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Packages that use StartDistanceForecaster | |
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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 |
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Classes in de.jstacs.algorithms.optimization that implement StartDistanceForecaster | |
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class |
ConstantStartDistance
The most simple StartDistanceForecaster that returns always the same
value. |
class |
LimitedMedianStartDistance
This class implements a StartDistanceForecaster that returns the
median of a limited memory over the last values. |
Methods in de.jstacs.algorithms.optimization with parameters of type StartDistanceForecaster | |
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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 Ribière. |
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 Ribière called "Polak-Ribière-Positive". |
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 the 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 the 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. |
Uses of StartDistanceForecaster in de.jstacs.motifDiscovery |
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Methods in de.jstacs.motifDiscovery with parameters of type StartDistanceForecaster | |
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static double[][] |
MutableMotifDiscovererToolbox.optimize(ScoringFunction[] funs,
OptimizableFunction opt,
byte algorithm,
double eps,
double linEps,
StartDistanceForecaster startDistance,
SafeOutputStream out,
boolean breakOnChanged,
History[][] hist,
int[][] minimalNewLength,
OptimizableFunction.KindOfParameter plugIn)
This method tries to optimize the problem at hand as good as possible. |
static double[][] |
MutableMotifDiscovererToolbox.optimize(ScoringFunction[] funs,
OptimizableFunction opt,
byte algorithm,
double eps,
double linEps,
StartDistanceForecaster startDistance,
SafeOutputStream out,
boolean breakOnChanged,
History template,
OptimizableFunction.KindOfParameter plugIn)
This method tries to optimize the problem at hand as good as possible. |
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