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

Packages that use ScoringFunction
de.jstacs.classifier.scoringFunctionBased | Provides the classes for `Classifier`s that are based on `ScoringFunction`s.
de.jstacs.classifier.scoringFunctionBased.gendismix | Provides an implementation of a classifier that allows to train the parameters of a set of `NormalizableScoringFunction`s by a unified generative-discriminative learning principle
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.classifier.scoringFunctionBased.msp | Provides an implementation of a classifier that allows to train the parameters of a set of `ScoringFunctions` either by maximum supervised posterior (MSP) or by maximum conditional likelihood (MCL)
de.jstacs.models.hmm.models | The package provides different implementations of hidden Markov models based on `AbstractHMM`
de.jstacs.motifDiscovery | This package provides the framework including the interface for any de novo motif discoverer
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 ScoringFunction in de.jstacs.classifier.scoringFunctionBased

Fields in de.jstacs.classifier.scoringFunctionBased declared as ScoringFunction
`protected ScoringFunction[][]` | `SFBasedOptimizableFunction.score` These `ScoringFunction`s are used during the parallel computation.
`protected ScoringFunction[]` | `ScoreClassifier.score` The internally used scoring functions.

Methods in de.jstacs.classifier.scoringFunctionBased that return ScoringFunction
`ScoringFunction` | `ScoreClassifier.getScoringFunction(int i)` Returns the internally used `ScoringFunction` with index `i`.
`ScoringFunction[]` | `ScoreClassifier.getScoringFunctions()` Returns all internally used `ScoringFunction`s in the internal order.

Methods in de.jstacs.classifier.scoringFunctionBased with parameters of type ScoringFunction
`abstract void` | `SFBasedOptimizableFunction.reset(ScoringFunction[] funs)` This method allows to reset the internally used functions and the corresponding objects.

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

Uses of ScoringFunction in de.jstacs.classifier.scoringFunctionBased.gendismix

Methods in de.jstacs.classifier.scoringFunctionBased.gendismix with parameters of type ScoringFunction
`void` | `LogGenDisMixFunction.reset(ScoringFunction[] funs)`

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

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

Uses of ScoringFunction in de.jstacs.classifier.scoringFunctionBased.msp

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

Uses of ScoringFunction in de.jstacs.models.hmm.models

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

Uses of ScoringFunction in de.jstacs.motifDiscovery

Methods in de.jstacs.motifDiscovery with parameters of type ScoringFunction
`static History[][]` | `MutableMotifDiscovererToolbox.createHistoryArray(ScoringFunction[] funs, History template)` This method creates a History-array that can be used in an optimization.
`static int[][]` | `MutableMotifDiscovererToolbox.createMinimalNewLengthArray(ScoringFunction[] funs)` This method creates a minimalNewLength-array that can be used in an optimization.
`static boolean` | `MutableMotifDiscovererToolbox.doHeuristicSteps(ScoringFunction[] funs, Sample[] data, double[][] weights, SFBasedOptimizableFunction 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 `MutableMotifDiscovererToolbox.InitMethodForScoringFunction` is a `MutableMotifDiscoverer`.
`static Sequence[]` | `MutableMotifDiscovererToolbox.enumerate(ScoringFunction[] funs, int[] classIndex, int[] motifIndex, RecyclableSequenceEnumerator[] rse, double weight, SFBasedOptimizableFunction 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(ScoringFunction[] funs, int classIndex, int motifIndex, RecyclableSequenceEnumerator rse, double weight, SFBasedOptimizableFunction 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, ScoringFunction[] score, Sample[] data, double[][] weights, SFBasedOptimizableFunction 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(ScoringFunction[] funs, MutableMotifDiscovererToolbox.InitMethodForScoringFunction[] init, SFBasedOptimizableFunction opt, int n, OutputStream stream, int optimizationSteps)` This method allows to initialize the `MutableMotifDiscovererToolbox.InitMethodForScoringFunction` using different `MutableMotifDiscovererToolbox.InitMethodForScoringFunction`.
`static double[][]` | `MutableMotifDiscovererToolbox.optimize(ScoringFunction[] funs, SFBasedOptimizableFunction opt, byte algorithm, double eps, 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(ScoringFunction[] funs, SFBasedOptimizableFunction opt, byte algorithm, double eps, 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 ScoringFunction in de.jstacs.scoringFunctions

Subinterfaces of ScoringFunction in de.jstacs.scoringFunctions
`interface` | `NormalizableScoringFunction` The interface for normalizable `ScoringFunction`s.
`interface` | `SamplingScoringFunction` Interface for `NormalizableScoringFunction`s that can be used for Metropolis-Hastings sampling in a `SamplingScoreBasedClassifier`.
`interface` | `VariableLengthScoringFunction` This is an interface for all `NormalizableScoringFunction`s that allow to score subsequences of arbitrary length.

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

Methods in de.jstacs.scoringFunctions that return ScoringFunction
`ScoringFunction` | `ScoringFunction.clone()` Creates a clone (deep copy) of the current `ScoringFunction` instance.

Methods in de.jstacs.scoringFunctions with parameters of type ScoringFunction
`static int` | `AbstractNormalizableScoringFunction.getNumberOfStarts(ScoringFunction[] score)` Returns the number of recommended starts in a numerical optimization.

Uses of ScoringFunction in de.jstacs.scoringFunctions.directedGraphicalModels

Classes in de.jstacs.scoringFunctions.directedGraphicalModels that implement ScoringFunction
`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 `AbstractNormalizableScoringFunction` for an inhomogeneous Markov model.

Uses of ScoringFunction in de.jstacs.scoringFunctions.homogeneous

Classes in de.jstacs.scoringFunctions.homogeneous that implement ScoringFunction
`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 ScoringFunction in de.jstacs.scoringFunctions.mix

Classes in de.jstacs.scoringFunctions.mix that implement ScoringFunction
`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.
`class` | `VariableLengthMixtureScoringFunction` This class implements a mixture of `VariableLengthScoringFunction` by extending `MixtureScoringFunction` and implementing the methods of `VariableLengthScoringFunction`.

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

Classes in de.jstacs.scoringFunctions.mix.motifSearch that implement ScoringFunction
`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` | `MixtureDuration` This class implements a mixture of `DurationScoringFunction`s.
`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.

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