S-Index

A B C D E F G H I J K L M N O P Q R S T U V W X Z

S

s1 - Variable in class de.jstacs.algorithms.alignment.Alignment: The first sequence
s2 - Variable in class de.jstacs.algorithms.alignment.Alignment: The second sequence
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 data set the sequences of the non-reference classes have the same length as the corresponding sequence of the reference class.
sample(SamplingScoreBasedClassifier.DiffSMSamplingComponent, Function) - 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 data set 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( ...
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 data sets 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 data set.
Sampled_RepeatedHoldOutExperiment(AbstractClassifier[], TrainableStatisticalModel[][], boolean, boolean) - Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutExperiment: Creates a new Sampled_RepeatedHoldOutExperiment from an array of AbstractClassifiers 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 AbstractClassifiers.
Sampled_RepeatedHoldOutExperiment(boolean, TrainableStatisticalModel[]...) - Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutExperiment: Creates a new Sampled_RepeatedHoldOutExperiment from a set of TrainableStatisticalModels.
Sampled_RepeatedHoldOutExperiment(AbstractClassifier[], boolean, TrainableStatisticalModel[]...) - Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutExperiment: This constructor allows to assess a collection of given AbstractClassifiers and those constructed using the given TrainableStatisticalModels by a Sampled_RepeatedHoldOutExperiment.
sampleNSteps(Function, 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 DifferentiableStatisticalModels 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 SamplingDifferentiableStatisticalModels 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 SamplingDifferentiableStatisticalModels 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.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 Sequences including their SequenceAnnotations 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 DifferentiableSequenceScores 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 DifferentiableSequenceScores.
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 DifferentiableSequenceScores .
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 Parameters 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: SamplingDifferentiableStatisticalModels
sd(int, int) - Method in class de.jstacs.utils.DoubleList: This method computes the standard deviation of a part of the list.
sd(int, int, double[]) - Static method in class de.jstacs.utils.ToolBox: This method returns the standard deviation of the elements of an array between start and end.
SectionDefinedAlphabetParameterSet(AlphabetContainer.AlphabetContainerType) - Constructor for class de.jstacs.data.AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet: Creates a new AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet for a set of discrete or continuous Alphabets.
SectionDefinedAlphabetParameterSet() - Constructor for class de.jstacs.data.AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet: Creates a new AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet for a set of discrete and continuous Alphabets.
SectionDefinedAlphabetParameterSet(Alphabet[], int[]) - Constructor for class de.jstacs.data.AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet: Creates a new AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet from an array of Alphabets and an array of indexes that define the index of the Alphabet in alphabets belonging to that position in indexes.
SectionDefinedAlphabetParameterSet(StringBuffer) - Constructor for class de.jstacs.data.AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet: The standard constructor for the interface Storable .
SelectionParameter - Class in de.jstacs.parameters: Class for a collection parameter, i.e.
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 ParameterSets.
SelectionParameter(String, String, boolean, Class<? extends ParameterSet>...) - Constructor for class de.jstacs.parameters.SelectionParameter: Constructor for a SelectionParameter from an array of Classes of ParameterSets.
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 DifferentiableStatisticalModels 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 DifferentiableStatisticalModels 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.
SeqLogoPlotGenerator(double[][], int) - Constructor for class de.jstacs.utils.SeqLogoPlotter.SeqLogoPlotGenerator: Creates a new SeqLogoPlotter.SeqLogoPlotGenerator for the given PWM using the specified height of the plot.
SeqLogoPlotGenerator(StringBuffer) - Constructor for class de.jstacs.utils.SeqLogoPlotter.SeqLogoPlotGenerator: Creates a SeqLogoPlotter.SeqLogoPlotGenerator from its XML representation.
SeqLogoPlotter - Class in de.jstacs.utils: Class with static methods for plotting sequence logos of DNA motifs, i.e., position weight matrices defined over a DNAAlphabet.
SeqLogoPlotter() - Constructor for class de.jstacs.utils.SeqLogoPlotter
SeqLogoPlotter.SeqLogoPlotGenerator - Class in de.jstacs.utils: PlotGeneratorResult.PlotGenerator for plotting sequence logos.
seqs - Variable in class de.jstacs.clustering.distances.SequenceScoreDistance: The De Bruijn sequences
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 Sequences.
Sequence.RecursiveSequence<T> - Class in de.jstacs.data.sequences: This is the main class for subsequences, composite sequences, ...
Sequence.SubSequence<T> - Class in de.jstacs.data.sequences: This class handles subsequences.
