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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
Sampled_RepeatedHoldOutExperiment(boolean, TrainableStatisticalModel[]...) - Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutExperiment
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
SamplingGenDisMixClassifierParameterSet(AlphabetContainer, int, int, SamplingScoreBasedClassifier.SamplingScheme, int, int, boolean, boolean, String, int) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.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
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
SamplingScoreBasedClassifierParameterSet(Class<? extends SamplingScoreBasedClassifier>, AlphabetContainer, int, int, SamplingScoreBasedClassifier.SamplingScheme, int, int, boolean, boolean, String) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.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
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
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
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
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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