- Galaxy - Class in de.jstacs.tools.ui.galaxy
-
Class for generating a generic Galaxy interface for a set of
JstacsTool
s.
- Galaxy(String, boolean, JstacsTool...) - Constructor for class de.jstacs.tools.ui.galaxy.Galaxy
-
Creates a new Galaxy interface from a set of
JstacsTool
s.
- GalaxyAdaptor - Class in de.jstacs.tools.ui.galaxy
-
Adaptor class between the parameter representation of Jstacs in
Parameter
s and
ParameterSet
s and the parameter representation
in
Galaxy.
- GalaxyAdaptor(ParameterSet, JstacsTool.ResultEntry[], boolean[], String, String, String, String, String) - Constructor for class de.jstacs.tools.ui.galaxy.GalaxyAdaptor
-
Creates a new
GalaxyAdaptor
from a given
ParameterSet
containing all parameters
that are necessary for a program is shall be included in a Galaxy installation.
- GalaxyAdaptor.FileResult - Class in de.jstacs.tools.ui.galaxy
-
Result
for files that are results of some computation.
- GalaxyAdaptor.HeadResult - Class in de.jstacs.tools.ui.galaxy
-
Class for a result that is basically a
CategoricalResult
,
but has its own name for checking purposes.
- GalaxyAdaptor.LineBasedResult - Class in de.jstacs.tools.ui.galaxy
-
Superclass for all
Result
that may be saved line by line.
- GalaxyAdaptor.LinkedImageResult - Class in de.jstacs.tools.ui.galaxy
-
Class for an
ImageResult
that is linked to a file that can be downloaded.
- GalaxyAdaptor.Protocol - Class in de.jstacs.tools.ui.galaxy
-
Class for a Protocol writer.
- GalaxyConvertible - Interface in de.jstacs.parameters
-
Interface for
Parameter
s that can be converted to and extracted from
Galaxy representations.
- gammas - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.MultivariateGaussianEmission
-
Contains the emission sequences and corresponding gammas (state-posteriors) required for the estimation of the standard deviation.
- GaussianEmission - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous
-
Emission for continuous values following a Gaussian distribution.
- GaussianEmission(AlphabetContainer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
-
- GaussianEmission(AlphabetContainer, double, double, double, double, boolean) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
-
Creates a
GaussianEmission
with normal-gamma prior by directly defining the hyper-parameters of the prior.
- GaussianEmission(double, AlphabetContainer, double, double, double, boolean) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
-
Creates a
GaussianEmission
with normal-gamma prior by defining the expected precision and the expected standard deviation of the precision, i.e.
- GaussianEmission(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
-
- GaussianLikePositionPrior - Class in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior
-
This class implements a gaussian like discrete truncated prior.
- GaussianLikePositionPrior(int, double, double) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.GaussianLikePositionPrior
-
This constructor creates an instance with given sequence length, maximal
value and sigma for the Gaussian.
- GaussianLikePositionPrior(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.GaussianLikePositionPrior
-
The standard constructor for the interface
Storable
.
- GenDisMixClassifier - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix
-
This class implements a classifier the optimizes the following function
The weights
have to sum to 1.
- GenDisMixClassifier(GenDisMixClassifierParameterSet, LogPrior, double, double[], DifferentiableSequenceScore...) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier
-
This constructor creates an instance and sets the value of the last (external) optimization.
- GenDisMixClassifier(GenDisMixClassifierParameterSet, LogPrior, double, double[], DifferentiableStatisticalModel...) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier
-
This constructor creates an instance and sets the value of the last (external) optimization.
- GenDisMixClassifier(GenDisMixClassifierParameterSet, LogPrior, double[], DifferentiableStatisticalModel...) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier
-
The main constructor.
- GenDisMixClassifier(GenDisMixClassifierParameterSet, LogPrior, double, double, double, DifferentiableStatisticalModel...) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier
-
This convenience constructor agglomerates the genBeta, disBeta,
and priorBeta
into an array and calls the main constructor.
- GenDisMixClassifier(GenDisMixClassifierParameterSet, LogPrior, LearningPrinciple, DifferentiableStatisticalModel...) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier
-
This convenience constructor creates an array of weights for an elementary learning principle and calls the main constructor.
- GenDisMixClassifier(StringBuffer) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier
-
- GenDisMixClassifierParameterSet - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix
-
- GenDisMixClassifierParameterSet(StringBuffer) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifierParameterSet
-
The standard constructor for the interface
Storable
.
- GenDisMixClassifierParameterSet(AlphabetContainer, int, byte, double, double, double, boolean, OptimizableFunction.KindOfParameter, boolean, int) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifierParameterSet
-
- GenDisMixClassifierParameterSet(Class<? extends ScoreClassifier>, AlphabetContainer, int, byte, double, double, double, boolean, OptimizableFunction.KindOfParameter, boolean, int) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifierParameterSet
-
- generate(DiscreteAlphabet, int) - Static method in class de.jstacs.data.DeBruijnGraphSequenceGenerator
-
Generates a De Bruijn sequence of length
, where A denotes the alphabet.
- generate(DiscreteAlphabet, int, int) - Static method in class de.jstacs.data.DeBruijnGraphSequenceGenerator
-
Generates a De Bruijn sequence using the supplied alphabet and the given alphabet shift, i.e., for a cyclic shift of the symbols
of the alphabet.
- generate(DiscreteAlphabet, int) - Static method in class de.jstacs.data.DeBruijnSequenceGenerator
-
Generates a De Bruijn sequence of length
, where A denotes the alphabet.
- generate(DiscreteAlphabet, int, int) - Static method in class de.jstacs.data.DeBruijnSequenceGenerator
-
Generates a De Bruijn sequence using the supplied alphabet and the given alphabet shift, i.e., for a cyclic shift of the symbols
of the alphabet.
- generate(double[], int, int, MRGParams) - Method in class de.jstacs.utils.random.DirichletMRG
-
- generate(double[], int, int, MRGParams) - Method in class de.jstacs.utils.random.EqualParts
-
- generate(int) - Method in class de.jstacs.utils.random.EqualParts
-
Returns an array of length n
with entries 1/n
.
- generate(double[], int, int) - Method in class de.jstacs.utils.random.EqualParts
-
Modifies a given number
of array entries to
1/number
.
- generate(double[], int, int, MRGParams) - Method in class de.jstacs.utils.random.ErlangMRG
-
- generate(int, MRGParams) - Method in class de.jstacs.utils.random.MultivariateRandomGenerator
-
Generates a n
-dimensional random array.
- generate(double[], int, int, MRGParams) - Method in class de.jstacs.utils.random.MultivariateRandomGenerator
-
Generates a n
-dimensional random array as part of the array
d
beginning at start
.
- generate(double[], int, int, MRGParams) - Method in class de.jstacs.utils.random.SoftOneOfN
-
- generate(int) - Method in class de.jstacs.utils.random.SoftOneOfN
-
Generates an array of length number
with one entry getting
the value 1-epsilon
and all the others equal parts of
epsilon
.
- generate(double[], int, int) - Method in class de.jstacs.utils.random.SoftOneOfN
-
Generates an array of length 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
.
- generateLog(double[], int, int, MRGParams) - Method in class de.jstacs.utils.random.DirichletMRG
-
Fills a part of the array d
beginning at start
with n
logarithmic values.
- generateOutput(File) - Method in class de.jstacs.utils.graphics.EPSAdaptor
-
- generateOutput(String) - Method in class de.jstacs.utils.graphics.GraphicsAdaptor
-
Generates the outputs and saves the results to a file with the supplied name.
- generateOutput(File) - Method in class de.jstacs.utils.graphics.GraphicsAdaptor
-
Generates the outputs and saves the results to the supplied file.
- generateOutput(File) - Method in class de.jstacs.utils.graphics.PDFAdaptor
-
- generateOutput(File) - Method in class de.jstacs.utils.graphics.RasterizedAdaptor
-
- generateOutput(File) - Method in class de.jstacs.utils.graphics.SVGAdaptor
-
- generatePlot(GraphicsAdaptor) - Method in interface de.jstacs.results.PlotGeneratorResult.PlotGenerator
-
- generatePlot(GraphicsAdaptor) - Method in class de.jstacs.utils.SeqLogoPlotter.SeqLogoPlotGenerator
-
- GenericComplementableDiscreteAlphabet - Class in de.jstacs.data.alphabets
-
This class implements an generic complementable discrete alphabet.
- GenericComplementableDiscreteAlphabet(StringBuffer) - Constructor for class de.jstacs.data.alphabets.GenericComplementableDiscreteAlphabet
-
The standard constructor for the interface
Storable
.
- GenericComplementableDiscreteAlphabet(GenericComplementableDiscreteAlphabet.GenericComplementableDiscreteAlphabetParameterSet) - Constructor for class de.jstacs.data.alphabets.GenericComplementableDiscreteAlphabet
-
- GenericComplementableDiscreteAlphabet(boolean, String[], int[]) - Constructor for class de.jstacs.data.alphabets.GenericComplementableDiscreteAlphabet
-
The main constructor.
- GenericComplementableDiscreteAlphabet.GenericComplementableDiscreteAlphabetParameterSet - Class in de.jstacs.data.alphabets
-
- GenericComplementableDiscreteAlphabetParameterSet() - Constructor for class de.jstacs.data.alphabets.GenericComplementableDiscreteAlphabet.GenericComplementableDiscreteAlphabetParameterSet
-
- GenericComplementableDiscreteAlphabetParameterSet(String[], boolean, int[]) - Constructor for class de.jstacs.data.alphabets.GenericComplementableDiscreteAlphabet.GenericComplementableDiscreteAlphabetParameterSet
-
The main constructor.
- GenericComplementableDiscreteAlphabetParameterSet(StringBuffer) - Constructor for class de.jstacs.data.alphabets.GenericComplementableDiscreteAlphabet.GenericComplementableDiscreteAlphabetParameterSet
-
The standard constructor for the interface
Storable
.
- GEQ - Static variable in interface de.jstacs.parameters.validation.Constraint
-
The condition is greater or equal
- get(int) - Method in class de.jstacs.AnnotatedEntityList
-
- get(String) - Method in class de.jstacs.AnnotatedEntityList
-
- get(int) - Method in class de.jstacs.utils.DoubleList
-
Returns the element with the specified index
.
- get(int) - Method in class de.jstacs.utils.IntList
-
Returns the element with the specified index
.
- getAbsoluteKMereFrequencies(DataSet, int, boolean) - Static method in class de.jstacs.motifDiscovery.KMereStatistic
-
This method enables the user to get a statistic over all k
-mers
in the data
.
- getAbsoluteKMereFrequencies(DataSet, int, boolean, DataSet.WeightedDataSetFactory.SortOperation) - Static method in class de.jstacs.motifDiscovery.KMereStatistic
-
This method enables the user to get a statistic over all k
-mers
in the data
.
- getAcceptedMimeType() - Method in class de.jstacs.parameters.FileParameter
-
Returns the type(s) of the allowed files as list of file extensions, separated by commas
- getAdaptor(GraphicsAdaptorFactory.OutputFormat) - Static method in class de.jstacs.utils.graphics.GraphicsAdaptorFactory
-
- getAdaptVariance() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifierParameterSet
-
Returns true if the sampling variance shall be adapted to the size
of the event space of a random variable
- getAdditionalInformation() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.GaussianLikePositionPrior
-
- getAdditionalInformation() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.PositionPrior
-
This method returns a
StringBuffer
containing additional
information for the XML representation.
