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## Uses of Interface
de.jstacs.sequenceScores.statisticalModels.trainable.TrainableStatisticalModel

Packages that use TrainableStatisticalModel
de.jstacs.classifiers.assessment | This package allows to assess classifiers.
de.jstacs.classifiers.trainSMBased | Provides the classes for `Classifier`s that are based on `TrainableStatisticalModel`s
de.jstacs.sequenceScores.statisticalModels.trainable | Provides all `TrainableStatisticalModel`s, which can be learned from a single `DataSet`.
de.jstacs.sequenceScores.statisticalModels.trainable.discrete |
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous |
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous | This package contains various inhomogeneous models.
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared |
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.mixture | This package is the super package for any mixture model.
de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif |

Uses of TrainableStatisticalModel in de.jstacs.classifiers.assessment

Fields in de.jstacs.classifiers.assessment declared as TrainableStatisticalModel
`protected TrainableStatisticalModel[][]` | `ClassifierAssessment.myModel` This array contains for each class the internal used models.

Constructors in de.jstacs.classifiers.assessment with parameters of type TrainableStatisticalModel
`ClassifierAssessment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)` This constructor allows to assess a collection of given `AbstractClassifier`s and, in addition, classifiers that will be constructed using the given `TrainableStatisticalModel`s.
`ClassifierAssessment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)` Creates a new `ClassifierAssessment` from an array of `AbstractClassifier`s and a two-dimensional array of `TrainableStatisticalModel` s, which are combined to additional classifiers.
`ClassifierAssessment(boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)` Creates a new `ClassifierAssessment` from a set of `TrainableStatisticalModel`s.
`KFoldCrossValidation(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)` This constructor allows to assess a collection of given `AbstractClassifier`s and those constructed using the given `TrainableStatisticalModel`s by a `KFoldCrossValidation` .
`KFoldCrossValidation(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)` Creates a new `KFoldCrossValidation` from an array of `AbstractClassifier`s and a two-dimensional array of `TrainableStatisticalModel` s, which are combined to additional classifiers.
`KFoldCrossValidation(boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)` Creates a new `KFoldCrossValidation` from a set of `TrainableStatisticalModel`s.
`RepeatedHoldOutExperiment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)` This constructor allows to assess a collection of given `AbstractClassifier`s and those constructed using the given `TrainableStatisticalModel`s by a `RepeatedHoldOutExperiment`.
`RepeatedHoldOutExperiment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)` Creates a new `RepeatedHoldOutExperiment` from an array of `AbstractClassifier`s and a two-dimensional array of `TrainableStatisticalModel` s, which are combined to additional classifiers.
`RepeatedHoldOutExperiment(boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)` Creates a new `RepeatedHoldOutExperiment` from a set of `TrainableStatisticalModel`s.
`RepeatedSubSamplingExperiment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)` This constructor allows to assess a collection of given `AbstractClassifier`s and those constructed using the given `TrainableStatisticalModel`s by a `RepeatedSubSamplingExperiment`.
`RepeatedSubSamplingExperiment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)` Creates a new `RepeatedSubSamplingExperiment` from an array of `AbstractClassifier`s and a two-dimensional array of `TrainableStatisticalModel` s, which are combined to additional classifiers.
`RepeatedSubSamplingExperiment(boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)` Creates a new `RepeatedSubSamplingExperiment` from a set of `TrainableStatisticalModel`s.
`Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)` This constructor allows to assess a collection of given `AbstractClassifier`s and those constructed using the given `TrainableStatisticalModel`s by a `Sampled_RepeatedHoldOutExperiment`.
`Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)` Creates a new `Sampled_RepeatedHoldOutExperiment` from an array of `AbstractClassifier`s and a two-dimensional array of `TrainableStatisticalModel` s, which are combined to additional classifiers.
`Sampled_RepeatedHoldOutExperiment(boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)` Creates a new `Sampled_RepeatedHoldOutExperiment` from a set of `TrainableStatisticalModel`s.

