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

Packages that use DistanceFunction

de.lmu.ifi.dbs.elki.algorithm	Package to collect algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.result	Package to collect result classes for the results of algorithms.
de.lmu.ifi.dbs.elki.algorithm.result.clustering	Package to collect result classes for the results of clustering algorithms.
de.lmu.ifi.dbs.elki.database	Package collects variants of databases and related classes.
de.lmu.ifi.dbs.elki.distance.distancefunction	Package collects distance functions.
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel	Package collects kernel functions.
de.lmu.ifi.dbs.elki.index.tree.metrical	Package collects metrical tree-based index structures.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants	Package collects variants of the M-Tree.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mkcop	Package collects classes for the MkCoPTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mkmax	Package collects classes for the MkMaxTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktab	Package collects classes for the MkTabTree
de.lmu.ifi.dbs.elki.index.tree.spatial	Package collects spatial tree-based index structures.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants	Package collects variants of the R*-Tree.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn	Package collects classes for the RdKNNTree
de.lmu.ifi.dbs.elki.parser	Package collects parser for different file formats and data types.
de.lmu.ifi.dbs.elki.preprocessing	Package collects preprocessors used for data preparation in a first step of various algorithms or distance 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>

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.result

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

private DistanceFunction<O,D>

PointerRepresentation.distanceFunction The distance function this pointer representation was computed with.

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

PointerRepresentation(HashMap<Integer,Integer> pi, HashMap<Integer,SLINK.SLinkDistance> lambda, DistanceFunction<O,D> distanceFunction, Database<O> database)
Creates a new pointer representation.

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

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

private DistanceFunction<O,D>

ClusterOrder.distanceFunction The distance function of the OPTICS algorithm.

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

ClusterOrder(Database<O> database, DistanceFunction<O,D> distanceFunction)
Provides the cluster order of the OPTICS algorithm.

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<QueryResult<D>>>	SequentialDatabase.bulkKNNQueryForID(List<Integer> ids, int k, DistanceFunction<O,D> distanceFunction)
<D extends Distance<D>> List<List<QueryResult<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<QueryResult<D>>>	InvertedListDatabase.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<QueryResult<D>>>	SpatialIndexDatabase.bulkKNNQueryForID(List<Integer> ids, int k, DistanceFunction<O,D> distanceFunction)
<D extends Distance<D>> List<List<QueryResult<D>>>	MetricalIndexDatabase.bulkKNNQueryForID(List<Integer> ids, int k, DistanceFunction<O,D> distanceFunction)
<D extends Distance<D>> List<QueryResult<D>>	SequentialDatabase.kNNQueryForID(Integer id, int k, DistanceFunction<O,D> distanceFunction)
<D extends Distance<D>> List<QueryResult<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<QueryResult<D>>	InvertedListDatabase.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<QueryResult<D>>	SpatialIndexDatabase.kNNQueryForID(Integer id, int k, DistanceFunction<O,D> distanceFunction)
<T extends Distance<T>> List<QueryResult<T>>	MetricalIndexDatabase.kNNQueryForID(Integer id, int k, DistanceFunction<O,T> distanceFunction)
<D extends Distance<D>> List<QueryResult<D>>	SequentialDatabase.kNNQueryForObject(O queryObject, int k, DistanceFunction<O,D> distanceFunction)
<D extends Distance<D>> List<QueryResult<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<QueryResult<D>>	InvertedListDatabase.kNNQueryForObject(O queryObject, int k, DistanceFunction<O,D> distanceFunction) Performs a k-nearest neighbor query for the given object.
<D extends Distance<D>> List<QueryResult<D>>	SpatialIndexDatabase.kNNQueryForObject(O queryObject, int k, DistanceFunction<O,D> distanceFunction)
<T extends Distance<T>> List<QueryResult<T>>	MetricalIndexDatabase.kNNQueryForObject(O queryObject, int k, DistanceFunction<O,T> distanceFunction)
<D extends Distance<D>> List<QueryResult<D>>	SequentialDatabase.rangeQuery(Integer id, String epsilon, DistanceFunction<O,D> distanceFunction)
<D extends Distance<D>> List<QueryResult<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<QueryResult<D>>	InvertedListDatabase.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<QueryResult<D>>	SpatialIndexDatabase.rangeQuery(Integer id, String epsilon, DistanceFunction<O,D> distanceFunction)
<T extends Distance<T>> List<QueryResult<T>>	MetricalIndexDatabase.rangeQuery(Integer id, String epsilon, DistanceFunction<O,T> distanceFunction)
<D extends Distance<D>> List<QueryResult<D>>	SequentialDatabase.reverseKNNQuery(Integer id, int k, DistanceFunction<O,D> distanceFunction)
<D extends Distance<D>> List<QueryResult<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<QueryResult<D>>	InvertedListDatabase.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<QueryResult<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<QueryResult<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	AbstractCorrelationDistanceFunction<O extends RealVector<O,?>,P extends Preprocessor<O>,D extends CorrelationDistance<D>> Abstract super class for correlation based distance functions.
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	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	CosineDistanceFunction<V extends FeatureVector<V,?>> CosineDistanceFunction for FeatureVectors.
class	DimensionSelectingDistanceFunction<N extends Number,O extends FeatureVector<O,N>> Provides a distance function that computes the distance between feature vectors as the absolute difference of their values in a specified dimension.
class	DimensionsSelectingEuklideanDistanceFunction<V extends NumberVector<V,?>> Provides a distance function that computes the Euklidean distance between feature vectors only in specified dimensions.
class	DirectSupportDependentItemsetDistanceFunction Provides a DistanceFunction to compute a Distance between BitVectors based on the number of shared bits.
class	DiSHDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>> Distance function used in the DiSH algorithm.
class	ERiCDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>> Provides a distance function for building the hierarchiy in the ERiC algorithm.
class	EuklideanDistanceFunction<T extends NumberVector<T,?>> Provides the Euklidean distance for FeatureVectors.
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.
class	FractalDimensionBasedDistanceFunction<V extends RealVector<V,?>>
class	FrequencyDependentItemsetDistanceFunction Provides a DistanceFunction to compute a Distance between BitVectors based on the number of shared bits.
class	HiSCDistanceFunction<O extends RealVector<O,?>,P extends Preprocessor<O>> Distance function used in the HiSC algorithm.
class	KernelBasedLocallyWeightedDistanceFunction<O extends RealVector<O,?>,P extends Preprocessor<O>> Provides a kernel based locally weighted distance function.
class	LocallyWeightedDistanceFunction<O extends RealVector<O,?>,P extends Preprocessor<O>> Provides a locally weighted distance function.
class	LPNormDistanceFunction<V extends FeatureVector<V,N>,N extends Number> Provides a LP-Norm for FeatureVectors.
class	ManhattanDistanceFunction<T extends NumberVector<T,?>> Manhattan distance function to compute the Manhattan distance for a pair of NumberVectors.
class	PCABasedCorrelationDistanceFunction<O extends RealVector<O,?>,P extends Preprocessor<O>,D extends CorrelationDistance<D>> Provides the Correlation distance for real valued vectors.
class	PreferenceVectorBasedCorrelationDistanceFunction<O extends RealVector<O,?>,P extends Preprocessor<O>> XXX unify CorrelationDistanceFunction and VarianceDistanceFunction
class	ReciprocalSupportDependentItemsetDistanceFunction Provides a DistanceFunction to compute a Distance between BitVectors based on the number of shared bits.
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	SharedMaximumDistanceFunction Provides a DistanceFunction to compute a Distance between BitVectors based on the number of shared bits.
class	SharedUnitedDistanceFunction Provides a DistanceFunction to compute a Distance between BitVectors based on the number of shared bits.
class	SharingDependentItemsetDistanceFunction Provides a DistanceFunction to compute a Distance between BitVectors based on the number of shared bits.
class	SquareRootSupportLengthDependentItemsetDistanceFunction Provides a DistanceFunction to compute a Distance between BitVectors based on the number of shared bits.
class	SubspaceDistanceFunction<O extends RealVector<O,?>,P extends Preprocessor<O>,D extends SubspaceDistance<D>> 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	SupportLengthDependentItemsetDistanceFunction Provides a DistanceFunction to compute a Distance between BitVectors based on the number of shared bits.
class	WeightedDistanceFunction<O extends NumberVector<O,?>> 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 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.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 definded 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 definded 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 The distance function.

