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

S

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, DiffSSBasedOptimizableFunction) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier

Samples as many steps as needed to get into the stationary phase according to SamplingScoreBasedClassifier.burnInTest and then samples the number of stationary steps as set in SamplingScoreBasedClassifier.params.

sample - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM

The data set that was used in the last training.

Sampled_RepeatedHoldOutAssessParameterSet - Class in de.jstacs.classifiers.assessment

This class implements a ClassifierAssessmentAssessParameterSet that must be used to call the method assess( ...

Sampled_RepeatedHoldOutAssessParameterSet() - Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutAssessParameterSet

Constructs a new Sampled_RepeatedHoldOutAssessParameterSet with empty parameter values.

Sampled_RepeatedHoldOutAssessParameterSet(StringBuffer) - Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutAssessParameterSet

The standard constructor for the interface Storable.

Sampled_RepeatedHoldOutAssessParameterSet(DataSet.PartitionMethod, int, boolean, int, int, double, boolean) - Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutAssessParameterSet

Constructs a new Sampled_RepeatedHoldOutAssessParameterSet with given parameter values.

Sampled_RepeatedHoldOutExperiment - Class in de.jstacs.classifiers.assessment

This class is a special ClassifierAssessment that partitions the data of a user-specified reference class (typically the smallest class) and data sets non-overlapping for all other classes, so that one gets the same number of sequences (and the same lengths of the sequences) in each train and test data set.

Sampled_RepeatedHoldOutExperiment(AbstractClassifier[], TrainableStatisticalModel[][], boolean, boolean) - Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutExperiment

Creates a new Sampled_RepeatedHoldOutExperiment from an array of AbstractClassifiers and a two-dimensional array of TrainableStatisticalModel s, which are combined to additional classifiers.

Sampled_RepeatedHoldOutExperiment(AbstractClassifier...) - Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutExperiment

Creates a new Sampled_RepeatedHoldOutExperiment from a set of AbstractClassifiers.

Sampled_RepeatedHoldOutExperiment(boolean, TrainableStatisticalModel[]...) - Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutExperiment

Creates a new Sampled_RepeatedHoldOutExperiment from a set of TrainableStatisticalModels.

Sampled_RepeatedHoldOutExperiment(AbstractClassifier[], boolean, TrainableStatisticalModel[]...) - Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutExperiment

This constructor allows to assess a collection of given AbstractClassifiers and those constructed using the given TrainableStatisticalModels by a Sampled_RepeatedHoldOutExperiment.

sampleNSteps(DiffSSBasedOptimizableFunction, SamplingScoreBasedClassifier.DiffSMSamplingComponent, BurnInTest, int, SamplingScoreBasedClassifier.SamplingScheme) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier

Samples a predefined number of steps appended to the current sampling

samplePath(IntList, int, int, Sequence) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM

This method samples a valid path for the given sequence seq using the internal parameters.

SamplingComponent - Interface in de.jstacs.sampling

This interface defines methods that are used during a sampling.

SamplingDifferentiableStatisticalModel - Interface in de.jstacs.sequenceScores.statisticalModels.differentiable

Interface for DifferentiableStatisticalModels that can be used for Metropolis-Hastings sampling in a SamplingScoreBasedClassifier.

SamplingEmission - Interface in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions

SamplingFromStatistic - Interface in de.jstacs.sampling

This is the interface for sampling based on a sufficient statistic.

SamplingGenDisMixClassifier - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling

A classifier that samples its parameters from a LogGenDisMixFunction using the Metropolis-Hastings algorithm.

SamplingGenDisMixClassifier(SamplingGenDisMixClassifierParameterSet, BurnInTest, double[], LogPrior, double[], SamplingDifferentiableStatisticalModel...) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifier

Creates a new SamplingGenDisMixClassifier using the external parameters params, a burn-in test, a set of sampling variances for the different classes, a prior on the parameters, weights beta for the three components of the LogGenDisMixFunction, i.e., likelihood, conditional likelihood, and prior, and scoring functions that model the distribution for each of the classes.

SamplingGenDisMixClassifier(SamplingGenDisMixClassifierParameterSet, BurnInTest, double[], LogPrior, LearningPrinciple, SamplingDifferentiableStatisticalModel...) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifier

Creates a new SamplingGenDisMixClassifier using the external parameters params, a burn-in test, a set of sampling variances for the different classes, a prior on the parameters, a learning principle, and scoring functions that model the distribution for each of the classes.

SamplingGenDisMixClassifier(StringBuffer) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifier

Creates a new SamplingGenDisMixClassifier from its XML-representation

SamplingGenDisMixClassifierParameterSet - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling

ParameterSet to instantiate a SamplingGenDisMixClassifier.

SamplingGenDisMixClassifierParameterSet(AlphabetContainer, int, int, SamplingScoreBasedClassifier.SamplingScheme, int, int, boolean, boolean, String, int) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifierParameterSet

Create a new SamplingGenDisMixClassifierParameterSet.

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

Create a new SamplingGenDisMixClassifierParameterSet.

SamplingGenDisMixClassifierParameterSet(AlphabetContainer, int, int, int, int, String, int) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifierParameterSet

Create a new SamplingGenDisMixClassifierParameterSet with a grouped sampling scheme, sampling all parameters (and not only the free ones), and adaption of the variance.

SamplingHigherOrderHMM - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models

SamplingHigherOrderHMM(SamplingHMMTrainingParameterSet, String[], int[], boolean[], SamplingEmission[], TransitionElement...) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM

This is the main constructor.

