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## Uses of Class
de.jstacs.data.DataSet

Packages that use DataSet
de.jstacs.classifiers | This package provides the framework for any classifier.
de.jstacs.classifiers.assessment | This package allows to assess classifiers.
de.jstacs.classifiers.differentiableSequenceScoreBased | Provides the classes for `Classifier`s that are based on `SequenceScore`s.
de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix | Provides an implementation of a classifier that allows to train the parameters of a set of `DifferentiableStatisticalModel`s by a unified generative-discriminative learning principle
de.jstacs.classifiers.differentiableSequenceScoreBased.sampling | Provides the classes for `AbstractScoreBasedClassifier`s that are based on `SamplingDifferentiableStatisticalModel`s and that sample parameters using the Metropolis-Hastings algorithm.
de.jstacs.classifiers.trainSMBased | Provides the classes for `Classifier`s that are based on `TrainableStatisticalModel`s
de.jstacs.classifiers.utils | Provides some useful classes for working with classifiers
de.jstacs.data | Provides classes for the representation of data.
de.jstacs.data.bioJava | Provides an adapter between the representation of data in BioJava and the representation used in Jstacs.
de.jstacs.data.sequences | Provides classes for representing sequences.
de.jstacs.motifDiscovery | This package provides the framework including the interface for any de novo motif discoverer
de.jstacs.results | This package provides classes for results and sets of results.
de.jstacs.sampling | This package contains many classes that can be used while a sampling.
de.jstacs.sequenceScores | Provides all `SequenceScore`s, which can be used to score a `Sequence`, typically using some model assumptions.
de.jstacs.sequenceScores.differentiable |
de.jstacs.sequenceScores.differentiable.logistic |
de.jstacs.sequenceScores.statisticalModels | Provides all `StatisticalModel`s, which can compute a proper (i.e., normalized) likelihood over the input space of sequences.
de.jstacs.sequenceScores.statisticalModels.differentiable | Provides all `DifferentiableStatisticalModel`s, which can compute the gradient with respect to their parameters for a given input `Sequence`.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels | Provides `DifferentiableStatisticalModel`s that are directed graphical models.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures | Provides the facilities to learn the structure of a `BayesianNetworkDiffSM`.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures | Provides the facilities to learn the structure of a `BayesianNetworkDiffSM` as a Bayesian tree using a number of measures to define a rating of structures
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures | Provides the facilities to learn the structure of a `BayesianNetworkDiffSM` as a permuted Markov model using a number of measures to define a rating of structures
de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous | Provides `DifferentiableStatisticalModel`s that are homogeneous, i.e. model probabilities or scores independent of the position within a sequence
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture | Provides `DifferentiableSequenceScore`s that are mixtures of other `DifferentiableSequenceScore`s.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif |
de.jstacs.sequenceScores.statisticalModels.trainable | Provides all `TrainableStatisticalModel`s, which can be learned from a single `DataSet`.
de.jstacs.sequenceScores.statisticalModels.trainable.discrete |
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous |
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous | This package contains various inhomogeneous models.
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared |
de.jstacs.sequenceScores.statisticalModels.trainable.hmm | The package provides all interfaces and classes for a hidden Markov model (HMM).
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models | The package provides different implementations of hidden Markov models based on `AbstractHMM`
de.jstacs.sequenceScores.statisticalModels.trainable.mixture | This package is the super package for any mixture model.
de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif |
de.jstacs.utils | This package contains a bundle of useful classes and interfaces like ...

Uses of DataSet in de.jstacs.classifiers

Methods in de.jstacs.classifiers that return DataSet
`DataSet[]` | `MappingClassifier.mapDataSet(DataSet[] s)` This method maps the given `DataSet`s to the internal classes.

Methods in de.jstacs.classifiers with parameters of type DataSet
`protected void` | `AbstractScoreBasedClassifier.check(DataSet s)` This method checks if the given `DataSet` can be used.
`byte[]` | `AbstractClassifier.classify(DataSet s)` This method classifies all sequences of a sample and returns an array of indices of the classes to which the respective sequences are assigned with for each index `i` in the array `0 < i < getNumberOfClasses()`.
`ResultSet` | `AbstractClassifier.evaluate(PerformanceMeasureParameterSet params, boolean exceptionIfNotComputeable, DataSet... s)` This method evaluates the classifier and computes, for instance, the sensitivity for a given specificity, the area under the ROC curve and so on.
`protected double[][][]` | `AbstractScoreBasedClassifier.getMultiClassScores(DataSet[] s)`
`protected double[][][]` | `AbstractClassifier.getMultiClassScores(DataSet[] s)` This method returns a multidimensional array with class specific scores.
`double[]` | `AbstractScoreBasedClassifier.getPValue(DataSet candidates, DataSet bg)` Returns the p-values for all `Sequence`s in the `DataSet` `candidates` with respect to a given background `DataSet` .
`double` | `AbstractScoreBasedClassifier.getPValue(Sequence candidate, DataSet bg)` Returns the p-value for a `Sequence` `candidate` with respect to a given background `DataSet`.
`protected boolean` | `MappingClassifier.getResults(LinkedList list, DataSet[] s, PerformanceMeasureParameterSet params, boolean exceptionIfNotComputeable)`
`protected boolean` | `AbstractScoreBasedClassifier.getResults(LinkedList list, DataSet[] s, PerformanceMeasureParameterSet params, boolean exceptionIfNotComputeable)`
`protected boolean` | `AbstractClassifier.getResults(LinkedList list, DataSet[] s, PerformanceMeasureParameterSet params, boolean exceptionIfNotComputeable)` This method computes the results for any evaluation of the classifier.
`double[]` | `AbstractScoreBasedClassifier.getScores(DataSet s)` This method returns the scores of the classifier for any `Sequence` in the `DataSet`.
`DataSet[]` | `MappingClassifier.mapDataSet(DataSet[] s)` This method maps the given `DataSet`s to the internal classes.
`void` | `AbstractClassifier.train(DataSet... s)` Trains the `AbstractClassifier` object given the data as `DataSet`s.
`void` | `MappingClassifier.train(DataSet[] s, double[][] weights)`
`abstract void` | `AbstractClassifier.train(DataSet[] s, double[][] weights)` This method trains a classifier over an array of weighted `DataSet` s.

