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Packages that use MRGParams | |
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de.jstacs.sequenceScores.statisticalModels.trainable.mixture | This package is the super package for any mixture model. |
de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif | |
de.jstacs.utils.random | This package contains some classes for generating random numbers |
Uses of MRGParams in de.jstacs.sequenceScores.statisticalModels.trainable.mixture |
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Methods in de.jstacs.sequenceScores.statisticalModels.trainable.mixture that return MRGParams | |
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protected MRGParams |
AbstractMixtureTrainSM.getMRGParams()
This method creates the parameters used in a multivariate random generator while initialization. |
Methods in de.jstacs.sequenceScores.statisticalModels.trainable.mixture with parameters of type MRGParams | |
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protected double[][] |
AbstractMixtureTrainSM.doFirstIteration(DataSet data,
double[] dataWeights,
MultivariateRandomGenerator m,
MRGParams[] params)
This method will do the first step in the train algorithm for the current model. |
protected double[][] |
StrandTrainSM.doFirstIteration(double[] dataWeights,
MultivariateRandomGenerator m,
MRGParams[] params)
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protected double[][] |
MixtureTrainSM.doFirstIteration(double[] dataWeights,
MultivariateRandomGenerator m,
MRGParams[] params)
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protected abstract double[][] |
AbstractMixtureTrainSM.doFirstIteration(double[] dataWeights,
MultivariateRandomGenerator m,
MRGParams[] params)
This method will do the first step in the train algorithm for the current model on the internal sample. |
double |
AbstractMixtureTrainSM.iterate(DataSet data,
double[] dataWeights,
MultivariateRandomGenerator m,
MRGParams[] params)
This method runs the train algorithm for the current model. |
protected double |
AbstractMixtureTrainSM.iterate(int start,
double[] dataWeights,
MultivariateRandomGenerator m,
MRGParams[] params)
This method runs the train algorithm for the current model and the internal data set. |
Uses of MRGParams in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif |
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Methods in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif with parameters of type MRGParams | |
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protected double[][] |
ZOOPSTrainSM.doFirstIteration(double[] dataWeights,
MultivariateRandomGenerator m,
MRGParams[] params)
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protected double |
ZOOPSTrainSM.iterate(int start,
double[] dataWeights,
MultivariateRandomGenerator m,
MRGParams[] params)
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Uses of MRGParams in de.jstacs.utils.random |
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Subclasses of MRGParams in de.jstacs.utils.random | |
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class |
DiMRGParams
The super container for parameters of Dirichlet multivariate random generators. |
class |
DirichletMRGParams
The container for parameters of a Dirichlet random generator. |
class |
ErlangMRGParams
The container for parameters of an Erlang multivariate random generator. |
class |
FastDirichletMRGParams
The container for parameters of a Dirichlet random generator that uses the same hyperparameter at all positions. |
Methods in de.jstacs.utils.random with parameters of type MRGParams | |
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void |
SoftOneOfN.generate(double[] d,
int start,
int number,
MRGParams p)
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abstract void |
MultivariateRandomGenerator.generate(double[] d,
int start,
int n,
MRGParams p)
Generates a n -dimensional random array as part of the array
d beginning at start . |
void |
ErlangMRG.generate(double[] d,
int start,
int n,
MRGParams p)
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void |
EqualParts.generate(double[] d,
int start,
int number,
MRGParams p)
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void |
DirichletMRG.generate(double[] d,
int start,
int n,
MRGParams p)
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double[] |
MultivariateRandomGenerator.generate(int n,
MRGParams p)
Generates a n -dimensional random array. |
void |
DirichletMRG.generateLog(double[] d,
int start,
int n,
MRGParams p)
Fills a part of the array d beginning at start with n logarithmic values. |
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