SamplingHigherOrderHMM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM

The standard constructor for the interface Storable.

SamplingHigherOrderHMM.ViterbiComputation - Enum in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models

Emumeration of all possible Viterbi-Path methods

SamplingHMMTrainingParameterSet - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training

This class contains the parameters for training training an AbstractHMM using a sampling strategy.

SamplingHMMTrainingParameterSet() - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.SamplingHMMTrainingParameterSet

This is the empty constructor that can be used to fill the parameters after creation.

SamplingHMMTrainingParameterSet(int, int, int, AbstractBurnInTestParameterSet) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.SamplingHMMTrainingParameterSet

This is the main constructor creating an already filled parameter set for training an AbstractHMM using a sampling strategy.

SamplingHMMTrainingParameterSet(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.SamplingHMMTrainingParameterSet

The standard constructor for the interface Storable.

samplingIndex - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGModelForGibbsSampling

The index of the current sampling.

samplingIndex - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission

The index of the current sampling.

samplingIndex - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition

The index of the current sampling.

samplingIndex - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM

The current index of the sampling.

SamplingPhyloHMM - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models

This class implements an (higher order) HMM that contains multi-dimensional emissions described by a phylogenetic tree.

SamplingPhyloHMM(SamplingHMMTrainingParameterSet, String[], int[], boolean[], PhyloDiscreteEmission[], TransitionElement...) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingPhyloHMM

This is the main constructor for a hidden markov model with phylogenetic emission(s) This model can be trained using a metropolis hastings algorithm

SamplingPhyloHMM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingPhyloHMM

The standard constructor for the interface Storable.

SamplingScoreBasedClassifier - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling

A classifier that samples the parameters of SamplingDifferentiableStatisticalModels by the Metropolis-Hastings algorithm.

SamplingScoreBasedClassifier(StringBuffer) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier

This is the constructor for Storable.

SamplingScoreBasedClassifier(SamplingScoreBasedClassifierParameterSet, BurnInTest, double[], SamplingDifferentiableStatisticalModel...) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier

Creates a new SamplingScoreBasedClassifier using the parameters in params, a specified BurnInTest (or null for no burn-in test), a set of sampling variances, which may be different for each of the classes (in analogy to equivalent sample size for the Dirichlet distribution), and set set of SamplingDifferentiableStatisticalModels for each of the classes.

SamplingScoreBasedClassifier.DiffSMSamplingComponent - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling

The SamplingComponent that handles storing and loading sampled parameters values to and from files.

SamplingScoreBasedClassifier.DiffSMSamplingComponent(String) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier.DiffSMSamplingComponent

Creates a new SamplingScoreBasedClassifier.DiffSMSamplingComponent that uses temporary files with name prefix outfilePrefix to store sampled parameters.

SamplingScoreBasedClassifier.SamplingScheme - Enum in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling

Sampling scheme for sampling the parameters of the scoring functions.

SamplingScoreBasedClassifierParameterSet - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling

ParameterSet to instantiate a SamplingScoreBasedClassifier.

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

Create a new SamplingScoreBasedClassifierParameterSet.

SamplingScoreBasedClassifierParameterSet(Class<? extends SamplingScoreBasedClassifier>, AlphabetContainer, int, int, int, int, String) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifierParameterSet

Create a new SamplingScoreBasedClassifierParameterSet with a grouped sampling scheme, sampling all parameters (and not only the free ones), and adaption of the variance.

SamplingState - Interface in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states

samplingStopped() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier.DiffSMSamplingComponent

samplingStopped() - Method in interface de.jstacs.sampling.SamplingComponent

This method is the opposite of the method SamplingComponent.extendSampling(int, boolean).

samplingStopped() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGModelForGibbsSampling

samplingStopped() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission

samplingStopped() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission

samplingStopped() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleSamplingState

samplingStopped() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition

samplingStopped() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM

This method is the opposite of the method AbstractMixtureTrainSM.initModelForSampling(int).

SamplingTransition - Interface in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions

This interface declares all method used during a sampling.

satisfiesSpecificConstraint(Sequence, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint

This method returns the index of the specific constraint that is fulfilled by the Sequence seq beginning at position start.

satisfiesSpecificConstraint(Sequence, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM.HomCondProb

satisfiesSpecificConstraint(Sequence, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhConstraint

satisfiesSpecificConstraint(SequenceIterator) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint

Returns the index of the constraint that is satisfied by sequence.

save(File) - Method in class de.jstacs.data.DataSet

This method writes the DataSet to a file f.

save(OutputStream, char, SequenceAnnotationParser) - Method in class de.jstacs.data.DataSet

This method allows to write all Sequences including their SequenceAnnotations into a OutputStream.

saveParameters() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier.DiffSMSamplingComponent

Saves the parameter values of all parameter files to a StringBuffer representing these as XML.

ScaledTransitionElement - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements

Scaled transition element for an HMM with scaled transition matrices (SHMM).

ScaledTransitionElement(int[], int[], double[], double, double[], String) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ScaledTransitionElement

Creates an object representing the transition probabilities of a Hidden Markov TrainableStatisticalModel with scaled transition matrices (SHMM) for the given context.

ScaledTransitionElement(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ScaledTransitionElement

The standard constructor for the interface Storable.

scalingFactor - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.DistanceBasedScaledTransitionElement

The maximal scaling factor.

scalingFactor - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ScaledTransitionElement

The scaling factors of the individual transition classes.

score - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.DiffSSBasedOptimizableFunction

These DifferentiableSequenceScores are used during the parallel computation.

score - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier

The internally used scoring functions.

score - Variable in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS

The internally used DifferentiableSequenceScores.

score - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM

The type of the score that is evaluated

ScoreClassifier - Class in de.jstacs.classifiers.differentiableSequenceScoreBased

This abstract class implements the main functionality of a DifferentiableSequenceScore based classifier.

