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## S

SafeOutputStream - Class in de.jstacs.utils
  This class is for any output.
SafeOutputStream(OutputStream) - Constructor for class de.jstacs.utils.SafeOutputStream
  Creates a new `SafeOutputStream`.
sameLength() - Method in class de.jstacs.classifier.assessment.Sampled_RepeatedHoldOutAssessParameterSet
  Returns `true` if for test and train dataset the sequences of the non-reference classes have the same length as the corresponding sequence of the reference class.
Sample - Class in de.jstacs.data
  This is the class for any sample of `Sequence`s.
Sample(AlphabetContainer, AbstractStringExtractor) - Constructor for class de.jstacs.data.Sample
  Creates a new `Sample` from a `StringExtractor` using the given `AlphabetContainer`.
Sample(AlphabetContainer, AbstractStringExtractor, int) - Constructor for class de.jstacs.data.Sample
  Creates a new `Sample` from a `StringExtractor` using the given `AlphabetContainer` and all overlapping windows of length `subsequenceLength`.
Sample(AlphabetContainer, AbstractStringExtractor, String) - Constructor for class de.jstacs.data.Sample
  Creates a new `Sample` from a `StringExtractor` using the given `AlphabetContainer` and a delimiter `delim`.
Sample(AlphabetContainer, AbstractStringExtractor, String, int) - Constructor for class de.jstacs.data.Sample
  Creates a new `Sample` from a `StringExtractor` using the given `AlphabetContainer`, the given delimiter `delim` and all overlapping windows of length `subsequenceLength`.
Sample(Sample, int) - Constructor for class de.jstacs.data.Sample
  Creates a new `Sample` from a given `Sample` and a given length `subsequenceLength`.
Sample(String, Sequence...) - Constructor for class de.jstacs.data.Sample
  Creates a new `Sample` from an array of `Sequence`s and a given annotation.
sample - Variable in class de.jstacs.models.mixture.AbstractMixtureModel
  The sample that was used in the last training.
Sample.ElementEnumerator - Class in de.jstacs.data
  This class can be used to have a fast sequential access to a `Sample`.
Sample.ElementEnumerator(Sample) - Constructor for class de.jstacs.data.Sample.ElementEnumerator
  Creates a new `Sample.ElementEnumerator` on the given `Sample` `data`.
Sample.PartitionMethod - Enum in de.jstacs.data
  This `enum` defines different partition methods for a `Sample`.
Sample.WeightedSampleFactory - Class in de.jstacs.data
  This class enables you to eliminate `Sequence`s that occur more than once in one or more `Sample`s.
Sample.WeightedSampleFactory(Sample.WeightedSampleFactory.SortOperation, Sample...) - Constructor for class de.jstacs.data.Sample.WeightedSampleFactory
  Creates a new `Sample.WeightedSampleFactory` on the given `Sample`(s) with `Sample.WeightedSampleFactory.SortOperation` `sort`.
Sample.WeightedSampleFactory(Sample.WeightedSampleFactory.SortOperation, Sample, double[]) - Constructor for class de.jstacs.data.Sample.WeightedSampleFactory
  Creates a new `Sample.WeightedSampleFactory` on the given `Sample` and an array of `weights` with `Sample.WeightedSampleFactory.SortOperation` `sort`.
Sample.WeightedSampleFactory(Sample.WeightedSampleFactory.SortOperation, Sample, double[], int) - Constructor for class de.jstacs.data.Sample.WeightedSampleFactory
  Creates a new `Sample.WeightedSampleFactory` on the given `Sample` and an array of `weights` with a given `length` and `Sample.WeightedSampleFactory.SortOperation` `sort`.
Sample.WeightedSampleFactory(Sample.WeightedSampleFactory.SortOperation, Sample[], double[][], int) - Constructor for class de.jstacs.data.Sample.WeightedSampleFactory
  Creates a new `Sample.WeightedSampleFactory` on the given array of `Sample`s and an array of `weights` with a given `length` and `Sample.WeightedSampleFactory.SortOperation` `sort`.
Sample.WeightedSampleFactory.SortOperation - Enum in de.jstacs.data
  This `enum` defines the different types of sort operations that can be performed while creating a `Sample.WeightedSampleFactory`.
Sampled_RepeatedHoldOutAssessParameterSet - Class in de.jstacs.classifier.assessment
  This class implements a `ClassifierAssessmentAssessParameterSet` that must be used to call the method `assess( ... )` of a `Sampled_RepeatedHoldOutExperiment`.
Sampled_RepeatedHoldOutAssessParameterSet() - Constructor for class de.jstacs.classifier.assessment.Sampled_RepeatedHoldOutAssessParameterSet
  Constructs a new `Sampled_RepeatedHoldOutAssessParameterSet` with empty parameter values.
Sampled_RepeatedHoldOutAssessParameterSet(StringBuffer) - Constructor for class de.jstacs.classifier.assessment.Sampled_RepeatedHoldOutAssessParameterSet
  The standard constructor for the interface `Storable`.
Sampled_RepeatedHoldOutAssessParameterSet(Sample.PartitionMethod, int, boolean, int, int, double, boolean) - Constructor for class de.jstacs.classifier.assessment.Sampled_RepeatedHoldOutAssessParameterSet
  Constructs a new `Sampled_RepeatedHoldOutAssessParameterSet` with given parameter values.
Sampled_RepeatedHoldOutExperiment - Class in de.jstacs.classifier.assessment
  This class is a special `ClassifierAssessment` that partitions the data of a user-specified reference class (typically the smallest class) and samples non-overlapping for all other classes, so that one gets the same number of sequences (and the same lengths of the sequences) in each train and test dataset.
Sampled_RepeatedHoldOutExperiment(AbstractClassifier[], Model[][], boolean, boolean) - Constructor for class de.jstacs.classifier.assessment.Sampled_RepeatedHoldOutExperiment
  Creates a new `Sampled_RepeatedHoldOutExperiment` from an array of `AbstractClassifier`s and a two-dimensional array of `Model` s, which are combined to additional classifiers.
Sampled_RepeatedHoldOutExperiment(AbstractClassifier...) - Constructor for class de.jstacs.classifier.assessment.Sampled_RepeatedHoldOutExperiment
  Creates a new `Sampled_RepeatedHoldOutExperiment` from a set of `AbstractClassifier`s.
Sampled_RepeatedHoldOutExperiment(boolean, Model[]...) - Constructor for class de.jstacs.classifier.assessment.Sampled_RepeatedHoldOutExperiment
  Creates a new `Sampled_RepeatedHoldOutExperiment` from a set of `Model`s.
Sampled_RepeatedHoldOutExperiment(AbstractClassifier[], boolean, Model[]...) - Constructor for class de.jstacs.classifier.assessment.Sampled_RepeatedHoldOutExperiment
  This constructor allows to assess a collection of given `AbstractClassifier`s and those constructed using the given `AbstractModel`s by a `Sampled_RepeatedHoldOutExperiment`.
SampleKMerEnumerator - Class in de.jstacs.data
  Class for an `RecyclableSequenceEnumerator` of `Sequence`s that enumerates all k-mers that exist in a given `Sample`, optionally ignoring reverse complements.
SampleKMerEnumerator(Sample, int, boolean) - Constructor for class de.jstacs.data.SampleKMerEnumerator
  Constructs a new SampleKMerEnumerator from a `Sample` `data` by extracting all k-mers.
SampleResult - Class in de.jstacs.results
  `Result` that contains a `Sample`.
SampleResult(String, String, Sample) - Constructor for class de.jstacs.results.SampleResult
  Creates a new `SampleResult` from a `Sample` with the annotation `name` and `comment`.
SampleResult(StringBuffer) - Constructor for class de.jstacs.results.SampleResult
  The standard constructor for the interface `Storable`.
sampleToSequenceIterator(Sample, boolean) - Static method in class de.jstacs.data.bioJava.BioJavaAdapter
  Creates a `SequenceIterator` from the `Sample` `sample` preserving as much annotation as possible.
samplingIndex - Variable in class de.jstacs.models.mixture.AbstractMixtureModel
  The current index of the sampling.
samplingIndex - Variable in class de.jstacs.models.mixture.gibbssampling.FSDAGModelForGibbsSampling
  The index of the current sampling.
samplingStopped() - Method in class de.jstacs.models.mixture.AbstractMixtureModel
  This method is the opposite of the method `AbstractMixtureModel.initModelForSampling(int)`.
samplingStopped() - Method in class de.jstacs.models.mixture.gibbssampling.FSDAGModelForGibbsSampling
samplingStopped() - Method in interface de.jstacs.models.mixture.gibbssampling.GibbsSamplingComponent
  This method is the opposite of the method `GibbsSamplingComponent.extendSampling(int, boolean)`.
satisfiesSpecificConstraint(Sequence, int) - Method in class de.jstacs.models.discrete.Constraint
  This method returns the index of the specific constraint that is fulfilled by the `Sequence` `seq` beginning at position `start`.
satisfiesSpecificConstraint(Sequence, int) - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousModel.HomCondProb
satisfiesSpecificConstraint(Sequence, int) - Method in class de.jstacs.models.discrete.inhomogeneous.InhConstraint
satisfiesSpecificConstraint(SequenceIterator) - Method in class de.jstacs.models.discrete.inhomogeneous.MEMConstraint
  Returns the index of the constraint that is satisfied by `sequence`.
save(File) - Method in class de.jstacs.data.Sample
  This method writes the `Sample` to a file `f`.
save(OutputStream, char, SequenceAnnotationParser) - Method in class de.jstacs.data.Sample
  This method allows to write all `Sequence`s including their `SequenceAnnotation`s into a `OutputStream`.
score - Variable in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier
  The internally used scoring functions.
score - Variable in class de.jstacs.classifier.scoringFunctionBased.SFBasedOptimizableFunction
  These `ScoringFunction`s are used during the parallel computation.
