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DataSet
s to the internal classes.
Sequence
to an new Sequence
using some DiscreteAlphabetMapping
s.MappedDiscreteSequence
.
MappedDiscreteSequence
from a given Sequence
and some given DiscreteAlphabetMapping
s.
MappingClassifier
from a given classifier and a
class mapping.
Storable
.
DifferentiableStatisticalModel
that works on
mapped Sequence
s.MappingDiffSM
.
Storable
.
Sequence
weights to the internal classes.
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
.
array
between start
and end
.
NumericalResultSet
s.MeanResultSet
with an empty set of
NumericalResultSet
s and allows to collect all
values via the switch aggregateAll
.
MeanResultSet
with an empty set of
NumericalResultSet
s.
MeanResultSet
with an empty set of
NumericalResultSet
s and no further information.
Storable
.
MeanResultSet
s
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
.
Storable
interface.
MEManager
from a given
MEManagerParameterSet
.
Storable
.
Storable
interface.
MEMConstraint
as part of a (whole) model.
MEMConstraint
as part of a model.
Storable
.
array
.
array
between start
and end
.
MixtureDiffSM
.
Storable
.
DurationDiffSM
s.MixtureDurationDiffSM
.
Storable
.
Emission
s.MixtureEmission
from a set of emissions.
Storable
.
TrainableStatisticalModel
s.MixtureTrainSM
.
Storable
.
TrainableStatisticalModel
s.
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
Result
s additionalAnnotation
.
Storable
.
MotifAnnotation
s.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 DifferentiableSequenceScore
s for the
classes.
MSPClassifier
from a
given parameter set, a prior and DifferentiableSequenceScore
s for the
classes.
Storable
.
MultiDimensionalDiscreteSequence
from a set of individual Sequence
s.
MultiDimensionalDiscreteSequence
from a set of individual Sequence
s.
MultiDimensionalSequence
from a set of individual Sequence
s.
Storable
.
SimpleParameter
that renders as a textarea in Galaxy, which is only suitable for DataType.STRING
s.MultilineSimpleParameter
with given default value.
MultilineSimpleParameter
with given default value and a ParameterValidator
.
MultilineSimpleParameter
with no default value and a ParameterValidator
.
MultilineSimpleParameter
with no default value.
Storable
.
TerminationCondition
requires another provided TerminationCondition
to fail a contiguous specified number of times
before the optimization is terminated.Storable
.
MultipleIterationsCondition
.Storable
.
start
to
end
with the value factor
.
val
:
.
Parameter
that provides a collection of possible values.MultiSelectionParameter
.
MultiSelectionParameter
.
MultiSelectionParameter
from an array of
ParameterSet
s.
MultiSelectionParameter
from an array of
Class
es of ParameterSet
s.
Storable
.
MaxHMMTrainingParameterSet
that
is used for a multi-threaded maximizing training algorithm of a hidden Markov model.Storable
.
MultivariateGaussianEmission
from its XML representation.
MutableMotifDiscoverer
.MutableMotifDiscovererToolbox.getSortedInitialParameters(DifferentiableSequenceScore[], InitMethodForDiffSM[], DiffSSBasedOptimizableFunction, int, OutputStream, int)
.
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