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

Packages that use TerminationCondition
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
 

Methods in de.jstacs.algorithms.optimization with parameters of type TerminationCondition
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
 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 TerminationConditions.
 class CombinedCondition
          This class allows to use many TerminationConditions 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., $|f(\underline{x}_{i-1}) - f(\underline{x}_i)| < \epsilon$.
 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.shared
 

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