ScoreClassifier(ScoreClassifierParameterSet, double, DifferentiableSequenceScore...) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier

Creates a new ScoreClassifier from a given ScoreClassifierParameterSet and DifferentiableSequenceScores .

ScoreClassifier(StringBuffer) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier

The standard constructor for the interface Storable.

ScoreClassifierParameterSet - Class in de.jstacs.classifiers.differentiableSequenceScoreBased

A set of Parameters for any ScoreClassifier.

ScoreClassifierParameterSet(Class<? extends ScoreClassifier>, boolean, AlphabetContainer.AlphabetContainerType, boolean) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifierParameterSet

Creates a new ScoreClassifierParameterSet with empty parameter values.

ScoreClassifierParameterSet(StringBuffer) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifierParameterSet

The standard constructor for the interface Storable.

ScoreClassifierParameterSet(Class<? extends ScoreClassifier>, AlphabetContainer, int, byte, double, double, double, boolean, OptimizableFunction.KindOfParameter) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifierParameterSet

The constructor for a simple, instantiated parameter set.

ScoreClassifierParameterSet(Class<? extends ScoreClassifier>, AlphabetContainer, int, byte, AbstractTerminationCondition, double, double, boolean, OptimizableFunction.KindOfParameter) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifierParameterSet

The constructor for a simple, instantiated parameter set.

scoringFunctions - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier

SamplingDifferentiableStatisticalModels

sd(int, int) - Method in class de.jstacs.utils.DoubleList

This method computes the standard deviation of a part of the list.

sd(int, int, double[]) - Static method in class de.jstacs.utils.ToolBox

This method returns the standard deviation of the elements of an array between start and end.

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.

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

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.CompositeSequence(Sequence, int[], int[]) - Constructor for class de.jstacs.data.sequences.Sequence.CompositeSequence

This is a very efficient way to create a Sequence.CompositeSequence for Sequences with a simple AlphabetContainer.

Sequence.CompositeSequence(AlphabetContainer, Sequence<T>, int[], int[]) - Constructor for class de.jstacs.data.sequences.Sequence.CompositeSequence

This constructor should be used if one wants to create a DataSet of Sequence.CompositeSequences.

Sequence.RecursiveSequence<T> - Class in de.jstacs.data.sequences

This is the main class for subsequences, composite sequences, ...

Sequence.RecursiveSequence(AlphabetContainer, SequenceAnnotation[], Sequence<T>) - Constructor for class de.jstacs.data.sequences.Sequence.RecursiveSequence

Creates a new Sequence.RecursiveSequence on the Sequence seq with the AlphabetContainer alphabet and the annotation annotation.

Sequence.RecursiveSequence(AlphabetContainer, Sequence<T>) - Constructor for class de.jstacs.data.sequences.Sequence.RecursiveSequence

Creates a new Sequence.RecursiveSequence on the Sequence seq with the AlphabetContainer alphabet using the annotation of the given Sequence.

Sequence.SubSequence<T> - Class in de.jstacs.data.sequences

This class handles subsequences.

Sequence.SubSequence(AlphabetContainer, Sequence, int, int) - Constructor for class de.jstacs.data.sequences.Sequence.SubSequence

This constructor should be used if one wants to create a DataSet of Sequence.SubSequences of defined length.

Sequence.SubSequence(Sequence, int, int) - Constructor for class de.jstacs.data.sequences.Sequence.SubSequence

This is a very efficient way to create a Sequence.SubSequence of defined length for Sequences with a simple AlphabetContainer.

SequenceAnnotation - Class in de.jstacs.data.sequences.annotation

Class for a general annotation of a Sequence.

SequenceAnnotation(String, String, Result) - Constructor for class de.jstacs.data.sequences.annotation.SequenceAnnotation

Creates a new SequenceAnnotation of type type with identifier identifier and additional annotation (that does not fit the SequenceAnnotation definitions) given as a Result result.

SequenceAnnotation(String, String, Result[]...) - Constructor for class de.jstacs.data.sequences.annotation.SequenceAnnotation

Creates a new SequenceAnnotation of type type with identifier identifier and additional annotation (that does not fit the SequenceAnnotation definitions) given as an array of 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) - Static method in class de.jstacs.data.bioJava.BioJavaAdapter

This method creates a new DataSet from a SequenceIterator.

SequenceScore - Interface in de.jstacs.sequenceScores

This interface defines a scoring function that assigns a score to each input sequence.

SequenceScoringParameterSet<T extends InstantiableFromParameterSet> - Class in de.jstacs.parameters

Abstract class for a ParameterSet containing all parameters necessary to construct an Object that implements InstantiableFromParameterSet.

SequenceScoringParameterSet(Class<T>, AlphabetContainer.AlphabetContainerType, boolean) - Constructor for class de.jstacs.parameters.SequenceScoringParameterSet

Constructs an InstanceParameterSet having empty parameter values.

SequenceScoringParameterSet(Class<T>, AlphabetContainer.AlphabetContainerType, boolean, boolean) - Constructor for class de.jstacs.parameters.SequenceScoringParameterSet

Constructs a SequenceScoringParameterSet having empty parameter values.

