DataSets to the internal classes.
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.
DifferentiableStatisticalModel that works on
mapped Sequences.MappingDiffSM.
Storable.
AbstractDifferentiableStatisticalModel for an inhomogeneous Markov model.Storable.
MarkovRandomFieldDiffSM with
equivalent sample size (ess) 0.
MarkovRandomFieldDiffSM.
Storable.
MatrixCosts where the costs
for mismatch and match are given in 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.
.MaximumCorrelationCoefficient.
Storable.
.MaximumFMeasure with empty parameters.
MaximumFMeasure with given beta.
Storable.
MaximumNumericalTwoClassMeasure.
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.
Measure from its XML-representation.
Measure from its Measure.MeasureParameterSet.
ParameterSet that can be used to instantiate a Measure.Measure.MeasureParameterSet for the given sub-class
of Measure,
Storable.
array.
array
between start and end.
MEMConstraint as part of a (whole) model.
MEMConstraint as part of a model.
Storable.
array.
array
between start and end.
MixtureDiffSM.
Storable.
DurationDiffSMs.MixtureDurationDiffSM.
Storable.
Emissions.MixtureEmission from a set of emissions.
Storable.
TrainableStatisticalModels.MixtureTrainSM.
Storable.
TrainableStatisticalModels.
AlphabetContainer.
SamplingScoreBasedClassifier.getFunction(DataSet[], 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.MSPClassifier that used MCL principle for training.
MSPClassifier from a
given parameter set, a prior and DifferentiableSequenceScores for the
classes.
MSPClassifier from a
given parameter set, a prior and DifferentiableSequenceScores for the
classes.
Storable.
MultiDimensionalDiscreteSequence from a set of individual Sequences.
MultiDimensionalDiscreteSequence from a set of individual Sequences.
MultiDimensionalSequence from a set of individual Sequences.
Storable.
start to
end with the value factor.
with the factor val:
.
Parameter that provides a collection of possible values.MultiSelectionParameter.
MultiSelectionParameter.
MultiSelectionParameter from an array of
ParameterSets.
MultiSelectionParameter from an array of
Classes of ParameterSets.
Storable.
MaxHMMTrainingParameterSet that
is used for a multi-threaded maximizing training algorithm of a hidden Markov model.Storable.
MutableMotifDiscoverer.MutableMotifDiscovererToolbox.getSortedInitialParameters(DifferentiableSequenceScore[], InitMethodForDiffSM[], DiffSSBasedOptimizableFunction, int, OutputStream, int).