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Uses of Package
de.jstacs.algorithms.optimization

Packages that use de.jstacs.algorithms.optimization
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 Classifiers that are based on ScoringFunctions. 
de.jstacs.classifier.scoringFunctionBased.gendismix Provides an implementation of a classifier that allows to train the parameters of a set of NormalizableScoringFunctions 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 
 

Classes in de.jstacs.algorithms.optimization used by de.jstacs.algorithms.optimization
DifferentiableFunction
          This class is the framework for any (at least) one time differentiable function $f: \mathbb{R}^n \to \mathbb{R}$.
DimensionException
          This class is for Exceptions depending on wrong dimensions of vectors for a given function.
EvaluationException
          This class indicates that there was a problem to evaluate a function or the gradient of the function.
Function
          This interface is the framework for any mathematical function f: R^n -> R.
OneDimensionalFunction
          This class implements the interface Function for an one-dimensional function.
StartDistanceForecaster
          This interface is used to determine the next start distance that will be used in a line search.
TerminationException
          This class is for an Exception that is thrown if something with a termination was not correct.
 

Classes in de.jstacs.algorithms.optimization used by de.jstacs.classifier.scoringFunctionBased
DifferentiableFunction
          This class is the framework for any (at least) one time differentiable function $f: \mathbb{R}^n \to \mathbb{R}$.
DimensionException
          This class is for Exceptions depending on wrong dimensions of vectors for a given function.
EvaluationException
          This class indicates that there was a problem to evaluate a function or the gradient of the function.
Function
          This interface is the framework for any mathematical function f: R^n -> R.
 

Classes in de.jstacs.algorithms.optimization used by de.jstacs.classifier.scoringFunctionBased.gendismix
DifferentiableFunction
          This class is the framework for any (at least) one time differentiable function $f: \mathbb{R}^n \to \mathbb{R}$.
DimensionException
          This class is for Exceptions depending on wrong dimensions of vectors for a given function.
EvaluationException
          This class indicates that there was a problem to evaluate a function or the gradient of the function.
Function
          This interface is the framework for any mathematical function f: R^n -> R.
 

Classes in de.jstacs.algorithms.optimization used by de.jstacs.classifier.scoringFunctionBased.logPrior
DifferentiableFunction
          This class is the framework for any (at least) one time differentiable function $f: \mathbb{R}^n \to \mathbb{R}$.
DimensionException
          This class is for Exceptions depending on wrong dimensions of vectors for a given function.
EvaluationException
          This class indicates that there was a problem to evaluate a function or the gradient of the function.
Function
          This interface is the framework for any mathematical function f: R^n -> R.
 

Classes in de.jstacs.algorithms.optimization used by de.jstacs.motifDiscovery
DifferentiableFunction
          This class is the framework for any (at least) one time differentiable function $f: \mathbb{R}^n \to \mathbb{R}$.
StartDistanceForecaster
          This interface is used to determine the next start distance that will be used in a line search.
 

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