Uses of Class
de.jstacs.data.Sample

Packages that use Sample

de.jstacs.classifier	This package provides the framework for any classifier.
de.jstacs.classifier.assessment	This package allows to assess classifiers.
de.jstacs.classifier.modelBased	Provides the classes for Classifiers that are based on Models
de.jstacs.classifier.scoringFunctionBased	Provides the classes for Classifiers that are based on ScoringFunctions.
de.jstacs.classifier.scoringFunctionBased.gendismix	Provides an implementation of a classifier that allows to train the parameters of a set of NormalizableScoringFunctions by a unified generative-discriminative learning principle
de.jstacs.classifier.scoringFunctionBased.sampling	Provides the classes for AbstractScoreBasedClassifiers that are based on SamplingScoringFunctions and that sample parameters using the Metropolis-Hastings algorithm.
de.jstacs.classifier.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.models	Provides the interface Model and its abstract implementation AbstractModel, which is the super class of all other models.
de.jstacs.models.discrete
de.jstacs.models.discrete.homogeneous
de.jstacs.models.discrete.inhomogeneous	This package contains various inhomogeneous models.
de.jstacs.models.discrete.inhomogeneous.shared
de.jstacs.models.hmm	The package provides all interfaces and classes for a hidden Markov model (HMM).
de.jstacs.models.hmm.models	The package provides different implementations of hidden Markov models based on AbstractHMM
de.jstacs.models.mixture	This package is the super package for any mixture model.
de.jstacs.models.mixture.motif
de.jstacs.models.utils
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.scoringFunctions	Provides ScoringFunctions that can be used in a ScoreClassifier.
de.jstacs.scoringFunctions.directedGraphicalModels	Provides ScoringFunctions that are equivalent to directed graphical models.
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures	Provides the facilities to learn the structure of a BayesianNetworkScoringFunction.
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures	Provides the facilities to learn the structure of a BayesianNetworkScoringFunction as a Bayesian tree using a number of measures to define a rating of structures
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures	Provides the facilities to learn the structure of a BayesianNetworkScoringFunction as a permuted Markov model using a number of measures to define a rating of structures
de.jstacs.scoringFunctions.homogeneous	Provides ScoringFunctions that are homogeneous, i.e. model probabilities or scores independent of the position within a sequence
de.jstacs.scoringFunctions.mix	Provides ScoringFunctions that are mixtures of other ScoringFunctions.
de.jstacs.scoringFunctions.mix.motifSearch

Uses of Sample in de.jstacs.classifier

Methods in de.jstacs.classifier that return Sample

Sample[]

MappingClassifier.mapSample(Sample[] s) This method maps the given Samples to the internal classes.

Methods in de.jstacs.classifier with parameters of type Sample

protected void	AbstractScoreBasedClassifier.check(Sample s) This method checks if the given Sample can be used.
byte[]	AbstractClassifier.classify(Sample 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().
NumericalResultSet	AbstractClassifier.evaluate(MeasureParameters params, boolean exceptionIfNotComputeable, Sample... s) This method evaluates the classifier and computes all numerical results as, for instance, the sensitivity for a given specificity, the area under the ROC curve and so on.
ResultSet	AbstractClassifier.evaluateAll(MeasureParameters params, boolean exceptionIfNotComputeable, Sample... s) This method evaluates the classifier and computes all results.
protected NumericalResult	AbstractClassifier.getClassificationRate(Sample[] s) This method computes the classification rate for a given array of samples.
double[]	AbstractScoreBasedClassifier.getPValue(Sample candidates, Sample bg) Returns the p-values for all Sequences in the Sample candidates with respect to a given background Sample .
double	AbstractScoreBasedClassifier.getPValue(Sequence candidate, Sample bg) Returns the p-value for a Sequence candidate with respect to a given background Sample.
protected LinkedList<? extends Result>	MappingClassifier.getResults(Sample[] s, MeasureParameters params, boolean exceptionIfNotComputeable, boolean all)
protected LinkedList<? extends Result>	AbstractScoreBasedClassifier.getResults(Sample[] s, MeasureParameters params, boolean exceptionIfNotComputeable, boolean all)
protected LinkedList<? extends Result>	AbstractClassifier.getResults(Sample[] s, MeasureParameters params, boolean exceptionIfNotComputeable, boolean all) This method computes the results for any evaluation of the classifier.
double[]	AbstractScoreBasedClassifier.getScores(Sample s) This method returns the scores of the classifier for any Sequence in the Sample.
Sample[]	MappingClassifier.mapSample(Sample[] s) This method maps the given Samples to the internal classes.
ConfusionMatrix	AbstractScoreBasedClassifier.test(Sample... testData)
ConfusionMatrix	AbstractClassifier.test(Sample... testData) This method computes the confusion matrix for a given array of test data.
void	AbstractClassifier.train(Sample... s) Trains the AbstractClassifier object given the data as Samples.
void	MappingClassifier.train(Sample[] s, double[][] weights)
abstract void	AbstractClassifier.train(Sample[] s, double[][] weights) This method trains a classifier over an array of weighted Sample s.