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 Results 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 Results 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 Results 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 Sequences.
SequenceEnumeration(Sequence...) - Constructor for class de.jstacs.data.SequenceEnumeration: This constructor creates an instance based on the user-specified Sequences sequences.
SequenceEnumeration(Collection<Sequence>) - Constructor for class de.jstacs.data.SequenceEnumeration: This constructor creates an instance based on the user-specified Collection of Sequences 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, AlphabetContainer) - 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.
SequenceScoreDistance - Class in de.jstacs.clustering.distances: Class for a distance metric between StatisticalModels based on the correlation of score profiles on De Bruijn sequences.
SequenceScoreDistance(DiscreteAlphabet, int, boolean) - Constructor for class de.jstacs.clustering.distances.SequenceScoreDistance: Creates a new distance.
SequenceScoreDistance(CyclicSequenceAdaptor[], boolean) - Constructor for class de.jstacs.clustering.distances.SequenceScoreDistance: Creates a new distance for a given set of sequences.
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(double[], double[]) - Method in class de.jstacs.algorithms.optimization.OneDimensionalSubFunction: Sets the current values and direction.
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.MultipleIterationsCondition
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
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 DifferentiableSequenceScores 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(int, double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder: Sets the (conditional) probability parameters at a specific position and sets the mixture parameters (largely) to the unconditional PWM component.
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.FSMEManager
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.
setCurrent(double) - Method in class de.jstacs.tools.ProgressUpdater: Sets the value corresponding to the current progress
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.
setElements() - Method in class de.jstacs.clustering.hierachical.ClusterTree: Sets the copy references of the leave nodes of this cluster tree to the elements of its leaves in the current order.
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 $\exp(\lambda_{index}) = val$ .
(Additionally it sets the value of $\lambda_{index}$ to the logarithmic value of val: $\lambda_{index} = \log(val)$ .)
setExport(boolean) - Method in class de.jstacs.results.ListResult: Sets if this ListResult will be exported in Galaxy.
setExtendedType(String) - Method in class de.jstacs.parameters.FileParameter: Sets the extended type of this FileParameter.
setExtendedType(String) - Method in class de.jstacs.results.TextResult: Sets the extended type of this TextResult.
setExtension(String) - Method in class de.jstacs.parameters.FileParameter.FileRepresentation: Sets the extension of this FileParameter.
setExtension(String) - Method in class de.jstacs.tools.ui.galaxy.GalaxyAdaptor.FileResult: Sets the filename extension
setFilename(String) - Method in class de.jstacs.parameters.FileParameter.FileRepresentation: Sets the file name of this FileParameter
setFilename(String) - Method in class de.jstacs.tools.ui.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.
setFromStoredParameters(ParameterSet) - Method in class de.jstacs.tools.ToolResult: Sets the values of all parameters in other to those stored in the internal parameters that have been supplied upon construction.
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
setFurtherModelInfos(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEManager
setHelp(String) - Method in class de.jstacs.tools.ui.galaxy.GalaxyAdaptor: Sets the help, i.e., a more detailed description of the program to help.
setHelp(File) - Method in class de.jstacs.tools.ui.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
setIndeterminate() - Method in class de.jstacs.tools.ProgressUpdater: Sets the progress to indeterminate.
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 $\lambda_{index} = val$ .
(Additionally it sets the exponential value of $\lambda_{index} = val$ to the exponential value of val: $\exp(\lambda_{index}) = \exp(val)$ .
setLast(double) - Method in class de.jstacs.tools.ProgressUpdater: Sets the value that is reached upon completion of the monitored task.
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.
setLength(int) - Method in class de.jstacs.parameters.ArrayParameterSet
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: Deprecated.

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.
setMaxTicks(double) - Method in class de.jstacs.utils.NiceScale: Sets maximum number of tick marks we're comfortable with
setMeasure(T) - Method in class de.jstacs.classifiers.performanceMeasures.AbstractPerformanceMeasureParameterSet: Sets the given measure as content of the internally last ParameterSetContainer.
setMinMaxPoints(double, double) - Method in class de.jstacs.utils.NiceScale: Sets the minimum and maximum data points for the axis.
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).
setOriginalIndex(int) - Method in class de.jstacs.clustering.hierachical.ClusterTree: Sets the original index (e.g., if elements have been removed from the tree) referring to indexes in the distance matrix that has been used to build a tree.
setOutputStream(OutputStream) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier: Sets the OutputStream that is used e.g.
setOutputStream(OutputStream) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.DifferentiableStatisticalModelWrapperTrainSM: Sets the OutputStream that is used e.g.
