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## 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 `Classifier`s that are based on `Model`s
de.jstacs.classifier.scoringFunctionBased | Provides the classes for `Classifier`s that are based on `ScoringFunction`s.
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 `ScoringFunction`s that can be used in a `ScoreClassifier`.
de.jstacs.scoringFunctions.directedGraphicalModels | Provides `ScoringFunction`s 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 `ScoringFunction`s that are homogeneous, i.e. model probabilities or scores independent of the position within a sequence
de.jstacs.scoringFunctions.mix | Provides `ScoringFunction`s that are mixtures of other `ScoringFunction`s.
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 `Sample`s 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 `Sequence`s 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 `Sample`s 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 `Sample`s.
`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 `AbstractClassifier`s.
`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 `Sequence`s 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 `Sequence`s, 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 `Sequence`s that are contained in all `Sample`s of the array.
`Sample[]` | `Sample.partition(double p, Sample.PartitionMethod method, int subsequenceLength)` This method partitions the elements, i.e. the `Sequence`s, of the `Sample` in two distinct parts.
`Sample[]` | `Sample.partition(int k, Sample.PartitionMethod method)` This method partitions the elements, i.e. the `Sequence`s, of the `Sample` in `k` distinct parts.
`Sample[]` | `Sample.partition(Sample.PartitionMethod method, double... percentage)` This method partitions the elements, i.e. the `Sequence`s, 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 `Sample`s of the array `s`.
`static Sample` | `Sample.union(Sample[] s, boolean[] in)` This method unites all `Sample`s of the array `s` regarding the array `in`.
`static Sample` | `Sample.union(Sample[] s, boolean[] in, int subsequenceLength)` This method unites all `Sample`s 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 `Sample`s of the array `s` and sets the element length in the united sample to `subsequenceLength`.

Methods in de.jstacs.data with parameters of type Sample
`static String` | `Sample.getAnnotation(Sample... s)` Returns the annotation for an array of `Sample`s.
`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 `Sequence`s that are contained in all `Sample`s of the array.
`static Sample` | `Sample.union(Sample... s)` Unites all `Sample`s of the array `s`.
`static Sample` | `Sample.union(Sample[] s, boolean[] in)` This method unites all `Sample`s of the array `s` regarding the array `in`.
`static Sample` | `Sample.union(Sample[] s, boolean[] in, int subsequenceLength)` This method unites all `Sample`s 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 `Sample`s of the array `s` and sets the element length in the united sample to `subsequenceLength`.

Constructors in de.jstacs.data with parameters of type 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 `Sample`s 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 `Sequence`s 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 `Sample`s.
`abstract void` | `HomogeneousModel.train(Sample[] data, double[][] weights)` Trains the homogeneous model using an array of weighted `Sample`s.
`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` | `HiddenMotifsMixture.initializeMotif(int motif, Sample data, double[] weights)`
`protected void` | `HiddenMotifsMixture.initializeUsingPlugIn(int index, boolean freeParams, Sample[] data, double[][] weights)`

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