SequenceScoringParameterSet(StringBuffer) - Constructor for class de.jstacs.parameters.SequenceScoringParameterSet

The standard constructor for the interface Storable.

SequenceScoringParameterSet(Class<T>, AlphabetContainer, int, boolean) - Constructor for class de.jstacs.parameters.SequenceScoringParameterSet

Constructs a SequenceScoringParameterSet from an AlphabetContainer and the length of a sequence.

SequenceScoringParameterSet(Class<T>, AlphabetContainer) - Constructor for class de.jstacs.parameters.SequenceScoringParameterSet

Constructs a SequenceScoringParameterSet for an object that can handle sequences of variable length and with the AlphabetContainer alphabet.

seqWeights - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM

The weights of the (sub-)sequence used to train the components (internal models).

set() - Method in class de.jstacs.algorithms.optimization.termination.AbsoluteValueCondition

Deprecated.

set() - Method in class de.jstacs.algorithms.optimization.termination.AbstractTerminationCondition

This method sets internal member variables from AbstractTerminationCondition.parameter.

set() - Method in class de.jstacs.algorithms.optimization.termination.CombinedCondition

set() - Method in class de.jstacs.algorithms.optimization.termination.IterationCondition

set() - Method in class de.jstacs.algorithms.optimization.termination.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/code>

set(boolean, DifferentiableSequenceScore...) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.CompositeLogPrior

set(boolean, DifferentiableSequenceScore...) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.LogPrior

Resets all pre-computed values to their initial values using the 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(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.

setCurrentLength(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.CombinationIterator

This method sets the current used number of selected elements.

setCurrentSamplingIndex(int) - Method in class de.jstacs.sampling.AbstractBurnInTest

setCurrentSamplingIndex(int) - Method in interface de.jstacs.sampling.BurnInTest

This method sets the value of the current sampling.

setCurrentSamplingIndex(int) - Method in class de.jstacs.sampling.SimpleBurnInTest

Deprecated.

setDataAndWeights(DataSet[], double[][]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractMultiThreadedOptimizableFunction

setDataAndWeights(DataSet[], double[][]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractOptimizableFunction

setDataAndWeights(DataSet[], double[][]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.OneDataSetLogGenDisMixFunction

setDataAndWeights(DataSet[], double[][]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.OptimizableFunction

This method sets the data set and the sequence weights to be used.

setDefault(Object) - Method in class de.jstacs.parameters.AbstractSelectionParameter

Sets the default value of this AbstractSelectionParameter to defaultValue.

setDefault(Object) - Method in class de.jstacs.parameters.EnumParameter

setDefault(Object) - Method in class de.jstacs.parameters.FileParameter

setDefault(Object) - Method in class de.jstacs.parameters.MultiSelectionParameter

setDefault(Object) - Method in class de.jstacs.parameters.Parameter

Sets the default value of the Parameter to defaultValue.

setDefault(Object) - Method in class de.jstacs.parameters.ParameterSetContainer

setDefault(Object) - Method in class de.jstacs.parameters.RangeParameter

setDefault(Object) - Method in class de.jstacs.parameters.SelectionParameter

setDefault(Object) - Method in class de.jstacs.parameters.SimpleParameter

setDefaultSelected(int[]) - Method in class de.jstacs.parameters.MultiSelectionParameter

Sets the default selection of this MultiSelectionParameter to defaultSelection.

setDeleteOnExit(boolean) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier

If set to true (which is the default), the temporary files for storing sampled parameter values are deleted on exit of the program.

setEss(double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DGTrainSMParameterSet

This method can be used to set the ess (equivalent sample size) of this parameter set.

setESS(double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.StructureLearner

This method sets the ess (equivalent sample size) of the StructureLearner.

setExpLambda(int, double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint

Sets the exponential value of .
(Additionally it sets the value of to the logarithmic value of val: .)

setExtension(String) - Method in class de.jstacs.parameters.FileParameter.FileRepresentation

Sets the extension of this FileParameter.

setExtension(String) - Method in class de.jstacs.utils.galaxy.GalaxyAdaptor.FileResult

Sets the filename extension

setFilename(String) - Method in class de.jstacs.utils.galaxy.GalaxyAdaptor.FileResult

Sets the file

setForwardProb(double) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM

This method can be used to set the forward strand probability.

setFrameParameterOptimization(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM

This method enables the user to choose whether the frame parameters should be optimized or not.

setFreqs(String[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb

This method is used to restore the values of a Gibbs Sampling run.

setFurtherInformation(StringBuffer) - Method in class de.jstacs.sampling.AbstractBurnInTest

This method sets further information for the AbstractBurnInTest.

setFurtherInformation(StringBuffer) - Method in class de.jstacs.sampling.VarianceRatioBurnInTest

setFurtherModelInfos(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DiscreteGraphicalTrainSM

This method replaces the internal model information with those from a StringBuffer.

setFurtherModelInfos(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousMM

setFurtherModelInfos(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.DAGTrainSM

setFurtherModelInfos(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGModelForGibbsSampling

setFurtherModelInfos(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEManager

setHelp(String) - Method in class de.jstacs.utils.galaxy.GalaxyAdaptor

Sets the help, i.e., a more detailed description of the program to help.

setHelp(File) - Method in class de.jstacs.utils.galaxy.GalaxyAdaptor

Sets the help, i.e., a more detailed description of the program to the contents of helpfile.

setHiddenParameters(double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM

This method sets the hidden parameters of the model.

setId(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.phylo.PhyloNode

This method set the ID of the current PhyloNode The ID should be unique in the PhyloTree

setIndexOfDescendantTransitionElement(int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement

