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## Uses of Class
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.HMMTrainingParameterSet

Packages that use HMMTrainingParameterSet
de.jstacs.sequenceScores.statisticalModels.trainable.hmm | The package provides all interfaces and classes for a hidden Markov model (HMM).
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models | The package provides different implementations of hidden Markov models based on `AbstractHMM`
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training | The package provides all classes used to determine the training algorithm of a hidden Markov model

Uses of HMMTrainingParameterSet in de.jstacs.sequenceScores.statisticalModels.trainable.hmm

Fields in de.jstacs.sequenceScores.statisticalModels.trainable.hmm declared as HMMTrainingParameterSet
`protected HMMTrainingParameterSet` | `AbstractHMM.trainingParameter` The `ParameterSet` containing all `Parameter`s for the training of the HMM.

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm with parameters of type HMMTrainingParameterSet
`static AbstractHMM` | `HMMFactory.createErgodicHMM(HMMTrainingParameterSet pars, int order, double ess, double selfTranistionPart, double expectedSequenceLength, Emission... emission)` This method creates an ergodic, i.e. a completely connected, HMM using the given emissions.
`static AbstractHMM` | `HMMFactory.createPseudoErgodicHMM(HMMTrainingParameterSet pars, double ess, double selfTranistionPart, double finalTranistionPart, AlphabetContainer con, int numStates, boolean insertUniform)` Creates an HMM with `numStates+1` states, where `numStates` emitting build a clique and each of those states is connected to the absorbing silent final state.
`static AbstractHMM` | `HMMFactory.createSunflowerHMM(HMMTrainingParameterSet pars, AlphabetContainer con, double ess, int expectedSequenceLength, boolean startCentral, int... motifLength)` This method creates a first order sunflower HMM.
`static AbstractHMM` | `HMMFactory.createSunflowerHMM(HMMTrainingParameterSet pars, AlphabetContainer con, double ess, int expectedSequenceLength, boolean startCentral, PhyloTree[] t, double[] motifProb, int[] motifLength)` This method creates a first order sunflower HMM allowing phylogenetic emissions.

Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.hmm with parameters of type HMMTrainingParameterSet
`AbstractHMM(HMMTrainingParameterSet trainingParameterSet, String[] name, int[] emissionIdx, boolean[] forward, Emission[] emission)` This is the main constructor for an HMM.

Uses of HMMTrainingParameterSet in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models

Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models with parameters of type HMMTrainingParameterSet
`HigherOrderHMM(HMMTrainingParameterSet trainingParameterSet, String[] name, Emission[] emission, BasicHigherOrderTransition.AbstractTransitionElement... te)` This is a convenience constructor.
`HigherOrderHMM(HMMTrainingParameterSet trainingParameterSet, String[] name, int[] emissionIdx, boolean[] forward, Emission[] emission, BasicHigherOrderTransition.AbstractTransitionElement... te)` This is the main constructor.

Uses of HMMTrainingParameterSet in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training

Subclasses of HMMTrainingParameterSet in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training
`class` | `BaumWelchParameterSet` This class implements an `HMMTrainingParameterSet` for the Baum-Welch training of an `AbstractHMM`.
`class` | `MaxHMMTrainingParameterSet` This class is the super class for any `HMMTrainingParameterSet` that is used for a maximizing training algorithm of a hidden Markov model.
`class` | `MultiThreadedTrainingParameterSet` This class is the super class for any `MaxHMMTrainingParameterSet` that is used for a multi-threaded maximizing training algorithm of a hidden Markov model.
`class` | `NumericalHMMTrainingParameterSet` This class implements an `ParameterSet` for numerical training of an `AbstractHMM`.
`class` | `SamplingHMMTrainingParameterSet` This class contains the parameters for training training an `AbstractHMM` using a sampling strategy.
`class` | `ViterbiParameterSet` This class implements an `ParameterSet` for the viterbi training of an `AbstractHMM`.

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