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## Uses of Interface
de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModel

Packages that use DifferentiableStatisticalModel
de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix | Provides an implementation of a classifier that allows to train the parameters of a set of `DifferentiableStatisticalModel`s by a unified generative-discriminative learning principle
de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior | Provides a general definition of a parameter log-prior and a number of implementations of Laplace and Gaussian priors
de.jstacs.sequenceScores.statisticalModels.differentiable | Provides all `DifferentiableStatisticalModel`s, which can compute the gradient with respect to their parameters for a given input `Sequence`.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels | Provides `DifferentiableStatisticalModel`s that are directed graphical models.
de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous | Provides `DifferentiableStatisticalModel`s that are homogeneous, i.e. model probabilities or scores independent of the position within a sequence
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture | Provides `DifferentiableSequenceScore`s that are mixtures of other `DifferentiableSequenceScore`s.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif |
de.jstacs.sequenceScores.statisticalModels.trainable | Provides all `TrainableStatisticalModel`s, which can be learned from a single `DataSet`.
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models | The package provides different implementations of hidden Markov models based on `AbstractHMM`

Uses of DifferentiableStatisticalModel in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix with parameters of type DifferentiableStatisticalModel
`static GenDisMixClassifier[]` | `GenDisMixClassifier.create(GenDisMixClassifierParameterSet params, LogPrior prior, double[] weights, DifferentiableStatisticalModel[]... functions)` This method creates an array of GenDisMixClassifiers by using the cross-product of the given `DifferentiableStatisticalModel`s.

Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix with parameters of type DifferentiableStatisticalModel
`GenDisMixClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, double[] beta, DifferentiableStatisticalModel... score)` The main constructor.
`GenDisMixClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, double lastScore, double[] beta, DifferentiableStatisticalModel... score)` This constructor creates an instance and sets the value of the last (external) optimization.
`GenDisMixClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, double genBeta, double disBeta, double priorBeta, DifferentiableStatisticalModel... score)` This convenience constructor agglomerates the `genBeta, disBeta,` and `priorBeta` into an array and calls the main constructor.
`GenDisMixClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, LearningPrinciple key, DifferentiableStatisticalModel... score)` This convenience constructor creates an array of weights for an elementary learning principle and calls the main constructor.

Uses of DifferentiableStatisticalModel in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior

Fields in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior declared as DifferentiableStatisticalModel
`protected DifferentiableStatisticalModel[]` | `SeparateLogPrior.funs` The `DifferentiableSequenceScore`s using the parameters that shall be penalized.

Uses of DifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable

Subinterfaces of DifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable
`interface` | `SamplingDifferentiableStatisticalModel` Interface for `DifferentiableStatisticalModel`s that can be used for Metropolis-Hastings sampling in a `SamplingScoreBasedClassifier`.
`interface` | `VariableLengthDiffSM` This is an interface for all `DifferentiableStatisticalModel`s that allow to score subsequences of arbitrary length.

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable that implement DifferentiableStatisticalModel
`class` | `AbstractDifferentiableStatisticalModel` This class is the main part of any `ScoreClassifier`.
`class` | `AbstractVariableLengthDiffSM` This abstract class implements some methods declared in `DifferentiableStatisticalModel` based on the declaration of methods in `VariableLengthDiffSM`.
`class` | `CyclicMarkovModelDiffSM` This scoring function implements a cyclic Markov model of arbitrary order and periodicity for any sequence length.
`class` | `IndependentProductDiffSM` This class enables the user to model parts of a sequence independent of each other.
`class` | `MappingDiffSM` This class implements a `DifferentiableStatisticalModel` that works on mapped `Sequence`s.
`class` | `MarkovRandomFieldDiffSM` This class implements the scoring function for any MRF (Markov Random Field).
`class` | `NormalizedDiffSM` This class makes an unnormalized `DifferentiableStatisticalModel` to a normalized `DifferentiableStatisticalModel`.
`class` | `UniformDiffSM` This `DifferentiableStatisticalModel` does nothing.

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable that return DifferentiableStatisticalModel
`DifferentiableStatisticalModel` | `NormalizedDiffSM.getFunction()` This method returns the internal function.
`DifferentiableStatisticalModel` | `MappingDiffSM.getFunction()` This method return the internal function.
`static DifferentiableStatisticalModel` | `NormalizedDiffSM.getNormalizedVersion(DifferentiableStatisticalModel nsf, int starts)` This method returns a normalized version of a DifferentiableStatisticalModel.

