Uses of Class de.jstacs.algorithms.optimization.termination.AbstractTerminationCondition

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Packages that use AbstractTerminationCondition
Package	Description
de.jstacs.algorithms.optimization.termination	Provides classes for termination conditions that can be used in algorithms.
de.jstacs.classifiers.differentiableSequenceScoreBased	Provides the classes for `Classifier`s that are based on `SequenceScore`s. It includes a sub-package for discriminative objective functions, namely conditional likelihood and supervised posterior, and a separate sub-package for the parameter priors, that can be used for the supervised posterior.
de.jstacs.motifDiscovery	This package provides the framework including the interface for any de novo motif discoverer.
de.jstacs.sequenceScores.statisticalModels.trainable	Provides all `TrainableStatisticalModel`s, which can be learned from a single `DataSet`.
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training	The package provides all classes used to determine the training algorithm of a hidden Markov model.

Uses of AbstractTerminationCondition in de.jstacs.algorithms.optimization.termination

Subclasses of AbstractTerminationCondition in de.jstacs.algorithms.optimization.termination
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`	`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.

Methods in de.jstacs.algorithms.optimization.termination that return AbstractTerminationCondition
Modifier and Type	Method and Description
`AbstractTerminationCondition`	AbstractTerminationCondition.`clone()`

Constructors in de.jstacs.algorithms.optimization.termination with parameters of type AbstractTerminationCondition
Constructor and Description
`CombinedCondition(int threshold, AbstractTerminationCondition... condition)` This constructor creates an instance that allows to use many `TerminationCondition`s at once.
`CombinedConditionParameterSet(int threshold, AbstractTerminationCondition[] condition)` This constructor creates a filled instance of a parameters set.
`MultipleIterationsCondition(int threshold, AbstractTerminationCondition condition)` This constructor creates an instance that stops the optimization if provided termination condition fails a contiguously a specified number of times.
`MultipleIterationsConditionParameterSet(int threshold, AbstractTerminationCondition condition)` This constructor creates a filled instance of the parameter set.

Constructor parameters in de.jstacs.algorithms.optimization.termination with type arguments of type AbstractTerminationCondition
Constructor and Description
`AbstractTerminationConditionParameterSet(Class<? extends AbstractTerminationCondition> instanceClass)` Constructs an `AbstractTerminationCondition.AbstractTerminationConditionParameterSet` from the class that can be instantiated using this `AbstractTerminationCondition.AbstractTerminationConditionParameterSet`.

Uses of AbstractTerminationCondition in de.jstacs.classifiers.differentiableSequenceScoreBased

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased that return AbstractTerminationCondition
Modifier and Type	Method and Description
`AbstractTerminationCondition`	ScoreClassifierParameterSet.`getTerminantionCondition()` This method returns the `AbstractTerminationCondition` for stopping the training, e.g., if the difference of the scores between two iterations is smaller than a given threshold the training is stopped.

Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased with parameters of type AbstractTerminationCondition
Constructor and Description
`ScoreClassifierParameterSet(Class<? extends ScoreClassifier> instanceClass, AlphabetContainer alphabet, int length, byte algo, AbstractTerminationCondition tc, double lineps, double startD, boolean free, OptimizableFunction.KindOfParameter kind)` The constructor for a simple, instantiated parameter set.

Uses of AbstractTerminationCondition in de.jstacs.motifDiscovery

Methods in de.jstacs.motifDiscovery with parameters of type AbstractTerminationCondition
Modifier and Type	Method and Description
`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.

Uses of AbstractTerminationCondition in de.jstacs.sequenceScores.statisticalModels.trainable

Constructors in de.jstacs.sequenceScores.statisticalModels.trainable with parameters of type AbstractTerminationCondition
Constructor and Description
`DifferentiableStatisticalModelWrapperTrainSM(DifferentiableStatisticalModel nsf, int threads, byte algo, AbstractTerminationCondition tc, double lineps, double startD)` The main constructor that creates an instance with the user given parameters and `CompositeLogPrior`.
`DifferentiableStatisticalModelWrapperTrainSM(DifferentiableStatisticalModel nsf, int threads, byte algo, AbstractTerminationCondition tc, double lineps, double startD, LogPrior prior)` Constructor that creates an instance with the user given parameters.

Uses of AbstractTerminationCondition in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training that return AbstractTerminationCondition
Modifier and Type	Method and Description
`AbstractTerminationCondition`	MaxHMMTrainingParameterSet.`getTerminationCondition()` This method returns the `AbstractTerminationCondition` for stopping the training, e.g., if the difference of the scores between two iterations is smaller than a given threshold the training is stopped.

Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training with parameters of type AbstractTerminationCondition
Constructor and Description
`BaumWelchParameterSet(int starts, AbstractTerminationCondition tc, int threads)` This constructor can be used to create an instance with specified parameters.
`MaxHMMTrainingParameterSet(int starts, AbstractTerminationCondition tc)` This constructor can be used to create an instance with specified parameters.
`MultiThreadedTrainingParameterSet(int starts, AbstractTerminationCondition tc, int threads)` This constructor can be used to create an instance with specified parameters.
`NumericalHMMTrainingParameterSet(int starts, AbstractTerminationCondition tc, int threads, byte algorithm, double lineEps, double startDist)` This constructor can be used to create an instance with specified parameters.
`ViterbiParameterSet(int starts, AbstractTerminationCondition tc, int threads)` This constructor can be used to create an instance with specified parameters.

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