Uses of DataSet in de.jstacs.classifiers.assessment

Methods in de.jstacs.classifiers.assessment with parameters of type DataSet
`ListResult` | `ClassifierAssessment.assess(NumericalPerformanceMeasureParameterSet mp, ClassifierAssessmentAssessParameterSet assessPS, DataSet... s)` Assesses the contained classifiers.
`ListResult` | `ClassifierAssessment.assess(NumericalPerformanceMeasureParameterSet mp, ClassifierAssessmentAssessParameterSet assessPS, ProgressUpdater pU, DataSet... s)` Assesses the contained classifiers.
`ListResult` | `ClassifierAssessment.assess(NumericalPerformanceMeasureParameterSet mp, ClassifierAssessmentAssessParameterSet assessPS, ProgressUpdater pU, DataSet[][]... s)` Assesses the contained classifiers.
`ListResult` | `KFoldCrossValidation.assessWithPredefinedSplits(NumericalPerformanceMeasureParameterSet mp, ClassifierAssessmentAssessParameterSet caaps, ProgressUpdater pU, DataSet[]... splitData)` This method implements a k-fold crossvalidation on previously split data.
`protected void` | `Sampled_RepeatedHoldOutExperiment.evaluateClassifier(NumericalPerformanceMeasureParameterSet mp, ClassifierAssessmentAssessParameterSet assessPS, DataSet[] s, ProgressUpdater pU)`
`protected void` | `RepeatedSubSamplingExperiment.evaluateClassifier(NumericalPerformanceMeasureParameterSet mp, ClassifierAssessmentAssessParameterSet assessPS, DataSet[] s, ProgressUpdater pU)` Evaluates the classifier.
`protected void` | `RepeatedHoldOutExperiment.evaluateClassifier(NumericalPerformanceMeasureParameterSet mp, ClassifierAssessmentAssessParameterSet assessPS, DataSet[] s, ProgressUpdater pU)` Evaluates the classifier.
`protected void` | `KFoldCrossValidation.evaluateClassifier(NumericalPerformanceMeasureParameterSet mp, ClassifierAssessmentAssessParameterSet assessPS, DataSet[] s, ProgressUpdater pU)` Evaluates a classifier.
`protected abstract void` | `ClassifierAssessment.evaluateClassifier(NumericalPerformanceMeasureParameterSet mp, ClassifierAssessmentAssessParameterSet assessPS, DataSet[] s, ProgressUpdater pU)` This method must be implemented in all subclasses.
`protected void` | `ClassifierAssessment.prepareAssessment(DataSet... s)` Prepares an assessment.
`protected void` | `ClassifierAssessment.test(NumericalPerformanceMeasureParameterSet mp, boolean exception, DataSet... testS)` Uses the given test samples to call the `evaluate( ... )` -methods of the local `AbstractClassifier`s.
`protected void` | `ClassifierAssessment.train(DataSet... trainS)` Trains the local classifiers using the given training samples.

Uses of DataSet in de.jstacs.classifiers.differentiableSequenceScoreBased

Fields in de.jstacs.classifiers.differentiableSequenceScoreBased declared as DataSet
`protected DataSet[]` | `AbstractOptimizableFunction.data` The data that is used to evaluate this function.

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased that return DataSet
`abstract DataSet[]` | `OptimizableFunction.getData()` Returns the data for each class used in this `OptimizableFunction`.
`DataSet[]` | `AbstractOptimizableFunction.getData()`

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased with parameters of type DataSet
`protected void` | `ScoreClassifier.createStructure(DataSet[] data, double[][] weights)` Creates the structure that will be used in the optimization.
`protected void` | `ScoreClassifier.createStructure(DataSet[] data, double[][] weights, boolean initRandomly)` Creates the structure that will be used in the optimization.
`protected double` | `ScoreClassifier.doOptimization(DataSet[] reduced, double[][] newWeights)` This method does the optimization of the `train`-method
`protected abstract DiffSSBasedOptimizableFunction` | `ScoreClassifier.getFunction(DataSet[] data, double[][] weights)` Returns the function that should be optimized.
`abstract void` | `OptimizableFunction.setDataAndWeights(DataSet[] data, double[][] weights)` This method sets the data set and the sequence weights to be used.
`void` | `AbstractOptimizableFunction.setDataAndWeights(DataSet[] data, double[][] weights)`
`void` | `AbstractMultiThreadedOptimizableFunction.setDataAndWeights(DataSet[] data, double[][] weights)`
`void` | `ScoreClassifier.train(DataSet[] data, double[][] weights)`

Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased with parameters of type DataSet
`AbstractMultiThreadedOptimizableFunction(int threads, DataSet[] data, double[][] weights, boolean norm, boolean freeParams)` The constructor for an multi-threaded instance.
`AbstractOptimizableFunction(DataSet[] data, double[][] weights, boolean norm, boolean freeParams)` The constructor creates an instance using the given weighted data.
`DiffSSBasedOptimizableFunction(int threads, DifferentiableSequenceScore[] score, DataSet[] data, double[][] weights, LogPrior prior, boolean norm, boolean freeParams)` Creates an instance with the underlying infrastructure.

Uses of DataSet in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix that return DataSet
`DataSet[]` | `OneDataSetLogGenDisMixFunction.getData()`

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix with parameters of type DataSet
`protected LogGenDisMixFunction` | `GenDisMixClassifier.getFunction(DataSet[] data, double[][] weights)`
`void` | `OneDataSetLogGenDisMixFunction.setDataAndWeights(DataSet[] data, double[][] weights)`

Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix with parameters of type DataSet
`LogGenDisMixFunction(int threads, DifferentiableSequenceScore[] score, DataSet[] data, double[][] weights, LogPrior prior, double[] beta, boolean norm, boolean freeParams)` The constructor for creating an instance that can be used in an `Optimizer`.
`OneDataSetLogGenDisMixFunction(int threads, DifferentiableSequenceScore[] score, DataSet data, double[][] weights, LogPrior prior, double[] beta, boolean norm, boolean freeParams)` The constructor for creating an instance that can be used in an `Optimizer`.

Uses of DataSet in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling with parameters of type DataSet
`void` | `SamplingScoreBasedClassifier.doSingleSampling(DataSet[] s, double[][] weights, int numSteps, String outfilePrefix)` Does a single sampling run for a predefined number of steps.
`protected abstract DiffSSBasedOptimizableFunction` | `SamplingScoreBasedClassifier.getFunction(DataSet[] data, double[][] weights)` Returns the function that should be sampled from.
`protected DiffSSBasedOptimizableFunction` | `SamplingGenDisMixClassifier.getFunction(DataSet[] data, double[][] weights)`
`double[]` | `SamplingScoreBasedClassifier.getScores(DataSet s)`
`void` | `SamplingScoreBasedClassifier.train(DataSet[] s, double[][] weights)`

Uses of DataSet in de.jstacs.classifiers.trainSMBased

Methods in de.jstacs.classifiers.trainSMBased with parameters of type DataSet
`byte[]` | `TrainSMBasedClassifier.classify(DataSet s)`
`double[]` | `TrainSMBasedClassifier.getScores(DataSet s)`
`void` | `TrainSMBasedClassifier.train(DataSet[] s, double[][] weights)`

