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## S

SafeOutputStream - Class in de.jstacs.utils
  This class is for any output.
sameLength() - Method in class de.jstacs.classifiers.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(SamplingScoreBasedClassifier.DiffSMSamplingComponent, DiffSSBasedOptimizableFunction) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
  Samples as many steps as needed to get into the stationary phase according to `SamplingScoreBasedClassifier.burnInTest` and then samples the number of stationary steps as set in `SamplingScoreBasedClassifier.params`.
sample - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  The sample that was used in the last training.
Sampled_RepeatedHoldOutAssessParameterSet - Class in de.jstacs.classifiers.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.classifiers.assessment.Sampled_RepeatedHoldOutAssessParameterSet
  Constructs a new `Sampled_RepeatedHoldOutAssessParameterSet` with empty parameter values.
Sampled_RepeatedHoldOutAssessParameterSet(StringBuffer) - Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutAssessParameterSet
  The standard constructor for the interface `Storable`.
Sampled_RepeatedHoldOutAssessParameterSet(DataSet.PartitionMethod, int, boolean, int, int, double, boolean) - Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutAssessParameterSet
  Constructs a new `Sampled_RepeatedHoldOutAssessParameterSet` with given parameter values.
Sampled_RepeatedHoldOutExperiment - Class in de.jstacs.classifiers.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[], TrainableStatisticalModel[][], boolean, boolean) - Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutExperiment
  Creates a new `Sampled_RepeatedHoldOutExperiment` from an array of `AbstractClassifier`s and a two-dimensional array of `TrainableStatisticalModel` s, which are combined to additional classifiers.
Sampled_RepeatedHoldOutExperiment(AbstractClassifier...) - Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutExperiment
  Creates a new `Sampled_RepeatedHoldOutExperiment` from a set of `AbstractClassifier`s.
Sampled_RepeatedHoldOutExperiment(boolean, TrainableStatisticalModel[]...) - Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutExperiment
  Creates a new `Sampled_RepeatedHoldOutExperiment` from a set of `TrainableStatisticalModel`s.
Sampled_RepeatedHoldOutExperiment(AbstractClassifier[], boolean, TrainableStatisticalModel[]...) - Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutExperiment
  This constructor allows to assess a collection of given `AbstractClassifier`s and those constructed using the given `TrainableStatisticalModel`s by a `Sampled_RepeatedHoldOutExperiment`.
sampleNSteps(DiffSSBasedOptimizableFunction, SamplingScoreBasedClassifier.DiffSMSamplingComponent, BurnInTest, int, SamplingScoreBasedClassifier.SamplingScheme) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
  Samples a predefined number of steps appended to the current sampling
samplePath(IntList, int, int, Sequence) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
  This method samples a valid path for the given sequence `seq` using the internal parameters.
SamplingComponent - Interface in de.jstacs.sampling
  This interface defines methods that are used during a sampling.
SamplingDifferentiableStatisticalModel - Interface in de.jstacs.sequenceScores.statisticalModels.differentiable
  Interface for `DifferentiableStatisticalModel`s that can be used for Metropolis-Hastings sampling in a `SamplingScoreBasedClassifier`.
SamplingEmission - Interface in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions
SamplingFromStatistic - Interface in de.jstacs.sampling
  This is the interface for sampling based on a sufficient statistic.
SamplingGenDisMixClassifier - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling
  A classifier that samples its parameters from a `LogGenDisMixFunction` using the Metropolis-Hastings algorithm.
SamplingGenDisMixClassifier(SamplingGenDisMixClassifierParameterSet, BurnInTest, double[], LogPrior, double[], SamplingDifferentiableStatisticalModel...) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifier
  Creates a new `SamplingGenDisMixClassifier` using the external parameters `params`, a burn-in test, a set of sampling variances for the different classes, a prior on the parameters, weights `beta` for the three components of the `LogGenDisMixFunction`, i.e., likelihood, conditional likelihood, and prior, and scoring functions that model the distribution for each of the classes.
SamplingGenDisMixClassifier(SamplingGenDisMixClassifierParameterSet, BurnInTest, double[], LogPrior, LearningPrinciple, SamplingDifferentiableStatisticalModel...) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifier
  Creates a new `SamplingGenDisMixClassifier` using the external parameters `params`, a burn-in test, a set of sampling variances for the different classes, a prior on the parameters, a learning principle, and scoring functions that model the distribution for each of the classes.
SamplingGenDisMixClassifier(StringBuffer) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifier
  Creates a new `SamplingGenDisMixClassifier` from its XML-representation
SamplingGenDisMixClassifierParameterSet - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling
  `ParameterSet` to instantiate a `SamplingGenDisMixClassifier`.
SamplingGenDisMixClassifierParameterSet(AlphabetContainer, int, int, SamplingScoreBasedClassifier.SamplingScheme, int, int, boolean, boolean, String, int) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifierParameterSet
  Create a new `SamplingGenDisMixClassifierParameterSet`.
SamplingGenDisMixClassifierParameterSet(Class<? extends SamplingScoreBasedClassifier>, AlphabetContainer, int, int, SamplingScoreBasedClassifier.SamplingScheme, int, int, boolean, boolean, String, int) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifierParameterSet
  Create a new `SamplingGenDisMixClassifierParameterSet`.
SamplingGenDisMixClassifierParameterSet(AlphabetContainer, int, int, int, int, String, int) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifierParameterSet
  Create a new `SamplingGenDisMixClassifierParameterSet` with a grouped sampling scheme, sampling all parameters (and not only the free ones), and adaption of the variance.
SamplingHigherOrderHMM - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models
SamplingHigherOrderHMM(SamplingHMMTrainingParameterSet, String[], int[], boolean[], SamplingEmission[], TransitionElement...) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM
  This is the main constructor.
SamplingHigherOrderHMM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM
  The standard constructor for the interface `Storable`.
SamplingHigherOrderHMM.ViterbiComputation - Enum in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models
  Emumeration of all possible Viterbi-Path methods
SamplingHMMTrainingParameterSet - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training
  This class contains the parameters for training training an `AbstractHMM` using a sampling strategy.
SamplingHMMTrainingParameterSet() - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.SamplingHMMTrainingParameterSet
  This is the empty constructor that can be used to fill the parameters after creation.