Uses of TrainableStatisticalModel in de.jstacs.classifiers.trainSMBased

Fields in de.jstacs.classifiers.trainSMBased declared as TrainableStatisticalModel
`protected TrainableStatisticalModel[]` | `TrainSMBasedClassifier.models` The internal `TrainableStatisticalModel`s.

Methods in de.jstacs.classifiers.trainSMBased that return TrainableStatisticalModel
`TrainableStatisticalModel` | `TrainSMBasedClassifier.getModel(int classIndex)` Returns a clone of the `TrainableStatisticalModel` for a specified class.

Methods in de.jstacs.classifiers.trainSMBased with parameters of type TrainableStatisticalModel
`static int` | `TrainSMBasedClassifier.getPossibleLength(TrainableStatisticalModel... models)` This method returns the possible length of a classifier that would use the given `TrainableStatisticalModel`s.

Constructors in de.jstacs.classifiers.trainSMBased with parameters of type TrainableStatisticalModel
`TrainSMBasedClassifier(boolean cloneModels, TrainableStatisticalModel... models)` This constructor creates a new instance with the given `TrainableStatisticalModel`s and clones these if necessary.
`TrainSMBasedClassifier(TrainableStatisticalModel... models)` The default constructor that creates a new instance with the given `TrainableStatisticalModel`s.

Uses of TrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable

Classes in de.jstacs.sequenceScores.statisticalModels.trainable that implement TrainableStatisticalModel
`class` | `AbstractTrainableStatisticalModel` Abstract class for a model for pattern recognition.
`class` | `CompositeTrainSM` This class is for modelling sequences by modelling the different positions of the each sequence by different models.
`class` | `DifferentiableStatisticalModelWrapperTrainSM` This model can be used to use a DifferentiableStatisticalModel as model.
`class` | `UniformTrainSM` This class represents a uniform model.
`class` | `VariableLengthWrapperTrainSM` This class allows to train any `TrainableStatisticalModel` on `DataSet`s of `Sequence`s with variable length if each individual length is at least `SequenceScore.getLength()`.

Fields in de.jstacs.sequenceScores.statisticalModels.trainable declared as TrainableStatisticalModel
`protected TrainableStatisticalModel[]` | `CompositeTrainSM.models` The models for the components

Methods in de.jstacs.sequenceScores.statisticalModels.trainable that return TrainableStatisticalModel
`TrainableStatisticalModel` | `TrainableStatisticalModel.clone()` Creates a clone (deep copy) of the current `TrainableStatisticalModel` instance.
`TrainableStatisticalModel[]` | `CompositeTrainSM.getModels()` Returns the a deep copy of the models.

Methods in de.jstacs.sequenceScores.statisticalModels.trainable with parameters of type TrainableStatisticalModel
`static MixtureTrainSM` | `TrainableStatisticalModelFactory.createMixtureModel(double[] hyper, TrainableStatisticalModel[] model)` This method allows to create a `MixtureTrainSM` that allows to model a `DataSet` as a mixture of individual components.
`static StrandTrainSM` | `TrainableStatisticalModelFactory.createStrandModel(TrainableStatisticalModel model)` This method allows to create a `StrandTrainSM` that allows to score binding sites on both strand of DNA.
`static ZOOPSTrainSM` | `TrainableStatisticalModelFactory.createZOOPS(TrainableStatisticalModel motif, TrainableStatisticalModel bg, double[] hyper, boolean trainOnlyMotifModel)` This method allows to create a "zero or one occurrence per sequence" (ZOOPS) model that allows to discover binding sites in a `DataSet`.

Constructors in de.jstacs.sequenceScores.statisticalModels.trainable with parameters of type TrainableStatisticalModel
`CompositeTrainSM(AlphabetContainer alphabets, int[] assignment, TrainableStatisticalModel... models)` Creates a new `CompositeTrainSM`.
`VariableLengthWrapperTrainSM(TrainableStatisticalModel m)` This is the main constructor that creates an instance from any `TrainableStatisticalModel`.

Uses of TrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete that implement TrainableStatisticalModel
`class` | `DiscreteGraphicalTrainSM` This is the main class for all discrete graphical models (DGM).

