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GaussianLikePositionPrior - Class in de.jstacs.models.mixture.motif.positionprior: This class implements a gaussian like discrete truncated prior.
GaussianLikePositionPrior(int, double, double) - Constructor for class de.jstacs.models.mixture.motif.positionprior.GaussianLikePositionPrior: This constructor creates an instance with given sequence length, maximal value and sigma for the Gaussian.
GaussianLikePositionPrior(StringBuffer) - Constructor for class de.jstacs.models.mixture.motif.positionprior.GaussianLikePositionPrior: The standard constructor for the interface Storable.
generate(double[], int, int, MRGParams) - Method in class de.jstacs.utils.random.DirichletMRG
generate(double[], int, int, MRGParams) - Method in class de.jstacs.utils.random.EqualParts
generate(int) - Method in class de.jstacs.utils.random.EqualParts: Returns an array of length n with entries 1/n.
generate(double[], int, int) - Method in class de.jstacs.utils.random.EqualParts: Modifies a given number of array entries to 1/number.
generate(double[], int, int, MRGParams) - Method in class de.jstacs.utils.random.ErlangMRG
generate(int, MRGParams) - Method in class de.jstacs.utils.random.MultivariateRandomGenerator: Generates a n-dimensional random array.
generate(double[], int, int, MRGParams) - Method in class de.jstacs.utils.random.MultivariateRandomGenerator: Generates a n-dimensional random array as part of the array d beginning at start.
generate(double[], int, int, MRGParams) - Method in class de.jstacs.utils.random.SoftOneOfN
generate(int) - Method in class de.jstacs.utils.random.SoftOneOfN: Generates an array of length number with one entry getting the value 1-epsilon and all the others equal parts of epsilon.
generate(double[], int, int) - Method in class de.jstacs.utils.random.SoftOneOfN: Generates an array of length number as part of the array d beginning at index start with one entry getting the value 1-epsilon and all the others equal parts of epsilon.
GEQ - Static variable in interface de.jstacs.parameters.validation.Constraint: The condition is greater or equal
get(int) - Method in class de.jstacs.utils.DoubleList: Returns the element with the specified index.
get(int) - Method in class de.jstacs.utils.IntList: Returns the element with the specified index.
getAcceptedMimeType() - Method in class de.jstacs.parameters.FileParameter: Returns the mime type of the allowed files
getAdditionalInformation() - Method in class de.jstacs.models.mixture.motif.positionprior.GaussianLikePositionPrior
getAdditionalInformation() - Method in class de.jstacs.models.mixture.motif.positionprior.PositionPrior: This method returns a StringBuffer containing additional information for the XML representation.
getAdditionalInformation() - Method in class de.jstacs.models.mixture.motif.positionprior.UniformPositionPrior
getAlignment() - Method in class de.jstacs.algorithms.Alignment: Computes and returns the alignment of s1 and s2 (Alignment.Alignment(Sequence, Sequence, Costs)).
getAllConditionalStationaryDistributions(double[], int) - Static method in class de.jstacs.models.utils.StationaryDistribution: This method return the all conditional stationary distribution, i.e., for a homogeneous Markov model of order 2 it returns an array containing the stationary symbol distribution as first entry, the conditional stationary distribution of order 1 as second entry and the conditional distribution of order 2 as third entry.
getAllConditionalStationaryDistributions() - Method in class de.jstacs.scoringFunctions.homogeneous.HMM0ScoringFunction
getAllConditionalStationaryDistributions() - Method in class de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction
getAllConditionalStationaryDistributions() - Method in class de.jstacs.scoringFunctions.homogeneous.UniformHomogeneousScoringFunction
getAllConditionalStationaryDistributions() - Method in class de.jstacs.scoringFunctions.VariableLengthScoringFunction: This method returns the all stationary conditional distribution.
getAllElements() - Method in class de.jstacs.data.Sample: Returns an array of sequences containing all elements of this Sample.