SamplingHMMTrainingParameterSet(int, int, int, AbstractBurnInTestParameterSet) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.SamplingHMMTrainingParameterSet
  This is the main constructor creating an already filled parameter set for training an `AbstractHMM` using a sampling strategy.
SamplingHMMTrainingParameterSet(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.SamplingHMMTrainingParameterSet
  The standard constructor for the interface `Storable`.
samplingIndex - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGModelForGibbsSampling
  The index of the current sampling.
samplingIndex - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
  The index of the current sampling.
samplingIndex - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
  The index of the current sampling.
samplingIndex - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  The current index of the sampling.
SamplingPhyloHMM - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models
  This class implements an (higher order) HMM that contains multi-dimensional emissions described by a phylogenetic tree.
SamplingPhyloHMM(SamplingHMMTrainingParameterSet, String[], int[], boolean[], PhyloDiscreteEmission[], TransitionElement...) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingPhyloHMM
  This is the main constructor for a hidden markov model with phylogenetic emission(s) This model can be trained using a metropolis hastings algorithm
SamplingPhyloHMM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingPhyloHMM
  The standard constructor for the interface `Storable`.
SamplingScoreBasedClassifier - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling
  A classifier that samples the parameters of `SamplingDifferentiableStatisticalModel`s by the Metropolis-Hastings algorithm.
SamplingScoreBasedClassifier(StringBuffer) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
  This is the constructor for `Storable`.
SamplingScoreBasedClassifier(SamplingScoreBasedClassifierParameterSet, BurnInTest, double[], SamplingDifferentiableStatisticalModel...) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
  Creates a new `SamplingScoreBasedClassifier` using the parameters in `params`, a specified `BurnInTest` (or `null` for no burn-in test), a set of sampling variances, which may be different for each of the classes (in analogy to equivalent sample size for the Dirichlet distribution), and set set of `SamplingDifferentiableStatisticalModel`s for each of the classes.
SamplingScoreBasedClassifier.DiffSMSamplingComponent - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling
  The `SamplingComponent` that handles storing and loading sampled parameters values to and from files.
SamplingScoreBasedClassifier.DiffSMSamplingComponent(String) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier.DiffSMSamplingComponent
  Creates a new `SamplingScoreBasedClassifier.DiffSMSamplingComponent` that uses temporary files with name prefix `outfilePrefix` to store sampled parameters.
SamplingScoreBasedClassifier.SamplingScheme - Enum in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling
  Sampling scheme for sampling the parameters of the scoring functions.
SamplingScoreBasedClassifierParameterSet - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling
  `ParameterSet` to instantiate a `SamplingScoreBasedClassifier`.
SamplingScoreBasedClassifierParameterSet(Class<? extends SamplingScoreBasedClassifier>, AlphabetContainer, int, int, SamplingScoreBasedClassifier.SamplingScheme, int, int, boolean, boolean, String) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifierParameterSet
  Create a new `SamplingScoreBasedClassifierParameterSet`.
SamplingScoreBasedClassifierParameterSet(Class<? extends SamplingScoreBasedClassifier>, AlphabetContainer, int, int, int, int, String) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifierParameterSet
  Create a new `SamplingScoreBasedClassifierParameterSet` with a grouped sampling scheme, sampling all parameters (and not only the free ones), and adaption of the variance.
SamplingState - Interface in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states
samplingStopped() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier.DiffSMSamplingComponent
samplingStopped() - Method in interface de.jstacs.sampling.SamplingComponent
  This method is the opposite of the method `SamplingComponent.extendSampling(int, boolean)`.
samplingStopped() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGModelForGibbsSampling
samplingStopped() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
samplingStopped() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
samplingStopped() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleSamplingState
samplingStopped() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
samplingStopped() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  This method is the opposite of the method `AbstractMixtureTrainSM.initModelForSampling(int)`.
SamplingTransition - Interface in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions
  This interface declares all method used during a sampling.
satisfiesSpecificConstraint(Sequence, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.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.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM.HomCondProb
satisfiesSpecificConstraint(Sequence, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhConstraint
satisfiesSpecificConstraint(SequenceIterator) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint
  Returns the index of the constraint that is satisfied by `sequence`.
save(File) - Method in class de.jstacs.data.DataSet
  This method writes the `DataSet` to a file `f`.
save(OutputStream, char, SequenceAnnotationParser) - Method in class de.jstacs.data.DataSet
  This method allows to write all `Sequence`s including their `SequenceAnnotation`s into a `OutputStream`.
saveParameters() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier.DiffSMSamplingComponent
  Saves the parameter values of all parameter files to a `StringBuffer` representing these as XML.
ScaledTransitionElement - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements
  Scaled transition element for an HMM with scaled transition matrices (SHMM).
ScaledTransitionElement(int[], int[], double[], double, double[], String) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ScaledTransitionElement
  Creates an object representing the transition probabilities of a Hidden Markov TrainableStatisticalModel with scaled transition matrices (SHMM) for the given context.
ScaledTransitionElement(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ScaledTransitionElement
  The standard constructor for the interface `Storable`.
scalingFactor - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.DistanceBasedScaledTransitionElement
  The maximal scaling factor.
scalingFactor - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ScaledTransitionElement
  The scaling factors of the individual transition classes.
score - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.DiffSSBasedOptimizableFunction
  These `DifferentiableSequenceScore`s are used during the parallel computation.
score - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
  The internally used scoring functions.
score - Variable in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
  The internally used `DifferentiableSequenceScore`s.
score - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
  The type of the score that is evaluated
ScoreClassifier - Class in de.jstacs.classifiers.differentiableSequenceScoreBased
  This abstract class implements the main functionality of a `DifferentiableSequenceScore` based classifier.
ScoreClassifier(ScoreClassifierParameterSet, double, DifferentiableSequenceScore...) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
  Creates a new `ScoreClassifier` from a given `ScoreClassifierParameterSet` and `DifferentiableSequenceScore`s .
ScoreClassifier(StringBuffer) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
  The standard constructor for the interface `Storable`.
ScoreClassifierParameterSet - Class in de.jstacs.classifiers.differentiableSequenceScoreBased
  A set of `Parameter`s for any `ScoreClassifier`.
ScoreClassifierParameterSet(Class<? extends ScoreClassifier>, boolean, AlphabetContainer.AlphabetContainerType, boolean) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifierParameterSet
  Creates a new `ScoreClassifierParameterSet` with empty parameter values.
