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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
.
Storable
.
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.
SamplingScoreBasedClassifier.scoringFunctions
s randomly
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
.
SamplingScoreBasedClassifier.setInitParameters(double[])
, null
otherwise
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.
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.
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
.
true
if the sequence is multidimensional, otherwise .
- isMultiDimensional() -
Method in class de.jstacs.data.Sequence.RecursiveSequence
-
- isMultiDimensional() -
Method in class de.jstacs.data.sequences.ArbitrarySequence
-
- isMultiDimensional() -
Method in class de.jstacs.data.sequences.MultiDimensionalDiscreteSequence
-
- isMultiDimensional() -
Method in class de.jstacs.data.sequences.SimpleDiscreteSequence
-
- isNormalized() -
Method in class de.jstacs.models.hmm.models.DifferentiableHigherOrderHMM
-
- isNormalized() -
Method in class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
-
- isNormalized(NormalizableScoringFunction...) -
Static method in class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
- This method checks whether all given
NormalizableScoringFunction
s
are normalized.
- isNormalized() -
Method in class de.jstacs.scoringFunctions.CMMScoringFunction
-
- isNormalized() -
Method in class de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction
-
- isNormalized() -
Method in class de.jstacs.scoringFunctions.homogeneous.UniformHomogeneousScoringFunction
-
- isNormalized() -
Method in class de.jstacs.scoringFunctions.IndependentProductScoringFunction
-
- isNormalized() -
Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
-
- isNormalized() -
Method in class de.jstacs.scoringFunctions.mix.motifSearch.MixtureDuration
-
- isNormalized() -
Method in class de.jstacs.scoringFunctions.mix.motifSearch.SkewNormalLikeScoringFunction
-
- isNormalized() -
Method in class de.jstacs.scoringFunctions.mix.motifSearch.UniformDurationScoringFunction
-
- isNormalized() -
Method in interface de.jstacs.scoringFunctions.NormalizableScoringFunction
- This method indicates whether the implemented score is already normalized
to 1 or not.
- isNormalized() -
Method in class de.jstacs.scoringFunctions.NormalizedScoringFunction
-
- isNormalized() -
Method in class de.jstacs.scoringFunctions.UniformScoringFunction
-
- isPossible(int...) -
Method in class de.jstacs.scoringFunctions.mix.motifSearch.DurationScoringFunction
-
- isPossible(int...) -
Method in class de.jstacs.scoringFunctions.mix.motifSearch.PositionScoringFunction
- This method returns
true
if the given positions
are in the domain of the
PositionScoringFunction.
- isRangeable() -
Method in class de.jstacs.parameters.CollectionParameter
-
- isRangeable() -
Method in class de.jstacs.parameters.ParameterSetContainer
-
- isRangeable() -
Method in interface de.jstacs.parameters.Rangeable
- Returns
true
if the parameters can be varied over a range of
values.
- isRangeable() -
Method in class de.jstacs.parameters.SimpleParameter
-
- isRanged() -
Method in class de.jstacs.parameters.MultiSelectionCollectionParameter
-
- isRanged() -
Method in class de.jstacs.parameters.ParameterSet
-
- isRanged() -
Method in class de.jstacs.parameters.ParameterSetContainer
-
- isRanged() -
Method in interface de.jstacs.parameters.RangeIterator
- Returns
true
if this RangeIterator
is ranging over a
set of values.
- isRanged() -
Method in class de.jstacs.parameters.RangeParameter
-
- isRequired() -
Method in class de.jstacs.parameters.CollectionParameter
-
- isRequired() -
Method in class de.jstacs.parameters.FileParameter
-
- isRequired() -
Method in class de.jstacs.parameters.Parameter
- Returns
true
if the Parameter
is required,
false
otherwise.
- isRequired() -
Method in class de.jstacs.parameters.ParameterSetContainer
-
- isRequired() -
Method in class de.jstacs.parameters.RangeParameter
-
- isRequired() -
Method in class de.jstacs.parameters.SimpleParameter
-
- isReverseComplementable() -
Method in class de.jstacs.data.AlphabetContainer
- This method helps to determine if the
AlphabetContainer
also
computes the reverse complement of a Sequence
.
