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

Packages that use DistanceFunction

de.lmu.ifi.dbs.elki.algorithm	Algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.outlier	Outlier detection algorithms
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.visualization	Visualization applications in ELKI.
de.lmu.ifi.dbs.elki.database	ELKI database layer - loading, storing, indexing and accessing data
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.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.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.kernel	Kernel functions.
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.index.tree.metrical.mtreevariants.mktrees.mkcop	MkCoPTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax	MkMaxTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab	MkTabTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.split	Splitting strategies of nodes in an M-Tree (and variants).
de.lmu.ifi.dbs.elki.index.tree.spatial	Tree-based index structures for spatial indexing.
de.lmu.ifi.dbs.elki.math.linearalgebra.pca	Principal Component Analysis (PCA) and Eigenvector processing.
de.lmu.ifi.dbs.elki.parser	Parsers for different file formats and data types.
de.lmu.ifi.dbs.elki.preprocessing	Preprocessors used for data preparation in a first step of various algorithms or distance and similarity measures.
de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints	Constraints allow to restrict possible values for parameters.

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

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

private DistanceFunction<O,D>

DistanceBasedAlgorithm.distanceFunction Holds the instance of the distance function specified by DistanceBasedAlgorithm.DISTANCE_FUNCTION_PARAM.

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

protected ClassParameter<DistanceFunction<O,D>>

DistanceBasedAlgorithm.DISTANCE_FUNCTION_PARAM Parameter to specify the distance function to determine the distance between database objects, must extend DistanceFunction.

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

DistanceFunction<O,D>

DistanceBasedAlgorithm.getDistanceFunction() Returns the distanceFunction.

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

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

private DistanceFunction<O,D>

LOF.reachabilityDistanceFunction Holds the instance of the reachability distance function specified by LOF.REACHABILITY_DISTANCE_FUNCTION_PARAM.

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

private ClassParameter<DistanceFunction<O,D>>

LOF.REACHABILITY_DISTANCE_FUNCTION_PARAM The distance function to determine the reachability distance between database objects.

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

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

private DoubleMinMax	DistanceStatisticsWithClasses.exactMinMax(Database<V> database, DistanceFunction<V,D> distFunc)
private DoubleMinMax	DistanceStatisticsWithClasses.sampleMinMax(Database<V> database, DistanceFunction<V,D> distFunc)

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

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

private DistanceFunction<O,N>	CacheFloatDistanceInOnDiskMatrix.distance Distance function that is to be cached.
private DistanceFunction<O,N>	CacheDoubleDistanceInOnDiskMatrix.distance Distance function that is to be cached.

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

private ClassParameter<DistanceFunction<O,N>>	CacheFloatDistanceInOnDiskMatrix.DISTANCE_PARAM Parameter that specifies the name of the directory to be re-parsed.
private ClassParameter<DistanceFunction<O,N>>	CacheDoubleDistanceInOnDiskMatrix.DISTANCE_PARAM Parameter that specifies the name of the directory to be re-parsed.

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

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

private DistanceFunction<O,N>	KNNExplorer.distanceFunction Holds the instance of the distance function specified by KNNExplorer.DISTANCE_FUNCTION_PARAM.
private DistanceFunction<O,N>	KNNExplorer.ExplorerWindow.distanceFunction Holds the instance of the distance function specified by KNNExplorer.DISTANCE_FUNCTION_PARAM.

Fields in de.lmu.ifi.dbs.elki.application.visualization with type parameters of type DistanceFunction

protected ClassParameter<DistanceFunction<O,N>>

KNNExplorer.DISTANCE_FUNCTION_PARAM Parameter to specify the distance function to determine the distance between database objects, must extend DistanceFunction.

