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Packages that use DifferentiableFunction | |
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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 |
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Subclasses of DifferentiableFunction in de.jstacs.algorithms.optimization | |
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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 | |
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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 Ribière. |
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 Ribière called "Polak-Ribière-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 the 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 the 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 | |
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NegativeDifferentiableFunction(DifferentiableFunction f)
Creates the DifferentiableFunction f for which
-f should be calculated. |
Uses of DifferentiableFunction in de.jstacs.classifier.scoringFunctionBased |
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Subclasses of DifferentiableFunction in de.jstacs.classifier.scoringFunctionBased | |
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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 |
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Subclasses of DifferentiableFunction in de.jstacs.classifier.scoringFunctionBased.cll | |
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class |
NormConditionalLogLikelihood
This class implements the normalized log conditional likelihood. |
Uses of DifferentiableFunction in de.jstacs.classifier.scoringFunctionBased.logPrior |
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Subclasses of DifferentiableFunction in de.jstacs.classifier.scoringFunctionBased.logPrior | |
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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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