de.lmu.ifi.dbs.elki.distance.distancefunction.correlation
Class ERiCDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<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,BitDistance>
                      de.lmu.ifi.dbs.elki.distance.distancefunction.correlation.ERiCDistanceFunction<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,BitDistance>, MeasurementFunction<V,BitDistance>, PreprocessorBasedMeasurementFunction<V,P,BitDistance>, PreprocessorClient<P,V>, Parameterizable

public class ERiCDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>>
extends AbstractPreprocessorBasedDistanceFunction<V,P,BitDistance>

Provides a distance function for building the hierarchiy in the ERiC algorithm.

Author:

Elke Achtert

Field Summary

private double	delta Holds the value of DELTA_PARAM.
static OptionID	DELTA_ID OptionID for DELTA_PARAM
private DoubleParameter	DELTA_PARAM Parameter to specify the threshold for approximate linear dependency: the strong eigenvectors of q are approximately linear dependent from the strong eigenvectors p if the following condition holds for all stroneg eigenvectors q_i of q (lambda_q < lambda_p): q_i' * M^check_p * q_i <= delta^2, must be a double equal to or greater than 0.
private double	tau Holds the value of TAU_PARAM.
static OptionID	TAU_ID OptionID for TAU_PARAM
private DoubleParameter	TAU_PARAM Parameter to specify the threshold for the maximum distance between two approximately linear dependent subspaces of two objects p and q (lambda_q < lambda_p) 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.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

ERiCDistanceFunction()
Provides a distance function for the ERiC algorithm, adding parameters DELTA_PARAM and {#TAU_PARAM} to the option handler additionally to parameters of super class.

Method Summary

private boolean	approximatelyLinearDependent(PCAFilteredResult pca1, PCAFilteredResult pca2) Returns true, if the strong eigenvectors of the two specified pcas span up the same space.
BitDistance	distance(V v1, V v2) Note, that the pca of o1 must have equal ore more strong eigenvectors than the pca of o2.
BitDistance	distance(V v1, V v2, 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.
String	getDefaultPreprocessorClassName() 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.
BitDistance	infiniteDistance() Provides an infinite distance.
BitDistance	nullDistance() Provides a null distance.
List<String>	setParameters(List<String> args) Calls the super method AbstractPreprocessorBasedDistanceFunction#setParameters(args)} and sets additionally the values of the parameters DELTA_PARAM and {#TAU_PARAM}.
BitDistance	undefinedDistance() Provides an undefined distance.
BitDistance	valueOf(String pattern) Provides a measurement suitable to this measurement function based on the given pattern.

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, shortDescription

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.utilities.optionhandling.Parameterizable

checkGlobalParameterConstraints, collectOptions, getParameters, shortDescription

Field Detail

DELTA_ID

public static final OptionID DELTA_ID

OptionID for DELTA_PARAM

DELTA_PARAM

private final DoubleParameter DELTA_PARAM

Parameter to specify the threshold for approximate linear dependency: the strong eigenvectors of q are approximately linear dependent from the strong eigenvectors p if the following condition holds for all stroneg eigenvectors q_i of q (lambda_q < lambda_p): q_i' * M^check_p * q_i <= delta^2, must be a double equal to or greater than 0.

Default value: 0.1

Key: -ericdf.delta

TAU_ID

public static final OptionID TAU_ID

OptionID for TAU_PARAM

TAU_PARAM

private final DoubleParameter TAU_PARAM

Parameter to specify the threshold for the maximum distance between two approximately linear dependent subspaces of two objects p and q (lambda_q < lambda_p) before considering them as parallel, must be a double equal to or greater than 0.

Default value: 0.1

Key: -ericdf.tau

delta

private double delta

Holds the value of DELTA_PARAM.

tau

private double tau

Holds the value of TAU_PARAM.

Constructor Detail

ERiCDistanceFunction

public ERiCDistanceFunction()

Provides a distance function for the ERiC algorithm, adding parameters DELTA_PARAM and {#TAU_PARAM} to the option handler additionally to parameters of super class.

Method Detail

setParameters

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

Calls the super method AbstractPreprocessorBasedDistanceFunction#setParameters(args)} and sets additionally the values of the parameters DELTA_PARAM and {#TAU_PARAM}.

Specified by:

setParameters in interface Parameterizable

Overrides:

setParameters in class AbstractPreprocessorBasedDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>,BitDistance>

Parameters:

args - parameters to set the attributes accordingly to

Returns:

a list containing the unused parameters

Throws:

ParameterException - in case of wrong parameter-setting

getDefaultPreprocessorClassName

public String getDefaultPreprocessorClassName()

Returns the name of the default preprocessor.

Returns:

the name of the default preprocessor, which is KnnQueryBasedHiCOPreprocessor

getPreprocessorDescription

public String getPreprocessorDescription()

Description copied from interface: PreprocessorClient

Returns the description for the preprocessor parameter.

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.

Returns:

the super class for the preprocessor parameter, 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.

Returns:

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

valueOf

public BitDistance 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 BitDistance infiniteDistance()

Description copied from interface: MeasurementFunction

Provides an infinite distance.

Returns:

an infinite distance

nullDistance

public BitDistance nullDistance()

Description copied from interface: MeasurementFunction

Provides a null distance.

Returns:

a null distance

undefinedDistance

public BitDistance undefinedDistance()

Description copied from interface: MeasurementFunction

Provides an undefined distance.

Returns:

an undefined distance

distance

public BitDistance distance(V v1,
                            V v2)

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

Parameters:

v1 - first DatabaseObject

v2 - second DatabaseObject

Returns:

the distance between two given DatabaseObjects according to this distance function

distance

public BitDistance distance(V v1,
                            V v2,
                            PCAFilteredResult pca1,
                            PCAFilteredResult pca2)

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

Parameters:

v1 - first DatabaseObject

v2 - second DatabaseObject

pca1 - first PCA

pca2 - second PCA

Returns:

the distance between two given DatabaseObjects according to this distance function

approximatelyLinearDependent

private boolean approximatelyLinearDependent(PCAFilteredResult pca1,
                                             PCAFilteredResult pca2)

Returns true, if the strong eigenvectors of the two specified pcas span up the same space. Note, that the first pca must have equal ore more strong eigenvectors than the second pca.

Parameters:

pca1 - first PCA

pca2 - second PCA

Returns:

true, if the strong eigenvectors of the two specified pcas span up the same space