Uses of DataSet in de.jstacs.classifiers.utils

Methods in de.jstacs.classifiers.utils with parameters of type DataSet
`static ImageResult` | `ClassificationVisualizer.getScatterplot(AbstractScoreBasedClassifier cl1, AbstractScoreBasedClassifier cl2, DataSet class0, DataSet class1, REnvironment e, boolean drawThreshold)` This method returns an `ImageResult` containing a scatter plot of the scores for the given classifiers `cl1` and `cl2`.
`static ImageResult` | `ClassificationVisualizer.plotScores(AbstractScoreBasedClassifier cl, DataSet class0, DataSet class1, REnvironment e, int bins, double density, String plotOptions)` This method returns an `ImageResult` containing a plot of the histograms of the scores.
`static void` | `ClassificationVisualizer.plotScores(AbstractScoreBasedClassifier cl, DataSet class0, DataSet class1, REnvironment e, int bins, double density, String plotOptions, String fName)` This method creates a pdf containing a plot of the histograms of the scores.

Uses of DataSet in de.jstacs.data

Subclasses of DataSet in de.jstacs.data
`class` | `DNADataSet` This class exist for convenience to allow the user an easy creation of `DataSet`s of DNA `Sequence`s.

Methods in de.jstacs.data that return DataSet
`static DataSet` | `DataSet.diff(DataSet data, DataSet... samples)` This method computes the difference between the `DataSet` `data` and the `DataSet`s `samples`.
`DataSet` | `DataSet.getCompositeDataSet(int[] starts, int[] lengths)` This method enables you to use only composite `Sequence`s of all elements in the current `DataSet`.
`DataSet` | `DataSet.WeightedDataSetFactory.getDataSet()` Returns the `DataSet`, where each `Sequence` occurs only once.
`static DataSet` | `DinucleotideProperty.getDataSetForProperty(DataSet original, DinucleotideProperty... properties)` Creates a new `DataSet` by converting each `Sequence` in `original` to the `DinucleotideProperty`s `properties` and setting these as `ReferenceSequenceAnnotation` of each original sequence.
`static DataSet` | `DinucleotideProperty.getDataSetForProperty(DataSet original, DinucleotideProperty.Smoothing smoothing, boolean addToAnnotation, DinucleotideProperty... properties)` Creates a new `DataSet` by converting each `Sequence` in `original` to the `DinucleotideProperty`s `properties` and adding or setting these as `ReferenceSequenceAnnotation` of each original sequence.
`static DataSet` | `DinucleotideProperty.getDataSetForProperty(DataSet original, DinucleotideProperty.Smoothing smoothing, boolean originalAsAnnotation, DinucleotideProperty property)` Creates a new `DataSet` by converting each `Sequence` in `original` to the `DinucleotideProperty` `property` using the `DinucleotideProperty.Smoothing` smoothing.
`static DataSet` | `DinucleotideProperty.getDataSetForProperty(DataSet original, DinucleotideProperty property)` Creates a new `DataSet` by converting each `Sequence` in `original` to the `DinucleotideProperty` `property`.
`DataSet` | `DataSet.getInfixDataSet(int start, int length)` This method enables you to use only an infix of all elements, i.e. the `Sequence`s, in the current `DataSet`.
`DataSet` | `DataSet.getReverseComplementaryDataSet()` Returns a `DataSet` that contains the reverse complement of all `Sequence`s in this `DataSet`.
`DataSet` | `DataSet.getSuffixDataSet(int start)` This method enables you to use only a suffix of all elements, i.e. the `Sequence`, in the current `DataSet`.
`static DataSet` | `DataSet.intersection(DataSet... samples)` This method computes the intersection between all elements/`DataSet` s of the array, i.e. it returns a `DataSet` containing only `Sequence`s that are contained in all `DataSet`s of the array.
`DataSet[]` | `DataSet.partition(DataSet.PartitionMethod method, double... percentage)` This method partitions the elements, i.e. the `Sequence`s, of the `DataSet` in distinct parts where each part holds the corresponding percentage given in the array `percentage`.
`DataSet[]` | `DataSet.partition(double p, DataSet.PartitionMethod method, int subsequenceLength)` This method partitions the elements, i.e. the `Sequence`s, of the `DataSet` in two distinct parts.
`DataSet[]` | `DataSet.partition(int k, DataSet.PartitionMethod method)` This method partitions the elements, i.e. the `Sequence`s, of the `DataSet` in `k` distinct parts.
`DataSet` | `DataSet.subSampling(int number)` Randomly samples elements, i.e.
`static DataSet` | `DataSet.union(DataSet... s)` Unites all `DataSet`s of the array `s`.
`static DataSet` | `DataSet.union(DataSet[] s, boolean[] in)` This method unites all `DataSet`s of the array `s` regarding the array `in`.
`static DataSet` | `DataSet.union(DataSet[] s, boolean[] in, int subsequenceLength)` This method unites all `DataSet`s of the array `s` regarding the array `in` and sets the element length in the united `DataSet` to `subsequenceLength`.
`static DataSet` | `DataSet.union(DataSet[] s, int subsequenceLength)` This method unites all `DataSet`s of the array `s` and sets the element length in the united sample to `subsequenceLength`.

