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Parameter
s and ParameterSet
s and the parameter representation
in Galaxy.GalaxyAdaptor
from a given ParameterSet
containing all parameters
that are necessary for a program is shall be included in a Galaxy installation.
Result
for files that are results of some computation.GalaxyAdaptor.FileResult
with name, comment, and path to the file.
GalaxyAdaptor.FileResult
with name, comment, path to the file, filename and extension.
GalaxyAdaptor.FileResult
from its XML-representation
ImageResult
that is linked to a file that can be downloaded.ImageResult
with linked GalaxyAdaptor.FileResult
link
GalaxyAdaptor.LinkedImageResult
from its XML-representation
Parameter
s that can be converted to and extracted from
Galaxy representations.GaussianEmission
which can be used for maximum likelihood.
GaussianEmission
with normal-gamma prior by directly defining the hyper-parameters of the prior.
GaussianEmission
with normal-gamma prior by defining the expected precision and the expected standard deviation of the precision, i.e. via the
expectation and variance of the gamma part of the normal-gamma prior.
GaussianEmission
from its XML representation.
Storable
.
genBeta, disBeta,
and priorBeta
into an array and calls the main constructor.
Storable
.
GenDisMixClassifier
.Storable
.
GenDisMixClassifierParameterSet
.
GenDisMixClassifierParameterSet
.
n
with entries 1/n
.
number
of array entries to
1/number
.
n
-dimensional random array.
n
-dimensional random array as part of the array
d
beginning at start
.
number
with one entry getting
the value 1-epsilon
and all the others equal parts of
epsilon
.
number
as part of the array
d
beginning at index start
with one entry
getting the value 1-epsilon
and all the others equal parts
of epsilon
.
d
beginning at start
with n
logarithmic values.
Storable
.
GenericComplementableDiscreteAlphabet
from a parameter set.
GenericComplementableDiscreteAlphabet
.GenericComplementableDiscreteAlphabet
.
Storable
.
AnnotatedEntity
at index index
in the list.
AnnotatedEntity
with name name
in the list.
index
.
index
.
k
-mers
in the data
.
k
-mers
in the data
.
StringBuffer
containing additional
information for the XML representation.
String
with index index
.
s1
and s2
(Alignment.Alignment(AlignmentType, Costs)
).
s1
and s2
(Alignment.Alignment(AlignmentType, Costs)
).
Sequence
s containing all elements of this
DataSet
.
PhyloNode
s that are leafs in the subtree starting from this instance
PhyloNode
s that represent the leafs of the tree
Parameter
s in this ParameterSet
.
Alphabet
of position pos
.
AlphabetContainer
of this DataSet
.
AlphabetContainer
, used in this
Sequence
.
AlphabetContainer
of the current instance.
AlphabetContainer
of the
StructureLearner
.
AlphabetContainer
of this emission.
Alphabet
that is used for the given position.
Alphabet
of position
pos
.
Collection
of parameters containing informations about
this ClassifierAssessmentAssessParameterSet
.
DataSet
s.
DataSet
.
Sequence
.
ListResult
.
SequenceAnnotation
as
given in the constructor.
SequenceAnnotation
[] for each dimension of this multidimensional sequence.
SequenceAnnotation
types and the corresponding
identifier which occur in this DataSet
.
Sequence
s in this DataSet
.
k
nodes from the (encoded) set par
to the node
child
.
DataSet
containing the predicted binding sites.
DataSet
containing the predicted binding sites.
boolean
which is the value of the
Parameter
par
.
byte
which is the value of the Parameter
par
.
PhyloNode
s that are children of this instance
Result
s of dimension
AbstractClassifier.getNumberOfClasses()
that contains information about the
classifier and for each class.
GenDisMixClassifier
, where the parameters
are set to those that yielded the maximum value of the objective functions among all sampled
parameter values.
GenDisMixClassifier
, where the parameters
are set to the mean values over all sampled
parameter values in the stationary phase.
Storable
corresponding to
the XML representation stored in this StorableResult
.
index
.
AbstractScoreBasedClassifier
.
fgStats
and
bgStats
counted on sequences with a total weight of
n
.
fgStats
and
bgStats
counted on sequences with a total weight of
nFg
and nBg
, respectively.
sym
of the Alphabet
of position pos
of this AlphabetContainer
.
SelectionParameter
that can be used to create
an instance of PerformanceMeasureParameterSet
or NumericalPerformanceMeasureParameterSet
.
EnumParameter
that allows the user to choose
between different scales.
AnnotatedEntity
.
ParameterSet
.
motifLength
so that each String is contained in all
sequences of the sample respectively in the sample and the reverse
complementary sample.
code
.
AlphabetContainer
of Alphabet
s e.g. for
composite motifs/sequences.
Sequence
s of all
elements in the current DataSet
.
DataSet
of
Sequence.CompositeSequence
s.
Sequence.CompositeSequence
for
sequences with a simple AlphabetContainer
.
Sequence
s.
String
representation of the context.
index
.
s1(i)
and
s2(j)
.
index
.
SequenceAnnotation
.
ParameterSet
of the classifier.
InstanceParameterSet
that has been used to
instantiate the current instance of the implementing class.
double
array of dimension
DifferentiableSequenceScore.getNumberOfParameters()
containing the current parameter values.
SequenceAnnotation
or null
if no SequenceAnnotation
is available.
OptimizableFunction
.
DataSet
, where each Sequence
occurs only
once.
DataSet
containing ArbitraryFloatSequence
s using
a file name.
DataSet
containing ArbitraryFloatSequence
s using
a file name.
DataSet
containing ArbitraryFloatSequence
s.
DataSet
containing SparseSequence
s using
a file name.
DataSet
containing SparseSequence
s using
a file name.
DataSet
containing SparseSequence
s.
DataSet
by converting each Sequence
in original
to the DinucleotideProperty
property
.
DataSet
by converting each Sequence
in original
to the DinucleotideProperty
property
using the DinucleotideProperty.Smoothing
smoothing.
DataSet
by converting each Sequence
in original
to the DinucleotideProperty
s properties
and setting these as ReferenceSequenceAnnotation
of each original sequence.
DataSet
by converting each Sequence
in original
to the DinucleotideProperty
s properties
and adding or setting these as ReferenceSequenceAnnotation
of each original sequence.
DataSet.PartitionMethod
defining how the mutually exclusive
random-splits of user supplied data are generated.
DataSet.PartitionMethod
defining how the mutually exclusive
random-splits of user supplied data are generated.
DataSet.PartitionMethod
defining how the mutually exclusive
random-splits of user supplied data are generated.
AnnotatedEntity
.
true
if the temporary parameter files shall
be deleted on exit of the program.
index
i
.
DifferentiableSequenceScore
with index
i
.
DifferentiableSequenceScore
s in the internal
order.
DifferentiableStatisticalModel
s.
Function
.
DinucleotideProperty
as a two-dimensional double
array, where the rows correspond to the first nucleotide
and the columns correspond to the second nucleotide in the dinucleotide in order A, C, G, and T.
double
which is the value of the
Parameter
par
.
fgStats
and
bgStats
counted on sequences with a total weight of
nFg
and nBg
, respectively.
fgStats
and
bgStats
counted on sequences with a total weight of
nFg
and nBg
, respectively.
String
number idx
that has been extracted.
Sequence
, with index
i
.
Sequence
with index index
.
ClassifierAssessmentAssessParameterSet
.
Sequence
s, in this
DataSet
.
String
representation of this instance in the method Sequence.toString(String, int, int)
.
LocatedSequenceAnnotationWithLength
, i.e.
null
if no range was specified.
Constraint
as exact as
possible.
null
if the constraint was fulfilled by the last checked
value.
ParameterValidator.checkValue(Object)
returned false.
StructureLearner
.
ClassifierAssessmentAssessParameterSet
.
index
: .
Math.exp(BNDiffSMParameter.getValue()
)
, which is pre-computed.
QuadraticFunction
.
AbstractHMM.fillLogStatePosteriorMatrix(double[][], int, int, Sequence, boolean)
is used with code>silentZero==true
to eliminate the first row.
BNDiffSMParameterTree
in the topological ordering of the network
structure of the enclosing BayesianNetworkDiffSM
.
float
which is the value of the
Parameter
par
.
true
if only free parameters shall be used
index
.
Sequence
seq
beginning at position
start
.
DifferentiableStatisticalModel
.
DifferentiableSequenceScore
.
StringBuffer
.
AbstractBurnInTest
.
StringBuffer
.
length
.
motif
used in
component
.
String
representation of the structure that
can be used in Graphviz to create an image.
String
representation of the node options that
can be used in Graphviz to create the node for this state.
String
representation of the structure that
can be used in Graphviz to create an image.
String
representation of the structure that
can be used in Graphviz to create an image.
String
representation of the structure that
can be used in Graphviz to create an image.
String
representation of the structure that
can be used in Graphviz to create an image.
Sequence
and seq
.
HashMap
that can be used in
AbstractHMM.getGraphvizRepresentation(java.text.NumberFormat, de.jstacs.data.DataSet, double[], HashMap)
to create a Graphviz representation of the AbstractHMM
i
of the hyperparameter vector
of the underlying Dirichlet distribution.
i
of the hyperparameter vector
of the underlying Erlang distribution.
index
.
SequenceAnnotation
as given in the
constructor.
NullProgressUpdater
that is
immutable.
sortedScores
for the index
i
so that
sortedScores[i-1] < myScore <= sortedScores[i]
.
sortedScores
beginning at
start
for the index i
so that
sortedScores[i-1] < myScore <= sortedScores[i]
.
String
) for your
choice.
combi
.
pos - 1
to pos
in sequences seq
.
pos - 1
to pos
in sequences seq
.
Sequence
s to specific Alphabet
s.
sequence
.
Sequence
.
i
of the component with
P(i|s) maximal.
- getIndices() -
Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
- This method returns a deep copy of the internally used indices of the
DifferentiableSequenceScore
for the parts.
- getIndices(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
- This array is used to compute the relative indices of a parameter index.
- getInfixDataSet(int, int) -
Method in class de.jstacs.data.DataSet
- This method enables you to use only an infix of all elements, i.e. the
Sequence
s, in the current DataSet
.
- getInfos() -
Method in class de.jstacs.results.MeanResultSet
- Returns some information for this
MeanResultSet
.
- getInitialClassParam(double) -
Method in class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
-
- getInitialClassParam(double) -
Method in interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore
- Returns the initial class parameter for the class this
DifferentiableSequenceScore
is responsible for, based on the class
probability classProb
.
- getInitialClassParam(double) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
-
- getInitialClassParam(double) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
-
- getInstance(SequenceAnnotation[], Sequence...) -
Method in class de.jstacs.data.sequences.MultiDimensionalArbitrarySequence
-
- getInstance(SequenceAnnotation[], Sequence...) -
Method in class de.jstacs.data.sequences.MultiDimensionalDiscreteSequence
-
- getInstance(SequenceAnnotation[], Sequence...) -
Method in class de.jstacs.data.sequences.MultiDimensionalSequence
-
- getInstance() -
Method in class de.jstacs.parameters.InstanceParameterSet
- Returns a new instance of the class of
InstanceParameterSet.getInstanceClass()
that
was created using this ParameterSet
.
- getInstanceClass() -
Method in class de.jstacs.parameters.InstanceParameterSet
- Returns the class of the instances that can be constructed using this
set.
- getInstanceComment() -
Method in class de.jstacs.algorithms.optimization.termination.AbsoluteValueCondition.AbsoluteValueConditionParameterSet
- Deprecated.
- getInstanceComment() -
Method in class de.jstacs.algorithms.optimization.termination.CombinedCondition.CombinedConditionParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.algorithms.optimization.termination.IterationCondition.IterationConditionParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.algorithms.optimization.termination.SmallGradientConditon.SmallGradientConditonParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.algorithms.optimization.termination.SmallStepCondition.SmallStepConditionParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.algorithms.optimization.termination.TimeCondition.TimeConditionParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifierParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifierParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.data.AlphabetContainerParameterSet.AlphabetArrayParameterSet
- Returns a descriptive comment on this
AlphabetContainerParameterSet.AlphabetArrayParameterSet
.
- getInstanceComment() -
Method in class de.jstacs.data.AlphabetContainerParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.data.AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet
- Returns a descriptive comment on this
AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet
.
- getInstanceComment() -
Method in class de.jstacs.data.alphabets.ContinuousAlphabet.ContinuousAlphabetParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.data.alphabets.DiscreteAlphabet.DiscreteAlphabetParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.data.alphabets.DNAAlphabet.DNAAlphabetParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.data.alphabets.DNAAlphabetContainer.DNAAlphabetContainerParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.data.alphabets.ProteinAlphabet.ProteinAlphabetParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.parameters.InstanceParameterSet
- Returns a comment (a textual description) of the class that can be
constructed using this
ParameterSet
.
- getInstanceComment() -
Method in class de.jstacs.sampling.VarianceRatioBurnInTestParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSMParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTExplainingAwayResidual.BTExplainingAwayResidualParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTMutualInformation.BTMutualInformationParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov.InhomogeneousMarkovParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMExplainingAwayResidual.PMMExplainingAwayResidualParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMMutualInformation.PMMMutualInformationParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.parameters.HomMMParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.BayesianNetworkTrainSMParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.FSDAGTrainSMParameterSet
-
- getInstanceFromParameterSet(InstanceParameterSet<T>) -
Static method in class de.jstacs.io.ParameterSetParser
- Returns an instance of a subclass of
InstantiableFromParameterSet
that can be instantiated by the InstanceParameterSet
pars
.
- getInstanceFromParameterSet(ParameterSet, Class<T>) -
Static method in class de.jstacs.io.ParameterSetParser
- Returns an instance of a subclass of
InstantiableFromParameterSet
that can be instantiated by the ParameterSet
pars
.
- getInstanceName() -
Method in class de.jstacs.algorithms.optimization.termination.AbsoluteValueCondition.AbsoluteValueConditionParameterSet
- Deprecated.
- getInstanceName() -
Method in class de.jstacs.algorithms.optimization.termination.CombinedCondition.CombinedConditionParameterSet
-
- getInstanceName() -
Method in class de.jstacs.algorithms.optimization.termination.IterationCondition.IterationConditionParameterSet
-
- getInstanceName() -
Method in class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet
-
- getInstanceName() -
Method in class de.jstacs.algorithms.optimization.termination.SmallGradientConditon.SmallGradientConditonParameterSet
-
- getInstanceName() -
Method in class de.jstacs.algorithms.optimization.termination.SmallStepCondition.SmallStepConditionParameterSet
-
- getInstanceName() -
Method in class de.jstacs.algorithms.optimization.termination.TimeCondition.TimeConditionParameterSet
-
- getInstanceName() -
Method in class de.jstacs.classifiers.AbstractClassifier
- Returns a short description of the classifier.
