Uses of Class
de.jstacs.io.ParameterSetParser.NotInstantiableException

Packages that use ParameterSetParser.NotInstantiableException

de.jstacs.classifiers.differentiableSequenceScoreBased	Provides the classes for Classifiers that are based on SequenceScores.
de.jstacs.data	Provides classes for the representation of data.
de.jstacs.io	Provides classes for reading data from and writing to a file and storing a number of datatypes, including all primitives, arrays of primitives, and Storables to an XML-representation
de.jstacs.parameters	This package provides classes for parameters that establish a general convention for the description of parameters as defined in the Parameter-interface.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels	Provides DifferentiableStatisticalModels that are directed graphical models.
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 ParameterSetParser.NotInstantiableException in de.jstacs.classifiers.differentiableSequenceScoreBased

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased that throw ParameterSetParser.NotInstantiableException

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.

Uses of ParameterSetParser.NotInstantiableException in de.jstacs.data

Constructors in de.jstacs.data that throw ParameterSetParser.NotInstantiableException

AlphabetContainer(AlphabetContainerParameterSet parameters)
Creates a new AlphabetContainer from an AlphabetContainerParameterSet that contains all necessary parameters.

Uses of ParameterSetParser.NotInstantiableException in de.jstacs.io

Methods in de.jstacs.io that throw ParameterSetParser.NotInstantiableException

static <T extends InstantiableFromParameterSet> T	ParameterSetParser.getInstanceFromParameterSet(InstanceParameterSet<T> pars) Returns an instance of a subclass of InstantiableFromParameterSet that can be instantiated by the InstanceParameterSet pars.
static <T extends InstantiableFromParameterSet> T	ParameterSetParser.getInstanceFromParameterSet(ParameterSet pars, Class<T> instanceClass) Returns an instance of a subclass of InstantiableFromParameterSet that can be instantiated by the ParameterSet pars.

Uses of ParameterSetParser.NotInstantiableException in de.jstacs.parameters

Methods in de.jstacs.parameters that throw ParameterSetParser.NotInstantiableException

T	InstanceParameterSet.getInstance() Returns a new instance of the class of InstanceParameterSet.getInstanceClass() that was created using this ParameterSet.

Uses of ParameterSetParser.NotInstantiableException in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels that throw ParameterSetParser.NotInstantiableException

Measure

BayesianNetworkDiffSMParameterSet.getMeasure() Returns the structure Measure defined by this set of parameters.

Constructors in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels that throw ParameterSetParser.NotInstantiableException

BayesianNetworkDiffSM(BayesianNetworkDiffSMParameterSet parameters)
Creates a new BayesianNetworkDiffSM that has neither been initialized nor trained from a BayesianNetworkDiffSMParameterSet.

Uses of ParameterSetParser.NotInstantiableException in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training that throw ParameterSetParser.NotInstantiableException

AbstractBurnInTest	SamplingHMMTrainingParameterSet.getBurnInTest() This method return the burn in test to be used during sampling.
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.