Methods in de.jstacs.data with parameters of type DataSet
`static DataSet` | `DataSet.diff(DataSet data, DataSet... samples)` This method computes the difference between the `DataSet` `data` and the `DataSet`s `samples`.
`static DataSet` | `DataSet.diff(DataSet data, DataSet... samples)` This method computes the difference between the `DataSet` `data` and the `DataSet`s `samples`.
`static String` | `DataSet.getAnnotation(DataSet... s)` Returns the annotation for an array of `DataSet`s.
`static DataSet` | `DinucleotideProperty.getDataSetForProperty(DataSet original, DinucleotideProperty... properties)` Creates a new `DataSet` by converting each `Sequence` in `original` to the `DinucleotideProperty`s `properties` and setting these as `ReferenceSequenceAnnotation` of each original sequence.
`static DataSet` | `DinucleotideProperty.getDataSetForProperty(DataSet original, DinucleotideProperty.Smoothing smoothing, boolean addToAnnotation, DinucleotideProperty... properties)` Creates a new `DataSet` by converting each `Sequence` in `original` to the `DinucleotideProperty`s `properties` and adding or setting these as `ReferenceSequenceAnnotation` of each original sequence.
`static DataSet` | `DinucleotideProperty.getDataSetForProperty(DataSet original, DinucleotideProperty.Smoothing smoothing, boolean originalAsAnnotation, DinucleotideProperty property)` Creates a new `DataSet` by converting each `Sequence` in `original` to the `DinucleotideProperty` `property` using the `DinucleotideProperty.Smoothing` smoothing.
`static DataSet` | `DinucleotideProperty.getDataSetForProperty(DataSet original, DinucleotideProperty property)` Creates a new `DataSet` by converting each `Sequence` in `original` to the `DinucleotideProperty` `property`.
`static ImageResult` | `DinucleotideProperty.getPropertyImage(DataSet original, DinucleotideProperty prop, DinucleotideProperty.Smoothing smoothing, REnvironment re, int xLeft, String pltOptions, int width, int height)`
`static DataSet` | `DataSet.intersection(DataSet... samples)` This method computes the intersection between all elements/`DataSet` s of the array, i.e. it returns a `DataSet` containing only `Sequence`s that are contained in all `DataSet`s of the array.
`static DataSet` | `DataSet.union(DataSet... s)` Unites all `DataSet`s of the array `s`.
`static DataSet` | `DataSet.union(DataSet[] s, boolean[] in)` This method unites all `DataSet`s of the array `s` regarding the array `in`.
`static DataSet` | `DataSet.union(DataSet[] s, boolean[] in, int subsequenceLength)` This method unites all `DataSet`s of the array `s` regarding the array `in` and sets the element length in the united `DataSet` to `subsequenceLength`.
`static DataSet` | `DataSet.union(DataSet[] s, int subsequenceLength)` This method unites all `DataSet`s of the array `s` and sets the element length in the united sample to `subsequenceLength`.

Constructors in de.jstacs.data with parameters of type DataSet
`DataSet.ElementEnumerator(DataSet data)` Creates a new `DataSet.ElementEnumerator` on the given `DataSet` `data`.
`DataSet.WeightedDataSetFactory(DataSet.WeightedDataSetFactory.SortOperation sort, DataSet... data)` Creates a new `DataSet.WeightedDataSetFactory` on the given `DataSet`(s) with `DataSet.WeightedDataSetFactory.SortOperation` `sort`.
`DataSet.WeightedDataSetFactory(DataSet.WeightedDataSetFactory.SortOperation sort, DataSet[] data, double[][] weights, int length)` Creates a new `DataSet.WeightedDataSetFactory` on the given array of `DataSet`s and an array of `weights` with a given `length` and `DataSet.WeightedDataSetFactory.SortOperation` `sort`.
`DataSet.WeightedDataSetFactory(DataSet.WeightedDataSetFactory.SortOperation sort, DataSet data, double[] weights)` Creates a new `DataSet.WeightedDataSetFactory` on the given `DataSet` and an array of `weights` with `DataSet.WeightedDataSetFactory.SortOperation` `sort`.
`DataSet.WeightedDataSetFactory(DataSet.WeightedDataSetFactory.SortOperation sort, DataSet data, double[] weights, int length)` Creates a new `DataSet.WeightedDataSetFactory` on the given `DataSet` and an array of `weights` with a given `length` and `DataSet.WeightedDataSetFactory.SortOperation` `sort`.
`DataSet(DataSet s, int subsequenceLength)` Creates a new `DataSet` from a given `DataSet` and a given length `subsequenceLength`.
`DataSetKMerEnumerator(DataSet data, int k, boolean eliminateRevComp)` Constructs a new DataSetKMerEnumerator from a `DataSet` `data` by extracting all k-mers.

Uses of DataSet in de.jstacs.data.bioJava

Methods in de.jstacs.data.bioJava that return DataSet
`static DataSet` | `BioJavaAdapter.sequenceIteratorToDataSet(SequenceIterator it, FeatureFilter filter)` This method creates a new `DataSet` from a `SequenceIterator`.

Methods in de.jstacs.data.bioJava with parameters of type DataSet
`static SequenceIterator` | `BioJavaAdapter.dataSetToSequenceIterator(DataSet sample, boolean flat)` Creates a `SequenceIterator` from the `DataSet` `sample` preserving as much annotation as possible.

Uses of DataSet in de.jstacs.data.sequences

Methods in de.jstacs.data.sequences that return DataSet
`static DataSet` | `SparseSequence.getDataSet(AlphabetContainer con, AbstractStringExtractor... se)` This method allows to create a `DataSet` containing `SparseSequence`s.
`static DataSet` | `ArbitraryFloatSequence.getDataSet(AlphabetContainer con, AbstractStringExtractor... se)` This method allows to create a `DataSet` containing `ArbitraryFloatSequence`s.
`static DataSet` | `SparseSequence.getDataSet(AlphabetContainer con, String filename)` This method allows to create a `DataSet` containing `SparseSequence`s using a file name.
`static DataSet` | `ArbitraryFloatSequence.getDataSet(AlphabetContainer con, String filename)` This method allows to create a `DataSet` containing `ArbitraryFloatSequence`s using a file name.
`static DataSet` | `SparseSequence.getDataSet(AlphabetContainer con, String filename, SequenceAnnotationParser parser)` This method allows to create a `DataSet` containing `SparseSequence`s using a file name.
`static DataSet` | `ArbitraryFloatSequence.getDataSet(AlphabetContainer con, String filename, SequenceAnnotationParser parser)` This method allows to create a `DataSet` containing `ArbitraryFloatSequence`s using a file name.

Uses of DataSet in de.jstacs.motifDiscovery

Methods in de.jstacs.motifDiscovery that return DataSet
`DataSet` | `SignificantMotifOccurrencesFinder.annotateMotif(DataSet data, int motifIndex)` This method annotates a `DataSet`.
`DataSet` | `SignificantMotifOccurrencesFinder.annotateMotif(DataSet data, int motifIndex, int addMax)` This method annotates a `DataSet`.
`DataSet` | `SignificantMotifOccurrencesFinder.annotateMotif(int startPos, DataSet data, int motifIndex)` This method annotates a `DataSet` starting in each sequence at `startPos`.
`DataSet` | `SignificantMotifOccurrencesFinder.annotateMotif(int startPos, DataSet data, int motifIndex, int addMax, boolean addAnnotation)` This method annotates a `DataSet` starting in each sequence at `startPos`.
`DataSet` | `SignificantMotifOccurrencesFinder.getBindingSites(DataSet data, int motifIndex)` This method returns a `DataSet` containing the predicted binding sites.
`DataSet` | `SignificantMotifOccurrencesFinder.getBindingSites(int startPos, DataSet data, int motifIndex, int addMax, int addLeft, int addRight)` This method returns a `DataSet` containing the predicted binding sites.