- getAdditionalInformation() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.UniformPositionPrior
-
- getAlgorithm() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.NumericalHMMTrainingParameterSet
-
This method returns a byte encoding for the algorithm that should be used for optimization.
- getAlignedString(int) - Method in class de.jstacs.algorithms.alignment.StringAlignment
-
Returns the aligned
String
with index
index
.
- getAlignment(Alignment.AlignmentType, Sequence, Sequence) - Method in class de.jstacs.algorithms.alignment.Alignment
-
- getAlignment(Alignment.AlignmentType, Sequence, int, int, Sequence, int, int) - Method in class de.jstacs.algorithms.alignment.Alignment
-
- getAlignment(int[]) - Method in class de.jstacs.algorithms.alignment.Alignment
-
Returns the optimal alignment (backtrace) according to matrix index[0]
until positions index[1]
and
index[2]
in the first and second sequence, respectively.
- getAllConditionalStationaryDistributions() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
-
This method returns the stationary conditional distributions.
- getAllConditionalStationaryDistributions(double[], int) - Static method in class de.jstacs.utils.StationaryDistribution
-
This method returns the conditional stationary distributions, i.e.
- getAllElements() - Method in class de.jstacs.data.DataSet
-
- getAllLeafs() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.phylo.PhyloNode
-
This method returns a list of
PhyloNode
s that are leafs in the subtree starting from this instance
- getAllLeafs() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.phylo.PhyloTree
-
This method returns a list of
PhyloNode
s that represent the leafs of the tree
- getAllMeasures() - Method in class de.jstacs.classifiers.performanceMeasures.AbstractPerformanceMeasureParameterSet
-
Returns an array of all contained performance measures.
- getAllowedNumberOfClasses() - Method in class de.jstacs.classifiers.performanceMeasures.AbstractTwoClassPerformanceMeasure
-
- getAllowedNumberOfClasses() - Method in class de.jstacs.classifiers.performanceMeasures.ClassificationRate
-
- getAllowedNumberOfClasses() - Method in class de.jstacs.classifiers.performanceMeasures.ConfusionMatrix
-
- getAllowedNumberOfClasses() - Method in interface de.jstacs.classifiers.performanceMeasures.PerformanceMeasure
-
This method returns the allowed number of classes.
- getAllParameterNames() - Method in class de.jstacs.parameters.ParameterSet
-
- getAllValues(int) - Method in class de.jstacs.results.MeanResultSet
-
Returns all values of the result with index index
if available otherwise null
.
- getAlphabetAt(int) - Method in class de.jstacs.data.AlphabetContainer
-
Returns the underlying
Alphabet
of position
pos
.
- getAlphabetContainer() - Method in class de.jstacs.classifiers.AbstractClassifier
-
This method returns the container of alphabets that is used in the
classifier.
- getAlphabetContainer() - Method in class de.jstacs.data.DataSet
-
- getAlphabetContainer() - Method in class de.jstacs.data.sequences.Sequence
-
Return the alphabets, i.e.
- getAlphabetContainer() - Method in class de.jstacs.parameters.SequenceScoringParameterSet
-
- getAlphabetContainer() - Method in class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
-
- getAlphabetContainer() - Method in interface de.jstacs.sequenceScores.SequenceScore
-
Returns the container of alphabets that were used when constructing the instance.
- getAlphabetContainer() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
-
- getAlphabetContainer() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.StructureLearner
-
- getAlphabetContainer() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
-
- getAlphabetContainer() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.MultivariateGaussianEmission
-
- getAlphabetContainer() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
-
- getAlphabetContainer() - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.Emission
-
- getAlphabetContainer() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.MixtureEmission
-
- getAlphabetContainer() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
-
- getAlphabetContainer() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.UniformEmission
-
- getAlphabetIndexForPosition(int) - Method in class de.jstacs.data.AlphabetContainer
-
This method returns the index of the
Alphabet
that is used for the given position.
- getAlphabetLengthAt(int) - Method in class de.jstacs.data.AlphabetContainer
-
Returns the length of the underlying
Alphabet
of position
pos
.
- getAncestorProbabilities(int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
Returns the probabilities that the preceding positions considered are used as context.
- getAnnotation() - Method in class de.jstacs.classifiers.assessment.ClassifierAssessmentAssessParameterSet
-
- getAnnotation() - Method in class de.jstacs.classifiers.assessment.KFoldCrossValidationAssessParameterSet
-
- getAnnotation() - Method in class de.jstacs.classifiers.assessment.RepeatedHoldOutAssessParameterSet
-
- getAnnotation() - Method in class de.jstacs.classifiers.assessment.RepeatedSubSamplingAssessParameterSet
-
- getAnnotation() - Method in class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutAssessParameterSet
-
- getAnnotation(DataSet...) - Static method in class de.jstacs.data.DataSet
-
Returns the annotation for an array of
DataSet
s.
- getAnnotation() - Method in class de.jstacs.data.DataSet
-
Returns some annotation of the
DataSet
.
- getAnnotation() - Method in class de.jstacs.data.sequences.Sequence
-
- getAnnotation() - Method in class de.jstacs.io.AbstractStringExtractor
-
Returns the annotation of the source.
- getAnnotation() - Method in class de.jstacs.results.ListResult
-
Returns a reference to the annotation of this
ListResult
.
- getAnnotationResult() - Method in class de.jstacs.algorithms.alignment.StringAlignment
-
Returns the annotation result.
- getAnnotations() - Method in class de.jstacs.data.sequences.annotation.SequenceAnnotation
-
Returns the additional annotations of this
SequenceAnnotation
as
given in the constructor.
- getAnnotations() - Method in class de.jstacs.data.sequences.MultiDimensionalSequence
-
This method returns the
SequenceAnnotation
[] for each dimension of this multidimensional sequence.
- getAnnotationTypesAndIdentifier() - Method in class de.jstacs.data.DataSet
-
- getAPrioriMixtureProbabilities() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
-
Returns the mixture probabilities (i.e., the a-priori probabilities of the different components).
- getArrowOption(NumberFormat, double, double, String, boolean) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
-
This method returns the option for an edge in Graphviz.
- getAssessParameterSet() - Method in class de.jstacs.classifiers.assessment.ClassifierAssessment
-
- getAssessParameterSet() - Method in class de.jstacs.classifiers.assessment.KFoldCrossValidation
-
- getAssessParameterSet() - Method in class de.jstacs.classifiers.assessment.RepeatedHoldOutExperiment
-
- getAssessParameterSet() - Method in class de.jstacs.classifiers.assessment.RepeatedSubSamplingExperiment
-
- getAssessParameterSet() - Method in class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutExperiment
-
- getAsymIndex(int, int[], byte) - Method in class de.jstacs.algorithms.graphs.tensor.Tensor
-
Returns the index for an asymmetric tensor.
- getAverageElementLength() - Method in class de.jstacs.data.DataSet
-
- getBest(int, int[], byte) - Method in class de.jstacs.algorithms.graphs.tensor.SymmetricTensor
-
Returns the maximal weight which can be reached for an edge from
k
nodes from the (encoded) set par
to the node
child
.
- getBestParameters() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
-
Returns the sampled parameter values with the maximum value of the objective function
- getBeta(LearningPrinciple) - Static method in enum de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.LearningPrinciple
-
This method returns the standard weights for a predefined key.
- getBindingSites(DataSet, int) - Method in class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
-
This method returns a
DataSet
containing the predicted binding sites.
- getBindingSites(int, DataSet, int, int, int, int) - Method in class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
-
This method returns a
DataSet
containing the predicted binding sites.
- getBooleanFromParameter(Parameter) - Static method in class de.jstacs.io.ParameterSetParser
-
Returns the
boolean
which is the value of the
Parameter
par
.
- getBorder(double[], double) - Static method in class de.jstacs.classifiers.utils.PValueComputation
-
This method finds the first index that has a significant score.
- getBufferedImageAndGraphics(int, double[][]) - Static method in class de.jstacs.utils.SeqLogoPlotter
-
- getBurnInTest() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.SamplingHMMTrainingParameterSet
-
This method return the burn in test to be used during sampling.
- getByteFromParameter(Parameter) - Static method in class de.jstacs.io.ParameterSetParser
-
Returns the
byte
which is the value of the
Parameter
par
.
- getCharacteristics() - Method in class de.jstacs.classifiers.AbstractClassifier
-
Returns some information characterizing or describing the current
instance of the classifier.
- getCharacteristics() - Method in class de.jstacs.classifiers.trainSMBased.TrainSMBasedClassifier
-
- getCharacteristics() - Method in class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
-
- getCharacteristics() - Method in interface de.jstacs.sequenceScores.SequenceScore
-
Returns some information characterizing or describing the current
instance.
- getCharacteristics() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
-
- getCharacteristics() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.CompositeTrainSM
-
- getCharacteristics() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
-
- getCharacteristics() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
-
- getChild(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
-
This method returns the state index encoded by the child index.
- getChildIdx(int, int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
-
- getChildIdx(int, int, int) - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.Transition
-
This method returns the child index of the state, if this state is no child of the context -1 is returned
- getChildrenNodes() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.phylo.PhyloNode
-
This method returns a list of
PhyloNode
s that are children of this instance
- getClassifier() - Method in class de.jstacs.classifiers.assessment.ClassifierAssessment
-
Returns a deep copy of all classifiers that have been or will be used in
this assessment.
- getClassifierAnnotation() - Method in class de.jstacs.classifiers.AbstractClassifier
-
Returns an array of
Result
s of dimension
AbstractClassifier.getNumberOfClasses()
that contains information about the
classifier and for each class.
res[0] = new CategoricalResult( "classifier", "the kind of classifier", getInstanceName() );
res[1] = new CategoricalResult( "class info 0", "some information about the class", "info0" );
res[2] = new CategoricalResult( "class info 1", "some information about the class", "info1" );
...
- getClassifierAnnotation() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
-
- getClassifierAnnotation() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
-
- getClassifierAnnotation() - Method in class de.jstacs.classifiers.MappingClassifier
-
- getClassifierAnnotation() - Method in class de.jstacs.classifiers.trainSMBased.TrainSMBasedClassifier
-
- getClassifierAnnotation() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureClassifier
-
- getClassifierForBestParameters(GenDisMixClassifierParameterSet) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifier
-
Returns a standard, i.e., non-sampling,
GenDisMixClassifier
, where the parameters
are set to those that yielded the maximum value of the objective functions among all sampled
parameter values.
- getClassifierForMeanParameters(GenDisMixClassifierParameterSet, boolean, int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifier
-
Returns a standard, i.e., non-sampling,
GenDisMixClassifier
, where the parameters
are set to the mean values over all sampled
parameter values in the stationary phase.
- getClassName() - Method in class de.jstacs.results.StorableResult
-
Returns the name of the class of the
Storable
corresponding to
the XML representation stored in this
StorableResult
.
- getClassParams(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.DiffSSBasedOptimizableFunction
-
Returns from the complete vector of parameters those that are for the
classes.
- getClassWeight(int) - Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
-
Returns the class weight for the class with a given index
.
- getClassWeights() - Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
-
- getClazz() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTMutualInformation.BTMutualInformationParameterSet
-
Returns the source of the data to compute the mutual information as
defined by this set of parameters.
