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## Uses of Class
de.jstacs.classifier.scoringFunctionBased.SFBasedOptimizableFunction

Packages that use SFBasedOptimizableFunction
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.sampling | Provides the classes for `AbstractScoreBasedClassifier`s that are based on `SamplingScoringFunction`s and that sample parameters using the Metropolis-Hastings algorithm.
de.jstacs.motifDiscovery | This package provides the framework including the interface for any de novo motif discoverer

Uses of SFBasedOptimizableFunction in de.jstacs.classifier.scoringFunctionBased

Methods in de.jstacs.classifier.scoringFunctionBased that return SFBasedOptimizableFunction
`protected abstract SFBasedOptimizableFunction` | `ScoreClassifier.getFunction(Sample[] data, double[][] weights)` Returns the function that should be optimized.

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

Subclasses of SFBasedOptimizableFunction in de.jstacs.classifier.scoringFunctionBased.gendismix
`class` | `LogGenDisMixFunction` This class implements the the following function The weights have to sum to 1.
`class` | `OneSampleLogGenDisMixFunction` This class implements the the following function where is the weight for sequence and class .

Uses of SFBasedOptimizableFunction in de.jstacs.classifier.scoringFunctionBased.sampling

Methods in de.jstacs.classifier.scoringFunctionBased.sampling that return SFBasedOptimizableFunction
`protected abstract SFBasedOptimizableFunction` | `SamplingScoreBasedClassifier.getFunction(Sample[] data, double[][] weights)` Returns the function that should be sampled from.
`protected SFBasedOptimizableFunction` | `SamplingGenDisMixClassifier.getFunction(Sample[] data, double[][] weights)`

Methods in de.jstacs.classifier.scoringFunctionBased.sampling with parameters of type SFBasedOptimizableFunction
`protected double` | `SamplingScoreBasedClassifier.doOneSamplingStep(SFBasedOptimizableFunction function, SamplingScoreBasedClassifier.SamplingScheme scheme, double previousValue)` Performs one sampling step, i.e., one sampling of all parameter values.
`protected void` | `SamplingScoreBasedClassifier.sample(SamplingScoreBasedClassifier.ScoringFunctionSamplingComponent sfsc, SFBasedOptimizableFunction function)` Samples as many steps as needed to get into the stationary phase according to `SamplingScoreBasedClassifier.burnInTest` and then samples the number of stationary steps as set in `SamplingScoreBasedClassifier.params`.
`protected double` | `SamplingScoreBasedClassifier.sampleNSteps(SFBasedOptimizableFunction function, SamplingScoreBasedClassifier.ScoringFunctionSamplingComponent component, BurnInTest test, int numSteps, SamplingScoreBasedClassifier.SamplingScheme scheme)` Samples a predefined number of steps appended to the current sampling

Uses of SFBasedOptimizableFunction in de.jstacs.motifDiscovery

Methods in de.jstacs.motifDiscovery with parameters of type SFBasedOptimizableFunction
`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.

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