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## Package de.jstacs.scoringFunctions

Provides `ScoringFunction`s that can be used in a `ScoreClassifier`.

See:
Description

Interface Summary
NormalizableScoringFunction | The interface for normalizable `ScoringFunction`s.
SamplingScoringFunction | Interface for `NormalizableScoringFunction`s that can be used for Metropolis-Hastings sampling in a `SamplingScoreBasedClassifier`.
ScoringFunction | This interface is the main part of any `ScoreClassifier`.
VariableLengthScoringFunction | This is an interface for all `NormalizableScoringFunction`s that allow to score subsequences of arbitrary length.

Class Summary
AbstractNormalizableScoringFunction | This class is the main part of any `ScoreClassifier`.
AbstractVariableLengthScoringFunction | This abstract class implements some methods declared in `NormalizableScoringFunction` based on the declaration of methods in `VariableLengthScoringFunction`.
CMMScoringFunction | This scoring function implements a cyclic Markov model of arbitrary order and periodicity for any sequence length.
IndependentProductScoringFunction | This class enables the user to model parts of a sequence independent of each other.
MappingScoringFunction | This class implements a `NormalizableScoringFunction` that works on mapped `Sequence`s.
MRFScoringFunction | This class implements the scoring function for any MRF (Markov Random Field).
NormalizedScoringFunction | This class makes an unnormalized `NormalizableScoringFunction` to a normalized `NormalizableScoringFunction`.
UniformScoringFunction | This `ScoringFunction` does nothing.

## Package de.jstacs.scoringFunctions Description

Provides `ScoringFunction`s that can be used in a `ScoreClassifier`. Among the currently implemented `ScoringFunction`s are

- the `BayesianNetworkScoringFunction`, which provides structure learning for inhomogeneous Markov models, Bayesian trees, and permuted Markov models,
- the `MixtureScoringFunction` for mixtures of `ScoringFunctions`, and
- homogeneous `ScoringFunction`s.

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