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 Classifiers that are based on ScoringFunctions. 
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 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 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 onedimensional 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
 

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)