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

void	KNNExplorer.ExplorerWindow.run(Database<O> db, DistanceFunction<O,N> distanceFunction)

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

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

<D extends Distance<D>> List<List<DistanceResultPair<D>>>	Database.bulkKNNQueryForID(List<Integer> ids, int k, DistanceFunction<O,D> distanceFunction) Performs k-nearest neighbor queries for the given object IDs.
<D extends Distance<D>> List<List<DistanceResultPair<D>>>	SequentialDatabase.bulkKNNQueryForID(List<Integer> ids, int k, DistanceFunction<O,D> distanceFunction) Retrieves the k nearest neighbors for the query objects.
<D extends Distance<D>> List<List<DistanceResultPair<D>>>	SpatialIndexDatabase.bulkKNNQueryForID(List<Integer> ids, int k, DistanceFunction<O,D> distanceFunction)
<T extends Distance<T>> List<List<DistanceResultPair<T>>>	MetricalIndexDatabase.bulkKNNQueryForID(List<Integer> ids, int k, DistanceFunction<O,T> distanceFunction)
<D extends Distance<D>> List<DistanceResultPair<D>>	Database.kNNQueryForID(Integer id, int k, DistanceFunction<O,D> distanceFunction) Performs a k-nearest neighbor query for the given object ID.
<D extends Distance<D>> List<DistanceResultPair<D>>	SequentialDatabase.kNNQueryForID(Integer id, int k, DistanceFunction<O,D> distanceFunction) Retrieves the k nearest neighbors for the query object.
<D extends Distance<D>> List<DistanceResultPair<D>>	SpatialIndexDatabase.kNNQueryForID(Integer id, int k, DistanceFunction<O,D> distanceFunction)
<T extends Distance<T>> List<DistanceResultPair<T>>	MetricalIndexDatabase.kNNQueryForID(Integer id, int k, DistanceFunction<O,T> distanceFunction)
<D extends Distance<D>> List<DistanceResultPair<D>>	Database.kNNQueryForObject(O queryObject, int k, DistanceFunction<O,D> distanceFunction) Performs a k-nearest neighbor query for the given object.
<D extends Distance<D>> List<DistanceResultPair<D>>	SequentialDatabase.kNNQueryForObject(O queryObject, int k, DistanceFunction<O,D> distanceFunction) Retrieves the k nearest neighbors for the query object.
<D extends Distance<D>> List<DistanceResultPair<D>>	SpatialIndexDatabase.kNNQueryForObject(O queryObject, int k, DistanceFunction<O,D> distanceFunction)
<T extends Distance<T>> List<DistanceResultPair<T>>	MetricalIndexDatabase.kNNQueryForObject(O queryObject, int k, DistanceFunction<O,T> distanceFunction)
<D extends Distance<D>> List<DistanceResultPair<D>>	Database.rangeQuery(Integer id, String epsilon, DistanceFunction<O,D> distanceFunction) Performs a range query for the given object ID with the given epsilon range and the according distance function.
<D extends Distance<D>> List<DistanceResultPair<D>>	SequentialDatabase.rangeQuery(Integer id, String epsilon, DistanceFunction<O,D> distanceFunction)
<D extends Distance<D>> List<DistanceResultPair<D>>	SpatialIndexDatabase.rangeQuery(Integer id, String epsilon, DistanceFunction<O,D> distanceFunction)
<T extends Distance<T>> List<DistanceResultPair<T>>	MetricalIndexDatabase.rangeQuery(Integer id, String epsilon, DistanceFunction<O,T> distanceFunction)
<D extends Distance<D>> List<DistanceResultPair<D>>	Database.reverseKNNQuery(Integer id, int k, DistanceFunction<O,D> distanceFunction) Performs a reverse k-nearest neighbor query for the given object ID.
<D extends Distance<D>> List<DistanceResultPair<D>>	SequentialDatabase.reverseKNNQuery(Integer id, int k, DistanceFunction<O,D> distanceFunction)
<D extends Distance<D>> List<DistanceResultPair<D>>	SpatialIndexDatabase.reverseKNNQuery(Integer id, int k, DistanceFunction<O,D> distanceFunction) Performs a reverse k-nearest neighbor query for the given object ID.
<T extends Distance<T>> List<DistanceResultPair<T>>	MetricalIndexDatabase.reverseKNNQuery(Integer id, int k, DistanceFunction<O,T> distanceFunction)