ScoreClassifierParameterSet(StringBuffer) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.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.classifiers.differentiableSequenceScoreBased.ScoreClassifierParameterSet
  The constructor for a simple, instantiated parameter set.
ScoreClassifierParameterSet(Class<? extends ScoreClassifier>, AlphabetContainer, int, byte, AbstractTerminationCondition, double, double, boolean, OptimizableFunction.KindOfParameter) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifierParameterSet
  The constructor for a simple, instantiated parameter set.
scoringFunctions - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
  `SamplingDifferentiableStatisticalModel`s
SelectionParameter - Class in de.jstacs.parameters
  Class for a collection parameter, i.e. a parameter that provides some collection of possible values the user can choose from.
SelectionParameter(DataType, String[], Object[], String, String, boolean) - Constructor for class de.jstacs.parameters.SelectionParameter
  Constructor for a `SelectionParameter`.
SelectionParameter(DataType, String[], Object[], String[], String, String, boolean) - Constructor for class de.jstacs.parameters.SelectionParameter
  Constructor for a `SelectionParameter`.
SelectionParameter(String, String, boolean, ParameterSet...) - Constructor for class de.jstacs.parameters.SelectionParameter
  Constructor for a `SelectionParameter` from an array of `ParameterSet`s.
SelectionParameter(String, String, boolean, Class<? extends ParameterSet>...) - Constructor for class de.jstacs.parameters.SelectionParameter
  Constructor for a `SelectionParameter` from an array of `Class`es of `ParameterSet`s.
SelectionParameter(StringBuffer) - Constructor for class de.jstacs.parameters.SelectionParameter
  The standard constructor for the interface `Storable`.
SensitivityForFixedSpecificity - Class in de.jstacs.classifiers.performanceMeasures
  This class implements the sensitivity for a fixed specificity.
SensitivityForFixedSpecificity() - Constructor for class de.jstacs.classifiers.performanceMeasures.SensitivityForFixedSpecificity
  Constructs a new instance of the performance measure `SensitivityForFixedSpecificity` with empty parameter values.
SensitivityForFixedSpecificity(double) - Constructor for class de.jstacs.classifiers.performanceMeasures.SensitivityForFixedSpecificity
  Constructs a new instance of the performance measure `SensitivityForFixedSpecificity` with given `specificity`.
SensitivityForFixedSpecificity(StringBuffer) - Constructor for class de.jstacs.classifiers.performanceMeasures.SensitivityForFixedSpecificity
  The standard constructor for the interface `Storable`.
SeparateGaussianLogPrior - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior
  Class for a `LogPrior` that defines a Gaussian prior on the parameters of a set of `DifferentiableStatisticalModel`s and a set of class parameters.
SeparateGaussianLogPrior(double[], double[], double[]) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.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.classifiers.differentiableSequenceScoreBased.logPrior.SeparateGaussianLogPrior
  The standard constructor for the interface `Storable`.
SeparateLaplaceLogPrior - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior
  Class for a `LogPrior` that defines a Laplace prior on the parameters of a set of `DifferentiableStatisticalModel`s and a set of class parameters.
SeparateLaplaceLogPrior(double[], double[], double[]) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.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.classifiers.differentiableSequenceScoreBased.logPrior.SeparateLaplaceLogPrior
  The standard constructor for the interface `Storable`.
SeparateLogPrior - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.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.classifiers.differentiableSequenceScoreBased.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.classifiers.differentiableSequenceScoreBased.logPrior.SeparateLogPrior
  The standard constructor for the interface `Storable`.
Sequence<T> - Class in de.jstacs.data.sequences
  This is the main class for all sequences.
Sequence(AlphabetContainer, SequenceAnnotation[]) - Constructor for class de.jstacs.data.sequences.Sequence
  Creates a new `Sequence` with the given `AlphabetContainer` and the given annotation, but without the content.
Sequence.CompositeSequence<T> - Class in de.jstacs.data.sequences
  The class handles composite `Sequence`s.
Sequence.CompositeSequence(Sequence, int[], int[]) - Constructor for class de.jstacs.data.sequences.Sequence.CompositeSequence
  This is a very efficient way to create a `Sequence.CompositeSequence` for `Sequence`s with a simple `AlphabetContainer`.
Sequence.CompositeSequence(AlphabetContainer, Sequence<T>, int[], int[]) - Constructor for class de.jstacs.data.sequences.Sequence.CompositeSequence
  This constructor should be used if one wants to create a `DataSet` of `Sequence.CompositeSequence`s.
Sequence.RecursiveSequence<T> - Class in de.jstacs.data.sequences
  This is the main class for subsequences, composite sequences, ... .
Sequence.RecursiveSequence(AlphabetContainer, SequenceAnnotation[], Sequence<T>) - Constructor for class de.jstacs.data.sequences.Sequence.RecursiveSequence
  Creates a new `Sequence.RecursiveSequence` on the `Sequence` `seq` with the `AlphabetContainer` `alphabet` and the annotation `annotation`.
Sequence.RecursiveSequence(AlphabetContainer, Sequence<T>) - Constructor for class de.jstacs.data.sequences.Sequence.RecursiveSequence
  Creates a new `Sequence.RecursiveSequence` on the `Sequence` `seq` with the `AlphabetContainer` `alphabet` using the annotation of the given `Sequence`.
Sequence.SubSequence<T> - Class in de.jstacs.data.sequences
  This class handles subsequences.
Sequence.SubSequence(AlphabetContainer, Sequence, int, int) - Constructor for class de.jstacs.data.sequences.Sequence.SubSequence
  This constructor should be used if one wants to create a `DataSet` of `Sequence.SubSequence`s of defined length.
Sequence.SubSequence(Sequence, int, int) - Constructor for class de.jstacs.data.sequences.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.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous
  This class is used to iterate over a discrete sequence.
SequenceIterator(int) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.SequenceIterator
  Creates a new `SequenceIterator` with maximal `length`.
sequenceIteratorToDataSet(SequenceIterator, FeatureFilter) - Static method in class de.jstacs.data.bioJava.BioJavaAdapter
  This method creates a new `DataSet` from a `SequenceIterator`.
SequenceScore - Interface in de.jstacs.sequenceScores
  This interface defines a scoring function that assigns a score to each input sequence.