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable with parameters of type DifferentiableStatisticalModel
`static MixtureDiffSM` | `DifferentiableStatisticalModelFactory.createMixtureModel(DifferentiableStatisticalModel[] models)` This method allows to create a `MixtureDiffSM` that models a mixture of individual component `DifferentiableStatisticalModel`s.
`static StrandDiffSM` | `DifferentiableStatisticalModelFactory.createStrandModel(DifferentiableStatisticalModel model)` This method allows to create a `StrandDiffSM` that allows to score binding sites on both strand of DNA.
`static ExtendedZOOPSDiffSM` | `DifferentiableStatisticalModelFactory.createZOOPS(int length, DifferentiableStatisticalModel motif, HomogeneousDiffSM bg)` This method allows to create a "zero or one occurrence per sequence" (ZOOPS) model that allows to discover binding sites in a `DataSet`.
`static DifferentiableStatisticalModel` | `NormalizedDiffSM.getNormalizedVersion(DifferentiableStatisticalModel nsf, int starts)` This method returns a normalized version of a DifferentiableStatisticalModel.

Constructors in de.jstacs.sequenceScores.statisticalModels.differentiable with parameters of type DifferentiableStatisticalModel
`IndependentProductDiffSM(double ess, boolean plugIn, DifferentiableStatisticalModel... functions)` This constructor creates an instance of an `IndependentProductDiffSM` from a given series of independent `DifferentiableStatisticalModel`s.
`IndependentProductDiffSM(double ess, boolean plugIn, DifferentiableStatisticalModel[] functions, int[] length)` This constructor creates an instance of an `IndependentProductDiffSM` from given series of independent `DifferentiableStatisticalModel`s and lengths.
`IndependentProductDiffSM(double ess, boolean plugIn, DifferentiableStatisticalModel[] functions, int[] index, int[] length, boolean[] reverse)` This is the main constructor.
`MappingDiffSM(AlphabetContainer originalAlphabetContainer, DifferentiableStatisticalModel nsf, DiscreteAlphabetMapping... mapping)` The main constructor creating a `MappingDiffSM`.
`NormalizedDiffSM(DifferentiableStatisticalModel nsf, int starts)` Creates a new instance using a given DifferentiableStatisticalModel.

Uses of DifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels that implement DifferentiableStatisticalModel
`class` | `BayesianNetworkDiffSM` This class implements a scoring function that is a moral directed graphical model, i.e. a moral Bayesian network.
`class` | `MarkovModelDiffSM` This class implements a `AbstractDifferentiableStatisticalModel` for an inhomogeneous Markov model.

Uses of DifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous that implement DifferentiableStatisticalModel
`class` | `HomogeneousDiffSM` This is the main class for all homogeneous `DifferentiableSequenceScore`s.
`class` | `HomogeneousMM0DiffSM` This scoring function implements a homogeneous Markov model of order zero (hMM(0)) for a fixed sequence length.
`class` | `HomogeneousMMDiffSM` This scoring function implements a homogeneous Markov model of arbitrary order for any sequence length.
`class` | `UniformHomogeneousDiffSM` This scoring function does nothing.

Uses of DifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture that implement DifferentiableStatisticalModel
`class` | `AbstractMixtureDiffSM` This main abstract class for any mixture scoring function (e.g.
`class` | `MixtureDiffSM` This class implements a real mixture model.
`class` | `StrandDiffSM` This class enables the user to search on both strand.
`class` | `VariableLengthMixtureDiffSM` This class implements a mixture of `VariableLengthDiffSM` by extending `MixtureDiffSM` and implementing the methods of `VariableLengthDiffSM`.

Fields in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture declared as DifferentiableStatisticalModel
`protected DifferentiableStatisticalModel[]` | `AbstractMixtureDiffSM.function` This array contains the internal `DifferentiableStatisticalModel`s that are used to determine the score.

