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## Uses of Interface
de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore

Packages that use DifferentiableSequenceScore
de.jstacs.classifiers.differentiableSequenceScoreBased | Provides the classes for `Classifier`s that are based on `SequenceScore`s.
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.classifiers.differentiableSequenceScoreBased.msp | Provides an implementation of a classifier that allows to train the parameters of a set of `DifferentiableStatisticalModel`s either by maximum supervised posterior (MSP) or by maximum conditional likelihood (MCL)
de.jstacs.motifDiscovery | This package provides the framework including the interface for any de novo motif discoverer
de.jstacs.sequenceScores.differentiable |
de.jstacs.sequenceScores.differentiable.logistic |
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.hmm.models | The package provides different implementations of hidden Markov models based on `AbstractHMM`

Uses of DifferentiableSequenceScore in de.jstacs.classifiers.differentiableSequenceScoreBased

Fields in de.jstacs.classifiers.differentiableSequenceScoreBased declared as DifferentiableSequenceScore
`protected DifferentiableSequenceScore[]` | `ScoreClassifier.score` The internally used scoring functions.
`protected DifferentiableSequenceScore[][]` | `DiffSSBasedOptimizableFunction.score` These `DifferentiableSequenceScore`s are used during the parallel computation.

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased that return DifferentiableSequenceScore
`DifferentiableSequenceScore` | `ScoreClassifier.getDifferentiableSequenceScore(int i)` Returns the internally used `DifferentiableSequenceScore` with index `i`.
`DifferentiableSequenceScore[]` | `ScoreClassifier.getDifferentiableSequenceScores()` Returns all internally used `DifferentiableSequenceScore`s in the internal order.

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased with parameters of type DifferentiableSequenceScore
`abstract void` | `DiffSSBasedOptimizableFunction.reset(DifferentiableSequenceScore[] funs)` This method allows to reset the internally used functions and the corresponding objects.

Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased with parameters of type DifferentiableSequenceScore
`DiffSSBasedOptimizableFunction(int threads, DifferentiableSequenceScore[] score, DataSet[] data, double[][] weights, LogPrior prior, boolean norm, boolean freeParams)` Creates an instance with the underlying infrastructure.
`ScoreClassifier(ScoreClassifierParameterSet params, double lastScore, DifferentiableSequenceScore... score)` Creates a new `ScoreClassifier` from a given `ScoreClassifierParameterSet` and `DifferentiableSequenceScore`s .

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

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix with parameters of type DifferentiableSequenceScore
`void` | `LogGenDisMixFunction.reset(DifferentiableSequenceScore[] funs)`

Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix with parameters of type DifferentiableSequenceScore
`GenDisMixClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, double lastScore, double[] beta, DifferentiableSequenceScore... score)` This constructor creates an instance and sets the value of the last (external) optimization.
`LogGenDisMixFunction(int threads, DifferentiableSequenceScore[] score, DataSet[] data, double[][] weights, LogPrior prior, double[] beta, boolean norm, boolean freeParams)` The constructor for creating an instance that can be used in an `Optimizer`.
`OneDataSetLogGenDisMixFunction(int threads, DifferentiableSequenceScore[] score, DataSet data, double[][] weights, LogPrior prior, double[] beta, boolean norm, boolean freeParams)` The constructor for creating an instance that can be used in an `Optimizer`.

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

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior with parameters of type DifferentiableSequenceScore
`void` | `SeparateLogPrior.set(boolean freeParameters, DifferentiableSequenceScore... funs)`
`void` | `LogPrior.set(boolean freeParameters, DifferentiableSequenceScore... funs)` Resets all pre-computed values to their initial values using the `DifferentiableSequenceScore`s `funs`.
`void` | `CompositeLogPrior.set(boolean freeParameters, DifferentiableSequenceScore... funs)`

Uses of DifferentiableSequenceScore in de.jstacs.classifiers.differentiableSequenceScoreBased.msp

Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased.msp with parameters of type DifferentiableSequenceScore
`MSPClassifier(GenDisMixClassifierParameterSet params, DifferentiableSequenceScore... score)` This convenience constructor creates an `MSPClassifier` that used MCL principle for training.
`MSPClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, DifferentiableSequenceScore... score)` The default constructor that creates a new `MSPClassifier` from a given parameter set, a prior and `DifferentiableSequenceScore`s for the classes.
`MSPClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, double lastScore, DifferentiableSequenceScore... score)` This constructor that creates a new `MSPClassifier` from a given parameter set, a prior and `DifferentiableSequenceScore`s for the classes.