SequenceScoringParameterSet<T extends InstantiableFromParameterSet> - Class in de.jstacs.parameters
  Abstract class for a `ParameterSet` containing all parameters necessary to construct an `Object` that implements `InstantiableFromParameterSet`.
SequenceScoringParameterSet(Class<T>, AlphabetContainer.AlphabetContainerType, boolean) - Constructor for class de.jstacs.parameters.SequenceScoringParameterSet
  Constructs an `InstanceParameterSet` having empty parameter values.
SequenceScoringParameterSet(Class<T>, 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<T>, AlphabetContainer, int, boolean) - Constructor for class de.jstacs.parameters.SequenceScoringParameterSet
  Constructs a `SequenceScoringParameterSet` from an `AlphabetContainer` and the length of a sequence.
SequenceScoringParameterSet(Class<T>, 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`.
seqWeights - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  The weights of the (sub-)sequence used to train the components (internal models).
set() - Method in class de.jstacs.algorithms.optimization.termination.AbsoluteValueCondition
  Deprecated.
set() - Method in class de.jstacs.algorithms.optimization.termination.AbstractTerminationCondition
  This method sets internal member variables from `AbstractTerminationCondition.parameter`.
set() - Method in class de.jstacs.algorithms.optimization.termination.CombinedCondition
set() - Method in class de.jstacs.algorithms.optimization.termination.IterationCondition
set() - Method in class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition
set() - Method in class de.jstacs.algorithms.optimization.termination.SmallGradientConditon
set() - Method in class de.jstacs.algorithms.optimization.termination.SmallStepCondition
set() - Method in class de.jstacs.algorithms.optimization.termination.TimeCondition
set(int, T) - Method in class de.jstacs.AnnotatedEntityList
  Replaces the `AnnotatedEntity` at index `idx` with the `AnnotatedEntity` `entity/code>`
set(boolean, DifferentiableSequenceScore...) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.CompositeLogPrior
set(boolean, DifferentiableSequenceScore...) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.LogPrior
  Resets all pre-computed values to their initial values using the `DifferentiableSequenceScore`s `funs`.
set(boolean, DifferentiableSequenceScore...) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateLogPrior
set(int, Parameter) - Method in class de.jstacs.parameters.ParameterSet.ParameterList
set(DGTrainSMParameterSet, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DiscreteGraphicalTrainSM
  Sets the parameters as internal parameters and does some essential computations.
set(DGTrainSMParameterSet, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousMM
set(DGTrainSMParameterSet, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM
set(DGTrainSMParameterSet, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.BayesianNetworkTrainSM
set(DGTrainSMParameterSet, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGTrainSM
set(DGTrainSMParameterSet, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhomogeneousDGTrainSM
setAlpha(double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  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.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.SequenceIterator
  This method sets the bounds for each position.
setClassWeights(boolean, double...) - Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
  Sets new class weights.
setClassWeights(boolean, double[], int) - Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
  Sets new class weights.
setCurrentLength(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.CombinationIterator
  This method sets the current used number of selected elements.
setCurrentSamplingIndex(int) - Method in class de.jstacs.sampling.AbstractBurnInTest
setCurrentSamplingIndex(int) - Method in interface de.jstacs.sampling.BurnInTest
  This method sets the value of the current sampling.
setCurrentSamplingIndex(int) - Method in class de.jstacs.sampling.SimpleBurnInTest
  Deprecated.
setDataAndWeights(DataSet[], double[][]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractMultiThreadedOptimizableFunction
setDataAndWeights(DataSet[], double[][]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractOptimizableFunction
setDataAndWeights(DataSet[], double[][]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.OneDataSetLogGenDisMixFunction
setDataAndWeights(DataSet[], double[][]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.OptimizableFunction
  This method sets the data set and the sequence weights to be used.
setDefault(Object) - Method in class de.jstacs.parameters.AbstractSelectionParameter
  Sets the default value of this `AbstractSelectionParameter` to `defaultValue`.
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.MultiSelectionParameter
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.SelectionParameter
setDefault(Object) - Method in class de.jstacs.parameters.SimpleParameter
setDefaultSelected(int[]) - Method in class de.jstacs.parameters.MultiSelectionParameter
  Sets the default selection of this `MultiSelectionParameter` to `defaultSelection`.
setDeleteOnExit(boolean) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
  If set to `true` (which is the default), the temporary files for storing sampled parameter values are deleted on exit of the program.
setEss(double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DGTrainSMParameterSet
  This method can be used to set the ess (equivalent sample size) of this parameter set.
setESS(double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.StructureLearner
  This method sets the ess (equivalent sample size) of the `StructureLearner`.
setExpLambda(int, double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint
  Sets the exponential value of .
setExtension(String) - Method in class de.jstacs.utils.galaxy.GalaxyAdaptor.FileResult
  Sets the filename extension
setFilename(String) - Method in class de.jstacs.utils.galaxy.GalaxyAdaptor.FileResult
  Sets the file
setForwardProb(double) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
  This method can be used to set the forward strand probability.
setFrameParameterOptimization(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
  This method enables the user to choose whether the frame parameters should be optimized or not.
setFreqs(String[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb
  This method is used to restore the values of a Gibbs Sampling run.
setFurtherInformation(StringBuffer) - Method in class de.jstacs.sampling.AbstractBurnInTest
  This method sets further information for the `AbstractBurnInTest`.
setFurtherInformation(StringBuffer) - Method in class de.jstacs.sampling.VarianceRatioBurnInTest
setFurtherModelInfos(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DiscreteGraphicalTrainSM
  This method replaces the internal model information with those from a `StringBuffer`.
setFurtherModelInfos(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousMM
setFurtherModelInfos(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.DAGTrainSM
setFurtherModelInfos(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGModelForGibbsSampling
setHelp(String) - Method in class de.jstacs.utils.galaxy.GalaxyAdaptor
  Sets the help, i.e., a more detailed description of the program to `help`.
setHelp(File) - Method in class de.jstacs.utils.galaxy.GalaxyAdaptor
  Sets the help, i.e., a more detailed description of the program to the contents of `helpfile`.
setHiddenParameters(double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
  This method sets the hidden parameters of the model.
setId(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.phylo.PhyloNode
  This method set the ID of the current PhyloNode The ID should be unique in the PhyloTree
setIndexOfDescendantTransitionElement(int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
  This method sets the index of the descendant transition element for the child with index `index`.
setInitParameters(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
  Sets the initial parameters of the sampling to `parameters`.
setLambda(int, double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.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.
setLinear(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
  If set to true, the probabilities are mapped to colors by directly, otherwise a logistic mapping is used to emphasize deviations from the uniform distribution.