ScoreBasedPerformanceMeasureDefinitions - Class in de.jstacs.classifier
  This class contains the methods that are needed to evaluate a score based 2-class-classifier.
ScoreBasedPerformanceMeasureDefinitions() - Constructor for class de.jstacs.classifier.ScoreBasedPerformanceMeasureDefinitions
ScoreBasedPerformanceMeasureDefinitions.ThresholdMeasurePair - Class in de.jstacs.classifier
  This class is used as a container that allows to store a threshold and the result of a measure together.
ScoreBasedPerformanceMeasureDefinitions.ThresholdMeasurePair(double, double) - Constructor for class de.jstacs.classifier.ScoreBasedPerformanceMeasureDefinitions.ThresholdMeasurePair
  Creates a filled instance of a `ScoreBasedPerformanceMeasureDefinitions.ThresholdMeasurePair`.
ScoreClassifier - Class in de.jstacs.classifier.scoringFunctionBased
  This abstract class implements the main functionality of a `ScoringFunction` based classifier.
ScoreClassifier(ScoreClassifierParameterSet, double, ScoringFunction...) - Constructor for class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier
  Creates a new `ScoreClassifier` from a given `ScoreClassifierParameterSet` and `ScoringFunction`s .
ScoreClassifier(StringBuffer) - Constructor for class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier
  The standard constructor for the interface `Storable`.
ScoreClassifierParameterSet - Class in de.jstacs.classifier.scoringFunctionBased
  A set of `Parameter`s for any `ScoreClassifier`.
ScoreClassifierParameterSet(Class<? extends ScoreClassifier>, boolean, AlphabetContainer.AlphabetContainerType, boolean) - Constructor for class de.jstacs.classifier.scoringFunctionBased.ScoreClassifierParameterSet
  Creates a new `ScoreClassifierParameterSet` with empty parameter values.
ScoreClassifierParameterSet(StringBuffer) - Constructor for class de.jstacs.classifier.scoringFunctionBased.ScoreClassifierParameterSet
  The standard constructor for the interface `Storable`.
ScoreClassifierParameterSet(Class<? extends ScoreClassifier>, AlphabetContainer, int, byte, double, double, double, boolean, OptimizableFunction.KindOfParameter) - Constructor for class de.jstacs.classifier.scoringFunctionBased.ScoreClassifierParameterSet
  The constructor for a simple, instantiated parameter set.
ScoringFunction - Interface in de.jstacs.scoringFunctions
  This interface is the main part of any `ScoreClassifier`.
SeparateGaussianLogPrior - Class in de.jstacs.classifier.scoringFunctionBased.logPrior
  Class for a `LogPrior` that defines a Gaussian prior on the parameters of a set of `NormalizableScoringFunction`s and a set of class parameters.
SeparateGaussianLogPrior(double[], double[], double[]) - Constructor for class de.jstacs.classifier.scoringFunctionBased.logPrior.SeparateGaussianLogPrior
  Creates a new `SeparateGaussianLogPrior` from a set of base variances `vars`, a set of class variances `classVars` and a set of class means `classMus`.
SeparateGaussianLogPrior(StringBuffer) - Constructor for class de.jstacs.classifier.scoringFunctionBased.logPrior.SeparateGaussianLogPrior
  The standard constructor for the interface `Storable`.
SeparateLaplaceLogPrior - Class in de.jstacs.classifier.scoringFunctionBased.logPrior
  Class for a `LogPrior` that defines a Laplace prior on the parameters of a set of `NormalizableScoringFunction`s and a set of class parameters.
SeparateLaplaceLogPrior(double[], double[], double[]) - Constructor for class de.jstacs.classifier.scoringFunctionBased.logPrior.SeparateLaplaceLogPrior
  Creates a new `SeparateLaplaceLogPrior` from a set of base variances `vars`, a set of class variances `classVars` and a set of class means `classMus`.
SeparateLaplaceLogPrior(StringBuffer) - Constructor for class de.jstacs.classifier.scoringFunctionBased.logPrior.SeparateLaplaceLogPrior
  The standard constructor for the interface `Storable`.
SeparateLogPrior - Class in de.jstacs.classifier.scoringFunctionBased.logPrior
  Abstract class for priors that penalize each parameter value independently and have some variances (and possible means) as hyperparameters.
SeparateLogPrior(double[], double[], double[]) - Constructor for class de.jstacs.classifier.scoringFunctionBased.logPrior.SeparateLogPrior
  Creates a new `SeparateLogPrior` using the class-specific base variances `vars`, the variances `classVars` and the means `classMus` for the class parameters.
SeparateLogPrior(StringBuffer) - Constructor for class de.jstacs.classifier.scoringFunctionBased.logPrior.SeparateLogPrior
  The standard constructor for the interface `Storable`.
Sequence - Class in de.jstacs.data
  This is the main class for all sequences.
Sequence(AlphabetContainer, SequenceAnnotation[]) - Constructor for class de.jstacs.data.Sequence
  Creates a new `Sequence.CompositeSequence` with the given `AlphabetContainer` and the given annotation, but without the content.
Sequence.CompositeSequence - Class in de.jstacs.data
  The class handles composite `Sequence`s.
Sequence.CompositeSequence(Sequence, int[], int[]) - Constructor for class de.jstacs.data.Sequence.CompositeSequence
  This is a very efficient way to create a `Sequence.CompositeSequence` for `Sequence`s with a simple `AlphabetContainer`.
Sequence.CompositeSequence(AlphabetContainer, Sequence, int[], int[]) - Constructor for class de.jstacs.data.Sequence.CompositeSequence
  This constructor should be used if one wants to create a `Sample` of `Sequence.CompositeSequence`s.
Sequence.SubSequence - Class in de.jstacs.data
  This class handles subsequences.
Sequence.SubSequence(AlphabetContainer, Sequence, int, int) - Constructor for class de.jstacs.data.Sequence.SubSequence
  This constructor should be used if one wants to create a `Sample` of `Sequence.SubSequence`s of defined length.
Sequence.SubSequence(Sequence, int, int) - Constructor for class de.jstacs.data.Sequence.SubSequence
  This is a very efficient way to create a `Sequence.SubSequence` of defined length for `Sequence`s with a simple `AlphabetContainer`.
SequenceAnnotation - Class in de.jstacs.data.sequences.annotation
  Class for a general annotation of a `Sequence`.
SequenceAnnotation(String, String, Result) - Constructor for class de.jstacs.data.sequences.annotation.SequenceAnnotation
  Creates a new `SequenceAnnotation` of type `type` with identifier `identifier` and additional annotation (that does not fit the `SequenceAnnotation` definitions) given as a `Result` `result`.
SequenceAnnotation(String, String, Result[]...) - Constructor for class de.jstacs.data.sequences.annotation.SequenceAnnotation
  Creates a new `SequenceAnnotation` of type `type` with identifier `identifier` and additional annotation (that does not fit the `SequenceAnnotation` definitions) given as an array of `Result`s `results`.
SequenceAnnotation(String, String, SequenceAnnotation[], Result...) - Constructor for class de.jstacs.data.sequences.annotation.SequenceAnnotation
  Creates a new `SequenceAnnotation` of type `type` with identifier `identifier` and additional annotation (that does not fit the `SequenceAnnotation` definitions) given as an array of `Result`s `additionalAnnotation`.
SequenceAnnotation(String, String, Collection<? extends Result>) - Constructor for class de.jstacs.data.sequences.annotation.SequenceAnnotation
  Creates a new `SequenceAnnotation` of type `type` with identifier `identifier` and additional annotation (that does not fit the `SequenceAnnotation` definitions) given as a `Collection` of `Result`s `results`.
SequenceAnnotation(StringBuffer) - Constructor for class de.jstacs.data.sequences.annotation.SequenceAnnotation
  The standard constructor for the interface `Storable`.
SequenceAnnotationParser - Interface in de.jstacs.data.sequences.annotation
  This interface declares the methods which are used by `AbstractStringExtractor` to annotate a `String` which will be parsed to a `Sequence`.
SequenceEnumeration - Class in de.jstacs.data
  This class implements a `RecyclableSequenceEnumerator` on user-specified `Sequence`s.
SequenceEnumeration(Sequence...) - Constructor for class de.jstacs.data.SequenceEnumeration
  This constructor creates an instance based on the user-specified `Sequence`s `sequences`.
SequenceEnumeration(Collection<Sequence>) - Constructor for class de.jstacs.data.SequenceEnumeration
  This constructor creates an instance based on the user-specified `Collection` of `Sequence`s `sequences`.
SequenceIterator - Class in de.jstacs.models.discrete.inhomogeneous
  This class is used to iterate over a discrete sequence.