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.
setOutputStream(OutputStream) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM: Sets the OutputStream that is used e.g.
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.
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[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier: Sets the current parameters for the class weights and in all scoring functions
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.MultiDimensionalSequenceWrapperDiffSS
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.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
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.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.continuous.MultivariateGaussianEmission
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.
setParametersToValue(MEMConstraint[], double) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMTools: This method is a convenience method that sets the same value for all parameter of the constraints
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(Parameter) - 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 Sequences to a file
setPath(String) - Method in class de.jstacs.tools.ui.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.
setSkiptInit(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM: Sets if the model should be initialized (randomly) before optimization
setStartParamsToConditionalStationaryDistributions() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM: Sets the start parameters of this homogeneous Markov model to the corresponding stationary distributions of the transition probabilities.
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.
setStoreAll(boolean) - Method in class de.jstacs.classifiers.assessment.ClassifierAssessmentAssessParameterSet: This method allows to set the switch for storing all individual performance measure values of each iteration of the ClassifierAssessment.
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.
Only available if this AbstractScoreBasedClassifier distinguishes between 2 classes 0 and 1.
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 data set the data set 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],...,parents[k-1] -> child.
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: Deprecated.

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.
setValues(double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMTools.DualFunction: This method set the values of the Lagrange multiplicators of the constraints
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.
SGIS - Static variable in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMTools: This constant can be used to specify that the model should use the iterative scaling for training.
SGIS_P - Static variable in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMTools: This constant can be used to specify that the model should use the iterative scaling for training.
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 FSDAGTrainSMs.
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 shortss and can therefore be used for discrete AlphabetContainers 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.
shuffle(SimpleDiscreteSequence, int) - Static method in class de.jstacs.data.sequences.SimpleDiscreteSequence: This method implements the algorithm of D.
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.
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).
SimpleCosts(StringBuffer) - Constructor for class de.jstacs.algorithms.alignment.cost.SimpleCosts: Restores SimpleCosts object from its XML representation.
SimpleDifferentiableState - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states: This class implements a State based on Emission that allows to reuse Emissions for different States.
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.IllegalValueException - Exception in de.jstacs.parameters: This exception is thrown if a parameter is not valid.
SimpleParameterSet - Class in de.jstacs.parameters: Class for a ParameterSet that is constructed from an array of Parameters.
SimpleParameterSet(Parameter...) - Constructor for class de.jstacs.parameters.SimpleParameterSet: Creates a new SimpleParameterSet from an array of Parameters.
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 Sequences.
SimpleSequenceIterator(Sequence...) - Constructor for class de.jstacs.data.bioJava.SimpleSequenceIterator: Creates a new SimpleSequenceIterator from an array of Sequences.
SimpleState - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states: This class implements a State based on Emission that allows to reuse Emissions for different States.
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 Strings.
SimpleStringExtractor(String...) - Constructor for class de.jstacs.io.SimpleStringExtractor: This constructor packs the Strings in an instance of SimpleStringExtractor.
simpleWeights(double[]) - Static method in class de.jstacs.classifiers.performanceMeasures.AbstractPerformanceMeasure: Returns true if all weights in weight are 1.
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.IUPACDNAAlphabet.IUPACDNAAlphabetParameterSet: The only instance of this class.
SINGLETON - Static variable in class de.jstacs.data.alphabets.IUPACDNAAlphabet: 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.
SingletonHandler() - Constructor for class de.jstacs.Singleton.SingletonHandler
size() - Method in class de.jstacs.AnnotatedEntityList: Returns the number of AnnotatedEntitys (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., $|f(\underline{x}_{i-1}) - f(\underline{x}_i)| < \epsilon$ .
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.
SmallDifferenceOfFunctionEvaluationsConditionParameterSet() - Constructor for class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet: This constructor creates an empty parameter set.
SmallDifferenceOfFunctionEvaluationsConditionParameterSet(StringBuffer) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet: The standard constructor for the interface Storable.
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., $\sum_i \left|\frac{\partial f(\underline{x})}{\partial x_i}\right| < \epsilon$ .
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.
SmallGradientConditonParameterSet() - Constructor for class de.jstacs.algorithms.optimization.termination.SmallGradientConditon.SmallGradientConditonParameterSet: This constructor creates an empty parameter set.