- getInstanceName() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier
-
- getInstanceName() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.CompositeLogPrior
-
- getInstanceName() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.DoesNothingLogPrior
-
- getInstanceName() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.LogPrior
- Returns a short instance name.
- getInstanceName() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateGaussianLogPrior
-
- getInstanceName() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateLaplaceLogPrior
-
- getInstanceName() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SimpleGaussianSumLogPrior
-
- getInstanceName() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.msp.MSPClassifier
-
- getInstanceName() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifierParameterSet
-
- getInstanceName() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
-
- getInstanceName() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
-
- getInstanceName() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifierParameterSet
-
- getInstanceName() -
Method in class de.jstacs.classifiers.MappingClassifier
-
- getInstanceName() -
Method in class de.jstacs.classifiers.trainSMBased.TrainSMBasedClassifier
-
- getInstanceName() -
Method in class de.jstacs.data.AlphabetContainerParameterSet.AlphabetArrayParameterSet
- Returns a descriptive name for this
AlphabetContainerParameterSet.AlphabetArrayParameterSet
.
- getInstanceName() -
Method in class de.jstacs.data.AlphabetContainerParameterSet
-
- getInstanceName() -
Method in class de.jstacs.data.AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet
- Returns a descriptive name for this
AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet
.
- getInstanceName() -
Method in class de.jstacs.data.alphabets.Alphabet.AlphabetParameterSet
-
- getInstanceName() -
Method in class de.jstacs.data.alphabets.DNAAlphabetContainer.DNAAlphabetContainerParameterSet
-
- getInstanceName() -
Method in class de.jstacs.parameters.InstanceParameterSet
- Returns the name of an instance of the class that can be constructed
using this
ParameterSet
.
- getInstanceName() -
Method in interface de.jstacs.sampling.BurnInTest
- Returns a short description of the burn-in test.
- getInstanceName() -
Method in class de.jstacs.sampling.SimpleBurnInTest
- Deprecated.
- getInstanceName() -
Method in class de.jstacs.sampling.VarianceRatioBurnInTest
-
- getInstanceName() -
Method in class de.jstacs.sampling.VarianceRatioBurnInTestParameterSet
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.differentiable.logistic.LogisticDiffSS
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.differentiable.UniformDiffSS
-
- getInstanceName() -
Method in interface de.jstacs.sequenceScores.SequenceScore
- Should return a short instance name such as iMM(0), BN(2), ...
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSMParameterSet
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTExplainingAwayResidual.BTExplainingAwayResidualParameterSet
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTExplainingAwayResidual
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTMutualInformation.BTMutualInformationParameterSet
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTMutualInformation
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov.InhomogeneousMarkovParameterSet
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure
- Returns the name of the
Measure
and possibly some additional
information about the current instance.
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMExplainingAwayResidual
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMExplainingAwayResidual.PMMExplainingAwayResidualParameterSet
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMMutualInformation
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMMutualInformation.PMMMutualInformationParameterSet
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMM0DiffSM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.UniformHomogeneousDiffSM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MarkovRandomFieldDiffSM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.MixtureDiffSM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.MixtureDurationDiffSM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.UniformDurationDiffSM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MultiDimensionalSequenceWrapperDiffSM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.CompositeTrainSM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.DifferentiableStatisticalModelWrapperTrainSM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DGTrainSMParameterSet
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousMM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.BayesianNetworkTrainSM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGTrainSM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureClassifier
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureMixture
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingPhyloHMM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.HiddenMotifMixture
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.GaussianLikePositionPrior
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.PositionPrior
- Returns the instance name.
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.UniformPositionPrior
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.UniformTrainSM
-
- getInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.VariableLengthWrapperTrainSM
-
- getInstanceParameterSets() -
Method in enum de.jstacs.data.AlphabetContainer.AlphabetContainerType
- This method returns a
LinkedList
of
InstanceParameterSet
s which can be used to create
Alphabet
s that can be used in a AlphabetContainer
of
the given AlphabetContainer.AlphabetContainerType
.
- getInstanceParameterSets(Class<T>, String) -
Static method in class de.jstacs.utils.SubclassFinder
- This method returns a list of
InstanceParameterSet
s that can be used to create a subclass of clazz
.
- getInternalPosition(int[]) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.PositionDiffSM
- Copies the current value of the internal iterator in the given array.
- getIntFromParameter(Parameter) -
Static method in class de.jstacs.io.ParameterSetParser
- Returns the
int
which is the value of the Parameter
par
.
- getK() -
Method in class de.jstacs.classifiers.assessment.KFoldCrossValidationAssessParameterSet
- Returns the number of mutually exclusive random-splits of user supplied
data defined by this
KFoldCrossValidationAssessParameterSet
.
- getKLDivergence(double[][][]) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
- Returns the KL-divergence of the distribution of this
BNDiffSMParameterTree
and the distribution given by
ds
.
- getKLDivergence(double[], double[][][][]) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
- Returns the KL-divergence of the distribution of this
BNDiffSMParameterTree
and a number of distribution given by
ds
and weighted by weight
- getKLDivergence(StatisticalModel, StatisticalModel, int) -
Static method in class de.jstacs.utils.StatisticalModelTester
- Returns the Kullback-Leibler-divergence
D(p_m1||p_m2)
.
- getKmereSequenceStatistic(int, boolean, int, DataSet...) -
Static method in class de.jstacs.motifDiscovery.KMereStatistic
- This method enables the user to get a statistic over all
k
-mers
in the sequences.
- getKmereSequenceStatistic(boolean, int, HashSet<Sequence>, DataSet...) -
Static method in class de.jstacs.motifDiscovery.KMereStatistic
- This method enables the user to get a statistic for a set of
k
-mers.
- getLabel(String[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
- This method returns a label for the state.
- getLambda(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint
- Returns the value of .
- getLastContextState(int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
-
- getLastContextState(int, int) -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.Transition
- The method returns the index of the state of the context, if there is no context -1 is returned.
- getLastScore() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
- Returns the score that was computed in the last optimization of the
parameters.
- getLegalName(String) -
Static method in class de.jstacs.utils.galaxy.GalaxyAdaptor
- Returns a legal variable name in Galaxy
- getLength() -
Method in class de.jstacs.algorithms.alignment.StringAlignment
- This method return the length of the alignment.
- getLength() -
Method in class de.jstacs.classifiers.AbstractClassifier
- Returns the length of the sequences this classifier can handle or
0
for sequences of arbitrary length.
- getLength() -
Method in class de.jstacs.data.sequences.annotation.LocatedSequenceAnnotationWithLength
- Returns the length of this
LocatedSequenceAnnotationWithLength
as
given in the constructor.
- getLength() -
Method in class de.jstacs.data.sequences.ArbitraryFloatSequence
-
- getLength() -
Method in class de.jstacs.data.sequences.ArbitrarySequence
-
- getLength() -
Method in class de.jstacs.data.sequences.ByteSequence
-
- getLength() -
Method in class de.jstacs.data.sequences.IntSequence
-
- getLength() -
Method in class de.jstacs.data.sequences.MappedDiscreteSequence
-
- getLength() -
Method in class de.jstacs.data.sequences.MultiDimensionalSequence
-
- getLength() -
Method in class de.jstacs.data.sequences.PermutedSequence
-
- getLength() -
Method in class de.jstacs.data.sequences.Sequence.CompositeSequence
-
- getLength() -
Method in class de.jstacs.data.sequences.Sequence
- Returns the length of the
Sequence
.
- getLength() -
Method in class de.jstacs.data.sequences.Sequence.SubSequence
-
- getLength() -
Method in class de.jstacs.data.sequences.ShortSequence
-
- getLength() -
Method in class de.jstacs.data.sequences.SparseSequence
-
- getLength() -
Method in class de.jstacs.parameters.SequenceScoringParameterSet
- Returns the length of the sequences the model can work on.
- getLength() -
Method in class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
-
- getLength() -
Method in interface de.jstacs.sequenceScores.SequenceScore
- Returns the length of sequences this instance can score.
- getLength() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
-
- getLength() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.GaussianLikePositionPrior
-
- getLength() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.PositionPrior
- Returns the length that is supported by this prior.
- getLength() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.UniformPositionPrior
-
- getLengthArray(DifferentiableSequenceScore...) -
Static method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
- This method provides an array of lengths that can be used for instance as
IndependentProductDiffSS.partialLength
.
- getLengthOfBurnIn() -
Method in class de.jstacs.sampling.AbstractBurnInTest
-
- getLengthOfBurnIn() -
Method in interface de.jstacs.sampling.BurnInTest
- Computes and returns the length of the burn-in phase using the values
from
BurnInTest.setValue(double)
.
- getLengthOfBurnIn() -
Method in class de.jstacs.sampling.SimpleBurnInTest
- Deprecated.
- getLengthOfModels() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.CompositeTrainSM
- This method returns the length of the models in the
CompositeTrainSM
.
- getLine(int) -
Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier.DoubleTableResult
- Return the line with a given
index
from the table.
- getLineEps() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.NumericalHMMTrainingParameterSet
- This method returns the threshold that should be used for stopping the line search during the optimization.
- getLink() -
Method in class de.jstacs.utils.galaxy.GalaxyAdaptor.LinkedImageResult
- Returns the linked file
- getList() -
Method in class de.jstacs.parameters.RangeParameter
- Returns a list of all parameter values as a
String
or
null
if no parameter values have been set.
- getLnFreq(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM.HomCondProb
- Returns the logarithmic frequency at a given position
index
.
- getLnFreq(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb
- Returns the logarithm of the relative frequency (=probability) at
position
index
in the distribution.
- getLnFreq(Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb
- Returns the logarithm of the relative frequency (=probability) with the
position in the distribution given by the index of the specific
constraint that is fulfilled by the
Sequence
s
beginning at start
.
- getLogCDF(double) -
Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.CDFOfNormal
- This method computes the logarithm of the cumulative density function of a standard normal distribution.
- getLogGammaScoreForCurrentStatistic() -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SamplingState
- This method calculates a score for the current statistics, which is independent from the current parameters
In general the gamma-score is a product of gamma-functions parameterized with the current statistics
- getLogGammaScoreForCurrentStatistic() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleSamplingState
-
- getLogGammaScoreFromStatistic() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
-
- getLogGammaScoreFromStatistic() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.PhyloDiscreteEmission
-
- getLogGammaScoreFromStatistic() -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SamplingEmission
- This method calculates a score for the current statistics, which is independent from the current parameters
In general the gamma-score is a product of gamma-functions parameterized with the current statistics
- getLogGammaScoreFromStatistic() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
-
- getLogGammaScoreFromStatistic() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
- This method calculates a score for the current statistics, which is independent from the current parameters
In general the gamma-score is a product of gamma-functions parameterized with the current statistics
- getLogGammaScoreFromStatistic() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
-
- getLogGammaScoreFromStatistic() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ReferenceBasedTransitionElement
-
- getLogGammaScoreFromStatistic() -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.TransitionWithSufficientStatistic
- This method calculates a score for the current statistics, which is independent from the current parameters
In general the gamma-score is a product of gamma-functions parameterized with the current statistics
- getLogGammaSum(Constraint, double) -
Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.ConstraintManager
- Computes the sum of the differences between the logarithmic values of the
prior knowledge and all counts of a
Constraint
c
.
- getLogLikelihood(StatisticalModel, DataSet) -
Static method in class de.jstacs.utils.StatisticalModelTester
- Returns the log-likelihood of a
DataSet
data
for a
given model m
.
- getLogLikelihood(StatisticalModel, DataSet, double[]) -
Static method in class de.jstacs.utils.StatisticalModelTester
- Returns the log-likelihood of a
DataSet
data
for a
given model m
.
- getLogNormalizationConstant() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractVariableLengthDiffSM
-
- getLogNormalizationConstant(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
-
- getLogNormalizationConstant() -
Method in interface de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModel
- Returns the logarithm of the sum of the scores over all sequences of the event space.
- getLogNormalizationConstant() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
-
- getLogNormalizationConstant(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMM0DiffSM
-
- getLogNormalizationConstant(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
-
- getLogNormalizationConstant(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.UniformHomogeneousDiffSM
-
- getLogNormalizationConstant() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
-
- getLogNormalizationConstant() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
-
- getLogNormalizationConstant() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MarkovRandomFieldDiffSM
-
- getLogNormalizationConstant() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
-
- getLogNormalizationConstant() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.DurationDiffSM
-
- getLogNormalizationConstant(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.VariableLengthMixtureDiffSM
-
- getLogNormalizationConstant() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
-
- getLogNormalizationConstant() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.UniformDiffSM
-
- getLogNormalizationConstant(int) -
Method in interface de.jstacs.sequenceScores.statisticalModels.differentiable.VariableLengthDiffSM
- This method returns the logarithm of the normalization constant for a given sequence
length.
- getLogNormalizationConstant() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
-
- getLogNormalizationConstantForComponent(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
- Computes the logarithm of the normalization constant for the component
i
.
- getLogNormalizationConstantForComponent(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.MixtureDiffSM
-
- getLogNormalizationConstantForComponent(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
-
- getLogNormalizationConstantForComponent(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
-
- getLogNumberOfPossibleOriginalSequences() -
Method in class de.jstacs.data.sequences.MappedDiscreteSequence
- This method returns the logarithm of the number of original
Sequence
s that yield the same mapped Sequence
.
- getLogNumberOfPossibleOriginalSequences(int, int) -
Method in class de.jstacs.data.sequences.MappedDiscreteSequence
- This method returns the logarithm of the number of original
Sequence
s that yield the same mapped Sequence
.
- getLogNumberOfSimilarSymbols(int) -
Method in class de.jstacs.data.alphabets.DiscreteAlphabetMapping
- This method returns the logarithm of the number of old values that yield the same new value.
- getLogPartialNormalizationConstant(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractVariableLengthDiffSM
-
- getLogPartialNormalizationConstant(int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
-
- getLogPartialNormalizationConstant(int) -
Method in interface de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModel
- Returns the logarithm of the partial normalization constant for the parameter with index
parameterIndex
.
