org.apache.mahout.common.distance

Class TanimotoDistanceMeasure

java.lang.Object

org.apache.mahout.common.distance.WeightedDistanceMeasure

org.apache.mahout.common.distance.TanimotoDistanceMeasure

All Implemented Interfaces:

DistanceMeasure, Parametered

public class TanimotoDistanceMeasure
extends WeightedDistanceMeasure

Tanimoto coefficient implementation. http://en.wikipedia.org/wiki/Jaccard_index

Nested Class Summary

Nested classes/interfaces inherited from interface org.apache.mahout.common.parameters.Parametered

Parametered.ParameteredGeneralizations

Field Summary

Fields inherited from interface org.apache.mahout.common.parameters.Parametered

log

Constructor Summary

Constructors

Constructor and Description
TanimotoDistanceMeasure()

Method Summary

Methods

Modifier and Type	Method and Description
double	distance(double centroidLengthSquare, Vector centroid, Vector v) Optimized version of distance metric for sparse vectors.
double	distance(Vector a, Vector b) Calculates the distance between two vectors.

Methods inherited from class org.apache.mahout.common.distance.WeightedDistanceMeasure

configure, createParameters, getParameters, getWeights, setWeights

Methods inherited from class java.lang.Object

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

Constructor Detail

TanimotoDistanceMeasure

public TanimotoDistanceMeasure()

Method Detail

distance

public double distance(Vector a,
              Vector b)

Calculates the distance between two vectors. The coefficient (a measure of similarity) is: T(a, b) = a.b / (|a|^2 + |b|^2 - a.b) The distance d(a,b) = 1 - T(a,b)

Parameters:

a - a Vector defining a multidimensional point in some feature space

b - a Vector defining a multidimensional point in some feature space

Returns:

0 for perfect match, > 0 for greater distance

distance

public double distance(double centroidLengthSquare,
              Vector centroid,
              Vector v)

Description copied from interface: DistanceMeasure

Optimized version of distance metric for sparse vectors. This distance computation requires operations proportional to the number of non-zero elements in the vector instead of the cardinality of the vector.

Parameters:

centroidLengthSquare - Square of the length of centroid

centroid - Centroid vector