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.
setMeasure(AbstractPerformanceMeasure) - Method in class de.jstacs.classifiers.performanceMeasures.PerformanceMeasureParameterSet
  Sets the given measure as content of the internally last `ParameterSetContainer`.
setModelType(String) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.BayesianNetworkTrainSMParameterSet
  This method allows a simple change of the model type.
setMotifLength(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.GaussianLikePositionPrior
setMotifLength(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.PositionPrior
  Sets the length of the current motif.
setName(String) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.phylo.PhyloNode
  This method set a name for the current instance
setNumberOfStarts(int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifierParameterSet
  Sets the number of starts to `i`
setNumberOfThreads(int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier
  This method allows to set the number of threads used while optimization.
setNumberOfThreads(int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.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.classifiers.differentiableSequenceScoreBased.ScoreClassifier
  Sets the `OutputStream` that is used e.g. for writing information during training.
setOutputStream(OutputStream) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.DifferentiableStatisticalModelWrapperTrainSM
  Sets the OutputStream that is used e.g. for writing information while training.
setOutputStream(OutputStream) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhomogeneousDGTrainSM
  Sets the `OutputStream` for the model.
setOutputStream(OutputStream) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
  Sets the `OutputStream` that is used e.g. for writing information while training.
setOutputStream(OutputStream) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  Sets the `OutputStream` that is used e.g. for writing information while training.
setParameter(double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
setParameter(double[], int) - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.DifferentiableEmission
  This method sets the internal parameters using the given global parameter array, the global offset of the HMM and the internal offset.
setParameter(double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
setParameter(double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
setParameter(double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.UniformEmission
setParameterFor(int, int[][], BNDiffSMParameter) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
  Sets the instance of the `BNDiffSMParameter` for symbol `symbol` and context `context` to `BNDiffSMParameter` `par`.
setParameterOffset(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
setParameterOffset(int) - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.DifferentiableEmission
  This method sets the internal parameter offset and returns the new parameter offset for further use.
setParameterOffset(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
setParameterOffset(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
setParameterOffset(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.UniformEmission
setParameterOffset(int) - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.DifferentiableTransition
  This method sets the internal offset of the parameter index.
setParameterOffset(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.TransitionElement
  This method sets the internal `TransitionElement.offset` used for several methods (cf. see tags).
setParameterOffset(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
setParameterOffset() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
  This method allows to set the parameter offset in each internally used `TransitionElement`.
setParameterOptimization(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
  This method enables the user to choose whether the parameters should be optimized or not.
setParameterOptimization(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
  This method allows the user to specify whether the parameters should be optimized or not.
setParameters(double[], int) - Method in interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore
  This method sets the internal parameters to the values of `params` between `start` and `start + `DifferentiableSequenceScore.getNumberOfParameters()` - 1`
setParameters(double[], int) - Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
setParameters(double[], int) - Method in class de.jstacs.sequenceScores.differentiable.logistic.LogisticDiffSS
setParameters(double[], int) - Method in class de.jstacs.sequenceScores.differentiable.UniformDiffSS
setParameters(double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
setParameters(double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
setParameters(double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMM0DiffSM
setParameters(double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
setParameters(double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.UniformHomogeneousDiffSM
setParameters(double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
setParameters(double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
setParameters(double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MarkovRandomFieldDiffSM
setParameters(double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
setParameters(double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.MixtureDurationDiffSM
setParameters(double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
setParameters(double, double, double) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
  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.sequenceScores.statisticalModels.differentiable.mixture.motif.UniformDurationDiffSM
setParameters(double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MultiDimensionalSequenceWrapperDiffSM
setParameters(double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
setParameters(double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
setParameters(Emission) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
setParameters(Emission) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
setParameters(Emission) - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.Emission
  Set values of parameters of the instance to the value of the parameters of the given instance.
setParameters(Emission) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.MixtureEmission
setParameters(Emission) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
setParameters(Emission) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.UniformEmission
setParameters(BasicHigherOrderTransition.AbstractTransitionElement) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
  Set values of parameters of the instance to the value of the parameters of the given instance.
setParameters(Transition) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
setParameters(double[], int) - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.DifferentiableTransition
  This method allows to set the parameters of the transition.
setParameters(double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.TransitionElement
  This method sets the internal parameters to the values of `params` beginning at index `start`.
setParameters(double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
setParameters(Transition) - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.Transition
  Set values of parameters of the instance to the value of the parameters of the given instance.
setParametersForFunction(int, double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
  This method allows to set the parameters for specific functions.
setParams(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractMultiThreadedOptimizableFunction
setParams(int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractMultiThreadedOptimizableFunction
  This method sets the parameters for thread `index`
setParams(int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.DiffSSBasedOptimizableFunction
setParams(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.OptimizableFunction
  Sets the current values as parameters.
setParams(double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
  This method allows to set the new parameters using a specific offset.
setParamsStarts() - Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
  This method set the value of the array `IndependentProductDiffSS.startIndexOfParams`.
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`.
setParser(SequenceAnnotationParser) - Method in class de.jstacs.results.DataSetResult
  Sets the `SequenceAnnotationParser` that can be used to write this `DataSetResult` including annotations on the contained `Sequence`s to a file
setPath(String) - Method in class de.jstacs.utils.galaxy.GalaxyAdaptor.FileResult
  Sets the path of the directory containing the file to `path`
setPlugInParameters(int, boolean, DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
  Computes and sets the plug-in parameters (MAP estimated parameters) from `data` using `weights`.
setPrior(LogPrior) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier
  This method set a new prior that should be used for optimization.
setRangeable(boolean) - Method in class de.jstacs.parameters.AbstractSelectionParameter
  Sets the value returned by `AbstractSelectionParameter.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(String, boolean) - Method in class de.jstacs.parameters.MultiSelectionParameter
  Sets the selection of the option with key `key` to the value of `selected`.
setSelected(int, boolean) - Method in class de.jstacs.parameters.MultiSelectionParameter
  Sets the selection of option with no.
