends FilteredLocalPCAIndex<? super O>,D extends Distance<D>> Interface for local PCA based preprocessors.
interface	IndexBasedDistanceFunction<O,D extends Distance<D>> Distance function relying on an index (such as preprocessed neighborhoods).
interface	Norm<O,D extends Distance<D>> Abstract interface for a mathematical norm.
interface	NumberVectorDistanceFunction<D extends Distance<D>> Base interface for the common case of distance functions defined on numerical vectors.
interface	PrimitiveDistanceFunction<O,D extends Distance<?>> Primitive distance function that is defined on some kind of object.
interface	PrimitiveDoubleDistanceFunction<O> Interface for distance functions that can provide a raw double value.
interface	SpatialPrimitiveDistanceFunction<V extends SpatialComparable,D extends Distance<D>> API for a spatial primitive distance function.
interface	SpatialPrimitiveDoubleDistanceFunction<V extends SpatialComparable> Interface combining spatial primitive distance functions with primitive number distance functions.

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

Modifier and Type	Class and Description
class	AbstractDatabaseDistanceFunction<O,D extends Distance<D>> Abstract super class for distance functions needing a database context.
class	AbstractDBIDDistanceFunction<D extends Distance<D>> AbstractDistanceFunction provides some methods valid for any extending class.
class	AbstractIndexBasedDistanceFunction<O,I extends Index,D extends Distance<D>> Abstract super class for distance functions needing a database index.
class	AbstractPrimitiveDistanceFunction<O,D extends Distance<D>> AbstractDistanceFunction provides some methods valid for any extending class.
class	AbstractSpatialDoubleDistanceFunction Abstract base class for typical distance functions that allow rectangle-to-rectangle lower bounds.
class	AbstractSpatialDoubleDistanceNorm Abstract base class for typical distance functions that allow rectangle-to-rectangle lower bounds.
class	AbstractVectorDoubleDistanceFunction Abstract base class for the most common family of distance functions: defined on number vectors and returning double values.
class	AbstractVectorDoubleDistanceNorm Abstract base class for double-valued number-vector-based distances based on norms.
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	LocallyWeightedDistanceFunction<V extends NumberVector<?>> Provides a locally weighted distance function.
class	LorentzianDistanceFunction Lorentzian distance function for vector spaces.
class	MinKDistance<O,D extends Distance<D>> A distance that is at least the distance to the kth nearest neighbor.
class	ProxyDistanceFunction<O,D extends Distance<D>> Distance function to proxy computations to another distance (that probably was run before).
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.
class	WeightedDistanceFunction Provides the Weighted distance for feature vectors.

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

Modifier and Type	Field and Description
(package private) DistanceFunction<? super O,D>	AbstractDatabaseDistanceFunction.Instance.parent Parent distance
protected F	AbstractIndexBasedDistanceFunction.Instance.parent Our parent distance function
protected DistanceFunction<? super O,D>	MinKDistance.parentDistance The distance function to determine the exact distance.
protected DistanceFunction<? super O,D>	MinKDistance.Parameterizer.parentDistance The distance function to determine the exact distance.

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

Modifier and Type	Method and Description
DistanceFunction<? super T,D>	MinKDistance.Instance.getDistanceFunction()
DistanceFunction<? super O,D>	AbstractDatabaseDistanceFunction.Instance.getDistanceFunction()
static <V,T extends V,D extends Distance<D>> DistanceFunction<? super V,D>	ProxyDistanceFunction.unwrapDistance(DistanceFunction<V,D> dfun) Helper function, to resolve any wrapped Proxy Distances

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

Modifier and Type	Method and Description
static <V,T extends V,D extends Distance<D>> DistanceFunction<? super V,D>	ProxyDistanceFunction.unwrapDistance(DistanceFunction<V,D> dfun) Helper function, to resolve any wrapped Proxy Distances

