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Packages that use MixtureDensity | |
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statistics |
Uses of MixtureDensity in statistics |
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Fields in statistics declared as MixtureDensity | |
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MixtureDensity |
ParallelEM.current
current estimate |
MixtureDensity |
ParallelEM.previous
previous estimate |
Methods in statistics that return MixtureDensity | |
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MixtureDensity |
MixtureDensity.clone()
Return a deep copy of this instance |
static MixtureDensity |
Trainer.em(MixtureDensity initial,
java.util.List<Sample> data)
Given an initial mixture density, perform a single EM iteration w/ out the use of parallelization. |
static MixtureDensity |
Trainer.em(MixtureDensity initial,
java.util.List<Sample> data,
int iterations)
Perform a number of EM iterations (single-core, cached posteriors) using the initial density and the given data. |
static MixtureDensity |
Initialization.gMeansClustering(java.util.List<Sample> data,
double alpha,
int maxc,
boolean diagonalCovariances)
Perform a Gaussian-means (G-means) clustering on the given data set. |
static MixtureDensity |
Initialization.hierarchicalGaussianClustering(java.util.List<Sample> data,
int maxc,
boolean diagonalCovariances,
Initialization.DensityRankingMethod rank)
Perform a hierarchical Gaussian clustering: Beginning with only one density, always split the cluster with highest variance in two parts, finding the new means by following the strongest eigen vector. |
static MixtureDensity |
Initialization.kMeansClustering(java.util.List<Sample> data,
int nd,
boolean diagonalCovariances)
Perform a simple k-means clustering on the data. |
static MixtureDensity |
Trainer.map(MixtureDensity initial,
java.util.List<Sample> data,
double r,
int iterations,
java.lang.String update)
Perform a number of MAP iterations, based on the initial estimate |
static MixtureDensity |
Trainer.map(MixtureDensity initial,
java.util.List<Sample> data,
double r,
java.lang.String update)
Perform a single MAP iteration on the given initial estimate |
static MixtureDensity |
MixtureDensity.readFromFile(java.lang.String file)
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Methods in statistics with parameters of type MixtureDensity | |
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static MixtureDensity |
Trainer.em(MixtureDensity initial,
java.util.List<Sample> data)
Given an initial mixture density, perform a single EM iteration w/ out the use of parallelization. |
static MixtureDensity |
Trainer.em(MixtureDensity initial,
java.util.List<Sample> data,
int iterations)
Perform a number of EM iterations (single-core, cached posteriors) using the initial density and the given data. |
static MixtureDensity |
Trainer.map(MixtureDensity initial,
java.util.List<Sample> data,
double r,
int iterations,
java.lang.String update)
Perform a number of MAP iterations, based on the initial estimate |
static MixtureDensity |
Trainer.map(MixtureDensity initial,
java.util.List<Sample> data,
double r,
java.lang.String update)
Perform a single MAP iteration on the given initial estimate |
Constructors in statistics with parameters of type MixtureDensity | |
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MixtureDensity(MixtureDensity copy)
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ParallelEM(MixtureDensity initial,
ChunkedDataSet data,
int numThreads)
Generate a new Estimator for parallel EM iterations. |
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