Uses of Class
de.jstacs.classifiers.differentiableSequenceScoreBased.DiffSSBasedOptimizableFunction

Packages that use DiffSSBasedOptimizableFunction

Package	Description
de.jstacs.classifiers.differentiableSequenceScoreBased	Provides the classes for Classifiers that are based on SequenceScores. It includes a sub-package for discriminative objective functions, namely conditional likelihood and supervised posterior, and a separate sub-package for the parameter priors, that can be used for the supervised posterior.
de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix	Provides an implementation of a classifier that allows to train the parameters of a set of DifferentiableStatisticalModels by a unified generative-discriminative learning principle.
de.jstacs.classifiers.differentiableSequenceScoreBased.sampling	Provides the classes for AbstractScoreBasedClassifiers that are based on SamplingDifferentiableStatisticalModels 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 DiffSSBasedOptimizableFunction in de.jstacs.classifiers.differentiableSequenceScoreBased

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased that return DiffSSBasedOptimizableFunction

Modifier and Type	Method and Description
protected abstract DiffSSBasedOptimizableFunction	ScoreClassifier.getFunction(DataSet[] data, double[][] weights) Returns the function that should be optimized.

Uses of DiffSSBasedOptimizableFunction in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix

Subclasses of DiffSSBasedOptimizableFunction in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix

Modifier and Type	Class and Description
class	LogGenDisMixFunction This class implements the the following function The weights have to sum to 1.
class	OneDataSetLogGenDisMixFunction This class implements the the following function where is the weight for sequence and class .

Uses of DiffSSBasedOptimizableFunction in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling that return DiffSSBasedOptimizableFunction

Modifier and Type	Method and Description
protected DiffSSBasedOptimizableFunction	SamplingGenDisMixClassifier.getFunction(DataSet[] data, double[][] weights)

Uses of DiffSSBasedOptimizableFunction in de.jstacs.motifDiscovery

Methods in de.jstacs.motifDiscovery with parameters of type DiffSSBasedOptimizableFunction

Modifier and Type	Method and Description
static boolean	MutableMotifDiscovererToolbox.doHeuristicSteps(DifferentiableSequenceScore[] funs, DataSet[] data, double[][] weights, DiffSSBasedOptimizableFunction 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 DifferentiableSequenceScore is a MutableMotifDiscoverer.
static Sequence[]	MutableMotifDiscovererToolbox.enumerate(DifferentiableSequenceScore[] funs, int[] classIndex, int[] motifIndex, RecyclableSequenceEnumerator[] rse, double weight, DiffSSBasedOptimizableFunction 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(DifferentiableSequenceScore[] funs, int classIndex, int motifIndex, RecyclableSequenceEnumerator rse, double weight, DiffSSBasedOptimizableFunction 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, DifferentiableSequenceScore[] score, DataSet[] data, double[][] weights, DiffSSBasedOptimizableFunction 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.
static ComparableElement<double[],Double>[]	MutableMotifDiscovererToolbox.getSortedInitialParameters(DifferentiableSequenceScore[] funs, MutableMotifDiscovererToolbox.InitMethodForDiffSM[] init, DiffSSBasedOptimizableFunction opt, int n, OutputStream stream, int optimizationSteps) This method allows to initialize the DifferentiableSequenceScore using different MutableMotifDiscovererToolbox.InitMethodForDiffSM.
static double[][]	MutableMotifDiscovererToolbox.optimize(DifferentiableSequenceScore[] funs, DiffSSBasedOptimizableFunction opt, byte algorithm, AbstractTerminationCondition condition, 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(DifferentiableSequenceScore[] funs, DiffSSBasedOptimizableFunction opt, byte algorithm, AbstractTerminationCondition condition, 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.