de.lmu.ifi.dbs.elki.distance.distancefunction.subspace

Class SubspaceLPNormDistanceFunction

java.lang.Object

de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractPrimitiveDistanceFunction<V,DoubleDistance>

de.lmu.ifi.dbs.elki.distance.distancefunction.subspace.AbstractDimensionsSelectingDoubleDistanceFunction<NumberVector<?>>

de.lmu.ifi.dbs.elki.distance.distancefunction.subspace.SubspaceLPNormDistanceFunction

All Implemented Interfaces:

DistanceFunction<NumberVector<?>,DoubleDistance>, DoubleNorm<NumberVector<?>>, Norm<NumberVector<?>,DoubleDistance>, PrimitiveDistanceFunction<NumberVector<?>,DoubleDistance>, PrimitiveDoubleDistanceFunction<NumberVector<?>>, SpatialPrimitiveDistanceFunction<NumberVector<?>,DoubleDistance>, SpatialPrimitiveDoubleDistanceFunction<NumberVector<?>>, DimensionSelectingSubspaceDistanceFunction<NumberVector<?>,DoubleDistance>, InspectionUtilFrequentlyScanned, Parameterizable

Direct Known Subclasses:

SubspaceEuclideanDistanceFunction, SubspaceManhattanDistanceFunction

public class SubspaceLPNormDistanceFunction
extends AbstractDimensionsSelectingDoubleDistanceFunction<NumberVector<?>>
implements SpatialPrimitiveDoubleDistanceFunction<NumberVector<?>>, DoubleNorm<NumberVector<?>>

Provides a distance function that computes the Euclidean distance between feature vectors only in specified dimensions.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	SubspaceLPNormDistanceFunction.Parameterizer Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
private double	p Value of p

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

dimensions, DIMS_ID

Constructor Summary

Constructors

Constructor and Description
SubspaceLPNormDistanceFunction(double p, BitSet dimensions) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
double	doubleDistance(NumberVector<?> v1, NumberVector<?> v2) Provides the Euclidean distance between two given feature vectors in the selected dimensions.
double	doubleMinDist(SpatialComparable mbr1, SpatialComparable mbr2) Computes the distance between the two given MBRs according to this distance function.
protected double	doubleMinDistObject(SpatialComparable mbr, NumberVector<?> v)
double	doubleNorm(NumberVector<?> obj) Compute the norm of object obj as double value.
VectorFieldTypeInformation<? super NumberVector<?>>	getInputTypeRestriction() Get the input data type of the function.
double	getP() Get the value of p.
<T extends NumberVector<?>> SpatialPrimitiveDistanceQuery<T,DoubleDistance>	instantiate(Relation<T> database) Instantiate with a database to get the actual distance query.
boolean	isMetric() Is this distance function metric (in particular, does it satisfy the triangle equation?)
DoubleDistance	minDist(SpatialComparable mbr1, SpatialComparable mbr2) Computes the distance between the two given MBRs according to this distance function.
DoubleDistance	norm(NumberVector<?> obj) Compute the norm of object obj.

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

distance, equals, getDistanceFactory, getSelectedDimensions, setSelectedDimensions

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

isSymmetric

Methods inherited from class java.lang.Object

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

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

distance

Field Detail

p

private double p

Value of p

Constructor Detail

SubspaceLPNormDistanceFunction

public SubspaceLPNormDistanceFunction(double p,
                              BitSet dimensions)

Constructor.

Parameters:

dimensions - Selected dimensions

p - p value

Method Detail

getP

public double getP()

Get the value of p.

Returns:

p

doubleDistance

public double doubleDistance(NumberVector<?> v1,
                    NumberVector<?> v2)

Provides the Euclidean distance between two given feature vectors in the selected dimensions.

Specified by:

doubleDistance in interface PrimitiveDoubleDistanceFunction<NumberVector<?>>

Parameters:

v1 - first feature vector

v2 - second feature vector

Returns:

the Euclidean distance between two given feature vectors in the selected dimensions

doubleMinDistObject

protected double doubleMinDistObject(SpatialComparable mbr,
                         NumberVector<?> v)

doubleMinDist

public double doubleMinDist(SpatialComparable mbr1,
                   SpatialComparable mbr2)

Description copied from interface: SpatialPrimitiveDoubleDistanceFunction

Computes the distance between the two given MBRs according to this distance function.

Specified by:

doubleMinDist in interface SpatialPrimitiveDoubleDistanceFunction<NumberVector<?>>

Parameters:

mbr1 - the first MBR object

mbr2 - the second MBR object

Returns:

the distance between the two given MBRs according to this distance function

minDist

public DoubleDistance minDist(SpatialComparable mbr1,
                     SpatialComparable mbr2)

Description copied from interface: SpatialPrimitiveDistanceFunction

Computes the distance between the two given MBRs according to this distance function.

Specified by:

minDist in interface SpatialPrimitiveDistanceFunction<NumberVector<?>,DoubleDistance>

Parameters:

mbr1 - the first MBR object

mbr2 - the second MBR object

Returns:

the distance between the two given MBRs according to this distance function

norm

public DoubleDistance norm(NumberVector<?> obj)

Description copied from interface: Norm

Compute the norm of object obj.

Specified by:

norm in interface Norm<NumberVector<?>,DoubleDistance>

Parameters:

obj - Object

Returns:

Norm

doubleNorm

public double doubleNorm(NumberVector<?> obj)

Description copied from interface: DoubleNorm

Compute the norm of object obj as double value.

Specified by:

doubleNorm in interface DoubleNorm<NumberVector<?>>

Parameters:

obj - Object

Returns:

Double

instantiate

public <T extends NumberVector<?>> SpatialPrimitiveDistanceQuery<T,DoubleDistance> instantiate(Relation<T> database)

Description copied from class: AbstractPrimitiveDistanceFunction

Instantiate with a database to get the actual distance query.

Specified by:

instantiate in interface DistanceFunction<NumberVector<?>,DoubleDistance>

Specified by:

instantiate in interface SpatialPrimitiveDistanceFunction<NumberVector<?>,DoubleDistance>

Overrides:

instantiate in class AbstractPrimitiveDistanceFunction<NumberVector<?>,DoubleDistance>

Parameters:

database - Representation

Returns:

Actual distance query.

getInputTypeRestriction

public VectorFieldTypeInformation<? super NumberVector<?>> getInputTypeRestriction()

Description copied from interface: DistanceFunction

Get the input data type of the function.

Specified by:

getInputTypeRestriction in interface DistanceFunction<NumberVector<?>,DoubleDistance>

Specified by:

getInputTypeRestriction in interface PrimitiveDistanceFunction<NumberVector<?>,DoubleDistance>

Returns:

Type restriction

isMetric

public boolean isMetric()

Description copied from interface: DistanceFunction

Is this distance function metric (in particular, does it satisfy the triangle equation?)

Specified by:

isMetric in interface DistanceFunction<NumberVector<?>,DoubleDistance>

Overrides:

isMetric in class AbstractPrimitiveDistanceFunction<NumberVector<?>,DoubleDistance>

Returns:

true when metric.