Methods in de.jstacs.motifDiscovery with parameters of type DataSet
`void` | `MutableMotifDiscoverer.adjustHiddenParameters(int index, DataSet[] data, double[][] weights)` Adjusts all hidden parameters including duration and mixture parameters according to the current values of the remaining parameters.
`DataSet` | `SignificantMotifOccurrencesFinder.annotateMotif(DataSet data, int motifIndex)` This method annotates a `DataSet`.
`DataSet` | `SignificantMotifOccurrencesFinder.annotateMotif(DataSet data, int motifIndex, int addMax)` This method annotates a `DataSet`.
`DataSet` | `SignificantMotifOccurrencesFinder.annotateMotif(int startPos, DataSet data, int motifIndex)` This method annotates a `DataSet` starting in each sequence at `startPos`.
`DataSet` | `SignificantMotifOccurrencesFinder.annotateMotif(int startPos, DataSet data, int motifIndex, int addMax, boolean addAnnotation)` This method annotates a `DataSet` starting in each sequence at `startPos`.
`static ListResult` | `MotifDiscoveryAssessment.assess(DataSet truth, DataSet prediction, int maxDiff)` This method computes the nucleotide and site measures.
`static boolean` | `MutableMotifDiscovererToolbox.doHeuristicSteps(DifferentiableSequenceScore[] funs, DataSet[] data, double[][] weights, DiffSSBasedOptimizableFunction opt, DifferentiableFunction neg, byte algorithm, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, boolean breakOnChanged, History[][] hist, int[][] minimalNewLength, boolean maxPos)` This method tries to make some heuristic step if at least one `DifferentiableSequenceScore` is a `MutableMotifDiscoverer`.
`static boolean` | `MutableMotifDiscovererToolbox.findModification(int clazz, int motif, MutableMotifDiscoverer mmd, DifferentiableSequenceScore[] score, DataSet[] data, double[][] weights, DiffSSBasedOptimizableFunction opt, DifferentiableFunction neg, byte algo, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, History hist, int minimalNewLength, boolean maxPos)` This method tries to find a modification, i.e. shifting, shrinking, or expanding a motif, that is promising.
`static DataSet.WeightedDataSetFactory` | `KMereStatistic.getAbsoluteKMereFrequencies(DataSet data, int k, boolean bothStrands)` This method enables the user to get a statistic over all `k`-mers in the `data`.
`static DataSet.WeightedDataSetFactory` | `KMereStatistic.getAbsoluteKMereFrequencies(DataSet data, int k, boolean bothStrands, DataSet.WeightedDataSetFactory.SortOperation sortOp)` This method enables the user to get a statistic over all `k`-mers in the `data`.
`DataSet` | `SignificantMotifOccurrencesFinder.getBindingSites(DataSet data, int motifIndex)` This method returns a `DataSet` containing the predicted binding sites.
`DataSet` | `SignificantMotifOccurrencesFinder.getBindingSites(int startPos, DataSet data, int motifIndex, int addMax, int addLeft, int addRight)` This method returns a `DataSet` containing the predicted binding sites.
`static Sequence[]` | `KMereStatistic.getCommonString(DataSet data, int motifLength, boolean bothStrands)` This method returns an array of strings of length `motifLength` so that each String is contained in all sequences of the sample respectively in the sample and the reverse complementary sample.
`static Pair<Sequence,BitSet[]>[]` | `KMereStatistic.getKmereSequenceStatistic(boolean bothStrands, int maxMismatch, HashSet<Sequence> filter, DataSet... data)` This method enables the user to get a statistic for a set of `k`-mers.
`static Hashtable<Sequence,BitSet[]>` | `KMereStatistic.getKmereSequenceStatistic(int k, boolean bothStrands, int addIndex, DataSet... data)` This method enables the user to get a statistic over all `k`-mers in the sequences.
`double` | `SignificantMotifOccurrencesFinder.getNumberOfBoundSequences(DataSet data, double[] weights, int motifIndex)` Returns the number of sequences in `data` that are predicted to be bound at least once by motif no.
`static double[][]` | `MotifDiscoveryAssessment.getSortedScoresForMotifAndFlanking(DataSet data, DataSet pred, String identifier)` Returns the scores read from the prediction `pred` for the motif with identifier `identifier` and flanking sequences as annotated in the `DataSet` data.
`static double[][]` | `MotifDiscoveryAssessment.getSortedValuesForMotifAndFlanking(DataSet data, double[][] values, double offset, double factor, String identifier)` This method provides some score arrays that can be used in `AbstractPerformanceMeasure` to determine some curves or area under curves based on the values of the predictions.
`IntList` | `SignificantMotifOccurrencesFinder.getStartPositions(int startPos, DataSet data, int motifIndex, int addMax)` This method returns a list of start positions of binding sites.
`double[][]` | `SignificantMotifOccurrencesFinder.getValuesForEachNucleotide(DataSet data, int motif, boolean addOnlyBest)` This method determines a score for each possible starting position in each of the sequences in `data` that this position is covered by at least one motif occurrence of the motif with index `index`.
`void` | `MutableMotifDiscoverer.initializeMotif(int motifIndex, DataSet data, double[] weights)` This method allows to initialize the model of a motif manually using a weighted sample.
`static void` | `MutableMotifDiscovererToolbox.initMotif(int idx, int[] classIndex, int[] motifIndex, DataSet[] s, double[][] seqWeights, boolean[] adjust, MutableMotifDiscoverer[] mmd, int[] len, DataSet[] data, double[][] dataWeights)` This method allows to initialize a number of motifs.
`static void` | `MutableMotifDiscovererToolbox.initMotif(int idx, int[] classIndex, int[] motifIndex, DataSet[] s, double[][] seqWeights, boolean[] adjust, MutableMotifDiscoverer[] mmd, int[] len, DataSet[] data, double[][] dataWeights)` This method allows to initialize a number of motifs.

Constructors in de.jstacs.motifDiscovery with parameters of type DataSet
`KMereStatistic(DataSet data, int k)` This constructor creates an internal statistic counting all `k`-mers in the `data`.
`SignificantMotifOccurrencesFinder(MotifDiscoverer disc, DataSet bg, double[] weights, double sign)` This constructor creates an instance of `SignificantMotifOccurrencesFinder` that uses a `DataSet` to determine the siginificance level.
`SignificantMotifOccurrencesFinder(MotifDiscoverer disc, SignificantMotifOccurrencesFinder.JoinMethod joiner, DataSet bg, double[] weights, double sign)` This constructor creates an instance of `SignificantMotifOccurrencesFinder` that uses a `DataSet` to determine the siginificance level.

Uses of DataSet in de.jstacs.results

Methods in de.jstacs.results that return DataSet
`DataSet` | `DataSetResult.getValue()`

Constructors in de.jstacs.results with parameters of type DataSet
`DataSetResult(String name, String comment, DataSet data)` Creates a new `DataSetResult` from a `DataSet` with the annotation `name` and `comment`.