Uses of Sample in de.jstacs.classifier.assessment

Methods in de.jstacs.classifier.assessment with parameters of type Sample

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

Uses of Sample in de.jstacs.classifier.modelBased

Methods in de.jstacs.classifier.modelBased with parameters of type Sample

byte[]	ModelBasedClassifier.classify(Sample s)
double[]	ModelBasedClassifier.getScores(Sample s)
void	ModelBasedClassifier.train(Sample[] s, double[][] weights)

Uses of Sample in de.jstacs.classifier.scoringFunctionBased

Fields in de.jstacs.classifier.scoringFunctionBased declared as Sample

protected Sample[]

AbstractOptimizableFunction.data The data that is used to evaluate this function.

Methods in de.jstacs.classifier.scoringFunctionBased that return Sample

abstract Sample[]	OptimizableFunction.getData() Returns the data for each class used in this OptimizableFunction.
Sample[]	AbstractOptimizableFunction.getData()

Methods in de.jstacs.classifier.scoringFunctionBased with parameters of type Sample

protected void	ScoreClassifier.createStructure(Sample[] data, double[][] weights) Creates the structure that will be used in the optimization.
protected void	ScoreClassifier.createStructure(Sample[] data, double[][] weights, boolean initRandomly) Creates the structure that will be used in the optimization.
protected double	ScoreClassifier.doOptimization(Sample[] reduced, double[][] newWeights) This method does the optimization of the train-method
protected abstract SFBasedOptimizableFunction	ScoreClassifier.getFunction(Sample[] data, double[][] weights) Returns the function that should be optimized.
abstract void	OptimizableFunction.setDataAndWeights(Sample[] data, double[][] weights) This method sets the data set and the sequence weights to be used.
void	AbstractOptimizableFunction.setDataAndWeights(Sample[] data, double[][] weights)
void	AbstractMultiThreadedOptimizableFunction.setDataAndWeights(Sample[] data, double[][] weights)
void	ScoreClassifier.train(Sample[] data, double[][] weights)

Constructors in de.jstacs.classifier.scoringFunctionBased with parameters of type Sample

AbstractMultiThreadedOptimizableFunction(int threads, Sample[] data, double[][] weights, boolean norm, boolean freeParams)
The constructor for an multi-threaded instance.

AbstractOptimizableFunction(Sample[] data, double[][] weights, boolean norm, boolean freeParams)
The constructor creates an instance using the given weighted data.

SFBasedOptimizableFunction(int threads, ScoringFunction[] score, Sample[] data, double[][] weights, LogPrior prior, boolean norm, boolean freeParams)
Creates an instance with the underlying infrastructure.

Uses of Sample in de.jstacs.classifier.scoringFunctionBased.gendismix

Methods in de.jstacs.classifier.scoringFunctionBased.gendismix that return Sample

Sample[]

OneSampleLogGenDisMixFunction.getData()

Methods in de.jstacs.classifier.scoringFunctionBased.gendismix with parameters of type Sample

protected LogGenDisMixFunction	GenDisMixClassifier.getFunction(Sample[] data, double[][] weights)
void	OneSampleLogGenDisMixFunction.setDataAndWeights(Sample[] data, double[][] weights)

Constructors in de.jstacs.classifier.scoringFunctionBased.gendismix with parameters of type Sample

LogGenDisMixFunction(int threads, ScoringFunction[] score, Sample[] data, double[][] weights, LogPrior prior, double[] beta, boolean norm, boolean freeParams)
The constructor for creating an instance that can be used in an Optimizer.