- getClazz() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMMutualInformation.PMMMutualInformationParameterSet
-
Returns the source of the data to compute the mutual information as
defined by this set of parameters.
- getClusterElements() - Method in class de.jstacs.clustering.hierachical.ClusterTree
-
Returns the elements at all leaves in this cluster tree, in the order
of the leaves, from left to right.
- getClusterRepresentative(ClusterTree<StatisticalModel>, int) - Static method in class de.jstacs.clustering.distances.DeBruijnMotifComparison
-
Returns a position weight matrix (PWM) representation of the root node of the given cluster tree and
also computed the relative shifts of the motifs such that they align best with the consensus motif at the root.
- getCMI(double[][][][][][], double[][][][][][], double) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure
-
Computes the conditional mutual information from fgStats
and
bgStats
counted on sequences with a total weight of
n
.
- getCMI(double[][][][], double[][][][], double, double, double) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure
-
Computes the conditional mutual information from fgStats
and
bgStats
counted on sequences with a total weight of
nFg
and nBg
, respectively.
- getCode(int, String) - Method in class de.jstacs.data.AlphabetContainer
-
- getCode(String) - Method in class de.jstacs.data.alphabets.DiscreteAlphabet
-
Returns the code of a given symbol.
- getCode(String) - Method in class de.jstacs.data.alphabets.DNAAlphabet
-
- getCollectionOfAllMeasures(int, boolean) - Static method in class de.jstacs.classifiers.performanceMeasures.AbstractPerformanceMeasure
-
- getCollectionOfScales() - Static method in class de.jstacs.parameters.RangeParameter
-
Returns a
EnumParameter
that allows the user to choose
between different scales.
- getColor(int) - Static method in class de.jstacs.tools.ui.galaxy.GalaxyAdaptor
-
Returns the color for a specified depth within the parameter hierarchy.
- getColumnWidth(int) - Static method in class de.jstacs.utils.SeqLogoPlotter
-
Returns the width of one column in the sequence logo of the given height for a PWM with the given number of columns.
- getCombination() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.CombinationIterator
-
Returns a clone of the internal combination.
- getComment() - Method in class de.jstacs.AnnotatedEntity
-
- getComment(Class<? extends ParameterSet>) - Static method in class de.jstacs.parameters.ParameterSet
-
Returns a comment for the class.
- getComment(ParameterSet) - Static method in class de.jstacs.parameters.ParameterSet
-
- getComments() - Method in enum de.jstacs.data.DinucleotideProperty
-
Returns additional comments on this property.
- getCommonString(DataSet, int, boolean) - Static method in class de.jstacs.motifDiscovery.KMereStatistic
-
This method returns an array of sequences of length
motifLength
so that each string is contained in all
sequences of the data set, more precisely in the data set or the reverse
complementary data set.
- getComplementaryCode(int) - Method in class de.jstacs.data.alphabets.ComplementableDiscreteAlphabet
-
This method returns the code of the symbol that is the complement of the
symbol encoded by code
.
- getComplementaryCode(int) - Method in class de.jstacs.data.alphabets.DNAAlphabet
-
- getComplementaryCode(int) - Method in class de.jstacs.data.alphabets.GenericComplementableDiscreteAlphabet
-
- getComplementaryCode(int) - Method in class de.jstacs.data.alphabets.IUPACDNAAlphabet
-
- getComponents() - Method in class de.jstacs.algorithms.graphs.UnionFind
-
Returns the connected components of the graph.
- getComponentScores(Sequence, int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
-
Return the scores for the individual components.
- getCompositeContainer(int[], int[]) - Method in class de.jstacs.data.AlphabetContainer
-
- getCompositeDataSet(int[], int[]) - Method in class de.jstacs.data.DataSet
-
This method enables you to use only composite
Sequence
s of all
elements in the current
DataSet
.
- getCompositeSequence(AlphabetContainer, int[], int[]) - Method in class de.jstacs.data.sequences.Sequence
-
- getCompositeSequence(int[], int[]) - Method in class de.jstacs.data.sequences.Sequence
-
- getConditionalProbabilities(int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
Returns the conditional probabilities for the specified component.
- getConditionIndex(boolean, int, Sequence) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
-
This method returns an index encoding the condition.
- getConditionIndex(boolean, int, Sequence) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.DiscreteEmission
-
- getConditionIndex(boolean, int, Sequence) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.ReferenceSequenceDiscreteEmission
-
- getConsensus(AlphabetContainer, double[][]) - Static method in class de.jstacs.utils.PFMComparator
-
This method extracts the
The method does not use any degenerated IUPAC code.
- getConservedPatterns(Hashtable<Sequence, BitSet[]>, int, int) - Static method in class de.jstacs.motifDiscovery.KMereStatistic
-
This method returns a list of
Sequence
s.
- getContent() - Method in class de.jstacs.parameters.FileParameter.FileRepresentation
-
Returns the content of the file.
- getContext(String[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
-
This method returns a
String
representation of the context.
- getCorrectedPosition(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint
-
Returns the value of the corrected position index
.
- getCost(int, int) - Method in class de.jstacs.algorithms.alignment.Alignment
-
- getCost() - Method in class de.jstacs.algorithms.alignment.StringAlignment
-
Returns the costs.
- getCostFor(Sequence, Sequence, int, int) - Method in class de.jstacs.algorithms.alignment.cost.AffineCosts
-
- getCostFor(Sequence, Sequence, int, int) - Method in interface de.jstacs.algorithms.alignment.cost.Costs
-
Returns the costs for the alignment of s1(i)
and
s2(j)
.
- getCostFor(Sequence, Sequence, int, int) - Method in class de.jstacs.algorithms.alignment.cost.MatrixCosts
-
- getCostFor(Sequence, Sequence, int, int) - Method in class de.jstacs.algorithms.alignment.cost.SimpleCosts
-
- getCount(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint
-
Returns the current count with index index
.
- getCounts() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter
-
Returns the current counts for this parameter.
- getCounts(DataSet...) - Static method in class de.jstacs.utils.PFMComparator
-
This method counts the occurrences of symbols in the given data sets.
- getCum_Complex(int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
- getCum_Naive(int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
- getCurrentAnnotation() - Method in class de.jstacs.data.sequences.annotation.NullSequenceAnnotationParser
-
- getCurrentAnnotation() - Method in interface de.jstacs.data.sequences.annotation.SequenceAnnotationParser
-
- getCurrentAnnotation() - Method in class de.jstacs.data.sequences.annotation.SimpleSequenceAnnotationParser
-
- getCurrentAnnotation() - Method in class de.jstacs.data.sequences.annotation.SplitSequenceAnnotationParser
-
- getCurrentParameterSet() - Method in class de.jstacs.algorithms.optimization.termination.AbstractTerminationCondition
-
- getCurrentParameterSet() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
-
This method returns the current
ParameterSet
of the classifier.
- getCurrentParameterSet() - Method in class de.jstacs.data.AlphabetContainer
-
- getCurrentParameterSet() - Method in class de.jstacs.data.alphabets.Alphabet
-
- getCurrentParameterSet() - Method in class de.jstacs.data.alphabets.ContinuousAlphabet
-
- getCurrentParameterSet() - Method in class de.jstacs.data.alphabets.DiscreteAlphabet
-
- getCurrentParameterSet() - Method in class de.jstacs.data.alphabets.GenericComplementableDiscreteAlphabet
-
- getCurrentParameterSet() - Method in interface de.jstacs.InstantiableFromParameterSet
-
Returns the
InstanceParameterSet
that has been used to
instantiate the current instance of the implementing class.
- getCurrentParameterSet() - Method in class de.jstacs.sampling.AbstractBurnInTest
-
- getCurrentParameterSet() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
-
- getCurrentParameterSet() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure
-
- getCurrentParameterSet() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DiscreteGraphicalTrainSM
-
- getCurrentParameterValues() - Method in interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore
-
- getCurrentParameterValues() - Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
-
- getCurrentParameterValues() - Method in class de.jstacs.sequenceScores.differentiable.logistic.LogisticDiffSS
-
- getCurrentParameterValues() - Method in class de.jstacs.sequenceScores.differentiable.MultiDimensionalSequenceWrapperDiffSS
-
- getCurrentParameterValues() - Method in class de.jstacs.sequenceScores.differentiable.UniformDiffSS
-
- getCurrentParameterValues() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
-
- getCurrentParameterValues() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
-
- getCurrentParameterValues() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMM0DiffSM
-
- getCurrentParameterValues() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
-
- getCurrentParameterValues() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.UniformHomogeneousDiffSM
-
- getCurrentParameterValues() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
- getCurrentParameterValues() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
-
- getCurrentParameterValues() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MarkovRandomFieldDiffSM
-
- getCurrentParameterValues() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
-
- getCurrentParameterValues() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.MixtureDurationDiffSM
-
- getCurrentParameterValues() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
-
- getCurrentParameterValues() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.UniformDurationDiffSM
-
- getCurrentParameterValues() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
-
- getCurrentParameterValues() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
-
- getCurrentSequenceAnnotations() - Method in class de.jstacs.io.AbstractStringExtractor
-
- getCurrentSequenceAnnotations() - Method in class de.jstacs.io.InfixStringExtractor
-
- getCurrentSequenceAnnotations() - Method in class de.jstacs.io.LimitedStringExtractor
-
- getCurrentSequenceAnnotations() - Method in class de.jstacs.io.SparseStringExtractor
-
- getData() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractOptimizableFunction
-
- getData() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.OneDataSetLogGenDisMixFunction
-
- getData() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.OptimizableFunction
-
- getDataRef() - Method in class de.jstacs.tools.DataColumnParameter
-
Returns the ID of the referenced parameter (tabular) in Galaxy.
- getDataSet() - Method in class de.jstacs.data.DataSet.WeightedDataSetFactory
-
- getDataSet(AlphabetContainer, String, SequenceAnnotationParser) - Static method in class de.jstacs.data.sequences.ArbitraryFloatSequence
-
- getDataSet(AlphabetContainer, String) - Static method in class de.jstacs.data.sequences.ArbitraryFloatSequence
-
- getDataSet(AlphabetContainer, AbstractStringExtractor...) - Static method in class de.jstacs.data.sequences.ArbitraryFloatSequence
-
- getDataSet(AlphabetContainer, String, SequenceAnnotationParser) - Static method in class de.jstacs.data.sequences.SparseSequence
-
- getDataSet(AlphabetContainer, String) - Static method in class de.jstacs.data.sequences.SparseSequence
-
- getDataSet(AlphabetContainer, AbstractStringExtractor...) - Static method in class de.jstacs.data.sequences.SparseSequence
-
- getDataSetForProperty(DataSet, DinucleotideProperty) - Static method in enum de.jstacs.data.DinucleotideProperty
-
- getDataSetForProperty(DataSet, DinucleotideProperty.Smoothing, boolean, DinucleotideProperty) - Static method in enum de.jstacs.data.DinucleotideProperty
-
- getDataSetForProperty(DataSet, DinucleotideProperty...) - Static method in enum de.jstacs.data.DinucleotideProperty
-
- getDataSetForProperty(DataSet, DinucleotideProperty.Smoothing, boolean, DinucleotideProperty...) - Static method in enum de.jstacs.data.DinucleotideProperty
-
- getDataSplitMethod() - Method in class de.jstacs.classifiers.assessment.KFoldCrossValidationAssessParameterSet
-
Returns the
DataSet.PartitionMethod
defining how the mutually exclusive
random-splits of user supplied data are generated.
