Package elki.clustering.em.models

Class TwoPassMultivariateGaussianModel

java.lang.Object

elki.clustering.em.models.TwoPassMultivariateGaussianModel

All Implemented Interfaces:

EMClusterModel<NumberVector,EMModel>

public class TwoPassMultivariateGaussianModel
extends java.lang.Object
implements EMClusterModel<NumberVector,EMModel>

Model for a single Gaussian cluster, using two-passes for slightly better numerics.

This is the more classic approach, but the savings in numerical precision are usually negligible, since we already use a very stable and fast approach.

Since:

0.7.5

Author:

Erich Schubert

Field Summary

Fields

Modifier and Type	Field	Description
(package private) CholeskyDecomposition	chol	Decomposition of covariance matrix.
(package private) double[][]	covariance	Covariance matrix.
(package private) double	logNorm	Normalization factor.
(package private) double	logNormDet	Normalization factor.
(package private) double[]	mean	Mean vector.
(package private) double[][]	priormatrix	Matrix for prior conditioning.
(package private) double[]	tmp	Temporary storage, to avoid reallocations.
(package private) double	weight	Weight aggregation sum.
(package private) double	wsum	Weight aggregation sum.

Constructor Summary

Constructors

Constructor	Description
TwoPassMultivariateGaussianModel(double weight, double[] mean)	Constructor.
TwoPassMultivariateGaussianModel(double weight, double[] mean, double[][] covariance)	Constructor.

Method Summary

Modifier and Type	Method	Description
void	beginEStep()	Begin the E step.
double	estimateLogDensity(NumberVector vec)	Estimate the log likelihood of a vector.
EMModel	finalizeCluster()	Finalize a cluster model.
void	finalizeEStep(double weight, double prior)	Finalize the E step.
void	finalizeFirstPassE()	Finish computation of the mean.
void	firstPassE(NumberVector vec, double wei)	First pass: update the mean only.
double	getWeight()	Get the cluster weight.
double	mahalanobisDistance(NumberVector vec)	Compute the Mahalanobis distance from the centroid for a given vector.
boolean	needsTwoPass()	True, if the model needs two passes in the E step.
void	setWeight(double weight)	Set the cluster weight.
void	updateE(NumberVector vec, double wei)	Second pass: compute the covariance matrix.

Methods inherited from class java.lang.Object

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

Field Detail

mean

double[] mean

Mean vector.

covariance

double[][] covariance

Covariance matrix.

chol

CholeskyDecomposition chol

Decomposition of covariance matrix.

tmp

double[] tmp

Temporary storage, to avoid reallocations.

logNorm

double logNorm

Normalization factor.

logNormDet

double logNormDet

Normalization factor.

weight

double weight

Weight aggregation sum.

wsum

double wsum

Weight aggregation sum.

priormatrix

double[][] priormatrix

Matrix for prior conditioning.

Constructor Detail

TwoPassMultivariateGaussianModel

public TwoPassMultivariateGaussianModel(double weight,
                                        double[] mean)

Constructor.

Parameters:

weight - Cluster weight

mean - Initial mean

TwoPassMultivariateGaussianModel

public TwoPassMultivariateGaussianModel(double weight,
                                        double[] mean,
                                        double[][] covariance)

Constructor.

Parameters:

weight - Cluster weight

mean - Initial mean

covariance - initial covariance matrix

Method Detail

beginEStep

public void beginEStep()

Description copied from interface: EMClusterModel

Begin the E step.

Specified by:

beginEStep in interface EMClusterModel<NumberVector,EMModel>

needsTwoPass

public boolean needsTwoPass()

Description copied from interface: EMClusterModel

True, if the model needs two passes in the E step.

Specified by:

needsTwoPass in interface EMClusterModel<NumberVector,EMModel>

Returns:

true when an initial pass is needed.

firstPassE

public void firstPassE(NumberVector vec,
                       double wei)

First pass: update the mean only.

Specified by:

firstPassE in interface EMClusterModel<NumberVector,EMModel>

Parameters:

vec - Vector to process

wei - Weight of point ("responsibility" of the cluster)

finalizeFirstPassE

public void finalizeFirstPassE()

Finish computation of the mean.

Specified by:

finalizeFirstPassE in interface EMClusterModel<NumberVector,EMModel>

updateE

public void updateE(NumberVector vec,
                    double wei)

Second pass: compute the covariance matrix.

Specified by:

updateE in interface EMClusterModel<NumberVector,EMModel>

Parameters:

vec - Vector to process

wei - Weight of point ("responsibility" of the cluster)

finalizeEStep

public void finalizeEStep(double weight,
                          double prior)

Description copied from interface: EMClusterModel

Finalize the E step.

Specified by:

finalizeEStep in interface EMClusterModel<NumberVector,EMModel>

Parameters:

weight - weight of the cluster

prior - MAP prior (0 for MLE)

mahalanobisDistance

public double mahalanobisDistance(NumberVector vec)

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

Parameters:

vec - Vector

Returns:

Mahalanobis distance

estimateLogDensity

public double estimateLogDensity(NumberVector vec)

Description copied from interface: EMClusterModel

Estimate the log likelihood of a vector.

Specified by:

estimateLogDensity in interface EMClusterModel<NumberVector,EMModel>

Parameters:

vec - Vector

Returns:

log likelihood.

getWeight

public double getWeight()

Description copied from interface: EMClusterModel

Get the cluster weight.

Specified by:

getWeight in interface EMClusterModel<NumberVector,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<NumberVector,EMModel>

Parameters:

weight - Cluster weight

finalizeCluster

public EMModel finalizeCluster()

Description copied from interface: EMClusterModel

Finalize a cluster model.

Specified by:

finalizeCluster in interface EMClusterModel<NumberVector,EMModel>

Returns:

Cluster model