SequenceIterator(int) - Constructor for class de.jstacs.models.discrete.inhomogeneous.SequenceIterator
  Creates a new `SequenceIterator` with maximal `length`.
sequenceIteratorToSample(SequenceIterator, FeatureFilter) - Static method in class de.jstacs.data.bioJava.BioJavaAdapter
  This method creates a new `Sample` from a `SequenceIterator`.
SequenceScoringParameterSet - Class in de.jstacs.parameters
  Abstract class for a `ParameterSet` containing all parameters necessary to construct an `Object` that implements `InstantiableFromParameterSet`.
SequenceScoringParameterSet(Class, AlphabetContainer.AlphabetContainerType, boolean) - Constructor for class de.jstacs.parameters.SequenceScoringParameterSet
  Constructs an `InstanceParameterSet` having empty parameter values.
SequenceScoringParameterSet(Class, AlphabetContainer.AlphabetContainerType, boolean, boolean) - Constructor for class de.jstacs.parameters.SequenceScoringParameterSet
  Constructs a `SequenceScoringParameterSet` having empty parameter values.
SequenceScoringParameterSet(StringBuffer) - Constructor for class de.jstacs.parameters.SequenceScoringParameterSet
  The standard constructor for the interface `Storable`.
SequenceScoringParameterSet(Class, AlphabetContainer, int, boolean) - Constructor for class de.jstacs.parameters.SequenceScoringParameterSet
  Constructs a `SequenceScoringParameterSet` from an `AlphabetContainer` and the length of a sequence.
SequenceScoringParameterSet(Class, AlphabetContainer) - Constructor for class de.jstacs.parameters.SequenceScoringParameterSet
  Constructs a `SequenceScoringParameterSet` for an object that can handle sequences of variable length and with the `AlphabetContainer` `alphabet`.
set(boolean, ScoringFunction...) - Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.CompositeLogPrior
set(boolean, ScoringFunction...) - Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.LogPrior
  Resets all pre-computed values to their initial values using the `ScoringFunction`s `funs`.
set(boolean, ScoringFunction...) - Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.SeparateLogPrior
set(AlphabetContainer) - Method in class de.jstacs.models.AbstractModel
  This method should only be invoked by the method `AbstractModel.setNewAlphabetContainerInstance(AlphabetContainer)` and not be made public.
set(AlphabetContainer) - Method in class de.jstacs.models.CompositeModel
set(DGMParameterSet, boolean) - Method in class de.jstacs.models.discrete.DiscreteGraphicalModel
  Sets the parameters as internal parameters and does some essential computations.
set(DGMParameterSet, boolean) - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousMM
set(DGMParameterSet, boolean) - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousModel
set(DGMParameterSet, boolean) - Method in class de.jstacs.models.discrete.inhomogeneous.BayesianNetworkModel
set(DGMParameterSet, boolean) - Method in class de.jstacs.models.discrete.inhomogeneous.FSDAGModel
set(DGMParameterSet, boolean) - Method in class de.jstacs.models.discrete.inhomogeneous.InhomogeneousDGM
set(AlphabetContainer) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
setAlpha(double) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
  Sets the parameter of the Dirichlet distribution which is used when you invoke `train` to init the gammas.
setBounds(int[]) - Method in class de.jstacs.models.discrete.inhomogeneous.SequenceIterator
  This method sets the bounds for each position.
setClassWeights(boolean, double...) - Method in class de.jstacs.classifier.AbstractScoreBasedClassifier
  Sets new class weights.
setClassWeights(boolean, double[], int) - Method in class de.jstacs.classifier.AbstractScoreBasedClassifier
  Sets new class weights.
setCurrentLength(int) - Method in class de.jstacs.models.discrete.inhomogeneous.CombinationIterator
  This method sets the current used number of selected elements.
setCurrentSamplingIndex(int) - Method in class de.jstacs.models.mixture.gibbssampling.AbstractBurnInTest
setCurrentSamplingIndex(int) - Method in class de.jstacs.models.mixture.gibbssampling.BurnInTest
  This method sets the value of the current sampling.
setCurrentSamplingIndex(int) - Method in class de.jstacs.models.mixture.gibbssampling.SimpleBurnInTest
setDataAndWeights(Sample[], double[][]) - Method in class de.jstacs.classifier.scoringFunctionBased.AbstractMultiThreadedOptimizableFunction
setDataAndWeights(Sample[], double[][]) - Method in class de.jstacs.classifier.scoringFunctionBased.AbstractOptimizableFunction
setDataAndWeights(Sample[], double[][]) - Method in class de.jstacs.classifier.scoringFunctionBased.OptimizableFunction
  This method sets the data set and the sequence weights to be used.
setDefault(Object) - Method in class de.jstacs.parameters.CollectionParameter
setDefault(Object) - Method in class de.jstacs.parameters.EnumParameter
setDefault(Object) - Method in class de.jstacs.parameters.FileParameter
setDefault(Object) - Method in class de.jstacs.parameters.MultiSelectionCollectionParameter
setDefault(Object) - Method in class de.jstacs.parameters.Parameter
  Sets the default value of the `Parameter` to `defaultValue`.
setDefault(Object) - Method in class de.jstacs.parameters.ParameterSetContainer
setDefault(Object) - Method in class de.jstacs.parameters.RangeParameter
setDefault(Object) - Method in class de.jstacs.parameters.SimpleParameter
setEss(double) - Method in class de.jstacs.models.discrete.DGMParameterSet
  This method can be used to set the ess (equivalent sample size) of this parameter set.
setESS(double) - Method in class de.jstacs.models.discrete.inhomogeneous.StructureLearner
  This method sets the ess (equivalent sample size) of the `StructureLearner`.
setExpLambda(int, double) - Method in class de.jstacs.models.discrete.inhomogeneous.MEMConstraint
  Sets the exponential value of .
setForwardProb(double) - Method in class de.jstacs.scoringFunctions.mix.StrandScoringFunction
  This method can be used to set the forward strand probability.
setFrameParameterOptimization(boolean) - Method in class de.jstacs.scoringFunctions.CMMScoringFunction
  This method enables the user to choose whether the frame parameters should be optimized or not.
setFreqs(String[], int) - Method in class de.jstacs.models.discrete.inhomogeneous.InhCondProb
  This method is used to restore the values of a Gibbs Sampling run.
setFurtherInformation(StringBuffer) - Method in class de.jstacs.models.mixture.gibbssampling.AbstractBurnInTest
  This method sets further information for the `AbstractBurnInTest`.
setFurtherInformation(StringBuffer) - Method in class de.jstacs.models.mixture.gibbssampling.VarianceRatioBurnInTest
setFurtherModelInfos(StringBuffer) - Method in class de.jstacs.models.discrete.DiscreteGraphicalModel
  This method replaces the internal model information with those from a `StringBuffer`.
setFurtherModelInfos(StringBuffer) - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousMM
setFurtherModelInfos(StringBuffer) - Method in class de.jstacs.models.discrete.inhomogeneous.DAGModel
setFurtherModelInfos(StringBuffer) - Method in class de.jstacs.models.mixture.gibbssampling.FSDAGModelForGibbsSampling
setHiddenParameters(double[], int) - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
  This method sets the hidden parameters of the model.
setLambda(int, double) - Method in class de.jstacs.models.discrete.inhomogeneous.MEMConstraint
  Sets the value of .
setLastDistance(double) - Method in class de.jstacs.algorithms.optimization.ConstantStartDistance
setLastDistance(double) - Method in class de.jstacs.algorithms.optimization.LimitedMedianStartDistance
setLastDistance(double) - Method in interface de.jstacs.algorithms.optimization.StartDistanceForecaster
  Sets the last used distance.
setMax(int) - Method in class de.jstacs.utils.DefaultProgressUpdater
setMax(int) - Method in class de.jstacs.utils.GUIProgressUpdater
setMax(int) - Method in class de.jstacs.utils.NullProgressUpdater
setMax(int) - Method in interface de.jstacs.utils.ProgressUpdater
  Sets the maximal value that will be set by `ProgressUpdater.setValue(int)`, so a value of max indicates the end of the supervised method call.
setModelType(String) - Method in class de.jstacs.models.discrete.inhomogeneous.parameters.BayesianNetworkModelParameterSet
  This method allows a simple change of the model type.
setMotifLength(int) - Method in class de.jstacs.models.mixture.motif.positionprior.GaussianLikePositionPrior
setMotifLength(int) - Method in class de.jstacs.models.mixture.motif.positionprior.PositionPrior
  Sets the length of the current motif.
setNeededReference(ParameterSet) - Method in class de.jstacs.parameters.Parameter
  Sets an internal reference to a `ParameterSet` whose validity depends on the value of this `Parameter`.
setNeededReference(ParameterSet) - Method in class de.jstacs.parameters.RangeParameter
setNewAlphabetContainerInstance(AlphabetContainer) - Method in class de.jstacs.classifier.AbstractClassifier
  This method tries to set a new instance of an `AlphabetContainer` for the current classifier.