OneSampleLogGenDisMixFunction(int threads, ScoringFunction[] score, Sample 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 Sample in de.jstacs.classifier.scoringFunctionBased.sampling

Methods in de.jstacs.classifier.scoringFunctionBased.sampling with parameters of type Sample

void	SamplingScoreBasedClassifier.doSingleSampling(Sample[] s, double[][] weights, int numSteps, String outfilePrefix) Does a single sampling run for a predefined number of steps.
protected abstract SFBasedOptimizableFunction	SamplingScoreBasedClassifier.getFunction(Sample[] data, double[][] weights) Returns the function that should be sampled from.
protected SFBasedOptimizableFunction	SamplingGenDisMixClassifier.getFunction(Sample[] data, double[][] weights)
double[]	SamplingScoreBasedClassifier.getScores(Sample s)
void	SamplingScoreBasedClassifier.train(Sample[] s, double[][] weights)

Uses of Sample in de.jstacs.classifier.utils

Methods in de.jstacs.classifier.utils with parameters of type Sample

static ImageResult	ClassificationVisualizer.getScatterplot(AbstractScoreBasedClassifier cl1, AbstractScoreBasedClassifier cl2, Sample class0, Sample 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, Sample class0, Sample 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, Sample class0, Sample 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 Sample in de.jstacs.data

Subclasses of Sample in de.jstacs.data

class

DNASample This class exist for convenience to allow the user an easy creation of Samples of DNA Sequences.

Methods in de.jstacs.data that return Sample

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

Methods in de.jstacs.data with parameters of type Sample

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

Constructors in de.jstacs.data with parameters of type Sample

Sample.ElementEnumerator(Sample data)
Creates a new Sample.ElementEnumerator on the given Sample data.

Sample.WeightedSampleFactory(Sample.WeightedSampleFactory.SortOperation sort, Sample... data)
Creates a new Sample.WeightedSampleFactory on the given Sample(s) with Sample.WeightedSampleFactory.SortOperation sort.

Sample.WeightedSampleFactory(Sample.WeightedSampleFactory.SortOperation sort, Sample[] data, double[][] weights, int length)
Creates a new Sample.WeightedSampleFactory on the given array of Samples and an array of weights with a given length and Sample.WeightedSampleFactory.SortOperation sort.

Sample.WeightedSampleFactory(Sample.WeightedSampleFactory.SortOperation sort, Sample data, double[] weights)
Creates a new Sample.WeightedSampleFactory on the given Sample and an array of weights with Sample.WeightedSampleFactory.SortOperation sort.

Sample.WeightedSampleFactory(Sample.WeightedSampleFactory.SortOperation sort, Sample data, double[] weights, int length)
Creates a new Sample.WeightedSampleFactory on the given Sample and an array of weights with a given length and Sample.WeightedSampleFactory.SortOperation sort.

Sample(Sample s, int subsequenceLength)
Creates a new Sample from a given Sample and a given length subsequenceLength.

SampleKMerEnumerator(Sample data, int k, boolean eliminateRevComp)
Constructs a new SampleKMerEnumerator from a Sample data by extracting all k-mers.

Uses of Sample in de.jstacs.data.bioJava

Methods in de.jstacs.data.bioJava that return Sample

static Sample

BioJavaAdapter.sequenceIteratorToSample(SequenceIterator it, FeatureFilter filter) This method creates a new Sample from a SequenceIterator.

Methods in de.jstacs.data.bioJava with parameters of type Sample

static SequenceIterator

BioJavaAdapter.sampleToSequenceIterator(Sample sample, boolean flat) Creates a SequenceIterator from the Sample sample preserving as much annotation as possible.

Uses of Sample in de.jstacs.data.sequences

Methods in de.jstacs.data.sequences that return Sample

static Sample

SparseSequence.getSample(AlphabetContainer con, AbstractStringExtractor... se) This method allows to create a Sample containing SparseSequences.