setShape(String) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
  Sets the graphviz shape of the node that uses this emission to some non-standard value (standard is "house").
setShiftCorrection(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.ZOOPSTrainSM
  Enables or disables the phase shift correction.
setStatisticForHyperparameters(int[], double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
setStatisticForHyperparameters(int[], double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMM0DiffSM
setStatisticForHyperparameters(int[], double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
setStatisticForHyperparameters(int[], double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.UniformHomogeneousDiffSM
setStatisticForHyperparameters(int[], double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.VariableLengthMixtureDiffSM
setStatisticForHyperparameters(int[], double[]) - Method in interface de.jstacs.sequenceScores.statisticalModels.differentiable.VariableLengthDiffSM
  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.
setTempDir(File) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
  Sets the directory for parameter files set in this `SamplingScoreBasedClassifier`.
setThreadIndependentParameters() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractMultiThreadedOptimizableFunction
  This method allows to set thread independent parameters.
setThreadIndependentParameters() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.DiffSSBasedOptimizableFunction
setThresholdClassWeights(boolean, double) - Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
  Sets a new threshold for 2-class-classifiers.
setTrainData(DataSet) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  This method is invoked by the `train`-method and sets for a given sample the sample that should be used for `train`.
setTrainData(DataSet) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.MixtureTrainSM
setTrainData(DataSet) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.ZOOPSTrainSM
setTrainData(DataSet) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM
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(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.MultiSelectionParameter
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.SelectionParameter
  Sets the selected value to the one that is specified by the key `value`.
setValue(Object) - Method in class de.jstacs.parameters.SimpleParameter
setValue(double) - Method in class de.jstacs.sampling.AbstractBurnInTest
setValue(double) - Method in interface de.jstacs.sampling.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.sampling.SimpleBurnInTest
  Deprecated.
setValue(double) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter
  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.
setWeight(double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.phylo.PhyloNode
  This method set the weight (length, rate ...) for the incoming edge
setWeights(double...) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier
  This method set the weights for the summand of the function.
setWeights(double...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  Sets the weights of each component.
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.sequenceScores.statisticalModels.trainable.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, FSDAGTrainSM...) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureClassifier
  Creates a new `SharedStructureClassifier` from given `FSDAGTrainSM`s.
SharedStructureClassifier(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureClassifier
  The standard constructor for the interface `Storable`.
SharedStructureMixture - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared
  This class handles a mixture of models with the same structure that is learned via EM.
SharedStructureMixture(FSDAGTrainSM[], StructureLearner.ModelType, byte, int, double, TerminationCondition) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureMixture
  Creates a new `SharedStructureMixture` instance which estimates the component probabilities/weights.
SharedStructureMixture(FSDAGTrainSM[], StructureLearner.ModelType, byte, int, double[], double, TerminationCondition) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureMixture
  Creates a new `SharedStructureMixture` instance with fixed component weights.
SharedStructureMixture(FSDAGTrainSM[], StructureLearner.ModelType, byte, int, boolean, double[], double, TerminationCondition) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureMixture
  Creates a new `SharedStructureMixture` instance with all relevant values.
SharedStructureMixture(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureMixture
  The standard constructor for the interface `Storable`.
shortcut - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.DiffSSBasedOptimizableFunction
  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.classifiers.differentiableSequenceScoreBased.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, SignificantMotifOccurrencesFinder.RandomSeqType, SignificantMotifOccurrencesFinder.JoinMethod, 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, DataSet, double[], double) - Constructor for class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
  This constructor creates an instance of `SignificantMotifOccurrencesFinder` that uses a `DataSet` to determine the siginificance level.
SignificantMotifOccurrencesFinder(MotifDiscoverer, SignificantMotifOccurrencesFinder.JoinMethod, DataSet, double[], double) - Constructor for class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
  This constructor creates an instance of `SignificantMotifOccurrencesFinder` that uses a `DataSet` to determine the siginificance level.
SignificantMotifOccurrencesFinder.JoinMethod - Interface in de.jstacs.motifDiscovery
  Interface for methods that combine several profiles over the same sequence into one common profile
SignificantMotifOccurrencesFinder.RandomSeqType - Enum in de.jstacs.motifDiscovery
SignificantMotifOccurrencesFinder.SumOfProbabilities - Class in de.jstacs.motifDiscovery
  Joins several profiles containing log-probabilities into one profile containing the logarithm of the sum of the probabilities of the single profiles.
SignificantMotifOccurrencesFinder.SumOfProbabilities() - Constructor for class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder.SumOfProbabilities
SilentEmission - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions
  This class implements a silent emission which is used to create silent states.
SilentEmission() - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
  The main constructor.
SilentEmission(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
  The standard constructor for the interface `Storable`.
SimpleBurnInTest - Class in de.jstacs.sampling
  Deprecated. since this burn test ignore the data coming from the sampling, it might be problematic to use this test
SimpleBurnInTest(int) - Constructor for class de.jstacs.sampling.SimpleBurnInTest
  Deprecated. This is the main constructor that creates an instance of `SimpleBurnInTest` with fixed burn-in length.
SimpleBurnInTest(StringBuffer) - Constructor for class de.jstacs.sampling.SimpleBurnInTest
  Deprecated. The standard constructor for the interface `Storable`.
SimpleCosts - Class in de.jstacs.algorithms.alignment.cost
  Class for simple costs with costs `match` for a match, `mismatch` for a mismatch, and `gap` for a gap (of length 1).
SimpleCosts(double, double, double) - Constructor for class de.jstacs.algorithms.alignment.cost.SimpleCosts
  Creates a new instance of simple costs with costs `match` for a match, `mismatch` for a mismatch, and `gap` for a gap (of length 1).
SimpleDifferentiableState - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states
  This class implements a `State` based on `Emission` that allows to reuse `Emission`s for different `State`s.
SimpleDifferentiableState(DifferentiableEmission, String, boolean) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleDifferentiableState
  This is the constructor of a `SimpleState`.
SimpleDiscreteSequence - Class in de.jstacs.data.sequences
  This is the main class for any discrete sequence.
SimpleDiscreteSequence(AlphabetContainer, SequenceAnnotation[]) - Constructor for class de.jstacs.data.sequences.SimpleDiscreteSequence
  This constructor creates a new `SimpleDiscreteSequence` with the `AlphabetContainer` `container` and the annotation `annotation` but without the content.
