de.lmu.ifi.dbs.elki.algorithm.clustering.em

Class MultivariateGaussianModel

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.clustering.em.MultivariateGaussianModel

All Implemented Interfaces:

EMClusterModel<EMModel>

public class MultivariateGaussianModel
extends Object
implements EMClusterModel<EMModel>

Model for a single Gaussian cluster.

Author:

Erich Schubert

Field Summary

Fields

Modifier and Type	Field and Description
(package private) Matrix	covariance Covariance matrix, and inverse.
(package private) double[][]	elements Matrix element reference.
(package private) Matrix	invCovMatr Covariance matrix, and inverse.
private static Logging	LOG Class logger.
(package private) Vector	mean Mean vector.
(package private) double[]	mref Temporary storage, to avoid reallocations.
(package private) double[]	nmea Temporary storage, to avoid reallocations.
(package private) double	norm Normalization factor.
(package private) double	normDistrFactor Normalization factor.
(package private) double	weight Weight aggregation sum
(package private) double	wsum Weight aggregation sum

Constructor Summary

Constructors

Constructor and Description
MultivariateGaussianModel(double weight, Vector mean) Constructor.
MultivariateGaussianModel(double weight, Vector mean, double norm) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
void	beginEStep() Begin the E step.
double	estimateDensity(NumberVector vec) Estimate the likelihood of a vector.
EMModel	finalizeCluster() Finalize a cluster model.
void	finalizeEStep() Finalize the E step.
double	getWeight() Get the cluster weight.
double	mahalanobisDistance(NumberVector vec) Compute the Mahalanobis distance from the centroid for a given vector.
double	mahalanobisDistance(Vector vec) Compute the Mahalanobis distance from the centroid for a given vector.
void	setWeight(double weight) Set the cluster weight.
void	updateE(NumberVector vec, double wei) Update the

Methods inherited from class java.lang.Object

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

Field Detail

LOG

private static Logging LOG

Class logger.

mean

Vector mean

Mean vector.

covariance

Matrix covariance

Covariance matrix, and inverse.

invCovMatr

Matrix invCovMatr

Covariance matrix, and inverse.

nmea

double[] nmea

Temporary storage, to avoid reallocations.

mref

double[] mref

Temporary storage, to avoid reallocations.

elements

double[][] elements

Matrix element reference.

norm

double norm

Normalization factor.

normDistrFactor

double normDistrFactor

Normalization factor.

weight

double weight

Weight aggregation sum

wsum

double wsum

Weight aggregation sum

Constructor Detail

MultivariateGaussianModel

public MultivariateGaussianModel(double weight,
                         Vector mean)

Constructor.

Parameters:

weight - Cluster weight

mean - Initial mean

MultivariateGaussianModel

public MultivariateGaussianModel(double weight,
                         Vector mean,
                         double norm)

Constructor.

Parameters:

weight - Cluster weight

mean - Initial mean

norm - Normalization factor.

Method Detail

beginEStep

public void beginEStep()

Description copied from interface: EMClusterModel

Begin the E step.

Specified by:

beginEStep in interface EMClusterModel<EMModel>

updateE

public void updateE(NumberVector vec,
           double wei)

Description copied from interface: EMClusterModel

Update the

Specified by:

updateE in interface EMClusterModel<EMModel>

Parameters:

vec - Vector to process

wei - Weight

finalizeEStep

public void finalizeEStep()

Description copied from interface: EMClusterModel

Finalize the E step.

Specified by:

finalizeEStep in interface EMClusterModel<EMModel>

mahalanobisDistance

public double mahalanobisDistance(Vector vec)

Compute the Mahalanobis distance from the centroid for a given vector.

Parameters:

vec - Vector

Returns:

Mahalanobis distance

mahalanobisDistance

public double mahalanobisDistance(NumberVector vec)

Compute the Mahalanobis distance from the centroid for a given vector.

Parameters:

vec - Vector

Returns:

Mahalanobis distance

estimateDensity

public double estimateDensity(NumberVector vec)

Description copied from interface: EMClusterModel

Estimate the likelihood of a vector.

Specified by:

estimateDensity in interface EMClusterModel<EMModel>

Parameters:

vec - Vector

Returns:

Likelihood.

getWeight

public double getWeight()

Description copied from interface: EMClusterModel

Get the cluster weight.

Specified by:

getWeight in interface EMClusterModel<EMModel>

Returns:

Cluster weight

setWeight

public void setWeight(double weight)

Description copied from interface: EMClusterModel

Set the cluster weight.

Specified by:

setWeight in interface EMClusterModel<EMModel>

Parameters:

weight - Cluster weight

finalizeCluster

public EMModel finalizeCluster()

Description copied from interface: EMClusterModel

Finalize a cluster model.

Specified by:

finalizeCluster in interface EMClusterModel<EMModel>

Returns:

Cluster model