Uses of Sample in de.jstacs.models

Methods in de.jstacs.models that return Sample

Sample	UniformModel.emitSample(int n, int... lengths)
Sample	Model.emitSample(int numberOfSequences, int... seqLength) This method returns a Sample object containing artificial sequence(s).
Sample	AbstractModel.emitSample(int numberOfSequences, int... seqLength)

Methods in de.jstacs.models with parameters of type Sample

double[]	Model.getLogProbFor(Sample data) This method computes the logarithm of the probabilities of all sequences in the given sample.
double[]	AbstractModel.getLogProbFor(Sample data)
void	Model.getLogProbFor(Sample data, double[] res) This method computes and stores the logarithm of the probabilities for any sequence in the sample in the given double-array.
void	AbstractModel.getLogProbFor(Sample data, double[] res)
void	Model.train(Sample data) Trains the Model object given the data as Sample.
void	AbstractModel.train(Sample data)
void	VariableLengthWrapperModel.train(Sample data, double[] weights)
void	UniformModel.train(Sample data, double[] weights) Deprecated.
void	NormalizableScoringFunctionModel.train(Sample data, double[] weights)
void	Model.train(Sample data, double[] weights) Trains the Model object given the data as Sample using the specified weights.
void	CompositeModel.train(Sample data, double[] weights)

Uses of Sample in de.jstacs.models.discrete

Methods in de.jstacs.models.discrete with parameters of type Sample

static double

ConstraintManager.countInhomogeneous(AlphabetContainer alphabets, int length, Sample data, double[] weights, boolean reset, Constraint... constr) Fills the (inhomogeneous) Constraint constr with the weighted absolute frequencies of the Sample data.

Uses of Sample in de.jstacs.models.discrete.homogeneous

Methods in de.jstacs.models.discrete.homogeneous that return Sample

Sample

HomogeneousModel.emitSample(int no, int... length) Creates a Sample of a given number of Sequences from a trained homogeneous model.

Methods in de.jstacs.models.discrete.homogeneous with parameters of type Sample

void	HomogeneousModel.train(Sample[] data) Trains the homogeneous model on all given Samples.
abstract void	HomogeneousModel.train(Sample[] data, double[][] weights) Trains the homogeneous model using an array of weighted Samples.
void	HomogeneousMM.train(Sample[] data, double[][] weights)
void	HomogeneousMM.train(Sample data, double[] weights)

Uses of Sample in de.jstacs.models.discrete.inhomogeneous

Methods in de.jstacs.models.discrete.inhomogeneous that return Sample

Sample

DAGModel.emitSample(int n, int... lengths)

Methods in de.jstacs.models.discrete.inhomogeneous with parameters of type Sample

void	FSDAGModelForGibbsSampling.drawParameters(Sample data, double[] weights)
protected void	DAGModel.drawParameters(Sample data, double[] weights) This method draws the parameter of the model from the likelihood or the posterior, respectively.
void	FSDAGModelForGibbsSampling.drawParameters(Sample data, double[] weights, int[][] graph)
void	FSDAGModel.drawParameters(Sample data, double[] weights, int[][] graph) This method draws the parameters of the model from the a posteriori density.
protected void	DAGModel.estimateParameters(Sample data, double[] weights) This method estimates the parameter of the model from the likelihood or the posterior, respectively.
static double[][]	TwoPointEvaluater.getMIInBits(Sample s, double[] weights) This method computes the pairwise mutual information (in bits) between the sequence positions.
int[][]	StructureLearner.getStructure(Sample 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(Sample 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.
static void	FSDAGModel.train(Model[] models, int[][] graph, double[][] weights, Sample... data) Computes the models with structure graph.
void	FSDAGModelForGibbsSampling.train(Sample data, double[] weights)
void	FSDAGModel.train(Sample data, double[] weights)
void	BayesianNetworkModel.train(Sample data, double[] weights)
void	FSDAGModelForGibbsSampling.train(Sample data, double[] weights, int[][] graph)
void	FSDAGModel.train(Sample data, double[] weights, int[][] graph) Computes the model with structure graph.

Uses of Sample in de.jstacs.models.discrete.inhomogeneous.shared

Methods in de.jstacs.models.discrete.inhomogeneous.shared with parameters of type Sample

void	SharedStructureClassifier.train(Sample[] data, double[][] weights)

Uses of Sample in de.jstacs.models.hmm

Methods in de.jstacs.models.hmm with parameters of type Sample

String	AbstractHMM.getGraphvizRepresentation(NumberFormat nf, Sample data, double[] weight, boolean sameTypeSameRank) This method returns a String representation of the structure that can be used in Graphviz to create an image.
double[][][]	AbstractHMM.getLogStatePosteriorMatrixFor(Sample data) This method returns the log state posteriors for all sequences of the sample data.
double[][][]	AbstractHMM.getStatePosteriorMatrixFor(Sample data) This method returns the state posteriors for all sequences of the sample data.
Pair<IntList,Double>[]	AbstractHMM.getViterbiPathsFor(Sample data) This method returns the viterbi paths and scores for all sequences of the sample data.
void	AbstractHMM.train(Sample data)