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

DistanceFunction<O,D>

AbstractMTree.getDistanceFunction() Returns the distance function.

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants 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 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.metrical.mtreevariants.mkcop

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

<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	MkCoPLeafEntry.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>> 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.mkmax

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

protected D

MkMaxTreeNode.kNNDistance(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.mktab

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.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.spatial

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

interface

SpatialDistanceFunction<O extends FeatureVector<O,?>,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.

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

abstract <D extends Distance<D>> List<QueryResult<D>>	SpatialIndex.kNNQuery(O obj, int k, DistanceFunction<O,D> distanceFunction) Performs a k-nearest neighbor query for the given object with the given parameter k and the according distance function.
abstract <D extends Distance<D>> List<QueryResult<D>>	SpatialIndex.rangeQuery(O obj, String epsilon, DistanceFunction<O,D> distanceFunction) Performs a range query for the given object with the given epsilon range and the according distance function.
abstract <D extends Distance<D>> List<QueryResult<D>>	SpatialIndex.reverseKNNQuery(O object, int k, DistanceFunction<O,D> distanceFunction) Performs a reverse k-nearest neighbor query for the given object ID.

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

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

protected <D extends Distance<D>> void	AbstractRStarTree.doKNNQuery(Object object, DistanceFunction<O,D> distanceFunction, KNNList<D> knnList) Performs a k-nearest neighbor query for the given NumberVector with the given parameter k and the according distance function.
<D extends Distance<D>> List<QueryResult<D>>	AbstractRStarTree.kNNQuery(O object, int k, DistanceFunction<O,D> distanceFunction) Performs a k-nearest neighbor query for the given NumberVector with the given parameter k and the according distance function.
<D extends Distance<D>> List<QueryResult<D>>	AbstractRStarTree.rangeQuery(O object, String epsilon, DistanceFunction<O,D> distanceFunction) Performs a range query for the given spatial objec with the given epsilon range and the according distance function.
<D extends Distance<D>> List<QueryResult<D>>	AbstractRStarTree.reverseKNNQuery(O object, int k, DistanceFunction<O,D> distanceFunction) Performs a reverse k-nearest neighbor query for the given object ID.

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

Methods in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn with parameters of type DistanceFunction

<T extends Distance<T>> List<QueryResult<T>>

RdKNNTree.reverseKNNQuery(O object, int k, DistanceFunction<O,T> distanceFunction) Performs a reverse k-nearest neighbor query for the given object ID.

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.

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

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

private DistanceFunction<O,D>	SharedNearestNeighborsPreprocessor.distanceFunction Hold the distance funciton 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

static ClassParameter<DistanceFunction>

SharedNearestNeighborsPreprocessor.DISTANCE_FUNCTION_PARAM Parameter to indicate the distance function to be used to ascertain the nearest neighbors.

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