setNewAlphabetContainerInstance(AlphabetContainer) - Method in class de.jstacs.classifier.modelBased.ModelBasedClassifier
setNewAlphabetContainerInstance(AlphabetContainer) - Method in class de.jstacs.models.AbstractModel
setNewAlphabetContainerInstance(AlphabetContainer) - Method in interface de.jstacs.models.Model
  This method tries to set a new instance of an `AlphabetContainer` for the current model.
setNumberOfThreads(int) - Method in class de.jstacs.classifier.scoringFunctionBased.gendismix.GenDisMixClassifier
  This method allows to set the number of threads used while optimization.
setNumberOfThreads(int) - Method in class de.jstacs.classifier.scoringFunctionBased.gendismix.GenDisMixClassifierParameterSet
  This method set the number of threads used during optimization.
setOffset() - Method in class de.jstacs.utils.NullProgressUpdater
  After `NullProgressUpdater.setOffset()` is called the current value will be added to every value set by `NullProgressUpdater.setValue(int)`.
setOutputStream(OutputStream) - Method in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier
  Sets the `OutputStream` that is used e.g. for writing information during training.
setOutputStream(OutputStream) - Method in class de.jstacs.models.discrete.inhomogeneous.InhomogeneousDGM
  Sets the `OutputStream` for the model.
setOutputStream(OutputStream) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
  Sets the `OutputStream` that is used e.g. for writing information while training.
setOutputStream(OutputStream) - Method in class de.jstacs.models.NormalizableScoringFunctionModel
  Sets the OutputStream that is used e.g. for writing information while training.
setParameterFor(int, int[][], Parameter) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.ParameterTree
  Sets the instance of the `Parameter` for symbol `symbol` and context `context` to `Parameter` `par`.
setParameterOptimization(boolean) - Method in class de.jstacs.scoringFunctions.CMMScoringFunction
  This method enables the user to choose whether the parameters should be optimized or not.
setParameterOptimization(boolean) - Method in class de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction
  This method allows the user to specify whether the parameters should be optimized or not.
setParameters(double[], int) - Method in class de.jstacs.scoringFunctions.CMMScoringFunction
setParameters(double[], int) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
setParameters(double[], int) - Method in class de.jstacs.scoringFunctions.homogeneous.HMM0ScoringFunction
setParameters(double[], int) - Method in class de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction
setParameters(double[], int) - Method in class de.jstacs.scoringFunctions.homogeneous.UniformHomogeneousScoringFunction
setParameters(double[], int) - Method in class de.jstacs.scoringFunctions.IndependentProductScoringFunction
setParameters(double[], int) - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
setParameters(double[], int) - Method in class de.jstacs.scoringFunctions.mix.motifSearch.MixtureDuration
setParameters(double[], int) - Method in class de.jstacs.scoringFunctions.mix.motifSearch.SkewNormalLikeScoringFunction
setParameters(double, double, double) - Method in class de.jstacs.scoringFunctions.mix.motifSearch.SkewNormalLikeScoringFunction
  this method can be used to set the parameters even if the parameters are not allowed to be optimized.
setParameters(double[], int) - Method in class de.jstacs.scoringFunctions.mix.motifSearch.UniformDurationScoringFunction
setParameters(double[], int) - Method in class de.jstacs.scoringFunctions.MRFScoringFunction
setParameters(double[], int) - Method in class de.jstacs.scoringFunctions.NormalizedScoringFunction
setParameters(double[], int) - Method in interface de.jstacs.scoringFunctions.ScoringFunction
  This method sets the internal parameters to the values of `params` between `start` and `start + `ScoringFunction.getNumberOfParameters()` - 1`
setParameters(double[], int) - Method in class de.jstacs.scoringFunctions.UniformScoringFunction
setParametersForFunction(int, double[], int) - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
  This method allows to set the parameters for specific functions.
setParams(double[]) - Method in class de.jstacs.classifier.scoringFunctionBased.AbstractMultiThreadedOptimizableFunction
setParams(int) - Method in class de.jstacs.classifier.scoringFunctionBased.AbstractMultiThreadedOptimizableFunction
  This method sets the parameters for thread `index`
setParams(double[]) - Method in class de.jstacs.classifier.scoringFunctionBased.OptimizableFunction
  Sets the current values as parameters.
setParams(int) - Method in class de.jstacs.classifier.scoringFunctionBased.SFBasedOptimizableFunction
setParent(ParameterSet) - Method in class de.jstacs.parameters.Parameter
  Sets the reference of the enclosing `ParameterSet` of this `Parameter` to `parent`.
setParent(ParameterSetContainer) - Method in class de.jstacs.parameters.ParameterSet
  Sets the enclosing `ParameterSetContainer` of this `ParameterSet` to `parent`.
setPlugInParameters(int, boolean, Sample[], double[][]) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
  Computes and sets the plug-in parameters (MAP estimated parameters) from `data` using `weights`.
setPrior(LogPrior) - Method in class de.jstacs.classifier.scoringFunctionBased.gendismix.GenDisMixClassifier
  This method set a new prior that should be used for optimization.
setRangeable(boolean) - Method in class de.jstacs.parameters.CollectionParameter
  Sets the value returned by `CollectionParameter.isRangeable()` to `rangeable`.
setRangeable(boolean) - Method in class de.jstacs.parameters.SimpleParameter
  Sets the value returned by `SimpleParameter.isRangeable()` to `rangeable`.
setRootValue(int, double) - Method in class de.jstacs.algorithms.graphs.tensor.AsymmetricTensor
setRootValue(int, double) - Method in class de.jstacs.algorithms.graphs.tensor.SubTensor
setRootValue(int, double) - Method in class de.jstacs.algorithms.graphs.tensor.SymmetricTensor
setRootValue(int, double) - Method in class de.jstacs.algorithms.graphs.tensor.Tensor
  Sets the value `val` for the root node `child`.
setSeed(long) - Method in class de.jstacs.utils.random.RandomNumberGenerator
setSelected(MeasureParameters.Measure, boolean) - Method in class de.jstacs.classifier.MeasureParameters
  Selects or deselects an option `sel` depending on the new selection `b`.
setSelected(String, boolean) - Method in class de.jstacs.parameters.MultiSelectionCollectionParameter
  Sets the selection of the option with key `key` to the value of `selected`.
setSelected(int, boolean) - Method in class de.jstacs.parameters.MultiSelectionCollectionParameter
  Sets the selection of option with no.
setShallBeRanged(RangeParameter.RangeType) - Method in class de.jstacs.parameters.RangeParameter
  Sets the type of this `RangeParameter` to one of `LIST, RANGE` or `NO`.
setStatisticForHyperparameters(int[], double[]) - Method in class de.jstacs.scoringFunctions.CMMScoringFunction
setStatisticForHyperparameters(int[], double[]) - Method in class de.jstacs.scoringFunctions.homogeneous.HMM0ScoringFunction
setStatisticForHyperparameters(int[], double[]) - Method in class de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction
setStatisticForHyperparameters(int[], double[]) - Method in class de.jstacs.scoringFunctions.homogeneous.UniformHomogeneousScoringFunction
setStatisticForHyperparameters(int[], double[]) - Method in class de.jstacs.scoringFunctions.mix.VariableLengthMixtureScoringFunction
setStatisticForHyperparameters(int[], double[]) - Method in interface de.jstacs.scoringFunctions.VariableLengthScoringFunction
  This method sets the hyperparameters for the model parameters by evaluating the given statistic.
setStringToBeParsed(String) - Method in class de.jstacs.io.SymbolExtractor
  Sets a new `String` to be parsed.
setThreadIndependentParameters() - Method in class de.jstacs.classifier.scoringFunctionBased.AbstractMultiThreadedOptimizableFunction
  This method allows to set thread independent parameters.
setThreadIndependentParameters() - Method in class de.jstacs.classifier.scoringFunctionBased.SFBasedOptimizableFunction
setThreshold(double) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
  Sets the threshold for terminating the train algorithm.
setThresholdClassWeights(boolean, double) - Method in class de.jstacs.classifier.AbstractScoreBasedClassifier
  Sets a new threshold for 2-class-classifiers.
setTrainData(Sample) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
  This method is invoked by the `train`-method and sets for a given sample the sample that should be used for `train`.
setTrainData(Sample) - Method in class de.jstacs.models.mixture.MixtureModel
setTrainData(Sample) - Method in class de.jstacs.models.mixture.motif.SingleHiddenMotifMixture
setTrainData(Sample) - Method in class de.jstacs.models.mixture.StrandModel
setValidator(ParameterValidator) - Method in class de.jstacs.parameters.SimpleParameter
  Sets a new `ParameterValidator` for this `SimpleParameter`.
setValue(byte, double, int, int...) - Method in class de.jstacs.algorithms.graphs.tensor.AsymmetricTensor
setValue(byte, double, int, int...) - Method in class de.jstacs.algorithms.graphs.tensor.SubTensor
setValue(byte, double, int, int...) - Method in class de.jstacs.algorithms.graphs.tensor.SymmetricTensor
  Sets the value if it is bigger than the current value and keeps the parents information.
setValue(byte, double, int, int...) - Method in class de.jstacs.algorithms.graphs.tensor.Tensor
  Sets the value for the edge `parents[0],...`
setValue(double) - Method in class de.jstacs.models.mixture.gibbssampling.AbstractBurnInTest
setValue(double) - Method in class de.jstacs.models.mixture.gibbssampling.BurnInTest
  This method can be used to fill the internal memory with the values that will be used to determine the length of the burn-in phase.