getAlphabet() - Method in class de.jstacs.parameters.SequenceScoringParameterSet: Returns the alphabet
getAlphabetAt(int) - Method in class de.jstacs.data.AlphabetContainer: Returns the underlying alphabet of position pos.
getAlphabetContainer() - Method in class de.jstacs.classifier.AbstractClassifier: This method returns the container of alphabets that is used in the classifier.
getAlphabetContainer() - Method in class de.jstacs.data.Sample: Returns the AlphabetContainer of this Sample.
getAlphabetContainer() - Method in class de.jstacs.data.Sequence: Return the alphabets used in this sequence.
getAlphabetContainer() - Method in class de.jstacs.models.AbstractModel
getAlphabetContainer() - Method in class de.jstacs.models.discrete.inhomogeneous.StructureLearner: This method returns the AlphabetContainer of the StructureLearner.
getAlphabetContainer() - Method in interface de.jstacs.models.Model: Returns the container of alphabets that were used when constructing the model.
getAlphabetContainer() - Method in class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
getAlphabetContainer() - Method in interface de.jstacs.scoringFunctions.ScoringFunction: Returns the AlphabetContainer for this ScoringFunction.
getAlphabetLengthAt(int) - Method in class de.jstacs.data.AlphabetContainer: Returns the length of the underlying alphabet of position pos.
getAnnotation() - Method in class de.jstacs.classifier.assessment.ClassifierAssessmentAssessParameterSet: Returns a collection of parameters containing informations about this ClassifierAssessmentAssessParameterSet.
getAnnotation() - Method in class de.jstacs.classifier.assessment.KFoldCVAssessParameterSet
getAnnotation() - Method in class de.jstacs.classifier.assessment.RepeatedHoldOutAssessParameterSet
getAnnotation() - Method in class de.jstacs.classifier.assessment.RepeatedSubSamplingAssessParameterSet
getAnnotation() - Method in class de.jstacs.classifier.assessment.Sampled_RepeatedHoldOutAssessParameterSet
getAnnotation() - Method in class de.jstacs.classifier.MeasureParameters: Returns the selected parameters and their values as a list of results.
getAnnotation(Sample...) - Static method in class de.jstacs.data.Sample: Returns the annotation for an array of Samples.
getAnnotation() - Method in class de.jstacs.data.Sample: This method returns some annotation of the Sample.
getAnnotation() - Method in class de.jstacs.data.Sequence: Returns the annotation of the sequence.
getAnnotation() - Method in class de.jstacs.io.AbstractStringExtractor: Returns the annotation of the source.
getAnnotation() - Method in class de.jstacs.results.ListResult: Returns a reference to the annotation of this ListResult
getAnnotations() - Method in class de.jstacs.data.sequences.annotation.SequenceAnnotation: Returns the additional annotations of this SequenceAnnotation as given in the constructor.
getAsymIndex(int, int[], byte) - Method in class de.jstacs.algorithms.graphs.tensor.Tensor
getAUC_PR(double[], double[], ArrayList<double[]>) - Static method in class de.jstacs.classifier.ScoreBasedPerformanceMeasureDefinitions: This method computes the area under the precision recall curve.
getAUC_ROC(double[], double[], ArrayList<double[]>) - Static method in class de.jstacs.classifier.ScoreBasedPerformanceMeasureDefinitions: This method computes the area under the receiver operator characteristics curve.
getBest(int, int[], byte) - Method in class de.jstacs.algorithms.graphs.tensor.SymmetricTensor: Returns the maximal weight which can be reached for an edge from k nodes from the (encoded) set par to the node child.
getBooleanFromParameter(Parameter) - Static method in class de.jstacs.io.ParameterSetParser: Returns the boolean which is the value of the Parameter par.
getBorder(double[], double) - Static method in class de.jstacs.classifier.utils.PValueComputation: This method finds the first index that has a significant score.
getByteFromParameter(Parameter) - Static method in class de.jstacs.io.ParameterSetParser: Returns the byte which is the value of the Parameter par.