Uses of DataSet in de.jstacs.sampling

Methods in de.jstacs.sampling with parameters of type DataSet
`void` | `GibbsSamplingModel.drawParameters(DataSet data, double[] weights)` This method draws the parameters of the model from the a posteriori density.

Uses of DataSet in de.jstacs.sequenceScores

Methods in de.jstacs.sequenceScores with parameters of type DataSet
`double[]` | `SequenceScore.getLogScoreFor(DataSet data)` This method computes the logarithm of the scores of all sequences in the given sample.
`void` | `SequenceScore.getLogScoreFor(DataSet data, double[] res)` This method computes and stores the logarithm of the scores for any sequence in the sample in the given `double`-array.

Uses of DataSet in de.jstacs.sequenceScores.differentiable

Methods in de.jstacs.sequenceScores.differentiable with parameters of type DataSet
`int` | `IndependentProductDiffSS.extractSequenceParts(int scoringFunctionIndex, DataSet[] data, DataSet[] result)` This method extracts the corresponding `Sequence` parts for a specific `DifferentiableSequenceScore`.
`int` | `IndependentProductDiffSS.extractSequenceParts(int scoringFunctionIndex, DataSet[] data, DataSet[] result)` This method extracts the corresponding `Sequence` parts for a specific `DifferentiableSequenceScore`.
`double[]` | `AbstractDifferentiableSequenceScore.getLogScoreFor(DataSet data)`
`void` | `AbstractDifferentiableSequenceScore.getLogScoreFor(DataSet data, double[] res)`
`void` | `UniformDiffSS.initializeFunction(int index, boolean meila, DataSet[] data, double[][] weights)`
`void` | `IndependentProductDiffSS.initializeFunction(int index, boolean freeParams, DataSet[] data, double[][] weights)`
`void` | `DifferentiableSequenceScore.initializeFunction(int index, boolean freeParams, DataSet[] data, double[][] weights)` This method creates the underlying structure of the `DifferentiableSequenceScore`.

Uses of DataSet in de.jstacs.sequenceScores.differentiable.logistic

Methods in de.jstacs.sequenceScores.differentiable.logistic with parameters of type DataSet
`void` | `LogisticDiffSS.initializeFunction(int index, boolean freeParams, DataSet[] data, double[][] weights)`

Uses of DataSet in de.jstacs.sequenceScores.statisticalModels

Methods in de.jstacs.sequenceScores.statisticalModels that return DataSet
`DataSet` | `StatisticalModel.emitDataSet(int numberOfSequences, int... seqLength)` This method returns a `DataSet` object containing artificial sequence(s).

Uses of DataSet in de.jstacs.sequenceScores.statisticalModels.differentiable

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable that return DataSet
`DataSet` | `UniformDiffSM.emitDataSet(int numberOfSequences, int... seqLength)`
`DataSet` | `IndependentProductDiffSM.emitDataSet(int numberOfSequences, int... seqLength)`
`DataSet` | `AbstractDifferentiableStatisticalModel.emitDataSet(int numberOfSequences, int... seqLength)`

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable with parameters of type DataSet
`void` | `MappingDiffSM.adjustHiddenParameters(int index, DataSet[] data, double[][] weights)`
`void` | `IndependentProductDiffSM.adjustHiddenParameters(int index, DataSet[] data, double[][] weights)`
`double[]` | `AbstractDifferentiableStatisticalModel.getLogScoreFor(DataSet data)`
`void` | `AbstractDifferentiableStatisticalModel.getLogScoreFor(DataSet data, double[] res)`
`void` | `UniformDiffSM.initializeFunction(int index, boolean meila, DataSet[] data, double[][] weights)`
`void` | `NormalizedDiffSM.initializeFunction(int index, boolean freeParams, DataSet[] data, double[][] weights)`
`void` | `MultiDimensionalSequenceWrapperDiffSM.initializeFunction(int index, boolean freeParams, DataSet[] data, double[][] weights)`
`void` | `MarkovRandomFieldDiffSM.initializeFunction(int index, boolean freeParams, DataSet[] data, double[][] weights)`
`void` | `MappingDiffSM.initializeFunction(int index, boolean freeParams, DataSet[] data, double[][] weights)`
`void` | `CyclicMarkovModelDiffSM.initializeFunction(int index, boolean freeParams, DataSet[] data, double[][] weights)`
`void` | `MappingDiffSM.initializeMotif(int motifIndex, DataSet data, double[] weights)`
`void` | `IndependentProductDiffSM.initializeMotif(int motifIndex, DataSet data, double[] weights)`

Uses of DataSet in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels that return DataSet
`DataSet` | `BayesianNetworkDiffSM.emitDataSet(int numberOfSequences, int... seqLength)`

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels with parameters of type DataSet
`protected void` | `BayesianNetworkDiffSM.createTrees(DataSet[] data2, double[][] weights2)` Creates the tree structures that represent the context (array `BayesianNetworkDiffSM.trees`) and the parameter objects `BayesianNetworkDiffSM.parameters` using the given `Measure` `BayesianNetworkDiffSM.structureMeasure`.
`void` | `BayesianNetworkDiffSM.initializeFunction(int index, boolean freeParams, DataSet[] data, double[][] weights)`
`protected void` | `BayesianNetworkDiffSM.setPlugInParameters(int index, boolean freeParameters, DataSet[] data, double[][] weights)` Computes and sets the plug-in parameters (MAP estimated parameters) from `data` using `weights`.

Uses of DataSet in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures with parameters of type DataSet
`abstract int[][]` | `Measure.getParents(DataSet fg, DataSet bg, double[] weightsFg, double[] weightsBg, int length)` Returns the optimal parents for the given data and weights.
`int[][]` | `InhomogeneousMarkov.getParents(DataSet fg, DataSet bg, double[] weightsFg, double[] weightsBg, int length)`
`protected static double[][][][]` | `Measure.getStatistics(DataSet s, double[] weights, int length, double ess)` Counts the occurrences of symbols of the `AlphabetContainer` of `DataSet` `s` using `weights`.
`protected static double[][][][][][]` | `Measure.getStatisticsOrderTwo(DataSet s, double[] weights, int length, double ess)` Counts the occurrences of symbols of the `AlphabetContainer` of `DataSet` `s` using `weights`.

