org.apache.mahout.common.distance

Interface DistanceMeasure

All Superinterfaces:

Parametered

All Known Implementing Classes:

ChebyshevDistanceMeasure, CosineDistanceMeasure, EuclideanDistanceMeasure, MahalanobisDistanceMeasure, ManhattanDistanceMeasure, MinkowskiDistanceMeasure, SquaredEuclideanDistanceMeasure, TanimotoDistanceMeasure, WeightedDistanceMeasure, WeightedEuclideanDistanceMeasure, WeightedManhattanDistanceMeasure

public interface DistanceMeasure
extends Parametered

This interface is used for objects which can determine a distance metric between two points

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

Method Summary

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 v1, Vector v2) Returns the distance metric applied to the arguments

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

configure, createParameters, getParameters

Method Detail

distance

double distance(Vector v1,
                Vector v2)

Returns the distance metric applied to the arguments

Parameters:

v1 - a Vector defining a multidimensional point in some feature space

v2 - a Vector defining a multidimensional point in some feature space

Returns:

a scalar doubles of the distance

distance

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

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