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## G

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

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