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## Uses of Interface
de.jstacs.scoringFunctions.NormalizableScoringFunction

Packages that use NormalizableScoringFunction
de.jstacs.classifier.scoringFunctionBased.logPrior | Provides a general definition of a parameter log-prior and a number of implementations of Laplace and Gaussian priors
de.jstacs.scoringFunctions | Provides `ScoringFunction`s that can be used in a `ScoreClassifier`.
de.jstacs.scoringFunctions.directedGraphicalModels | Provides `ScoringFunction`s that are equivalent to directed graphical models.
de.jstacs.scoringFunctions.homogeneous | Provides `ScoringFunction`s that are homogeneous, i.e. model probabilities or scores independent of the position within a sequence
de.jstacs.scoringFunctions.mix | Provides `ScoringFunction`s that are mixtures of other `ScoringFunction`s.
de.jstacs.scoringFunctions.mix.motifSearch |

Uses of NormalizableScoringFunction in de.jstacs.classifier.scoringFunctionBased.logPrior

Fields in de.jstacs.classifier.scoringFunctionBased.logPrior declared as NormalizableScoringFunction
`protected NormalizableScoringFunction[]` | `SeparateLogPrior.funs` The `ScoringFunction`s using the parameters that shall be penalized.

Uses of NormalizableScoringFunction in de.jstacs.scoringFunctions

Classes in de.jstacs.scoringFunctions that implement NormalizableScoringFunction
`class` | `AbstractNormalizableScoringFunction` This class is the main part of any `ScoreClassifier`.
`class` | `IndependentProductScoringFunction` This class enables the user to model parts of a sequence independent of each other.
`class` | `MRFScoringFunction` This class implements the scoring function for any MRF (Markov Random Field).
`class` | `NormalizedScoringFunction` This class makes an unnormalized ScoringFunction to a normalized ScoringFunction.
`class` | `UniformScoringFunction` This `ScoringFunction` does nothing.
`class` | `VariableLengthScoringFunction` This is the main class for all `ScoringFunction`s that allow to score subsequences of arbitrary length.

Methods in de.jstacs.scoringFunctions that return NormalizableScoringFunction
`NormalizableScoringFunction` | `NormalizedScoringFunction.getFunction()` This method returns the internal function.
`static NormalizableScoringFunction` | `NormalizedScoringFunction.getNormalizedVersion(NormalizableScoringFunction nsf, int starts)` This method returns a normalized version of a NormalizableScoringFunction.

Methods in de.jstacs.scoringFunctions with parameters of type NormalizableScoringFunction
`static NormalizableScoringFunction` | `NormalizedScoringFunction.getNormalizedVersion(NormalizableScoringFunction nsf, int starts)` This method returns a normalized version of a NormalizableScoringFunction.
`static boolean` | `AbstractNormalizableScoringFunction.isNormalized(NormalizableScoringFunction... function)` This method checks whether all given `NormalizableScoringFunction`s are normalized.

Constructors in de.jstacs.scoringFunctions with parameters of type NormalizableScoringFunction
`IndependentProductScoringFunction(NormalizableScoringFunction... functions)` This constructor creates an instance of an `IndependentProductScoringFunction` from a given series of independent `NormalizableScoringFunction`s.
`IndependentProductScoringFunction(NormalizableScoringFunction[] functions, int[] length)` This constructor creates an instance of an `IndependentProductScoringFunction` from given series of independent `NormalizableScoringFunction`s and lengths.
`NormalizedScoringFunction(NormalizableScoringFunction nsf, int starts)` Creates a new instance using a given NormalizableScoringFunction.

Uses of NormalizableScoringFunction in de.jstacs.scoringFunctions.directedGraphicalModels

Classes in de.jstacs.scoringFunctions.directedGraphicalModels that implement NormalizableScoringFunction
`class` | `BayesianNetworkScoringFunction` This class implements a scoring function that is a moral directed graphical model, i.e. a moral Bayesian network.
`class` | `MutableMarkovModelScoringFunction` This class implements a `NormalizableScoringFunction` for an inhomogeneous Markov model.

Uses of NormalizableScoringFunction in de.jstacs.scoringFunctions.homogeneous

Classes in de.jstacs.scoringFunctions.homogeneous that implement NormalizableScoringFunction
`class` | `HMM0ScoringFunction` This scoring function implements a homogeneous Markov model of order zero (hMM(0)) for a fixed sequence length.
`class` | `HMMScoringFunction` This scoring function implements a homogeneous Markov model of arbitrary order for any sequence length.
`class` | `HomogeneousScoringFunction` This is the main class for all homogeneous `ScoringFunction`s.
`class` | `UniformHomogeneousScoringFunction` This scoring function does nothing.

