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## Uses of Interface
de.jstacs.algorithms.optimization.Function

Packages that use Function
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

Uses of Function in de.jstacs.algorithms.optimization

Classes in de.jstacs.algorithms.optimization that implement Function
`class` | `DifferentiableFunction` This class is the framework for any (at least) one time differentiable function .
`class` | `NegativeDifferentiableFunction` The negative function `-f` for a given `DifferentiableFunction` `f`.
`class` | `NegativeFunction` The negative function -f for a given `Function` `f`.
`class` | `NegativeOneDimensionalFunction` This class extends the class `OneDimensionalFunction`.
`class` | `NumericalDifferentiableFunction` This class is the framework for any numerical differentiable function `f: R^n -> R`.
`class` | `OneDimensionalFunction` This class implements the interface `Function` for an one-dimensional function.
`class` | `OneDimensionalSubFunction` This class is used to do the line search.
`class` | `QuadraticFunction` This class implements a quadratic function.

Constructors in de.jstacs.algorithms.optimization with parameters of type Function
`NegativeFunction(Function f)` Creates the `Function` `f` for which `-f` should be calculated.
`OneDimensionalSubFunction(Function f, double[] current, double[] d)` Creates a new `OneDimensionalSubFunction` from a `Function` `f` for the line search.

Uses of Function in de.jstacs.classifier.scoringFunctionBased

Classes in de.jstacs.classifier.scoringFunctionBased that implement Function
`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 Function in de.jstacs.classifier.scoringFunctionBased.gendismix

Classes in de.jstacs.classifier.scoringFunctionBased.gendismix that implement Function
`class` | `LogGenDisMixFunction` This class implements the the following function The weights have to sum to 1.
`class` | `OneSampleLogGenDisMixFunction` This class implements the the following function where is the weight for sequence and class .

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

Classes in de.jstacs.classifier.scoringFunctionBased.logPrior that implement Function
`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.

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