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

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

Packages that use DifferentiableFunction
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 `Classifier`s that are based on `SequenceScore`s.
de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix	Provides an implementation of a classifier that allows to train the parameters of a set of `DifferentiableStatisticalModel`s 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

Uses of DifferentiableFunction in de.jstacs.algorithms.optimization

Subclasses of DifferentiableFunction in de.jstacs.algorithms.optimization
`class`	`NegativeDifferentiableFunction` The negative function `-f` for a given `DifferentiableFunction` `f`.
`class`	`NumericalDifferentiableFunction` This class is the framework for any numerical differentiable function $f: \mathbb{R}^n \to \mathbb{R}$ .

Methods in de.jstacs.algorithms.optimization with parameters of type DifferentiableFunction
`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
`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
`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 `OptimizableFunction`s that are based on `DifferentiableSequenceScore`s.
`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
`class`	`LogGenDisMixFunction` This class implements the the following function $\[f(\underline{\lambda}\|C,D,\underline{\alpha},\underline{\beta})$ The weights $\beta_i$ have to sum to 1.
`class`	`OneDataSetLogGenDisMixFunction` This class implements the the following function $\[f(\underline{\lambda}\|C,D,\underline{w},\underline{\alpha},\underline{\beta})$ where $w_{c,n}$ 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
`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. for maximum supervised posterior optimization.
`class`	`SeparateGaussianLogPrior` Class for a `LogPrior` that defines a Gaussian prior on the parameters of a set of `DifferentiableStatisticalModel`s and a set of class parameters.
`class`	`SeparateLaplaceLogPrior` Class for a `LogPrior` that defines a Laplace prior on the parameters of a set of `DifferentiableStatisticalModel`s 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
`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. shifting, shrinking, or expanding a motif, that is promising.

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

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. shifting, shrinking, or expanding a motif, that is promising.