- getDataSplitMethod() - Method in class de.jstacs.classifiers.assessment.RepeatedHoldOutAssessParameterSet
-
Returns the
DataSet.PartitionMethod
defining how the mutually exclusive
random-splits of user supplied data are generated.
- getDataSplitMethod() - Method in class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutAssessParameterSet
-
Returns the
DataSet.PartitionMethod
defining how the mutually exclusive
random-splits of user supplied data are generated.
- getDatatype() - Method in class de.jstacs.AnnotatedEntity
-
- getDeclaredClass() - Method in class de.jstacs.tools.JstacsTool.ResultEntry
-
Returns the class declared for the default result.
- getDefault() - Method in class de.jstacs.parameters.SelectionParameter
-
Returns the index of the default selected value.
- getDefaultExtension(Class<? extends Result>) - Static method in class de.jstacs.tools.ui.galaxy.GalaxyAdaptor
-
Returns the default extension (Galaxy format) for a given result class.
- getDefaultResultInfos() - Method in interface de.jstacs.tools.JstacsTool
-
- getDeleteCosts() - Method in class de.jstacs.algorithms.alignment.cost.AffineCosts
-
- getDeleteCosts() - Method in interface de.jstacs.algorithms.alignment.cost.Costs
-
Returns the costs for a delete gap, i.e., a gap in the second string.
- getDeleteCosts() - Method in class de.jstacs.algorithms.alignment.cost.MatrixCosts
-
- getDeleteCosts() - Method in class de.jstacs.algorithms.alignment.cost.SimpleCosts
-
- getDeleteCostsFor(int) - Method in class de.jstacs.algorithms.alignment.cost.AffineCosts
-
Returns the costs for a delete gap of length length
.
- getDeleteOnExit() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
-
Returns true
if the temporary parameter files shall
be deleted on exit of the program.
- getDelim() - Method in class de.jstacs.data.AlphabetContainer
-
Returns the delimiter that should be used (for writing e.g.
- getDepth() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter
-
Returns the depth of the tree, i.e.
- getDescendant(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
-
This method returns the index of the descendant transition element when following the child with index index
- getDescription() - Method in class de.jstacs.io.RegExFilenameFilter
-
- getDescription(AlphabetContainer, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint
-
Returns the decoded symbol for the encoded symbol i
.
- getDescription() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DiscreteGraphicalTrainSM
-
Returns a short description of the model that was given by the user in
the parameter set.
- getDescription(AlphabetContainer, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM.HomCondProb
-
- getDescription(AlphabetContainer, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb
-
- getDescription(AlphabetContainer, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhConstraint
-
- getDescription() - Method in interface de.jstacs.tools.JstacsTool
-
Returns a short description (half a sentence) on what this tool does.
- getDifferentiableSequenceScore(int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
-
- getDifferentiableSequenceScores() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
-
- getDifferentiableStatisticalModels() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
-
- getDimension() - Method in enum de.jstacs.data.DinucleotideProperty
-
Returns the dimension of this property, e.g.
- getDimension() - Method in class de.jstacs.utils.random.DiMRGParams
-
Returns the dimension of the hyperparameter vector of the underlying
Dirichlet distribution and therefore the dimension of the generated
random array.
- getDimension() - Method in class de.jstacs.utils.random.DirichletMRGParams
-
- getDimension() - Method in class de.jstacs.utils.random.ErlangMRGParams
-
Returns the dimension of the hyperparameter vector of the underlying
Erlang distribution.
- getDimension() - Method in class de.jstacs.utils.random.FastDirichletMRGParams
-
- getDimensionOfScope() - Method in interface de.jstacs.algorithms.optimization.Function
-
Returns the dimension of the scope of the
Function
.
- getDimensionOfScope() - Method in class de.jstacs.algorithms.optimization.NegativeDifferentiableFunction
-
- getDimensionOfScope() - Method in class de.jstacs.algorithms.optimization.NegativeFunction
-
- getDimensionOfScope() - Method in class de.jstacs.algorithms.optimization.NumericalDifferentiableFunction
-
- getDimensionOfScope() - Method in class de.jstacs.algorithms.optimization.OneDimensionalFunction
-
- getDimensionOfScope() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.DiffSSBasedOptimizableFunction
-
- getDimensionOfScope() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.CompositeLogPrior
-
- getDimensionOfScope() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.DoesNothingLogPrior
-
- getDimensionOfScope() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateGaussianLogPrior
-
- getDimensionOfScope() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateLaplaceLogPrior
-
- getDimensionOfScope() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SimpleGaussianSumLogPrior
-
- getDimensionOfScope() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMTools.DualFunction
-
- getDinucleotideParameters() - Method in enum de.jstacs.data.DinucleotideProperty
-
Returns the dinucleotide parameters of this
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.
- getDistance(T, T) - Method in class de.jstacs.clustering.distances.DistanceMetric
-
Returns the distance according to the metric of the two supplied objects.
- getDistance(double[], double[]) - Method in class de.jstacs.clustering.distances.PearsonCorrelationDistanceMetric
-
- getDistance(double[][], double[][], double[][], int) - Method in class de.jstacs.clustering.distances.SequenceScoreDistance
-
Returns the distance between the two score profiles.
- getDistance(double[][], double[][], StatisticalModel, int) - Method in class de.jstacs.clustering.distances.SequenceScoreDistance
-
Returns the distance between a score profile and a model.
- getDistance(StatisticalModel, StatisticalModel) - Method in class de.jstacs.clustering.distances.SequenceScoreDistance
-
- getDistance() - Method in class de.jstacs.clustering.hierachical.ClusterTree
-
Returns the distance between the child trees of this cluster tree root node.
- getDistance(double[][], ClusterTree<Integer>, ClusterTree<Integer>) - Method in class de.jstacs.clustering.hierachical.Hclust
-
Returns the distance between the two supplied trees using the linkage method of this
Hclust
object
and the given distance matrix.
- getDistance() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
Returns the maximum distance of preceding positions considered in the LSlim model.
- getDistance(double[][], double[][], int, int) - Method in class de.jstacs.utils.PFMComparator.NormalizedEuclideanDistance
-
- getDistance(double[][], double[][], int, int) - Method in class de.jstacs.utils.PFMComparator.OneMinusPearsonCorrelationCoefficient
-
- getDistance(double[][], double[][], int) - Method in class de.jstacs.utils.PFMComparator.PFMDistance
-
This method computes the distance between two PFMs.
- getDistance(double[][], double[][], int, int) - Method in class de.jstacs.utils.PFMComparator.PFMDistance
-
Computes the mean distance between the overlapping parts of pfm1
and pfm2
starting at the offsets
l1
and l2
, respectively.
- getDistance(double[][], double[][], int, int) - Method in class de.jstacs.utils.PFMComparator.SymmetricKullbackLeiblerDivergence
-
- getDistance(double[][], double[][], int, int) - Method in class de.jstacs.utils.PFMComparator.UniformBorderWrapper
-
- getDoubleFromParameter(Parameter) - Static method in class de.jstacs.io.ParameterSetParser
-
Returns the
double
which is the value of the
Parameter
par
.
- getEAR(DataSet, DataSet, double[], double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTExplainingAwayResidual
-
Returns the explaining away residual (EAR) between all pairs of positions
as a matrix.
- getEAR(double[][][][][][], double[][][][][][], double, double) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure
-
Computes the explaining away residual from fgStats
and
bgStats
counted on sequences with a total weight of
nFg
and nBg
, respectively.
- getEAR(double[][][][], double[][][][], double, double) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure
-
Computes the explaining away residual from fgStats
and
bgStats
counted on sequences with a total weight of
nFg
and nBg
, respectively.
- getEdgeFromIndex(int, int) - Method in class de.jstacs.algorithms.graphs.tensor.SymmetricTensor
-
This method decodes an index in an edge.
- getElapsedTime() - Method in class de.jstacs.utils.RealTime
-
- getElapsedTime() - Method in class de.jstacs.utils.Time
-
Returns the elapsed time since invoking the constructor.
- getElapsedTime() - Method in class de.jstacs.utils.UserTime
-
- getElement(int) - Method in class de.jstacs.io.StringExtractor
-
Returns
String
number
idx
that has been extracted.
- getElement() - Method in class de.jstacs.utils.ComparableElement
-
This method returns the element.
- getElementAt(int) - Method in class de.jstacs.data.DataSet
-
This method returns the element, i.e.
- getElementAt(int) - Method in class de.jstacs.data.DataSet.WeightedDataSetFactory
-
- getElementLength() - Method in class de.jstacs.classifiers.assessment.ClassifierAssessmentAssessParameterSet
-
- getElementLength() - Method in class de.jstacs.data.DataSet
-
Returns the length of the elements, i.e.
- getElongateDeleteCosts() - Method in class de.jstacs.algorithms.alignment.cost.AffineCosts
-
Returns the costs for elongating a delete gap by one position.
- getElongateInsertCosts() - Method in class de.jstacs.algorithms.alignment.cost.AffineCosts
-
Returns the costs for elongating an insert gap by one position.
- getEmissionIndexes() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
-
Returns a clone of the internal array of emission indexes that represent which emission is used in which state.
- getEmissions() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
-
Returns a clone of the internal emissions.
- getEmptyContainer() - Method in class de.jstacs.data.sequences.ArbitraryFloatSequence
-
- getEmptyContainer() - Method in class de.jstacs.data.sequences.ArbitrarySequence
-
- getEmptyContainer() - Method in class de.jstacs.data.sequences.CyclicSequenceAdaptor
-
- getEmptyContainer() - Method in class de.jstacs.data.sequences.MappedDiscreteSequence
-
- getEmptyContainer() - Method in class de.jstacs.data.sequences.MultiDimensionalArbitrarySequence
-
- getEmptyContainer() - Method in class de.jstacs.data.sequences.MultiDimensionalDiscreteSequence
-
- getEmptyContainer() - Method in class de.jstacs.data.sequences.Sequence
-
The method returns a container that can be used for accessing the symbols for each position.
- getEmptyContainer() - Method in class de.jstacs.data.sequences.Sequence.RecursiveSequence
-
- getEmptyContainer() - Method in class de.jstacs.data.sequences.SimpleDiscreteSequence
-
- getEmptyRepresentation() - Method in class de.jstacs.data.sequences.ArbitraryFloatSequence
-
- getEmptyRepresentation() - Method in class de.jstacs.data.sequences.ArbitrarySequence
-
- getEmptyRepresentation() - Method in class de.jstacs.data.sequences.CyclicSequenceAdaptor
-
- getEmptyRepresentation() - Method in class de.jstacs.data.sequences.MultiDimensionalSequence
-
- getEmptyRepresentation() - Method in class de.jstacs.data.sequences.Sequence
-
- getEmptyRepresentation() - Method in class de.jstacs.data.sequences.Sequence.RecursiveSequence
-
- getEmptyRepresentation() - Method in class de.jstacs.data.sequences.SimpleDiscreteSequence
-
- getEnd() - Method in class de.jstacs.data.sequences.annotation.LocatedSequenceAnnotationWithLength
-
- getEndIndexOfAlignmentForFirst() - Method in class de.jstacs.algorithms.alignment.PairwiseStringAlignment
-
This method returns the end index of the alignment in the first sequence.
- getEndNode() - Method in class de.jstacs.algorithms.graphs.Edge
-
Returns the end node of the edge.