- getLogPartialNormalizationConstant(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
-
- getLogPartialNormalizationConstant(int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMM0DiffSM
-
- getLogPartialNormalizationConstant(int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
-
- getLogPartialNormalizationConstant(int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.UniformHomogeneousDiffSM
-
- getLogPartialNormalizationConstant(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
-
- getLogPartialNormalizationConstant(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
-
- getLogPartialNormalizationConstant(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MarkovRandomFieldDiffSM
-
- getLogPartialNormalizationConstant(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.MixtureDiffSM
-
- getLogPartialNormalizationConstant(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.DurationDiffSM
-
- getLogPartialNormalizationConstant(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
-
- getLogPartialNormalizationConstant(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
-
- getLogPartialNormalizationConstant(int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.VariableLengthMixtureDiffSM
-
- getLogPartialNormalizationConstant(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
-
- getLogPartialNormalizationConstant(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.UniformDiffSM
-
- getLogPartialNormalizationConstant(int, int) -
Method in interface de.jstacs.sequenceScores.statisticalModels.differentiable.VariableLengthDiffSM
- This method returns the logarithm of the partial normalization constant for a given
parameter index and a sequence length.
- getLogPartialNormalizationConstant(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
-
- getLogPartialNormalizer() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter
- Returns the partial derivative of the normalization constant with respect
to this parameter.
- getLogPosteriorFromStatistic() -
Method in interface de.jstacs.sampling.SamplingFromStatistic
- This method calculates the a-posteriori probability for the current statistics
- getLogPosteriorFromStatistic() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM
- This method calculates the a posteriori probability for the current statistics
- getLogPosteriorFromStatistic() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
-
- getLogPosteriorFromStatistic() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.PhyloDiscreteEmission
-
- getLogPosteriorFromStatistic() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
-
- getLogPosteriorFromStatistic() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleSamplingState
-
- getLogPosteriorFromStatistic() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.TransitionElement
- This method computes the log posterior from the internal sufficient statistic.
- getLogPosteriorFromStatistic() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
-
- getLogPriorForPositions(int, int...) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.GaussianLikePositionPrior
- Returns only the important part and leaving the logarithm of the
normalization constant out.
- getLogPriorForPositions(int, int...) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.PositionPrior
- The logarithmic value of the prior for specified start positions of the
part motifs.
- getLogPriorForPositions(int, int...) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.UniformPositionPrior
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
-
- getLogPriorTerm() -
Method in interface de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModel
- This method computes a value that is proportional to
DifferentiableStatisticalModel.getESS()
* DifferentiableStatisticalModel.getLogNormalizationConstant()
+ Math.log( prior )
where prior
is the prior for the parameters of this model.
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.MarkovModelDiffSM
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMM0DiffSM
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.UniformHomogeneousDiffSM
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MarkovRandomFieldDiffSM
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.MixtureDurationDiffSM
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.UniformDurationDiffSM
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.UniformDiffSM
-
- getLogPriorTerm() -
Method in interface de.jstacs.sequenceScores.statisticalModels.StatisticalModel
- Returns a value that is proportional to the log of the prior.
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.CompositeTrainSM
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.DifferentiableStatisticalModelWrapperTrainSM
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousMM
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.BayesianNetworkTrainSM
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.DAGTrainSM
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
-
- getLogPriorTerm() -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.Emission
- Returns a value that is proportional to the log of the prior.
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.MixtureEmission
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.UniformEmission
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
- Returns a value that is proportional to the log of the prior.
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ReferenceBasedTransitionElement
-
- getLogPriorTerm() -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.Transition
- Returns a value that is proportional to the log of the prior.
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.UniformTrainSM
-
- getLogPriorTerm() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.VariableLengthWrapperTrainSM
-
- getLogPriorTermForComponentProbs() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- This method computes the part of the prior that comes from the component
probabilities.
- getLogProbAndPartialDerivationFor(boolean, int, int, IntList, DoubleList, Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
-
- getLogProbAndPartialDerivationFor(boolean, int, int, IntList, DoubleList, Sequence) -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.DifferentiableEmission
- Returns the logarithmic score for a
Sequence
beginning at
position start
in the Sequence
and fills lists with
the indices and the partial derivations.
- getLogProbAndPartialDerivationFor(boolean, int, int, IntList, DoubleList, Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
-
- getLogProbAndPartialDerivationFor(boolean, int, int, IntList, DoubleList, Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.PhyloDiscreteEmission
-
- getLogProbAndPartialDerivationFor(boolean, int, int, IntList, DoubleList, Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
-
- getLogProbAndPartialDerivationFor(boolean, int, int, IntList, DoubleList, Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.UniformEmission
-
- getLogProbFor(Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
-
- getLogProbFor(Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
-
- getLogProbFor(Sequence, int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
-
- getLogProbFor(Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
-
- getLogProbFor(Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
-
- getLogProbFor(Sequence, int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
-
- getLogProbFor(Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.UniformDiffSM
-
- getLogProbFor(Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.UniformDiffSM
-
- getLogProbFor(Sequence, int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.UniformDiffSM
-
- getLogProbFor(Sequence, int, int) -
Method in interface de.jstacs.sequenceScores.statisticalModels.StatisticalModel
- Returns the logarithm of the probability of (a part of) the given
sequence given the model.
- getLogProbFor(Sequence, int) -
Method in interface de.jstacs.sequenceScores.statisticalModels.StatisticalModel
- Returns the logarithm of the probability of (a part of) the given
sequence given the model.
- getLogProbFor(Sequence) -
Method in interface de.jstacs.sequenceScores.statisticalModels.StatisticalModel
- Returns the logarithm of the probability of the given sequence given the
model.
- getLogProbFor(Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
-
- getLogProbFor(Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
-
- getLogProbFor(Sequence, int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.CompositeTrainSM
-
- getLogProbFor(Sequence, int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.DifferentiableStatisticalModelWrapperTrainSM
-
- getLogProbFor(Sequence, int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM
-
- getLogProbFor(Sequence, int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.DAGTrainSM
-
- getLogProbFor(Sequence, int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
-
- getLogProbFor(boolean, int, int, Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
-
- getLogProbFor(boolean, int, int, Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
-
- getLogProbFor(boolean, int, int, Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.PhyloDiscreteEmission
-
- getLogProbFor(boolean, int, int, Sequence) -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.Emission
- This method computes the logarithm of the likelihood.
- getLogProbFor(boolean, int, int, Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.MixtureEmission
-
- getLogProbFor(boolean, int, int, Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
-
- getLogProbFor(boolean, int, int, Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.UniformEmission
-
- getLogProbFor(int, Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- Returns the logarithmic probability for the sequence and the given
component.
- getLogProbFor(Sequence, int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
-
- getLogProbFor(Sequence, int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.UniformTrainSM
-
- getLogProbFor(Sequence, int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.VariableLengthWrapperTrainSM
-
- getLogProbForPath(IntList, int, Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
-
- getLogProbForPath(IntList, int, Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
-
- getLogProbForPath(IntList, int, Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM
-
- getLogProbUsingCurrentParameterSetFor(int, Sequence, int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- Returns the logarithmic probability for the sequence and the given
component using the current parameter set.
- getLogProbUsingCurrentParameterSetFor(int, Sequence, int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.MixtureTrainSM
-
- getLogProbUsingCurrentParameterSetFor(int, Sequence, int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.ZOOPSTrainSM
-
- getLogProbUsingCurrentParameterSetFor(int, Sequence, int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM
-
- getLogProposalPosteriorFromStatistic() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.PhyloDiscreteEmission
- Returns the log posterior of the proposal distribution for the current statistic
- getLogScore(int...) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.MixtureDurationDiffSM
-
- getLogScore(int...) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.PositionDiffSM
- This method enables the user to get the log-score without using a sequence object.
- getLogScore(int...) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
-
- getLogScore(int...) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.UniformDurationDiffSM
-
- getLogScoreAndPartialDerivation(Sequence, IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
-
- getLogScoreAndPartialDerivation(Sequence, int, int, IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
-
- getLogScoreAndPartialDerivation(Sequence, IntList, DoubleList) -
Method in interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore
- Returns the logarithmic score for a
Sequence
seq
and
fills lists with the indices and the partial derivations.
- getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList) -
Method in interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore
- Returns the logarithmic score for a
Sequence
beginning at
position start
in the Sequence
and fills lists with
the indices and the partial derivations.
- getLogScoreAndPartialDerivation(Sequence, int, int, IntList, DoubleList) -
Method in interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore
- Returns the logarithmic score for a
Sequence
beginning at
position start
in the Sequence
and fills lists with
the indices and the partial derivations.
- getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
-
- getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.differentiable.logistic.LogisticDiffSS
-
- getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.differentiable.UniformDiffSS
-
- getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractVariableLengthDiffSM
-
- getLogScoreAndPartialDerivation(Sequence, int, int, IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractVariableLengthDiffSM
-
- getLogScoreAndPartialDerivation(Sequence, int, int, IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
-
- getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
-
- getLogScoreAndPartialDerivation(Sequence, int, int, IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMM0DiffSM
-
- getLogScoreAndPartialDerivation(Sequence, int, int, IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
-
- getLogScoreAndPartialDerivation(Sequence, int, int, IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.UniformHomogeneousDiffSM
-
- getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
-
- getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MarkovRandomFieldDiffSM
-
- getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.MixtureDiffSM
-
- getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
-
- getLogScoreAndPartialDerivation(IntList, DoubleList, int...) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.MixtureDurationDiffSM
-
- getLogScoreAndPartialDerivation(IntList, DoubleList, int...) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.PositionDiffSM
- This method enables the user to get the log-score and the partial derivations without using a sequence object.
- getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.PositionDiffSM
-
- getLogScoreAndPartialDerivation(IntList, DoubleList, int...) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
-
- getLogScoreAndPartialDerivation(IntList, DoubleList, int...) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.UniformDurationDiffSM
-
- getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
-
- getLogScoreAndPartialDerivation(Sequence, int, int, IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.VariableLengthMixtureDiffSM
-
- getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MultiDimensionalSequenceWrapperDiffSM
-
- getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
-
- getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.UniformDiffSM
-
- getLogScoreAndPartialDerivation(Sequence, int, int, IntList, DoubleList) -
Method in interface de.jstacs.sequenceScores.statisticalModels.differentiable.VariableLengthDiffSM
-
- getLogScoreAndPartialDerivation(Sequence, IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
-
- getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
-
- getLogScoreAndPartialDerivation(Sequence, int, int, IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
-
- getLogScoreAndPartialDerivation(int, int, IntList, DoubleList, Sequence) -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.DifferentiableState
- This method allows to compute the logarithm of the score and the gradient for the given subsequences.
- getLogScoreAndPartialDerivation(int, int, IntList, DoubleList, Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleDifferentiableState
-
- getLogScoreAndPartialDerivation(int, int, int, IntList, DoubleList, Sequence, int) -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.DifferentiableTransition
- This method allows to compute the logarithm of the score and the gradient for a specific transition.
- getLogScoreAndPartialDerivation(int, IntList, DoubleList, Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.TransitionElement
- Returns the logarithmic score and fills lists with
the indices and the partial derivations.
- getLogScoreAndPartialDerivation(int, int, int, IntList, DoubleList, Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
-
- getLogScoreAndPartialDerivationForInternal(IntList, DoubleList) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.PositionDiffSM
- This method enables the user to get the log-score and the partial derivations without using a sequence object by using the internal iterator.
- getLogScoreFor(Sequence) -
Method in class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
-
- getLogScoreFor(Sequence, int, int) -
Method in class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
-
- getLogScoreFor(DataSet) -
Method in class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
-
- getLogScoreFor(DataSet, double[]) -
Method in class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
-
- getLogScoreFor(Sequence, int) -
Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
-
- getLogScoreFor(Sequence, int) -
Method in class de.jstacs.sequenceScores.differentiable.logistic.LogisticDiffSS
-
- getLogScoreFor(Sequence, int) -
Method in class de.jstacs.sequenceScores.differentiable.UniformDiffSS
-
- getLogScoreFor(Sequence) -
Method in interface de.jstacs.sequenceScores.SequenceScore
- Returns the logarithmic score for the
Sequence
seq
.
- getLogScoreFor(Sequence, int) -
Method in interface de.jstacs.sequenceScores.SequenceScore
- Returns the logarithmic score for the
Sequence
seq
beginning at position start
in the Sequence
.
- getLogScoreFor(Sequence, int, int) -
Method in interface de.jstacs.sequenceScores.SequenceScore
- Returns the logarithmic score for the
Sequence
seq
beginning at position start
in the Sequence
.
- getLogScoreFor(DataSet) -
Method in interface de.jstacs.sequenceScores.SequenceScore
- This method computes the logarithm of the scores of all sequences
in the given sample.
- getLogScoreFor(DataSet, double[]) -
Method in interface de.jstacs.sequenceScores.SequenceScore
- This method computes and stores the logarithm of the scores for
any sequence in the sample in the given
double
-array.
- getLogScoreFor(DataSet) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
-
- getLogScoreFor(DataSet, double[]) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
-
- getLogScoreFor(Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractVariableLengthDiffSM
-
- getLogScoreFor(Sequence, int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractVariableLengthDiffSM
-
- getLogScoreFor(Sequence, int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
-
- getLogScoreFor(Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
-
- getLogScoreFor(Sequence, int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMM0DiffSM
-
- getLogScoreFor(Sequence, int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
-
- getLogScoreFor(Sequence, int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.UniformHomogeneousDiffSM
-
- getLogScoreFor(Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
-
- getLogScoreFor(Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MarkovRandomFieldDiffSM
-
- getLogScoreFor(Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
-
- getLogScoreFor(Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
-
- getLogScoreFor(Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.PositionDiffSM
-
- getLogScoreFor(Sequence, int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.VariableLengthMixtureDiffSM
-
- getLogScoreFor(Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MultiDimensionalSequenceWrapperDiffSM
-
- getLogScoreFor(Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
-
- getLogScoreFor(Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.UniformDiffSM
-
- getLogScoreFor(Sequence, int, int) -
Method in interface de.jstacs.sequenceScores.statisticalModels.differentiable.VariableLengthDiffSM
-
- getLogScoreFor(Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
-
- getLogScoreFor(Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
-
- getLogScoreFor(Sequence, int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
-
- getLogScoreFor(DataSet) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
-
- getLogScoreFor(DataSet, double[]) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
-
- getLogScoreFor(Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
-
- getLogScoreFor(Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
-
- getLogScoreFor(Sequence, int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
-
- getLogScoreFor(DataSet) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
-
- getLogScoreFor(DataSet, double[]) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
-
- getLogScoreFor(int, int, Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleState
-
- getLogScoreFor(int, int, Sequence) -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.State
- This method returns the logarithm of the score for a given sequence with given start and end position.
- getLogScoreFor(int, Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
- This method returns the score for the transition from the current context to the state with index
index
.