getBytesFromFileOnServer(String, RConnection) - Static method in class de.jstacs.utils.RUtils: This method returns the content of a file on the server as byte array.
getCharacteristics() - Method in class de.jstacs.classifier.AbstractClassifier: Returns some information characterizing or describing the current instance of the model.
getCharacteristics() - Method in class de.jstacs.classifier.modelBased.ModelBasedClassifier
getCharacteristics() - Method in class de.jstacs.models.AbstractModel
getCharacteristics() - Method in class de.jstacs.models.CompositeModel
getCharacteristics() - Method in class de.jstacs.models.mixture.AbstractMixtureModel
getCharacteristics() - Method in interface de.jstacs.models.Model: Returns some information characterizing or describing the current instance of the model.
getClassificationRate(Sample[]) - Method in class de.jstacs.classifier.AbstractClassifier: This method computes the classification rate for a given array of samples.
getClassificationRateFor2Classes(double[], double[]) - Static method in class de.jstacs.classifier.ScoreBasedPerformanceMeasureDefinitions: This method computes the classification rate.
getClassifier() - Method in class de.jstacs.classifier.assessment.ClassifierAssessment: Returns a deep copy of all classifiers that have been or will be used in this assessment.
getClassifierAnnotation() - Method in class de.jstacs.classifier.AbstractClassifier: Returns an array of Results of dimension AbstractClassifier.getNumberOfClasses() that contains information about the classifier and for each class.
getClassifierAnnotation() - Method in class de.jstacs.classifier.MappingClassifier
getClassifierAnnotation() - Method in class de.jstacs.classifier.modelBased.ModelBasedClassifier
getClassifierAnnotation() - Method in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier
getClassifierAnnotation() - Method in class de.jstacs.models.discrete.inhomogeneous.shared.SharedStructureClassifier
getClassName() - Method in class de.jstacs.results.StorableResult: Returns the name of the class of the Storable corresponding to the XML-representation stored in this ObjectResult.
getClassParams(double[]) - Method in class de.jstacs.classifier.scoringFunctionBased.AbstractOptimizableFunction
getClassParams(double[]) - Method in class de.jstacs.classifier.scoringFunctionBased.OptimizableFunction: Returns from the complete vector of parameters those that are for the classes.
getClassWeight(int) - Method in class de.jstacs.classifier.AbstractScoreBasedClassifier: Returns the class weight for class index.
getClassWeights() - Method in class de.jstacs.classifier.AbstractScoreBasedClassifier: Returns the specific class weights of a AbstractScoreBasedClassifier.
getClazz() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures.BTMutualInformation.BTMutualInformationParameterSet: Returns the source of the data to compute the mutual information as defined by this set of parameters.
getClazz() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMMutualInformation.PMMMutualInformationParameterSet: Returns the source of the data to compute the mutual information as defined by this set of parameters.
getCMI(double[][][][][][], double[][][][][][], double) - Static method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.Measure: Computes the conditional mutual information from fgStats and bgStats counted on sequences with a total weight of n.
getCMI(double[][][][], double[][][][], double, double, double) - Static method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.Measure: Computes the conditional mutual information from fgStats and bgStats counted on sequences with a total weight of nFg and nBg, respectively.
getCode(int, String) - Method in class de.jstacs.data.AlphabetContainer: Returns the encoded symbol sym for position pos .
getCode(String) - Method in class de.jstacs.data.alphabets.DiscreteAlphabet: Returns the code of a given symbol.
getCollection(Class<T>, String, String, String, boolean) - Static method in class de.jstacs.utils.SubclassFinder: This method creates an CollectionParameter that contains InstanceParameterSet for each possible class.
getCollectionOfScales() - Static method in class de.jstacs.parameters.RangeParameter: Returns a CollectionsParameter that allows the user to choose between different scales.
getCombination() - Method in class de.jstacs.models.discrete.inhomogeneous.CombinationIterator: Returns a clone of the internal combination.
