Uses of Class
de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM.Parameterization

Packages that use AbstractMixtureTrainSM.Parameterization

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

Uses of AbstractMixtureTrainSM.Parameterization in de.jstacs.sequenceScores.statisticalModels.trainable.mixture

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.mixture that return AbstractMixtureTrainSM.Parameterization

Modifier and Type	Method and Description
static AbstractMixtureTrainSM.Parameterization	AbstractMixtureTrainSM.Parameterization.valueOf(String name) Returns the enum constant of this type with the specified name.
static AbstractMixtureTrainSM.Parameterization[]	AbstractMixtureTrainSM.Parameterization.values() Returns an array containing the constants of this enum type, in the order they are declared.

Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.mixture with parameters of type AbstractMixtureTrainSM.Parameterization

Constructor and Description
AbstractMixtureTrainSM(int length, TrainableStatisticalModel[] models, boolean[] optimizeModel, int dimension, int starts, boolean estimateComponentProbs, double[] componentHyperParams, double[] weights, AbstractMixtureTrainSM.Algorithm algorithm, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest) Creates a new AbstractMixtureTrainSM.
MixtureTrainSM(int length, TrainableStatisticalModel[] models, double[] weights, int starts, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization) Creates an instance using EM and fixed component probabilities.
MixtureTrainSM(int length, TrainableStatisticalModel[] models, int starts, boolean estimateComponentProbs, double[] componentHyperParams, double[] weights, AbstractMixtureTrainSM.Algorithm algorithm, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest) Creates a new MixtureTrainSM.
MixtureTrainSM(int length, TrainableStatisticalModel[] models, int starts, double[] componentHyperParams, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization) Creates an instance using EM and estimating the component probabilities.
StrandTrainSM(TrainableStatisticalModel model, int starts, boolean estimateComponentProbs, double[] componentHyperParams, double forwardStrandProb, AbstractMixtureTrainSM.Algorithm algorithm, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest) Creates a new StrandTrainSM.
StrandTrainSM(TrainableStatisticalModel model, int starts, double[] componentHyperParams, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization) Creates an instance using EM and estimating the component probabilities.
StrandTrainSM(TrainableStatisticalModel model, int starts, double forwardStrandProb, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization) Creates an instance using EM and fixed component probabilities.

Uses of AbstractMixtureTrainSM.Parameterization in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif

Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif with parameters of type AbstractMixtureTrainSM.Parameterization

Constructor and Description
HiddenMotifMixture(TrainableStatisticalModel[] models, boolean[] optimzeArray, int components, int starts, boolean estimateComponentProbs, double[] componentHyperParams, double[] weights, PositionPrior posPrior, AbstractMixtureTrainSM.Algorithm algorithm, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest) Creates a new HiddenMotifMixture.
ZOOPSTrainSM(TrainableStatisticalModel motif, TrainableStatisticalModel bg, boolean trainOnlyMotifModel, int starts, double[] componentHyperParams, double[] weights, PositionPrior posPrior, AbstractMixtureTrainSM.Algorithm algorithm, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest) Creates a new ZOOPSTrainSM.
ZOOPSTrainSM(TrainableStatisticalModel motif, TrainableStatisticalModel bg, boolean trainOnlyMotifModel, int starts, double[] componentHyperParams, PositionPrior posPrior, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization) Creates a new ZOOPSTrainSM using EM and estimating the probability for finding a motif.
ZOOPSTrainSM(TrainableStatisticalModel motif, TrainableStatisticalModel bg, boolean trainOnlyMotifModel, int starts, double motifProb, PositionPrior posPrior, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization) Creates a new ZOOPSTrainSM using EM and fixed probability for finding a motif.