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. It contains the class ClassifierAssessment that is used as a super-class of all implemented methodologies of an assessment to assess classifiers.
de.jstacs.classifiers.differentiableSequenceScoreBased	Provides the classes for Classifiers that are based on SequenceScores. It includes a sub-package for discriminative objective functions, namely conditional likelihood and supervised posterior, and a separate sub-package for the parameter priors, that can be used for the supervised posterior.
de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix	Provides an implementation of a classifier that allows to train the parameters of a set of DifferentiableStatisticalModels by a unified generative-discriminative learning principle.
de.jstacs.classifiers.differentiableSequenceScoreBased.sampling	Provides the classes for AbstractScoreBasedClassifiers that are based on SamplingDifferentiableStatisticalModels and that sample parameters using the Metropolis-Hastings algorithm.
de.jstacs.classifiers.trainSMBased	Provides the classes for Classifiers that are based on TrainableStatisticalModels.
de.jstacs.classifiers.utils	Provides some useful classes for working with classifiers.
de.jstacs.data	Provides classes for the representation of data. The base classes to represent data are Alphabet and AlphabetContainer for representing alphabets, Sequence and its sub-classes to represent continuous and discrete sequences, and DataSet to represent data sets comprising a set of sequences.
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. The implementations of sequences currently include DiscreteSequences prepared for alphabets of different sizes, and ArbitrarySequences that may contain continuous values as well. As sub-package provides the facilities to annotate 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 SequenceScores, 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 StatisticalModels, which can compute a proper (i.e., normalized) likelihood over the input space of sequences. StatisticalModels can be further differentiated into TrainableStatisticalModels, which can be learned from a single input DataSet, and DifferentiableStatisticalModels, which define a proper likelihood but can also compute gradients like DifferentiableSequenceScores.
de.jstacs.sequenceScores.statisticalModels.differentiable	Provides all DifferentiableStatisticalModels, which can compute the gradient with respect to their parameters for a given input Sequence.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels	Provides DifferentiableStatisticalModels 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 DifferentiableStatisticalModels that are homogeneous, i.e.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture	Provides DifferentiableSequenceScores that are mixtures of other DifferentiableSequenceScores.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif
de.jstacs.sequenceScores.statisticalModels.trainable	Provides all TrainableStatisticalModels, 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 DataSets 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 data set 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(AbstractPerformanceMeasureParameterSet<? extends PerformanceMeasure> 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.
ResultSet	AbstractClassifier.evaluate(AbstractPerformanceMeasureParameterSet<? extends PerformanceMeasure> params, boolean exceptionIfNotComputeable, DataSet[] s, double[][] weights) 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 Sequences 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, double[][] weights, AbstractPerformanceMeasureParameterSet<? extends PerformanceMeasure> params, boolean exceptionIfNotComputeable)
protected boolean	AbstractScoreBasedClassifier.getResults(LinkedList list, DataSet[] s, double[][] weights, AbstractPerformanceMeasureParameterSet<? extends PerformanceMeasure> params, boolean exceptionIfNotComputeable)
protected boolean	AbstractClassifier.getResults(LinkedList list, DataSet[] s, double[][] weights, AbstractPerformanceMeasureParameterSet<? extends PerformanceMeasure> 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 DataSets to the internal classes.
void	AbstractClassifier.train(DataSet... s) Trains the AbstractClassifier object given the data as DataSets. This method should work non-incrementally.
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, T assessPS, DataSet... s) Assesses the contained classifiers.
ListResult	ClassifierAssessment.assess(NumericalPerformanceMeasureParameterSet mp, T assessPS, ProgressUpdater pU, DataSet[] s) Assesses the contained classifiers.
ListResult	ClassifierAssessment.assess(NumericalPerformanceMeasureParameterSet mp, T assessPS, ProgressUpdater pU, DataSet[][]... s) Assesses the contained classifiers.
ListResult	ClassifierAssessment.assess(NumericalPerformanceMeasureParameterSet mp, T assessPS, ProgressUpdater pU, DataSet[] s, double[][] weights) Assesses the contained classifiers.
ListResult	KFoldCrossValidation.assessWithPredefinedSplits(NumericalPerformanceMeasureParameterSet mp, ClassifierAssessmentAssessParameterSet caaps, ProgressUpdater pU, DataSet[][] splitData, double[][][] splitWeights) This method implements a k-fold crossvalidation on previously split data.
protected void	KFoldCrossValidation.evaluateClassifier(NumericalPerformanceMeasureParameterSet mp, KFoldCrossValidationAssessParameterSet assessPS, DataSet[] s, double[][] weights, ProgressUpdater pU) Evaluates a classifier.
protected void	RepeatedHoldOutExperiment.evaluateClassifier(NumericalPerformanceMeasureParameterSet mp, RepeatedHoldOutAssessParameterSet assessPS, DataSet[] s, double[][] weights, ProgressUpdater pU) Evaluates the classifier.
protected void	RepeatedSubSamplingExperiment.evaluateClassifier(NumericalPerformanceMeasureParameterSet mp, RepeatedSubSamplingAssessParameterSet assessPS, DataSet[] s, double[][] weights, ProgressUpdater pU) Evaluates the classifier.
protected void	Sampled_RepeatedHoldOutExperiment.evaluateClassifier(NumericalPerformanceMeasureParameterSet mp, Sampled_RepeatedHoldOutAssessParameterSet assessPS, DataSet[] s, double[][] weights, ProgressUpdater pU)
protected abstract void	ClassifierAssessment.evaluateClassifier(NumericalPerformanceMeasureParameterSet mp, T assessPS, DataSet[] s, double[][] weights, ProgressUpdater pU) This method must be implemented in all subclasses.
protected void	ClassifierAssessment.prepareAssessment(boolean storeAll, DataSet... s) Prepares an assessment.
protected void	ClassifierAssessment.test(NumericalPerformanceMeasureParameterSet mp, boolean exception, DataSet[] testS, double[][] weights) Uses the given test data sets to call the evaluate( ...
protected void	ClassifierAssessment.train(DataSet[] trainS, double[][] weights) Trains the local classifiers using the given training data sets.

