Uses of Class
de.jstacs.utils.random.MRGParams

Packages that use MRGParams

Package	Description
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

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.mixture that return MRGParams

Modifier and Type	Method and Description
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

Modifier and Type	Method and Description
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)
protected double[][]	MixtureTrainSM.doFirstIteration(double[] dataWeights, MultivariateRandomGenerator m, MRGParams[] params)
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 data set.
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

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif with parameters of type MRGParams

Modifier and Type	Method and Description
protected double[][]	ZOOPSTrainSM.doFirstIteration(double[] dataWeights, MultivariateRandomGenerator m, MRGParams[] params)
protected double	ZOOPSTrainSM.iterate(int start, double[] dataWeights, MultivariateRandomGenerator m, MRGParams[] params)

Uses of MRGParams in de.jstacs.utils.random

Subclasses of MRGParams in de.jstacs.utils.random

Modifier and Type	Class and Description
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

Modifier and Type	Method and Description
void	SoftOneOfN.generate(double[] d, int start, int number, MRGParams p)
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)
void	EqualParts.generate(double[] d, int start, int number, MRGParams p)
void	DirichletMRG.generate(double[] d, int start, int n, MRGParams p)
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.