setValue(double) - Method in class de.jstacs.models.mixture.gibbssampling.SimpleBurnInTest
setValue(Object) - Method in class de.jstacs.parameters.CollectionParameter
  Sets the selected value to the one that is specified by the key `value`.
setValue(Object) - Method in class de.jstacs.parameters.EnumParameter
setValue(Object) - Method in class de.jstacs.parameters.FileParameter
setValue(Object) - Method in class de.jstacs.parameters.MultiSelectionCollectionParameter
setValue(Object) - Method in class de.jstacs.parameters.Parameter
  Sets the value of this `Parameter` to `value`.
setValue(Object) - Method in class de.jstacs.parameters.ParameterSetContainer
setValue(Object) - Method in class de.jstacs.parameters.RangeParameter
setValue(Object) - Method in class de.jstacs.parameters.SimpleParameter
setValue(double) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.Parameter
  Sets the current value of this parameter.
setValue(int) - Method in class de.jstacs.utils.DefaultProgressUpdater
setValue(int) - Method in class de.jstacs.utils.GUIProgressUpdater
setValue(int) - Method in class de.jstacs.utils.NullProgressUpdater
setValue(int) - Method in interface de.jstacs.utils.ProgressUpdater
  Sets the current value the supervised process has reached.
setValue(int) - Method in class de.jstacs.utils.TimeLimitedProgressUpdater
setValueFromTag(String, Object) - Method in class de.jstacs.parameters.ParameterSetTagger
  This method allows to easily set the value of a parameter defined by the tag.
setValues(String) - Method in class de.jstacs.parameters.RangeParameter
  Sets a list of values from a `String` containing a space separated list of values.
setValues(Object, int, Object, RangeParameter.Scale) - Method in class de.jstacs.parameters.RangeParameter
  Sets the values of this `RangeParameter` as a range of values, specified by a start value, a last value, a number of steps between these values (without the last value) and a scale in that the values between the first and the last value are chosen.
setValuesInLogScale(boolean, double, Object, int, Object) - Method in class de.jstacs.parameters.RangeParameter
  This method enables you to set a list of values in an easy manner.
setWeights(double...) - Method in class de.jstacs.classifier.scoringFunctionBased.gendismix.GenDisMixClassifier
  This method set the weights for the summand of the function.
setWeights(double...) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
  Sets the weights of each component.
SFBasedOptimizableFunction - Class in de.jstacs.classifier.scoringFunctionBased
  This abstract class is the basis of all multi-threaded `OptimizableFunction`s that are based on `ScoringFunction`s.
SFBasedOptimizableFunction(int, ScoringFunction[], Sample[], double[][], LogPrior, boolean, boolean) - Constructor for class de.jstacs.classifier.scoringFunctionBased.SFBasedOptimizableFunction
  Creates an instance with the underlying infrastructure.
shallBeRanged() - Method in class de.jstacs.parameters.RangeParameter
  Returns one of `LIST, RANGE` or `NO` depending on the input used to specify this `RangeParameter`.
SharedStructureClassifier - Class in de.jstacs.models.discrete.inhomogeneous.shared
  This class enables you to learn the structure on all classes of the classifier together.
SharedStructureClassifier(int, StructureLearner.ModelType, byte, StructureLearner.LearningType, FSDAGModel...) - Constructor for class de.jstacs.models.discrete.inhomogeneous.shared.SharedStructureClassifier
  Creates a new `SharedStructureClassifier` from given `FSDAGModel`s.
SharedStructureClassifier(StringBuffer) - Constructor for class de.jstacs.models.discrete.inhomogeneous.shared.SharedStructureClassifier
  The standard constructor for the interface `Storable`.
SharedStructureMixture - Class in de.jstacs.models.discrete.inhomogeneous.shared
  This class handles a mixture of models with the same structure that is learned via EM.
SharedStructureMixture(FSDAGModel[], StructureLearner.ModelType, byte, int, double, double) - Constructor for class de.jstacs.models.discrete.inhomogeneous.shared.SharedStructureMixture
  Creates a new `SharedStructureMixture` instance which estimates the component probabilities/weights.
SharedStructureMixture(FSDAGModel[], StructureLearner.ModelType, byte, int, double[], double, double) - Constructor for class de.jstacs.models.discrete.inhomogeneous.shared.SharedStructureMixture
  Creates a new `SharedStructureMixture` instance with fixed component weights.
SharedStructureMixture(FSDAGModel[], StructureLearner.ModelType, byte, int, boolean, double[], double, double) - Constructor for class de.jstacs.models.discrete.inhomogeneous.shared.SharedStructureMixture
  Creates a new `SharedStructureMixture` instance with all relevant values.
SharedStructureMixture(StringBuffer) - Constructor for class de.jstacs.models.discrete.inhomogeneous.shared.SharedStructureMixture
  The standard constructor for the interface `Storable`.
shortcut - Variable in class de.jstacs.classifier.scoringFunctionBased.SFBasedOptimizableFunction
  These shortcuts indicate the beginning of a new part in the parameter vector.
ShortSequence - Class in de.jstacs.data.sequences
  This class is for sequences with the alphabet symbols encoded as `shorts`s and can therefore be used for discrete `AlphabetContainer`s with alphabets that use many different symbols.
ShortSequence(AlphabetContainer, short[]) - Constructor for class de.jstacs.data.sequences.ShortSequence
  Creates a new `ShortSequence` from an array of `short`- encoded alphabet symbols.
ShortSequence(AlphabetContainer, String) - Constructor for class de.jstacs.data.sequences.ShortSequence
  Creates a new `ShortSequence` from a `String` representation using the default delimiter.
ShortSequence(AlphabetContainer, SequenceAnnotation[], String, String) - Constructor for class de.jstacs.data.sequences.ShortSequence
  Creates a new `ShortSequence` from a `String` representation using the delimiter `delim`.
ShortSequence(AlphabetContainer, SequenceAnnotation[], SymbolExtractor) - Constructor for class de.jstacs.data.sequences.ShortSequence
  Creates a new `ShortSequence` from a `SymbolExtractor`.
shouldBeNormalized() - Method in class de.jstacs.classifier.scoringFunctionBased.gendismix.GenDisMixClassifierParameterSet
  This method indicates if a normalization shall be used while optimization.
showImage(String, BufferedImage) - Static method in class de.jstacs.utils.REnvironment
  Enables you to show an image.
showImage(String, BufferedImage, int) - Static method in class de.jstacs.utils.REnvironment
  Enables you to show an image.
SignificantMotifOccurrencesFinder - Class in de.jstacs.motifDiscovery
  This class enables the user to predict motif occurrences given a specific significance level.
SignificantMotifOccurrencesFinder(MotifDiscoverer, SignificantMotifOccurrencesFinder.RandomSeqType, boolean, int, double) - Constructor for class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
  This constructor creates an instance of `SignificantMotifOccurrencesFinder` that uses the given `SignificantMotifOccurrencesFinder.RandomSeqType` to determine the siginificance level.
SignificantMotifOccurrencesFinder(MotifDiscoverer, Sample, double) - Constructor for class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
  This constructor creates an instance of `SignificantMotifOccurrencesFinder` that uses a `Sample` to determine the siginificance level.
SignificantMotifOccurrencesFinder.RandomSeqType - Enum in de.jstacs.motifDiscovery
SimpleBurnInTest - Class in de.jstacs.models.mixture.gibbssampling
  This is a very simple test for the length of the burn-in phase.
SimpleBurnInTest(int) - Constructor for class de.jstacs.models.mixture.gibbssampling.SimpleBurnInTest
  This is the main constructor that creates an instance of `SimpleBurnInTest` with fixed burn-in length.
SimpleBurnInTest(StringBuffer) - Constructor for class de.jstacs.models.mixture.gibbssampling.SimpleBurnInTest
  The standard constructor for the interface `Storable`.
SimpleGaussianSumLogPrior - Class in de.jstacs.classifier.scoringFunctionBased.logPrior
  This class implements a prior that is a product of Gaussian distributions with mean 0 and equal variance for each parameter.
SimpleGaussianSumLogPrior(double) - Constructor for class de.jstacs.classifier.scoringFunctionBased.logPrior.SimpleGaussianSumLogPrior
  Creates a new `SimpleGaussianSumLogPrior` with mean 0 and variance `sigma` for all parameters, including the class parameters.
SimpleGaussianSumLogPrior(StringBuffer) - Constructor for class de.jstacs.classifier.scoringFunctionBased.logPrior.SimpleGaussianSumLogPrior
  The standard constructor for the interface `Storable`.
SimpleHistory - Class in de.jstacs.motifDiscovery.history
  This class implements a simple history that has a limited memory that will be used cyclicly.
SimpleHistory(int) - Constructor for class de.jstacs.motifDiscovery.history.SimpleHistory
  This constructor creates a simple history with limited memory.
SimpleHistory(int, boolean, boolean, boolean) - Constructor for class de.jstacs.motifDiscovery.history.SimpleHistory
  This constructor creates a simple history with limited memory.
SimpleParameter - Class in de.jstacs.parameters
  Class for a "simple" parameter.
SimpleParameter(StringBuffer) - Constructor for class de.jstacs.parameters.SimpleParameter
  The standard constructor for the interface `Storable`.