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

Constructor and Description
AbstractDatabaseDistanceFunction.Instance(Relation<O> database, DistanceFunction<? super O,D> parent) Constructor.
MinKDistance.Instance(Relation<T> relation, int k, DistanceFunction<? super O,D> parentDistance) Constructor.
MinKDistance(DistanceFunction<? super O,D> parentDistance, int k) Full 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 normalized 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
AbstractSimilarityAdapter.Instance(Relation<O> database, DistanceFunction<? super O,DoubleDistance> parent, SimilarityQuery<? super O,? extends NumberDistance<?,?>> similarityQuery) Constructor.
ArccosSimilarityAdapter.Instance(Relation<O> database, DistanceFunction<? super O,DoubleDistance> parent, SimilarityQuery<O,? extends NumberDistance<?,?>> similarityQuery) Constructor.
LinearAdapterLinear.Instance(Relation<O> database, DistanceFunction<? super O,DoubleDistance> parent, SimilarityQuery<? super O,? extends NumberDistance<?,?>> similarityQuery) Constructor.
LnSimilarityAdapter.Instance(Relation<O> database, DistanceFunction<? super O,DoubleDistance> parent, SimilarityQuery<O,? extends NumberDistance<?,?>> 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	ERiCDistanceFunction Provides a distance function for building the hierarchy in the ERiC algorithm.
class	PCABasedCorrelationDistanceFunction Provides the correlation distance for real valued vectors.
class	PearsonCorrelationDistanceFunction Pearson correlation distance function for feature vectors.
class	SquaredPearsonCorrelationDistanceFunction Squared Pearson correlation distance function for feature vectors.
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 Provides a DistanceFunction that is based on double distances given by a distance matrix of an external file.
class	DiskCacheBasedFloatDistanceFunction Provides a DistanceFunction that is based on float distances given by a distance matrix of an external file.
class	FileBasedDoubleDistanceFunction Provides a DistanceFunction that is based on double distances given by a distance matrix of an external file.
class	FileBasedFloatDistanceFunction Provides a DistanceFunction that is based on float distances given by a distance matrix of an external 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 Provides the Euclidean distance for FeatureVectors.
class	LPIntegerNormDistanceFunction Provides a LP-Norm for number vectors.
class	LPNormDistanceFunction Provides a LP-Norm for FeatureVectors.
class	ManhattanDistanceFunction Manhattan distance function to compute the Manhattan distance for a pair of FeatureVectors.
class	MaximumDistanceFunction Maximum distance function to compute the Maximum distance for a pair of FeatureVectors.
class	MinimumDistanceFunction Maximum distance function to compute the Minimum distance for a pair of FeatureVectors.
class	SparseEuclideanDistanceFunction Euclidean distance function.
class	SparseLPNormDistanceFunction Provides a LP-Norm for FeatureVectors.
class	SparseManhattanDistanceFunction Manhattan distance function.
class	SparseMaximumDistanceFunction Maximum distance function.
class	SquaredEuclideanDistanceFunction Provides the squared Euclidean distance for FeatureVectors.
class	WeightedEuclideanDistanceFunction Provides the Euclidean distance for FeatureVectors.
class	WeightedLPNormDistanceFunction Weighted version of the Minkowski L_p metrics distance function.
class	WeightedManhattanDistanceFunction Weighted version of the Minkowski L_p metrics distance function.
class	WeightedMaximumDistanceFunction Weighted version of the Minkowski L_p metrics distance function.
class	WeightedSquaredEuclideanDistanceFunction Provides the squared Euclidean distance for FeatureVectors.