- getLogScoreFor(int, int, int, Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
-
- getLogScoreFor(int, Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.DistanceBasedScaledTransitionElement
-
- getLogScoreFor(int, Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ScaledTransitionElement
-
- getLogScoreFor(int, int, int, Sequence, int) -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.Transition
- This method returns the logarithm of the score for the transition.
- getLogScoreFor(DataSet) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
-
- getLogScoreForInternal() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.PositionDiffSM
- This method enables the user to get the log-score without using a sequence object by using the internal iterator.
- getLogStatePosteriorMatrixFor(int, int, Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
- This method returns the log state posterior of all states for a sequence.
- getLogStatePosteriorMatrixFor(DataSet) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
- This method returns the log state posteriors for all sequences of the sample
data
.
- getLogStatePosteriorMatrixFor(int, int, Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM
-
- getLogSum(double...) -
Static method in class de.jstacs.utils.Normalisation
- Returns the logarithm of the sum of values
val[i]
given as
lnVal[i] = Math.log( val[i] )
.
- getLogSum(int, int, double...) -
Static method in class de.jstacs.utils.Normalisation
- Returns the logarithm of the sum of values
v[i]
given as
lnVal[i] = Math.log( val[i] )
between a start and end index.
- getLogT() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter
- Returns the part of the normalization constant of parameters before this
parameter in the structure of the network.
- getLogZ() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter
- Returns the part of the normalization constant of parameters after this
parameter in the structure of the network.
- getLongFromParameter(Parameter) -
Static method in class de.jstacs.io.ParameterSetParser
- Returns the
long
which is the value of the Parameter
par
.
- getLowerBound() -
Method in class de.jstacs.parameters.validation.NumberValidator
- Returns the lower bound of the
NumberValidator
.
- getMarginalDistribution(StatisticalModel, int[]) -
Static method in class de.jstacs.utils.StatisticalModelTester
- This method computes the marginal distribution for any discrete model
m
and all sequences that fulfill the constraint
, if possible.
- getMarginalOrder() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint
- Returns the marginal order, i.e. the number of used random variables.
- getMax() -
Method in class de.jstacs.data.alphabets.ContinuousAlphabet
- Returns the maximal value of this alphabet.
- getMax() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.DurationDiffSM
- Returns the maximal value that can be scored.
- getMax(double[][]) -
Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.TwoPointEvaluater
- This method can be used to determine the maximal value of the matrix of
mutual informations.
- getMaximalAlphabetLength() -
Method in class de.jstacs.data.AlphabetContainer
- Returns the maximal
Alphabet
length of this
AlphabetContainer
.
- getMaximalEdgeFor(byte, int, int...) -
Method in class de.jstacs.algorithms.graphs.tensor.AsymmetricTensor
-
- getMaximalEdgeFor(byte, int, int...) -
Method in class de.jstacs.algorithms.graphs.tensor.SubTensor
-
- getMaximalEdgeFor(byte, int, int...) -
Method in class de.jstacs.algorithms.graphs.tensor.SymmetricTensor
-
- getMaximalEdgeFor(byte, int, int...) -
Method in class de.jstacs.algorithms.graphs.tensor.Tensor
- Returns the edge
permute(parents[0],...
- getMaximalElementLength() -
Method in class de.jstacs.data.DataSet
- Returns the maximal length of an element, i.e. a
Sequence
, in
this DataSet
.
- getMaximalInDegree() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
-
- getMaximalInDegree() -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.Transition
- This method returns the maximal out degree of any context used in this transition instance.
- getMaximalMarkovOrder() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
-
- getMaximalMarkovOrder() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousDiffSM
- Returns the maximal used markov oder.
- getMaximalMarkovOrder() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMM0DiffSM
-
- getMaximalMarkovOrder() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
-
- getMaximalMarkovOrder() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.UniformHomogeneousDiffSM
-
- getMaximalMarkovOrder() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
-
- getMaximalMarkovOrder() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.UniformDiffSM
-
- getMaximalMarkovOrder() -
Method in interface de.jstacs.sequenceScores.statisticalModels.StatisticalModel
- This method returns the maximal used Markov order, if possible.
- getMaximalMarkovOrder() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
-
- getMaximalMarkovOrder() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.CompositeTrainSM
-
- getMaximalMarkovOrder() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM
-
- getMaximalMarkovOrder() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.BayesianNetworkTrainSM
-
- getMaximalMarkovOrder() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGTrainSM
-
- getMaximalMarkovOrder() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
-
- getMaximalMarkovOrder() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
-
- getMaximalMarkovOrder() -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.Transition
- This method returns the maximal used Markov order.
- getMaximalMarkovOrder() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.UniformTrainSM
-
- getMaximalNumberOfChildren() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
-
- getMaximalNumberOfChildren() -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.Transition
- This method returns the maximal number of children for any context used in this transition instance.
- getMaximalSymbolLength() -
Method in class de.jstacs.data.alphabets.DiscreteAlphabet
- Returns the length of the longest "symbol" in the alphabet.
- getMaxIndex(double[]) -
Static method in class de.jstacs.utils.ToolBox
- Returns the index with maximal value in a
double
array.
- getMaxIndex(int, int, double[]) -
Static method in class de.jstacs.utils.ToolBox
- Returns the index with maximal value in a
double
array.
- getMaxOfDeviation(StatisticalModel, StatisticalModel, int) -
Static method in class de.jstacs.utils.StatisticalModelTester
- This method computes the maximum deviation between the probabilities for
all sequences of
length
for discrete models m1
and m2
.
- getMeanParameters(boolean, int) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
- Returns the mean parameters over all samplings of all stationary phases.
- getMeasure(double, double, double, double) -
Method in class de.jstacs.classifiers.performanceMeasures.MaximumCorrelationCoefficient
-
- getMeasure(double, double, double, double) -
Method in class de.jstacs.classifiers.performanceMeasures.MaximumFMeasure
-
- getMeasure(double, double, double, double) -
Method in class de.jstacs.classifiers.performanceMeasures.MaximumNumericalTwoClassMeasure
- This measure compute the measure for a given confusion matrix
- getMeasure() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSMParameterSet
- Returns the structure
Measure
defined by this set of parameters.
- getMeasureName() -
Method in class de.jstacs.classifiers.performanceMeasures.MaximumCorrelationCoefficient
-
- getMeasureName() -
Method in class de.jstacs.classifiers.performanceMeasures.MaximumFMeasure
-
- getMeasureName() -
Method in class de.jstacs.classifiers.performanceMeasures.MaximumNumericalTwoClassMeasure
- This method returns a short name of the measure without any parameters.
- getMI(double[][][][][][], double) -
Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure
- Computes the mutual information from
counts
counted on
sequences with a total weight of n
.
- getMI(double[][][][], double) -
Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure
- Computes the mutual information from
counts
counted on
sequences with a total weight of n
.
- getMIInBits(DataSet, double[]) -
Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.TwoPointEvaluater
- This method computes the pairwise mutual information (in bits) between
the sequence positions.
- getMin(int) -
Method in class de.jstacs.data.AlphabetContainer
- Returns the minimal value of the underlying
Alphabet
of position
pos
.
- getMin() -
Method in class de.jstacs.data.alphabets.Alphabet
- Returns the minimal value of the
Alphabet
.
- getMin() -
Method in class de.jstacs.data.alphabets.ContinuousAlphabet
-
- getMin() -
Method in class de.jstacs.data.alphabets.DiscreteAlphabet
-
- getMin() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.DurationDiffSM
- Returns the minimal value that can be scored.
- getMinimalAlphabetLength() -
Method in class de.jstacs.data.AlphabetContainer
- Returns the minimal
Alphabet
length of this
AlphabetContainer
.
- getMinimalElementLength() -
Method in class de.jstacs.data.DataSet
- Returns the minimal length of an element, i.e. a
Sequence
, in
this DataSet
.
- getMinimalHyperparameter() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.TransitionElement
- This method returns the minimal hyper parameters of this
TransitionElement
.
- getMinimalSequenceLength() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.HiddenMotifMixture
- Returns the minimal length a sequence respectively a sample has to have.
- getMinimalSequenceLength() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.ZOOPSTrainSM
-
- getModel(int) -
Method in class de.jstacs.classifiers.trainSMBased.TrainSMBasedClassifier
- Returns a clone of the
TrainableStatisticalModel
for a specified class.
- getModel(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- Returns a deep copy of the
i
-th model.
- getModelInstanceName() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.BayesianNetworkTrainSMParameterSet
- This method returns a short description of the model.
- getModelInstanceName(StructureLearner.ModelType, byte, StructureLearner.LearningType, double) -
Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.IDGTrainSMParameterSet
- This method returns a short textual representation of the model instance.
- getModels() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.CompositeTrainSM
- Returns the a deep copy of the models.
- getModels() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- Returns a deep copy of the models.
- getMostProbableSequence(SequenceScore, int) -
Static method in class de.jstacs.utils.StatisticalModelTester
- Returns one most probable sequence for the discrete model
m
.
- getMotifDiscoverer() -
Method in class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
- This method returns a clone of the internally used
MotifDiscoverer
.
- getMotifLength(int) -
Method in interface de.jstacs.motifDiscovery.MotifDiscoverer
- This method returns the length of the motif with index
motif
.
- getMotifLength(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
-
- getMotifLength(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
-
- getMotifLength(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.MixtureDiffSM
-
- getMotifLength(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
-
- getMotifLength(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.ZOOPSTrainSM
-
- getMRG() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- This method creates the multivariate random generator that will be used
during initialization.
- getMRGParams() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- This method creates the parameters used in a multivariate random
generator while initialization.
- getMultiClassScores(DataSet[]) -
Method in class de.jstacs.classifiers.AbstractClassifier
- This method returns a multidimensional array with class specific scores.
- getMultiClassScores(DataSet[]) -
Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
-
- getName() -
Method in class de.jstacs.AnnotatedEntity
- Returns the name of the
AnnotatedEntity
.
- getName() -
Method in class de.jstacs.classifiers.performanceMeasures.AbstractPerformanceMeasure
- The method returns the name of the performance measure.
- getName() -
Method in class de.jstacs.classifiers.performanceMeasures.AucPR
-
- getName() -
Method in class de.jstacs.classifiers.performanceMeasures.AucROC
-
- getName() -
Method in class de.jstacs.classifiers.performanceMeasures.ClassificationRate
-
- getName() -
Method in class de.jstacs.classifiers.performanceMeasures.ConfusionMatrix
-
- getName() -
Method in class de.jstacs.classifiers.performanceMeasures.FalsePositiveRateForFixedSensitivity
-
- getName() -
Method in class de.jstacs.classifiers.performanceMeasures.MaximumNumericalTwoClassMeasure
-
- getName() -
Method in class de.jstacs.classifiers.performanceMeasures.PositivePredictiveValueForFixedSensitivity
-
- getName() -
Method in class de.jstacs.classifiers.performanceMeasures.PRCurve
-
- getName() -
Method in class de.jstacs.classifiers.performanceMeasures.ROCCurve
-
- getName() -
Method in class de.jstacs.classifiers.performanceMeasures.SensitivityForFixedSpecificity
-
- getName(Class<? extends ParameterSet>) -
Static method in class de.jstacs.parameters.ParameterSet
- Returns a name for the class.
- getName(ParameterSet) -
Static method in class de.jstacs.parameters.ParameterSet
- Returns a name for the
ParameterSet
.
- getName() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.phylo.PhyloNode
- This method returns the name of the current instance
- getName() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.phylo.PhyloTree
- This method returns the name of the PhyloTree
- getNameOfAlgorithm() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- Returns the name of the used algorithm.
- getNameOfAssessment() -
Method in class de.jstacs.classifiers.assessment.ClassifierAssessment
- Returns the name of this class.
- getNames() -
Method in class de.jstacs.AnnotatedEntityList
- Returns the names of all
AnnotatedEntity
s in the list.
- getNewAlphabet() -
Method in class de.jstacs.data.alphabets.DiscreteAlphabetMapping
- Returns the new Alphabet that is used for mapping.
- getNewAlphabetContainer(AlphabetContainer, DiscreteAlphabetMapping...) -
Static method in class de.jstacs.data.sequences.MappedDiscreteSequence
- This method allows to create a new
AlphabetContainer
given an old AlphabetContainer
and some DiscreteAlphabetMapping
s.
- getNewComponentProbs(double[]) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- Estimates the weights of each component.
- getNewDiscreteValue(int) -
Method in class de.jstacs.data.alphabets.DiscreteAlphabetMapping
- This method implements the main transformation of the values.
- getNewInstance() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.CompositeLogPrior
-
- getNewInstance() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.DoesNothingLogPrior
-
- getNewInstance() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.LogPrior
- This method returns an empty new instance of the current prior.
- getNewInstance() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateLogPrior
-
- getNewInstance() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SimpleGaussianSumLogPrior
-
- getNewParameters(int, double[][], double[]) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureMixture
-
- getNewParameters() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM
- This method set all parameters for the next sampling step
- getNewParameters() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingPhyloHMM
-
- getNewParameters(int, double[][], double[]) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- This method trains the internal models on the internal sample and the
given weights.
- getNewParameters(int, double[][], double[]) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.HiddenMotifMixture
-
- getNewParametersForModel(int, int, int, double[]) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- This method trains the internal model with index
modelIndex
on the internal sample and the given weights.
- getNewStartDistance() -
Method in class de.jstacs.algorithms.optimization.ConstantStartDistance
-
- getNewStartDistance() -
Method in class de.jstacs.algorithms.optimization.LimitedMedianStartDistance
-
- getNewStartDistance() -
Method in interface de.jstacs.algorithms.optimization.StartDistanceForecaster
- This method returns the new positive start distance.
- getNewWeights(double[], double[], double[][]) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- Computes sequence weights and returns the score.
- getNewWeights(double[], double[], double[][]) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.MixtureTrainSM
- Computes sequence weights and returns the score.
- getNewWeights(double[], double[], double[][]) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.ZOOPSTrainSM
-
- getNewWeights(double[], double[], double[][]) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM
- Computes sequence weights and returns the score.
- getNextContext(int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
- This method returns the next context that will be visited when visiting the child with index
index
.
- getNodeLabel(double, String, NumberFormat) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
-
- getNodeLabel(double, String, NumberFormat) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
-
- getNodeLabel(double, String, NumberFormat) -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.Emission
- Returns the graphviz label of the node containing this emission.
