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Storable.
IDGTrainSMParameterSet instance from
the class that can be instantiated using this IDGTrainSMParameterSet.
IDGTrainSMParameterSet instance for the
specified class.
Alphabets ignore the case.
IntLists are used during the parallel computation of the gradient.
IntLists that are used while computing
the partial derivation.
ImageResult from a BufferedImage.
Storable.
SubclassFinder.findSubclasses(Class, String)
thereby enabling to find self-implemented classes not included in the Jstacs class hierarchy.
IndependentProductDiffSM from a given series of
independent DifferentiableStatisticalModels.
IndependentProductDiffSM from given series of
independent DifferentiableStatisticalModels and lengths.
Storable.
IndependentProductDiffSS from a given series of
independent DifferentiableSequenceScores.
IndependentProductDiffSS from given series of
independent DifferentiableSequenceScores and lengths.
Storable.
IndependentProductDiffSS.score should be used for the specific parts.
AbstractStringExtractor that can be seen as a filter.se.
InhCondProb instance.
InhCondProb instance.
Storable.
InhConstraint instance.
Storable.
InhomogeneousDGTrainSM).InhomogeneousDGTrainSM from a given
IDGTrainSMParameterSet.
Storable.
BayesianNetworkDiffSM
that is an inhomogeneous Markov model.order.
InhomogeneousMarkov from the corresponding
InstanceParameterSet parameters.
Storable.
InstanceParameterSet that defines the parameters of
an InhomogeneousMarkov structure Measure.InhomogeneousMarkov.InhomogeneousMarkovParameterSet with empty
parameter values.
InhomogeneousMarkov.InhomogeneousMarkovParameterSet with the
parameter for the order set to order.
InhomogeneousMarkov.InhomogeneousMarkovParameterSet from its XML
representation as defined by the Storable
interface.
SamplingScoreBasedClassifier.scoringFunctionss randomly
DifferentiableSequenceScore.
DifferentiableSequenceScore randomly.
DifferentiableStatisticalModel uniformly if it is a AbstractMixtureDiffSM.
motif randomly using for instance DifferentiableSequenceScore.initializeFunctionRandomly(boolean).
BNDiffSMParameterTree randomly.
Parameters, which is a
ParameterSet.ParameterList.
Parameters, which is a
ParameterSet.ParameterList, with an initial number of Parameters of
initCapacity.
SamplingScoreBasedClassifier.setInitParameters(double[]), null otherwise
AlphabetContainer by
incorporating additional Alphabets into an existing
AlphabetContainer.
probs.
Parameters that can be used to
instantiate another class.InstanceParameterSet from the class that can be
instantiated using this InstanceParameterSet.
Storable.
InstanceParameterSet.DurationDiffSM that use an internal memory
DataSet
s of the array, i.e. it returns a DataSet containing only
Sequences that are contained in all DataSets of the array.
int.IntList with
initial length 10.
IntList with
initial length size.
IntronAnnotation from a donor
SinglePositionSequenceAnnotation and an acceptor
SinglePositionSequenceAnnotation and a set of additional
annotations.
Storable.
ints and can therefore be used for discrete
AlphabetContainers with alphabets that use a huge number of symbols.IntSequence from an array of int-
encoded alphabet symbols.
IntSequence from a part of the array of
int- encoded alphabet symbols.
IntSequence from a String representation
using the default delimiter.
IntSequence from a String representation
using the delimiter delim.
IntSequence from a SymbolExtractor.
null.
true if the parameter is of an atomic data type,
false otherwise.
true if this ParameterSet contains only
atomic parameters, i.e. the parameters do not contain
ParameterSets themselves.
true if the data type of the Result
test can be casted to that of this instance and both have
the same name and comment for the Result.
Parameter is comparable to the current instance, i.e. whether
the Class, the DataType, the name and the comment are identical.
ParameterSet is comparable to the current instance, i.e. whether
the Class and the number of parameters are identical, and the individual Parameters are comparable.
true if the Result test and the
current object have the same data type, name and comment for the result.
DiscreteAlphabet.DiscreteAlphabetParameterSet, i.e.
Alphabets.
DiscreteAlphabet.DiscreteAlphabetParameterSet, i.e.
pos is a discrete random variable,
i.e. if the Alphabet of position pos is discrete.
true if this property has been determined for a double-stranded nucleic acid.
continuous is a symbol of the Alphabet
used at position pos of the AlphabetContainer.
candidat is an element of the internal
interval.
candidate is an element of the internal
interval.
StorableResult.TRUE if the model or classifier was trained when
obtaining its XML representation stored in this StorableResult,
StorableResult.FALSE if it was not, and StorableResult.NA if the object could not be
trained anyway.
true if the model is trained, false otherwise.
true if the object is currently used in
a sampling, otherwise false.
true if the object is currently used in
a sampling, otherwise false.
BNDiffSMParameterTree is a
leaf, i.e. it has no children in the network structure of the enclosing
BayesianNetworkDiffSM.
true if the sequence is multidimensional, otherwise DifferentiableStatisticalModels
are normalized.
true if the given positions are in the domain of the
PositionDiffSM.
true if the parameters can be varied over a range of
values.
true if this RangeIterator is ranging over a
set of values.
true if the Parameter is required,
false otherwise.
AlphabetContainer also
computes the reverse complement of a Sequence.
true if the option at position idx is
selected.
key.
true if the option at position idx is
selected.
true if the parameter was set by the user,
false otherwise.
Measure supports shifts.
false if the TerminationCondition uses either
the gradient or the direction for the decision, otherwise it returns true.
Alphabet, i.e. if the
corresponding AlphabetContainer is simple.
AlphabetContainer is simple and all positions use the
same (fixed) Alphabet.
DifferentiableStatisticalModel is a StrandDiffSM.
true if the internal DifferentiableStatisticalModel is a StrandDiffSM otherwise false.
true if the value was selected by the user.
IndependentProductDiffSS.score whether it is able to score sequences of variable length.
maxIter iterations.
Storable.
IterationCondition.Storable.
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