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

Packages that use OptimizableFunction
de.jstacs.classifier.scoringFunctionBased Provides the classes for Classifiers that are based on ScoringFunctions. 
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.