Package elki.clustering.em.models
Class TextbookSphericalGaussianModel
- java.lang.Object
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- elki.clustering.em.models.TextbookSphericalGaussianModel
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- All Implemented Interfaces:
EMClusterModel<NumberVector,EMModel>
public class TextbookSphericalGaussianModel extends java.lang.Object implements EMClusterModel<NumberVector,EMModel>
Simple spherical Gaussian cluster.- Since:
- 0.7.0
- Author:
- Andreas Lang
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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
priorvar
Prior variance, for MAP estimation.(package private) double
variance
Variances.(package private) double
weight
Weight aggregation sum.(package private) double
wsum
Weight aggregation sum.
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Constructor Summary
Constructors Constructor Description TextbookSphericalGaussianModel(double weight, double[] mean)
Constructor.TextbookSphericalGaussianModel(double weight, double[] mean, double var)
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.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-
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
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Methods inherited from interface elki.clustering.em.models.EMClusterModel
finalizeFirstPassE, firstPassE, needsTwoPass
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Field Detail
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mean
double[] mean
Mean vector.
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variance
double variance
Variances.
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nmea
double[] nmea
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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priorvar
double priorvar
Prior variance, for MAP estimation.
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Constructor Detail
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TextbookSphericalGaussianModel
public TextbookSphericalGaussianModel(double weight, double[] mean)
Constructor.- Parameters:
weight
- Cluster weightmean
- Initial mean
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TextbookSphericalGaussianModel
public TextbookSphericalGaussianModel(double weight, double[] mean, double var)
Constructor.- Parameters:
weight
- Cluster weightmean
- Initial meanvar
- Initial variance
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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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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 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(double[] vec)
Compute the Mahalanobis distance from the centroid for a given vector.- Parameters:
vec
- Vector- Returns:
- Mahalanobis distance
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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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