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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.cll | Provides the implementation of the log conditional likelihood as an `OptimizableFunction` and a classifier that uses log conditional likelihood or supervised posterior to learn the parameters of a set of `ScoringFunctions`
de.jstacs.classifier.scoringFunctionBased.logPrior | Provides a general definition of a parameter log-prior and a number of implementations of Laplace and Gaussian priors

Uses of DifferentiableFunction in de.jstacs.algorithms.optimization

Subclasses of DifferentiableFunction in de.jstacs.algorithms.optimization
`class` | `NegativeDifferentiableFunction` The function -f for a given function f.
`class` | `NumericalDifferentiableFunction` This class is the framework for any function f: R^n -> R.

Methods in de.jstacs.algorithms.optimization with parameters of type DifferentiableFunction
`static int` | `Optimizer.conjugateGradientsFR(DifferentiableFunction f, double[] currentValues, Optimizer.TerminationCondition terminationMode, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, Time t)` The conjugate gradient algorithm by Fletcher and Reeves.
`static int` | `Optimizer.conjugateGradientsPR(DifferentiableFunction f, double[] currentValues, Optimizer.TerminationCondition terminationMode, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, Time t)` The conjugate gradient algorithm by Polak and Ribiere.
`static int` | `Optimizer.conjugateGradientsPRP(DifferentiableFunction f, double[] currentValues, Optimizer.TerminationCondition terminationMode, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, Time t)` The conjugate gradient algorithm by Polak and Ribiere called Polak-Ribiere-Positive.
`static int` | `Optimizer.limitedMemoryBFGS(DifferentiableFunction f, double[] currentValues, byte m, Optimizer.TerminationCondition terminationMode, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream 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, Optimizer.TerminationCondition terminationMode, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream 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, Optimizer.TerminationCondition terminationMode, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream 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, Optimizer.TerminationCondition terminationMode, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, Time t)` The Broyden-Fletcher-Goldfarb-Shanno version of quasi Newton method.
`static int` | `Optimizer.quasiNewtonDFP(DifferentiableFunction f, double[] currentValues, Optimizer.TerminationCondition terminationMode, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, Time t)` The Davidon-Fletcher-Powell version of quasi Newton method.
`static int` | `Optimizer.steepestDescent(DifferentiableFunction f, double[] currentValues, Optimizer.TerminationCondition terminationMode, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, Time t)` The steepest descent.

Constructors in de.jstacs.algorithms.optimization with parameters of type DifferentiableFunction
`NegativeDifferentiableFunction(DifferentiableFunction f)` Creates the function f for which -f should be calculated.

Uses of DifferentiableFunction in de.jstacs.classifier.scoringFunctionBased

Subclasses of DifferentiableFunction in de.jstacs.classifier.scoringFunctionBased
`class` | `AbstractOptimizableFunction` This class extends `OptimizableFunction` and implements some common methods.
`class` | `OptimizableFunction` This is the main function for the `ScoreClassifier`.

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

Subclasses of DifferentiableFunction in de.jstacs.classifier.scoringFunctionBased.cll
`class` | `NormConditionalLogLikelihood` This class implements the normalized log conditional likelihood.

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

Subclasses of DifferentiableFunction in de.jstacs.classifier.scoringFunctionBased.logPrior
`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 variance (and possible mean) 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.

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