- getEndValue() - Method in class de.jstacs.parameters.RangeParameter
-
Returns the last value of a range of parameter values or
null
if no range was specified.
- getEntropy(Constraint) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.ConstraintManager
-
Tries to compute the entropy as exact as possible.
- getErrorMessage() - Method in class de.jstacs.parameters.AbstractSelectionParameter
-
- getErrorMessage() - Method in class de.jstacs.parameters.FileParameter
-
- getErrorMessage() - Method in class de.jstacs.parameters.Parameter
-
If a value could not be set successfully this method returns the
corresponding error message.
- getErrorMessage() - Method in class de.jstacs.parameters.ParameterSet
-
Returns the message of the last error that occurred.
- getErrorMessage() - Method in class de.jstacs.parameters.ParameterSetContainer
-
- getErrorMessage() - Method in class de.jstacs.parameters.RangeParameter
-
- getErrorMessage() - Method in class de.jstacs.parameters.SelectionParameter
-
- getErrorMessage() - Method in class de.jstacs.parameters.SimpleParameter
-
- getErrorMessage() - Method in interface de.jstacs.parameters.validation.Constraint
-
Returns the message of the last error (missed constraint) or
null
if the constraint was fulfilled by the last checked
value.
- getErrorMessage() - Method in class de.jstacs.parameters.validation.ConstraintValidator
-
- getErrorMessage() - Method in class de.jstacs.parameters.validation.NumberValidator
-
- getErrorMessage() - Method in interface de.jstacs.parameters.validation.ParameterValidator
-
- getErrorMessage() - Method in class de.jstacs.parameters.validation.RegExpValidator
-
- getErrorMessage() - Method in class de.jstacs.parameters.validation.SimpleStaticConstraint
-
- getErrorMessage() - Method in class de.jstacs.parameters.validation.StorableValidator
-
- getESS() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
-
- getESS() - Method in interface de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModel
-
Returns the equivalent sample size (ess) of this model, i.e.
- getESS() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
-
- getEss() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSMParameterSet
-
Returns the equivalent samples size (ess) defined in this set of
parameters.
- getEss() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTExplainingAwayResidual.BTExplainingAwayResidualParameterSet
-
Returns the equivalent sample sizes (ess) defined by this set of
parameters.
- getEss() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTMutualInformation.BTMutualInformationParameterSet
-
Returns the equivalent sample sizes (ess) defined by this set of
parameters.
- getEss() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMExplainingAwayResidual.PMMExplainingAwayResidualParameterSet
-
Returns the equivalent sample sizes (ess) defined by this set of
parameters.
- getEss() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMMutualInformation.PMMMutualInformationParameterSet
-
Returns the equivalent sample sizes (ess) defined by this set of parameters.
- getESS() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMM0DiffSM
-
- getESS() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
-
- getESS() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.UniformHomogeneousDiffSM
-
- getESS() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
-
- getESS() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
- getESS() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
-
- getESS() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MarkovRandomFieldDiffSM
-
- getESS() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.MixtureDiffSM
-
- getESS() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.DurationDiffSM
-
- getESS() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
-
- getESS() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
-
- getESS() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
-
- getESS() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.UniformDiffSM
-
- getESS() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DiscreteGraphicalTrainSM
-
This method returns the ess (equivalent sample size)
that is used in this model.
- getEss() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.StructureLearner
-
- getESS() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
-
- getExceptionIfMPNotComputable() - Method in class de.jstacs.classifiers.assessment.ClassifierAssessmentAssessParameterSet
-
- getExpLambda(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint
-
Returns the exponential value of
at position
index
:
.
- getExport() - Method in class de.jstacs.results.ListResult
-
- getExport() - Method in class de.jstacs.results.TextResult
-
Returns
true
if the contents are saved to a separate file in
Galaxy
.
- getExpPartOfProb(MEMConstraint[], int[], SequenceIterator) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMTools
-
This method computes the exponential part of the probability, i.e., everything except the normalization constant.
- getExpValue() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter
-
- getExtendedType() - Method in class de.jstacs.parameters.FileParameter
-
Returns the extended type (or
null
if not set) of this
FileParameter
.
- getExtendedType() - Method in class de.jstacs.results.TextResult
-
- getExtension() - Method in class de.jstacs.parameters.FileParameter.FileRepresentation
-
- getExtension() - Method in class de.jstacs.tools.ui.galaxy.GalaxyAdaptor.FileResult
-
Returns the filename extension
- getExtremum() - Method in class de.jstacs.algorithms.optimization.QuadraticFunction
-
- getFactorForAucPR() - Method in class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
-
This method returns a factor that must be multiplied to scores for computing PR curves.
- getFactors(String, boolean, ConstraintManager.Decomposition) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEManager
-
This method returns an array of independent maximum entropy models parsed from the given constraints.
- getFactors(ArrayList<int[]>, boolean, ConstraintManager.Decomposition) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEManager
-
This method returns an array of independent maximum entropy models parsed from the given constraints.
- getFancyScatterplot(AbstractScoreBasedClassifier, AbstractScoreBasedClassifier, REnvironment, DataSet...) - Static method in class de.jstacs.classifiers.utils.ClassificationVisualizer
-
Scatters the classification scores of two binary classifiers for given data.
- getFileContents() - Method in class de.jstacs.parameters.FileParameter
-
Returns the content of the file.
- getFileExtensions(DataSetResult) - Method in class de.jstacs.results.savers.DataSetResultSaver
-
- getFileExtensions(ListResult) - Method in class de.jstacs.results.savers.ListResultSaver
-
- getFileExtensions(PlotGeneratorResult) - Method in class de.jstacs.results.savers.PlotGeneratorResultSaver
-
- getFileExtensions(T) - Method in interface de.jstacs.results.savers.ResultSaver
-
Returns the file extensions (in descending preference) for storing the given
Result
- getFileExtensions(ResultSetResult) - Method in class de.jstacs.results.savers.ResultSetResultSaver
-
- getFileExtensions(StorableResult) - Method in class de.jstacs.results.savers.StorableResultSaver
-
- getFileExtensions(TextResult) - Method in class de.jstacs.results.savers.TextResultSaver
-
- getFilename() - Method in class de.jstacs.parameters.FileParameter.FileRepresentation
-
Returns the filename.
- getFilename() - Method in class de.jstacs.tools.ui.galaxy.GalaxyAdaptor.FileResult
-
Returns the filename.
- getFilesize() - Method in class de.jstacs.parameters.FileParameter.FileRepresentation
-
- getFinalStatePosterioriMatrix(double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
-
- getFinishedDate() - Method in class de.jstacs.tools.ToolResult
-
Returns the date and time, when the tool's run resulting in this
ToolResult
finished.
- getFirstElement() - Method in class de.jstacs.utils.Pair
-
This method returns the first element.
- getFirstParent() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
-
- getFloatFromParameter(Parameter) - Static method in class de.jstacs.io.ParameterSetParser
-
Returns the
float
which is the value of the
Parameter
par
.
- getFormat() - Method in class de.jstacs.tools.JstacsTool.ResultEntry
-
Returns the format of the result.
- getForwardProbability() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
-
This methoth returns the a-priori probability for the forward strand.
- getFreeParameters() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifierParameterSet
-
Returns true
if only free parameters shall be used
- getFreq(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint
-
Returns the current frequency with index index
.
- getFreq(Sequence, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint
-
This method determines the specific constraint that is fulfilled by the
Sequence
seq
beginning at position
start
.
- getFreq(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint
-
- getFreqInfo(AlphabetContainer, NumberFormat) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint
-
Returns an information about the stored frequencies.
- getFunction(DataSet[], double[][]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier
-
- getFunction(DataSet[], double[][]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifier
-
- getFunction(DataSet[], double[][]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
-
Returns the function that should be sampled from.
- getFunction(DataSet[], double[][]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
-
Returns the function that should be optimized.
- getFunction() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
-
This method return the internal function.
- getFunction(int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
-
This method returns a specific internal function.
- getFunction() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
-
This method returns the internal function.
- getFunction() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.DifferentiableStatisticalModelWrapperTrainSM
-
- getFunctions() - Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
-
- getFunctions() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
-
This method returns an array of clones of the internal used functions.
- getFurtherClassifierInfos() - Method in class de.jstacs.classifiers.AbstractClassifier
-
This method returns further information of a classifier as a
StringBuffer
.
- getFurtherClassifierInfos() - Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
-
- getFurtherClassifierInfos() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier
-
- getFurtherClassifierInfos() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifier
-
- getFurtherClassifierInfos() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
-
- getFurtherClassifierInfos() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
-
- getFurtherClassifierInfos() - Method in class de.jstacs.classifiers.MappingClassifier
-
- getFurtherClassifierInfos() - Method in class de.jstacs.classifiers.trainSMBased.TrainSMBasedClassifier
-
- getFurtherClassifierInfos() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureClassifier
-
- getFurtherInformation() - Method in class de.jstacs.sampling.AbstractBurnInTest
-
- getFurtherInformation() - Method in class de.jstacs.sampling.VarianceRatioBurnInTest
-
- getFurtherInformation() - Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
-
This method is used to append further information of the instance to the
XML representation.
- getFurtherInformation() - Method in class de.jstacs.sequenceScores.differentiable.UniformDiffSS
-
This method is used to append further information of the instance to the
XML representation.
- getFurtherInformation() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
-
- getFurtherInformation() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
-
This method is used to append further information of the instance to the
XML representation.
- getFurtherInformation() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
-
- getFurtherInformation() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
-
- getFurtherInformation() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.UniformDiffSM
-
- getFurtherInformation() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
-
This method is used in the subclasses to append further information to
the XML representation.
- getFurtherInformation() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.HiddenMotifMixture
-
- getFurtherModelInfos() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DiscreteGraphicalTrainSM
-
- getFurtherModelInfos() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousMM
-
- getFurtherModelInfos() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.DAGTrainSM
-
- getFurtherModelInfos() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGModelForGibbsSampling
-
- getFurtherModelInfos() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEManager
-
- getGapCostsFor(int) - Method in class de.jstacs.algorithms.alignment.cost.AffineCosts
-
Returns the costs for a gap of length length
.
- getGeneralizedDivergence(double[][], double[][], double) - Static method in class de.jstacs.utils.StatisticalTest
-
Computes the generalized divergence for two given stochastic matrices
over the same domain, i.e.
- getGeneralizedDivergence(double[][], double[], double[], double) - Static method in class de.jstacs.utils.StatisticalTest
-
Computes the generalized divergence for two stochastic matrices over the
same domain, i.e.
- getGeneralizedDivergence(double[][], double) - Static method in class de.jstacs.utils.StatisticalTest
-
Computes the generalized divergence for two stochastic matrices over the
same domain, i.e.
- getGlobalIndexOfMotifInComponent(int, int) - Method in interface de.jstacs.motifDiscovery.MotifDiscoverer
-
Returns the global index of the motif
used in
component
.
