de.lmu.ifi.dbs.elki.distance.distancefunction
Class PreferenceVectorBasedCorrelationDistanceFunction<O extends RealVector<O,?>,P extends Preprocessor<O>>

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<O,P,D>
                      de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractCorrelationDistanceFunction<O,P,PreferenceVectorBasedCorrelationDistance>
                          de.lmu.ifi.dbs.elki.distance.distancefunction.PreferenceVectorBasedCorrelationDistanceFunction<O,P>

All Implemented Interfaces:

DistanceFunction<O,PreferenceVectorBasedCorrelationDistance>, MeasurementFunction<O,PreferenceVectorBasedCorrelationDistance>, Loggable, Parameterizable

Direct Known Subclasses:

DiSHDistanceFunction, HiSCDistanceFunction

public abstract class PreferenceVectorBasedCorrelationDistanceFunction<O extends RealVector<O,?>,P extends Preprocessor<O>>
extends AbstractCorrelationDistanceFunction<O,P,PreferenceVectorBasedCorrelationDistance>

XXX unify CorrelationDistanceFunction and VarianceDistanceFunction

Author:

Arthur Zimek

Field Summary

static AssociationID	ASSOCIATION_ID The Assocoiation ID for the association to be set by the preprocessor.
static double	DEFAULT_EPSILON The default value for epsilon.
private double	epsilon Holds the value of epsilon parameter.
static String	EPSILON_D Description for parameter epsilon.
static String	EPSILON_P Option string for parameter epsilon.
static Class<PreferenceVectorPreprocessor>	PREPROCESSOR_SUPER_CLASS The super class for the preprocessor.

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

Constructor Summary

PreferenceVectorBasedCorrelationDistanceFunction()
Provides a preference vector based CorrelationDistanceFunction.

Method Summary

protected PreferenceVectorBasedCorrelationDistance	correlationDistance(O v1, O v2) Computes the correlation distance between the two specified vectors.
abstract PreferenceVectorBasedCorrelationDistance	correlationDistance(O v1, O v2, BitSet pv1, BitSet pv2) Computes the correlation distance between the two specified vectors according to the specified preference vectors.
(package private) AssociationID	getAssociationID() Returns the assocoiation ID for the association to be set by the preprocessor.
double	getEpsilon() Returns epsilon.
(package private) Class<PreferenceVectorPreprocessor>	getPreprocessorSuperClassName() Returns the super class for the preprocessor.
PreferenceVectorBasedCorrelationDistance	infiniteDistance() Provides an infinite distance.
PreferenceVectorBasedCorrelationDistance	nullDistance() Provides a null distance.
String[]	setParameters(String[] args) Sets the values for the parameters delta and preprocessor if specified.
PreferenceVectorBasedCorrelationDistance	undefinedDistance() Provides an undefined distance.
PreferenceVectorBasedCorrelationDistance	valueOf(String pattern) Provides a distance suitable to this DistanceFunction 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(O dv1, O dv2, 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(O rv1, O rv2) 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.AbstractCorrelationDistanceFunction

description, distance, getDefaultPreprocessorClassName, getPreprocessorClassDescription

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

getAttributeSettings, 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, checkGlobalParameterConstraints, deleteOption, description, description, getParameters, getParameterValue, getPossibleOptions, inlineDescription, isSet, setParameters

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

debugFine, debugFiner, debugFinest, exception, message, progress, progress, progress, verbose, 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

requiredInputPattern

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

checkGlobalParameterConstraints, getParameters, getPossibleOptions, inlineDescription

Field Detail

ASSOCIATION_ID

public static final AssociationID ASSOCIATION_ID

The Assocoiation ID for the association to be set by the preprocessor.

PREPROCESSOR_SUPER_CLASS

public static final Class<PreferenceVectorPreprocessor> PREPROCESSOR_SUPER_CLASS

The super class for the preprocessor.

DEFAULT_EPSILON

public static final double DEFAULT_EPSILON

The default value for epsilon.

See Also:

Constant Field Values

EPSILON_P

public static final String EPSILON_P

Option string for parameter epsilon.

See Also:

Constant Field Values

EPSILON_D

public static String EPSILON_D

Description for parameter epsilon.

epsilon

private double epsilon

Holds the value of epsilon parameter.

Constructor Detail

PreferenceVectorBasedCorrelationDistanceFunction

public PreferenceVectorBasedCorrelationDistanceFunction()

Provides a preference vector based CorrelationDistanceFunction.

Method Detail

valueOf

public PreferenceVectorBasedCorrelationDistance valueOf(String pattern)
                                                 throws IllegalArgumentException

Provides a distance suitable to this DistanceFunction based on the given pattern.

Parameters:

pattern - A pattern defining a distance suitable to this DistanceFunction

Returns:

a distance suitable to this DistanceFunction based on the given pattern

Throws:

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

infiniteDistance

public PreferenceVectorBasedCorrelationDistance infiniteDistance()

Provides an infinite distance.

Returns:

an infinite distance

nullDistance

public PreferenceVectorBasedCorrelationDistance nullDistance()

Provides a null distance.

Returns:

a null distance

undefinedDistance

public PreferenceVectorBasedCorrelationDistance undefinedDistance()

Provides an undefined distance.

Returns:

an undefined distance

correlationDistance

protected PreferenceVectorBasedCorrelationDistance correlationDistance(O v1,
                                                                       O v2)

Description copied from class: AbstractCorrelationDistanceFunction

Computes the correlation distance between the two specified vectors.

Specified by:

correlationDistance in class AbstractCorrelationDistanceFunction<O extends RealVector<O,?>,P extends Preprocessor<O>,PreferenceVectorBasedCorrelationDistance>

Parameters:

v1 - first RealVector

v2 - second RealVector

Returns:

the correlation distance between the two specified vectors

See Also:

AbstractCorrelationDistanceFunction.correlationDistance(de.lmu.ifi.dbs.elki.data.RealVector,de.lmu.ifi.dbs.elki.data.RealVector)

correlationDistance

public abstract PreferenceVectorBasedCorrelationDistance correlationDistance(O v1,
                                                                             O 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(O dv1,
                               O dv2,
                               BitSet weightVector)

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

Parameters:

dv1 - the first vector

dv2 - 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(O rv1,
                                                O rv2)

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

Parameters:

rv1 - the first vector

rv2 - 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 String[] setParameters(String[] args)
                       throws ParameterException

Sets the values for the parameters delta and preprocessor if specified. If the parameters are not specified default values are set.

Specified by:

setParameters in interface Parameterizable

Overrides:

setParameters in class AbstractPreprocessorBasedDistanceFunction<O extends RealVector<O,?>,P extends Preprocessor<O>,PreferenceVectorBasedCorrelationDistance>

Parameters:

args - parameters to set the attributes accordingly to

Returns:

String[] an array containing the unused parameters

Throws:

ParameterException - in case of wrong parameter-setting

See Also:

Parameterizable.setParameters(String[])

getEpsilon

public double getEpsilon()

Returns epsilon.

Returns:

epsilon

getAssociationID

final AssociationID getAssociationID()

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

Specified by:

getAssociationID in class AbstractCorrelationDistanceFunction<O extends RealVector<O,?>,P extends Preprocessor<O>,PreferenceVectorBasedCorrelationDistance>

getPreprocessorSuperClassName

final Class<PreferenceVectorPreprocessor> getPreprocessorSuperClassName()

Returns the super class for the preprocessor.

Specified by:

getPreprocessorSuperClassName in class AbstractCorrelationDistanceFunction<O extends RealVector<O,?>,P extends Preprocessor<O>,PreferenceVectorBasedCorrelationDistance>