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Storable
.
IDGTrainSMParameterSet
instance from
the class that can be instantiated using this IDGTrainSMParameterSet
.
IDGTrainSMParameterSet
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
.
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 DifferentiableStatisticalModel
s.
IndependentProductDiffSM
from given series of
independent DifferentiableStatisticalModel
s and lengths.
Storable
.
IndependentProductDiffSS
from a given series of
independent DifferentiableSequenceScore
s.
IndependentProductDiffSS
from given series of
independent DifferentiableSequenceScore
s 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.scoringFunctions
s randomly
DifferentiableSequenceScore
.
DifferentiableSequenceScore
randomly.
DifferentiableStatisticalModel
uniformly if it is a AbstractMixtureDiffSM
.
motif
randomly using for instance DifferentiableSequenceScore.initializeFunctionRandomly(boolean)
.
BNDiffSMParameterTree
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
.
Parameter
s 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.
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.
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.
ParameterSet
is comparable to the current instance, i.e.
true
if the Result
test
and the
current object have the same data type, name and comment for the result.
DiscreteAlphabet.DiscreteAlphabetParameterSet
, i.e.
Alphabet
s.
DiscreteAlphabet.DiscreteAlphabetParameterSet
, i.e.
pos
is a discrete random variable,
i.e.
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.
true
if the sequence is multidimensional, otherwise .
- isMultiDimensional() -
Method in class de.jstacs.data.sequences.Sequence.RecursiveSequence
-
- isMultiDimensional() -
Method in class de.jstacs.data.sequences.SimpleDiscreteSequence
-
- isNormalized() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
-
- isNormalized(DifferentiableSequenceScore...) -
Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
- This method checks whether all given
DifferentiableStatisticalModel
s
are normalized.
- isNormalized() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
-
- isNormalized() -
Method in interface de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModel
- This method indicates whether the implemented score is already normalized
to 1 or not.
- isNormalized() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
-
- isNormalized() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.UniformHomogeneousDiffSM
-
- isNormalized() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
-
- isNormalized() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
-
- isNormalized() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.MixtureDurationDiffSM
-
- isNormalized() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
-
- isNormalized() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.UniformDurationDiffSM
-
- isNormalized() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
-
- isNormalized() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.UniformDiffSM
-
- isNormalized() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
-
- isPossible(int...) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.DurationDiffSM
-
- isPossible(int...) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.PositionDiffSM
- This method returns
true
if the given positions
are in the domain of the
PositionDiffSM.
- isRangeable() -
Method in class de.jstacs.parameters.AbstractSelectionParameter
-
- 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.MultiSelectionParameter
-
- 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.AbstractSelectionParameter
-
- 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(int) -
Method in class de.jstacs.parameters.AbstractSelectionParameter
- Returns
true
if the option at position idx
is
selected.
- isSelected(String) -
Method in class de.jstacs.parameters.MultiSelectionParameter
- Returns the selection value of the option with key
key
.
- isSelected(int) -
Method in class de.jstacs.parameters.MultiSelectionParameter
-
- isSelected(int) -
Method in class de.jstacs.parameters.SelectionParameter
- Returns
true
if the option at position idx
is
selected.
- isSet() -
Method in class de.jstacs.parameters.AbstractSelectionParameter
-
- isSet() -
Method in class de.jstacs.parameters.FileParameter
-
- 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.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov
-
- isShiftable() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure
- Indicates if
Measure
supports shifts.
- isSilent() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleState
-
- isSilent() -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.State
- This method returns whether a state is silent or not.
- isSilent -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.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.MultipleIterationsCondition
-
- 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.AbstractAlphabetContainerParameterSet
- Indicates if all positions use the same
Alphabet
, i.e.
- isSimple() -
Method in class de.jstacs.data.AlphabetContainer
- Indicates whether all random variables are defined over the same range,
i.e.
- isSimple() -
Method in class de.jstacs.data.AlphabetContainerParameterSet
-
- isSimple() -
Method in class de.jstacs.data.alphabets.DNAAlphabetContainer.DNAAlphabetContainerParameterSet
-
- isSimpleDataSet() -
Method in class de.jstacs.data.DataSet
- This method indicates whether all random variables are defined over the
same range, i.e.
- isStrandModel(DifferentiableStatisticalModel) -
Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
- Check whether a
DifferentiableStatisticalModel
is a StrandDiffSM
.
- isStrandModel() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
- This method returns
true
if the internal DifferentiableStatisticalModel
is a StrandDiffSM
otherwise false
.
- isSymbol(String) -
Method in class de.jstacs.data.alphabets.DiscreteAlphabet
- Tests if a given symbol is contained in the alphabet.
- isTrained -
Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
- Indicates if the instance has been trained.
- isUserSelected() -
Method in class de.jstacs.parameters.AbstractSelectionParameter
- Returns
true
if the value was selected by the user.
- isVariable -
Variable in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
- This array specifies for each entry of
IndependentProductDiffSS.score
whether it is able to score sequences of variable length.
- iterate(DataSet, double[], MultivariateRandomGenerator, MRGParams[]) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- This method runs the train algorithm for the current model.
- iterate(int, double[], MultivariateRandomGenerator, MRGParams[]) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- 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.sequenceScores.statisticalModels.trainable.mixture.motif.ZOOPSTrainSM
-
- 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.DataSet
-
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