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	JeffreyDivergenceDistanceFunction Provides the Jeffrey Divergence Distance for FeatureVectors.
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.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,D extends Distance<D>> 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	AbstractDimensionsSelectingDoubleDistanceFunction<V extends FeatureVector<?>> Provides a distance function that computes the distance (which is a double distance) between feature vectors only in specified dimensions.
class	AbstractPreferenceVectorBasedCorrelationDistanceFunction<V extends NumberVector<?>,P extends PreferenceVectorIndex<V>> Abstract super class for all preference vector based correlation distance functions.
class	DimensionSelectingDistanceFunction Provides a distance function that computes the distance between feature vectors as the absolute difference of their values in a specified dimension.
class	DiSHDistanceFunction Distance function used in the DiSH algorithm.
class	HiSCDistanceFunction<V extends NumberVector<?>> Distance function used in the HiSC algorithm.
class	LocalSubspaceDistanceFunction Provides a distance function to determine a kind of correlation distance between two points, which is a pair consisting of the distance between the two subspaces spanned by the strong eigenvectors of the two points and the affine distance between the two subspaces.
class	SubspaceEuclideanDistanceFunction Provides a distance function that computes the Euclidean distance between feature vectors only in specified dimensions.
class	SubspaceLPNormDistanceFunction Provides a distance function that computes the Euclidean distance between feature vectors only in specified dimensions.
class	SubspaceManhattanDistanceFunction Provides a distance function that computes the Euclidean distance between feature vectors only in specified dimensions.
class	SubspaceMaximumDistanceFunction Provides a distance function that computes the Euclidean distance between feature vectors 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 Provides the Edit Distance for FeatureVectors.
class	DTWDistanceFunction Provides the Dynamic Time Warping distance for FeatureVectors.
class	EDRDistanceFunction Provides the Edit Distance on Real Sequence distance for FeatureVectors.
class	ERPDistanceFunction Provides the Edit Distance With Real Penalty distance for FeatureVectors.
class	LCSSDistanceFunction Provides the Longest Common Subsequence distance for FeatureVectors.

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

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

Modifier and Type	Class and Description
class	JaccardPrimitiveSimilarityFunction<O extends FeatureVector<?>> A flexible extension of Jaccard similarity to non-binary vectors.

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 Provides a linear Kernel function that computes a similarity between the two feature vectors V1 and V2 defined by V1^T*V2.
class	PolynomialKernelFunction Provides a 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.similaritymatrix

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

Modifier and Type	Field and Description
private DistanceFunction<? super O,? extends NumberDistance<?,?>>	ComputeSimilarityMatrixImage.distanceFunction The distance function to use
private DistanceFunction<O,? extends NumberDistance<?,?>>	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,? extends NumberDistance<?,?>> distanceFunction, ScalingFunction scaling, boolean skipzero) 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	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,D>	AbstractMaterializeKNNPreprocessor.distanceFunction The distance function to be used.
protected DistanceFunction<? super O,D>	AbstractMaterializeKNNPreprocessor.Factory.distanceFunction Hold the distance function to be used.
protected DistanceFunction<? super O,D>	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,D>	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.Factory(int k, DistanceFunction<? super O,D> distanceFunction) Index factory.
AbstractMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O,D> distanceFunction, int k) Constructor.
CachedDoubleDistanceKNNPreprocessor.Factory(int k, DistanceFunction<? super O,DoubleDistance> distanceFunction, File filename) Index factory.
CachedDoubleDistanceKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O,DoubleDistance> distanceFunction, int k, File file) Constructor.
KNNJoinMaterializeKNNPreprocessor.Factory(int k, DistanceFunction<? super O,D> distanceFunction) Constructor.
KNNJoinMaterializeKNNPreprocessor(Relation<V> relation, DistanceFunction<? super V,D> distanceFunction, int k) Constructor.
MaterializeKNNAndRKNNPreprocessor.Factory(int k, DistanceFunction<? super O,D> distanceFunction) Constructor.
MaterializeKNNAndRKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O,D> distanceFunction, int k) Constructor.
MaterializeKNNPreprocessor.Factory(int k, DistanceFunction<? super O,D> distanceFunction) Index factory.
MaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O,D> distanceFunction, int k) Constructor with preprocessing step.
MetricalIndexApproximationMaterializeKNNPreprocessor.Factory(int k, DistanceFunction<? super O,D> distanceFunction) Constructor.
MetricalIndexApproximationMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O,D> distanceFunction, int k) Constructor
PartitionApproximationMaterializeKNNPreprocessor.Factory(int k, DistanceFunction<? super O,D> distanceFunction, int partitions, RandomFactory rnd) Constructor.
PartitionApproximationMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O,D> distanceFunction, int k, int partitions, RandomFactory rnd) Constructor
RandomSampleKNNPreprocessor.Factory(int k, DistanceFunction<? super O,D> distanceFunction, double share, RandomFactory rnd) Constructor.
RandomSampleKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O,D> distanceFunction, int k, double share, RandomFactory rnd) Constructor.
SpatialApproximationMaterializeKNNPreprocessor.Factory(int k, DistanceFunction<? super NumberVector<?>,D> distanceFunction) Constructor.
SpatialApproximationMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O,D> 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,DoubleDistance>	AbstractFilteredPCAIndex.Factory.pcaDistanceFunction Holds the instance of the distance function specified by AbstractFilteredPCAIndex.Factory.PCA_DISTANCE_ID.
protected DistanceFunction<NV,DoubleDistance>	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
AbstractFilteredPCAIndex.Factory(DistanceFunction<NV,DoubleDistance> pcaDistanceFunction, PCAFilteredRunner<NV> pca) Constructor.
KNNQueryFilteredPCAIndex.Factory(DistanceFunction<V,DoubleDistance> pcaDistanceFunction, PCAFilteredRunner<V> pca, Integer k) Constructor.
RangeQueryFilteredPCAIndex.Factory(DistanceFunction<V,DoubleDistance> pcaDistanceFunction, PCAFilteredRunner<V> pca, DoubleDistance epsilon) 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,D>	SharedNearestNeighborPreprocessor.distanceFunction Hold the distance function to be used.
protected DistanceFunction<O,D>	SharedNearestNeighborPreprocessor.Factory.distanceFunction Hold the distance function to be used.
protected DistanceFunction<O,D>	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
SharedNearestNeighborPreprocessor.Factory(int numberOfNeighbors, DistanceFunction<O,D> distanceFunction) Constructor.
SharedNearestNeighborPreprocessor(Relation<O> relation, int numberOfNeighbors, DistanceFunction<O,D> distanceFunction) Constructor.

