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

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

Methods in de.jstacs.algorithms.optimization that throw DimensionException
`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 Ribiere.
`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 Ribiere called Polak-Ribiere-Positive.
`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`.
`protected double[]` | `DifferentiableFunction.findOneDimensionalMin(double[] current, 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, 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 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 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 that throw DimensionException
`OneDimensionalSubFunction(Function f, double[] current, double[] d)`

Uses of DimensionException in de.jstacs.classifier.scoringFunctionBased

Methods in de.jstacs.classifier.scoringFunctionBased that throw DimensionException
`abstract void` | `OptimizableFunction.setParams(double[] current)` Sets the current values as parameters.
`void` | `AbstractOptimizableFunction.setParams(double[] params)` Checks the dimension and sets the class parameters.

Uses of DimensionException in de.jstacs.classifier.scoringFunctionBased.cll

Methods in de.jstacs.classifier.scoringFunctionBased.cll that throw DimensionException
`double` | `NormConditionalLogLikelihood.evaluateFunction(double[] x)`
`double[]` | `NormConditionalLogLikelihood.evaluateGradientOfFunction(double[] x)`
`void` | `NormConditionalLogLikelihood.setParams(double[] params)`

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

Methods in de.jstacs.classifier.scoringFunctionBased.logPrior that throw DimensionException
`double` | `SeparateLaplaceLogPrior.evaluateFunction(double[] x)`
`double` | `SeparateGaussianLogPrior.evaluateFunction(double[] x)`

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