- getGlobalIndexOfMotifInComponent(int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
-
- getGlobalIndexOfMotifInComponent(int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
-
- getGlobalIndexOfMotifInComponent(int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.MixtureDiffSM
-
- getGlobalIndexOfMotifInComponent(int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
-
- getGlobalIndexOfMotifInComponent(int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.ZOOPSTrainSM
-
- getGraphics(int, int) - Method in class de.jstacs.utils.graphics.EPSAdaptor
-
- getGraphics(int, int) - Method in class de.jstacs.utils.graphics.GraphicsAdaptor
-
- getGraphics(int, int) - Method in class de.jstacs.utils.graphics.PDFAdaptor
-
- getGraphics(int, int) - Method in class de.jstacs.utils.graphics.RasterizedAdaptor
-
- getGraphics(int, int) - Method in class de.jstacs.utils.graphics.SVGAdaptor
-
- getGraphicsExtension() - Method in class de.jstacs.utils.graphics.EPSAdaptor
-
- getGraphicsExtension() - Method in class de.jstacs.utils.graphics.GraphicsAdaptor
-
Returns the file extension for the graphics file format of this
GraphicsAdaptor
.
- getGraphicsExtension() - Method in class de.jstacs.utils.graphics.PDFAdaptor
-
- getGraphicsExtension() - Method in class de.jstacs.utils.graphics.RasterizedAdaptor
-
- getGraphicsExtension() - Method in class de.jstacs.utils.graphics.SVGAdaptor
-
- getGraphizNetworkRepresentation(NumberFormat, String, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
-
- getGraphizNetworkRepresentation(NumberFormat, String, boolean) - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.Transition
-
This method returns a
String
representation of the structure that
can be used in
Graphviz to create an image.
- getGraphviz() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
Returns a Graphviz (dot) representation of the Slim model.
- getGraphvizEdgeWeight(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
-
This method returns the edge weight for plotting the edge with Graphviz.
- getGraphvizNodeOptions(double, double, NumberFormat) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleState
-
- getGraphvizNodeOptions(double, double, NumberFormat) - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.State
-
This method returns a
String
representation of the node options that
can be used in
Graphviz to create the node for this state.
- getGraphvizRepresentation(NumberFormat) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
-
This method returns a
String
representation of the structure that
can be used in
Graphviz to create an image.
- getGraphvizRepresentation(NumberFormat, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
-
This method returns a
String
representation of the structure that
can be used in
Graphviz to create an image.
- getGraphvizRepresentation(NumberFormat, DataSet, double[], boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
-
This method returns a
String
representation of the structure that
can be used in
Graphviz to create an image.
- getGraphvizRepresentation(NumberFormat, DataSet, double[], HashMap<String, String>) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
-
This method returns a
String
representation of the structure that
can be used in
Graphviz to create an image.
- getHammingDistance(Sequence) - Method in class de.jstacs.data.sequences.Sequence
-
This method returns the Hamming distance between the current
Sequence
and
seq
.
- getHashMap() - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.HMMFactory
-
- getHeight(int, double[][]) - Static method in class de.jstacs.utils.SeqLogoPlotter
-
Returns the automatically chosen height for a given width and position weight matrix.
- getHeightForDependencyLogo(int, int, int[], int, int) - Static method in class de.jstacs.utils.SeqLogoPlotter
-
Returns the height for a dependency logos of the given sequence length and chunks.
- getHelpText() - Method in interface de.jstacs.tools.JstacsTool
-
Returns a detailed help text for this tool, describing the purpose of the tool, all parameters and results.
- getHowCreated() - Method in enum de.jstacs.data.DinucleotideProperty
-
Returns how this property has been determined.
- getHtmlFilesPath() - Method in class de.jstacs.tools.ui.galaxy.GalaxyAdaptor
-
Returns the path where files, e.g.
- getHtmlId() - Static method in class de.jstacs.tools.ui.galaxy.GalaxyAdaptor
-
Gets a unique id that can be used, e.g.
- getHyperparameter(int) - Method in class de.jstacs.utils.random.DiMRGParams
-
Returns the value at position i
of the hyperparameter vector
of the underlying Dirichlet distribution.
- getHyperparameter(int) - Method in class de.jstacs.utils.random.DirichletMRGParams
-
- getHyperparameter(int) - Method in class de.jstacs.utils.random.ErlangMRGParams
-
Returns the value at position i
of the hyperparameter vector
of the underlying Erlang distribution.
- getHyperparameter(int) - Method in class de.jstacs.utils.random.FastDirichletMRGParams
-
- getHyperparameterForHiddenParameter(int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
-
This method returns the hyperparameter for the hidden parameter with
index index
.
- getHyperparameterForHiddenParameter(int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.MixtureDiffSM
-
- getHyperparameterForHiddenParameter(int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
-
- getHyperparameterForHiddenParameter(int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
-
- getHyperParams(int, int, double, double[], double[][][]) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
-
This method returns the hyper-parameters for a model given some a-priori probabilities.
- getHyperParams(double, int, int) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
-
Returns the hyper-parameters for all parameters and a given ess.
- getICScale(double[]) - Static method in class de.jstacs.utils.SeqLogoPlotter
-
Returns the information content scaled to [0,1].
- getId() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.phylo.PhyloNode
-
This method returns the ID of the current PhyloNode
- getIdentifier() - Method in class de.jstacs.data.sequences.annotation.SequenceAnnotation
-
- getImage(double[][], REnvironment) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.TwoPointEvaluater
-
This method can be used to create an image of a mutual information
matrix.
- getImage(DataSet, double[], REnvironment, double, int...) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.TwoPointEvaluater
-
- getImage() - Method in class de.jstacs.utils.graphics.RasterizedAdaptor
-
- getImmutableInstance() - Static method in class de.jstacs.utils.NullProgressUpdater
-
- getIndex(double[], double) - Static method in class de.jstacs.classifiers.utils.PValueComputation
-
This method searches in sortedScores
for the index
i
so that
sortedScores[i-1] < myScore <= sortedScores[i]
.
- getIndex(double[], double, int) - Static method in class de.jstacs.classifiers.utils.PValueComputation
-
This method searches in sortedScores
beginning at
start
for the index i
so that
sortedScores[i-1] < myScore <= sortedScores[i]
.
- getIndex(int) - Method in class de.jstacs.data.sequences.PermutedSequence
-
- getIndex(int) - Method in class de.jstacs.data.sequences.Sequence.CompositeSequence
-
- getIndex(int) - Method in class de.jstacs.data.sequences.Sequence.RecursiveSequence
-
Returns the index in the internal sequence.
- getIndex(int) - Method in class de.jstacs.data.sequences.Sequence.SubSequence
-
- getIndex(String[], Object[], Comparable, boolean) - Static method in class de.jstacs.parameters.ParameterSet
-
This method tries to find the correct name (
String
) for your
choice.
- getIndex() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter
-
Returns the index of this parameter as defined in the constructor.
- getIndex(int[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.CombinationIterator
-
This method returns an index for the sorted entries of a combination
combi
.
- getIndex(int, Sequence) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
-
Returns the index for position seqPos
in sequence seq
.
- getIndex(int, Sequence) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.DistanceBasedScaledTransitionElement
-
Returns the distance integrated into the transition from pos - 1
to pos
in sequences seq
.
- getIndex(int, Sequence) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ScaledTransitionElement
-
Returns the index of the transition matrix used for the transition from pos - 1
to pos
in sequences seq
.
- getIndexForAlphabets() - Method in class de.jstacs.data.AlphabetContainer
-
This method returns an object that is used for assigning the positions of the
Sequence
s to specific
Alphabet
s.
- getIndexOfMaximalComponentFor(Sequence) - Method in interface de.jstacs.motifDiscovery.MotifDiscoverer
-
Returns the index of the component with the maximal score for the
sequence sequence
.
- getIndexOfMaximalComponentFor(Sequence) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
-
- getIndexOfMaximalComponentFor(Sequence) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
-
- getIndexOfMaximalComponentFor(Sequence, int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
-
Returns the index of the component that has the greatest impact on the
complete score for a
Sequence
.
- getIndexOfMaximalComponentFor(Sequence) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.MixtureDiffSM
-
- getIndexOfMaximalComponentFor(Sequence) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
-
- getIndexOfMaximalComponentFor(Sequence) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
-
Returns the index i
of the component with
P(i|s)
maximal.
- getIndexTree() - Method in class de.jstacs.clustering.hierachical.ClusterTree
-
Returns a cluster tree with identical structure as this cluster tree but with all leaves replaced by
integer leaves holding the corresponding original indices.
- getIndices() - Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
-
- 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.
- getInfixFilter(int, double, int...) - Method in interface de.jstacs.sequenceScores.QuickScanningSequenceScore
-
Computes arrays that indicate, for a given set of starting positions and a given k-mer length, if a sequence
containing this k-mer may yield a score above threshold
, choosing the best-scoring option among
all non-specified positions (i.e., those outside the k-mer).
- getInfixFilter(int, double, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
-
- getInfixFilter(int, double, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
- getInfixScores(int, int, int, int, int[], double[][], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
- getInfos() - Method in class de.jstacs.results.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
-
- getInsertCosts() - Method in class de.jstacs.algorithms.alignment.cost.AffineCosts
-
- getInsertCosts() - Method in interface de.jstacs.algorithms.alignment.cost.Costs
-
Returns the costs for an insert gap, i.e., a gap in the first string.
- getInsertCosts() - Method in class de.jstacs.algorithms.alignment.cost.MatrixCosts
-
- getInsertCosts() - Method in class de.jstacs.algorithms.alignment.cost.SimpleCosts
-
- getInsertCostsFor(int) - Method in class de.jstacs.algorithms.alignment.cost.AffineCosts
-
Returns the costs for an insert gap of length length
.
- 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
-
- 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.MultipleIterationsCondition.MultipleIterationsConditionParameterSet
-
- 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
-
- getInstanceComment() - Method in class de.jstacs.data.AlphabetContainerParameterSet
-
- getInstanceComment() - Method in class de.jstacs.data.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.IUPACDNAAlphabet.IUPACDNAAlphabetParameterSet
-
- 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
-
- getInstanceComment() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.FSMEMParameterSet
-
- getInstanceFromParameterSet(InstanceParameterSet<T>) - Static method in class de.jstacs.io.ParameterSetParser
-
- getInstanceFromParameterSet(ParameterSet, Class<T>) - Static method in class de.jstacs.io.ParameterSetParser
-
- 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.MultipleIterationsCondition.MultipleIterationsConditionParameterSet
-
- 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
-
- getInstanceName() - Method in class de.jstacs.data.AlphabetContainerParameterSet
-
- getInstanceName() - Method in class de.jstacs.data.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.MultiDimensionalSequenceWrapperDiffSS
-
- 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.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
- 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.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.FSMEManager
-
- 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.PFMWrapperTrainSM
-
- 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
-
- getInstanceParameterSets(Class<T>, String) - Static method in class de.jstacs.utils.SubclassFinder
-
- getInternalCosts() - Method in class de.jstacs.algorithms.alignment.cost.AffineCosts
-
- 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
.
- getIterations() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
-
Returns the number of independent re-starts in the training.
- getK() - Method in class de.jstacs.classifiers.assessment.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() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
-
Returns the last state of the context
- 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.
- getLeaves() - Method in class de.jstacs.clustering.hierachical.ClusterTree
-
Returns all leaves of this cluster tree as
ClusterTree
objects comprising just the corresponding
leaf element
- getLegalName(String) - Static method in class de.jstacs.tools.ui.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
-
- 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.CyclicSequenceAdaptor
-
- 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
-
- 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
-
- getLengthOfBurnIn() - Method in class de.jstacs.sampling.AbstractBurnInTest
-
- getLengthOfBurnIn() - Method in interface de.jstacs.sampling.BurnInTest
-
- getLengthOfBurnIn() - Method in class de.jstacs.sampling.SimpleBurnInTest
-
Deprecated.