Uses of Sample in de.jstacs.models.hmm.models

Methods in de.jstacs.models.hmm.models that return Sample

Sample

HigherOrderHMM.emitSample(int numberOfSequences, int... seqLength)

Methods in de.jstacs.models.hmm.models with parameters of type Sample

protected void	SamplingHigherOrderHMM.furtherInits(Sample data, double[] weights) This method allows the implementation of further initialisations
double[]	HigherOrderHMM.getLogProbFor(Sample data)
void	HigherOrderHMM.getLogProbFor(Sample data, double[] res)
protected void	SamplingHigherOrderHMM.gibbsSamplingStep(int sampling, int steps, boolean append, Sample data, double[] weights) This method implements the next step(s) in the sampling procedure
void	DifferentiableHigherOrderHMM.initializeFunction(int index, boolean freeParams, Sample[] data, double[][] weights)
protected void	SamplingHigherOrderHMM.initTraining(Sample data, double[] weights) This methods initialize the training procedure with the given training data
void	SamplingHigherOrderHMM.train(Sample data, double[] weights)
void	HigherOrderHMM.train(Sample data, double[] weights)
void	DifferentiableHigherOrderHMM.train(Sample data, double[] weights)

Uses of Sample in de.jstacs.models.mixture

Fields in de.jstacs.models.mixture declared as Sample

protected Sample[]

AbstractMixtureModel.sample The sample that was used in the last training.

Methods in de.jstacs.models.mixture that return Sample

Sample

AbstractMixtureModel.emitSample(int n, int... lengths)

Methods in de.jstacs.models.mixture with parameters of type Sample

protected double[][]	AbstractMixtureModel.doFirstIteration(Sample data, double[] dataWeights) This method will do the first step in the train algorithm for the current model.
double[][]	MixtureModel.doFirstIteration(Sample data, double[] dataWeights, double[][] partitioning) This method enables you to train a mixture model with a fixed start partitioning.
protected double[][]	AbstractMixtureModel.doFirstIteration(Sample data, double[] dataWeights, MultivariateRandomGenerator m, MRGParams[] params) This method will do the first step in the train algorithm for the current model.
double[]	AbstractMixtureModel.getLogProbFor(Sample data)
double	AbstractMixtureModel.iterate(Sample data, double[] dataWeights, MultivariateRandomGenerator m, MRGParams[] params) This method runs the train algorithm for the current model.
void	StrandModel.setTrainData(Sample s)
protected void	MixtureModel.setTrainData(Sample data)
protected abstract void	AbstractMixtureModel.setTrainData(Sample data) This method is invoked by the train-method and sets for a given sample the sample that should be used for train.
void	AbstractMixtureModel.train(Sample data, double[] dataWeights)

Uses of Sample in de.jstacs.models.mixture.motif

Methods in de.jstacs.models.mixture.motif with parameters of type Sample

protected void	SingleHiddenMotifMixture.setTrainData(Sample data)
void	HiddenMotifMixture.train(Sample data, double[] weights)
void	SingleHiddenMotifMixture.trainBgModel(Sample data, double[] weights)
abstract void	HiddenMotifMixture.trainBgModel(Sample data, double[] weights) This method trains the background model.

Uses of Sample in de.jstacs.models.utils

Methods in de.jstacs.models.utils that return Sample

static Sample

DiscreteInhomogenousSampleEmitter.emitSample(Model m, int n) This method emits a sample with n sequences from the discrete inhomogeneous model m .

Methods in de.jstacs.models.utils with parameters of type Sample

static double	ModelTester.getLogLikelihood(Model m, Sample data) Returns the log-likelihood of a Sample data for a given model m.
static double	ModelTester.getLogLikelihood(Model m, Sample data, double[] weights) Returns the log-likelihood of a Sample data for a given model m.
static double	ModelTester.getValueOfAIC(Model m, Sample s, int k) This method computes the value of Akaikes Information Criterion (AIC).
static double	ModelTester.getValueOfBIC(Model m, Sample s, int k) This method computes the value of the Bayesian Information Criterion (BIC).

