Uses of Class
de.jstacs.algorithms.optimization.DifferentiableFunction

Packages that use DifferentiableFunction

Package	Description
de.jstacs.algorithms.optimization	Provides classes for different types of algorithms that are not directly linked to the modelling components of Jstacs: Algorithms on graphs, algorithms for numerical optimization, and a basic alignment algorithm.
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.logPrior	Provides a general definition of a parameter log-prior and a number of implementations of Laplace and Gaussian priors.
de.jstacs.motifDiscovery	This package provides the framework including the interface for any de novo motif discoverer.
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous	This package contains various inhomogeneous models.

Uses of DifferentiableFunction in de.jstacs.algorithms.optimization

Subclasses of DifferentiableFunction in de.jstacs.algorithms.optimization

Modifier and Type	Class and Description
class	NegativeDifferentiableFunction The negative function -f for a given DifferentiableFunction f.
class	NumericalDifferentiableFunction This class is the framework for any numerical differentiable function .

Methods in de.jstacs.algorithms.optimization with parameters of type DifferentiableFunction

Modifier and Type	Method and Description
static int	Optimizer.conjugateGradientsFR(DifferentiableFunction f, double[] currentValues, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream out, Time t) The conjugate gradient algorithm by Fletcher and Reeves.
static int	Optimizer.conjugateGradientsPR(DifferentiableFunction f, double[] currentValues, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream out, Time t) The conjugate gradient algorithm by Polak and Ribière.
static int	Optimizer.conjugateGradientsPRP(DifferentiableFunction f, double[] currentValues, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream out, Time t) The conjugate gradient algorithm by Polak and Ribière called "Polak-Ribière-Positive".
static int	Optimizer.limitedMemoryBFGS(DifferentiableFunction f, double[] currentValues, byte m, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream out, Time t) The Broyden-Fletcher-Goldfarb-Shanno version of limited memory quasi-Newton methods.
static int	Optimizer.optimize(byte algorithm, DifferentiableFunction f, double[] currentValues, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream out) This method enables you to use all different implemented optimization algorithms by only one method.
static int	Optimizer.optimize(byte algorithm, DifferentiableFunction f, double[] currentValues, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream out, Time t) This method enables you to use all different implemented optimization algorithms by only one method.
static int	Optimizer.quasiNewtonBFGS(DifferentiableFunction f, double[] currentValues, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream out, Time t) The Broyden-Fletcher-Goldfarb-Shanno version of the quasi-Newton method.
static int	Optimizer.quasiNewtonDFP(DifferentiableFunction f, double[] currentValues, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream out, Time t) The Davidon-Fletcher-Powell version of the quasi-Newton method.
static int	Optimizer.steepestDescent(DifferentiableFunction f, double[] currentValues, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream out, Time t) The steepest descent.

Constructors in de.jstacs.algorithms.optimization with parameters of type DifferentiableFunction

Constructor and Description
NegativeDifferentiableFunction(DifferentiableFunction f) Creates the DifferentiableFunction f for which -f should be calculated.

Uses of DifferentiableFunction in de.jstacs.classifiers.differentiableSequenceScoreBased

Subclasses of DifferentiableFunction in de.jstacs.classifiers.differentiableSequenceScoreBased

Modifier and Type	Class and Description
class	AbstractMultiThreadedOptimizableFunction This class enables the user to exploit all CPUs of an computer by using threads.
class	AbstractOptimizableFunction This class extends OptimizableFunction and implements some common methods.
class	DiffSSBasedOptimizableFunction This abstract class is the basis of all multi-threaded OptimizableFunctions that are based on DifferentiableSequenceScores.
class	OptimizableFunction This is the main function for the ScoreClassifier.

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

Subclasses of DifferentiableFunction 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 DifferentiableFunction in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior

Subclasses of DifferentiableFunction in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior

Modifier and Type	Class and Description
class	CompositeLogPrior This class implements a composite prior that can be used for DifferentiableStatisticalModel.
class	DoesNothingLogPrior This class defines a LogPrior that does not penalize any parameter.
class	LogPrior The abstract class for any log-prior used e.g.
class	SeparateGaussianLogPrior Class for a LogPrior that defines a Gaussian prior on the parameters of a set of DifferentiableStatisticalModels and a set of class parameters.
class	SeparateLaplaceLogPrior Class for a LogPrior that defines a Laplace prior on the parameters of a set of DifferentiableStatisticalModels and a set of class parameters.
class	SeparateLogPrior Abstract class for priors that penalize each parameter value independently and have some variances (and possible means) as hyperparameters.
class	SimpleGaussianSumLogPrior This class implements a prior that is a product of Gaussian distributions with mean 0 and equal variance for each parameter.

Uses of DifferentiableFunction in de.jstacs.motifDiscovery

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

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 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.

Uses of DifferentiableFunction in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous

Subclasses of DifferentiableFunction in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous

Modifier and Type	Class and Description
static class	MEMTools.DualFunction The dual function to the constraint problem of learning MEM's.