- getLengthOfModels() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.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.tools.ui.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
.
- getLocalScoreFor(Sequence, int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
-
- getLog() - Method in class de.jstacs.tools.ui.cli.CLI.SysProtocol
-
Returns the
StringBuffer
containing all messages since the creation of this
object.
- 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 differences of the logarithmic values of the prior knowledge and all counts.
- 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
.
- getLogLikelihoodRatio(Sequence) - Method in class de.jstacs.classifiers.trainSMBased.TrainSMBasedClassifier
-
- 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.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
- 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.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
- 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
-
- getLogPriorPart(double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEM
-
This method compute the prior for the current parameter ignoring some constants.
- 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
- 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.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
- 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.discrete.inhomogeneous.MEManager
-
- 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.continuous.MultivariateGaussianEmission
-
- 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.PFMWrapperTrainSM
-
- 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.discrete.inhomogeneous.MEManager
-
- 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.continuous.MultivariateGaussianEmission
-
- 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(int, Sequence, int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
-
Returns the logarithmic probability for the sequence between start and end 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.PFMWrapperTrainSM
-
- 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.MultiDimensionalSequenceWrapperDiffSS
-
- 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.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
- 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.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.MultiDimensionalSequenceWrapperDiffSS
-
- 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 data set.
- 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 data set 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.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
- 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.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, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEM
-
Returns the logarithmic score for the sequence
beginning at start
.
- 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 data set 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
-
- 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.
- getMatrixForKruskal(double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure
-
Prepares a matrix of pairwise association measures for the implementation of Kruskal's algorithm.
- 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
-
- 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],...,parents[k-1]) -> child
that maximizes
the score.
- getMaximalElementLength() - Method in class de.jstacs.data.DataSet
-
Returns the maximal length of an element, i.e.
- 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.directedGraphicalModels.BayesianNetworkDiffSM
-
- getMaximalMarkovOrder() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
-
Returns the maximal Markov order of this tree.
- 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.
- getMaximumDistance() - Method in class de.jstacs.clustering.hierachical.ClusterTree
-
Returns the maximum distance of trees under this root node.
- getMaximumScore() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
-
- getMaximumScore() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
-
Returns the maximum score in this tree.
- getMaxIndex() - Method in class de.jstacs.utils.DoubleList
-
Returns the index of the first value that is equal to the maximal value.
- 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
.
- getMI(DataSet, double[]) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.TwoPointEvaluater
-
This method computes the pairwise mutual information between
the sequence positions.
- 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.
- getMime() - Method in class de.jstacs.results.TextResult
-
- 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
-
- getMinimalElementLength() - Method in class de.jstacs.data.DataSet
-
Returns the minimal length of an element, i.e.
- getMinimalHyperparameter() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.TransitionElement
-
- getMinimalSequenceLength() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.HiddenMotifMixture
-
Returns the minimal length a sequence respectively a data set has to have.
- getMinimalSequenceLength() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.ZOOPSTrainSM
-
- getMinimumDistance() - Method in class de.jstacs.clustering.hierachical.ClusterTree
-
Returns the minimum distance of trees under this root node.
- getMinimumOriginalIndex() - Method in class de.jstacs.clustering.hierachical.ClusterTree
-
Returns the minimum original index in this cluster tree.
- getMinIndex() - Method in class de.jstacs.utils.DoubleList
-
Returns the index of the first value that is equal to the minimal value.
- getMinIndex(double[]) - Static method in class de.jstacs.utils.ToolBox
-
Returns the index with minimum value in a double
array.
- getMinIndex(int, int, double[]) - Static method in class de.jstacs.utils.ToolBox
-
Returns the index with minimum value in a double
array.
- getMixtureProbabilities() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
Returns the probabilities of the mixture components.
- getModel(int) - Method in class de.jstacs.classifiers.trainSMBased.TrainSMBasedClassifier
-
- 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
-
- 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
-
- getName() - Method in class de.jstacs.classifiers.performanceMeasures.AbstractPerformanceMeasure
-
- 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.CorrelationCoefficient
-
- getName() - Method in class de.jstacs.classifiers.performanceMeasures.FalsePositiveRateForFixedSensitivity
-
- getName() - Method in class de.jstacs.classifiers.performanceMeasures.MaximumNumericalTwoClassMeasure
-
- getName() - Method in interface de.jstacs.classifiers.performanceMeasures.PerformanceMeasure
-
The method returns the name of the performance measure.
- 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() - Method in interface de.jstacs.clustering.hierachical.PWMSupplier
-
Returns a name (e.g., an identifier from a database) for the PWM.
- 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
-
- getName() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleState
-
Returns the name of the state.
- getName() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.PFMWrapperTrainSM
-
- 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
- getName() - Method in class de.jstacs.tools.JstacsTool.ResultEntry
-
Returns the name of the result.
- 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
-
- getNames() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
-
Returns a clone of the state names.
- 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
-
- 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 data set 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 data set 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
.
- getNextLine(boolean) - Method in class de.jstacs.tools.ui.galaxy.GalaxyAdaptor.LineBasedResult
-
Returns the next line of the result
- getNiceMax() - Method in class de.jstacs.utils.NiceScale
-
Returns the "nice" maximum value
- getNiceMin() - Method in class de.jstacs.utils.NiceScale
-
Returns the "nice" minimum value
- 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.continuous.MultivariateGaussianEmission
-
- 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.continuous.MultivariateGaussianEmission
-
- 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
-
- 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.
- 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
-
- getNumberOfClasses(PerformanceMeasure[]) - Static method in class de.jstacs.classifiers.performanceMeasures.AbstractPerformanceMeasureParameterSet
-
- 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
-
- 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
-
- getNumberOfComponents() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
-
- getNumberOfElements() - Method in class de.jstacs.clustering.hierachical.ClusterTree
-
Returns the number of leaves in this cluster tree.
- getNumberOfElements() - Method in class de.jstacs.data.DataSet
-
Returns the number of elements, i.e.
- 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.
- getNumberOfMatches() - Method in class de.jstacs.algorithms.alignment.PairwiseStringAlignment
-
Returns the number of matches in this alignment.
- getNumberOfModels() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.CompositeTrainSM
-
- getNumberOfMotifs() - Method in interface de.jstacs.motifDiscovery.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
-
- getNumberOfNexts(int) - Method in class de.jstacs.parameters.RangeParameter
-
- 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.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
-
- getNumberOfParameters() - Method in class de.jstacs.parameters.ArrayParameterSet
-
- getNumberOfParameters() - Method in class de.jstacs.parameters.ParameterSet
-
- getNumberOfParameters() - Method in class de.jstacs.parameters.SequenceScoringParameterSet
-
- getNumberOfParameters() - Method in interface de.jstacs.sequenceScores.differentiable.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.MultiDimensionalSequenceWrapperDiffSS
-
- 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.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
- 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.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.
- getNumberOfParameterSets(int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier.DiffSMSamplingComponent
-
Returns the number of parameters set that can be retrieved from an internal file which has been creating while previous training.
- getNumberOfParents() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
-
- 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.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
- 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
-
- getNumberOfResultSets() - Method in class de.jstacs.results.ListResult
-
- getNumberOfSequenceAnnotationsByType(String) - Method in class de.jstacs.data.sequences.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
-
- 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 interface de.jstacs.algorithms.optimization.MultiThreadedFunction
-
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.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
-
- 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
-
- 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
-
- 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.discrete.inhomogeneous.MEManager
-
- 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.PFMWrapperTrainSM
-
- 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.
- getOrder() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
Returns the order of the Slim model
- getOriginal() - Method in class de.jstacs.data.sequences.Sequence.SubSequence
-
Returns the original sequence, this sequence is a sub-sequence of.
- getOriginalIndex() - Method in class de.jstacs.clustering.hierachical.ClusterTree
-
Returns the original index of the root node of this cluster tree
- getOriginalName() - Method in class de.jstacs.results.Result
-
Returns the original name (i.e., the name upon object creation) of this
Result
,
which may be just the name if
Result.rename(String)
has not been called on this object, yet.
- 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.tools.ui.galaxy.GalaxyAdaptor.Protocol
-
- getOutputStream() - Method in class de.jstacs.utils.SafeOutputStream
-
- getPairwiseDistanceMatrix(DistanceMetric<T>, T...) - Static method in class de.jstacs.clustering.distances.DistanceMetric
-
Returns the matrix of all pairwise distance of the supplied objects, where rows and colums are indexed in the order
of the supplied objects.
- getPairwiseDistanceMatrix(int, StatisticalModel...) - Method in class de.jstacs.clustering.distances.SequenceScoreDistance
-
Multi-threaded computation of the pairwise distance matrix.
- getParameterAt(int) - Method in class de.jstacs.parameters.ArrayParameterSet
-
- getParameterAt(int) - Method in class de.jstacs.parameters.ParameterSet
-
- 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
.
- getParameterFor(int[], 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
-
- 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
-
- getParametersInCollection() - Method in class de.jstacs.parameters.AbstractSelectionParameter
-
Returns the possible values in this collection.
- getParent() - Method in class de.jstacs.parameters.Parameter
-
- getParent() - Method in class de.jstacs.parameters.ParameterSet
-
- 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
-
- getPartialDataSet(int, int) - Method in class de.jstacs.data.DataSet
-
Returns a new
DataSet
that contains all elements of this
DataSet
that are specified
by the supplied
start
(inclusive) and
end
(exclusive) indexes.
- getPartialDataSet(int[]...) - Method in class de.jstacs.data.DataSet
-
Returns a new
DataSet
that contains all elements of this
DataSet
that are specified
by the supplied pairs of start and end indexes in
indexes
.
- 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.tools.ui.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 data set.
- getPercentage() - Method in class de.jstacs.tools.ProgressUpdater
-
Returns the percentage of a tool's work that has been completed so far.
- 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.
- getPFM() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.PFMWrapperTrainSM
-
Returns a deep copy of the internal PFM.
- getPFM(DataSet) - Static method in class de.jstacs.utils.PFMComparator
-
- getPFM(DataSet, int, int) - Static method in class de.jstacs.utils.PFMComparator
-
Returns a position frequency matrix (PFM, rows=positions, columns=symbols) for the given subset of
DataSet
.
- getPFM(DataSet, double[]) - Static method in class de.jstacs.utils.PFMComparator
-
- 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
-
- 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.differentiable.logistic.ProductConstraint
-
Returns a clone of the internal positions.
- 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
-
- getPossibleLength() - Method in class de.jstacs.data.AlphabetContainer.AbstractAlphabetContainerParameterSet
-
- getPossibleLength() - Method in class de.jstacs.data.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
-
- getProbsForComponent(Sequence) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
-
Returns the probabilities for each component given a
Sequence
.
- getProducer() - Method in class de.jstacs.results.TextResult
-
Returns the producer (i.e., the tool/application/program) that created this
TextResult
.
- getProfile(StatisticalModel, boolean) - Method in class de.jstacs.clustering.distances.RandomSequenceScoreDistance
-
- getProfile(StatisticalModel, boolean) - Method in class de.jstacs.clustering.distances.SequenceScoreDistance
-
Returns the score profile for the model.
- 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
-
- getProfilesForMotif(StatisticalModel, int, boolean, boolean) - Static method in class de.jstacs.clustering.distances.DeBruijnMotifComparison
-
Returns the score profile on a De Bruin sequence for a De Bruijn sequence.