Uses of Sample in de.jstacs.motifDiscovery

Methods in de.jstacs.motifDiscovery that return Sample

Sample	SignificantMotifOccurrencesFinder.annotateMotif(int startPos, Sample data, int motifIndex) This method annotates a Sample starting in each sequence at startPos.
Sample	SignificantMotifOccurrencesFinder.annotateMotif(int startPos, Sample data, int motifIndex, int addMax, boolean addAnnotation) This method annotates a Sample starting in each sequence at startPos.
Sample	SignificantMotifOccurrencesFinder.annotateMotif(Sample data, int motifIndex) This method annotates a Sample.
Sample	SignificantMotifOccurrencesFinder.annotateMotif(Sample data, int motifIndex, int addMax) This method annotates a Sample.
Sample	SignificantMotifOccurrencesFinder.getBindingSites(int startPos, Sample data, int motifIndex, int addMax, int addLeft, int addRight) This method returns a Sample containing the predicted binding sites.
Sample	SignificantMotifOccurrencesFinder.getBindingSites(Sample data, int motifIndex) This method returns a Sample containing the predicted binding sites.

Methods in de.jstacs.motifDiscovery with parameters of type Sample

void	MutableMotifDiscoverer.adjustHiddenParameters(int index, Sample[] data, double[][] weights) Adjusts all hidden parameters including duration and mixture parameters according to the current values of the remaining parameters.
Sample	SignificantMotifOccurrencesFinder.annotateMotif(int startPos, Sample data, int motifIndex) This method annotates a Sample starting in each sequence at startPos.
Sample	SignificantMotifOccurrencesFinder.annotateMotif(int startPos, Sample data, int motifIndex, int addMax, boolean addAnnotation) This method annotates a Sample starting in each sequence at startPos.
Sample	SignificantMotifOccurrencesFinder.annotateMotif(Sample data, int motifIndex) This method annotates a Sample.
Sample	SignificantMotifOccurrencesFinder.annotateMotif(Sample data, int motifIndex, int addMax) This method annotates a Sample.
static ListResult	MotifDiscoveryAssessment.assess(Sample truth, Sample prediction, int maxDiff) This method computes the nucleotide and site measures.
static boolean	MutableMotifDiscovererToolbox.doHeuristicSteps(ScoringFunction[] funs, Sample[] data, double[][] weights, SFBasedOptimizableFunction 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 MutableMotifDiscovererToolbox.InitMethodForScoringFunction is a MutableMotifDiscoverer.
static boolean	MutableMotifDiscovererToolbox.findModification(int clazz, int motif, MutableMotifDiscoverer mmd, ScoringFunction[] score, Sample[] data, double[][] weights, SFBasedOptimizableFunction 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 Sample.WeightedSampleFactory	KMereStatistic.getAbsoluteKMereFrequencies(Sample data, int k, boolean bothStrands) This method enables the user to get a statistic over all k-mers in the data.
static Sample.WeightedSampleFactory	KMereStatistic.getAbsoluteKMereFrequencies(Sample data, int k, boolean bothStrands, Sample.WeightedSampleFactory.SortOperation sortOp) This method enables the user to get a statistic over all k-mers in the data.
Sample	SignificantMotifOccurrencesFinder.getBindingSites(int startPos, Sample data, int motifIndex, int addMax, int addLeft, int addRight) This method returns a Sample containing the predicted binding sites.
Sample	SignificantMotifOccurrencesFinder.getBindingSites(Sample data, int motifIndex) This method returns a Sample containing the predicted binding sites.
static Sequence[]	KMereStatistic.getCommonString(Sample 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, Sample... 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, Sample... data) This method enables the user to get a statistic over all k-mers in the sequences.
double	SignificantMotifOccurrencesFinder.getNumberOfBoundSequences(Sample 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(Sample data, Sample pred, String identifier) Returns the scores read from the prediction pred for the motif with identifier identifier and flanking sequences as annotated in the Sample data.
static double[][]	MotifDiscoveryAssessment.getSortedValuesForMotifAndFlanking(Sample data, double[][] values, double offset, double factor, String identifier) This method provides some score arrays that can be used in ScoreBasedPerformanceMeasureDefinitions to determine some curves or area under curves based on the values of the predictions.
IntList	SignificantMotifOccurrencesFinder.getStartPositions(int startPos, Sample data, int motifIndex, int addMax) This method returns a list of start positions of binding sites.
double[][]	SignificantMotifOccurrencesFinder.getValuesForEachNucleotide(Sample 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, Sample 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, Sample[] s, double[][] seqWeights, boolean[] adjust, MutableMotifDiscoverer[] mmd, int[] len, Sample[] data, double[][] dataWeights) This method allows to initialize a number of motifs.
static void	MutableMotifDiscovererToolbox.initMotif(int idx, int[] classIndex, int[] motifIndex, Sample[] s, double[][] seqWeights, boolean[] adjust, MutableMotifDiscoverer[] mmd, int[] len, Sample[] data, double[][] dataWeights) This method allows to initialize a number of motifs.