SimpleGaussianSumLogPrior - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.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.classifiers.differentiableSequenceScoreBased.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.classifiers.differentiableSequenceScoreBased.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.
SimpleHistory(StringBuffer) - Constructor for class de.jstacs.motifDiscovery.history.SimpleHistory
  This is the constructor for the interface `Storable`.
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.
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`.
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`.
SimpleSamplingState - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states
  This class implements a state that can be used for a HMM that obtains its parameters from sampling.
SimpleSamplingState(SamplingEmission, String, boolean) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleSamplingState
  This constructor creates a state that can be used in a HMM that obtains its parameters from sampling.
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.
SimpleState - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states
  This class implements a `State` based on `Emission` that allows to reuse `Emission`s for different `State`s.
SimpleState(Emission, String, boolean) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleState
  This is the constructor of a `SimpleState`.
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`.
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`.
SINGLETON - Static variable in class de.jstacs.data.alphabets.DNAAlphabet.DNAAlphabetParameterSet
  The only instance of this class.
SINGLETON - Static variable in class de.jstacs.data.alphabets.DNAAlphabet
  The only instance of this class.
SINGLETON - Static variable in class de.jstacs.data.alphabets.DNAAlphabetContainer.DNAAlphabetContainerParameterSet
  The only instance of this class.
SINGLETON - Static variable in class de.jstacs.data.alphabets.DNAAlphabetContainer
  The only instance of this class.
SINGLETON - Static variable in class de.jstacs.data.alphabets.ProteinAlphabet.ProteinAlphabetParameterSet
  The only instance of this class.
SINGLETON - Static variable in class de.jstacs.data.alphabets.ProteinAlphabet
  The only instance of this class.
Singleton - Interface in de.jstacs
  This interface states that the implementing class has only one immutable instance.
Singleton.SingletonHandler - Class in de.jstacs
  This handler helps to retrieve the single instance of a `Singleton`.
Singleton.SingletonHandler() - Constructor for class de.jstacs.Singleton.SingletonHandler
size() - Method in class de.jstacs.AnnotatedEntityList
  Returns the number of `AnnotatedEntity`s (not the capacity) in the `AnnotatedEntityList`.
SkewNormalLikeDurationDiffSM - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif
  This class implements a skew normal like discrete truncated distribution.
SkewNormalLikeDurationDiffSM(int, int, double, double, double) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
  This is the main constructor if the parameters are fixed.
SkewNormalLikeDurationDiffSM(int, int, boolean, double, double, boolean, double, double, boolean, double, double, int) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
  This is the constructor that allows the most flexible handling of the parameters.
SkewNormalLikeDurationDiffSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
  This is the constructor for `Storable`.
skip(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.SequenceIterator
  This method skips some position.
skipInit - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
  Indicates if the model should be initialized (randomly) before optimization
skipLastClassifiersDuringClassifierTraining - Variable in class de.jstacs.classifiers.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`.
SmallDifferenceOfFunctionEvaluationsCondition(SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition
  This is the main constructor creating an instance from a given parameter set.
SmallDifferenceOfFunctionEvaluationsCondition(StringBuffer) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition
  The standard constructor for the interface `Storable`.
SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet - Class in de.jstacs.algorithms.optimization.termination
  This class implements the parameter set for a `SmallDifferenceOfFunctionEvaluationsCondition`.
SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet() - Constructor for class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet
  This constructor creates an empty parameter set.
SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet(StringBuffer) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet
  The standard constructor for the interface `Storable`.
SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet(double) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet
  This constructor creates a filled instance of a parameters set.
SmallGradientConditon - Class in de.jstacs.algorithms.optimization.termination
  This class implements a `TerminationCondition` that allows no further iteration in an optimization if the the gradient becomes small, i.e., .
SmallGradientConditon(double) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallGradientConditon
  This constructor creates an instance that stops the optimization if the sum of the absolute values of gradient components is smaller than `epsilon`.
SmallGradientConditon(SmallGradientConditon.SmallGradientConditonParameterSet) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallGradientConditon
  This is the main constructor creating an instance from a given parameter set.
SmallGradientConditon(StringBuffer) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallGradientConditon
  The standard constructor for the interface `Storable`.
SmallGradientConditon.SmallGradientConditonParameterSet - Class in de.jstacs.algorithms.optimization.termination
  This class implements the parameter set for a `SmallStepCondition`.
SmallGradientConditon.SmallGradientConditonParameterSet() - Constructor for class de.jstacs.algorithms.optimization.termination.SmallGradientConditon.SmallGradientConditonParameterSet
  This constructor creates an empty parameter set.
SmallGradientConditon.SmallGradientConditonParameterSet(StringBuffer) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallGradientConditon.SmallGradientConditonParameterSet
  The standard constructor for the interface `Storable`.
SmallGradientConditon.SmallGradientConditonParameterSet(double) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallGradientConditon.SmallGradientConditonParameterSet
  This constructor creates a filled instance of a parameters set.
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`.
SmallStepCondition(SmallStepCondition.SmallStepConditionParameterSet) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallStepCondition
  This is the main constructor creating an instance from a given parameter set.
SmallStepCondition(StringBuffer) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallStepCondition
  The standard constructor for the interface `Storable`.
SmallStepCondition.SmallStepConditionParameterSet - Class in de.jstacs.algorithms.optimization.termination
  This class implements the parameter set for a `SmallStepCondition`.
SmallStepCondition.SmallStepConditionParameterSet() - Constructor for class de.jstacs.algorithms.optimization.termination.SmallStepCondition.SmallStepConditionParameterSet
  This constructor creates an empty parameter set.
SmallStepCondition.SmallStepConditionParameterSet(StringBuffer) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallStepCondition.SmallStepConditionParameterSet
  The standard constructor for the interface `Storable`.
SmallStepCondition.SmallStepConditionParameterSet(double) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallStepCondition.SmallStepConditionParameterSet
  This constructor creates a filled instance of a parameters set.
smooth(double[]) - Method in class de.jstacs.data.DinucleotideProperty.MeanSmoothing
smooth(double[]) - Method in class de.jstacs.data.DinucleotideProperty.MedianSmoothing
smooth(double[]) - Method in class de.jstacs.data.DinucleotideProperty.NoSmoothing
smooth(double[]) - Method in class de.jstacs.data.DinucleotideProperty.Smoothing
  Returns the smoothed version of `original`.
