Parameters in this ParameterSet by their
equivalents implementing the Rangeable interface.
Sequence to an new Sequence using some DiscreteAlphabetMappings.MappedDiscreteSequence.
MappedDiscreteSequence from a given Sequence and some given DiscreteAlphabetMappings.
MappingClassifier from a given classifier and a
class mapping.
Storable.
NormalizableScoringFunction that works on
mapped Sequences.MappingScoringFunction.
Storable.
Samples to the internal classes.
matrix.
w between index start and end.
array.
array
between start and end.
HMMTrainingParameterSet that
is used for a maximizing training algorithm of a hidden Markov model.Storable.
NumericalResultSets.MeanResultSet with an empty set of
NumericalResultSets.
MeanResultSet with an empty set of
NumericalResultSets and no further information.
Storable.
MeanResultSets
should be added that do not match.MeanResultSet.AdditionImpossibleException with an
appropriate error message.
NumericalResultSet is
added to the MeanResultSet that has a number of results which is
not equal to the number of results of the previously added results.MeanResultSet.InconsistentResultNumberException with an
appropriate error message.
evaluate-methods of a
classifier.MeasureParameters.
MeasureParameters.
MeasureParameters.
Storable.
enum defines all measures that are currently
implemented in Jstacs.MEMConstraint as part of a (whole) model.
MEMConstraint as part of a model.
Storable.
array.
array
between start and end.
DurationScoringFunctions.MixtureDuration.
Storable.
Emissions.MixtureEmission from a set of emissions.
Storable.
Models.MixtureModel.
Storable.
MixtureScoringFunction.
Storable.
Models and
clones these if necessary.
Models.
Storable.
Models.
AlphabetContainer.
SamplingScoreBasedClassifier.getFunction(Sample[], double[][]).
motifIndex.
StrandedLocatedSequenceAnnotationWithLength that is a
motif.MotifAnnotation of type type with
identifier identifier and additional annotation (that does
not fit the SequenceAnnotation definitions) given as an array of
Results additionalAnnotation.
Storable.
MotifAnnotations.MotifAnnotationParser with default delimiters.
MotifAnnotationParser with the supplied delimiters
enum can be used to determine which kind of profile
should be returned.MotifDiscoverer.MRFScoringFunction with
equivalent sample size (ess) 0.
MRFScoringFunction.
Storable.
MSPClassifier that used MCL principle for training.
MSPClassifier from a
given parameter set, a prior and ScoringFunctions for the
classes.
MSPClassifier from a
given parameter set, a prior and ScoringFunctions for the
classes.
Storable.
MultiDimensionalDiscreteSequence from a set of individual SimpleDiscreteSequences.
start to
end with the value factor.
with the factor val:
.
Parameter that provides a collection of possible values.MultiSelectionCollectionParameter.
MultiSelectionCollectionParameter.
MultiSelectionCollectionParameter from an array of
ParameterSets.
MultiSelectionCollectionParameter from an array of
ParameterSets.
Storable.
MultiSelectionCollectionParameter from the
necessary field.
AbstractNormalizableScoringFunction for an inhomogeneous Markov model.Storable.
MutableMotifDiscoverer.MutableMotifDiscovererToolbox.getSortedInitialParameters(ScoringFunction[], InitMethodForScoringFunction[], SFBasedOptimizableFunction, int, OutputStream, int).