de.lmu.ifi.dbs.elki.distance.distancefunction.subspace
Class AbstractPreferenceVectorBasedCorrelationDistanceFunction.Instance<V extends NumberVector<?,?>,P extends PreferenceVectorIndex<V>>

java.lang.Object
  de.lmu.ifi.dbs.elki.database.query.AbstractDataBasedQuery<O>
      de.lmu.ifi.dbs.elki.database.query.distance.AbstractDistanceQuery<O,D>
          de.lmu.ifi.dbs.elki.database.query.distance.AbstractDatabaseDistanceQuery<O,D>
              de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractIndexBasedDistanceFunction.Instance<V,P,PreferenceVectorBasedCorrelationDistance,AbstractPreferenceVectorBasedCorrelationDistanceFunction<? super V,?>>
                  de.lmu.ifi.dbs.elki.distance.distancefunction.subspace.AbstractPreferenceVectorBasedCorrelationDistanceFunction.Instance<V,P>

All Implemented Interfaces:

DatabaseQuery, DistanceQuery<V,PreferenceVectorBasedCorrelationDistance>, IndexBasedDistanceFunction.Instance<V,P,PreferenceVectorBasedCorrelationDistance>

Direct Known Subclasses:

DiSHDistanceFunction.Instance, HiSCDistanceFunction.Instance

Enclosing class:

AbstractPreferenceVectorBasedCorrelationDistanceFunction<V extends NumberVector<?,?>,P extends PreferenceVectorIndex<V>>

public abstract static class AbstractPreferenceVectorBasedCorrelationDistanceFunction.Instance<V extends NumberVector<?,?>,P extends PreferenceVectorIndex<V>>
extends AbstractIndexBasedDistanceFunction.Instance<V,P,PreferenceVectorBasedCorrelationDistance,AbstractPreferenceVectorBasedCorrelationDistanceFunction<? super V,?>>

Instance to compute the distances on an actual database.

Field Summary

(package private) double

epsilon The epsilon value

Fields inherited from class de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractIndexBasedDistanceFunction.Instance

index, parent

Fields inherited from class de.lmu.ifi.dbs.elki.database.query.AbstractDataBasedQuery

relation

Fields inherited from interface de.lmu.ifi.dbs.elki.database.query.DatabaseQuery

HINT_BULK, HINT_EXACT, HINT_HEAVY_USE, HINT_NO_CACHE, HINT_OPTIMIZED_ONLY, HINT_SINGLE

Constructor Summary

AbstractPreferenceVectorBasedCorrelationDistanceFunction.Instance(Relation<V> database, P preprocessor, double epsilon, AbstractPreferenceVectorBasedCorrelationDistanceFunction<? super V,?> distanceFunction)
Constructor.

Method Summary

abstract PreferenceVectorBasedCorrelationDistance	correlationDistance(V v1, V v2, BitSet pv1, BitSet pv2) Computes the correlation distance between the two specified vectors according to the specified preference vectors.
PreferenceVectorBasedCorrelationDistance	distance(DBID id1, DBID id2) Returns the distance between the two objects specified by their object ids.
double	weightedDistance(DBID id1, DBID id2, BitSet weightVector) Computes the weighted distance between the two specified vectors according to the given preference vector.
double	weightedDistance(V v1, V v2, BitSet weightVector) Computes the weighted distance between the two specified vectors according to the given preference vector.
double	weightedPrefereneceVectorDistance(DBID id1, DBID id2) Computes the weighted distance between the two specified data vectors according to their preference vectors.

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

getDistanceFunction, getIndex

Methods inherited from class de.lmu.ifi.dbs.elki.database.query.distance.AbstractDatabaseDistanceQuery

distance, distance, distance

Methods inherited from class de.lmu.ifi.dbs.elki.database.query.distance.AbstractDistanceQuery

getDistanceFactory, infiniteDistance, nullDistance, undefinedDistance

Methods inherited from class de.lmu.ifi.dbs.elki.database.query.AbstractDataBasedQuery

getRelation

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.database.query.distance.DistanceQuery

distance, distance, distance, getDistanceFactory, getRelation, infiniteDistance, nullDistance, undefinedDistance

Field Detail

epsilon

final double epsilon

The epsilon value

Constructor Detail

AbstractPreferenceVectorBasedCorrelationDistanceFunction.Instance

public AbstractPreferenceVectorBasedCorrelationDistanceFunction.Instance(Relation<V> database,
                                                                         P preprocessor,
                                                                         double epsilon,
                                                                         AbstractPreferenceVectorBasedCorrelationDistanceFunction<? super V,?> distanceFunction)

Constructor.

Parameters:

database - Database

preprocessor - Preprocessor

epsilon - Epsilon

distanceFunction - parent distance function

Method Detail

distance

public PreferenceVectorBasedCorrelationDistance distance(DBID id1,
                                                         DBID id2)

Description copied from class: AbstractDistanceQuery

Returns the distance between the two objects specified by their object ids.

Specified by:

distance in interface DistanceQuery<V extends NumberVector<?,?>,PreferenceVectorBasedCorrelationDistance>

Specified by:

distance in class AbstractDistanceQuery<V extends NumberVector<?,?>,PreferenceVectorBasedCorrelationDistance>

Parameters:

id1 - first object id

id2 - second object id

Returns:

the distance between the two objects specified by their object ids

correlationDistance

public abstract PreferenceVectorBasedCorrelationDistance correlationDistance(V v1,
                                                                             V v2,
                                                                             BitSet pv1,
                                                                             BitSet pv2)

Computes the correlation distance between the two specified vectors according to the specified preference vectors.

Parameters:

v1 - first vector

v2 - second vector

pv1 - the first preference vector

pv2 - the second preference vector

Returns:

the correlation distance between the two specified vectors

weightedDistance

public double weightedDistance(V v1,
                               V v2,
                               BitSet weightVector)

Computes the weighted distance between the two specified vectors according to the given preference vector.

Parameters:

v1 - the first vector

v2 - the second vector

weightVector - the preference vector

Returns:

the weighted distance between the two specified vectors according to the given preference vector

weightedDistance

public double weightedDistance(DBID id1,
                               DBID id2,
                               BitSet weightVector)

Computes the weighted distance between the two specified vectors according to the given preference vector.

Parameters:

id1 - the id of the first vector

id2 - the id of the second vector

weightVector - the preference vector

Returns:

the weighted distance between the two specified vectors according to the given preference vector

weightedPrefereneceVectorDistance

public double weightedPrefereneceVectorDistance(DBID id1,
                                                DBID id2)

Computes the weighted distance between the two specified data vectors according to their preference vectors.

Parameters:

id1 - the id of the first vector

id2 - the id of the second vector

Returns:

the weighted distance between the two specified vectors according to the preference vector of the first data vector