Uses of DataSet in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures with parameters of type DataSet
`int[][]` | `BTMutualInformation.getParents(DataSet fg, DataSet bg, double[] weightsFg, double[] weightsBg, int length)`
`int[][]` | `BTExplainingAwayResidual.getParents(DataSet fg, DataSet bg, double[] weightsFg, double[] weightsBg, int length)`

Uses of DataSet in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures with parameters of type DataSet
`int[][]` | `PMMMutualInformation.getParents(DataSet fg, DataSet bg, double[] weightsFg, double[] weightsBg, int length)`
`int[][]` | `PMMExplainingAwayResidual.getParents(DataSet fg, DataSet bg, double[] weightsFg, double[] weightsBg, int length)`

Uses of DataSet in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous that return DataSet
`DataSet` | `HomogeneousMMDiffSM.emit(int numberOfSequences, int... seqLength)` This method returns a `DataSet` object containing artificial sequence(s).

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous with parameters of type DataSet
`void` | `UniformHomogeneousDiffSM.initializeFunction(int index, boolean meila, DataSet[] data, double[][] weights)`
`void` | `HomogeneousMMDiffSM.initializeFunction(int index, boolean freeParams, DataSet[] data, double[][] weights)`
`void` | `HomogeneousMM0DiffSM.initializeFunction(int index, boolean freeParams, DataSet[] data, double[][] weights)`

Uses of DataSet in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture with parameters of type DataSet
`void` | `MixtureDiffSM.adjustHiddenParameters(int index, DataSet[] data, double[][] weights)` Adjusts all hidden parameters including duration and mixture parameters according to the current values of the remaining parameters.
`void` | `AbstractMixtureDiffSM.initializeFunction(int index, boolean freeParams, DataSet[] data, double[][] weights)`
`void` | `MixtureDiffSM.initializeMotif(int motifIndex, DataSet data, double[] weights)`
`protected void` | `StrandDiffSM.initializeUsingPlugIn(int index, boolean freeParams, DataSet[] data, double[][] weights)`
`protected void` | `MixtureDiffSM.initializeUsingPlugIn(int index, boolean freeParams, DataSet[] data, double[][] weights)`
`protected abstract void` | `AbstractMixtureDiffSM.initializeUsingPlugIn(int index, boolean freeParams, DataSet[] data, double[][] weights)` This method initializes the functions using the data in some way.

Uses of DataSet in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif with parameters of type DataSet
`void` | `ExtendedZOOPSDiffSM.adjustHiddenParameters(int classIndex, DataSet[] data, double[][] dataWeights)`
`void` | `UniformDurationDiffSM.initializeFunction(int index, boolean meila, DataSet[] data, double[][] weights)`
`void` | `SkewNormalLikeDurationDiffSM.initializeFunction(int index, boolean freeParams, DataSet[] data, double[][] weights)`
`void` | `MixtureDurationDiffSM.initializeFunction(int index, boolean freeParams, DataSet[] data, double[][] weights)`
`void` | `ExtendedZOOPSDiffSM.initializeMotif(int motif, DataSet data, double[] weights)`
`protected void` | `ExtendedZOOPSDiffSM.initializeUsingPlugIn(int index, boolean freeParams, DataSet[] data, double[][] weights)`

Uses of DataSet in de.jstacs.sequenceScores.statisticalModels.trainable

Methods in de.jstacs.sequenceScores.statisticalModels.trainable that return DataSet
`DataSet` | `UniformTrainSM.emitDataSet(int n, int... lengths)`
`DataSet` | `AbstractTrainableStatisticalModel.emitDataSet(int numberOfSequences, int... seqLength)`

Methods in de.jstacs.sequenceScores.statisticalModels.trainable with parameters of type DataSet
`double[]` | `AbstractTrainableStatisticalModel.getLogScoreFor(DataSet data)`
`void` | `AbstractTrainableStatisticalModel.getLogScoreFor(DataSet data, double[] res)`
`void` | `TrainableStatisticalModel.train(DataSet data)` Trains the `TrainableStatisticalModel` object given the data as `DataSet`.
`void` | `AbstractTrainableStatisticalModel.train(DataSet data)`
`void` | `VariableLengthWrapperTrainSM.train(DataSet data, double[] weights)`
`void` | `UniformTrainSM.train(DataSet data, double[] weights)` Deprecated.
`void` | `TrainableStatisticalModel.train(DataSet data, double[] weights)` Trains the `TrainableStatisticalModel` object given the data as `DataSet` using the specified weights.
`void` | `DifferentiableStatisticalModelWrapperTrainSM.train(DataSet data, double[] weights)`
`void` | `CompositeTrainSM.train(DataSet data, double[] weights)`

Uses of DataSet in de.jstacs.sequenceScores.statisticalModels.trainable.discrete

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete with parameters of type DataSet
`static double` | `ConstraintManager.countInhomogeneous(AlphabetContainer alphabets, int length, DataSet data, double[] weights, boolean reset, Constraint... constr)` Fills the (inhomogeneous) `Constraint` `constr` with the weighted absolute frequencies of the `DataSet` `data`.

Uses of DataSet in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous that return DataSet
`DataSet` | `HomogeneousTrainSM.emitDataSet(int no, int... length)` Creates a `DataSet` of a given number of `Sequence`s from a trained homogeneous model.

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous with parameters of type DataSet
`void` | `HomogeneousTrainSM.train(DataSet[] data)` Trains the homogeneous model on all given `DataSet`s.
`abstract void` | `HomogeneousTrainSM.train(DataSet[] data, double[][] weights)` Trains the homogeneous model using an array of weighted `DataSet`s.
`void` | `HomogeneousMM.train(DataSet[] data, double[][] weights)`
`void` | `HomogeneousMM.train(DataSet data, double[] weights)`

Uses of DataSet in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous that return DataSet
`DataSet` | `DAGTrainSM.emitDataSet(int n, int... lengths)`

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous with parameters of type DataSet
`void` | `FSDAGModelForGibbsSampling.drawParameters(DataSet data, double[] weights)`
`protected void` | `DAGTrainSM.drawParameters(DataSet data, double[] weights)` This method draws the parameter of the model from the likelihood or the posterior, respectively.
`void` | `FSDAGTrainSM.drawParameters(DataSet data, double[] weights, int[][] graph)` This method draws the parameters of the model from the a posteriori density.
`void` | `FSDAGModelForGibbsSampling.drawParameters(DataSet data, double[] weights, int[][] graph)`
`protected void` | `DAGTrainSM.estimateParameters(DataSet data, double[] weights)` This method estimates the parameter of the model from the likelihood or the posterior, respectively.
`static double[][]` | `TwoPointEvaluater.getMIInBits(DataSet s, double[] weights)` This method computes the pairwise mutual information (in bits) between the sequence positions.
`int[][]` | `StructureLearner.getStructure(DataSet data, double[] weights, StructureLearner.ModelType model, byte order, StructureLearner.LearningType method)` This method finds the optimal structure of a model by using a given learning method (in some sense).
`SymmetricTensor` | `StructureLearner.getTensor(DataSet data, double[] weights, byte order, StructureLearner.LearningType method)` This method can be used to compute a `Tensor` that can be used to determine the optimal structure.
`void` | `FSDAGTrainSM.train(DataSet data, double[] weights)`
`void` | `FSDAGModelForGibbsSampling.train(DataSet data, double[] weights)`
`void` | `BayesianNetworkTrainSM.train(DataSet data, double[] weights)`
`void` | `FSDAGTrainSM.train(DataSet data, double[] weights, int[][] graph)` Computes the model with structure `graph`.
`void` | `FSDAGModelForGibbsSampling.train(DataSet data, double[] weights, int[][] graph)`
`static void` | `FSDAGTrainSM.train(TrainableStatisticalModel[] models, int[][] graph, double[][] weights, DataSet... data)` Computes the models with structure `graph`.