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 DataSets of DNA Sequences.

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 DataSets samples.
DataSet	DataSet.getCompositeDataSet(int[] starts, int[] lengths) This method enables you to use only composite Sequences 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 DinucleotidePropertys 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 DinucleotidePropertys 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.
DataSet	DataSet.getPartialDataSet(int[]... indexes) Returns a new DataSet that contains all elements of this DataSet that are specified by the supplied pairs of start and end indexes in indexes.
DataSet	DataSet.getPartialDataSet(int start, int end) Returns a new DataSet that contains all elements of this DataSet that are specified by the supplied start (inclusive) and end (exclusive) indexes.
DataSet	DataSet.getReverseComplementaryDataSet() Returns a DataSet that contains the reverse complement of all Sequences in this DataSet.
DataSet	DataSet.getSuffixDataSet(int start) This method enables you to use only a suffix of all elements, i.e.
static DataSet	DataSet.intersection(DataSet... samples) This method computes the intersection between all elements/DataSet s of the array, i.e.
DataSet[]	DataSet.partition(DataSet.PartitionMethod method, double... percentage) This method partitions the elements, i.e.
DataSet[]	DataSet.partition(DataSet.PartitionMethod method, int k) This method partitions the elements, i.e.
DataSet	DataSet.subSampling(double number) Randomly samples elements, i.e.
static DataSet	DataSet.union(DataSet... s) Unites all DataSets of the array s.
static DataSet	DataSet.union(DataSet[] s, boolean[] in) This method unites all DataSets of the array s regarding the array in.

Methods in de.jstacs.data that return types with arguments of type DataSet

Pair<DataSet,double[]>	DataSet.resize(double[] weights, int subsequenceLength)
Pair<DataSet,double[]>	DataSet.subSampling(double number, double[] weights)
static Pair<DataSet,double[]>	DataSet.union(DataSet[] s, double[][] weights, boolean[] in) This method unites all DataSets of the array s regarding the array in and sets the element length in the united DataSet 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 DataSets samples.
static DataSet	DataSet.diff(DataSet data, DataSet... samples) This method computes the difference between the DataSet data and the DataSets samples.
static String	DataSet.getAnnotation(DataSet... s) Returns the annotation for an array of DataSets.
static DataSet	DinucleotideProperty.getDataSetForProperty(DataSet original, DinucleotideProperty... properties) Creates a new DataSet by converting each Sequence in original to the DinucleotidePropertys 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 DinucleotidePropertys 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.
static DataSet	DataSet.union(DataSet... s) Unites all DataSets of the array s.
static DataSet	DataSet.union(DataSet[] s, boolean[] in) This method unites all DataSets of the array s regarding the array in.
static Pair<DataSet,double[]>	DataSet.union(DataSet[] s, double[][] weights, boolean[] in) This method unites all DataSets of the array s regarding the array in and sets the element length in the united DataSet 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 DataSets 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.
This constructor enables you to use subsequences of the elements of a DataSet.

