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

Packages that use OptimizableFunction
de.jstacs.classifier.scoringFunctionBased | Provides the classes for `Classifier`s that are based on `ScoringFunction`s.
de.jstacs.classifier.scoringFunctionBased.cll | Provides the implementation of the log conditional likelihood as an `OptimizableFunction` and a classifier that uses log conditional likelihood or supervised posterior to learn the parameters of a set of `ScoringFunctions`
de.jstacs.motifDiscovery | This package provides the framework including the interface for any de novo motif discoverer

Uses of OptimizableFunction in de.jstacs.classifier.scoringFunctionBased

Subclasses of OptimizableFunction in de.jstacs.classifier.scoringFunctionBased
`class` | `AbstractOptimizableFunction` This class extends `OptimizableFunction` and implements some common methods.

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

Uses of OptimizableFunction in de.jstacs.classifier.scoringFunctionBased.cll

Subclasses of OptimizableFunction in de.jstacs.classifier.scoringFunctionBased.cll
`class` | `NormConditionalLogLikelihood` This class implements the normalized log conditional likelihood.

Uses of OptimizableFunction in de.jstacs.motifDiscovery

Methods in de.jstacs.motifDiscovery with parameters of type OptimizableFunction
`static boolean` | `MutableMotifDiscovererToolbox.doHeuristicSteps(ScoringFunction[] funs, Sample[] data, double[][] weights, OptimizableFunction opt, SafeOutputStream out, boolean breakOnChanged, History[][] hist, int[][] minimalNewLength)` This method tries to make some heuristic step if at least one `MutableMotifDiscovererToolbox.InitMethodForScoringFunction` is a `MutableMotifDiscoverer`.
`static Sequence` | `MutableMotifDiscovererToolbox.enumerate(Sample[] data, ScoringFunction[] funs, int classIndex, int motifIndex, double weight, OptimizableFunction opt, OutputStream out)` This method allows to enumerate all possible seeds for a motif in the `HiddenMotifsMixture` of a specific class.
`static ComparableElement<double[],Double>[]` | `MutableMotifDiscovererToolbox.getSortedInitialParameters(Sample[] data, ScoringFunction[] funs, MutableMotifDiscovererToolbox.InitMethodForScoringFunction[] init, OptimizableFunction opt, int n, SafeOutputStream stream)` This method allows to initialize the `MutableMotifDiscovererToolbox.InitMethodForScoringFunction` using different `MutableMotifDiscovererToolbox.InitMethodForScoringFunction`.
`static double[][]` | `MutableMotifDiscovererToolbox.optimize(ScoringFunction[] funs, OptimizableFunction opt, byte algorithm, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, boolean breakOnChanged, History[][] hist, int[][] minimalNewLength, OptimizableFunction.KindOfParameter plugIn)` This method tries to optimize the problem at hand as good as possible.
`static double[][]` | `MutableMotifDiscovererToolbox.optimize(ScoringFunction[] funs, OptimizableFunction opt, byte algorithm, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, boolean breakOnChanged, History template, OptimizableFunction.KindOfParameter plugIn)` This method tries to optimize the problem at hand as good as possible.

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