de.lmu.ifi.dbs.elki.distance.distancefunction.correlation
Class AbstractPreferenceVectorBasedCorrelationDistanceFunction<V extends RealVector<V,?>,P extends PreferenceVectorPreprocessor<V>>

java.lang.Object
  de.lmu.ifi.dbs.elki.logging.AbstractLoggable
      de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizable
          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,D>
                      de.lmu.ifi.dbs.elki.distance.distancefunction.correlation.AbstractCorrelationDistanceFunction<V,P,PreferenceVectorBasedCorrelationDistance>
                          de.lmu.ifi.dbs.elki.distance.distancefunction.correlation.AbstractPreferenceVectorBasedCorrelationDistanceFunction<V,P>

Type Parameters:

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

P - the type of Preprocessor used

All Implemented Interfaces:

DistanceFunction<V,PreferenceVectorBasedCorrelationDistance>, MeasurementFunction<V,PreferenceVectorBasedCorrelationDistance>, PreprocessorBasedMeasurementFunction<V,P,PreferenceVectorBasedCorrelationDistance>, PreprocessorClient<P,V>, Parameterizable

Direct Known Subclasses:

DiSHDistanceFunction, HiSCDistanceFunction

public abstract class AbstractPreferenceVectorBasedCorrelationDistanceFunction<V extends RealVector<V,?>,P extends PreferenceVectorPreprocessor<V>>
extends AbstractCorrelationDistanceFunction<V,P,PreferenceVectorBasedCorrelationDistance>

Abstract super class for all preference vector based correlation distance functions.

Author:

Arthur Zimek

Field Summary

private double	epsilon Holds the value of EPSILON_PARAM.
static OptionID	EPSILON_ID OptionID for EPSILON_PARAM
private DoubleParameter	EPSILON_PARAM Parameter to specify the maximum distance between two vectors with equal preference vectors before considering them as parallel, must be a double equal to or greater than 0.

Fields inherited from class de.lmu.ifi.dbs.elki.distance.distancefunction.correlation.AbstractCorrelationDistanceFunction

SEPARATOR

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

INFINITY_PATTERN

Fields inherited from class de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizable

optionHandler

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

debug, logger

Constructor Summary

AbstractPreferenceVectorBasedCorrelationDistanceFunction()
Provides a preference vector based CorrelationDistanceFunction, adding parameter EPSILON_PARAM to the option handler additionally to parameters of super class.

Method Summary

protected PreferenceVectorBasedCorrelationDistance	correlationDistance(V v1, V v2) Computes the correlation distance between the two specified vectors.
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.
private int	dimensionality() Returns the dimensionality of the database.
AssociationID<?>	getAssociationID() Returns the association ID for the association to be set by the preprocessor.
double	getEpsilon() Returns epsilon.
String	getPreprocessorDescription() Returns the description for the preprocessor parameter.
Class<P>	getPreprocessorSuperClass() Returns the super class for the preprocessor parameter.
PreferenceVectorBasedCorrelationDistance	infiniteDistance() Provides an infinite distance.
PreferenceVectorBasedCorrelationDistance	nullDistance() Provides a null distance.
List<String>	setParameters(List<String> args) Calls the super method and sets additionally the value of the parameter EPSILON_PARAM.
PreferenceVectorBasedCorrelationDistance	undefinedDistance() Provides an undefined distance.
PreferenceVectorBasedCorrelationDistance	valueOf(String pattern) Provides a measurement suitable to this measurement function based on the given pattern.
double	weightedDistance(Integer id1, Integer 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(Integer id1, Integer id2) Computes the weighted distance between the two specified data vectors according to their preference vectors.
double	weightedPrefereneceVectorDistance(V v1, V v2) 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.correlation.AbstractCorrelationDistanceFunction

distance, shortDescription

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, isInfiniteDistance, isNullDistance, isUndefinedDistance

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

getDatabase, matches, requiredInputPattern, setRequiredInputPattern

Methods inherited from class de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizable

addOption, addParameterizable, addParameterizable, checkGlobalParameterConstraints, collectOptions, getAttributeSettings, getParameters, rememberParametersExcept, removeOption, removeParameterizable

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.MeasurementFunction

isInfiniteDistance, isNullDistance, isUndefinedDistance, requiredInputPattern

Methods inherited from interface de.lmu.ifi.dbs.elki.preprocessing.PreprocessorClient

getDefaultPreprocessorClassName

Methods inherited from interface de.lmu.ifi.dbs.elki.utilities.optionhandling.Parameterizable

checkGlobalParameterConstraints, collectOptions, getParameters

Field Detail

EPSILON_ID

public static final OptionID EPSILON_ID

OptionID for EPSILON_PARAM

EPSILON_PARAM

private final DoubleParameter EPSILON_PARAM

Parameter to specify the maximum distance between two vectors with equal preference vectors before considering them as parallel, must be a double equal to or greater than 0.

