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

Class Summary
Class	Description
AbstractDatabaseDistanceFunction<O,D extends Distance<D>>	Abstract super class for distance functions needing a database context.
AbstractDatabaseDistanceFunction.Instance<O,D extends Distance<D>>	The actual instance bound to a particular database.
AbstractDBIDDistanceFunction<D extends Distance<D>>	AbstractDistanceFunction provides some methods valid for any extending class.
AbstractIndexBasedDistanceFunction<O,I extends Index,D extends Distance<D>>	Abstract super class for distance functions needing a database index.
AbstractIndexBasedDistanceFunction.Instance<O,I extends Index,D extends Distance<D>,F extends DistanceFunction<? super O,D>>	The actual instance bound to a particular database.
AbstractIndexBasedDistanceFunction.Parameterizer<F extends IndexFactory<?,?>>	Parameterization class.
AbstractPrimitiveDistanceFunction<O,D extends Distance<D>>	AbstractDistanceFunction provides some methods valid for any extending class.
AbstractSpatialDoubleDistanceFunction	Abstract base class for typical distance functions that allow rectangle-to-rectangle lower bounds.
AbstractSpatialDoubleDistanceNorm	Abstract base class for typical distance functions that allow rectangle-to-rectangle lower bounds.
AbstractVectorDoubleDistanceFunction	Abstract base class for the most common family of distance functions: defined on number vectors and returning double values.
AbstractVectorDoubleDistanceNorm	Abstract base class for double-valued number-vector-based distances based on norms.
ArcCosineDistanceFunction	Cosine distance function for feature vectors.
ArcCosineDistanceFunction.Parameterizer	Parameterization class.
BrayCurtisDistanceFunction	Bray-Curtis distance function / Sørensen–Dice coefficient for continuous spaces.
BrayCurtisDistanceFunction.Parameterizer	Parameterization class.
CanberraDistanceFunction	Canberra distance function, a variation of Manhattan distance.
CanberraDistanceFunction.Parameterizer	Parameterization class.
ClarkDistanceFunction	Clark distance function for vector spaces.
ClarkDistanceFunction.Parameterizer	Parameterization class.
CosineDistanceFunction	Cosine distance function for feature vectors.
CosineDistanceFunction.Parameterizer	Parameterization class.
Kulczynski1DistanceFunction	Kulczynski similarity 1, in distance form.
Kulczynski1DistanceFunction.Parameterizer	Parameterization class.
LocallyWeightedDistanceFunction<V extends NumberVector<?>>	Provides a locally weighted distance function.
LocallyWeightedDistanceFunction.Instance<V extends NumberVector<?>>	Instance of this distance for a particular database.
LocallyWeightedDistanceFunction.Parameterizer<V extends NumberVector<?>>	Parameterization class.
LorentzianDistanceFunction	Lorentzian distance function for vector spaces.
LorentzianDistanceFunction.Parameterizer	Parameterization class.
MinKDistance<O,D extends Distance<D>>	A distance that is at least the distance to the kth nearest neighbor.
MinKDistance.Parameterizer<O,D extends Distance<D>>	Parameterization class.
ProxyDistanceFunction<O,D extends Distance<D>>	Distance function to proxy computations to another distance (that probably was run before).
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.
RandomStableDistanceFunction.Parameterizer	Parameterization class.
SharedNearestNeighborJaccardDistanceFunction<O>	SharedNearestNeighborJaccardDistanceFunction computes the Jaccard coefficient, which is a proper distance metric.
SharedNearestNeighborJaccardDistanceFunction.Instance<T>	Actual instance for a dataset.
SharedNearestNeighborJaccardDistanceFunction.Parameterizer<O>	Parameterization class.
WeightedCanberraDistanceFunction	Weighted Canberra distance function, a variation of Manhattan distance.
WeightedDistanceFunction	Provides the Weighted distance for feature vectors.

Package de.lmu.ifi.dbs.elki.distance.distancefunction Description

Distance functions for use within ELKI.

Distance functions

There are three basic types of distance functions:

Primitive Distance Functions that can be computed for any two objects.
DBID Distance Functions, that are only defined for object IDs, e.g. an external distance matrix
Index-Based Distance Functions, that require an indexing/preprocessing step, and are then valid for existing database objects.

These types differ significantly in both implementation and use.

Using distance functions

As a 'consumer' of distances, you usually do not care about the type of distance function you want to use. To facilitate this, a distance function can be bound to a database by calling the 'instantiate' method to obtain a DistanceQuery object. A distance query is a best-effort adapter for the given distance function. Usually, you pass it two DBIDs and get the distance value back. When required, the adapter will get the appropriate records from the database needed to compute the distance.

Note: instantiating a preprocessor based distance will invoke the preprocessing step. It is recommended to do this as soon as possible, and only instantiate the query once, then pass the query object through the various methods.

Code example

DistanceQuery<V, DoubleDistance> distanceQuery = database.getDistanceQuery(EuclideanDistanceFunction.STATIC);