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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., .
`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` | `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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