Uses of TrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous that implement TrainableStatisticalModel
`class` | `HomogeneousMM` This class implements homogeneous Markov models (hMM) of arbitrary order.
`class` | `HomogeneousTrainSM` This class implements homogeneous models of arbitrary order.

Uses of TrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous that implement TrainableStatisticalModel
`class` | `BayesianNetworkTrainSM` The class implements a Bayesian network ( `StructureLearner.ModelType.BN` ) of fixed order.
`class` | `DAGTrainSM` The abstract class for directed acyclic graphical models (`DAGTrainSM`).
`class` | `FSDAGModelForGibbsSampling` This is the class for a fixed structure directed acyclic graphical model (see `FSDAGTrainSM`) that can be used in a Gibbs sampling.
`class` | `FSDAGTrainSM` This class can be used for any discrete fixed structure directed acyclic graphical model ( `FSDAGTrainSM`).
`class` | `InhomogeneousDGTrainSM` This class is the main class for all inhomogeneous discrete graphical models (`InhomogeneousDGTrainSM`).

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous with parameters of type TrainableStatisticalModel
`static void` | `FSDAGTrainSM.train(TrainableStatisticalModel[] models, int[][] graph, double[][] weights, DataSet... data)` Computes the models with structure `graph`.

Uses of TrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared that implement TrainableStatisticalModel
`class` | `SharedStructureMixture` This class handles a mixture of models with the same structure that is learned via EM.

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

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm that implement TrainableStatisticalModel
`class` | `AbstractHMM` This class is the super class of all implementations hidden Markov models (HMMs) in Jstacs.

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

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models that implement TrainableStatisticalModel
`class` | `DifferentiableHigherOrderHMM` This class combines an `HigherOrderHMM` and a `DifferentiableStatisticalModel` by implementing some of the declared methods.
`class` | `HigherOrderHMM` This class implements a higher order hidden Markov model.
`class` | `SamplingHigherOrderHMM`
`class` | `SamplingPhyloHMM` This class implements an (higher order) HMM that contains multi-dimensional emissions described by a phylogenetic tree.

Uses of TrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.mixture

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.mixture that implement TrainableStatisticalModel
`class` | `AbstractMixtureTrainSM` This is the abstract class for all kinds of mixture models.
`class` | `MixtureTrainSM` The class for a mixture model of any `TrainableStatisticalModel`s.
`class` | `StrandTrainSM` This model handles sequences that can either lie on the forward strand or on the reverse complementary strand.

Fields in de.jstacs.sequenceScores.statisticalModels.trainable.mixture declared as TrainableStatisticalModel
`protected TrainableStatisticalModel[]` | `AbstractMixtureTrainSM.alternativeModel` The alternative models for the EM.
`protected TrainableStatisticalModel[]` | `AbstractMixtureTrainSM.model` The model for the sequences.

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.mixture that return TrainableStatisticalModel
`TrainableStatisticalModel` | `AbstractMixtureTrainSM.getModel(int i)` Returns a deep copy of the `i`-th model.
`TrainableStatisticalModel[]` | `AbstractMixtureTrainSM.getModels()` Returns a deep copy of the models.

Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.mixture with parameters of type TrainableStatisticalModel
`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, double[] weights, int starts, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates an instance using Gibbs Sampling 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.
`MixtureTrainSM(int length, TrainableStatisticalModel[] models, int starts, double[] componentHyperParams, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates an instance using Gibbs Sampling and sampling 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[] componentHyperParams, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates an instance using Gibbs Sampling and sampling 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.
`StrandTrainSM(TrainableStatisticalModel model, int starts, double forwardStrandProb, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates an instance using Gibbs Sampling and fixed component probabilities.

Uses of TrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif that implement TrainableStatisticalModel
`class` | `HiddenMotifMixture` This is the main class that every generative motif discoverer should implement.
`class` | `ZOOPSTrainSM` This class enables the user to search for a single motif in a sequence.

Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif with parameters of type TrainableStatisticalModel
`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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