Constructors in de.jstacs.motifDiscovery with parameters of type Sample

KMereStatistic(Sample data, int k)
This constructor creates an internal statistic counting all k-mers in the data.

SignificantMotifOccurrencesFinder(MotifDiscoverer disc, Sample bg, double[] weights, double sign)
This constructor creates an instance of SignificantMotifOccurrencesFinder that uses a Sample to determine the siginificance level.

SignificantMotifOccurrencesFinder(MotifDiscoverer disc, SignificantMotifOccurrencesFinder.JoinMethod joiner, Sample bg, double[] weights, double sign)
This constructor creates an instance of SignificantMotifOccurrencesFinder that uses a Sample to determine the siginificance level.

Uses of Sample in de.jstacs.results

Methods in de.jstacs.results that return Sample

Sample

SampleResult.getResult()

Constructors in de.jstacs.results with parameters of type Sample

SampleResult(String name, String comment, Sample data)
Creates a new SampleResult from a Sample with the annotation name and comment.

SampleResult(String name, String comment, Sample data, SequenceAnnotationParser parser)
Creates a new SampleResult from a Sample with the annotation name and comment.

Uses of Sample in de.jstacs.sampling

Methods in de.jstacs.sampling with parameters of type Sample

void	GibbsSamplingModel.drawParameters(Sample data, double[] weights) This method draws the parameters of the model from the a posteriori density.

Uses of Sample in de.jstacs.scoringFunctions

Methods in de.jstacs.scoringFunctions with parameters of type Sample

void	MappingScoringFunction.adjustHiddenParameters(int index, Sample[] data, double[][] weights)
void	IndependentProductScoringFunction.adjustHiddenParameters(int index, Sample[] data, double[][] weights)
int	IndependentProductScoringFunction.extractSequenceParts(int scoringFunctionIndex, Sample[] data, Sample[] result) This method extracts the corresponding Sequence parts for a specific ScoringFunction.
int	IndependentProductScoringFunction.extractSequenceParts(int scoringFunctionIndex, Sample[] data, Sample[] result) This method extracts the corresponding Sequence parts for a specific ScoringFunction.
void	UniformScoringFunction.initializeFunction(int index, boolean meila, Sample[] data, double[][] weights)
void	ScoringFunction.initializeFunction(int index, boolean freeParams, Sample[] data, double[][] weights) This method creates the underlying structure of the ScoringFunction.
void	NormalizedScoringFunction.initializeFunction(int index, boolean freeParams, Sample[] data, double[][] weights)
void	MRFScoringFunction.initializeFunction(int index, boolean freeParams, Sample[] data, double[][] weights)
void	MappingScoringFunction.initializeFunction(int index, boolean freeParams, Sample[] data, double[][] weights)
void	IndependentProductScoringFunction.initializeFunction(int index, boolean freeParams, Sample[] data, double[][] weights)
void	CMMScoringFunction.initializeFunction(int index, boolean freeParams, Sample[] data, double[][] weights)
void	MappingScoringFunction.initializeMotif(int motifIndex, Sample data, double[] weights)
void	IndependentProductScoringFunction.initializeMotif(int motifIndex, Sample data, double[] weights)

Uses of Sample in de.jstacs.scoringFunctions.directedGraphicalModels

Methods in de.jstacs.scoringFunctions.directedGraphicalModels with parameters of type Sample

protected void	BayesianNetworkScoringFunction.createTrees(Sample[] data2, double[][] weights2) Creates the tree structures that represent the context (array BayesianNetworkScoringFunction.trees) and the parameter objects BayesianNetworkScoringFunction.parameters using the given Measure BayesianNetworkScoringFunction.structureMeasure.
void	BayesianNetworkScoringFunction.initializeFunction(int index, boolean freeParams, Sample[] data, double[][] weights)
protected void	BayesianNetworkScoringFunction.setPlugInParameters(int index, boolean freeParameters, Sample[] data, double[][] weights) Computes and sets the plug-in parameters (MAP estimated parameters) from data using weights.

