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

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

Packages that use EvaluationException
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 EvaluationException in de.jstacs.algorithms.optimization

Methods in de.jstacs.algorithms.optimization that throw EvaluationException
`static double[]`	`Optimizer.brentsMethod(OneDimensionalFunction f, double a, double x, double b, double tol)` Approximates a minimum (not necessary the global) in the interval `[lower,upper]`.
`static double[]`	`Optimizer.brentsMethod(OneDimensionalFunction f, double a, double x, double fx, double b, double tol)` Approximates a minimum (not necessary the global) in the interval `[lower,upper]`.
`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`	`OneDimensionalSubFunction.evaluateFunction(double x)`
`abstract double`	`OneDimensionalFunction.evaluateFunction(double x)` Evaluates the function at position `x`.
`double`	`NegativeOneDimensionalFunction.evaluateFunction(double x)`
`double`	`OneDimensionalFunction.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., $\nabla f(\underline{x}) = \left(\frac{\partial f(\underline{x})}{\partial x_1},\ldots,\frac{\partial f(\underline{x})}{\partial x_n}\right)$ .
`static double[]`	`Optimizer.findBracket(OneDimensionalFunction f, double lower, double startDistance)` This method returns a bracket containing a minimum.
`static double[]`	`Optimizer.findBracket(OneDimensionalFunction f, double lower, double fLower, double startDistance)` This method returns a bracket containing a minimum.
`double[]`	`OneDimensionalFunction.findMin(double lower, double fLower, double eps, double startDistance)` This method returns a minimum `x` and the value `f(x)`, starting the search at `lower`.
`protected 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 double[]`	`Optimizer.goldenRatio(OneDimensionalFunction f, double lower, double upper, double eps)` Approximates a minimum (not necessary the global) in the interval `[lower,upper]`.
`static double[]`	`Optimizer.goldenRatio(OneDimensionalFunction f, double lower, double p1, double fP1, double upper, double eps)` Approximates a minimum (not necessary the global) in the interval `[lower,upper]`.
`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.
`static int`	`Optimizer.steepestDescent(DifferentiableFunction f, double[] currentValues, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream out, Time t)` The steepest descent.

Uses of EvaluationException in de.jstacs.classifier.scoringFunctionBased

Methods in de.jstacs.classifier.scoringFunctionBased that throw EvaluationException
`double`	`AbstractMultiThreadedOptimizableFunction.evaluateFunction(double[] x)`
`protected abstract void`	`AbstractMultiThreadedOptimizableFunction.evaluateFunction(int index, int startClass, int startSeq, int endClass, int endSeq)` This method evaluates the function for a part of the data.
`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)`.
`protected abstract double[]`	`AbstractMultiThreadedOptimizableFunction.joinGradients()` This method joins the gradients of each part that have been computed using `AbstractMultiThreadedOptimizableFunction.evaluateGradientOfFunction(int, int, int, int, int)`.

Uses of EvaluationException in de.jstacs.classifier.scoringFunctionBased.gendismix

Methods in de.jstacs.classifier.scoringFunctionBased.gendismix that throw EvaluationException
`protected void`	`OneSampleLogGenDisMixFunction.evaluateFunction(int index, int startClass, int startSeq, int endClass, int endSeq)`
`protected void`	`LogGenDisMixFunction.evaluateFunction(int index, int startClass, int startSeq, int endClass, int endSeq)`
`protected double`	`LogGenDisMixFunction.joinFunction()`
`protected double[]`	`LogGenDisMixFunction.joinGradients()`

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

Methods in de.jstacs.classifier.scoringFunctionBased.logPrior that throw EvaluationException
`abstract void`	`LogPrior.addGradientFor(double[] params, double[] vector)` Adds the gradient of the log-prior using the current parameters to a given vector.
`void`	`CompositeLogPrior.addGradientFor(double[] params, double[] grad)`
`double`	`SeparateLaplaceLogPrior.evaluateFunction(double[] x)`
`double`	`SeparateGaussianLogPrior.evaluateFunction(double[] x)`
`double`	`CompositeLogPrior.evaluateFunction(double[] x)`
`double[]`	`LogPrior.evaluateGradientOfFunction(double[] params)`

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Uses of Classde.jstacs.algorithms.optimization.EvaluationException

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