Uses of DifferentiableSequenceScore in de.jstacs.motifDiscovery

Methods in de.jstacs.motifDiscovery with parameters of type DifferentiableSequenceScore
`static History[][]` | `MutableMotifDiscovererToolbox.createHistoryArray(DifferentiableSequenceScore[] funs, History template)` This method creates a History-array that can be used in an optimization.
`static int[][]` | `MutableMotifDiscovererToolbox.createMinimalNewLengthArray(DifferentiableSequenceScore[] funs)` This method creates a minimalNewLength-array that can be used in an optimization.
`static boolean` | `MutableMotifDiscovererToolbox.doHeuristicSteps(DifferentiableSequenceScore[] funs, DataSet[] data, double[][] weights, DiffSSBasedOptimizableFunction opt, DifferentiableFunction neg, byte algorithm, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, boolean breakOnChanged, History[][] hist, int[][] minimalNewLength, boolean maxPos)` This method tries to make some heuristic step if at least one `DifferentiableSequenceScore` is a `MutableMotifDiscoverer`.
`static Sequence[]` | `MutableMotifDiscovererToolbox.enumerate(DifferentiableSequenceScore[] funs, int[] classIndex, int[] motifIndex, RecyclableSequenceEnumerator[] rse, double weight, DiffSSBasedOptimizableFunction opt, OutputStream out)` This method allows to enumerate all possible seeds for a number of motifs in the `MutableMotifDiscoverer`s of a specific classes.
`static Sequence` | `MutableMotifDiscovererToolbox.enumerate(DifferentiableSequenceScore[] funs, int classIndex, int motifIndex, RecyclableSequenceEnumerator rse, double weight, DiffSSBasedOptimizableFunction opt, OutputStream out)` This method allows to enumerate all possible seeds for a motif in the `MutableMotifDiscoverer` of a specific class.
`static boolean` | `MutableMotifDiscovererToolbox.findModification(int clazz, int motif, MutableMotifDiscoverer mmd, DifferentiableSequenceScore[] score, DataSet[] data, double[][] weights, DiffSSBasedOptimizableFunction opt, DifferentiableFunction neg, byte algo, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, History hist, int minimalNewLength, boolean maxPos)` This method tries to find a modification, i.e. shifting, shrinking, or expanding a motif, that is promising.
`static ComparableElement<double[],Double>[]` | `MutableMotifDiscovererToolbox.getSortedInitialParameters(DifferentiableSequenceScore[] funs, MutableMotifDiscovererToolbox.InitMethodForDiffSM[] init, DiffSSBasedOptimizableFunction opt, int n, OutputStream stream, int optimizationSteps)` This method allows to initialize the `DifferentiableSequenceScore` using different `MutableMotifDiscovererToolbox.InitMethodForDiffSM`.
`static double[][]` | `MutableMotifDiscovererToolbox.optimize(DifferentiableSequenceScore[] funs, DiffSSBasedOptimizableFunction opt, byte algorithm, AbstractTerminationCondition condition, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, boolean breakOnChanged, History[][] hist, int[][] minimalNewLength, OptimizableFunction.KindOfParameter plugIn, boolean maxPos)` This method tries to optimize the problem at hand as good as possible.
`static double[][]` | `MutableMotifDiscovererToolbox.optimize(DifferentiableSequenceScore[] funs, DiffSSBasedOptimizableFunction opt, byte algorithm, AbstractTerminationCondition condition, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, boolean breakOnChanged, History template, OptimizableFunction.KindOfParameter plugIn, boolean maxPos)` This method tries to optimize the problem at hand as good as possible.