- getNodeLabel(double, String, NumberFormat) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.MixtureEmission
-
- getNodeLabel(double, String, NumberFormat) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
-
- getNodeLabel(double, String, NumberFormat) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.UniformEmission
-
- getNodeShape(boolean) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
-
- getNodeShape(boolean) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
-
- getNodeShape(boolean) -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.Emission
- Returns the graphviz string for the shape of the node.
- getNodeShape(boolean) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.MixtureEmission
-
- getNodeShape(boolean) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
-
- getNodeShape(boolean) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.UniformEmission
-
- getNormalizedVersion(DifferentiableStatisticalModel, int) -
Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
- This method returns a normalized version of a DifferentiableStatisticalModel.
- getNucleicAcid() -
Method in enum de.jstacs.data.DinucleotideProperty
- Returns the kind of nucleic acid, e.g.
- getNumberOfAlignedSequences() -
Method in class de.jstacs.algorithms.alignment.StringAlignment
- Returns the number of sequences in this alignment.
- getNumberOfAllNodesBelow() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.phylo.PhyloNode
- This method returns the total number of
PhyloNode
s in the subtree starting from this instance
- getNumberOfAlphabets() -
Method in class de.jstacs.data.AlphabetContainer
- This method returns the number of
Alphabet
s used in the current AlphabetContainer
.
- getNumberOfAvailableProcessors() -
Static method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractMultiThreadedOptimizableFunction
- This method returns the number of available processors.
- getNumberOfBoundSequences(DataSet, double[], int) -
Method in class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
- Returns the number of sequences in
data
that are predicted to be bound at least once by motif no.
- getNumberOfChildren() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
- This method returns the number of states that can be visited.
- getNumberOfChildren(int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
-
- getNumberOfChildren(int, int) -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.Transition
- This method returns the number of children states for given index, i.e. context, and
a given layer of the matrix.
- getNumberOfClasses() -
Method in class de.jstacs.classifiers.AbstractClassifier
- Returns the number of classes that can be distinguished.
- getNumberOfClasses() -
Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
-
- getNumberOfCombinations(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.CombinationIterator
- Returns the number of possible combinations.
- getNumberOfComponents() -
Method in interface de.jstacs.motifDiscovery.MotifDiscoverer
- Returns the number of components in this
MotifDiscoverer
.
- getNumberOfComponents() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
-
- getNumberOfComponents() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
-
- getNumberOfComponents() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
- Returns the number of different components of this
AbstractMixtureDiffSM
.
- getNumberOfComponents() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- Returns the number of components the are modeled by this
AbstractMixtureTrainSM
.
- getNumberOfElements() -
Method in class de.jstacs.data.DataSet
- Returns the number of elements, i.e. the
Sequence
s, in this
DataSet
.
- getNumberOfElements() -
Method in class de.jstacs.data.DataSet.WeightedDataSetFactory
- Returns the number of elements, i.e.
- getNumberOfElements() -
Method in class de.jstacs.io.StringExtractor
- Returns the number of
String
s that have been read.
- getNumberOfElementsWithLength(int) -
Method in class de.jstacs.data.DataSet
- Returns the number of overlapping elements that can be extracted.
- getNumberOfElementsWithLength(int, double[]) -
Method in class de.jstacs.data.DataSet
- Returns the weighted number of overlapping elements that can be extracted.
- getNumberOfIndexes(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
-
- getNumberOfIndexes(int) -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.Transition
- This method computes the number of different indexes for a given layer of the matrix.
- getNumberOfLines() -
Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier.DoubleTableResult
- Returns the number of lines in this table.
- getNumberOfModels() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.CompositeTrainSM
- This method returns the number of models in the
CompositeTrainSM
.
- getNumberOfMotifs() -
Method in interface de.jstacs.motifDiscovery.MotifDiscoverer
- Returns the number of motifs for this
MotifDiscoverer
.
- getNumberOfMotifs() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
-
- getNumberOfMotifs() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
-
- getNumberOfMotifs() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.MixtureDiffSM
-
- getNumberOfMotifs() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
-
- getNumberOfMotifs() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.ZOOPSTrainSM
-
- getNumberOfMotifsInComponent(int) -
Method in interface de.jstacs.motifDiscovery.MotifDiscoverer
- Returns the number of motifs that are used in the component
component
of this MotifDiscoverer
.
- getNumberOfMotifsInComponent(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
-
- getNumberOfMotifsInComponent(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
-
- getNumberOfMotifsInComponent(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.MixtureDiffSM
-
- getNumberOfMotifsInComponent(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
-
- getNumberOfMotifsInComponent(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.ZOOPSTrainSM
-
- getNumberOfNexts(int) -
Method in class de.jstacs.parameters.MultiSelectionParameter
- Returns the number of calls of
MultiSelectionParameter.next()
that can be called
before false
is returned.
- getNumberOfNexts(int) -
Method in class de.jstacs.parameters.RangeParameter
- Returns the number of calls of
RangeParameter.next()
that can be done before
obtaining false
.
- getNumberOfNodes() -
Method in class de.jstacs.algorithms.graphs.tensor.Tensor
- Returns the number of nodes.
- getNumberOfNodes() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.phylo.PhyloTree
- This method returns the total number of nodes in the tree
- getNumberOfParameters() -
Method in class de.jstacs.parameters.ArrayParameterSet
-
- getNumberOfParameters() -
Method in class de.jstacs.parameters.ParameterSet
- Returns the number of parameters in the
ParameterSet
.
- getNumberOfParameters() -
Method in class de.jstacs.parameters.SequenceScoringParameterSet
-
- getNumberOfParameters() -
Method in interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore
- Returns the number of parameters in this
DifferentiableSequenceScore
.
- getNumberOfParameters() -
Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
-
- getNumberOfParameters() -
Method in class de.jstacs.sequenceScores.differentiable.logistic.LogisticDiffSS
-
- getNumberOfParameters() -
Method in class de.jstacs.sequenceScores.differentiable.UniformDiffSS
-
- getNumberOfParameters() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
-
- getNumberOfParameters() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
-
- getNumberOfParameters() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMM0DiffSM
-
- getNumberOfParameters() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
-
- getNumberOfParameters() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.UniformHomogeneousDiffSM
-
- getNumberOfParameters() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
-
- getNumberOfParameters() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
-
- getNumberOfParameters() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MarkovRandomFieldDiffSM
-
- getNumberOfParameters() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
-
- getNumberOfParameters() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.MixtureDurationDiffSM
-
- getNumberOfParameters() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
-
- getNumberOfParameters() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.UniformDurationDiffSM
-
- getNumberOfParameters() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MultiDimensionalSequenceWrapperDiffSM
-
- getNumberOfParameters() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
-
- getNumberOfParameters() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
-
- getNumberOfParameters() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
-
- getNumberOfParameters() -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.DifferentiableEmission
- Returns the number of parameters of this emission.
- getNumberOfParameters() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
-
- getNumberOfParameters() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
-
- getNumberOfParameters() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.UniformEmission
-
- getNumberOfParameters() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
- This method returns the number of parameters in this transition element.
- getNumberOfParents() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
- Returns the number of parents for the random variable of this
BNDiffSMParameterTree
in the network structure of the enclosing
BayesianNetworkDiffSM
.
- getNumberOfPossibilities() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.DurationDiffSM
- Returns the number of different possibilities that can be scored.
- getNumberOfRecommendedStarts() -
Method in class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
-
- getNumberOfRecommendedStarts() -
Method in interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore
- This method returns the number of recommended optimization starts.
- getNumberOfRecommendedStarts() -
Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
-
- getNumberOfRecommendedStarts() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
-
- getNumberOfRecommendedStarts() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
-
- getNumberOfRecommendedStarts() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
-
- getNumberOfRecommendedStarts() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
-
- getNumberOfRecommendedStarts() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.MixtureDurationDiffSM
-
- getNumberOfRecommendedStarts() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
-
- getNumberOfRecommendedStarts() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
-
- getNumberOfRecommendedStarts() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
-
- getNumberOfResults() -
Method in class de.jstacs.results.ResultSet
- Returns the number of
Result
s in this ResultSet
- getNumberOfSequenceAnnotationsByType(String) -
Method in class de.jstacs.data.sequences.Sequence
- Returns the number of
SequenceAnnotation
s of type type
for this Sequence
.
- getNumberOfSequences() -
Method in class de.jstacs.data.sequences.MultiDimensionalSequence
- This method returns the number of internal sequences.
- getNumberOfSequences() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.SequenceIterator
- This method returns the number of sequences in this iterator,
i.e., the number of times
SequenceIterator.next()
returns true
after using SequenceIterator.reset()
.
- getNumberOfSpecificConstraints() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint
- Returns the number of specific constraints.
- getNumberOfStarts() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifierParameterSet
- Returns the number of independent sampling starts
- getNumberOfStarts() -
Method in class de.jstacs.sampling.AbstractBurnInTestParameterSet
- Returns the number of starts.
- getNumberOfStarts(DifferentiableSequenceScore[]) -
Static method in class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
- Returns the number of recommended starts in a numerical optimization.
- getNumberOfStarts() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.HMMTrainingParameterSet
- The method returns the number of different starts.
- getNumberOfStates() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
- This method returns the number of the (hidden) states
- getNumberOfStates() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
-
- getNumberOfStates() -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.Transition
- This method returns the number of states underlying this transition
instance.
- getNumberOfStationarySamplings() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifierParameterSet
- Returns the number of samplings steps in the stationary phase
- getNumberOfStepsInStationaryPhase() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.SamplingHMMTrainingParameterSet
- The method returns the number of steps to be done in the stationary phase.
- getNumberOfStepsPerIteration() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.SamplingHMMTrainingParameterSet
- This method returns the number of steps to be done in each start before testing for the end of the burn in phase (again).
- getNumberOfTestSamplings() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifierParameterSet
- Returns the number of samplings between checks for the stationary phase
- getNumberOfThreads() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractMultiThreadedOptimizableFunction
- Returns the number of used threads for evaluating the function and for determining the gradient of the function.
- getNumberOfThreads() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier
- This method returns the number of used threads while optimization.
- getNumberOfThreads() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifierParameterSet
- This method returns the number of threads that should be used during optimization.
- getNumberOfThreads() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifierParameterSet
- Returns the number of threads for evaluating the
LogGenDisMixFunction
- getNumberOfThreads() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
- This method returns the number of threads that is internally used.
- getNumberOfThreads() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.MultiThreadedTrainingParameterSet
- This method returns the number of threads that should be used during optimization.
- getNumberOfValues() -
Method in class de.jstacs.parameters.MultiSelectionParameter
-
- getNumberOfValues() -
Method in interface de.jstacs.parameters.RangeIterator
- Returns the number of values in the collection.
- getNumberOfValues() -
Method in class de.jstacs.parameters.RangeParameter
- Returns the number of values in a list or range of parameter values.
- getNumericalCharacteristics() -
Method in class de.jstacs.classifiers.AbstractClassifier
- Returns the subset of numerical values that are also returned by
AbstractClassifier.getCharacteristics()
.
- getNumericalCharacteristics() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
-
- getNumericalCharacteristics() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
-
- getNumericalCharacteristics() -
Method in class de.jstacs.classifiers.MappingClassifier
-
- getNumericalCharacteristics() -
Method in class de.jstacs.classifiers.trainSMBased.TrainSMBasedClassifier
-
- getNumericalCharacteristics() -
Method in class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
-
- getNumericalCharacteristics() -
Method in interface de.jstacs.sequenceScores.SequenceScore
- Returns the subset of numerical values that are also returned by
SequenceScore.getCharacteristics()
.
- getNumericalCharacteristics() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.CompositeTrainSM
-
- getNumericalCharacteristics() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.DifferentiableStatisticalModelWrapperTrainSM
-
- getNumericalCharacteristics() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM
-
- getNumericalCharacteristics() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.DAGTrainSM
-
- getNumericalCharacteristics() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
-
- getNumericalCharacteristics() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
-
- getNumericalCharacteristics() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.UniformTrainSM
-
- getNumericalCharacteristics() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.VariableLengthWrapperTrainSM
-
- getOffsetForAucPR() -
Method in class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
- This method returns an offset that must be added to scores for computing PR curves.
- getOptimalBranching(double[][], double[][], byte) -
Static method in class de.jstacs.algorithms.graphs.Chu_Liu_Edmonds
- Returns an optimal branching.
- getOrder() -
Method in class de.jstacs.algorithms.graphs.tensor.Tensor
- Returns the order.
- getOrder() -
Method in enum de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder.RandomSeqType
- This method returns the Markov order.
- getOrder() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.MarkovModelDiffSM
- Returns the order of the inhomogeneous Markov model.
- getOrder() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov
- Returns the order of the Markov model as defined in the constructor
- getOrder() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov.InhomogeneousMarkovParameterSet
- Returns the order of the
InhomogeneousMarkov
structure
measure as defined by this set of parameters.
- getOrder() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMExplainingAwayResidual.PMMExplainingAwayResidualParameterSet
- Returns the order defined by this set of parameters.
- getOrder() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMMutualInformation.PMMMutualInformationParameterSet
- Returns the order defined by this set of parameters.
- getOutfilePrefix() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifierParameterSet
- Returns the prefix of the temporary files for storing sampled
parameter values
- getOutput(byte[], double) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb
- This method is used to create random sequences.
- getOutputStream() -
Method in class de.jstacs.utils.galaxy.GalaxyAdaptor.Protocol
- Returns the
ByteArrayOutputStream
of this protocol
- getOutputStream() -
Method in class de.jstacs.utils.SafeOutputStream
- Returns the internal used
OutputStream
.
- getParameterAt(int) -
Method in class de.jstacs.parameters.ArrayParameterSet
-
- getParameterAt(int) -
Method in class de.jstacs.parameters.ParameterSet
- Returns the
Parameter
at position i
.
- getParameterAt(int) -
Method in class de.jstacs.parameters.SequenceScoringParameterSet
-
- getParameterFor(Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
- Returns the
BNDiffSMParameter
that is responsible for the suffix of
sequence seq
starting at position start
.
- getParameterForName(String) -
Method in class de.jstacs.parameters.ParameterSet
- Returns the
Parameter
with name name
.
- getParameterFromTag(String) -
Method in class de.jstacs.parameters.ParameterSetTagger
- This method returns the
Parameter
specified by the tag
- getParameterIndexesForSamplingStep(int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
- Returns the indexes of the parameters, incremented by
offset
, that
shall be sampled in step step
of a grouped sampling process.
- getParameters(OptimizableFunction.KindOfParameter, double[]) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractOptimizableFunction
- This method enables the user to get the parameters without creating a new
array.