Uses of Sample in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures

Methods in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures with parameters of type Sample

abstract int[][]	Measure.getParents(Sample fg, Sample bg, double[] weightsFg, double[] weightsBg, int length) Returns the optimal parents for the given data and weights.
int[][]	InhomogeneousMarkov.getParents(Sample fg, Sample bg, double[] weightsFg, double[] weightsBg, int length)
protected static double[][][][]	Measure.getStatistics(Sample s, double[] weights, int length, double ess) Counts the occurrences of symbols of the AlphabetContainer of Sample s using weights.
protected static double[][][][][][]	Measure.getStatisticsOrderTwo(Sample s, double[] weights, int length, double ess) Counts the occurrences of symbols of the AlphabetContainer of Sample s using weights.

Uses of Sample in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures

Methods in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures with parameters of type Sample

int[][]	BTMutualInformation.getParents(Sample fg, Sample bg, double[] weightsFg, double[] weightsBg, int length)
int[][]	BTExplainingAwayResidual.getParents(Sample fg, Sample bg, double[] weightsFg, double[] weightsBg, int length)

Uses of Sample in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures

Methods in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures with parameters of type Sample

int[][]	PMMMutualInformation.getParents(Sample fg, Sample bg, double[] weightsFg, double[] weightsBg, int length)
int[][]	PMMExplainingAwayResidual.getParents(Sample fg, Sample bg, double[] weightsFg, double[] weightsBg, int length)

Uses of Sample in de.jstacs.scoringFunctions.homogeneous

Methods in de.jstacs.scoringFunctions.homogeneous that return Sample

Sample

HMMScoringFunction.emit(int numberOfSequences, int... seqLength) This method returns a Sample object containing artificial sequence(s).

Methods in de.jstacs.scoringFunctions.homogeneous with parameters of type Sample

void	UniformHomogeneousScoringFunction.initializeFunction(int index, boolean meila, Sample[] data, double[][] weights)
void	HMMScoringFunction.initializeFunction(int index, boolean freeParams, Sample[] data, double[][] weights)
void	HMM0ScoringFunction.initializeFunction(int index, boolean freeParams, Sample[] data, double[][] weights)

Uses of Sample in de.jstacs.scoringFunctions.mix

Methods in de.jstacs.scoringFunctions.mix with parameters of type Sample

void	MixtureScoringFunction.adjustHiddenParameters(int index, Sample[] data, double[][] weights) Adjusts all hidden parameters including duration and mixture parameters according to the current values of the remaining parameters.
void	AbstractMixtureScoringFunction.initializeFunction(int index, boolean freeParams, Sample[] data, double[][] weights)
void	MixtureScoringFunction.initializeMotif(int motifIndex, Sample data, double[] weights)
protected void	StrandScoringFunction.initializeUsingPlugIn(int index, boolean freeParams, Sample[] data, double[][] weights)
protected void	MixtureScoringFunction.initializeUsingPlugIn(int index, boolean freeParams, Sample[] data, double[][] weights)
protected abstract void	AbstractMixtureScoringFunction.initializeUsingPlugIn(int index, boolean freeParams, Sample[] data, double[][] weights) This method initializes the functions using the data in some way.

Uses of Sample in de.jstacs.scoringFunctions.mix.motifSearch

Methods in de.jstacs.scoringFunctions.mix.motifSearch with parameters of type Sample

void	HiddenMotifsMixture.adjustHiddenParameters(int classIndex, Sample[] data, double[][] dataWeights)
void	UniformDurationScoringFunction.initializeFunction(int index, boolean meila, Sample[] data, double[][] weights)
void	SkewNormalLikeScoringFunction.initializeFunction(int index, boolean freeParams, Sample[] data, double[][] weights)
void	MixtureDuration.initializeFunction(int index, boolean freeParams, Sample[] data, double[][] weights)
void	HiddenMotifsMixture.initializeMotif(int motif, Sample data, double[] weights)
protected void	HiddenMotifsMixture.initializeUsingPlugIn(int index, boolean freeParams, Sample[] data, double[][] weights)