SmallGradientConditonParameterSet(StringBuffer) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallGradientConditon.SmallGradientConditonParameterSet: The standard constructor for the interface Storable.
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., $(\underline{x}_i-\underline{x}_{i-1})^T (\underline{x}_i-\underline{x}_{i-1}) < \epsilon$ .
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.
SmallStepConditionParameterSet() - Constructor for class de.jstacs.algorithms.optimization.termination.SmallStepCondition.SmallStepConditionParameterSet: This constructor creates an empty parameter set.
SmallStepConditionParameterSet(StringBuffer) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallStepCondition.SmallStepConditionParameterSet: The standard constructor for the interface Storable.
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.
Smoothing() - Constructor for class de.jstacs.data.DinucleotideProperty.Smoothing
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.DoubleList: Sorts the elements of this DoubleList
sort() - Method in class de.jstacs.utils.IntList: This method sorts the elements of the list.
sortAlongWith(double[], double[]...) - Static method in class de.jstacs.utils.ToolBox: This method implements a sort algorithm on the array arrayToBeSorted.
sostream - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier: This stream is used for comments, e.g.
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, ...
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 Strings 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.
SparseStringExtractor(Reader, char, String, SequenceAnnotationParser) - Constructor for class de.jstacs.io.SparseStringExtractor: A constructor that reads the lines from a Reader, 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.
spearmanCorrelation(double[], double[], double[]) - Static method in class de.jstacs.utils.ToolBox: Computes the Spearman correlation of two vectors with weights on the individual entries.
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.
STARTDISTANCE - Static variable in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMTools: The start distance for the line search in an optimization using the Optimizer.
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.
startS1 - Variable in class de.jstacs.algorithms.alignment.Alignment: The start position in the first sequence
startS2 - Variable in class de.jstacs.algorithms.alignment.Alignment: The start position in the second sequence
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 Sequences.
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 interface de.jstacs.algorithms.optimization.MultiThreadedFunction: This method can and should be used to stop all threads if they are not needed any longer.
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.
StorableResult - Class in de.jstacs.results: Class for Results that are Storables.
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.
StorableResultSaver - Class in de.jstacs.results.savers: Implements a ResultSaver for StorableResult.
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 Results 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 Results 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 Results 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 Results 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.
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.
stream - Variable in class de.jstacs.utils.graphics.EPSAdaptor: The stream for saving the results
StringAlignment - Class in de.jstacs.algorithms.alignment: Class for the representation of an alignment of Strings.
StringAlignment(StringBuffer) - Constructor for class de.jstacs.algorithms.alignment.StringAlignment: Restores StringAlignment object from its XML representation.
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.
StringAlignment(double, String[], Result) - 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 Strings 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.
stylesheet - Static variable in class de.jstacs.tools.ui.galaxy.GalaxyAdaptor: The stylesheet used for the Galaxy HTML output.
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.
SubclassFinder() - Constructor for class de.jstacs.utils.SubclassFinder
subSampling(int) - Method in class de.jstacs.data.DataSet: Randomly samples elements, i.e.
subSampling(double, double[]) - Method in class de.jstacs.data.DataSet: Sub-samples sequences and corresponding weights from this DataSet.
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.SubSequences of defined length.
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 Sequences with a simple AlphabetContainer.
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.
sum(boolean[]) - Static method in class de.jstacs.utils.ToolBox: Counts the number of true values in bools (similar to sum on booleans in R).
sumNormalisation(double[]) - Static method in class de.jstacs.utils.Normalisation: The method does a sum-normalisation on d, i.e.
sumNormalisation(double[], double[], int) - Static method in class de.jstacs.utils.Normalisation: The method does a sum-normalisation on d, i.e.
SumOfProbabilities() - Constructor for class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder.SumOfProbabilities
SVGAdaptor - Class in de.jstacs.utils.graphics: GraphicsAdaptor for the SVG format.
SVGAdaptor() - Constructor for class de.jstacs.utils.graphics.SVGAdaptor: Creates a new adaptor for plotting to an SVG device.
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.
SymmetricKullbackLeiblerDivergence(double) - Constructor for class de.jstacs.utils.PFMComparator.SymmetricKullbackLeiblerDivergence: This constructor creates a new instance with a given value for the equivalent sample size.
SymmetricTensor - Class in de.jstacs.algorithms.graphs.tensor: This class can be used for Tensors 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 SymmetricTensors.
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.
SysProtocol() - Constructor for class de.jstacs.tools.ui.cli.CLI.SysProtocol: Creates a new, empty protocol.


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