- isSelected(MeasureParameters.Measure) -
Method in class de.jstacs.classifier.MeasureParameters
- Indicates if the option
sel
is selected.
- isSelected(int) -
Method in class de.jstacs.parameters.CollectionParameter
- Returns
true
if the option at position idx
is
selected.
- isSelected(String) -
Method in class de.jstacs.parameters.MultiSelectionCollectionParameter
- Returns the selection value of the option with key
key
.
- isSelected(int) -
Method in class de.jstacs.parameters.MultiSelectionCollectionParameter
-
- isSet() -
Method in class de.jstacs.parameters.CollectionParameter
-
- isSet() -
Method in class de.jstacs.parameters.FileParameter
-
- isSet() -
Method in class de.jstacs.parameters.MultiSelectionCollectionParameter
-
- isSet() -
Method in class de.jstacs.parameters.Parameter
- Returns
true
if the parameter was set by the user,
false
otherwise.
- isSet() -
Method in class de.jstacs.parameters.ParameterSetContainer
-
- isSet(String) -
Method in class de.jstacs.parameters.ParameterSetTagger
-
- isSet() -
Method in class de.jstacs.parameters.RangeParameter
-
- isSet() -
Method in class de.jstacs.parameters.SimpleParameter
-
- isShiftable() -
Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov
-
- isShiftable() -
Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.Measure
- Indicates if
Measure
supports shifts.
- isSilent() -
Method in interface de.jstacs.models.hmm.State
- This method returns whether a state is silent or not.
- isSilent() -
Method in class de.jstacs.models.hmm.states.SimpleState
-
- isSilent -
Variable in class de.jstacs.models.hmm.transitions.BasicHigherOrderTransition
- A vector indicating for each state whether it is silent or not.
- isSimple() -
Method in class de.jstacs.algorithms.optimization.termination.AbsoluteValueCondition
- Deprecated.
- isSimple() -
Method in class de.jstacs.algorithms.optimization.termination.CombinedCondition
-
- isSimple() -
Method in class de.jstacs.algorithms.optimization.termination.IterationCondition
-
- isSimple() -
Method in class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition
-
- isSimple() -
Method in class de.jstacs.algorithms.optimization.termination.SmallGradientConditon
-
- isSimple() -
Method in class de.jstacs.algorithms.optimization.termination.SmallStepCondition
-
- isSimple() -
Method in interface de.jstacs.algorithms.optimization.termination.TerminationCondition
- This method returns
false
if the TerminationCondition
uses either
the gradient or the direction for the decision, otherwise it returns true
.
- isSimple() -
Method in class de.jstacs.algorithms.optimization.termination.TimeCondition
-
- isSimple() -
Method in class de.jstacs.data.AlphabetContainer
- Indicates whether all random variables are defined over the same range,
i.e. if the
AlphabetContainer
is simple and all positions use the
same (fixed) Alphabet
.
- isSimple() -
Method in class de.jstacs.data.AlphabetContainerParameterSet
- Indicates if all positions use the same
Alphabet
, i.e. if the
corresponding AlphabetContainer
is simple.
- isSimpleSample() -
Method in class de.jstacs.data.Sample
- This method indicates whether all random variables are defined over the
same range, i.e. all positions use the same (fixed) alphabet.
- isStrandScoringFunction(NormalizableScoringFunction) -
Static method in class de.jstacs.scoringFunctions.mix.StrandScoringFunction
- Check whether a
NormalizableScoringFunction
is a StrandScoringFunction
.
- isStrandScoringFunction() -
Method in class de.jstacs.scoringFunctions.NormalizedScoringFunction
- This method returns
true
if the internal NormalizableScoringFunction
is a StrandScoringFunction
otherwise false
.
