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

Packages that use AbstractMixtureTrainSM.Parameterization
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
`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
`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
`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.

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