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

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

class	AbstractDistanceFunction<O extends DatabaseObject,D extends Distance<D>> AbstractDistanceFunction provides some methods valid for any extending class.
class	AbstractDoubleDistanceFunction<O extends DatabaseObject> Provides an abstract superclass for DistanceFunctions that are based on DoubleDistance.
class	AbstractFloatDistanceFunction<O extends DatabaseObject> Provides a DistanceFunction that is based on FloatDistance.
class	AbstractLocallyWeightedDistanceFunction<O extends RealVector<O,?>,P extends Preprocessor<O>> Abstract super class for locally weighted distance functions using a preprocessor to compute the local weight matrix.
class	AbstractPreprocessorBasedDistanceFunction<O extends DatabaseObject,P extends Preprocessor<O>,D extends Distance<D>> Abstract super class for distance functions needing a preprocessor.
class	ArcCosineDistanceFunction<V extends FeatureVector<V,?>> Cosine distance function for feature vectors.
class	CosineDistanceFunction<V extends FeatureVector<V,?>> Cosine distance function for feature vectors.
class	EuclideanDistanceFunction<V extends NumberVector<V,?>> Provides the Euclidean distance for NumberVectors.
class	KernelBasedLocallyWeightedDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>> Provides a kernel based locally weighted distance function.
class	LocallyWeightedDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>> Provides a locally weighted distance function.
class	LPNormDistanceFunction<V extends FeatureVector<V,N>,N extends Number> Provides a LP-Norm for FeatureVectors.
class	ManhattanDistanceFunction<V extends NumberVector<V,?>> Manhattan distance function to compute the Manhattan distance for a pair of NumberVectors.
class	RepresentationSelectingDistanceFunction<O extends DatabaseObject,M extends MultiRepresentedObject<O>,D extends Distance<D>> Distance function for multirepresented objects that selects one representation and computes the distances only within the selected representation.
class	WeightedDistanceFunction<V extends NumberVector<V,?>> Provides the Weighted distance for feature vectors.

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

private DistanceFunction<O,D>

RepresentationSelectingDistanceFunction.defaultDistanceFunction The default distance function.

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

private ClassListParameter<DistanceFunction<O,D>>	RepresentationSelectingDistanceFunction.DISTANCE_FUNCTIONS_PARAM Parameter to specify the distance functions
private List<DistanceFunction<O,D>>	RepresentationSelectingDistanceFunction.distanceFunctions The list of distance functions for each representation.

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

private DistanceFunction<O,D>

RepresentationSelectingDistanceFunction.getDistanceFunctionForCurrentRepresentation() Returns the distance function for the currently selected representation.

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

class	SimilarityAdapterAbstract<V extends FeatureVector<V,?>> Adapter from a normalized similarity function to a distance function.
class	SimilarityAdapterArccos<V extends FeatureVector<V,?>> Adapter from a normalized similarity function to a distance function using arccos(sim).
class	SimilarityAdapterLinear<V extends FeatureVector<V,?>> Adapter from a normalized similarity function to a distance function using 1 - sim.
class	SimilarityAdapterLn<V extends FeatureVector<V,?>> Adapter from a normalized similarity function to a distance function using -log(sim).

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

class	AbstractCorrelationDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>,D extends CorrelationDistance<D>> Abstract super class for correlation based distance functions.
class	AbstractPreferenceVectorBasedCorrelationDistanceFunction<V extends RealVector<V,?>,P extends PreferenceVectorPreprocessor<V>> Abstract super class for all preference vector based correlation distance functions.
class	ERiCDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>> Provides a distance function for building the hierarchiy in the ERiC algorithm.
class	PCABasedCorrelationDistanceFunction<V extends RealVector<V,?>,P extends HiCOPreprocessor<V>,D extends CorrelationDistance<D>> Provides the correlation distance for real valued 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

class	DiskCacheBasedDoubleDistanceFunction<V extends DatabaseObject> Provides a DistanceFunction that is based on double distances given by a distance matrix of an external file.
class	DiskCacheBasedFloatDistanceFunction<V extends DatabaseObject> Provides a DistanceFunction that is based on float distances given by a distance matrix of an external file.
class	FileBasedDoubleDistanceFunction<V extends DatabaseObject> Provides a DistanceFunction that is based on double distances given by a distance matrix of an external file.
class	FileBasedFloatDistanceFunction<V extends DatabaseObject> 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.subspace

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

class	AbstractDimensionsSelectingDoubleDistanceFunction<V extends NumberVector<V,?>> Provides a distance function that computes the distance (which is a double distance) between feature vectors only in specified dimensions.
class	DimensionSelectingDistanceFunction<N extends Number,V extends FeatureVector<V,N>> Provides a distance function that computes the distance between feature vectors as the absolute difference of their values in a specified dimension.
class	DimensionsSelectingEuclideanDistanceFunction<V extends NumberVector<V,?>> Provides a distance function that computes the Euclidean distance between feature vectors only in specified dimensions.
class	DiSHDistanceFunction<V extends RealVector<V,?>,P extends PreferenceVectorPreprocessor<V>> Distance function used in the DiSH algorithm.
class	HiSCDistanceFunction<V extends RealVector<V,?>,P extends PreferenceVectorPreprocessor<V>> Distance function used in the HiSC algorithm.
class	SubspaceDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>> 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.

