Package elki.clustering.em.models

Class MultivariateGaussianModel

java.lang.Object

elki.clustering.em.models.MultivariateGaussianModel

All Implemented Interfaces:

BetulaClusterModel, EMClusterModel<NumberVector,EMModel>

public class MultivariateGaussianModel
extends java.lang.Object
implements BetulaClusterModel

Model for a single multivariate Gaussian cluster with arbitrary rotation.

Since:

0.7.0

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.
private static Logging	LOG	Class logger.
(package private) double	logNorm	Normalization factor.
(package private) double	logNormDet	Normalization factor.
(package private) double[]	mean	Mean vector.
(package private) double[]	nmea	Temporary storage, to avoid reallocations.
(package private) double[][]	priormatrix	Matrix for prior conditioning.
private static double	SINGULARITY_CHEAT	Constant to avoid singular matrixes.
(package private) double	weight	Weight aggregation sum.
(package private) double	wsum	Weight aggregation sum.

Constructor Summary

Constructors

Constructor	Description
MultivariateGaussianModel(double weight, double[] mean)	Constructor.
MultivariateGaussianModel(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.
double	estimateLogDensity(ClusterFeature cf)	Estimate the log likelihood of a clustering feature.
EMModel	finalizeCluster()	Finalize a cluster model.
void	finalizeEStep(double weight, double prior)	Finalize the E step.
protected static double	getHalfLogDeterminant(CholeskyDecomposition chol)	Get 0.5 * log(det) of a cholesky decomposition.
double	getWeight()	Get the cluster weight.
double	mahalanobisDistance(double[] vec)	Compute the Mahalanobis distance from the centroid for a given vector.
double	mahalanobisDistance(NumberVector vec)	Compute the Mahalanobis distance from the centroid for a given vector.
void	setWeight(double weight)	Set the cluster weight.
protected static CholeskyDecomposition	updateCholesky(double[][] covariance, CholeskyDecomposition prev)	Update the cholesky decomposition.
void	updateE(NumberVector vec, double wei)	Process one data point in the E step
void	updateE(ClusterFeature cf, double wei)	Process one clustering feature in the E step.

Methods inherited from class java.lang.Object

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

Methods inherited from interface elki.clustering.em.models.EMClusterModel

finalizeFirstPassE, firstPassE, needsTwoPass

Field Detail

LOG

private static final Logging LOG

Class logger.

SINGULARITY_CHEAT

private static final double SINGULARITY_CHEAT

Constant to avoid singular matrixes.

See Also:

Constant Field Values

mean

double[] mean

Mean vector.

covariance

double[][] covariance

Covariance matrix.

chol

CholeskyDecomposition chol

Decomposition of covariance matrix.

nmea

double[] nmea

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

MultivariateGaussianModel

public MultivariateGaussianModel(double weight,
                                 double[] mean)

Constructor.

Parameters:

weight - Cluster weight

mean - Initial mean

MultivariateGaussianModel

public MultivariateGaussianModel(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>

updateE

public void updateE(NumberVector vec,
                    double wei)

Description copied from interface: EMClusterModel

Process one data point in the E step

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)

updateCholesky

protected static CholeskyDecomposition updateCholesky(double[][] covariance,
                                                      CholeskyDecomposition prev)

Update the cholesky decomposition.

Parameters:

covariance - Covariance matrix

prev - Previous Cholesky decomposition (reused in case of instability)

Returns:

New Cholesky decomposition

getHalfLogDeterminant

protected static double getHalfLogDeterminant(CholeskyDecomposition chol)

Get 0.5 * log(det) of a cholesky decomposition.

Parameters:

chol - Cholesky Decomposition

Returns:

log determinant.

mahalanobisDistance

public double mahalanobisDistance(double[] 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.

Note: used by P3C.

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

estimateLogDensity

public double estimateLogDensity(ClusterFeature cf)

Description copied from interface: BetulaClusterModel

Estimate the log likelihood of a clustering feature.

Specified by:

estimateLogDensity in interface BetulaClusterModel

Parameters:

cf - ClusteringFeature

Returns:

log likelihood

updateE

public void updateE(ClusterFeature cf,
                    double wei)

Description copied from interface: BetulaClusterModel

Process one clustering feature in the E step.

Specified by:

updateE in interface BetulaClusterModel

Parameters:

cf - Clustering feature to process.

wei - weight of the clustering feature.