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

Packages that use SFBasedOptimizableFunction

de.jstacs.classifier.scoringFunctionBased	Provides the classes for Classifiers that are based on ScoringFunctions.
de.jstacs.classifier.scoringFunctionBased.gendismix	Provides an implementation of a classifier that allows to train the parameters of a set of NormalizableScoringFunctions by a unified generative-discriminative learning principle
de.jstacs.classifier.scoringFunctionBased.sampling	Provides the classes for AbstractScoreBasedClassifiers that are based on SamplingScoringFunctions 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 MutableMotifDiscoverers 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.