Uses of Class
de.jstacs.NotTrainedException

Packages that use NotTrainedException

de.jstacs.classifiers	This package provides the framework for any classifier.
de.jstacs.classifiers.differentiableSequenceScoreBased	Provides the classes for Classifiers that are based on SequenceScores. 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.classifiers.differentiableSequenceScoreBased.sampling	Provides the classes for AbstractScoreBasedClassifiers that are based on SamplingDifferentiableStatisticalModels and that sample parameters using the Metropolis-Hastings algorithm.
de.jstacs.sequenceScores.statisticalModels	Provides all StatisticalModels, which can compute a proper (i.e., normalized) likelihood over the input space of sequences. StatisticalModels can be further differentiated into TrainableStatisticalModels, which can be learned from a single input DataSet, and DifferentiableStatisticalModels, which define a proper likelihood but can also compute gradients like DifferentiableSequenceScores.
de.jstacs.sequenceScores.statisticalModels.differentiable	Provides all DifferentiableStatisticalModels, which can compute the gradient with respect to their parameters for a given input Sequence.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels	Provides DifferentiableStatisticalModels that are directed graphical models.
de.jstacs.sequenceScores.statisticalModels.trainable	Provides all TrainableStatisticalModels, which can be learned from a single DataSet.
de.jstacs.sequenceScores.statisticalModels.trainable.discrete
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous
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.utils	This package contains a bundle of useful classes and interfaces like ...

Uses of NotTrainedException in de.jstacs.classifiers

Methods in de.jstacs.classifiers that throw NotTrainedException

protected void	AbstractScoreBasedClassifier.check(DataSet s) This method checks if the given DataSet can be used.
protected void	AbstractScoreBasedClassifier.check(Sequence seq) This method checks if the given Sequence can be used.
protected double	MappingClassifier.getScore(Sequence seq, int i, boolean check)
protected abstract double	AbstractScoreBasedClassifier.getScore(Sequence seq, int i, boolean check) This method returns the score for a given Sequence and a given class.

Uses of NotTrainedException in de.jstacs.classifiers.differentiableSequenceScoreBased

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased that throw NotTrainedException

protected double

ScoreClassifier.getScore(Sequence seq, int i, boolean check)

Uses of NotTrainedException in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling that throw NotTrainedException

protected double

SamplingScoreBasedClassifier.getScore(Sequence seq, int cls, boolean check)

Uses of NotTrainedException in de.jstacs.sequenceScores.statisticalModels

Methods in de.jstacs.sequenceScores.statisticalModels that throw NotTrainedException

DataSet

StatisticalModel.emitDataSet(int numberOfSequences, int... seqLength) This method returns a DataSet object containing artificial sequence(s).

Uses of NotTrainedException in de.jstacs.sequenceScores.statisticalModels.differentiable

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable that throw NotTrainedException

DataSet	UniformDiffSM.emitDataSet(int numberOfSequences, int... seqLength)
DataSet	MarkovRandomFieldDiffSM.emitDataSet(int numberOfSequences, int... seqLength)
DataSet	IndependentProductDiffSM.emitDataSet(int numberOfSequences, int... seqLength)
DataSet	AbstractDifferentiableStatisticalModel.emitDataSet(int numberOfSequences, int... seqLength)

Uses of NotTrainedException in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels that throw NotTrainedException

DataSet

BayesianNetworkDiffSM.emitDataSet(int numberOfSequences, int... seqLength)

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

Methods in de.jstacs.sequenceScores.statisticalModels.trainable that throw NotTrainedException

protected void	AbstractTrainableStatisticalModel.check(Sequence sequence, int startpos, int endpos) This method checks all parameters before a probability can be computed for a sequence.
DataSet	AbstractTrainableStatisticalModel.emitDataSet(int numberOfSequences, int... seqLength)
double	VariableLengthWrapperTrainSM.getLogProbFor(Sequence sequence, int startpos, int endpos)
double	DifferentiableStatisticalModelWrapperTrainSM.getLogProbFor(Sequence sequence, int startpos, int endpos)
double	CompositeTrainSM.getLogProbFor(Sequence sequence, int startpos, int endpos)

Uses of NotTrainedException in de.jstacs.sequenceScores.statisticalModels.trainable.discrete

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete that throw NotTrainedException

protected void

DiscreteGraphicalTrainSM.check(Sequence sequence, int startpos, int endpos) Checks some conditions on a Sequence.

Uses of NotTrainedException in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous that throw NotTrainedException

protected void	HomogeneousTrainSM.check(Sequence sequence, int startpos, int endpos) Checks some constraints, these are in general conditions on the AlphabetContainer of a (sub)Sequence between startpos und endpos.
DataSet	HomogeneousTrainSM.emitDataSet(int no, int... length) Creates a DataSet of a given number of Sequences from a trained homogeneous model.
double	HomogeneousTrainSM.getLogProbFor(Sequence sequence, int startpos, int endpos)

Uses of NotTrainedException in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous that throw NotTrainedException

protected void	InhomogeneousDGTrainSM.check(Sequence sequence, int startpos, int endpos)
DataSet	MEManager.emitDataSet(int n, int... lengths)
DataSet	DAGTrainSM.emitDataSet(int n, int... lengths)
double	MEManager.getLogProbFor(Sequence sequence, int startpos, int endpos)
double	DAGTrainSM.getLogProbFor(Sequence sequence, int startpos, int endpos)
String	MEManager.getStructure()
abstract String	InhomogeneousDGTrainSM.getStructure() Returns a String representation of the underlying graph.
String	DAGTrainSM.getStructure()

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

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared that throw NotTrainedException

String

SharedStructureMixture.getStructure() Returns a String representation of the structure of the used models.

Uses of NotTrainedException in de.jstacs.sequenceScores.statisticalModels.trainable.mixture

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.mixture that throw NotTrainedException

protected Sequence[]	StrandTrainSM.emitDataSetUsingCurrentParameterSet(int n, int... lengths)
double	AbstractMixtureTrainSM.getScoreForBestRun() Returns the value of the optimized function from the best run of the last training.

Uses of NotTrainedException in de.jstacs.utils

Methods in de.jstacs.utils that throw NotTrainedException

static DataSet

DiscreteInhomogenousDataSetEmitter.emitDataSet(StatisticalModel m, int n) This method emits a data set with n sequences from the discrete inhomogeneous model m .