Package elki.clustering.em.models
Class TwoPassMultivariateGaussianModel
- java.lang.Object
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- elki.clustering.em.models.TwoPassMultivariateGaussianModel
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- 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
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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.
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Constructor Summary
Constructors Constructor Description TwoPassMultivariateGaussianModel(double weight, double[] mean)
Constructor.TwoPassMultivariateGaussianModel(double weight, double[] mean, double[][] covariance)
Constructor.
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Method Summary
All Methods Instance Methods Concrete Methods 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.
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Field Detail
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mean
double[] mean
Mean vector.
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covariance
double[][] covariance
Covariance matrix.
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chol
CholeskyDecomposition chol
Decomposition of covariance matrix.
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tmp
double[] tmp
Temporary storage, to avoid reallocations.
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logNorm
double logNorm
Normalization factor.
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logNormDet
double logNormDet
Normalization factor.
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weight
double weight
Weight aggregation sum.
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wsum
double wsum
Weight aggregation sum.
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priormatrix
double[][] priormatrix
Matrix for prior conditioning.
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Constructor Detail
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TwoPassMultivariateGaussianModel
public TwoPassMultivariateGaussianModel(double weight, double[] mean)
Constructor.- Parameters:
weight
- Cluster weightmean
- Initial mean
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TwoPassMultivariateGaussianModel
public TwoPassMultivariateGaussianModel(double weight, double[] mean, double[][] covariance)
Constructor.- Parameters:
weight
- Cluster weightmean
- Initial meancovariance
- initial covariance matrix
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Method Detail
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beginEStep
public void beginEStep()
Description copied from interface:EMClusterModel
Begin the E step.- Specified by:
beginEStep
in interfaceEMClusterModel<NumberVector,EMModel>
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needsTwoPass
public boolean needsTwoPass()
Description copied from interface:EMClusterModel
True, if the model needs two passes in the E step.- Specified by:
needsTwoPass
in interfaceEMClusterModel<NumberVector,EMModel>
- Returns:
true
when an initial pass is needed.
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firstPassE
public void firstPassE(NumberVector vec, double wei)
First pass: update the mean only.- Specified by:
firstPassE
in interfaceEMClusterModel<NumberVector,EMModel>
- Parameters:
vec
- Vector to processwei
- Weight of point ("responsibility" of the cluster)
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finalizeFirstPassE
public void finalizeFirstPassE()
Finish computation of the mean.- Specified by:
finalizeFirstPassE
in interfaceEMClusterModel<NumberVector,EMModel>
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updateE
public void updateE(NumberVector vec, double wei)
Second pass: compute the covariance matrix.- Specified by:
updateE
in interfaceEMClusterModel<NumberVector,EMModel>
- Parameters:
vec
- Vector to processwei
- Weight of point ("responsibility" of the cluster)
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finalizeEStep
public void finalizeEStep(double weight, double prior)
Description copied from interface:EMClusterModel
Finalize the E step.- Specified by:
finalizeEStep
in interfaceEMClusterModel<NumberVector,EMModel>
- Parameters:
weight
- weight of the clusterprior
- MAP prior (0 for MLE)
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mahalanobisDistance
public double mahalanobisDistance(NumberVector vec)
Compute the Mahalanobis distance from the centroid for a given vector.- Parameters:
vec
- Vector- Returns:
- Mahalanobis distance
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estimateLogDensity
public double estimateLogDensity(NumberVector vec)
Description copied from interface:EMClusterModel
Estimate the log likelihood of a vector.- Specified by:
estimateLogDensity
in interfaceEMClusterModel<NumberVector,EMModel>
- Parameters:
vec
- Vector- Returns:
- log likelihood.
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getWeight
public double getWeight()
Description copied from interface:EMClusterModel
Get the cluster weight.- Specified by:
getWeight
in interfaceEMClusterModel<NumberVector,EMModel>
- Returns:
- Cluster weight
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setWeight
public void setWeight(double weight)
Description copied from interface:EMClusterModel
Set the cluster weight.- Specified by:
setWeight
in interfaceEMClusterModel<NumberVector,EMModel>
- Parameters:
weight
- Cluster weight
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finalizeCluster
public EMModel finalizeCluster()
Description copied from interface:EMClusterModel
Finalize a cluster model.- Specified by:
finalizeCluster
in interfaceEMClusterModel<NumberVector,EMModel>
- Returns:
- Cluster model
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