Uses of Interface de.jstacs.algorithms.optimization.termination.TerminationCondition

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Packages that use TerminationCondition
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.algorithms.optimization.termination	Provides classes for termination conditions that can be used in algorithms.
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous	This package contains various inhomogeneous models.
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

Methods in de.jstacs.algorithms.optimization with parameters of type TerminationCondition
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 TerminationCondition in de.jstacs.algorithms.optimization.termination

Classes in de.jstacs.algorithms.optimization.termination that implement TerminationCondition
Modifier and Type	Class and Description
`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`	`MultipleIterationsCondition` This `TerminationCondition` requires another provided `TerminationCondition` to fail a contiguous specified number of times before the optimization is terminated.
`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., $\|f(\underline{x}_{i-1}) - f(\underline{x}_i)\| < \epsilon$ .
`class`	`SmallGradientConditon` This class implements a `TerminationCondition` that allows no further iteration in an optimization if the the gradient becomes small, i.e., $\sum_i \left\|\frac{\partial f(\underline{x})}{\partial x_i}\right\| < \epsilon$ .
`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., $(\underline{x}_i-\underline{x}_{i-1})^T (\underline{x}_i-\underline{x}_{i-1}) < \epsilon$ .
`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

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous with parameters of type TerminationCondition
Modifier and Type	Method and Description
`static double`	MEMTools.`train(MEMConstraint[] constraints, int[][] cond, SequenceIterator sequence, byte algorithm, TerminationCondition condition, OutputStream stream, int[] alphLen)` This method approximates the distribution either analytically or numerically.
`void`	MEM.`train(SequenceIterator s, byte algo, TerminationCondition condition, SafeOutputStream sostream)` This method approximates the distribution either analytically or numerically.

Uses of TerminationCondition in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared

Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared with parameters of type TerminationCondition
Constructor and Description
`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.
`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.
`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

Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.mixture with parameters of type TerminationCondition
Constructor and Description
`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`.
`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.
`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`.
`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.
`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`.
`StrandTrainSM(TrainableStatisticalModel model, int starts, double[] componentHyperParams, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization)` Creates an instance using EM and estimating the component probabilities.
`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

Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif with parameters of type TerminationCondition
Constructor and Description
`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`.
`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`.
`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.
`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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