Uses of DifferentiableSequenceScore in de.jstacs.sequenceScores.differentiable

Classes in de.jstacs.sequenceScores.differentiable that implement DifferentiableSequenceScore
`class` | `AbstractDifferentiableSequenceScore` This class is the main part of any `ScoreClassifier`.
`class` | `IndependentProductDiffSS` This class enables the user to model parts of a sequence independent of each other.
`class` | `UniformDiffSS` This `DifferentiableSequenceScore` does nothing.

Fields in de.jstacs.sequenceScores.differentiable declared as DifferentiableSequenceScore
`protected DifferentiableSequenceScore[]` | `IndependentProductDiffSS.score` The internally used `DifferentiableSequenceScore`s.

Methods in de.jstacs.sequenceScores.differentiable that return DifferentiableSequenceScore
`DifferentiableSequenceScore` | `DifferentiableSequenceScore.clone()` Creates a clone (deep copy) of the current `DifferentiableSequenceScore` instance.
`DifferentiableSequenceScore[]` | `IndependentProductDiffSS.getFunctions()` This method returns a deep copy of the internally used `DifferentiableSequenceScore`.

Methods in de.jstacs.sequenceScores.differentiable with parameters of type DifferentiableSequenceScore
`protected static int[]` | `IndependentProductDiffSS.getLengthArray(DifferentiableSequenceScore... function)` This method provides an array of lengths that can be used for instance as `IndependentProductDiffSS.partialLength`.
`static int` | `AbstractDifferentiableSequenceScore.getNumberOfStarts(DifferentiableSequenceScore[] score)` Returns the number of recommended starts in a numerical optimization.

Constructors in de.jstacs.sequenceScores.differentiable with parameters of type DifferentiableSequenceScore
`IndependentProductDiffSS(boolean plugIn, DifferentiableSequenceScore... functions)` This constructor creates an instance of an `IndependentProductDiffSS` from a given series of independent `DifferentiableSequenceScore`s.
`IndependentProductDiffSS(boolean plugIn, DifferentiableSequenceScore[] functions, int[] length)` This constructor creates an instance of an `IndependentProductDiffSS` from given series of independent `DifferentiableSequenceScore`s and lengths.
`IndependentProductDiffSS(boolean plugIn, DifferentiableSequenceScore[] functions, int[] index, int[] length, boolean[] reverse)` This is the main constructor.

Uses of DifferentiableSequenceScore in de.jstacs.sequenceScores.differentiable.logistic

Classes in de.jstacs.sequenceScores.differentiable.logistic that implement DifferentiableSequenceScore
`class` | `LogisticDiffSS` This class implements a logistic function.

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

Subinterfaces of DifferentiableSequenceScore in de.jstacs.sequenceScores.statisticalModels.differentiable
`interface` | `DifferentiableStatisticalModel` The interface for normalizable `DifferentiableSequenceScore`s.
`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 DifferentiableSequenceScore
`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` | `MultiDimensionalSequenceWrapperDiffSM` This class implements a simple wrapper for multidimensional sequences.
`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 with parameters of type DifferentiableSequenceScore
`static boolean` | `AbstractDifferentiableStatisticalModel.isNormalized(DifferentiableSequenceScore... function)` This method checks whether all given `DifferentiableStatisticalModel`s are normalized.

Constructors in de.jstacs.sequenceScores.statisticalModels.differentiable with parameters of type DifferentiableSequenceScore
`MultiDimensionalSequenceWrapperDiffSM(DifferentiableSequenceScore function)` The main constructor.

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

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels that implement DifferentiableSequenceScore
`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 DifferentiableSequenceScore in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous that implement DifferentiableSequenceScore
`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 DifferentiableSequenceScore in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture that implement DifferentiableSequenceScore
`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`.

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

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif that implement DifferentiableSequenceScore
`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.

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

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

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