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture that return DifferentiableStatisticalModel
`DifferentiableStatisticalModel[]` | `AbstractMixtureDiffSM.getDifferentiableStatisticalModels()` Returns a deep copy of all internal used `DifferentiableStatisticalModel`s.
`DifferentiableStatisticalModel` | `AbstractMixtureDiffSM.getFunction(int index)` This method returns a specific internal function.
`DifferentiableStatisticalModel[]` | `AbstractMixtureDiffSM.getFunctions()` This method returns an array of clones of the internal used functions.

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture with parameters of type DifferentiableStatisticalModel
`protected void` | `AbstractMixtureDiffSM.cloneFunctions(DifferentiableStatisticalModel[] originalFunctions)` This method clones the given array of functions and enables the user to do some post-processing.
`static boolean` | `StrandDiffSM.isStrandModel(DifferentiableStatisticalModel nsf)` Check whether a `DifferentiableStatisticalModel` is a `StrandDiffSM`.

Constructors in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture with parameters of type DifferentiableStatisticalModel
`AbstractMixtureDiffSM(int length, int starts, int dimension, boolean optimizeHidden, boolean plugIn, DifferentiableStatisticalModel... function)` This constructor creates a new `AbstractMixtureDiffSM`.
`MixtureDiffSM(int starts, boolean plugIn, DifferentiableStatisticalModel... component)` This constructor creates a new `MixtureDiffSM`.
`StrandDiffSM(DifferentiableStatisticalModel function, double forwardPartOfESS, int starts, boolean plugIn, StrandDiffSM.InitMethod initMethod)` This constructor creates a StrandDiffSM that optimizes the usage of each strand.
`StrandDiffSM(DifferentiableStatisticalModel function, int starts, boolean plugIn, StrandDiffSM.InitMethod initMethod, double forward)` This constructor creates a StrandDiffSM that has a fixed frequency for the strand usage.

Uses of DifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif that implement DifferentiableStatisticalModel
`class` | `DurationDiffSM` This class is the super class for all one dimensional position scoring functions that can be used as durations for semi Markov models.
`class` | `ExtendedZOOPSDiffSM` This class handles mixtures with at least one hidden motif.
`class` | `MixtureDurationDiffSM` This class implements a mixture of `DurationDiffSM`s.
`class` | `PositionDiffSM` This class implements a position scoring function that enables the user to get a score without using a Sequence object.
`class` | `SkewNormalLikeDurationDiffSM` This class implements a skew normal like discrete truncated distribution.
`class` | `UniformDurationDiffSM` This scoring function implements a uniform distribution for positions.

Constructors in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif with parameters of type DifferentiableStatisticalModel
`ExtendedZOOPSDiffSM(boolean type, int length, int starts, boolean plugIn, HomogeneousDiffSM bg, DifferentiableStatisticalModel[] motif, DurationDiffSM[] posPrior, boolean plugInBg)` This constructor creates an instance of `ExtendedZOOPSDiffSM` that allows to have one site of the specified motifs in a `Sequence`.
`ExtendedZOOPSDiffSM(boolean type, int length, int starts, boolean plugIn, HomogeneousDiffSM bg, DifferentiableStatisticalModel motif, DurationDiffSM posPrior, boolean plugInBg)` This constructor creates an instance of `ExtendedZOOPSDiffSM` that is either an OOPS or a ZOOPS model depending on the chosen `type`.

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

Fields in de.jstacs.sequenceScores.statisticalModels.trainable declared as DifferentiableStatisticalModel
`protected DifferentiableStatisticalModel` | `DifferentiableStatisticalModelWrapperTrainSM.nsf` The internally used `DifferentiableStatisticalModel`.

Methods in de.jstacs.sequenceScores.statisticalModels.trainable that return DifferentiableStatisticalModel
`DifferentiableStatisticalModel` | `DifferentiableStatisticalModelWrapperTrainSM.getFunction()` Returns a copy of the internally used `DifferentiableStatisticalModel`.

Constructors in de.jstacs.sequenceScores.statisticalModels.trainable with parameters of type DifferentiableStatisticalModel
`DifferentiableStatisticalModelWrapperTrainSM(DifferentiableStatisticalModel nsf, int threads, byte algo, AbstractTerminationCondition tc, double lineps, double startD)` The main constructor that creates an instance with the user given parameters.

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

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models that implement DifferentiableStatisticalModel
`class` | `DifferentiableHigherOrderHMM` This class combines an `HigherOrderHMM` and a `DifferentiableStatisticalModel` by implementing some of the declared methods.

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