This method sets the index of the descendant transition element for the child with index index.

setInitParameters(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier

Sets the initial parameters of the sampling to parameters.

setLambda(int, double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint

Sets the value of .
(Additionally it sets the exponential value of to the exponential value of val: .

setLastDistance(double) - Method in class de.jstacs.algorithms.optimization.ConstantStartDistance

setLastDistance(double) - Method in class de.jstacs.algorithms.optimization.LimitedMedianStartDistance

setLastDistance(double) - Method in interface de.jstacs.algorithms.optimization.StartDistanceForecaster

Sets the last used distance.

setLinear(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission

If set to true, the probabilities are mapped to colors by directly, otherwise a logistic mapping is used to emphasize deviations from the uniform distribution.

setMax(int) - Method in class de.jstacs.utils.DefaultProgressUpdater

setMax(int) - Method in class de.jstacs.utils.GUIProgressUpdater

setMax(int) - Method in class de.jstacs.utils.NullProgressUpdater

setMax(int) - Method in interface de.jstacs.utils.ProgressUpdater

Sets the maximal value that will be set by ProgressUpdater.setValue(int), so a value of max indicates the end of the supervised method call.

setMeasure(T) - Method in class de.jstacs.classifiers.performanceMeasures.AbstractPerformanceMeasureParameterSet

Sets the given measure as content of the internally last ParameterSetContainer.

setModelType(String) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.BayesianNetworkTrainSMParameterSet

This method allows a simple change of the model type.

setMotifLength(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.GaussianLikePositionPrior

setMotifLength(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.PositionPrior

Sets the length of the current motif.

setName(String) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.phylo.PhyloNode

This method set a name for the current instance

setNumberOfStarts(int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifierParameterSet

Sets the number of starts to i

setNumberOfThreads(int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier

This method allows to set the number of threads used while optimization.

setNumberOfThreads(int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifierParameterSet

This method set the number of threads used during optimization.

setOffset() - Method in class de.jstacs.utils.NullProgressUpdater

After NullProgressUpdater.setOffset() is called the current value will be added to every value set by NullProgressUpdater.setValue(int).

setOutputStream(OutputStream) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier

Sets the OutputStream that is used e.g.

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[], 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.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(ParameterSetContainer) - Method in class de.jstacs.parameters.ParameterSet

Sets the enclosing ParameterSetContainer of this ParameterSet to parent.

setParser(SequenceAnnotationParser) - Method in class de.jstacs.results.DataSetResult

Sets the SequenceAnnotationParser that can be used to write this DataSetResult including annotations on the contained Sequences to a file

setPath(String) - Method in class de.jstacs.utils.galaxy.GalaxyAdaptor.FileResult

Sets the path of the directory containing the file to path

setPlugInParameters(int, boolean, DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM

Computes and sets the plug-in parameters (MAP estimated parameters) from data using weights.

setPrior(LogPrior) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier

This method set a new prior that should be used for optimization.

setRangeable(boolean) - Method in class de.jstacs.parameters.AbstractSelectionParameter

Sets the value returned by AbstractSelectionParameter.isRangeable() to rangeable.

setRangeable(boolean) - Method in class de.jstacs.parameters.SimpleParameter

Sets the value returned by SimpleParameter.isRangeable() to rangeable.

setRootValue(int, double) - Method in class de.jstacs.algorithms.graphs.tensor.AsymmetricTensor

setRootValue(int, double) - Method in class de.jstacs.algorithms.graphs.tensor.SubTensor

setRootValue(int, double) - Method in class de.jstacs.algorithms.graphs.tensor.SymmetricTensor

setRootValue(int, double) - Method in class de.jstacs.algorithms.graphs.tensor.Tensor

Sets the value val for the root node child.

setSeed(long) - Method in class de.jstacs.utils.random.RandomNumberGenerator

setSelected(String, boolean) - Method in class de.jstacs.parameters.MultiSelectionParameter

Sets the selection of the option with key key to the value of selected.

setSelected(int, boolean) - Method in class de.jstacs.parameters.MultiSelectionParameter

Sets the selection of option with no.

setShape(String) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission

Sets the graphviz shape of the node that uses this emission to some non-standard value (standard is "house").

setShiftCorrection(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.ZOOPSTrainSM

Enables or disables the phase shift correction.

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

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.

SignificantMotifOccurrencesFinder - Class in de.jstacs.motifDiscovery

This class enables the user to predict motif occurrences given a specific significance level.

SignificantMotifOccurrencesFinder(MotifDiscoverer, SignificantMotifOccurrencesFinder.RandomSeqType, boolean, int, double) - Constructor for class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder

This constructor creates an instance of SignificantMotifOccurrencesFinder that uses the given SignificantMotifOccurrencesFinder.RandomSeqType to determine the siginificance level.

SignificantMotifOccurrencesFinder(MotifDiscoverer, SignificantMotifOccurrencesFinder.RandomSeqType, SignificantMotifOccurrencesFinder.JoinMethod, boolean, int, double) - Constructor for class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder

This constructor creates an instance of SignificantMotifOccurrencesFinder that uses the given SignificantMotifOccurrencesFinder.RandomSeqType to determine the siginificance level.

SignificantMotifOccurrencesFinder(MotifDiscoverer, DataSet, double[], double) - Constructor for class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder

This constructor creates an instance of SignificantMotifOccurrencesFinder that uses a DataSet to determine the siginificance level.