SimpleParameter(DataType, String, String, boolean) - Constructor for class de.jstacs.parameters.SimpleParameter
  Constructor for a `SimpleParameter` without `ParameterValidator`.
SimpleParameter(DataType, String, String, boolean, Object) - Constructor for class de.jstacs.parameters.SimpleParameter
  Constructor for a `SimpleParameter` without `ParameterValidator` but with a default value.
SimpleParameter(DataType, String, String, boolean, ParameterValidator) - Constructor for class de.jstacs.parameters.SimpleParameter
  Constructor for a `SimpleParameter` with a `ParameterValidator`.
SimpleParameter(DataType, String, String, boolean, ParameterValidator, Object) - Constructor for class de.jstacs.parameters.SimpleParameter
  Constructor for a `SimpleParameter` with validator and default value.
SimpleParameter.DatatypeNotValidException - Exception in de.jstacs.parameters
  Class for an `Exception` that can be thrown if the provided `int`-value that represents a data type is not one of the values defined in `DataType`.
SimpleParameter.DatatypeNotValidException(String) - Constructor for exception de.jstacs.parameters.SimpleParameter.DatatypeNotValidException
  Creates a new `SimpleParameter.DatatypeNotValidException` with an error message.
SimpleParameter.IllegalValueException - Exception in de.jstacs.parameters
  This exception is thrown if a parameter is not valid.
SimpleParameter.IllegalValueException(String) - Constructor for exception de.jstacs.parameters.SimpleParameter.IllegalValueException
  Creates a new `SimpleParameter.IllegalValueException` with the reason of the exception `reason` as error message.
SimpleParameterSet - Class in de.jstacs.parameters
  Class for a `ParameterSet` that is constructed from an array of `Parameter`s and thus does nothing in the method `SimpleParameterSet.loadParameters()`.
SimpleParameterSet(Parameter[]) - Constructor for class de.jstacs.parameters.SimpleParameterSet
  Creates a new `SimpleParameterSet` from an array of `Parameter`s.
SimpleParameterSet(StringBuffer) - Constructor for class de.jstacs.parameters.SimpleParameterSet
  The standard constructor for the interface `Storable`.
SimpleReferenceConstraint - Class in de.jstacs.parameters.validation
  Class for a `ReferenceConstraint` that checks for "simple" conditions as defined in the interface `Constraint`.
SimpleReferenceConstraint(SimpleParameter, int) - Constructor for class de.jstacs.parameters.validation.SimpleReferenceConstraint
  Creates a new `SimpleReferenceConstraint` from a reference `SimpleParameter` and a comparison operator, which is one of the values defined in the `Constraint` interface.
SimpleReferenceConstraint(StringBuffer) - Constructor for class de.jstacs.parameters.validation.SimpleReferenceConstraint
  The standard constructor for the interface `Storable`.
SimpleResult - Class in de.jstacs.results
  Abstract class for a `Result` with a value of a primitive data type or `String`.
SimpleResult(String, String, DataType) - Constructor for class de.jstacs.results.SimpleResult
  The main constructor which takes the main information of a result.
SimpleResult(StringBuffer) - Constructor for class de.jstacs.results.SimpleResult
  This is the constructor for `Storable`.
SimpleSequenceAnnotationParser - Class in de.jstacs.data.sequences.annotation
  This class implements a naive `SequenceAnnotationParser` which simply paste the comments into `SequenceAnnotation`.
SimpleSequenceAnnotationParser() - Constructor for class de.jstacs.data.sequences.annotation.SimpleSequenceAnnotationParser
  The constructor of a `SimpleSequenceAnnotationParser` which simply paste the comments into `SequenceAnnotation`.
SimpleSequenceIterator - Class in de.jstacs.data.bioJava
  Class that implements the `SequenceIterator` interface of BioJava in a simple way, backed by an array of `Sequence`s.
SimpleSequenceIterator(Sequence...) - Constructor for class de.jstacs.data.bioJava.SimpleSequenceIterator
  Creates a new `SimpleSequenceIterator` from an array of `Sequence`s.
SimpleStaticConstraint - Class in de.jstacs.parameters.validation
  Class for a `Constraint` that checks values against static values using the comparison operators defined in the interface `Constraint`.
SimpleStaticConstraint(Number, int) - Constructor for class de.jstacs.parameters.validation.SimpleStaticConstraint
  Creates a new `SimpleStaticConstraint` from a `Number` -reference and a comparison operator as defined in `Constraint`.
SimpleStaticConstraint(String, int) - Constructor for class de.jstacs.parameters.validation.SimpleStaticConstraint
  Creates a new `SimpleStaticConstraint` from a `String` -reference and a comparison operator as defined in `Constraint`.
SimpleStaticConstraint(StringBuffer) - Constructor for class de.jstacs.parameters.validation.SimpleStaticConstraint
  The standard constructor for the interface `Storable`.
SimpleStringExtractor - Class in de.jstacs.io
  This is a simple class that extracts `String`s.
SimpleStringExtractor(String...) - Constructor for class de.jstacs.io.SimpleStringExtractor
  This constructor packs the `String`s in an instance of `SimpleStringExtractor`.
simplify() - Method in class de.jstacs.parameters.CollectionParameter
simplify() - Method in class de.jstacs.parameters.FileParameter
simplify() - Method in class de.jstacs.parameters.MultiSelectionCollectionParameter
simplify() - Method in class de.jstacs.parameters.Parameter
  Simplifies the `Parameter` and its contents to the relevant information.
simplify() - Method in class de.jstacs.parameters.ParameterSet
  Simplifies all `Parameter`s in this `ParameterSet`.
simplify() - Method in class de.jstacs.parameters.ParameterSetContainer
simplify() - Method in class de.jstacs.parameters.RangeParameter
simplify() - Method in class de.jstacs.parameters.SimpleParameter
SingleHiddenMotifMixture - Class in de.jstacs.models.mixture.motif
  This class enables the user to search for a single motif in a sequence.
SingleHiddenMotifMixture(Model, Model, boolean, int, double[], double[], PositionPrior, AbstractMixtureModel.Algorithm, double, double, AbstractMixtureModel.Parameterization, int, int, BurnInTest) - Constructor for class de.jstacs.models.mixture.motif.SingleHiddenMotifMixture
  Creates a new `SingleHiddenMotifMixture`.
SingleHiddenMotifMixture(Model, Model, boolean, int, double[], PositionPrior, double, double, AbstractMixtureModel.Parameterization) - Constructor for class de.jstacs.models.mixture.motif.SingleHiddenMotifMixture
  Creates a new `SingleHiddenMotifMixture` using EM and estimating the probability for finding a motif.
SingleHiddenMotifMixture(Model, Model, boolean, int, double, PositionPrior, double, double, AbstractMixtureModel.Parameterization) - Constructor for class de.jstacs.models.mixture.motif.SingleHiddenMotifMixture
  Creates a new `SingleHiddenMotifMixture` using EM and fixed probability for finding a motif.
SingleHiddenMotifMixture(StringBuffer) - Constructor for class de.jstacs.models.mixture.motif.SingleHiddenMotifMixture
  The standard constructor for the interface `Storable`.
SinglePositionSequenceAnnotation - Class in de.jstacs.data.sequences.annotation
  Class for some annotations that consist mainly of one position on a sequence.
SinglePositionSequenceAnnotation(SinglePositionSequenceAnnotation.Type, String, int) - Constructor for class de.jstacs.data.sequences.annotation.SinglePositionSequenceAnnotation
  Creates a new `SinglePositionSequenceAnnotation` of type `type` with identifier `identifier` and position `position`.
SinglePositionSequenceAnnotation(SinglePositionSequenceAnnotation.Type, String, int, Result...) - Constructor for class de.jstacs.data.sequences.annotation.SinglePositionSequenceAnnotation
  Creates a new `SinglePositionSequenceAnnotation` of type `type` with identifier `identifier`, position `position` and additional annotations `additionalAnnotation`.
SinglePositionSequenceAnnotation(StringBuffer) - Constructor for class de.jstacs.data.sequences.annotation.SinglePositionSequenceAnnotation
  The standard constructor for the interface `Storable`.
SinglePositionSequenceAnnotation.Type - Enum in de.jstacs.data.sequences.annotation
  This `enum` defines possible types of a `SinglePositionSequenceAnnotation`.
SkewNormalLikeScoringFunction - Class in de.jstacs.scoringFunctions.mix.motifSearch
  This class implements a skew normal like discrete truncated distribution.
SkewNormalLikeScoringFunction(int, int, double, double, double) - Constructor for class de.jstacs.scoringFunctions.mix.motifSearch.SkewNormalLikeScoringFunction
  This is the main constructor if the parameters are fixed.
SkewNormalLikeScoringFunction(int, int, boolean, double, double, boolean, double, double, boolean, double, double, int) - Constructor for class de.jstacs.scoringFunctions.mix.motifSearch.SkewNormalLikeScoringFunction
  This is the constructor that allows the most flexible handling of the parameters.