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.
sort() - Method in class de.jstacs.utils.IntList
  This method sorts the elements of the list.
sostream - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
  This stream is used for comments, e.g. during the training, ... .
sostream - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhomogeneousDGTrainSM
  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.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
  This is the stream for writing information while training.
sostream - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  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`.
spearmanCorrelation(double[], double[]) - Static method in class de.jstacs.utils.ToolBox
  The method computes the Spearman correlation of two vectors.
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.
standardDeviation - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.PluginGaussianEmission
  Initial standard deviation.
start - Variable in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
  This array specifies the start positions of the specific parts.
START_NODE - Static variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
  The `String` for the start node used in Graphviz annotation.
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.
startIndexOfParams - Variable in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
  This array contains the start indices for `DifferentiableSequenceScore.setParameters(double[], int)` on `IndependentProductDiffSS.score`.
starts - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.CompositeTrainSM
  The start indices.
starts - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  The number of starts.
State - Interface in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states
  This interface declares the methods of any state used in a hidden Markov model.
stateList - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
  Helper variable = only for internal use.
states - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
  The (hidden) states of the HMM.
states - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
  The states that can be visited
StationaryDistribution - Class in de.jstacs.utils
  This class can be used to determine the stationary distribution.
StationaryDistribution() - Constructor for class de.jstacs.utils.StationaryDistribution
stationaryIteration - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  The number of (stationary) iterations of the Gibbs Sampler.
statistic - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
  The array for storing the statistics for each parameter
statistic - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
  The sufficient statistic for determining the parameters during sampling, viterbi or Baum-Welch training.
StatisticalModel - Interface in de.jstacs.sequenceScores.statisticalModels
  This interface declares methods of a statistical model, i.e., a `SequenceScore` that defines a proper likelihood over the input `Sequence`s.
StatisticalModelTester - Class in de.jstacs.utils
  This class is useful for some test for any (discrete) models.
StatisticalModelTester() - Constructor for class de.jstacs.utils.StatisticalModelTester
StatisticalTest - Class in de.jstacs.utils
  This class enables the user to compute some divergences.
StatisticalTest() - Constructor for class de.jstacs.utils.StatisticalTest
statisticsTransitionProb - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.DistanceBasedScaledTransitionElement
  Represents the summarized epsilons required for estimating the transition probabilities from the `context`.
statisticsTransitionProb - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ReferenceBasedTransitionElement
  Represents the gammas required for estimating the transition probabilities not including pseudocounts.
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.classifiers.differentiableSequenceScoreBased.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 `AbstractTrainableStatisticalModel` 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`.
StrandDiffSM - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture
  This class enables the user to search on both strand.
StrandDiffSM(DifferentiableStatisticalModel, double, int, boolean, StrandDiffSM.InitMethod) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
  This constructor creates a StrandDiffSM that optimizes the usage of each strand.
StrandDiffSM(DifferentiableStatisticalModel, int, boolean, StrandDiffSM.InitMethod, double) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
  This constructor creates a StrandDiffSM that has a fixed frequency for the strand usage.
StrandDiffSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
  This is the constructor for `Storable`.
StrandDiffSM.InitMethod - Enum in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture
  This enum defines the different types of plug-in initialization of a `StrandDiffSM`.
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`.
StrandTrainSM - Class in de.jstacs.sequenceScores.statisticalModels.trainable.mixture
  This model handles sequences that can either lie on the forward strand or on the reverse complementary strand.
StrandTrainSM(TrainableStatisticalModel, int, boolean, double[], double, AbstractMixtureTrainSM.Algorithm, double, TerminationCondition, AbstractMixtureTrainSM.Parameterization, int, int, BurnInTest) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM
  Creates a new `StrandTrainSM`.
StrandTrainSM(TrainableStatisticalModel, int, double[], double, TerminationCondition, AbstractMixtureTrainSM.Parameterization) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM
  Creates an instance using EM and estimating the component probabilities.
StrandTrainSM(TrainableStatisticalModel, int, double, double, TerminationCondition, AbstractMixtureTrainSM.Parameterization) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM
  Creates an instance using EM and fixed component probabilities.
StrandTrainSM(TrainableStatisticalModel, int, double[], int, int, BurnInTest) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM
  Creates an instance using Gibbs Sampling and sampling the component probabilities.
StrandTrainSM(TrainableStatisticalModel, int, double, int, int, BurnInTest) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM
  Creates an instance using Gibbs Sampling and fixed component probabilities.
StrandTrainSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM
  The constructor for the interface `Storable`.
StringAlignment - Class in de.jstacs.algorithms.alignment
  Class for the representation of an alignment of `String`s.
StringAlignment(double, String...) - Constructor for class de.jstacs.algorithms.alignment.StringAlignment
  This constructor creates an instance storing the aligned Strings and the costs of the alignment.
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.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous
  This class can be used to learn the structure of any discrete model.
StructureLearner(AlphabetContainer, int, double) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.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.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.StructureLearner
  Creates a `StructureLearner` with equivalent sample size (ess) = 0.
StructureLearner.LearningType - Enum in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous
  This `enum` defines the different types of learning that are possible with the `StructureLearner`.
StructureLearner.ModelType - Enum in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous
  This `enum` defines the different types of models that can be learned with the `StructureLearner`.
structureMeasure - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
  `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.DataSet
  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.classifiers.differentiableSequenceScoreBased.AbstractOptimizableFunction
  The sums of the weighted data per class and additional the total weight sum.
sum(double[]) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure
  Computes the sum of all elements in the array `ar`.
sum(double...) - Static method in class de.jstacs.utils.ToolBox
  Computes the sum of the values in `array`
sum(int, int, double[]) - Static method in class de.jstacs.utils.ToolBox
  Computes the sum of the values in `array` starting at `start` until `end`.
sumNormalisation(double[]) - Static method in class de.jstacs.utils.Normalisation
  The method does a sum-normalisation on `d`, i.e. divides all values in `d` by the sum over all values in `d` and returns 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`, i.e. divides all values in `d` by the sum over all values in `d` and writes the result to `dest` starting at position `start` while `d` remains unchanged.
swap() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  This method swaps the current component models with the alternative model.
symbol - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter
  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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