Uses of NormalizableScoringFunction in de.jstacs.scoringFunctions.mix

Classes in de.jstacs.scoringFunctions.mix that implement NormalizableScoringFunction
`class` | `AbstractMixtureScoringFunction` This main abstract class for any mixture scoring function (e.g.
`class` | `MixtureScoringFunction` This class implements a real mixture model.
`class` | `StrandScoringFunction` This class enables the user to search on both strand.

Fields in de.jstacs.scoringFunctions.mix declared as NormalizableScoringFunction
`protected NormalizableScoringFunction[]` | `AbstractMixtureScoringFunction.function` This array contains the internal `ScoringFunction`s that are used to determine the score.

Methods in de.jstacs.scoringFunctions.mix that return NormalizableScoringFunction
`NormalizableScoringFunction` | `AbstractMixtureScoringFunction.getFunction(int index)` This method returns a specific internal function.
`NormalizableScoringFunction[]` | `AbstractMixtureScoringFunction.getFunctions()` This method returns an array of clones of the internal used functions.
`NormalizableScoringFunction[]` | `AbstractMixtureScoringFunction.getScoringFunctions()` Returns a deep copy of all internal used `ScoringFunction`s.

Methods in de.jstacs.scoringFunctions.mix with parameters of type NormalizableScoringFunction
`protected void` | `AbstractMixtureScoringFunction.cloneFunctions(NormalizableScoringFunction[] originalFunctions)` This method clones the given array of functions and enables the user to do some post-processing.

Constructors in de.jstacs.scoringFunctions.mix with parameters of type NormalizableScoringFunction
`AbstractMixtureScoringFunction(int length, int starts, int dimension, boolean optimizeHidden, boolean plugIn, NormalizableScoringFunction... function)` This constructor creates a new `AbstractMixtureScoringFunction`.
`MixtureScoringFunction(int starts, boolean plugIn, NormalizableScoringFunction... component)` This constructor creates a new `MixtureScoringFunction`.
`StrandScoringFunction(NormalizableScoringFunction function, double forwardPartOfESS, int starts, boolean plugIn, StrandScoringFunction.InitMethod initMethod)` This constructor creates a StrandScoringFunction that optimizes the usage of each strand.
`StrandScoringFunction(NormalizableScoringFunction function, int starts, boolean plugIn, StrandScoringFunction.InitMethod initMethod, double forward)` This constructor creates a StrandScoringFunction that has a fixed frequency for the strand usage.

Uses of NormalizableScoringFunction in de.jstacs.scoringFunctions.mix.motifSearch

Classes in de.jstacs.scoringFunctions.mix.motifSearch that implement NormalizableScoringFunction
`class` | `DurationScoringFunction` This class is the super class for all one dimensional position scoring functions that can be used as durations for semi Markov models.
`class` | `HiddenMotifsMixture` This class handles mixtures with at least one hidden motif.
`class` | `PositionScoringFunction` This class implements a position scoring function that enables the user to get a score without using a Sequence object.
`class` | `SkewNormalLikeScoringFunction` This class implements a skew normal like discrete truncated distribution.
`class` | `UniformDurationScoringFunction` This scoring function implements a uniform distribution for positions.

Constructors in de.jstacs.scoringFunctions.mix.motifSearch with parameters of type NormalizableScoringFunction
`HiddenMotifsMixture(boolean type, int length, int starts, boolean plugIn, HomogeneousScoringFunction bg, NormalizableScoringFunction[] motif, DurationScoringFunction[] posPrior, boolean plugInBg)` This constructor creates an instance of `HiddenMotifsMixture` that allows to have one site of the specified motifs in a `Sequence`.
`HiddenMotifsMixture(boolean type, int length, int starts, boolean plugIn, HomogeneousScoringFunction bg, NormalizableScoringFunction[] motif, DurationScoringFunction[] posPrior, double sign, boolean plugInBg)` This constructor creates an instance of `HiddenMotifsMixture` that allows to have one site of the specified motifs in a `Sequence`.
`HiddenMotifsMixture(boolean type, int length, int starts, boolean plugIn, HomogeneousScoringFunction bg, NormalizableScoringFunction motif, DurationScoringFunction posPrior, boolean plugInBg)` This constructor creates an instance of `HiddenMotifsMixture` that is either an OOPS or a ZOOPS model depending on the chosen `type`.
`HiddenMotifsMixture(boolean type, int length, int starts, boolean plugIn, HomogeneousScoringFunction bg, NormalizableScoringFunction motif, DurationScoringFunction posPrior, double sign, boolean plugInBg)` This constructor creates an instance of `HiddenMotifsMixture` that is either an OOPS or a ZOOPS model depending on the chosen `type`.

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