SkewNormalLikeScoringFunction(StringBuffer) - Constructor for class de.jstacs.scoringFunctions.mix.motifSearch.SkewNormalLikeScoringFunction
  This is the constructor for `Storable`.
skip(int) - Method in class de.jstacs.models.discrete.inhomogeneous.SequenceIterator
  This method skips some position.
skipLastClassifiersDuringClassifierTraining - Variable in class de.jstacs.classifier.assessment.ClassifierAssessment
  Skip last classifier.
SmallDifferenceOfFunctionEvaluationsCondition - Class in de.jstacs.algorithms.optimization.termination
  This class implements a `TerminationCondition` that stops an optimization if the difference of the current and the last function evaluations will be small, i.e., .
SmallDifferenceOfFunctionEvaluationsCondition(double) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition
  This constructor creates an instance that stops the optimization if the difference of the current and the last function evaluations is smaller than `epsilon`.
SmallStepCondition - Class in de.jstacs.algorithms.optimization.termination
  This class implements a `TerminationCondition` that allows no further iteration in an optimization if the scalar product of the current and the last values of `x` will be small, i.e., .
SmallStepCondition(double) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallStepCondition
  This constructor creates an instance that allows no further iteration in an optimization if the scalar product of the current and the last values of `x` is smaller than `epsilon`.
SoftOneOfN - Class in de.jstacs.utils.random
  This random generator returns `1-epsilon` for one and equal parts for the rest of a random vector.
SoftOneOfN(double) - Constructor for class de.jstacs.utils.random.SoftOneOfN
  This constructor can be used for (soft) sampling one of n.
SoftOneOfN() - Constructor for class de.jstacs.utils.random.SoftOneOfN
  This constructor can be used for (hard) sampling one of n.
sort(String) - Method in class de.jstacs.results.ListResult
  This method enables you to sort the entries of this container by a specified column.
sostream - Variable in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier
  This stream is used for comments, e.g. during the training, ... .
sostream - Variable in class de.jstacs.models.discrete.inhomogeneous.InhomogeneousDGM
  This stream is used for comments, computation steps/results or any other kind of output during the training, ... etc.
sostream - Variable in class de.jstacs.models.mixture.AbstractMixtureModel
  This is the stream for writing information while training.
source - Variable in class de.jstacs.algorithms.graphs.Edge
  The source node.
SparseSequence - Class in de.jstacs.data.sequences
  This class is an implementation for sequences on one alphabet with length 4.
SparseSequence(AlphabetContainer, String) - Constructor for class de.jstacs.data.sequences.SparseSequence
  Creates a new `SparseSequence` from a `String` representation.
SparseSequence(AlphabetContainer, SymbolExtractor) - Constructor for class de.jstacs.data.sequences.SparseSequence
  Creates a new `SparseSequence` from a `SymbolExtractor`.
SparseStringExtractor - Class in de.jstacs.io
  This `StringExtractor` reads the `String`s from a `File` as the user asks for the `String`.
SparseStringExtractor(String) - Constructor for class de.jstacs.io.SparseStringExtractor
  A constructor that reads the lines from a file.
SparseStringExtractor(File) - Constructor for class de.jstacs.io.SparseStringExtractor
  A constructor that reads the lines from a file.
SparseStringExtractor(String, SequenceAnnotationParser) - Constructor for class de.jstacs.io.SparseStringExtractor
  A constructor that reads the lines from a file.
SparseStringExtractor(String, char) - Constructor for class de.jstacs.io.SparseStringExtractor
  A constructor that reads the lines from a file and ignores those starting with the comment character `ignore`.
SparseStringExtractor(File, char) - Constructor for class de.jstacs.io.SparseStringExtractor
  A constructor that reads the lines from a file and ignores those starting with the comment character `ignore`.
SparseStringExtractor(String, char, SequenceAnnotationParser) - Constructor for class de.jstacs.io.SparseStringExtractor
  A constructor that reads the lines from a file and ignores those starting with the comment character `ignore`.
SparseStringExtractor(String, String, SequenceAnnotationParser) - Constructor for class de.jstacs.io.SparseStringExtractor
  A constructor that reads the lines from a file and sets the annotation of the source to `annotation`.
SparseStringExtractor(String, char, String, SequenceAnnotationParser) - Constructor for class de.jstacs.io.SparseStringExtractor
  A constructor that reads the lines from a file, ignores those starting with the comment character `ignore` and sets the annotation of the source to `annotation`.
SparseStringExtractor(File, char, String, SequenceAnnotationParser) - Constructor for class de.jstacs.io.SparseStringExtractor
  A constructor that reads the lines from a file, ignores those starting with the comment character `ignore` and sets the annotation of the source to `annotation`.
SplitSequenceAnnotationParser - Class in de.jstacs.data.sequences.annotation
  This class implements a simple `SequenceAnnotationParser` which simply splits the comments by specific delimiters.
SplitSequenceAnnotationParser() - Constructor for class de.jstacs.data.sequences.annotation.SplitSequenceAnnotationParser
  Creates a new `SplitSequenceAnnotationParser` with specific delimiters, i.e., key value delimiter "=" and annotation delimiter ";".
SplitSequenceAnnotationParser(String, String) - Constructor for class de.jstacs.data.sequences.annotation.SplitSequenceAnnotationParser
  Creates a new `SplitSequenceAnnotationParser` with user-specified delimiters.
StartDistanceForecaster - Interface in de.jstacs.algorithms.optimization
  This interface is used to determine the next start distance that will be used in a line search.
starts - Variable in class de.jstacs.models.CompositeModel
  The start indices.
starts - Variable in class de.jstacs.models.mixture.AbstractMixtureModel
  The number of starts.
StationaryDistribution - Class in de.jstacs.models.utils
  This class can be used to determine the stationary distribution.
StationaryDistribution() - Constructor for class de.jstacs.models.utils.StationaryDistribution
stationaryIteration - Variable in class de.jstacs.models.mixture.AbstractMixtureModel
  The number of (stationary) iterations of the Gibbs Sampler.
StatisticalTest - Class in de.jstacs.models.utils
  This class enables the user to compute some divergences.
StatisticalTest() - Constructor for class de.jstacs.models.utils.StatisticalTest
STEEPEST_DESCENT - Static variable in class de.jstacs.algorithms.optimization.Optimizer
  This constant can be used to specify that the steepest descent should be used in the `optimize`-method.
steepestDescent(DifferentiableFunction, double[], TerminationCondition, double, StartDistanceForecaster, OutputStream, Time) - Static method in class de.jstacs.algorithms.optimization.Optimizer
  The steepest descent.
stopThreads() - Method in class de.jstacs.classifier.scoringFunctionBased.AbstractMultiThreadedOptimizableFunction
  This method can and should be used to stop all threads if they are not needed any longer.
Storable - Interface in de.jstacs
  This is the root interface for all immutable objects that must be stored in e.g. a file or a database.
StorableResult - Class in de.jstacs.results
  Class for `Result`s that are `Storable`s.
StorableResult(String, String, Storable) - Constructor for class de.jstacs.results.StorableResult
  Creates a result for an XML representation of an object.
StorableResult(StringBuffer) - Constructor for class de.jstacs.results.StorableResult
  The standard constructor for the interface `Storable`.
StorableValidator - Class in de.jstacs.parameters.validation
  Class for a validator that validates instances and XML representations for the correct class types (e.g.
StorableValidator(Class<? extends Storable>, boolean) - Constructor for class de.jstacs.parameters.validation.StorableValidator
  Creates a new `StorableValidator` for a subclass of `AbstractModel` or `AbstractClassifier`.
StorableValidator(Class<? extends Storable>) - Constructor for class de.jstacs.parameters.validation.StorableValidator
  Creates a new `StorableValidator` for a subclass of `Storable`.
StorableValidator(StringBuffer) - Constructor for class de.jstacs.parameters.validation.StorableValidator
  The standard constructor for the interface `Storable`.
StrandedLocatedSequenceAnnotationWithLength - Class in de.jstacs.data.sequences.annotation
  Class for a `SequenceAnnotation` that has a position, a length and an orientation on the strand of a `Sequence`.
StrandedLocatedSequenceAnnotationWithLength(int, int, StrandedLocatedSequenceAnnotationWithLength.Strand, String, String, Result...) - Constructor for class de.jstacs.data.sequences.annotation.StrandedLocatedSequenceAnnotationWithLength
  Creates a new `StrandedLocatedSequenceAnnotationWithLength` of type `type` with identifier `identifier` and additional annotation (that does not fit the `SequenceAnnotation` definitions) given as an array of `Result`s `results`.
StrandedLocatedSequenceAnnotationWithLength(int, int, StrandedLocatedSequenceAnnotationWithLength.Strand, String, String, Collection<Result>) - Constructor for class de.jstacs.data.sequences.annotation.StrandedLocatedSequenceAnnotationWithLength
  Creates a new `StrandedLocatedSequenceAnnotationWithLength` of type `type` with identifier `identifier` and additional annotation (that does not fit the `SequenceAnnotation` definitions) given as a `Collection` of `Result`s `results`.
StrandedLocatedSequenceAnnotationWithLength(int, int, StrandedLocatedSequenceAnnotationWithLength.Strand, String, String, SequenceAnnotation[], Result...) - Constructor for class de.jstacs.data.sequences.annotation.StrandedLocatedSequenceAnnotationWithLength
  Creates a new `StrandedLocatedSequenceAnnotationWithLength` of type `type` with identifier `identifier`, additional annotation (that does not fit the `SequenceAnnotation` definitions) given as an array of `Result`s `additionalAnnotations` and sub-annotations `annotations`.
