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

Class WeightedCanberraDistanceFunction

java.lang.Object

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

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

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

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

All Implemented Interfaces:

DistanceFunction<NumberVector<?>,DoubleDistance>, NumberVectorDistanceFunction<DoubleDistance>, PrimitiveDistanceFunction<NumberVector<?>,DoubleDistance>, PrimitiveDoubleDistanceFunction<NumberVector<?>>, SpatialPrimitiveDistanceFunction<NumberVector<?>,DoubleDistance>, SpatialPrimitiveDoubleDistanceFunction<NumberVector<?>>, Parameterizable

public class WeightedCanberraDistanceFunction
extends AbstractSpatialDoubleDistanceFunction

Weighted Canberra distance function, a variation of Manhattan distance. TODO: add parameterizer. As of now, this can only be used from Java code.

Field Summary

Fields

Modifier and Type	Field and Description
protected double[]	weights Weight array

Constructor Summary

Constructors

Constructor and Description
WeightedCanberraDistanceFunction(double[] weights) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
double	doubleDistance(NumberVector<?> v1, NumberVector<?> v2) Computes the distance between two given Objects according to this distance function.
double	doubleMinDist(SpatialComparable mbr1, SpatialComparable mbr2) Computes the distance between the two given MBRs according to this distance function.
boolean	isMetric() Is this distance function metric (in particular, does it satisfy the triangle equation?)

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

instantiate, minDist

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

dimensionality, dimensionality, dimensionality, dimensionality, distance, getDistanceFactory, getInputTypeRestriction

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

isSymmetric

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.distancefunction.PrimitiveDistanceFunction

distance, getInputTypeRestriction

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

getDistanceFactory, isSymmetric

Field Detail

weights

protected double[] weights

Weight array

Constructor Detail

WeightedCanberraDistanceFunction

public WeightedCanberraDistanceFunction(double[] weights)

Constructor.

Method Detail

doubleDistance

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

Description copied from interface: PrimitiveDoubleDistanceFunction

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

Parameters:

v1 - first Object

v2 - second Object

Returns:

the distance between two given Objects according to this distance function

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.

Parameters:

mbr1 - the first MBR object

mbr2 - the second MBR object

Returns:

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

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.