Uses of Interface
de.jstacs.algorithms.optimization.StartDistanceForecaster

Packages that use StartDistanceForecaster

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.motifDiscovery	This package provides the framework including the interface for any de novo motif discoverer.

Uses of StartDistanceForecaster in de.jstacs.algorithms.optimization

Classes in de.jstacs.algorithms.optimization that implement StartDistanceForecaster

Modifier and Type	Class and Description
class	ConstantStartDistance The most simple StartDistanceForecaster that returns always the same value.
class	LimitedMedianStartDistance This class implements a StartDistanceForecaster that uses the median of a limited memory over the last values.

Methods in de.jstacs.algorithms.optimization with parameters of type StartDistanceForecaster

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".
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 StartDistanceForecaster in de.jstacs.motifDiscovery

Methods in de.jstacs.motifDiscovery with parameters of type StartDistanceForecaster

Modifier and Type	Method and Description
static boolean	MutableMotifDiscovererToolbox.doHeuristicSteps(DifferentiableSequenceScore[] funs, DataSet[] data, double[][] weights, DiffSSBasedOptimizableFunction opt, DifferentiableFunction neg, byte algorithm, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, boolean breakOnChanged, History[][] hist, int[][] minimalNewLength, boolean maxPos) This method tries to make some heuristic step if at least one DifferentiableSequenceScore is a MutableMotifDiscoverer.
static boolean	MutableMotifDiscovererToolbox.findModification(int clazz, int motif, MutableMotifDiscoverer mmd, DifferentiableSequenceScore[] score, DataSet[] data, double[][] weights, DiffSSBasedOptimizableFunction opt, DifferentiableFunction neg, byte algo, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, History hist, int minimalNewLength, boolean maxPos) This method tries to find a modification, i.e.
static double[][]	MutableMotifDiscovererToolbox.optimize(DifferentiableSequenceScore[] funs, DiffSSBasedOptimizableFunction opt, byte algorithm, AbstractTerminationCondition condition, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, boolean breakOnChanged, History[][] hist, int[][] minimalNewLength, OptimizableFunction.KindOfParameter plugIn, boolean maxPos) This method tries to optimize the problem at hand as good as possible.
static double[][]	MutableMotifDiscovererToolbox.optimize(DifferentiableSequenceScore[] funs, DiffSSBasedOptimizableFunction opt, byte algorithm, AbstractTerminationCondition condition, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, boolean breakOnChanged, History template, OptimizableFunction.KindOfParameter plugIn, boolean maxPos) This method tries to optimize the problem at hand as good as possible.