Uses of DataSet in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared with parameters of type DataSet
`void` | `SharedStructureClassifier.train(DataSet[] data, double[][] weights)`

Uses of DataSet in de.jstacs.sequenceScores.statisticalModels.trainable.hmm

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm with parameters of type DataSet
`String` | `AbstractHMM.getGraphvizRepresentation(NumberFormat nf, DataSet data, double[] weight, boolean sameTypeSameRank)` This method returns a `String` representation of the structure that can be used in Graphviz to create an image.
`String` | `AbstractHMM.getGraphvizRepresentation(NumberFormat nf, DataSet data, double[] weight, HashMap<String,String> rankPatterns)` This method returns a `String` representation of the structure that can be used in Graphviz to create an image.
`double[][][]` | `AbstractHMM.getLogStatePosteriorMatrixFor(DataSet data)` This method returns the log state posteriors for all sequences of the sample `data`.
`double[][][]` | `AbstractHMM.getStatePosteriorMatrixFor(DataSet data)` This method returns the state posteriors for all sequences of the sample `data`.
`Pair<IntList,Double>[]` | `AbstractHMM.getViterbiPathsFor(DataSet data)` This method returns the viterbi paths and scores for all sequences of the sample `data`.
`void` | `AbstractHMM.train(DataSet data)`

Uses of DataSet in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models with parameters of type DataSet
`protected void` | `SamplingHigherOrderHMM.furtherInits(DataSet data, double[] weights)` This method allows the implementation of further initialisations
`double[]` | `HigherOrderHMM.getLogScoreFor(DataSet data)`
`void` | `HigherOrderHMM.getLogScoreFor(DataSet data, double[] res)`
`protected void` | `SamplingHigherOrderHMM.gibbsSamplingStep(int sampling, int steps, boolean append, DataSet data, double[] weights)` This method implements the next step(s) in the sampling procedure
`protected void` | `HigherOrderHMM.initialize(DataSet data, double[] weight)` This method initializes all emissions and the transition.
`void` | `DifferentiableHigherOrderHMM.initializeFunction(int index, boolean freeParams, DataSet[] data, double[][] weights)`
`protected void` | `SamplingHigherOrderHMM.initTraining(DataSet data, double[] weights)` This methods initialize the training procedure with the given training data
`void` | `SamplingHigherOrderHMM.train(DataSet data, double[] weights)`
`void` | `HigherOrderHMM.train(DataSet data, double[] weights)`
`void` | `DifferentiableHigherOrderHMM.train(DataSet data, double[] weights)`

Uses of DataSet in de.jstacs.sequenceScores.statisticalModels.trainable.mixture

Fields in de.jstacs.sequenceScores.statisticalModels.trainable.mixture declared as DataSet
`protected DataSet[]` | `AbstractMixtureTrainSM.sample` The sample that was used in the last training.

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.mixture that return DataSet
`DataSet` | `AbstractMixtureTrainSM.emitDataSet(int n, int... lengths)`

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.mixture with parameters of type DataSet
`protected double[][]` | `AbstractMixtureTrainSM.doFirstIteration(DataSet data, double[] dataWeights)` This method will do the first step in the train algorithm for the current model.
`double[][]` | `MixtureTrainSM.doFirstIteration(DataSet data, double[] dataWeights, double[][] partitioning)` This method enables you to train a mixture model with a fixed start partitioning.
`protected double[][]` | `AbstractMixtureTrainSM.doFirstIteration(DataSet data, double[] dataWeights, MultivariateRandomGenerator m, MRGParams[] params)` This method will do the first step in the train algorithm for the current model.
`double[]` | `AbstractMixtureTrainSM.getLogScoreFor(DataSet data)`
`double` | `AbstractMixtureTrainSM.iterate(DataSet data, double[] dataWeights, MultivariateRandomGenerator m, MRGParams[] params)` This method runs the train algorithm for the current model.
`void` | `StrandTrainSM.setTrainData(DataSet s)`
`protected void` | `MixtureTrainSM.setTrainData(DataSet data)`
`protected abstract void` | `AbstractMixtureTrainSM.setTrainData(DataSet data)` This method is invoked by the `train`-method and sets for a given sample the sample that should be used for `train`.
`void` | `AbstractMixtureTrainSM.train(DataSet data, double[] dataWeights)`

Uses of DataSet in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif with parameters of type DataSet
`protected void` | `ZOOPSTrainSM.setTrainData(DataSet data)`
`void` | `HiddenMotifMixture.train(DataSet data, double[] weights)`
`void` | `ZOOPSTrainSM.trainBgModel(DataSet data, double[] weights)`
`abstract void` | `HiddenMotifMixture.trainBgModel(DataSet data, double[] weights)` This method trains the background model.

Uses of DataSet in de.jstacs.utils

Methods in de.jstacs.utils that return DataSet
`static DataSet` | `DiscreteInhomogenousDataSetEmitter.emitDataSet(StatisticalModel m, int n)` This method emits a sample with `n`sequences from the discrete inhomogeneous model `m` .``

Methods in de.jstacs.utils with parameters of type DataSet
`static double` | `StatisticalModelTester.getLogLikelihood(StatisticalModel m, DataSet data)` Returns the log-likelihood of a `DataSet` `data` for a given model `m`.
`static double` | `StatisticalModelTester.getLogLikelihood(StatisticalModel m, DataSet data, double[] weights)` Returns the log-likelihood of a `DataSet` `data` for a given model `m`.
`static double` | `StatisticalModelTester.getValueOfAIC(StatisticalModel m, DataSet s, int k)` This method computes the value of Akaikes Information Criterion (AIC).
`static double` | `StatisticalModelTester.getValueOfBIC(StatisticalModel m, DataSet s, int k)` This method computes the value of the Bayesian Information Criterion (BIC).

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