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## Uses of Class
de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore

Packages that use AbstractDifferentiableSequenceScore
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 |

Uses of AbstractDifferentiableSequenceScore in de.jstacs.sequenceScores.differentiable

Subclasses of AbstractDifferentiableSequenceScore in de.jstacs.sequenceScores.differentiable
`class` | `IndependentProductDiffSS` This class enables the user to model parts of a sequence independent of each other.
`class` | `UniformDiffSS` This `DifferentiableSequenceScore` does nothing.

Methods in de.jstacs.sequenceScores.differentiable that return AbstractDifferentiableSequenceScore
`AbstractDifferentiableSequenceScore` | `AbstractDifferentiableSequenceScore.clone()`

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

Subclasses of AbstractDifferentiableSequenceScore in de.jstacs.sequenceScores.differentiable.logistic
`class` | `LogisticDiffSS` This class implements a logistic function.

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

Subclasses of AbstractDifferentiableSequenceScore in de.jstacs.sequenceScores.statisticalModels.differentiable
`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.

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

Subclasses of AbstractDifferentiableSequenceScore in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels
`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 AbstractDifferentiableSequenceScore in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous

Subclasses of AbstractDifferentiableSequenceScore in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous
`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 AbstractDifferentiableSequenceScore in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture

Subclasses of AbstractDifferentiableSequenceScore in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture
`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 AbstractDifferentiableSequenceScore in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif

Subclasses of AbstractDifferentiableSequenceScore in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif
`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.

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