getComment() - Method in class de.jstacs.parameters.CollectionParameter
getComment() - Method in class de.jstacs.parameters.FileParameter
getComment() - Method in class de.jstacs.parameters.Parameter: Returns a comment on this parameter that tells something about useful values, domains, usage of this parameter, etc.
getComment() - Method in class de.jstacs.parameters.ParameterSetContainer
getComment() - Method in class de.jstacs.parameters.RangeParameter
getComment() - Method in class de.jstacs.parameters.SimpleParameter
getComment() - Method in class de.jstacs.results.Result: Returns the comment on the result.
getCommentString() - Method in enum de.jstacs.classifier.MeasureParameters.Measure: Returns a comment on the MeasureParameters.Measure.
getComplementaryCode(int) - Method in class de.jstacs.data.alphabets.ComplementableDiscreteAlphabet: This method returns the code of the symbol that is the complement of the symbol encoded by code.
getComplementaryCode(int) - Method in class de.jstacs.data.alphabets.DNAAlphabet
getComponents() - Method in class de.jstacs.algorithms.graphs.UnionFind: Returns the connected components of the graph.
getCompositeContainer(int[], int[]) - Method in class de.jstacs.data.AlphabetContainer: This method returns a container of alphabets e.g. for composite motifs/sequences.
getCompositeSample(int[], int[]) - Method in class de.jstacs.data.Sample: This method enables you to use only composite sequences of all elements in the current sample.
getCompositeSequence(AlphabetContainer, int[], int[]) - Method in class de.jstacs.data.Sequence: This constructor should be used if one wants to create a sample of composite sequences.
getCompositeSequence(int[], int[]) - Method in class de.jstacs.data.Sequence: This is an very efficient way to create a composite sequence for sequences with a simple AlphabetContainer.
getContent() - Method in class de.jstacs.parameters.FileParameter.FileRepresentation: Returns the content of the file
getCorrectedPosition(int) - Method in class de.jstacs.models.discrete.inhomogeneous.MEMConstraint: Returns the value of the corrected position
getCost() - Method in class de.jstacs.algorithms.Alignment.StringAlignment: Returns the costs.
getCostFor(Sequence, Sequence, int, int, Alignment.Costs.Direction) - Method in interface de.jstacs.algorithms.Alignment.Costs: Returns the costs for the alignment if s1(i) and s2(j) coming from from.
getCostFor(Sequence, Sequence, int, int, Alignment.Costs.Direction) - Method in class de.jstacs.algorithms.Alignment.SimpleCosts
getCount(int) - Method in class de.jstacs.models.discrete.Constraint: Returns the current count with index index
getCounts() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.Parameter: Returns the current counts for this parameter.
getCurrentParameterSet() - Method in class de.jstacs.data.AlphabetContainer
getCurrentParameterSet() - Method in class de.jstacs.data.alphabets.ComplementableDiscreteAlphabet
getCurrentParameterSet() - Method in class de.jstacs.data.alphabets.ContinuousAlphabet
getCurrentParameterSet() - Method in class de.jstacs.data.alphabets.DiscreteAlphabet
getCurrentParameterSet() - Method in class de.jstacs.data.alphabets.DNAAlphabet
getCurrentParameterSet() - Method in interface de.jstacs.InstantiableFromParameterSet: Returns the InstanceParameterSet that has been used to instantiate the current instance of the implementing class.
getCurrentParameterSet() - Method in class de.jstacs.models.discrete.DiscreteGraphicalModel
getCurrentParameterSet() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
getCurrentParameterSet() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures.BTExplainingAwayResidual
getCurrentParameterSet() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures.BTMutualInformation
getCurrentParameterSet() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov
getCurrentParameterSet() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMExplainingAwayResidual
getCurrentParameterSet() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMMutualInformation
getCurrentParameterValues() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
getCurrentParameterValues() - Method in class de.jstacs.scoringFunctions.homogeneous.HMM0ScoringFunction
getCurrentParameterValues() - Method in class de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction
getCurrentParameterValues() - Method in class de.jstacs.scoringFunctions.homogeneous.UniformHomogeneousScoringFunction
getCurrentParameterValues() - Method in class de.jstacs.scoringFunctions.IndependentProductScoringFunction
getCurrentParameterValues() - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
getCurrentParameterValues() - Method in class de.jstacs.scoringFunctions.MRFScoringFunction
getCurrentParameterValues() - Method in interface de.jstacs.scoringFunctions.ScoringFunction: Returns a double array of dimension getNumberOfParameters() containing the current parameter values.