- isSymbol(String) -
Method in class de.jstacs.data.alphabets.DiscreteAlphabet
- Tests if a given symbol is contained in the alphabet.
- isTrained() -
Method in class de.jstacs.classifier.AbstractClassifier
- This method gives information about the state of the classifier.
- isTrained() -
Method in class de.jstacs.classifier.MappingClassifier
-
- isTrained() -
Method in class de.jstacs.classifier.modelBased.ModelBasedClassifier
-
- isTrained() -
Method in class de.jstacs.classifier.scoringFunctionBased.sampling.SamplingScoreBasedClassifier
-
- isTrained() -
Method in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier
-
- isTrained() -
Method in class de.jstacs.models.CompositeModel
-
- isTrained() -
Method in class de.jstacs.models.discrete.DiscreteGraphicalModel
-
- isTrained() -
Method in class de.jstacs.models.hmm.models.HigherOrderHMM
-
- isTrained() -
Method in class de.jstacs.models.hmm.models.SamplingHigherOrderHMM
-
- isTrained() -
Method in class de.jstacs.models.mixture.AbstractMixtureModel
-
- isTrained() -
Method in interface de.jstacs.models.Model
- Returns
true
if the model has been trained successfully,
false
otherwise.
- isTrained() -
Method in class de.jstacs.models.NormalizableScoringFunctionModel
-
- isTrained() -
Method in class de.jstacs.models.UniformModel
- Returns
true
if the model is trained, false
otherwise.
- isTrained() -
Method in class de.jstacs.models.VariableLengthWrapperModel
-
- isTrained() -
Method in class de.jstacs.results.StorableResult
- Returns
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.
- isTrained -
Variable in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
- Indicates if the instance has been trained.
- isUserSelected() -
Method in class de.jstacs.parameters.CollectionParameter
- Returns
true
if the value was selected by the user.
- iterate(Sample, double[], MultivariateRandomGenerator, MRGParams[]) -
Method in class de.jstacs.models.mixture.AbstractMixtureModel
- This method runs the train algorithm for the current model.
- iterate(int, double[], MultivariateRandomGenerator, MRGParams[]) -
Method in class de.jstacs.models.mixture.AbstractMixtureModel
- This method runs the train algorithm for the current model and the
internal data set.
- iterate(int, double[], MultivariateRandomGenerator, MRGParams[]) -
Method in class de.jstacs.models.mixture.motif.SingleHiddenMotifMixture
-
- IterationCondition - Class in de.jstacs.algorithms.optimization.termination
- This class will stop an optimization if the number of iteration reaches a given number.
- IterationCondition(int) -
Constructor for class de.jstacs.algorithms.optimization.termination.IterationCondition
- This constructor creates an instance that does not allow any further iteration after
maxIter
iterations.
- IterationCondition(IterationCondition.IterationConditionParameterSet) -
Constructor for class de.jstacs.algorithms.optimization.termination.IterationCondition
- This is the main constructor creating an instance from a given parameter set.
- IterationCondition(StringBuffer) -
Constructor for class de.jstacs.algorithms.optimization.termination.IterationCondition
- The standard constructor for the interface
Storable
.
- IterationCondition.IterationConditionParameterSet - Class in de.jstacs.algorithms.optimization.termination
- This class implements the parameter set for a
IterationCondition
. - IterationCondition.IterationConditionParameterSet() -
Constructor for class de.jstacs.algorithms.optimization.termination.IterationCondition.IterationConditionParameterSet
- This constructor creates an empty parameter set.
- IterationCondition.IterationConditionParameterSet(StringBuffer) -
Constructor for class de.jstacs.algorithms.optimization.termination.IterationCondition.IterationConditionParameterSet
- The standard constructor for the interface
Storable
.
- IterationCondition.IterationConditionParameterSet(int) -
Constructor for class de.jstacs.algorithms.optimization.termination.IterationCondition.IterationConditionParameterSet
- This constructor creates a filled instance of a parameters set.
- iterator() -
Method in class de.jstacs.data.Sample
-
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