- getProfilesForMotif(CyclicSequenceAdaptor[], StatisticalModel, boolean, boolean) - Static method in class de.jstacs.clustering.distances.DeBruijnMotifComparison
-
Returns the score profile on a De Bruin sequence for a De Bruijn sequence.
- 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, int) - Method in enum de.jstacs.data.DinucleotideProperty
-
- 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 dimers 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 dimers 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.tools.ui.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
-
- 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 interface de.jstacs.clustering.hierachical.PWMSupplier
-
Returns the position weight matrix.
- getPWM(int, DataSet, double[], int, int) - Method in class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
-
- getPWM() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
-
- getPWM() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.PFMWrapperTrainSM
-
- getPWM(DataSet, int, int) - Static method in class de.jstacs.utils.PFMComparator
-
Returns a position weight matrix (PWM, rows=positions, columns=symbols, containing probabilities) for the given subset of
DataSet
.
- getPWMAndPosDist(int, DataSet, double[], double[], int, int) - Method in class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
-
Returns the Position weight matrix (PWM) of the binding sites of motif
motif
in the data set
data
of the
MotifDiscoverer
of this
SignificantMotifOccurrencesFinder
together with standard deviation of binding site positions computed using the provided
mean
values for each sequence.
- getPWMAndPosDist(int, DataSet, double[], double[], int, int, LinkedList<Sequence>, DoubleList, DoubleList) - Method in class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
-
Returns the position weight matrix and standard deviation of the position distribution using the given mean.
- getPWMAndPositions(int, DataSet, double[], int, int) - Method in class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
-
Returns the Position weight matrix (PWM) of the binding sites of motif
motif
in the data set
data
of the
MotifDiscoverer
of this
SignificantMotifOccurrencesFinder
together with the positions of the binding sites within the sequences of
data
and the corresponding p-values.
- getPWMAndPositions(int, DataSet, double[], int, int, int[][], double[][], double[], double[], LinkedList<Sequence>, DoubleList, DoubleList) - Method in class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
-
Returns the Position weight matrix (PWM) of the binding sites of motif
motif
in the data set
data
of the
MotifDiscoverer
of this
SignificantMotifOccurrencesFinder
and fills with the positions of the binding sites within the sequences of
data
and the corresponding p-values into the corresponding arrays.
- getPWMParameters() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
Returns the unconditional, normalized (PWM) probabilities of this Slim model
- 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.
- getReferenceSequence(Sequence) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.ReferenceSequenceDiscreteEmission
-
Returns the reference sequence annotated to seq
.
- 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
-
- getRepeats() - Method in class de.jstacs.classifiers.assessment.RepeatedSubSamplingAssessParameterSet
-
- getRepeats() - Method in class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutAssessParameterSet
-
- getResultAt(int) - Method in class de.jstacs.results.NumericalResultSet
-
- getResultAt(int) - Method in class de.jstacs.results.ResultSet
-
- getResultForName(String) - Method in class de.jstacs.results.ResultSet
-
- getResultInstance() - Method in class de.jstacs.results.StorableResult
-
- getResults(LinkedList, DataSet[], double[][], AbstractPerformanceMeasureParameterSet<? extends PerformanceMeasure>, boolean) - Method in class de.jstacs.classifiers.AbstractClassifier
-
This method computes the results for any evaluation of the classifier.
- getResults(LinkedList, DataSet[], double[][], AbstractPerformanceMeasureParameterSet<? extends PerformanceMeasure>, boolean) - Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
-
- getResults(LinkedList, DataSet[], double[][], AbstractPerformanceMeasureParameterSet<? extends PerformanceMeasure>, 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.
- getReverseComplement(ComplementableDiscreteAlphabet, double[][]) - Static method in class de.jstacs.utils.PFMComparator
-
This method returns the PFM that is the reverse complement of the given PFM.
- getReverseComplementaryDataSet() - Method in class de.jstacs.data.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, NumberFormat) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.DurationDiffSM
-
This method returns the distribution in
R notation.
- getRNotation(String, NumberFormat) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.MixtureDurationDiffSM
-
- getRNotation(String, NumberFormat) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
-
- getRNotation(String, NumberFormat) - 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
-
- getSafeOutputStream(OutputStream) - Static method in class de.jstacs.utils.SafeOutputStream
-
- getSamplingComponent() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.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.MarkovRandomFieldDiffSM
-
- 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
- getSaver(Class<? extends T>) - Static method in class de.jstacs.results.savers.ResultSaverLibrary
-
Returns the most suitable
ResultSaver
(if any) currently registered in the library.
- 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
-
- getScoreFor(Sequence, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEM
-
Returns the score for the sequence
beginning at start
.
- 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
-
- 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
-
- getSequenceAnnotationByTypeAndIdentifier(String, String) - Method in class de.jstacs.data.sequences.Sequence
-
- getSequenceAnnotationIndexMatrix(String, Hashtable<String, Integer>, String, Hashtable<String, Integer>) - Method in class de.jstacs.data.DataSet
-
- getSequenceWeights() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractOptimizableFunction
-
- getSequenceWeights() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.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
.
- getShortName() - Method in interface de.jstacs.tools.JstacsTool
-
Returns a name (preferably short and without spaces) for referring to this tool on the command line.
- getSimplifiedAlphabetContainer(Alphabet[], int[]) - Static method in class de.jstacs.data.AlphabetContainer
-
- 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.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
- 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
-
- 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.
- getStart() - Method in class de.jstacs.data.sequences.Sequence.SubSequence
-
Returns the start of this sub-sequence in the original sequence.
- 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 data set 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
-
- getStatistics(DataSet, double[], int, double) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure
-
- getStatisticsOrderTwo(DataSet, double[], int, double) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure
-
- 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.
- getStoreAll() - Method in class de.jstacs.classifiers.assessment.ClassifierAssessmentAssessParameterSet
-
- getStrand(Sequence, int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
-
- getStrand(Sequence, int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
-
- 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
-
- 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.CyclicSequenceAdaptor
-
- 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.MEManager
-
- 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
-
- 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
-
- getSubSequence(int, int) - Method in class de.jstacs.data.sequences.Sequence
-
- getSubTrees() - Method in class de.jstacs.clustering.hierachical.ClusterTree
-
Returns the sub-trees of this cluster tree root node
- getSuffixDataSet(int) - Method in class de.jstacs.data.DataSet
-
This method enables you to use only a suffix of all elements, i.e.
- 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
-
- 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.
- getSuperSequence(int) - Method in class de.jstacs.data.sequences.CyclicSequenceAdaptor
-
Returns a new cyclic sequence using the internal sequence of this
CyclicSequenceAdaptor
but with
the supplied virtual length
- getSymbol(int, double) - Method in class de.jstacs.data.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
-
- 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.
- getThreads() - Method in class de.jstacs.tools.ui.galaxy.GalaxyAdaptor
-
Returns the number of threads given by the Galaxy configuration
- 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
-
- getTickSpacing() - Method in class de.jstacs.utils.NiceScale
-
Returns the spacing between the tick marks
- 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.
- getToolName() - Method in interface de.jstacs.tools.JstacsTool
-
Returns a descriptive, human readable name for this tool.
- getToolName() - Method in class de.jstacs.tools.ToolResult
-
- getToolParameters() - Method in interface de.jstacs.tools.JstacsTool
-
Returns the input parameters of this tool.
- getToolParameters() - Method in class de.jstacs.tools.ToolResult
-
Returns the tool's parameters that have been used to create the results stored in this
ToolResult
.
- getToolVersion() - Method in interface de.jstacs.tools.JstacsTool
-
Returns a descriptive, human readable version for this tool.
- 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) data sets should consist of.
- getTrainingParams() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
-
Returns a clone of the training parameters
- getTransisionElements() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
-
Returns the transition elements of the internal
Transition
.
- getTransisionElements() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
-
Returns a clone of the internal transition elements.
- getTransitionElementIndex(int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
-
- getTrueIndexForLastGetBest() - Method in class de.jstacs.algorithms.graphs.tensor.SymmetricTensor
-
- getType() - Method in class de.jstacs.data.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
-
- 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
-
- 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],...,parents[k-1] -> child
.
- getValue() - Method in class de.jstacs.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.PlotGeneratorResult
-
- getValue() - Method in class de.jstacs.results.SimpleResult
-
- getValue() - Method in class de.jstacs.results.StorableResult
-
- getValue() - Method in class de.jstacs.results.TextResult
-
- getValue(Sequence, int) - Method in interface de.jstacs.sequenceScores.differentiable.logistic.LogisticConstraint
-
- 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.tools.ui.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 data set data
.
- getWeight() - Method in class de.jstacs.algorithms.graphs.Edge
-
Returns the weight of the edge.
- getWeight(double[], int) - Static method in class de.jstacs.classifiers.performanceMeasures.AbstractPerformanceMeasure
-
Returns the weight at index
in weight
or 1 if weight
is null
.
- 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.
- getWidth(int, double[][]) - Static method in class de.jstacs.utils.SeqLogoPlotter
-
Returns the automatically chosen width for a given height and position weight matrix.
- getWidth(int, int) - Static method in class de.jstacs.utils.SeqLogoPlotter
-
Returns the width of a sequence logo of the given height for a PWM with the given number of columns.
- getWriter() - Method in class de.jstacs.tools.ui.galaxy.GalaxyAdaptor.Protocol
-
- getXmlTag() - Method in class de.jstacs.algorithms.optimization.termination.AbsoluteValueCondition
-
Deprecated.
- getXmlTag() - Method in class de.jstacs.algorithms.optimization.termination.AbstractTerminationCondition
-
- 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.MultipleIterationsCondition
-
- 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.PlotGeneratorResult
-
- getXMLTag() - Method in class de.jstacs.results.StorableResult
-
- getXMLTag() - Method in class de.jstacs.results.TextResult
-
- getXMLTag() - Method in class de.jstacs.sampling.AbstractBurnInTest
-
- 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
-
- 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.FSMEManager
-
- 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
-
- 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.tools.ui.galaxy.GalaxyAdaptor.FileResult
-
- getXMLTag() - Method in class de.jstacs.tools.ui.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
-
- 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
- GIS - Static variable in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMTools
-
This constant can be used to specify that the model should use the iterative scaling for
training.
- 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
- graphics - Variable in class de.jstacs.utils.graphics.EPSAdaptor
-
The EPS document
- graphics - Variable in class de.jstacs.utils.graphics.RasterizedAdaptor
-
The graphics object used for plotting
- graphics - Variable in class de.jstacs.utils.graphics.SVGAdaptor
-
The internal graphics object
- GraphicsAdaptor - Class in de.jstacs.utils.graphics
-
Generic class for different adaptors for plotting graphics to a file
using different graphics formats.
- GraphicsAdaptor() - Constructor for class de.jstacs.utils.graphics.GraphicsAdaptor
-
- GraphicsAdaptorFactory - Class in de.jstacs.utils.graphics
-
- GraphicsAdaptorFactory() - Constructor for class de.jstacs.utils.graphics.GraphicsAdaptorFactory
-
- GraphicsAdaptorFactory.OutputFormat - Enum in de.jstacs.utils.graphics
-
The allowed output formats
- GT - Static variable in interface de.jstacs.parameters.validation.Constraint
-
The condition is greater than
- GUIProgressUpdater - Class in de.jstacs.utils
-
- GUIProgressUpdater(boolean) - Constructor for class de.jstacs.utils.GUIProgressUpdater
-