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 SparseSequences.
static DataSet	ArbitraryFloatSequence.getDataSet(AlphabetContainer con, AbstractStringExtractor... se) This method allows to create a DataSet containing ArbitraryFloatSequences.
static DataSet	SparseSequence.getDataSet(AlphabetContainer con, String filename) This method allows to create a DataSet containing SparseSequences using a file name.
static DataSet	ArbitraryFloatSequence.getDataSet(AlphabetContainer con, String filename) This method allows to create a DataSet containing ArbitraryFloatSequences using a file name.
static DataSet	SparseSequence.getDataSet(AlphabetContainer con, String filename, SequenceAnnotationParser parser) This method allows to create a DataSet containing SparseSequences using a file name.
static DataSet	ArbitraryFloatSequence.getDataSet(AlphabetContainer con, String filename, SequenceAnnotationParser parser) This method allows to create a DataSet containing ArbitraryFloatSequences 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.
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 sequences of length motifLength so that each string is contained in all sequences of the data set, more precisely in the data set or the reverse complementary data set.
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.
double[][]	SignificantMotifOccurrencesFinder.getPWM(int motif, DataSet data, double[] weights, int addLeft, int addRight) Returns the Position weight matrix (PWM) of the binding sites of motif motif in the data set data of the MotifDiscoverer of this SignificantMotifOccurrencesFinder.
Pair<double[][],double[]>	SignificantMotifOccurrencesFinder.getPWMAndPosDist(int motif, DataSet data, double[] weights, double[] mean, int addLeft, int addRight) Returns the Position weight matrix (PWM) of the binding sites of motif motif in the data set data of the MotifDiscoverer of this SignificantMotifOccurrencesFinder together with standard deviation of binding site positions computed using the provided mean values for each sequence.
Pair<double[][][],int[][]>	SignificantMotifOccurrencesFinder.getPWMAndPositions(int motif, DataSet data, double[] weights, int addLeft, int addRight) Returns the Position weight matrix (PWM) of the binding sites of motif motif in the data set data of the MotifDiscoverer of this SignificantMotifOccurrencesFinder together with the positions of the binding sites within the sequences of data and the corresponding p-values.
protected double[][]	SignificantMotifOccurrencesFinder.getPWMAndPositions(int motif, DataSet data, double[] weights, int addLeft, int addRight, int[][] positions, double[][] pvals, double[] mean, double[] sd) Returns the Position weight matrix (PWM) of the binding sites of motif motif in the data set data of the MotifDiscoverer of this SignificantMotifOccurrencesFinder and fills with the positions of the binding sites within the sequences of data and the corresponding p-values into the corresponding arrays.
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 data set.
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 data set.
void	SequenceScore.getLogScoreFor(DataSet data, double[] res) This method computes and stores the logarithm of the scores for any sequence in the data set 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	MultiDimensionalSequenceWrapperDiffSS.initializeFunction(int index, boolean freeParams, 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	MarkovRandomFieldDiffSM.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	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) constr with the weighted absolute frequency of the DataSet data and computes the frequencies will not be computed.

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 Sequences 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 DataSets.
abstract void	HomogeneousTrainSM.train(DataSet[] data, double[][] weights) Trains the homogeneous model using an array of weighted DataSets.
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	MEManager.emitDataSet(int n, int... lengths)
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 BufferedImage	TwoPointEvaluater.getImage(DataSet d, double[] weights, REnvironment r, double alpha, int... borders)
static double[][]	TwoPointEvaluater.getMI(DataSet s, double[] weights) This method computes the pairwise mutual information between the sequence positions.
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	FSMEManager.train(DataSet data, double[] weights)
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.
protected void	MEManager.trainFactors(DataSet data, double[] weights) This method trains the internal MEM array, i.e., it optimizes the parameters of the underlying MEMConstraints.

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 data set data.
double[][][]	AbstractHMM.getStatePosteriorMatrixFor(DataSet data) This method returns the state posteriors for all sequences of the data set data.
Pair<IntList,Double>[]	AbstractHMM.getViterbiPathsFor(DataSet data) This method returns the viterbi paths and scores for all sequences of the data set 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 initializations
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 data set 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 data set the data set 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 data set with n sequences from the discrete inhomogeneous model m .

Methods in de.jstacs.utils with parameters of type DataSet

static double[]	PFMComparator.getCounts(DataSet... data) This method counts the occurrences of symbols in the given data sets.
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[][]	PFMComparator.getPFM(DataSet data) This method creates a PFM from a DataSet of Sequences.
static double[][]	PFMComparator.getPFM(DataSet data, double[] weights) This method creates a PFM from a DataSet of Sequences.
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).