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

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

Modifier and Type	Field and Description
protected DistanceFunction<NV,D>	AbstractSubspaceProjectionIndex.rangeQueryDistanceFunction The distance function for the variance analysis.
protected DistanceFunction<NV,D>	AbstractSubspaceProjectionIndex.Factory.rangeQueryDistanceFunction The distance function for the variance analysis.
protected DistanceFunction<NV,D>	AbstractSubspaceProjectionIndex.Factory.Parameterizer.rangeQueryDistanceFunction The distance function for the variance analysis.

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

Modifier and Type	Method and Description
protected void	AbstractSubspaceProjectionIndex.Factory.Parameterizer.configEpsilon(Parameterization config, DistanceFunction<NV,D> rangeQueryDistanceFunction)

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

Constructor and Description
AbstractSubspaceProjectionIndex.Factory(D epsilon, DistanceFunction<NV,D> rangeQueryDistanceFunction, int minpts) Constructor.
AbstractSubspaceProjectionIndex(Relation<NV> relation, D epsilon, DistanceFunction<NV,D> rangeQueryDistanceFunction, int minpts) Constructor.
FourCSubspaceIndex.Factory(D epsilon, DistanceFunction<V,D> rangeQueryDistanceFunction, int minpts, PCAFilteredRunner<V> pca) Constructor.
FourCSubspaceIndex(Relation<V> relation, D epsilon, DistanceFunction<V,D> rangeQueryDistanceFunction, int minpts, PCAFilteredRunner<V> pca) Full constructor.
PreDeConSubspaceIndex.Factory(D epsilon, DistanceFunction<V,D> rangeQueryDistanceFunction, int minpts, double delta) Constructor.
PreDeConSubspaceIndex(Relation<V> relation, D epsilon, DistanceFunction<V,D> rangeQueryDistanceFunction, int minpts, double delta) 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,D>	MetricalIndexTree.getDistanceFunction() Returns the distance function of this metrical index.

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,D>	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,D>	AbstractMTree.getDistanceFunction()

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

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

Constructor and Description
DistanceParameter(OptionID optionID, DistanceFunction<?,D> dist) Constructs a double parameter with the given optionID.
DistanceParameter(OptionID optionID, DistanceFunction<?,D> dist, boolean optional) Constructs a double parameter with the given optionID and optional flag.
DistanceParameter(OptionID optionID, DistanceFunction<?,D> dist, D defaultValue) Constructs a double parameter with the given optionID and default value.

Uses of DistanceFunction in tutorial.clustering

Constructors in tutorial.clustering with parameters of type DistanceFunction

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