- getParameters(OptimizableFunction.KindOfParameter) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractOptimizableFunction
-
- getParameters(OptimizableFunction.KindOfParameter, double[]) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.DiffSSBasedOptimizableFunction
-
- getParameters(OptimizableFunction.KindOfParameter) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.OptimizableFunction
- Returns some parameters that can be used for instance as start
parameters.
- getParameterSetContainingASingleDoubleValue(double) -
Method in class de.jstacs.classifiers.assessment.RepeatedHoldOutAssessParameterSet
- Creates a new
ParameterSet
containing a single
double
-SimpleParameter
.
- getParameterSetFor(Class<? extends InstantiableFromParameterSet>) -
Static method in class de.jstacs.utils.SubclassFinder
- Returns a
LinkedList
of the classes of the
InstanceParameterSet
s that can be used to instantiate the
sub-class of InstantiableFromParameterSet
that is given by
clazz
- getParametersInCollection() -
Method in class de.jstacs.parameters.AbstractSelectionParameter
- Returns the possible values in this collection.
- getParent() -
Method in class de.jstacs.parameters.Parameter
- Returns a reference to the
ParameterSet
enclosing this
Parameter
.
- getParent() -
Method in class de.jstacs.parameters.ParameterSet
- Returns the enclosing
ParameterSetContainer
of this
ParameterSet
or null
if none exists.
- getParents(DataSet, DataSet, double[], double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTExplainingAwayResidual
-
- getParents(DataSet, DataSet, double[], double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTMutualInformation
-
- getParents(DataSet, DataSet, double[], double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov
-
- getParents(DataSet, DataSet, double[], double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure
- Returns the optimal parents for the given data and weights.
- getParents(DataSet, DataSet, double[], double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMExplainingAwayResidual
-
- getParents(DataSet, DataSet, double[], double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMMutualInformation
-
- getParser() -
Method in class de.jstacs.results.DataSetResult
- Returns the
SequenceAnnotationParser
that can be used to
write this DataSetResult
including annotations on the contained Sequence
s
to a file.
- getPartialLengths() -
Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
- This method returns a deep copy of the internally used partial lengths of the parts.
- getPath() -
Method in class de.jstacs.utils.galaxy.GalaxyAdaptor.FileResult
- Returns the path of the directory containing the file
- getPercent() -
Method in class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutAssessParameterSet
- Returns the percentage of user supplied data that is used in each
iteration as test dataset.
- getPercents() -
Method in class de.jstacs.classifiers.assessment.RepeatedHoldOutAssessParameterSet
- Returns an array containing for each class the percentage of user
supplied data that is used in each iteration as test dataset.
- getPlotCommands(REnvironment, String, AbstractScoreBasedClassifier.DoubleTableResult...) -
Static method in class de.jstacs.classifiers.AbstractScoreBasedClassifier.DoubleTableResult
- This method copies the data to the server side and creates a
StringBuffer
containing the plot commands.
- getPlotCommands(REnvironment, String, int[], AbstractScoreBasedClassifier.DoubleTableResult...) -
Static method in class de.jstacs.classifiers.AbstractScoreBasedClassifier.DoubleTableResult
- This method copies the data to the server side and creates a
StringBuffer
containing the plot commands.
- getPlotCommands(REnvironment, String, String[], AbstractScoreBasedClassifier.DoubleTableResult...) -
Static method in class de.jstacs.classifiers.AbstractScoreBasedClassifier.DoubleTableResult
- This method copies the data to the server side and creates a
StringBuffer
containing the plot commands.
- getPlugInParameters() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSMParameterSet
- Returns true if plug-in parameters shall be used when creating a
BayesianNetworkDiffSM
from this set of parameters.
- getPosition() -
Method in class de.jstacs.data.sequences.annotation.LocatedSequenceAnnotation
- Returns the position of this
LocatedSequenceAnnotation
on the
sequence.
- getPosition() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter
- Returns the position of the symbol this parameter is responsible for as
defined in the constructor.
- getPosition(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint
- Returns the position with index
index
.
- getPositionDependentKMerProb(Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
- Returns the probability of
kmer
for all possible positions in this BayesianNetworkDiffSM
starting at position kmer.getLength()-1.
- getPositionForParameter(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
- Returns the position in the sequence the parameter
index
is
responsible for.
- getPositions() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint
- Returns a clone of the array of used positions.
- getPossibleLength(TrainableStatisticalModel...) -
Static method in class de.jstacs.classifiers.trainSMBased.TrainSMBasedClassifier
- This method returns the possible length of a classifier that would use
the given
TrainableStatisticalModel
s.
- getPossibleLength() -
Method in class de.jstacs.data.AlphabetContainer.AbstractAlphabetContainerParameterSet
- Returns the length of the
AlphabetContainer
that can be instantiated using
this ParameterSet
.
- getPossibleLength() -
Method in class de.jstacs.data.AlphabetContainer
- Returns the possible length for
Sequence
s using this
AlphabetContainer
.
- getPossibleLength() -
Method in class de.jstacs.data.AlphabetContainerParameterSet
-
- getPossibleLength() -
Method in class de.jstacs.data.alphabets.DNAAlphabetContainer.DNAAlphabetContainerParameterSet
-
- getProbFor(Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
- Returns the probability of
Sequence
sequence
in this BNDiffSMParameterTree
.
- getProbsForComponent(Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
- Returns the probabilities for each component given a
Sequence
.
- getProfileOfScoresFor(int, int, Sequence, int, MotifDiscoverer.KindOfProfile) -
Method in interface de.jstacs.motifDiscovery.MotifDiscoverer
- Returns the profile of the scores for component
component
and motif motif
at all possible start positions of the motif
in the sequence sequence
beginning at startpos
.
- getProfileOfScoresFor(int, int, Sequence, int, MotifDiscoverer.KindOfProfile) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
-
- getProfileOfScoresFor(int, int, Sequence, int, MotifDiscoverer.KindOfProfile) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
-
- getProfileOfScoresFor(int, int, Sequence, int, MotifDiscoverer.KindOfProfile) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.MixtureDiffSM
-
- getProfileOfScoresFor(int, int, Sequence, int, MotifDiscoverer.KindOfProfile) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
-
- getProfileOfScoresFor(int, int, Sequence, int, MotifDiscoverer.KindOfProfile) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.ZOOPSTrainSM
-
- getProperty(Sequence, DinucleotideProperty.Smoothing) -
Method in enum de.jstacs.data.DinucleotideProperty
- Computes this dinucleotide property for all overlapping twomers in
original
, smoothes the result using smoothing
,
and returns the smoothed property as a double
array.
- getProperty(Sequence) -
Method in enum de.jstacs.data.DinucleotideProperty
- Computes this dinucleotide property for all overlapping twomers in
original
and returns the result as a double
array of length original.getLength()-1
- getPropertyAsSequence(Sequence) -
Method in enum de.jstacs.data.DinucleotideProperty
- Computes this dinucleotide property for all overlapping twomers in
original
and returns the result as a Sequence
of length original.getLength()-1
- getPropertyAsSequence(Sequence, DinucleotideProperty.Smoothing) -
Method in enum de.jstacs.data.DinucleotideProperty
- Computes this dinucleotide property for all overlapping twomers in
original
, smoothes the result using smoothing
,
and returns the smoothed property as a Sequence
.
- getPropertyImage(Sequence, DinucleotideProperty, DinucleotideProperty.Smoothing, REnvironment, int, String, int, int) -
Static method in enum de.jstacs.data.DinucleotideProperty
-
- getPropertyImage(DataSet, DinucleotideProperty, DinucleotideProperty.Smoothing, REnvironment, int, String, int, int) -
Static method in enum de.jstacs.data.DinucleotideProperty
-
- getProtocol(boolean) -
Method in class de.jstacs.utils.galaxy.GalaxyAdaptor
- Returns an object for writing a protocol of a program run
- getPseudoCount() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter
- Returns the pseudocount as given in the constructor.
- getPubMedID() -
Method in enum de.jstacs.data.DinucleotideProperty
- Returns the PubMed ID of the publication where the parameters of this property has been published.
- getPValue(Sequence, DataSet) -
Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
- Returns the p-value for a
Sequence
candidate
with
respect to a given background DataSet
.
- getPValue(DataSet, DataSet) -
Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
- Returns the p-values for all
Sequence
s in the DataSet
candidates
with respect to a given background DataSet
.
- getPValue(double[], double) -
Static method in class de.jstacs.classifiers.utils.PValueComputation
- This method searches for the insertion point of the score in a given
sorted array of scores and returns the p-value for this score.
- getPValue(double[], double, int) -
Static method in class de.jstacs.classifiers.utils.PValueComputation
- This method searches for the insertion point of the score in a given
sorted array of scores from index
start
and returns the
p-value for this score.
- getPWM() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
- If this
BayesianNetworkDiffSM
is a PWM, i.e.
- getRandomSequence(Random, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousMM
-
- getRandomSequence(Random, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM
- This method creates a random
Sequence
from a trained homogeneous
model.
- getRangedInstance() -
Method in class de.jstacs.parameters.AbstractSelectionParameter
-
- getRangedInstance() -
Method in interface de.jstacs.parameters.Rangeable
- Returns an instance of
RangeIterator
that has the same properties
as the current instance, but accepts a range or list of values.
- getRangedInstance() -
Method in class de.jstacs.parameters.SimpleParameter
-
- getRawResult() -
Method in class de.jstacs.results.ListResult
- Returns a copy of the internal list of
ResultSet
s.
- getReference() -
Method in enum de.jstacs.data.DinucleotideProperty
- Returns the reference of the publication where the parameters of this property has been published.
- getReferenceClass() -
Method in class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutAssessParameterSet
- Returns the index of the reference class.
- getReferenceSequence() -
Method in class de.jstacs.data.sequences.annotation.ReferenceSequenceAnnotation
- Returns the reference sequence.
- getRegEx() -
Method in class de.jstacs.io.RegExFilenameFilter
- Returns a representation of all used regular expressions.
- getRepeats() -
Method in class de.jstacs.classifiers.assessment.RepeatedHoldOutAssessParameterSet
- Returns the repeats defined by this
RepeatedHoldOutAssessParameterSet
(repeats define how many
iterations (train and test classifiers) of that
RepeatedHoldOutExperiment
this
RepeatedHoldOutAssessParameterSet
is used with are performed).
- getRepeats() -
Method in class de.jstacs.classifiers.assessment.RepeatedSubSamplingAssessParameterSet
- Returns the repeats defined by this
RepeatedSubSamplingAssessParameterSet
(repeats defines how many
iterations (train and test classifiers) of that
RepeatedSubSamplingExperiment
this
RepeatedSubSamplingAssessParameterSet
is used with are
performed).
- getRepeats() -
Method in class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutAssessParameterSet
- Returns the repeats defined by this
Sampled_RepeatedHoldOutAssessParameterSet
(repeats defines how
many iterations (train and test classifiers) of that
Sampled_RepeatedHoldOutExperiment
this
Sampled_RepeatedHoldOutAssessParameterSet
is used with are
performed).
- getResultAt(int) -
Method in class de.jstacs.results.NumericalResultSet
-
- getResultAt(int) -
Method in class de.jstacs.results.ResultSet
- Returns
Result
number index
in this
ResultSet
.
- getResultForName(String) -
Method in class de.jstacs.results.ResultSet
- Returns
Result
with name name
in this
ResultSet
.
- getResultInstance() -
Method in class de.jstacs.results.StorableResult
- Returns the instance of the
Storable
that is the result of this
StorableResult
.
- getResults(LinkedList, DataSet[], PerformanceMeasureParameterSet, boolean) -
Method in class de.jstacs.classifiers.AbstractClassifier
- This method computes the results for any evaluation of the classifier.
- getResults(LinkedList, DataSet[], PerformanceMeasureParameterSet, boolean) -
Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
-
- getResults(LinkedList, DataSet[], PerformanceMeasureParameterSet, boolean) -
Method in class de.jstacs.classifiers.MappingClassifier
-
- getResults() -
Method in class de.jstacs.results.ResultSet
- Returns all internal results as an array of
Result
s.
- getReverseComplementaryDataSet() -
Method in class de.jstacs.data.DataSet
- Returns a
DataSet
that contains the reverse complement of all Sequence
s in
this DataSet
.
- getReverseComplementDistributions(ComplementableDiscreteAlphabet, double[][][]) -
Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
- This method computes the reverse complement distributions for given conditional distributions.
- getReverseSwitches() -
Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
- This method returns a deep copy of the internally used switches for the parts whether to use the corresponding
DifferentiableSequenceScore
forward or as reverse complement.
- getRNotation(String) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.DurationDiffSM
- This method returns the distribution in R notation.
- getRNotation(String) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.MixtureDurationDiffSM
-
- getRNotation(String) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
-
- getRNotation(String) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.UniformDurationDiffSM
-
- getRoot() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.phylo.PhyloTree
- This method returns the root node of the tree
- getRootValue(int) -
Method in class de.jstacs.algorithms.graphs.tensor.AsymmetricTensor
-
- getRootValue(int) -
Method in class de.jstacs.algorithms.graphs.tensor.SubTensor
-
- getRootValue(int) -
Method in class de.jstacs.algorithms.graphs.tensor.SymmetricTensor
-
- getRootValue(int) -
Method in class de.jstacs.algorithms.graphs.tensor.Tensor
- Returns the value for
child
as root.
- getRunTimeException(Exception) -
Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
- This method creates an
RuntimeException
from any other Exception
- getSafeOutputStream(OutputStream) -
Static method in class de.jstacs.utils.SafeOutputStream
- This method returns an instance of
SafeOutputStream
for a given OutputStream
.
- getSamplingComponent() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
- Returns a sampling component suited for this
SamplingScoreBasedClassifier
- getSamplingGroups(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
-
- getSamplingGroups(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.MarkovModelDiffSM
-
- getSamplingGroups(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMM0DiffSM
-
- getSamplingGroups(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
-
- getSamplingGroups(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.UniformHomogeneousDiffSM
-
- getSamplingGroups(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
-
- getSamplingGroups(int) -
Method in interface de.jstacs.sequenceScores.statisticalModels.differentiable.SamplingDifferentiableStatisticalModel
- Returns groups of indexes of parameters that shall be drawn
together in a sampling procedure
- getSamplingGroups(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.UniformDiffSM
-
- getSamplingGroups(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
-
- getSamplingScheme() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifierParameterSet
- Returns the sampling scheme
- getScale() -
Method in class de.jstacs.parameters.RangeParameter
- Returns a description of the the scale of a range of parameter values.