SignificantMotifOccurrencesFinder(MotifDiscoverer, SignificantMotifOccurrencesFinder.JoinMethod, DataSet, double[], double) - Constructor for class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder

This constructor creates an instance of SignificantMotifOccurrencesFinder that uses a DataSet to determine the siginificance level.

SignificantMotifOccurrencesFinder.JoinMethod - Interface in de.jstacs.motifDiscovery

Interface for methods that combine several profiles over the same sequence into one common profile

SignificantMotifOccurrencesFinder.RandomSeqType - Enum in de.jstacs.motifDiscovery

SignificantMotifOccurrencesFinder.SumOfProbabilities - Class in de.jstacs.motifDiscovery

Joins several profiles containing log-probabilities into one profile containing the logarithm of the sum of the probabilities of the single profiles.

SignificantMotifOccurrencesFinder.SumOfProbabilities() - Constructor for class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder.SumOfProbabilities

SilentEmission - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions

This class implements a silent emission which is used to create silent states.

SilentEmission() - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission

The main constructor.

SilentEmission(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission

The standard constructor for the interface Storable.

SimpleBurnInTest - Class in de.jstacs.sampling

Deprecated. since this burn test ignore the data coming from the sampling, it might be problematic to use this test

SimpleBurnInTest(int) - Constructor for class de.jstacs.sampling.SimpleBurnInTest

Deprecated. This is the main constructor that creates an instance of SimpleBurnInTest with fixed burn-in length.

SimpleBurnInTest(StringBuffer) - Constructor for class de.jstacs.sampling.SimpleBurnInTest

Deprecated. The standard constructor for the interface Storable.

SimpleCosts - Class in de.jstacs.algorithms.alignment.cost

Class for simple costs with costs match for a match, mismatch for a mismatch, and gap for a gap (of length 1).

SimpleCosts(double, double, double) - Constructor for class de.jstacs.algorithms.alignment.cost.SimpleCosts

Creates a new instance of simple costs with costs match for a match, mismatch for a mismatch, and gap for a gap (of length 1).

SimpleDifferentiableState - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states

This class implements a State based on Emission that allows to reuse 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.DatatypeNotValidException(String) - Constructor for exception de.jstacs.parameters.SimpleParameter.DatatypeNotValidException

Creates a new SimpleParameter.DatatypeNotValidException with an error message.

SimpleParameter.IllegalValueException - Exception in de.jstacs.parameters

This exception is thrown if a parameter is not valid.

SimpleParameter.IllegalValueException(String) - Constructor for exception de.jstacs.parameters.SimpleParameter.IllegalValueException

Creates a new SimpleParameter.IllegalValueException with the reason of the exception reason as error message.

SimpleParameterSet - Class in de.jstacs.parameters

Class for a ParameterSet that is constructed from an array of 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.ProteinAlphabet.ProteinAlphabetParameterSet

The only instance of this class.

SINGLETON - Static variable in class de.jstacs.data.alphabets.ProteinAlphabet

The only instance of this class.

Singleton - Interface in de.jstacs

This interface states that the implementing class has only one immutable instance.

Singleton.SingletonHandler - Class in de.jstacs

This handler helps to retrieve the single instance of a Singleton.

Singleton.SingletonHandler() - Constructor for class de.jstacs.Singleton.SingletonHandler

size() - Method in class de.jstacs.AnnotatedEntityList

Returns the number of 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., .

SmallDifferenceOfFunctionEvaluationsCondition(double) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition

This constructor creates an instance that stops the optimization if the difference of the current and the last function evaluations is smaller than epsilon.

SmallDifferenceOfFunctionEvaluationsCondition(SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition

This is the main constructor creating an instance from a given parameter set.

SmallDifferenceOfFunctionEvaluationsCondition(StringBuffer) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition

The standard constructor for the interface Storable.

SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet - Class in de.jstacs.algorithms.optimization.termination

This class implements the parameter set for a SmallDifferenceOfFunctionEvaluationsCondition.

SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet() - Constructor for class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet

This constructor creates an empty parameter set.

SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet(StringBuffer) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet

The standard constructor for the interface Storable.

SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet(double) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet

This constructor creates a filled instance of a parameters set.

SmallGradientConditon - Class in de.jstacs.algorithms.optimization.termination

This class implements a TerminationCondition that allows no further iteration in an optimization if the the gradient becomes small, i.e., .

SmallGradientConditon(double) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallGradientConditon

This constructor creates an instance that stops the optimization if the sum of the absolute values of gradient components is smaller than epsilon.

SmallGradientConditon(SmallGradientConditon.SmallGradientConditonParameterSet) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallGradientConditon

This is the main constructor creating an instance from a given parameter set.

SmallGradientConditon(StringBuffer) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallGradientConditon

The standard constructor for the interface Storable.

SmallGradientConditon.SmallGradientConditonParameterSet - Class in de.jstacs.algorithms.optimization.termination

This class implements the parameter set for a SmallStepCondition.

SmallGradientConditon.SmallGradientConditonParameterSet() - Constructor for class de.jstacs.algorithms.optimization.termination.SmallGradientConditon.SmallGradientConditonParameterSet

This constructor creates an empty parameter set.

SmallGradientConditon.SmallGradientConditonParameterSet(StringBuffer) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallGradientConditon.SmallGradientConditonParameterSet

The standard constructor for the interface Storable.

SmallGradientConditon.SmallGradientConditonParameterSet(double) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallGradientConditon.SmallGradientConditonParameterSet

This constructor creates a filled instance of a parameters set.

