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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.classifier.scoringFunctionBased | Provides the classes for `Classifier`s that are based on `ScoringFunction`s.
de.jstacs.classifier.scoringFunctionBased.gendismix | Provides an implementation of a classifier that allows to train the parameters of a set of `NormalizableScoringFunction`s by a unified generative-discriminative learning principle
de.jstacs.classifier.scoringFunctionBased.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: R^n -> 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.classifier.scoringFunctionBased

Subclasses of DifferentiableFunction in de.jstacs.classifier.scoringFunctionBased
`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` | `OptimizableFunction` This is the main function for the `ScoreClassifier`.
`class` | `SFBasedOptimizableFunction` This abstract class is the basis of all multi-threaded `OptimizableFunction`s that are based on `ScoringFunction`s.

Uses of DifferentiableFunction in de.jstacs.classifier.scoringFunctionBased.gendismix

Subclasses of DifferentiableFunction in de.jstacs.classifier.scoringFunctionBased.gendismix
`class` | `LogGenDisMixFunction` This class implements the the following function The weights have to sum to 1.

Uses of DifferentiableFunction in de.jstacs.classifier.scoringFunctionBased.logPrior

Subclasses of DifferentiableFunction in de.jstacs.classifier.scoringFunctionBased.logPrior
`class` | `CompositeLogPrior` This class implements a composite prior that can be used for NormalizableScoringFunction.
`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 `NormalizableScoringFunction`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 `NormalizableScoringFunction`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(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 boolean` | `MutableMotifDiscovererToolbox.findModification(int clazz, int motif, MutableMotifDiscoverer mmd, ScoringFunction[] score, Sample[] data, 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.

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