de.lmu.ifi.dbs.elki.distance.distancefunction.subspace
Class SubspaceDistanceFunction<V extends NumberVector<V,?>,P extends LocalPCAPreprocessor<V>>

java.lang.Object
  de.lmu.ifi.dbs.elki.logging.AbstractLoggable
      de.lmu.ifi.dbs.elki.distance.AbstractMeasurementFunction<O,D>
          de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractDistanceFunction<O,D>
              de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractPreprocessorBasedDistanceFunction<V,P,SubspaceDistance>
                  de.lmu.ifi.dbs.elki.distance.distancefunction.subspace.SubspaceDistanceFunction<V,P>

Type Parameters:

V - the type of NumberVector to compute the distances in between

P - the type of Preprocessor used

All Implemented Interfaces:

DistanceFunction<V,SubspaceDistance>, LocalPCAPreprocessorBasedDistanceFunction<V,P,SubspaceDistance>, PreprocessorBasedDistanceFunction<V,P,SubspaceDistance>, MeasurementFunction<V,SubspaceDistance>, PreprocessorBasedMeasurementFunction<V,P,SubspaceDistance>, PreprocessorClient<P,V>, Parameterizable

public class SubspaceDistanceFunction<V extends NumberVector<V,?>,P extends LocalPCAPreprocessor<V>>
extends AbstractPreprocessorBasedDistanceFunction<V,P,SubspaceDistance>
implements LocalPCAPreprocessorBasedDistanceFunction<V,P,SubspaceDistance>

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.

Author:

Elke Achtert

Field Summary

Fields inherited from class de.lmu.ifi.dbs.elki.distance.AbstractMeasurementFunction

distanceFactory

Fields inherited from class de.lmu.ifi.dbs.elki.logging.AbstractLoggable

debug, logger

Constructor Summary

SubspaceDistanceFunction(Parameterization config)
Constructor, adhering to Parameterizable

Method Summary

SubspaceDistance	distance(V o1, V o2) Note, that the pca of o1 must have equal ore more strong eigenvectors than the pca of o2.
SubspaceDistance	distance(V o1, V o2, PCAFilteredResult pca1, PCAFilteredResult pca2) Computes the distance between two given DatabaseObjects according to this distance function.
AssociationID<?>	getAssociationID() Returns the association ID for the association to be set by the preprocessor.
Class<?>	getDefaultPreprocessorClass() Returns the name of the default preprocessor.
String	getPreprocessorDescription() Returns the description for the preprocessor parameter.
Class<P>	getPreprocessorSuperClass() Returns the super class for the preprocessor parameter.

Methods inherited from class de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractPreprocessorBasedDistanceFunction

getPreprocessor, setDatabase

Methods inherited from class de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractDistanceFunction

distance, distance

Methods inherited from class de.lmu.ifi.dbs.elki.distance.AbstractMeasurementFunction

getDatabase, getDistanceFactory, infiniteDistance, nullDistance, undefinedDistance, valueOf

Methods inherited from class de.lmu.ifi.dbs.elki.logging.AbstractLoggable

debugFine, debugFiner, debugFinest, exception, progress, verbose, warning

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction

distance, distance

Methods inherited from interface de.lmu.ifi.dbs.elki.distance.MeasurementFunction

getDistanceFactory, infiniteDistance, nullDistance, setDatabase, undefinedDistance, valueOf

Constructor Detail

SubspaceDistanceFunction

public SubspaceDistanceFunction(Parameterization config)

Constructor, adhering to Parameterizable

Parameters:

config - Parameterization

Method Detail

getDefaultPreprocessorClass

public Class<?> getDefaultPreprocessorClass()

Description copied from interface: PreprocessorClient

Returns the name of the default preprocessor.

Specified by:

getDefaultPreprocessorClass in interface PreprocessorClient<P extends LocalPCAPreprocessor<V>,V extends NumberVector<V,?>>

Returns:

the name of the default preprocessor, which is KnnQueryBasedLocalPCAPreprocessor

getPreprocessorDescription

public final String getPreprocessorDescription()

Description copied from interface: PreprocessorClient

Returns the description for the preprocessor parameter.

Specified by:

getPreprocessorDescription in interface PreprocessorClient<P extends LocalPCAPreprocessor<V>,V extends NumberVector<V,?>>

Returns:

the description for the preprocessor parameter

getPreprocessorSuperClass

public Class<P> getPreprocessorSuperClass()

Description copied from interface: PreprocessorClient

Returns the super class for the preprocessor parameter.

Specified by:

getPreprocessorSuperClass in interface PreprocessorClient<P extends LocalPCAPreprocessor<V>,V extends NumberVector<V,?>>

Returns:

the super class for the preprocessor, which is Preprocessor

getAssociationID

public AssociationID<?> getAssociationID()

Description copied from interface: PreprocessorClient

Returns the association ID for the association to be set by the preprocessor.

Specified by:

getAssociationID in interface PreprocessorClient<P extends LocalPCAPreprocessor<V>,V extends NumberVector<V,?>>

Returns:

the association ID for the association to be set by the preprocessor, which is AssociationID.LOCAL_PCA

distance

public SubspaceDistance distance(V o1,
                                 V o2)

Note, that the pca of o1 must have equal ore more strong eigenvectors than the pca of o2.

Specified by:

distance in interface DistanceFunction<V extends NumberVector<V,?>,SubspaceDistance>

Parameters:

o1 - first DatabaseObject

o2 - second DatabaseObject

Returns:

the distance between two given DatabaseObjects according to this distance function

distance

public SubspaceDistance distance(V o1,
                                 V o2,
                                 PCAFilteredResult pca1,
                                 PCAFilteredResult pca2)

Computes the distance between two given DatabaseObjects according to this distance function. Note, that the first pca must have an equal number of strong eigenvectors than the second pca.

Parameters:

o1 - first DatabaseObject

o2 - second DatabaseObject

pca1 - first PCA

pca2 - second PCA

Returns:

the distance between two given DatabaseObjects according to this distance function