SmallStepCondition - Class in de.jstacs.algorithms.optimization.termination

This class implements a TerminationCondition that allows no further iteration in an optimization if the scalar product of the current and the last values of x will be small, i.e., .

SmallStepCondition(double) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallStepCondition

This constructor creates an instance that allows no further iteration in an optimization if the scalar product of the current and the last values of x is smaller than epsilon.

SmallStepCondition(SmallStepCondition.SmallStepConditionParameterSet) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallStepCondition

This is the main constructor creating an instance from a given parameter set.

SmallStepCondition(StringBuffer) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallStepCondition

The standard constructor for the interface Storable.

SmallStepCondition.SmallStepConditionParameterSet - Class in de.jstacs.algorithms.optimization.termination

This class implements the parameter set for a SmallStepCondition.

SmallStepCondition.SmallStepConditionParameterSet() - Constructor for class de.jstacs.algorithms.optimization.termination.SmallStepCondition.SmallStepConditionParameterSet

This constructor creates an empty parameter set.

SmallStepCondition.SmallStepConditionParameterSet(StringBuffer) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallStepCondition.SmallStepConditionParameterSet

The standard constructor for the interface Storable.

SmallStepCondition.SmallStepConditionParameterSet(double) - Constructor for class de.jstacs.algorithms.optimization.termination.SmallStepCondition.SmallStepConditionParameterSet

This constructor creates a filled instance of a parameters set.

smooth(double[]) - Method in class de.jstacs.data.DinucleotideProperty.MeanSmoothing

smooth(double[]) - Method in class de.jstacs.data.DinucleotideProperty.MedianSmoothing

smooth(double[]) - Method in class de.jstacs.data.DinucleotideProperty.NoSmoothing

smooth(double[]) - Method in class de.jstacs.data.DinucleotideProperty.Smoothing

Returns the smoothed version of original.

SoftOneOfN - Class in de.jstacs.utils.random

This random generator returns 1-epsilon for one and equal parts for the rest of a random vector.

SoftOneOfN(double) - Constructor for class de.jstacs.utils.random.SoftOneOfN

This constructor can be used for (soft) sampling one of n.

SoftOneOfN() - Constructor for class de.jstacs.utils.random.SoftOneOfN

This constructor can be used for (hard) sampling one of n.

sort(String) - Method in class de.jstacs.results.ListResult

This method enables you to sort the entries of this container by a specified column.

sort() - Method in class de.jstacs.utils.IntList

This method sorts the elements of the list.

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.

SplitSequenceAnnotationParser - Class in de.jstacs.data.sequences.annotation

This class implements a simple SequenceAnnotationParser which simply splits the comments by specific delimiters.

SplitSequenceAnnotationParser() - Constructor for class de.jstacs.data.sequences.annotation.SplitSequenceAnnotationParser

Creates a new SplitSequenceAnnotationParser with specific delimiters, i.e., key value delimiter "=" and annotation delimiter ";".

SplitSequenceAnnotationParser(String, String) - Constructor for class de.jstacs.data.sequences.annotation.SplitSequenceAnnotationParser

Creates a new SplitSequenceAnnotationParser with user-specified delimiters.

standardDeviation - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.PluginGaussianEmission

Initial standard deviation.

start - Variable in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS

This array specifies the start positions of the specific parts.

START_NODE - Static variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM

The String for the start node used in Graphviz annotation.

STARTDISTANCE - Static variable in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMTools

The start distance for the line search in an optimization using the Optimizer.

StartDistanceForecaster - Interface in de.jstacs.algorithms.optimization

This interface is used to determine the next start distance that will be used in a line search.

startIndexOfParams - Variable in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS

This array contains the start indices for DifferentiableSequenceScore.setParameters(double[], int) on IndependentProductDiffSS.score.

starts - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.CompositeTrainSM

The start indices.

starts - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM

The number of starts.

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 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.

StorableValidator - Class in de.jstacs.parameters.validation

Class for a validator that validates instances and XML representations for the correct class types (e.g.

StorableValidator(Class<? extends Storable>, boolean) - Constructor for class de.jstacs.parameters.validation.StorableValidator

Creates a new StorableValidator for a subclass of AbstractTrainableStatisticalModel or AbstractClassifier.

StorableValidator(Class<? extends Storable>) - Constructor for class de.jstacs.parameters.validation.StorableValidator

Creates a new StorableValidator for a subclass of Storable.

StorableValidator(StringBuffer) - Constructor for class de.jstacs.parameters.validation.StorableValidator

The standard constructor for the interface Storable.

StrandDiffSM - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture

This class enables the user to search on both strand.

StrandDiffSM(DifferentiableStatisticalModel, double, int, boolean, StrandDiffSM.InitMethod) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM

This constructor creates a StrandDiffSM that optimizes the usage of each strand.

StrandDiffSM(DifferentiableStatisticalModel, int, boolean, StrandDiffSM.InitMethod, double) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM

This constructor creates a StrandDiffSM that has a fixed frequency for the strand usage.

StrandDiffSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM

This is the constructor for Storable.

StrandDiffSM.InitMethod - Enum in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture

This enum defines the different types of plug-in initialization of a StrandDiffSM.

StrandedLocatedSequenceAnnotationWithLength - Class in de.jstacs.data.sequences.annotation

Class for a SequenceAnnotation that has a position, a length and an orientation on the strand of a Sequence.

StrandedLocatedSequenceAnnotationWithLength(int, int, StrandedLocatedSequenceAnnotationWithLength.Strand, String, String, Result...) - Constructor for class de.jstacs.data.sequences.annotation.StrandedLocatedSequenceAnnotationWithLength

Creates a new StrandedLocatedSequenceAnnotationWithLength of type type with identifier identifier and additional annotation (that does not fit the SequenceAnnotation definitions) given as an array of Results results.