StrandedLocatedSequenceAnnotationWithLength(String, String, StrandedLocatedSequenceAnnotationWithLength.Strand, LocatedSequenceAnnotation[], Result...) - Constructor for class de.jstacs.data.sequences.annotation.StrandedLocatedSequenceAnnotationWithLength
  Creates a new `StrandedLocatedSequenceAnnotationWithLength` of type `type` with identifier `identifier`, additional annotation (that does not fit the `SequenceAnnotation` definitions) given as an array of `Result`s `additionalAnnotations` and sub-annotations `annotations`.
StrandedLocatedSequenceAnnotationWithLength(StringBuffer) - Constructor for class de.jstacs.data.sequences.annotation.StrandedLocatedSequenceAnnotationWithLength
  The standard constructor for the interface `Storable`.
StrandedLocatedSequenceAnnotationWithLength.Strand - Enum in de.jstacs.data.sequences.annotation
  This enum defines possible orientations on the strands.
strandedness() - Method in enum de.jstacs.data.sequences.annotation.StrandedLocatedSequenceAnnotationWithLength.Strand
  Returns the strandedness, i.e. the orientation on the strand of the sequence as a `String`.
StrandModel - Class in de.jstacs.models.mixture
  This model handles sequences that can either lie on the forward strand or on the reverse complementary strand.
StrandModel(Model, int, boolean, double[], double, AbstractMixtureModel.Algorithm, double, double, AbstractMixtureModel.Parameterization, int, int, BurnInTest) - Constructor for class de.jstacs.models.mixture.StrandModel
  Creates a new `StrandModel`.
StrandModel(Model, int, double[], double, double, AbstractMixtureModel.Parameterization) - Constructor for class de.jstacs.models.mixture.StrandModel
  Creates an instance using EM and estimating the component probabilities.
StrandModel(Model, int, double, double, double, AbstractMixtureModel.Parameterization) - Constructor for class de.jstacs.models.mixture.StrandModel
  Creates an instance using EM and fixed component probabilities.
StrandModel(Model, int, double[], int, int, BurnInTest) - Constructor for class de.jstacs.models.mixture.StrandModel
  Creates an instance using Gibbs Sampling and sampling the component probabilities.
StrandModel(Model, int, double, int, int, BurnInTest) - Constructor for class de.jstacs.models.mixture.StrandModel
  Creates an instance using Gibbs Sampling and fixed component probabilities.
StrandModel(StringBuffer) - Constructor for class de.jstacs.models.mixture.StrandModel
  The constructor for the interface `Storable`.
StrandScoringFunction - Class in de.jstacs.scoringFunctions.mix
  This class enables the user to search on both strand.
StrandScoringFunction(NormalizableScoringFunction, double, int, boolean, StrandScoringFunction.InitMethod) - Constructor for class de.jstacs.scoringFunctions.mix.StrandScoringFunction
  This constructor creates a StrandScoringFunction that optimizes the usage of each strand.
StrandScoringFunction(NormalizableScoringFunction, int, boolean, StrandScoringFunction.InitMethod, double) - Constructor for class de.jstacs.scoringFunctions.mix.StrandScoringFunction
  This constructor creates a StrandScoringFunction that has a fixed frequency for the strand usage.
StrandScoringFunction(StringBuffer) - Constructor for class de.jstacs.scoringFunctions.mix.StrandScoringFunction
  This is the constructor for `Storable`.
StrandScoringFunction.InitMethod - Enum in de.jstacs.scoringFunctions.mix
  This enum defines the different types of plug-in initialization of a `StrandScoringFunction`.
StringExtractor - Class in de.jstacs.io
  This class implements the reader that extracts `String`s from either a `File` or a `String`.
StringExtractor(File, int) - Constructor for class de.jstacs.io.StringExtractor
  A constructor that reads the lines from `file`.
StringExtractor(File, int, char) - Constructor for class de.jstacs.io.StringExtractor
  A constructor that reads the lines from `file` and ignores those starting with the comment character `ignore`.
StringExtractor(File, int, String) - Constructor for class de.jstacs.io.StringExtractor
  A constructor that reads the lines from `file` and sets the annotation of the source to `annotation`.
StringExtractor(File, int, char, String) - Constructor for class de.jstacs.io.StringExtractor
  A constructor that reads the lines from `file`, ignores those starting with the comment character `ignore` and sets the annotation of the source to `annotation`.
StringExtractor(String, int, String) - Constructor for class de.jstacs.io.StringExtractor
  A constructor that reads the lines from a `String` `content` and sets the annotation of the source to `annotation`.
StringExtractor(String, int, char, String) - Constructor for class de.jstacs.io.StringExtractor
  A constructor that reads the lines from a `String` `content`, ignores those starting with the comment character `ignore` and sets the annotation of the source to `annotation`.
StructureLearner - Class in de.jstacs.models.discrete.inhomogeneous
  This class can be used to learn the structure of any discrete model.
StructureLearner(AlphabetContainer, int, double) - Constructor for class de.jstacs.models.discrete.inhomogeneous.StructureLearner
  Creates a new `StructureLearner` for a given `AlphabetContainer`, a given length and a given equivalent sample size (ess).
StructureLearner(AlphabetContainer, int) - Constructor for class de.jstacs.models.discrete.inhomogeneous.StructureLearner
  Creates a `StructureLearner` with equivalent sample size (ess) = 0.
StructureLearner.LearningType - Enum in de.jstacs.models.discrete.inhomogeneous
  This `enum` defines the different types of learning that are possible with the `StructureLearner`.
StructureLearner.ModelType - Enum in de.jstacs.models.discrete.inhomogeneous
  This `enum` defines the different types of models that can be learned with the `StructureLearner`.
structureMeasure - Variable in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
  `Measure` that defines the network structure.
SubclassFinder - Class in de.jstacs.utils
  Utility-class with static methods to find all sub-classes of a certain class (or interface) within the scope of the current class-loader find all sub-classes of a certain class (or interface) within the scope of the current class-loader that can be instantiated, i.e. that are neither interfaces nor abstract filter a set of classes by inheritance from a super-class obtain the class of an `InstanceParameterSet` that can be used to instantiate a sub-class of `InstantiableFromParameterSet`.
SubclassFinder() - Constructor for class de.jstacs.utils.SubclassFinder
subSampling(int) - Method in class de.jstacs.data.Sample
  Randomly samples elements, i.e.
SubTensor - Class in de.jstacs.algorithms.graphs.tensor
  This Tensor can be used to extract or use only a part of a complete `Tensor`.
SubTensor(Tensor, int, int) - Constructor for class de.jstacs.algorithms.graphs.tensor.SubTensor
  This constructor creates a `SubTensor` using the `Tensor` `t` for the nodes `offset, offset+1, ..., offset+length-1`.
sum - Variable in class de.jstacs.classifier.scoringFunctionBased.AbstractOptimizableFunction
  The sums of the weighted data per class and additional the total weight sum.
sum(double[]) - Static method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.Measure
  Computes the sum of all elements in the array `ar`.
sumNormalisation(double[]) - Static method in class de.jstacs.utils.Normalisation
  The method does a sum-normalisation on `d` and returns the the sum of the values.
sumNormalisation(double[], double[], int) - Static method in class de.jstacs.utils.Normalisation
  The method does a sum-normalisation on `d`.
swap() - Method in class de.jstacs.models.mixture.AbstractMixtureModel
  This method swaps the current component models with the alternative model.
symbol - Variable in class de.jstacs.scoringFunctions.directedGraphicalModels.Parameter
  The symbol (out of some `Alphabet`) this parameter is responsible for.
SymbolExtractor - Class in de.jstacs.io
  This class enables you to extract elements (symbols) from a given `String` similar to a `StringTokenizer`.
SymbolExtractor(String) - Constructor for class de.jstacs.io.SymbolExtractor
  Creates a new `SymbolExtractor` using `delim` as delimiter.
SymbolExtractor(String, String) - Constructor for class de.jstacs.io.SymbolExtractor
  Creates a new `SymbolExtractor` using `delim` as delimiter and `string` as the `String` to be parsed.
SymmetricTensor - Class in de.jstacs.algorithms.graphs.tensor
  This class can be used for `Tensor`s with a special symmetry property.
SymmetricTensor(int, byte) - Constructor for class de.jstacs.algorithms.graphs.tensor.SymmetricTensor
  This constructor creates an empty symmetric tensor with given dimension.
SymmetricTensor(SymmetricTensor[], double[]) - Constructor for class de.jstacs.algorithms.graphs.tensor.SymmetricTensor
  The constructor can be used creating a new `SymmetricTensor` as weighted sum of `SymmetricTensor`s.
SymmetricTensor(AsymmetricTensor) - Constructor for class de.jstacs.algorithms.graphs.tensor.SymmetricTensor
  This constructor creates and checks a filled asymmetric tensor from an `AsymmetricTensor` instance.
SymmetricTensor(double[][][], int, byte) - Constructor for class de.jstacs.algorithms.graphs.tensor.SymmetricTensor
  This constructor creates and checks a filled asymmetric tensor with given dimension.

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