Package elki.clustering.em.models

Class DiagonalGaussianModel

java.lang.Object

elki.clustering.em.models.DiagonalGaussianModel

All Implemented Interfaces:

BetulaClusterModel, EMClusterModel<NumberVector,EMModel>

public class DiagonalGaussianModel
extends java.lang.Object
implements BetulaClusterModel

Simpler model for a single Gaussian cluster, without covariances.

Since:

0.7.0

Author:

Erich Schubert

Field Summary

Fields

Modifier and Type	Field	Description
(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[]	priordiag	Diagonal prior variances.
private static double	SINGULARITY_CHEAT	Constant to avoid zero values.
(package private) double[]	variances	Per-dimension variances.
(package private) double	weight	Weight aggregation sum.
(package private) double	wsum	Weight aggregation sum.

Constructor Summary

Constructors

Constructor	Description
DiagonalGaussianModel(double weight, double[] mean)	Constructor.
DiagonalGaussianModel(double weight, double[] mean, double[] vars)	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.
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.
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

SINGULARITY_CHEAT

private static final double SINGULARITY_CHEAT

Constant to avoid zero values.

See Also:

Constant Field Values

mean

double[] mean

Mean vector.

variances

double[] variances

Per-dimension variances.

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.

priordiag

double[] priordiag

Diagonal prior variances.

Constructor Detail

DiagonalGaussianModel

public DiagonalGaussianModel(double weight,
                             double[] mean)

Constructor.

Parameters:

weight - Cluster weight

mean - Initial mean

DiagonalGaussianModel

public DiagonalGaussianModel(double weight,
                             double[] mean,
                             double[] vars)

Constructor.

Parameters:

weight - Cluster weight

mean - Initial mean

vars - Initial variances

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)

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.

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.