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

Class DiagonalGaussianModel

java.lang.Object

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

All Implemented Interfaces:

EMClusterModel<EMModel>

public class DiagonalGaussianModel
extends java.lang.Object
implements EMClusterModel<EMModel>

Simpler model for a single Gaussian cluster, without covariances.

Since:

0.7.0

Author:

Erich Schubert

Field Summary

Fields

Modifier and Type	Field and 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 For the MAP version only, a prior diagonal
private static double	SINGULARITY_CHEAT Constant to avoid singular matrixes.
(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 and Description
DiagonalGaussianModel(double weight, double[] mean) Constructor.
DiagonalGaussianModel(double weight, double[] mean, double[] variances) Constructor.

Method Summary

Modifier and Type	Method and 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.
double	getWeight() Get the cluster weight.
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

Methods inherited from class java.lang.Object

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

Methods inherited from interface de.lmu.ifi.dbs.elki.algorithm.clustering.em.EMClusterModel

finalizeFirstPassE, firstPassE, needsTwoPass

Field Detail

SINGULARITY_CHEAT

private static final double SINGULARITY_CHEAT

Constant to avoid singular matrixes.

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

For the MAP version only, a prior diagonal

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[] variances)

Constructor.

Parameters:

weight - Cluster weight

mean - Initial mean

variances - Initial variances.

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

Process one data point in the E step

Specified by:

updateE in interface EMClusterModel<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<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<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<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