- getScatterplot(AbstractScoreBasedClassifier, AbstractScoreBasedClassifier, DataSet, DataSet, REnvironment, boolean) -
Static method in class de.jstacs.classifiers.utils.ClassificationVisualizer
- This method returns an
ImageResult
containing a scatter plot of
the scores for the given classifiers cl1
and
cl2
.
- getScore(Tensor, int[][]) -
Static method in class de.jstacs.algorithms.graphs.DAG
- Returns the score for any graph.
- getScore(Sequence, int) -
Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
- This method returns the score for a given
Sequence
and a given
class.
- getScore(Sequence, int, boolean) -
Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
- This method returns the score for a given
Sequence
and a given
class.
- getScore(Sequence, int, boolean) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
-
- getScore(Sequence, int, boolean) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
-
- getScore(Sequence, int, boolean) -
Method in class de.jstacs.classifiers.MappingClassifier
-
- getScore(Sequence, int, boolean) -
Method in class de.jstacs.classifiers.trainSMBased.TrainSMBasedClassifier
-
- getScoreForBestRun() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- Returns the value of the optimized function from the best run of the last
training.
- getScoreForPath(Tensor, int, byte, int[]) -
Static method in class de.jstacs.algorithms.graphs.DAG
- Returns the score for a given path
path
using the first
l
nodes and dependencies of order k
.
- getScores(DataSet) -
Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
- This method returns the scores of the classifier for any
Sequence
in the DataSet
.
- getScores(DataSet) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
-
- getScores(DataSet) -
Method in class de.jstacs.classifiers.trainSMBased.TrainSMBasedClassifier
-
- getSecondElement() -
Method in class de.jstacs.utils.Pair
- This method returns the second element.
- getSelected() -
Method in class de.jstacs.parameters.MultiSelectionParameter
- Returns the indexes of the selected options.
- getSelected() -
Method in class de.jstacs.parameters.SelectionParameter
- Returns the index of the selected value.
- getSelectionParameter(Class<? extends ParameterSet>, String, String, String, boolean) -
Static method in class de.jstacs.utils.SubclassFinder
- This method creates an
SelectionParameter
that contains
InstanceParameterSet
for each possible
class.
- getSequence(int) -
Method in class de.jstacs.data.sequences.MultiDimensionalSequence
- This method returns the internal sequence with index
index
.
- getSequenceAnnotationByType(String, int) -
Method in class de.jstacs.data.sequences.Sequence
- Returns the
SequenceAnnotation
no.
- getSequenceAnnotationByTypeAndIdentifier(String, String) -
Method in class de.jstacs.data.sequences.Sequence
- Returns the
SequenceAnnotation
of this Sequence
that has type type
and identifier identifier
.
- getSequenceAnnotationIndexMatrix(String, Hashtable<String, Integer>, String, Hashtable<String, Integer>) -
Method in class de.jstacs.data.DataSet
- This method creates a matrix which contains the index of the
Sequence
with specific SequenceAnnotation
combination or -1 if the DataSet
does not contain any Sequence
with such a combination.
- getSequenceWeights() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractOptimizableFunction
-
- getSequenceWeights() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.OptimizableFunction
- Returns the weights for each
Sequence
for each
class used in this OptimizableFunction
.
- getShannonEntropy(StatisticalModel, int) -
Static method in class de.jstacs.utils.StatisticalModelTester
- This method computes the Shannon entropy for any discrete model
m
and all sequences of length
, if possible.
- getShannonEntropyInBits(StatisticalModel, int) -
Static method in class de.jstacs.utils.StatisticalModelTester
- This method computes the Shannon entropy in bits for any discrete model
m
and all sequences of length
, if possible.
- getShortFromParameter(Parameter) -
Static method in class de.jstacs.io.ParameterSetParser
- Returns the
short
which is the value of the
Parameter
par
.
- getSimplifiedAlphabetContainer(Alphabet[], int[]) -
Static method in class de.jstacs.data.AlphabetContainer
- This method creates a new
AlphabetContainer
that uses as less as
possible Alphabet
s to describe the container.
- getSingelton(Class<? extends Singleton>) -
Static method in class de.jstacs.Singleton.SingletonHandler
- This method helps to retrieve the single instance of a
Singleton
singletonClass
.
- getSizeOfEventSpace() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
-
- getSizeOfEventSpace() -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.DifferentiableEmission
- Returns the size of the event space, i.e., the number of possible outcomes,
for the random variables of this emission
- getSizeOfEventSpace() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
-
- getSizeOfEventSpace() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
-
- getSizeOfEventSpace() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.UniformEmission
-
- getSizeOfEventSpace(int) -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.DifferentiableTransition
- Returns the size of the event space, i.e., the number of possible outcomes,
for the random variable of parameter
index
- getSizeOfEventSpace(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
-
- getSizeOfEventSpaceForRandomVariablesOfParameter(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
-
- getSizeOfEventSpaceForRandomVariablesOfParameter(int) -
Method in interface de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModel
- Returns the size of the event space of the random variables that are
affected by parameter no.
- getSizeOfEventSpaceForRandomVariablesOfParameter(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
-
- getSizeOfEventSpaceForRandomVariablesOfParameter(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMM0DiffSM
-
- getSizeOfEventSpaceForRandomVariablesOfParameter(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
-
- getSizeOfEventSpaceForRandomVariablesOfParameter(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.UniformHomogeneousDiffSM
-
- getSizeOfEventSpaceForRandomVariablesOfParameter(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
-
- getSizeOfEventSpaceForRandomVariablesOfParameter(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
-
- getSizeOfEventSpaceForRandomVariablesOfParameter(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MarkovRandomFieldDiffSM
-
- getSizeOfEventSpaceForRandomVariablesOfParameter(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
-
- getSizeOfEventSpaceForRandomVariablesOfParameter(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.DurationDiffSM
-
- getSizeOfEventSpaceForRandomVariablesOfParameter(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
-
- getSizeOfEventSpaceForRandomVariablesOfParameter(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.UniformDiffSM
-
- getSizeOfEventSpaceForRandomVariablesOfParameter(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
-
- getSmoothedProfile(int, String...) -
Method in class de.jstacs.motifDiscovery.KMereStatistic
- This method returns an array of smoothed profiles.
- getSmoothedProfile(int, Sequence...) -
Method in class de.jstacs.motifDiscovery.KMereStatistic
- This method returns an array of smoothed profiles.
- getSortedInitialParameters(DifferentiableSequenceScore[], MutableMotifDiscovererToolbox.InitMethodForDiffSM[], DiffSSBasedOptimizableFunction, int, OutputStream, int) -
Static method in class de.jstacs.motifDiscovery.MutableMotifDiscovererToolbox
- This method allows to initialize the
DifferentiableSequenceScore
using different MutableMotifDiscovererToolbox.InitMethodForDiffSM
.
- getSortedScoresForMotifAndFlanking(DataSet, DataSet, String) -
Static method in class de.jstacs.motifDiscovery.MotifDiscoveryAssessment
- Returns the scores read from the prediction
pred
for the motif with identifier identifier
and flanking sequences as annotated in
the DataSet
data.
- getSortedValuesForMotifAndFlanking(DataSet, double[][], double, double, String) -
Static method in class de.jstacs.motifDiscovery.MotifDiscoveryAssessment
- This method provides some score arrays that can be used in
AbstractPerformanceMeasure
to determine some
curves or area under curves based on the values of the predictions.
- getSpecificName() -
Method in class de.jstacs.classifiers.performanceMeasures.MaximumCorrelationCoefficient
-
- getSpecificName() -
Method in class de.jstacs.classifiers.performanceMeasures.MaximumFMeasure
-
- getSpecificName() -
Method in class de.jstacs.classifiers.performanceMeasures.MaximumNumericalTwoClassMeasure
- This method returns a specific name of the measure including any parameters.
- getStartDistance() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.NumericalHMMTrainingParameterSet
- This method returns the start distance that should be used in the line search during the optimization.
- getStartIndexOfAlignmentForFirst() -
Method in class de.jstacs.algorithms.alignment.PairwiseStringAlignment
- This method returns the start index of the alignment in the first sequence.
- getStartNode() -
Method in class de.jstacs.algorithms.graphs.Edge
- Returns the start node of the edge.
- getStartPositions(int, DataSet, int, int) -
Method in class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
- This method returns a list of start positions of binding sites.
- getStartValue() -
Method in class de.jstacs.parameters.RangeParameter
- Returns the start value of a range of parameter values or
null
if no range was specified.
- getStatePosteriorMatrixFor(Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
- This method returns the log state posterior of all states for a sequence.
- getStatePosteriorMatrixFor(DataSet) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
- This method returns the state posteriors for all sequences of the sample
data
.
- getStationaryDistribution(double[], int) -
Static method in class de.jstacs.utils.StationaryDistribution
- This method return the stationary distribution.
- getStatistics() -
Method in class de.jstacs.results.MeanResultSet
- Returns the means and (if possible the) standard errors of the results in
this
MeanResultSet
as a new NumericalResultSet
.
- getStatistics(DataSet, double[], int, double) -
Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure
- Counts the occurrences of symbols of the
AlphabetContainer
of
DataSet
s
using weights
.
- getStatisticsOrderTwo(DataSet, double[], int, double) -
Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure
- Counts the occurrences of symbols of the
AlphabetContainer
of
DataSet
s
using weights
.
- getSteps() -
Method in class de.jstacs.parameters.RangeParameter
- Returns the number of steps of a range of parameter values or
0
if no range was specified.
- getStrand(Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
- This method returns the preferred
StrandedLocatedSequenceAnnotationWithLength.Strand
for a given subsequence.
- getStrand(Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
- This method return the preferred
StrandedLocatedSequenceAnnotationWithLength.Strand
for a Sequence
beginning at startPos
.
- getStrandedness() -
Method in class de.jstacs.data.sequences.annotation.StrandedLocatedSequenceAnnotationWithLength
- Returns the strandedness, i.e the orientation of this annotation.
- getStrandProbabilitiesFor(int, int, Sequence, int) -
Method in interface de.jstacs.motifDiscovery.MotifDiscoverer
- This method returns the probabilities of the strand orientations for a given subsequence if it is
considered as site of the motif model in a specific component.
- getStrandProbabilitiesFor(int, int, Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
-
- getStrandProbabilitiesFor(int, int, Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
-
- getStrandProbabilitiesFor(int, int, Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.MixtureDiffSM
-
- getStrandProbabilitiesFor(int, int, Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
-
- getStrandProbabilitiesFor(int, int, Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.ZOOPSTrainSM
-
- getStringFromParameter(Parameter) -
Static method in class de.jstacs.io.ParameterSetParser
- Returns the
String
which is the value of the Parameter
par
.
- getStringRepresentation(Object) -
Method in class de.jstacs.data.sequences.ArbitraryFloatSequence
-
- getStringRepresentation(Object) -
Method in class de.jstacs.data.sequences.ArbitrarySequence
-
- getStringRepresentation(Object) -
Method in class de.jstacs.data.sequences.MultiDimensionalSequence
-
- getStringRepresentation(Object) -
Method in class de.jstacs.data.sequences.Sequence
- This method creates a String representation from the given representation.
- getStringRepresentation(Object) -
Method in class de.jstacs.data.sequences.Sequence.RecursiveSequence
-
- getStringRepresentation(Object) -
Method in class de.jstacs.data.sequences.SimpleDiscreteSequence
-
- getStructure() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.DAGTrainSM
-
- getStructure() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGTrainSM
-
- getStructure() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhomogeneousDGTrainSM
- Returns a
String
representation of the underlying graph.
- getStructure() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureMixture
- Returns a
String
representation of the structure of the used
models.
- getStructure(DataSet, double[], StructureLearner.ModelType, byte, StructureLearner.LearningType) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.StructureLearner
- This method finds the optimal structure of a model by using a given
learning method (in some sense).
- getStructure(Tensor, StructureLearner.ModelType, byte) -
Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.StructureLearner
- This method can be used to determine the optimal structure of a model.
- getStructureFromPath(int[], Tensor) -
Static method in class de.jstacs.algorithms.graphs.DAG
- Extracts the structure from a given path
path
and
score-"function".
- getSubAnnotations() -
Method in class de.jstacs.data.sequences.annotation.SequenceAnnotation
- Returns the sub-annotations of this
SequenceAnnotation
as given
in the constructor.
- getSubContainer(int, int) -
Method in class de.jstacs.data.AlphabetContainer
- Returns a sub-container with the
Alphabet
s for the positions
starting at start
and with length length
.
- getSubSequence(AlphabetContainer, int) -
Method in class de.jstacs.data.sequences.Sequence
- This method should be used if one wants to create a
DataSet
of
subsequences of defined length.
- getSubSequence(AlphabetContainer, int, int) -
Method in class de.jstacs.data.sequences.Sequence
- This method should be used if one wants to create a
DataSet
of
subsequences of defined length.
- getSubSequence(int) -
Method in class de.jstacs.data.sequences.Sequence
- This is a very efficient way to create a subsequence/suffix for
Sequence
s with a simple AlphabetContainer
.
- getSubSequence(int, int) -
Method in class de.jstacs.data.sequences.Sequence
- This is a very efficient way to create a subsequence of defined length
for
Sequence
s with a simple AlphabetContainer
.
- getSuffixDataSet(int) -
Method in class de.jstacs.data.DataSet
- This method enables you to use only a suffix of all elements, i.e. the
Sequence
, in the current DataSet
.
- getSumOfDeviation(StatisticalModel, StatisticalModel, int) -
Static method in class de.jstacs.utils.StatisticalModelTester
- This method computes the sum of deviations between the probabilities for
all sequences of
length
for discrete models m1
and m2
.
- getSumOfDistribution(StatisticalModel, int) -
Static method in class de.jstacs.utils.StatisticalModelTester
- This method computes the marginal distribution for any discrete model
m
and all sequences of length
, if possible.
- getSumOfHyperparameter() -
Method in class de.jstacs.utils.random.DirichletMRGParams
- Returns the sum of the hyperparameters (entries of the hyperparameter
vector) of the underlying Dirichlet distribution.
- getSumOfHyperparameter() -
Method in class de.jstacs.utils.random.ErlangMRGParams
- Returns the sum of the hyperparameters (entries of the hyperparameter
vector) of the underlying Erlang distribution.
- getSumOfHyperParameters(int, int, double) -
Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
- This method returns an array that can be used in the constructor
HomogeneousMMDiffSM.HomogeneousMMDiffSM(AlphabetContainer, int, double, double[], boolean, boolean, int)
containing the sums of the specific hyperparameters.
- getSumOfWeights() -
Method in class de.jstacs.data.DataSet.WeightedDataSetFactory
- Returns the sum of all weights.
