Uses of Class
de.jstacs.algorithms.optimization.DimensionException

Packages that use DimensionException

Package	Description
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. It includes a sub-package for discriminative objective functions, namely conditional likelihood and supervised posterior, and a separate sub-package for the parameter priors, that can be used for the supervised posterior.
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.
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous	This package contains various inhomogeneous models.

Uses of DimensionException in de.jstacs.algorithms.optimization

Methods in de.jstacs.algorithms.optimization that throw DimensionException

Modifier and Type	Method and Description
static int	Optimizer.conjugateGradientsFR(DifferentiableFunction f, double[] currentValues, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream out, Time t) The conjugate gradient algorithm by Fletcher and Reeves.
static int	Optimizer.conjugateGradientsPR(DifferentiableFunction f, double[] currentValues, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream out, Time t) The conjugate gradient algorithm by Polak and Ribière.
static int	Optimizer.conjugateGradientsPRP(DifferentiableFunction f, double[] currentValues, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream out, Time t) The conjugate gradient algorithm by Polak and Ribière called "Polak-Ribière-Positive".
double	OneDimensionalFunction.evaluateFunction(double[] x)
double	NumericalDifferentiableFunction.evaluateFunction(double[] x)
double	NegativeOneDimensionalFunction.evaluateFunction(double[] x)
double	NegativeFunction.evaluateFunction(double[] x)
double	NegativeDifferentiableFunction.evaluateFunction(double[] x)
double	Function.evaluateFunction(double[] x) Evaluates the function at a certain vector (in mathematical sense) x.
double[]	NumericalDifferentiableFunction.evaluateGradientOfFunction(double[] x) Evaluates the gradient of a function at a certain vector (in mathematical sense) x numerically.
double[]	NegativeDifferentiableFunction.evaluateGradientOfFunction(double[] x)
abstract double[]	DifferentiableFunction.evaluateGradientOfFunction(double[] x) Evaluates the gradient of a function at a certain vector (in mathematical sense) x, i.e., .
double[]	DifferentiableFunction.findOneDimensionalMin(double[] x, double[] d, double alpha_0, double fAlpha_0, double linEps, double startDistance) This method is used to find an approximation of an one-dimensional subfunction.
static int	Optimizer.limitedMemoryBFGS(DifferentiableFunction f, double[] currentValues, byte m, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream 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, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream 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, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream 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, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream out, Time t) The Broyden-Fletcher-Goldfarb-Shanno version of the quasi-Newton method.
static int	Optimizer.quasiNewtonDFP(DifferentiableFunction f, double[] currentValues, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream out, Time t) The Davidon-Fletcher-Powell version of the quasi-Newton method.
void	OneDimensionalSubFunction.set(double[] current, double[] d) Sets the current values and direction.
static int	Optimizer.steepestDescent(DifferentiableFunction f, double[] currentValues, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream out, Time t) The steepest descent.

Uses of DimensionException in de.jstacs.classifiers.differentiableSequenceScoreBased

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased that throw DimensionException

Modifier and Type	Method and Description
double	AbstractMultiThreadedOptimizableFunction.evaluateFunction(double[] x)
double[]	AbstractMultiThreadedOptimizableFunction.evaluateGradientOfFunction(double[] x)
protected abstract double	AbstractMultiThreadedOptimizableFunction.joinFunction() This method joins the partial results that have been computed using AbstractMultiThreadedOptimizableFunction.evaluateFunction(int, int, int, int, int).
abstract void	OptimizableFunction.setParams(double[] current) Sets the current values as parameters.
void	AbstractMultiThreadedOptimizableFunction.setParams(double[] params)
protected void	DiffSSBasedOptimizableFunction.setParams(int index)
protected abstract void	AbstractMultiThreadedOptimizableFunction.setParams(int index) This method sets the parameters for thread index
protected void	DiffSSBasedOptimizableFunction.setThreadIndependentParameters()
protected abstract void	AbstractMultiThreadedOptimizableFunction.setThreadIndependentParameters() This method allows to set thread independent parameters.

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

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix that throw DimensionException

Modifier and Type	Method and Description
protected double	LogGenDisMixFunction.joinFunction()

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

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior that throw DimensionException

Modifier and Type	Method and Description
double	SeparateLaplaceLogPrior.evaluateFunction(double[] x)
double	SeparateGaussianLogPrior.evaluateFunction(double[] x)
double	CompositeLogPrior.evaluateFunction(double[] x)

Uses of DimensionException in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous that throw DimensionException

Modifier and Type	Method and Description
double	MEMTools.DualFunction.evaluateFunction(double[] x)
double[]	MEMTools.DualFunction.evaluateGradientOfFunction(double[] x)