Default value: 0.001

Key: -pvbasedcorrelationdf.epsilon

epsilon

private double epsilon

Holds the value of EPSILON_PARAM.

Constructor Detail

AbstractPreferenceVectorBasedCorrelationDistanceFunction

public AbstractPreferenceVectorBasedCorrelationDistanceFunction()

Provides a preference vector based CorrelationDistanceFunction, adding parameter EPSILON_PARAM to the option handler additionally to parameters of super class.

Method Detail

valueOf

public PreferenceVectorBasedCorrelationDistance valueOf(String pattern)
                                                 throws IllegalArgumentException

Description copied from interface: MeasurementFunction

Provides a measurement suitable to this measurement function based on the given pattern.

Parameters:

pattern - a pattern defining a similarity suitable to this measurement function

Returns:

a measurement suitable to this measurement function based on the given pattern

Throws:

IllegalArgumentException - if the given pattern is not compatible with the requirements of this measurement function

infiniteDistance

public PreferenceVectorBasedCorrelationDistance infiniteDistance()

Description copied from interface: MeasurementFunction

Provides an infinite distance.

Returns:

an infinite distance

nullDistance

public PreferenceVectorBasedCorrelationDistance nullDistance()

Description copied from interface: MeasurementFunction

Provides a null distance.

Returns:

a null distance

undefinedDistance

public PreferenceVectorBasedCorrelationDistance undefinedDistance()

Description copied from interface: MeasurementFunction

Provides an undefined distance.

Returns:

an undefined distance

correlationDistance

protected PreferenceVectorBasedCorrelationDistance correlationDistance(V v1,
                                                                       V v2)

Description copied from class: AbstractCorrelationDistanceFunction

Computes the correlation distance between the two specified vectors.

Specified by:

correlationDistance in class AbstractCorrelationDistanceFunction<V extends RealVector<V,?>,P extends PreferenceVectorPreprocessor<V>,PreferenceVectorBasedCorrelationDistance>

Parameters:

v1 - first RealVector

v2 - second RealVector

Returns:

the correlation distance between the two specified vectors

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 RealVector

v2 - second RealVector

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(Integer id1,
                               Integer 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(V v1,
                                                V v2)

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

Parameters:

v1 - the first vector

v2 - the the second vector

Returns:

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

weightedPrefereneceVectorDistance

public double weightedPrefereneceVectorDistance(Integer id1,
                                                Integer 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

setParameters

public List<String> setParameters(List<String> args)
                           throws ParameterException

Calls the super method and sets additionally the value of the parameter EPSILON_PARAM.

Specified by:

setParameters in interface Parameterizable

Overrides:

setParameters in class AbstractPreprocessorBasedDistanceFunction<V extends RealVector<V,?>,P extends PreferenceVectorPreprocessor<V>,PreferenceVectorBasedCorrelationDistance>

Parameters:

args - parameters to set the attributes accordingly to

Returns:

a list containing the unused parameters

Throws:

ParameterException - in case of wrong parameter-setting

getEpsilon

public double getEpsilon()

Returns epsilon.

Returns:

epsilon

getAssociationID

public final AssociationID<?> getAssociationID()

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

Returns:

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

getPreprocessorSuperClass

public final Class<P> getPreprocessorSuperClass()

Description copied from interface: PreprocessorClient

Returns the super class for the preprocessor parameter.

Returns:

the super class for the preprocessor parameter, which is PreferenceVectorPreprocessor

getPreprocessorDescription

public final String getPreprocessorDescription()

Description copied from interface: PreprocessorClient

Returns the description for the preprocessor parameter.

Returns:

the description for the preprocessor parameter

dimensionality

private int dimensionality()

Returns the dimensionality of the database.

Returns:

the dimensionality of the database, -1 if no database is assigned.