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.cll	Provides the implementation of the log conditional likelihood as an OptimizableFunction and a classifier that uses log conditional likelihood or supervised posterior to learn the parameters of a set of ScoringFunctions
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.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.mixture	This package is the super package for any mixture model.
de.jstacs.models.mixture.gibbssampling	This package contains many classes that can be used while a Gibbs sampling.
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.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>	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 double	ScoreClassifier.doOptimization(Sample[] reduced, double[][] newWeights) This method does the optimization of the train-method
protected abstract OptimizableFunction	ScoreClassifier.getFunction(Sample[] data, double[][] weights) Returns the function that should be optimized.
void	ScoreClassifier.train(Sample[] data, double[][] weights)

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

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

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

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

protected NormConditionalLogLikelihood

CLLClassifier.getFunction(Sample[] data, double[][] weights)

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

NormConditionalLogLikelihood(ScoringFunction[] score, Sample[] data, double[][] weights, boolean norm, boolean freeParams)
The constructor creates an instance of the NormConditionalLogLikelihood.

NormConditionalLogLikelihood(ScoringFunction[] score, Sample[] data, double[][] weights, LogPrior prior, boolean norm, boolean freeParams)
The constructor creates an instance of the NormConditionalLogLikelihood using the given prior.

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

Methods in de.jstacs.data that return Sample

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.WeightedSampleFactory.getSample() Returns the Sample, where each Sequence occurs only once.
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 String	Sample.getAnnotation(Sample... s) Returns the annotation for an array of Samples.
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.

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.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	UniformModel.train(Sample data, double[] weights) Deprecated.
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

protected void	DAGModel.drawParameters(Sample data, double[] weights) This method draws the parameter of the model from the likelihood or the posterior, respectively.
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	FSDAGModel.train(Sample data, double[] weights)
void	BayesianNetworkModel.train(Sample data, double[] weights)
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.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.gibbssampling

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

void	GibbsSamplingComponent.drawParameters(Sample data, double[] weights) This method draws the parameters of the model from the a posteriori density.
void	FSDAGModelForGibbsSampling.drawParameters(Sample data, double[] weights)
void	FSDAGModelForGibbsSampling.drawParameters(Sample data, double[] weights, int[][] graph)
void	FSDAGModelForGibbsSampling.train(Sample data, double[] weights)
void	FSDAGModelForGibbsSampling.train(Sample data, double[] weights, int[][] graph)

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.annotateMotifs(int startPos, Sample data) This method annotates a Sample starting in each sequence at startPos.
Sample	SignificantMotifOccurrencesFinder.annotateMotifs(int startPos, Sample data, int addMax) This method annotates a Sample starting in each sequence at startPos.
Sample	SignificantMotifOccurrencesFinder.annotateMotifs(Sample data) This method annotates a Sample.
Sample	SignificantMotifOccurrencesFinder.annotateMotifs(Sample data, int addMax) This method annotates a Sample.

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

Sample	SignificantMotifOccurrencesFinder.annotateMotifs(int startPos, Sample data) This method annotates a Sample starting in each sequence at startPos.
Sample	SignificantMotifOccurrencesFinder.annotateMotifs(int startPos, Sample data, int addMax) This method annotates a Sample starting in each sequence at startPos.
Sample	SignificantMotifOccurrencesFinder.annotateMotifs(Sample data) This method annotates a Sample.
Sample	SignificantMotifOccurrencesFinder.annotateMotifs(Sample data, 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.
int[]	MutableMotifDiscoverer.determineNotSignificantPositionsFor(int motif, Sample[] data, double[][] weights, int classIdx) This method determines the number of not significant positions from each side of the motif with index motif.
static boolean	MutableMotifDiscovererToolbox.doHeuristicSteps(ScoringFunction[] funs, Sample[] data, double[][] weights, OptimizableFunction opt, SafeOutputStream out, boolean breakOnChanged, History[][] hist, int[][] minimalNewLength) This method tries to make some heuristic step if at least one MutableMotifDiscovererToolbox.InitMethodForScoringFunction is a MutableMotifDiscoverer.
static Sequence	MutableMotifDiscovererToolbox.enumerate(Sample[] data, ScoringFunction[] funs, int classIndex, int motifIndex, double weight, OptimizableFunction opt, OutputStream out) This method allows to enumerate all possible seeds for a motif in the HiddenMotifsMixture of a specific class.
double[][]	SignificantMotifOccurrencesFinder.getPValuesForEachNucleotide(Sample data, int component, int motif, boolean addOnlyBest) This method determines the p-value for each symbol to be annotated at least in one motif occurrence of the motif with index index in the component component.
static double[][]	MotifDiscoveryAssessment.getSorted1MinusPValuesForMotifAndFlanking(Sample data, double[][] pValues, 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 p-values of the predictions.
static ComparableElement<double[],Double>[]	MutableMotifDiscovererToolbox.getSortedInitialParameters(Sample[] data, ScoringFunction[] funs, MutableMotifDiscovererToolbox.InitMethodForScoringFunction[] init, OptimizableFunction opt, int n, SafeOutputStream stream) This method allows to initialize the MutableMotifDiscovererToolbox.InitMethodForScoringFunction using different MutableMotifDiscovererToolbox.InitMethodForScoringFunction.
void	MutableMotifDiscoverer.initializeMotif(int motifIndex, Sample data, double[] weights) This method allows to initialize the model of a motif manually using a weighted sample.

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

SignificantMotifOccurrencesFinder(MotifDiscoverer disc, Sample bg, 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.

Uses of Sample in de.jstacs.scoringFunctions

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

int[]	IndependentProductScoringFunction.determineNotSignificantPositionsFor(int motif, Sample[] data, double[][] weights, int classIdx)
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	IndependentProductScoringFunction.initializeFunction(int index, boolean freeParams, 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	AbstractMixtureScoringFunction.initializeFunction(int index, boolean freeParams, 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(Sample data, double[] dataWeights) This method allows to adjust the hidden parameter in some way.
int[]	HiddenMotifsMixture.determineNotSignificantPositionsFor(int motif, Sample[] data, double[][] weights, int classIdx)
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)