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

class	AbstractEditDistanceFunction<V extends NumberVector<V,?>> Provides the Edit Distance for NumberVectors.
class	DTWDistanceFunction<V extends NumberVector<V,?>> Provides the Dynamic Time Warping distance for NumberVectors.
class	EDRDistanceFunction<V extends NumberVector<V,?>> Provides the Edit Distance on Real Sequence distance for NumberVectors.
class	ERPDistanceFunction<V extends NumberVector<V,?>> Provides the Edit Distance With Real Penalty distance for NumberVectors.
class	LCSSDistanceFunction<V extends NumberVector<V,?>> Provides the Longest Common Subsequence distance for NumberVectors.

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

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

interface

KernelFunction<O extends DatabaseObject,D extends Distance<D>> Interface Kernel describes the requirements of any kernel function.

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

class	AbstractDoubleKernelFunction<O extends DatabaseObject> Provides an abstract superclass for KernelFunctions that are based on DoubleDistance.
class	AbstractKernelFunction<O extends DatabaseObject,D extends Distance<D>> AbstractKernelFunction provides some methods valid for any extending class.
class	ArbitraryKernelFunctionWrapper<O extends RealVector<O,?>> Provides a wrapper for arbitrary kernel functions whose kernel matrix has been precomputed.
class	FooKernelFunction<O extends FeatureVector<?,?>> Provides an experimental KernelDistanceFunction for RealVectors.
class	LinearKernelFunction<O extends FeatureVector<O,?>> Provides a linear Kernel function that computes a similarity between the two feature vectors V1 and V2 defined by V1^T*V2.
class	PolynomialKernelFunction<O extends FeatureVector<O,?>> 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.index.tree.metrical

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

abstract DistanceFunction<O,D>

MetricalIndex.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

private DistanceFunction<O,D>

AbstractMTree.distanceFunction Holds the instance of the distance function specified by AbstractMTree.DISTANCE_FUNCTION_PARAM.

Fields in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants with type parameters of type DistanceFunction

protected ClassParameter<DistanceFunction<O,D>>

AbstractMTree.DISTANCE_FUNCTION_PARAM Parameter to specify the distance function to determine the distance between database objects, must extend DistanceFunction.

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

DistanceFunction<O,D>

AbstractMTree.getDistanceFunction()

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

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop with parameters of type DistanceFunction

<O extends DatabaseObject> D	MkCoPLeafEntry.approximateConservativeKnnDistance(int k, DistanceFunction<O,D> distanceFunction) Returns the conservative approximated knn distance of the entry.
<O extends DatabaseObject> D	MkCoPDirectoryEntry.approximateConservativeKnnDistance(int k, DistanceFunction<O,D> distanceFunction) Returns the conservative approximated knn distance of the entry.
<O extends DatabaseObject> D	MkCoPEntry.approximateConservativeKnnDistance(int k, DistanceFunction<O,D> distanceFunction) Returns the conservative approximated knn distance of the entry.
<O extends DatabaseObject> D	MkCoPLeafEntry.approximateProgressiveKnnDistance(int k, DistanceFunction<O,D> distanceFunction) Returns the progressive approximated knn distance of the entry.
<O extends DatabaseObject,D extends NumberDistance<D,N>,N extends Number> D	ApproximationLine.getApproximatedKnnDistance(int k, DistanceFunction<O,D> distanceFunction) Returns the approximated knn-distance at the specified k.

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

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax with parameters of type DistanceFunction

protected D

MkMaxTreeNode.kNNDistance(DistanceFunction<O,D> distanceFunction) Determines and returns the k-nearest neighbor distance of this node as the maximum of the k-nearest neighbor distances of all entries.