- getSuperClassOf(T...) -
Static method in class de.jstacs.io.ArrayHandler
- This method returns the deepest class in the class hierarchy that is the
class or a superclass of all instances in the array.
- getSymbol(int, double) -
Method in class de.jstacs.data.AlphabetContainer
- Returns a
String
representation of the encoded symbol
val
of the Alphabet
of position pos
of
this AlphabetContainer
.
- getSymbolAt(int) -
Method in class de.jstacs.data.alphabets.DiscreteAlphabet
- Returns the symbol at position
i
in the alphabet.
- getSymKLDivergence(StatisticalModel, StatisticalModel, int) -
Static method in class de.jstacs.utils.StatisticalModelTester
- Returns the difference of the Kullback-Leibler-divergences, i.e.
- getTempDir() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
- Returns the directory for parameter files set in this
SamplingScoreBasedClassifier
.
- getTensor(DataSet, double[], byte, StructureLearner.LearningType) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.StructureLearner
- This method can be used to compute a
Tensor
that can be used to
determine the optimal structure.
- getTerminantionCondition() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifierParameterSet
- This method returns the
AbstractTerminationCondition
for stopping the training, e.g., if the
difference of the scores between two iterations is smaller than a given
threshold the training is stopped.
- getTerminationCondition() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.MaxHMMTrainingParameterSet
- This method returns the
AbstractTerminationCondition
for stopping the training, e.g., if the
difference of the scores between two iterations is smaller than a given
threshold the training is stopped.
- getThreshold(double[], int) -
Static method in class de.jstacs.classifiers.utils.PValueComputation
- This method returns the threshold that determines if an observed score is
significant.
- getThreshold() -
Method in class de.jstacs.sampling.VarianceRatioBurnInTestParameterSet
- Returns the threshold used in the
VarianceRatioBurnInTestParameterSet
.
- getTimeInstance(OutputStream) -
Static method in class de.jstacs.utils.Time
- This method tries to return a
UserTime
instance, if not possible (due to native code) it returns a RealTime
instance.
- getTopologicalOrder(int[][]) -
Static method in class de.jstacs.algorithms.graphs.TopSort
- Returns the topological order of indexes according to
parents2
.
- getTopologicalOrder2(byte[][]) -
Static method in class de.jstacs.algorithms.graphs.TopSort
- Computes a topological ordering for a given graph.
- getTrain_TestNumbers(boolean) -
Method in class de.jstacs.classifiers.assessment.RepeatedSubSamplingAssessParameterSet
- Returns an array containing the number of elements the subsampled (train
| test) datasets should consist of.
- getTransitionElementIndex(int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
- This method return the index of the
BasicHigherOrderTransition.AbstractTransitionElement
using the BasicHigherOrderTransition.lookup
table.
- getTrueIndexForLastGetBest() -
Method in class de.jstacs.algorithms.graphs.tensor.SymmetricTensor
- Returns the edge from
SymmetricTensor.getBest(int, int[], byte)
in an encoded
index.
- getType() -
Method in class de.jstacs.data.AlphabetContainer
- Returns the type of this
AlphabetContainer
.
- getType() -
Method in enum de.jstacs.data.DinucleotideProperty
- Returns the type of this property.
- getType() -
Method in class de.jstacs.data.sequences.annotation.SequenceAnnotation
- Returns the type of this
SequenceAnnotation
as given in the
constructor.
- getUniqueHueValues(int) -
Static method in class de.jstacs.utils.ToolBox
- Creates an array of hue values that can be used for the representation
of probabilities.
- getValidator() -
Method in class de.jstacs.parameters.SimpleParameter
- Returns the
ParameterValidator
used in this
SimpleParameter
.
- getValue(byte, int, int...) -
Method in class de.jstacs.algorithms.graphs.tensor.AsymmetricTensor
-
- getValue(byte, int, int...) -
Method in class de.jstacs.algorithms.graphs.tensor.SubTensor
-
- getValue(byte, int, int...) -
Method in class de.jstacs.algorithms.graphs.tensor.SymmetricTensor
-
- getValue(byte, int, int...) -
Method in class de.jstacs.algorithms.graphs.tensor.Tensor
- Returns the value for the edge
parents[0],...
- getValue() -
Method in class de.jstacs.AnnotatedEntity
- Returns the value of the
AnnotatedEntity
.
- getValue() -
Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier.DoubleTableResult
-
- getValue() -
Method in class de.jstacs.parameters.EnumParameter
-
- getValue() -
Method in class de.jstacs.parameters.FileParameter
-
- getValue() -
Method in class de.jstacs.parameters.MultiSelectionParameter
-
- getValue() -
Method in class de.jstacs.parameters.ParameterSetContainer
-
- getValue() -
Method in class de.jstacs.parameters.RangeParameter
-
- getValue() -
Method in class de.jstacs.parameters.SelectionParameter
-
- getValue() -
Method in class de.jstacs.parameters.SimpleParameter
-
- getValue() -
Method in class de.jstacs.results.DataSetResult
-
- getValue() -
Method in class de.jstacs.results.ImageResult
-
- getValue() -
Method in class de.jstacs.results.ListResult
-
- getValue() -
Method in class de.jstacs.results.SimpleResult
-
- getValue() -
Method in class de.jstacs.results.StorableResult
-
- getValue(Sequence, int) -
Method in interface de.jstacs.sequenceScores.differentiable.logistic.LogisticConstraint
- This method returns the value f(seq) used in
LogisticDiffSS
- getValue(Sequence, int) -
Method in class de.jstacs.sequenceScores.differentiable.logistic.ProductConstraint
-
- getValue() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter
- Returns the current value of this parameter.
- getValue() -
Method in class de.jstacs.utils.galaxy.GalaxyAdaptor.FileResult
-
- getValueFor(String) -
Method in class de.jstacs.parameters.MultiSelectionParameter
- Returns the value for the option with key
key
.
- getValueFor(int) -
Method in class de.jstacs.parameters.MultiSelectionParameter
- Returns the value of the option with no.
- getValueFromTag(String) -
Method in class de.jstacs.parameters.ParameterSetTagger
- This method returns the value of the
Parameter
specified by the tag
.
- getValueFromTag(String, Class<T>) -
Method in class de.jstacs.parameters.ParameterSetTagger
- This method returns the casted value of the
Parameter
specified by the tag
.
- getValueOfAIC(StatisticalModel, DataSet, int) -
Static method in class de.jstacs.utils.StatisticalModelTester
- This method computes the value of Akaikes Information Criterion (AIC).
- getValueOfBIC(StatisticalModel, DataSet, int) -
Static method in class de.jstacs.utils.StatisticalModelTester
- This method computes the value of the Bayesian Information Criterion
(BIC).
- getValues() -
Method in class de.jstacs.parameters.MultiSelectionParameter
- Returns the values of all selected options as an array.
- getValuesForEachNucleotide(DataSet, int, boolean) -
Method in class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
- This method determines a score for each possible starting position in each of the sequences in
data
that this position is covered by at least one motif occurrence of the
motif with index index
.
- getValuesFromSequence(Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.PositionDiffSM
- This method extracts the values form a sequence.
- getVersionInformation() -
Method in class de.jstacs.utils.REnvironment
- Returns information about the version of R that is used.
- getViterbiPath(int, int, Sequence, SamplingHigherOrderHMM.ViterbiComputation) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM
- This method returns a viterbi path that is the optimum for the choosen ViterbiComputation method
- getViterbiPathFor(int, int, Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
-
- getViterbiPathFor(Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
-
- getViterbiPathFor(int, int, Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
-
- getViterbiPathFor(int, int, Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM
-
- getViterbiPathsFor(DataSet) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
- This method returns the viterbi paths and scores for all sequences of the sample
data
.
- getWeight() -
Method in class de.jstacs.algorithms.graphs.Edge
- Returns the weight of the edge.
- getWeight(int) -
Method in class de.jstacs.data.DataSet.WeightedDataSetFactory
- Returns the weight for the
Sequence
with index
index
.
- getWeight() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.phylo.PhyloNode
- This method return the weight (length, rate ...) for the incoming edge
- getWeight() -
Method in class de.jstacs.utils.ComparableElement
- This method returns the weight of the element.
- getWeights() -
Method in class de.jstacs.data.DataSet.WeightedDataSetFactory
- Returns a copy of the weights for the
DataSet
.
- getWeights() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- This method returns a deep copy of the weights for each component.
- getWriter() -
Method in class de.jstacs.utils.galaxy.GalaxyAdaptor.Protocol
- Returns the
PrintWriter
of this protocol
- getXmlTag() -
Method in class de.jstacs.algorithms.optimization.termination.AbsoluteValueCondition
- Deprecated.
- getXmlTag() -
Method in class de.jstacs.algorithms.optimization.termination.AbstractTerminationCondition
- This method returns the xml tag that is used in the method
AbstractTerminationCondition.toXML()
and
in the constructor AbstractTerminationCondition.AbstractTerminationCondition(StringBuffer)
.
- getXmlTag() -
Method in class de.jstacs.algorithms.optimization.termination.CombinedCondition
-
- getXmlTag() -
Method in class de.jstacs.algorithms.optimization.termination.IterationCondition
-
- getXmlTag() -
Method in class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition
-
- getXmlTag() -
Method in class de.jstacs.algorithms.optimization.termination.SmallGradientConditon
-
- getXmlTag() -
Method in class de.jstacs.algorithms.optimization.termination.SmallStepCondition
-
- getXmlTag() -
Method in class de.jstacs.algorithms.optimization.termination.TimeCondition
-
- getXMLTag() -
Method in class de.jstacs.AnnotatedEntity
- This method returns a tag used as outer tag of the XML description.
- getXMLTag() -
Method in class de.jstacs.classifiers.AbstractClassifier
- Returns the
String
that is used as tag for the XML representation
of the classifier.
- getXMLTag() -
Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier.DoubleTableResult
-
- getXMLTag() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier
-
- getXMLTag() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.msp.MSPClassifier
-
- getXMLTag() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifier
-
- getXMLTag() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
-
- getXMLTag() -
Method in class de.jstacs.classifiers.MappingClassifier
-
- getXMLTag() -
Method in class de.jstacs.classifiers.trainSMBased.TrainSMBasedClassifier
-
- getXMLTag() -
Method in class de.jstacs.parameters.FileParameter
-
- getXMLTag() -
Method in class de.jstacs.parameters.MultiSelectionParameter
-
- getXMLTag() -
Method in class de.jstacs.parameters.ParameterSetContainer
-
- getXMLTag() -
Method in class de.jstacs.parameters.RangeParameter
-
- getXMLTag() -
Method in class de.jstacs.parameters.SelectionParameter
-
- getXMLTag() -
Method in class de.jstacs.parameters.SimpleParameter
-
- getXMLTag() -
Method in class de.jstacs.results.CategoricalResult
-
- getXMLTag() -
Method in class de.jstacs.results.DataSetResult
-
- getXMLTag() -
Method in class de.jstacs.results.ImageResult
-
- getXMLTag() -
Method in class de.jstacs.results.ListResult
-
- getXMLTag() -
Method in class de.jstacs.results.NumericalResult
-
- getXMLTag() -
Method in class de.jstacs.results.StorableResult
-
- getXMLTag() -
Method in class de.jstacs.sampling.AbstractBurnInTest
- This method returns the XML tag that is used in
AbstractBurnInTest.toXML()
and
AbstractBurnInTest.AbstractBurnInTest(StringBuffer)
.
- getXMLTag() -
Method in class de.jstacs.sampling.VarianceRatioBurnInTest
-
- getXMLTag() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTExplainingAwayResidual
-
- getXMLTag() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTMutualInformation
-
- getXMLTag() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov
-
- getXMLTag() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure
- Returns the XML-tag for storing this measure
- getXMLTag() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMExplainingAwayResidual
-
- getXMLTag() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMMutualInformation
-
- getXMLTag() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
- This method returns the XML tag of the instance that is used to build a
XML representation.
- getXMLTag() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint
- Returns the XML tag that is used for the class to en- or decode.
- getXMLTag() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DiscreteGraphicalTrainSM
- Returns the XML tag that is used for this model in
DiscreteGraphicalTrainSM.fromXML(StringBuffer)
and DiscreteGraphicalTrainSM.toXML()
.
- getXMLTag() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousMM
-
- getXMLTag() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM.HomCondProb
-
- getXMLTag() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.BayesianNetworkTrainSM
-
- getXMLTag() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGTrainSM
-
- getXMLTag() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb
-
- getXMLTag() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint
-
- getXMLTag() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
- Returns the tag for the XML representation.
- getXMLTag() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
-
- getXMLTag() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM
-
- getXMLTag() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
- This method returns the xml tag used in
BasicHigherOrderTransition.AbstractTransitionElement.toXML()
.
- getXMLTag() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
- The method returns the XML tag used during saving and loading the transition.
- getXMLTag() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.BasicPluginTransitionElement
-
- getXMLTag() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.DistanceBasedScaledTransitionElement
-
- getXMLTag() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ScaledTransitionElement
-
- getXMLTag() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
-
- getXMLTag() -
Method in class de.jstacs.utils.galaxy.GalaxyAdaptor.FileResult
-
- getXMLTag() -
Method in class de.jstacs.utils.galaxy.GalaxyAdaptor.LinkedImageResult
-
- gibbsSampling(int, int, double, Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM
- This method implements a sampling step in the sampling procedure
- GibbsSamplingModel - Interface in de.jstacs.sampling
- This is the interface that any
AbstractTrainableStatisticalModel
has to implement if it
should be used in a sampling. - gibbsSamplingStep(int, int, boolean, DataSet, double[]) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM
- This method implements the next step(s) in the sampling procedure
- goldenRatio(OneDimensionalFunction, double, double, double, double, double) -
Static method in class de.jstacs.algorithms.optimization.Optimizer
- Approximates a minimum (not necessary the global) in the interval
[lower,upper]
.
- goldenRatio(OneDimensionalFunction, double, double, double) -
Static method in class de.jstacs.algorithms.optimization.Optimizer
- Approximates a minimum (not necessary the global) in the interval
[lower,upper]
.
- grad -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
- The array for storing the gradients for
each parameter
- gradient -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
- Help array for the gradient
- GT -
Static variable in interface de.jstacs.parameters.validation.Constraint
- The condition is greater than
- GUIProgressUpdater - Class in de.jstacs.utils
- This class implements a
ProgressUpdater
with a GUI. - GUIProgressUpdater(boolean) -
Constructor for class de.jstacs.utils.GUIProgressUpdater
- This is the constructor for a
GUIProgressUpdater
.
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