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 Model
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.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.utils   
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. 
 

Uses of Sample in de.jstacs.classifier
 

Methods in de.jstacs.classifier that return Sample
 Sample[] MappingClassifier.mapSample(Sample[] s)
          This method maps the 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 the index i, with 0 < i < getNumberOfClasses(), of the class to which the sequence is assigned.
 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 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 in 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 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 Samples.
 

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)
           
 ListResult ClassifierAssessment.assess(MeasureParameters mp, ClassifierAssessmentAssessParameterSet assessPS, Sample... s)
          Assesses the contained classifiers.
protected  boolean Sampled_RepeatedHoldOutExperiment.evaluateClassifier(MeasureParameters mp, ClassifierAssessmentAssessParameterSet assessPS, Sample[] s, ProgressUpdater pU)
           
protected  boolean RepeatedSubSamplingExperiment.evaluateClassifier(MeasureParameters mp, ClassifierAssessmentAssessParameterSet assessPS, Sample[] s, ProgressUpdater pU)
           
protected  boolean RepeatedHoldOutExperiment.evaluateClassifier(MeasureParameters mp, ClassifierAssessmentAssessParameterSet assessPS, Sample[] s, ProgressUpdater pU)
           
protected  boolean KFoldCrossValidation.evaluateClassifier(MeasureParameters mp, ClassifierAssessmentAssessParameterSet assessPS, Sample[] s, ProgressUpdater pU)
           
protected abstract  boolean 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 trainingSamples.
 

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
 

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 the should be optimized
 void ScoreClassifier.train(Sample[] data, double[][] weights)
           
 

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 log conditional likelihood.
NormConditionalLogLikelihood(ScoringFunction[] score, Sample[] data, double[][] weights, LogPrior prior, boolean norm, boolean freeParams)
          The constructor creates an instance 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,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 an 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 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 an suffix of all elements in the current sample.
static Sample Sample.intersection(Sample... samples)
          This method computes the intersection between all elements of the array, i.e.
 Sample[] Sample.partition(double p, Sample.PartitionMethod method, int subsequenceLength)
          This method partitions the elements of the sample in 2 distinct parts.
 Sample[] Sample.partition(int k, Sample.PartitionMethod method)
          This method partitions the elements of the sample in k distinct parts.
 Sample[] Sample.partition(Sample.PartitionMethod method, double... percentage)
          This method partitions the elements of the sample in distinct parts.
 Sample Sample.subSampling(int number)
          Randomly samples elements (sequences) from the set of all elements (sequences) contained in this Sample.
static Sample Sample.union(Sample... s)
          Unites all samples in s
static Sample Sample.union(Sample[] s, boolean[] in)
          This method unites all Sample from s regarding in.
static Sample Sample.union(Sample[] s, boolean[] in, int subsequenceLength)
          This method unites all Sample from s regarding 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 from 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 of the array, i.e.
static Sample Sample.union(Sample... s)
          Unites all samples in s
static Sample Sample.union(Sample[] s, boolean[] in)
          This method unites all Sample from s regarding in.
static Sample Sample.union(Sample[] s, boolean[] in, int subsequenceLength)
          This method unites all Sample from s regarding 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 from 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)
          This constructor creates an new ElementEnumerator on the given data
Sample.WeightedSampleFactory(Sample.WeightedSampleFactory.SortOperation sort, Sample... data)
          This constructor creates a Sample.WeightedSampleFactory on the given Sample(s).
Sample.WeightedSampleFactory(Sample.WeightedSampleFactory.SortOperation sort, Sample[] data, double[][] weights, int length)
          This constructor creates a Sample.WeightedSampleFactory on the given array of Samples and weights.
Sample.WeightedSampleFactory(Sample.WeightedSampleFactory.SortOperation sort, Sample data, double[] weights)
          This constructor creates a Sample.WeightedSampleFactory on the given Sample and weights.
Sample.WeightedSampleFactory(Sample.WeightedSampleFactory.SortOperation sort, Sample data, double[] weights, int length)
          This constructor creates a Sample.WeightedSampleFactory on the given Sample and weights.
Sample(Sample s, int subsequenceLength)
          This constructor enables you to use subsequences of the elements of a sample.
 

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 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 AbstractModel 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) constr with the weighted absolute frequency of the sample data and computes the frequencies will not be computed.
 

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 respectively posterior.
 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 respectively posterior.
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 (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 set 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.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, inhomogenous model m.
 

Methods in de.jstacs.models.utils with parameters of type Sample
static double ModelTester.getLogLikelihood(Model m, Sample data)
          Returns the loglikelihood of a sample data for a given model m.
static double ModelTester.getLogLikelihood(Model m, Sample data, double[] weights)
          Returns the loglikelihood 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 Bayesian Information Criterion (BIC).
 

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
 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 scoring function.
 void MRFScoringFunction.initializeFunction(int index, boolean freeParams, Sample[] data, double[][] weights)
           
 void IndependentProductScoringFunction.initializeFunction(int index, boolean freeParams, 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 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 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 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 function using the data in some way.