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Storable
.
IDGMParameterSet
instance from
the class that can be instantiated using this IDGMParameterSet
.
IDGMParameterSet
instance for the
specified class.
Alphabet
s ignore the case.
IntList
s are used during the parallel computation of the gradient.
IntList
s that are used while computing
the partial derivation.
ImageResult
from a BufferedImage
.
IndependentProductScoringFunction
from a given series of
independent NormalizableScoringFunction
s.
IndependentProductScoringFunction
from given series of
independent NormalizableScoringFunction
s and lengths.
Storable
.
AbstractStringExtractor
that can be seen as a filter.se
.
InhCondProb
instance.
InhCondProb
instance.
Storable
.
InhConstraint
instance.
Storable
.
InhomogeneousDGM
).InhomogeneousDGM
from a given
IDGMParameterSet
.
Storable
.
BayesianNetworkScoringFunction
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.
ScoringFunction
.
ScoringFunction
randomly.
NormalizableScoringFunction
uniformly if it is a AbstractMixtureScoringFunction
.
motif
randomly using for instance ScoringFunction.initializeFunctionRandomly(boolean)
.
Parameter
s in this
ClassifierAssessmentAssessParameterSet
.
ParameterTree
randomly.
Parameter
s, which is a
ParameterSet.ParameterList
.
Parameter
s, which is a
ParameterSet.ParameterList
, with an initial number of Parameter
s of
initCapacity
.
AlphabetContainer
by
incorporating additional Alphabet
s into an existing
AlphabetContainer
.
probs
.
Parameter
s that can be used to
instantiate another class.InstanceParameterSet
from the class that can be
instantiated using this InstanceParameterSet
.
Storable
.
InstanceParameterSet
.DurationScoringFunction
that use an internal memory
Sample
s of the array, i.e. it returns a Sample
containing only
Sequence
s that are contained in all Sample
s 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
.
int
s and can therefore be used for discrete
AlphabetContainer
s 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
ParameterSet
s 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
.
true
if the Result
test
and the
current object have the same data type, name and comment for the result.
Alphabet
s.
DiscreteAlphabet.DiscreteAlphabetParameterSet
, i.e.
pos
is a discrete random variable,
i.e. if the Alphabet
of position pos
is discrete.
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.
true
if the object is currently used in
a sampling, otherwise false
.
true
if the object is currently used in
a sampling, otherwise false
.
ParameterTree
is a
leaf, i.e. it has no children in the network structure of the enclosing
BayesianNetworkScoringFunction
.
NormalizableScoringFunction
s
are normalized.
true
if the given positions
are in the domain of the
PositionScoringFunction.
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
.
sel
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.
AlphabetContainer
is simple and all positions use the
same (fixed) Alphabet
.
Alphabet
, i.e. if the
corresponding AlphabetContainer
is simple.
true
if the internal NormalizableScoringFunction
is a StrandScoringFunction
otherwise false
.
true
if the model has been trained successfully,
false
otherwise.
true
if the model is trained, false
otherwise.
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 value was selected by the user.
maxIter
iterations.
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