org.apache.mahout.common.distance

Class MahalanobisDistanceMeasure

java.lang.Object

org.apache.mahout.common.distance.MahalanobisDistanceMeasure

All Implemented Interfaces:

DistanceMeasure, Parametered

public class MahalanobisDistanceMeasure
extends Object
implements DistanceMeasure

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
MahalanobisDistanceMeasure()

Method Summary

Modifier and Type	Method and Description
void	configure(org.apache.hadoop.conf.Configuration jobConf)
void	createParameters(String prefix, org.apache.hadoop.conf.Configuration jobConf) EXPERT: consumers should never have to call this method.
double	distance(double centroidLengthSquare, Vector centroid, Vector v) Optimized version of distance metric for sparse vectors.
double	distance(Vector v)
double	distance(Vector v1, Vector v2) Returns the distance metric applied to the arguments
Matrix	getInverseCovarianceMatrix()
Vector	getMeanVector()
Collection<Parameter<?>>	getParameters()
void	setCovarianceMatrix(Matrix m) Computes the inverse covariance from the input covariance matrix given in input.
void	setInverseCovarianceMatrix(Matrix inverseCovarianceMatrix)
void	setMeanVector(Vector meanVector)

Methods inherited from class java.lang.Object

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

Constructor Detail

MahalanobisDistanceMeasure

public MahalanobisDistanceMeasure()

Method Detail

configure

public void configure(org.apache.hadoop.conf.Configuration jobConf)

Specified by:

configure in interface Parametered

getParameters

public Collection<Parameter<?>> getParameters()

Specified by:

getParameters in interface Parametered

createParameters

public void createParameters(String prefix,
                             org.apache.hadoop.conf.Configuration jobConf)

Description copied from interface: Parametered

EXPERT: consumers should never have to call this method. It would be friendly visible to Parametered.ParameteredGeneralizations if java supported it. Calling this method should create a new list of parameters and is called

Specified by:

createParameters in interface Parametered

Parameters:

prefix - ends with a dot if not empty.

jobConf - configuration used for retrieving values

See Also:

invoking method, invoking method

distance

public double distance(Vector v)

Parameters:

v - The vector to compute the distance to

Returns:

Mahalanobis distance of a multivariate vector

distance

public double distance(Vector v1,
                       Vector v2)

Description copied from interface: DistanceMeasure

Returns the distance metric applied to the arguments

Specified by:

distance in interface DistanceMeasure

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

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.

Specified by:

distance in interface DistanceMeasure

Parameters:

centroidLengthSquare - Square of the length of centroid

centroid - Centroid vector

setInverseCovarianceMatrix

public void setInverseCovarianceMatrix(Matrix inverseCovarianceMatrix)

setCovarianceMatrix

public void setCovarianceMatrix(Matrix m)

Computes the inverse covariance from the input covariance matrix given in input.

Parameters:

m - A covariance matrix.

Throws:

IllegalArgumentException - if eigen values equal to 0 found.

getInverseCovarianceMatrix

public Matrix getInverseCovarianceMatrix()

setMeanVector

public void setMeanVector(Vector meanVector)

getMeanVector

public Vector getMeanVector()