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Packages that use TerminationCondition | |
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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.algorithms.optimization.termination | Provides classes for termination conditions that can be used in algorithms |
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared | |
de.jstacs.sequenceScores.statisticalModels.trainable.mixture | This package is the super package for any mixture model. |
de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif |
Uses of TerminationCondition in de.jstacs.algorithms.optimization |
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Methods in de.jstacs.algorithms.optimization with parameters of type TerminationCondition | |
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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 TerminationCondition in de.jstacs.algorithms.optimization.termination |
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Classes in de.jstacs.algorithms.optimization.termination that implement TerminationCondition | |
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class |
AbsoluteValueCondition
Deprecated. use of the absolute value condition is not recommended and it may be removed in future releases |
class |
AbstractTerminationCondition
This class is the abstract super class of many TerminationCondition s. |
class |
CombinedCondition
This class allows to use many TerminationCondition s at once. |
class |
IterationCondition
This class will stop an optimization if the number of iteration reaches a given number. |
class |
SmallDifferenceOfFunctionEvaluationsCondition
This class implements a TerminationCondition that stops an optimization
if the difference of the current and the last function evaluations will be small, i.e.,
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class |
SmallGradientConditon
This class implements a TerminationCondition that allows no further iteration in an optimization if the
the gradient becomes small, i.e.,
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class |
SmallStepCondition
This class implements a TerminationCondition that allows no further iteration in an optimization if the
scalar product of the current and the last values of x will be small, i.e.,
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class |
TimeCondition
This class implements a TerminationCondition that stops the optimization if the elapsed time in seconds is
greater than a given value. |
Uses of TerminationCondition in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared |
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Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared with parameters of type TerminationCondition | |
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SharedStructureMixture(FSDAGTrainSM[] m,
StructureLearner.ModelType model,
byte order,
int starts,
boolean estimateComponentProbs,
double[] weights,
double alpha,
TerminationCondition tc)
Creates a new SharedStructureMixture instance with all relevant
values. |
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SharedStructureMixture(FSDAGTrainSM[] m,
StructureLearner.ModelType model,
byte order,
int starts,
double[] weights,
double alpha,
TerminationCondition tc)
Creates a new SharedStructureMixture instance with fixed
component weights. |
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SharedStructureMixture(FSDAGTrainSM[] m,
StructureLearner.ModelType model,
byte order,
int starts,
double alpha,
TerminationCondition tc)
Creates a new SharedStructureMixture instance which estimates the
component probabilities/weights. |
Uses of TerminationCondition in de.jstacs.sequenceScores.statisticalModels.trainable.mixture |
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Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.mixture with parameters of type TerminationCondition | |
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AbstractMixtureTrainSM(int length,
TrainableStatisticalModel[] models,
boolean[] optimizeModel,
int dimension,
int starts,
boolean estimateComponentProbs,
double[] componentHyperParams,
double[] weights,
AbstractMixtureTrainSM.Algorithm algorithm,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization,
int initialIteration,
int stationaryIteration,
BurnInTest burnInTest)
Creates a new AbstractMixtureTrainSM . |
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MixtureTrainSM(int length,
TrainableStatisticalModel[] models,
double[] weights,
int starts,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization)
Creates an instance using EM and fixed component probabilities. |
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MixtureTrainSM(int length,
TrainableStatisticalModel[] models,
int starts,
boolean estimateComponentProbs,
double[] componentHyperParams,
double[] weights,
AbstractMixtureTrainSM.Algorithm algorithm,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization,
int initialIteration,
int stationaryIteration,
BurnInTest burnInTest)
Creates a new MixtureTrainSM . |
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MixtureTrainSM(int length,
TrainableStatisticalModel[] models,
int starts,
double[] componentHyperParams,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization)
Creates an instance using EM and estimating the component probabilities. |
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StrandTrainSM(TrainableStatisticalModel model,
int starts,
boolean estimateComponentProbs,
double[] componentHyperParams,
double forwardStrandProb,
AbstractMixtureTrainSM.Algorithm algorithm,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization,
int initialIteration,
int stationaryIteration,
BurnInTest burnInTest)
Creates a new StrandTrainSM . |
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StrandTrainSM(TrainableStatisticalModel model,
int starts,
double[] componentHyperParams,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization)
Creates an instance using EM and estimating the component probabilities. |
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StrandTrainSM(TrainableStatisticalModel model,
int starts,
double forwardStrandProb,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization)
Creates an instance using EM and fixed component probabilities. |
Uses of TerminationCondition in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif |
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Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif with parameters of type TerminationCondition | |
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HiddenMotifMixture(TrainableStatisticalModel[] models,
boolean[] optimzeArray,
int components,
int starts,
boolean estimateComponentProbs,
double[] componentHyperParams,
double[] weights,
PositionPrior posPrior,
AbstractMixtureTrainSM.Algorithm algorithm,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization,
int initialIteration,
int stationaryIteration,
BurnInTest burnInTest)
Creates a new HiddenMotifMixture . |
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ZOOPSTrainSM(TrainableStatisticalModel motif,
TrainableStatisticalModel bg,
boolean trainOnlyMotifModel,
int starts,
double[] componentHyperParams,
double[] weights,
PositionPrior posPrior,
AbstractMixtureTrainSM.Algorithm algorithm,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization,
int initialIteration,
int stationaryIteration,
BurnInTest burnInTest)
Creates a new ZOOPSTrainSM . |
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ZOOPSTrainSM(TrainableStatisticalModel motif,
TrainableStatisticalModel bg,
boolean trainOnlyMotifModel,
int starts,
double[] componentHyperParams,
PositionPrior posPrior,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization)
Creates a new ZOOPSTrainSM using EM and estimating
the probability for finding a motif. |
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ZOOPSTrainSM(TrainableStatisticalModel motif,
TrainableStatisticalModel bg,
boolean trainOnlyMotifModel,
int starts,
double motifProb,
PositionPrior posPrior,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization)
Creates a new ZOOPSTrainSM using EM and fixed
probability for finding a motif. |
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