StrandedLocatedSequenceAnnotationWithLength(int, int, StrandedLocatedSequenceAnnotationWithLength.Strand, String, String, Collection<Result>) - Constructor for class de.jstacs.data.sequences.annotation.StrandedLocatedSequenceAnnotationWithLength

Creates a new StrandedLocatedSequenceAnnotationWithLength of type type with identifier identifier and additional annotation (that does not fit the SequenceAnnotation definitions) given as a Collection of Results results.

StrandedLocatedSequenceAnnotationWithLength(int, int, StrandedLocatedSequenceAnnotationWithLength.Strand, String, String, SequenceAnnotation[], Result...) - Constructor for class de.jstacs.data.sequences.annotation.StrandedLocatedSequenceAnnotationWithLength

Creates a new StrandedLocatedSequenceAnnotationWithLength of type type with identifier identifier, additional annotation (that does not fit the SequenceAnnotation definitions) given as an array of Results additionalAnnotations and sub-annotations annotations.

StrandedLocatedSequenceAnnotationWithLength(String, String, StrandedLocatedSequenceAnnotationWithLength.Strand, LocatedSequenceAnnotation[], Result...) - Constructor for class de.jstacs.data.sequences.annotation.StrandedLocatedSequenceAnnotationWithLength

Creates a new StrandedLocatedSequenceAnnotationWithLength of type type with identifier identifier, additional annotation (that does not fit the SequenceAnnotation definitions) given as an array of Results additionalAnnotations and sub-annotations annotations.

StrandedLocatedSequenceAnnotationWithLength(StringBuffer) - Constructor for class de.jstacs.data.sequences.annotation.StrandedLocatedSequenceAnnotationWithLength

The standard constructor for the interface Storable.

StrandedLocatedSequenceAnnotationWithLength.Strand - Enum in de.jstacs.data.sequences.annotation

This enum defines possible orientations on the strands.

strandedness() - Method in enum de.jstacs.data.sequences.annotation.StrandedLocatedSequenceAnnotationWithLength.Strand

Returns the strandedness, i.e.

StrandTrainSM - Class in de.jstacs.sequenceScores.statisticalModels.trainable.mixture

This model handles sequences that can either lie on the forward strand or on the reverse complementary strand.

StrandTrainSM(TrainableStatisticalModel, int, boolean, double[], double, AbstractMixtureTrainSM.Algorithm, double, TerminationCondition, AbstractMixtureTrainSM.Parameterization, int, int, BurnInTest) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM

Creates a new StrandTrainSM.

StrandTrainSM(TrainableStatisticalModel, int, double[], double, TerminationCondition, AbstractMixtureTrainSM.Parameterization) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM

Creates an instance using EM and estimating the component probabilities.

StrandTrainSM(TrainableStatisticalModel, int, double, double, TerminationCondition, AbstractMixtureTrainSM.Parameterization) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM

Creates an instance using EM and fixed component probabilities.

StrandTrainSM(TrainableStatisticalModel, int, double[], int, int, BurnInTest) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM

Creates an instance using Gibbs Sampling and sampling the component probabilities.

StrandTrainSM(TrainableStatisticalModel, int, double, int, int, BurnInTest) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM

Creates an instance using Gibbs Sampling and fixed component probabilities.

StrandTrainSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM

The constructor for the interface Storable.

StringAlignment - Class in de.jstacs.algorithms.alignment

Class for the representation of an alignment of Strings.

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.utils.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(double) - Method in class de.jstacs.data.DataSet

Randomly samples elements, i.e.

subSampling(double, double[]) - Method in class de.jstacs.data.DataSet

SubTensor - Class in de.jstacs.algorithms.graphs.tensor

This Tensor can be used to extract or use only a part of a complete Tensor.

SubTensor(Tensor, int, int) - Constructor for class de.jstacs.algorithms.graphs.tensor.SubTensor

This constructor creates a SubTensor using the Tensor t for the nodes offset, offset+1, ..., offset+length-1.

sum - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractOptimizableFunction

The sums of the weighted data per class and additional the total weight sum.

sum(double[]) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure

Computes the sum of all elements in the array ar.

sum(double...) - Static method in class de.jstacs.utils.ToolBox

Computes the sum of the values in array

sum(int, int, double[]) - Static method in class de.jstacs.utils.ToolBox

Computes the sum of the values in array starting at start until end.

sumNormalisation(double[]) - Static method in class de.jstacs.utils.Normalisation

The method does a sum-normalisation on d, i.e.

sumNormalisation(double[], double[], int) - Static method in class de.jstacs.utils.Normalisation

The method does a sum-normalisation on d, i.e.

swap() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM

This method swaps the current component models with the alternative model.

symbol - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter

The symbol (out of some Alphabet) this parameter is responsible for.

SymbolExtractor - Class in de.jstacs.io

This class enables you to extract elements (symbols) from a given String similar to a StringTokenizer.

SymbolExtractor(String) - Constructor for class de.jstacs.io.SymbolExtractor

Creates a new SymbolExtractor using delim as delimiter.

SymbolExtractor(String, String) - Constructor for class de.jstacs.io.SymbolExtractor

Creates a new SymbolExtractor using delim as delimiter and string as the String to be parsed.

SymmetricTensor - Class in de.jstacs.algorithms.graphs.tensor

This class can be used for 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.