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.classifiers.differentiableSequenceScoreBased	Provides the classes for Classifiers that are based on SequenceScores.
de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix	Provides an implementation of a classifier that allows to train the parameters of a set of DifferentiableStatisticalModels 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

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 .
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.classifiers.differentiableSequenceScoreBased

Classes in de.jstacs.classifiers.differentiableSequenceScoreBased 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	DiffSSBasedOptimizableFunction This abstract class is the basis of all multi-threaded OptimizableFunctions that are based on DifferentiableSequenceScores.
class	OptimizableFunction This is the main function for the ScoreClassifier.

Uses of Function in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix

Classes in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix that implement Function

class	LogGenDisMixFunction This class implements the the following function The weights have to sum to 1.
class	OneDataSetLogGenDisMixFunction This class implements the the following function where is the weight for sequence and class .

Uses of Function in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior

Classes in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior that implement Function

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 DifferentiableStatisticalModels and a set of class parameters.
class	SeparateLaplaceLogPrior Class for a LogPrior that defines a Laplace prior on the parameters of a set of DifferentiableStatisticalModels 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.