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

Packages that use MRGParams

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

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

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 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

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

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

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

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.