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

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab with parameters of type DistanceFunction

protected List<D>

MkTabTreeNode.kNNDistances(DistanceFunction<O,D> distanceFunction) Determines and returns the knn distance of this node as the maximum knn distance of all entries.

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

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.split with parameters of type DistanceFunction

(package private) Assignments<D,E>	MTreeSplit.balancedPartition(N node, Integer routingObject1, Integer routingObject2, DistanceFunction<O,D> distanceFunction) Creates a balanced partition of the entries of the specified node.
private void	MLBDistSplit.promote(N node, DistanceFunction<O,D> distanceFunction) Selects the second object of the specified node to be promoted and stored into the parent node and partitions the entries according to the M_LB_DIST strategy.
private void	MRadSplit.promote(N node, DistanceFunction<O,D> distanceFunction) Selects two objects of the specified node to be promoted and stored into the parent node.

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

MLBDistSplit(N node, DistanceFunction<O,D> distanceFunction)
Creates a new split object.

MRadSplit(N node, DistanceFunction<O,D> distanceFunction)
Creates a new split object.

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

Subinterfaces of DistanceFunction in de.lmu.ifi.dbs.elki.index.tree.spatial

interface

SpatialDistanceFunction<V extends FeatureVector<V,?>,D extends Distance<D>> Defines the requirements for a distance function that can used in spatial index to measure the dissimilarity between spatial data objects.

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

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

private DistanceFunction<V,DoubleDistance>

WeightedCovarianceMatrixBuilder.weightDistance Holds the distance function used for weight calculation

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

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

private DistanceFunction<ExternalObject,D>

NumberDistanceParser.distanceFunction The distance function.

Fields in de.lmu.ifi.dbs.elki.parser with type parameters of type DistanceFunction

(package private) ClassParameter<DistanceFunction<ExternalObject,D>>

NumberDistanceParser.DISTANCE_FUNCTION_PARAM Parameter for distance function.

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

DistanceFunction<O,D>	DistanceParser.getDistanceFunction() Returns the distance function of this parser.
DistanceFunction<ExternalObject,D>	NumberDistanceParser.getDistanceFunction() Returns the distance function of this parser.

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

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

protected DistanceFunction<O,D>	MaterializeKNNPreprocessor.distanceFunction Hold the distance function to be used.
private DistanceFunction<O,D>	SharedNearestNeighborsPreprocessor.distanceFunction Hold the distance function to be used.
protected DistanceFunction<V,DoubleDistance>	HiCOPreprocessor.pcaDistanceFunction The distance function for the PCA.
protected DistanceFunction<V,D>	ProjectedDBSCANPreprocessor.rangeQueryDistanceFunction The distance function for the variance analysis.

Fields in de.lmu.ifi.dbs.elki.preprocessing with type parameters of type DistanceFunction

ClassParameter<DistanceFunction<O,D>>	MaterializeKNNPreprocessor.DISTANCE_FUNCTION_PARAM Parameter to indicate the distance function to be used to ascertain the nearest neighbors.
ClassParameter<DistanceFunction<O,D>>	SharedNearestNeighborsPreprocessor.DISTANCE_FUNCTION_PARAM Parameter to indicate the distance function to be used to ascertain the nearest neighbors.
private ClassParameter<DistanceFunction<V,D>>	ProjectedDBSCANPreprocessor.DISTANCE_FUNCTION_PARAM Parameter distance function
protected ClassParameter<DistanceFunction<V,DoubleDistance>>	HiCOPreprocessor.PCA_DISTANCE_PARAM Parameter to specify the distance function used for running PCA.

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

DistanceFunction<O,D>

SharedNearestNeighborsPreprocessor.getDistanceFunction() Returns the distance function used by the preprocessor.

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

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

class

GlobalDistanceFunctionPatternConstraint<D extends DistanceFunction<?,?>> Global parameter constraint for testing if a given pattern parameter (PatternParameter) specifies a valid pattern for a given class parameter (ClassParameter) defining a specific distance function.

Fields in de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints declared as DistanceFunction

private DistanceFunction<?,?>

DistanceFunctionPatternConstraint.distanceFunction The distance function the pattern is checked for.

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

DistanceFunctionPatternConstraint(DistanceFunction<?,?> distFunction)
Constructs a distance function pattern constraint for testing if a given pattern parameter holds a valid pattern for the parameter distFunction