getCurrentParameterValues() - Method in class de.jstacs.scoringFunctions.UniformScoringFunction
getDataSplitMethod() - Method in class de.jstacs.classifier.assessment.KFoldCVAssessParameterSet: Returns the Sample.PartitionMethod defining how the mutually exclusive random-splits of user supplied data are generated.
getDataSplitMethod() - Method in class de.jstacs.classifier.assessment.RepeatedHoldOutAssessParameterSet: Returns the Sample.PartitionMethod defining how the mutually exclusive random-splits of user supplied data are generated.
getDataSplitMethod() - Method in class de.jstacs.classifier.assessment.Sampled_RepeatedHoldOutAssessParameterSet: Returns the Sample.PartitionMethod defining how the mutually exclusive random-splits of user supplied data are generated.
getDatatype() - Method in class de.jstacs.parameters.CollectionParameter
getDatatype() - Method in class de.jstacs.parameters.FileParameter
getDatatype() - Method in class de.jstacs.parameters.Parameter: Returns the data type of the parameter
getDatatype() - Method in class de.jstacs.parameters.ParameterSetContainer
getDatatype() - Method in class de.jstacs.parameters.RangeParameter
getDatatype() - Method in class de.jstacs.parameters.SimpleParameter
getDatatype() - Method in class de.jstacs.results.Result: Returns the datatype of the result.
getDefault() - Method in class de.jstacs.parameters.CollectionParameter: Returns the index of the default selected value.
getDelim() - Method in class de.jstacs.data.AlphabetContainer: Returns the delimiter that should be used (for writing e.g. a sequence).
getDepth() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.Parameter: Returns the depth of the tree, i.e. the number of parents of this parameter.
getDescription() - Method in class de.jstacs.io.SubstringFilenameFilter
getDescription() - Method in class de.jstacs.models.discrete.DiscreteGraphicalModel: Returns a short description of the model the was given by the user in the parameter set.
getDimension() - Method in class de.jstacs.utils.random.DiMRGParams: Returns the dimension of the hyperparameter vector of the underlying Dirichlet distribution and therefore the dimension of the generated random array.
getDimension() - Method in class de.jstacs.utils.random.DirichletMRGParams
getDimension() - Method in class de.jstacs.utils.random.ErlangMRGParams: Returns the dimension of the hyperparameter vector of the underlying Erlang distribution.
getDimension() - Method in class de.jstacs.utils.random.FastDirichletMRGParams
getDimensionOfScope() - Method in interface de.jstacs.algorithms.optimization.Function: Returns the dimension of the scope of the function.
getDimensionOfScope() - Method in class de.jstacs.algorithms.optimization.NegativeDifferentiableFunction
getDimensionOfScope() - Method in class de.jstacs.algorithms.optimization.NegativeFunction
getDimensionOfScope() - Method in class de.jstacs.algorithms.optimization.OneDimensionalFunction
getDimensionOfScope() - Method in class de.jstacs.classifier.scoringFunctionBased.AbstractOptimizableFunction
getDimensionOfScope() - Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.DoesNothingLogPrior
getDimensionOfScope() - Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.SeparateGaussianLogPrior
getDimensionOfScope() - Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.SeparateLaplaceLogPrior
getDimensionOfScope() - Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.SimpleGaussianSumLogPrior
getDoubleFromParameter(Parameter) - Static method in class de.jstacs.io.ParameterSetParser: Returns the double which is the value of the Parameter par.
getEAR(double[][][][][][], double[][][][][][], double, double) - Static method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.Measure: Computes the explaining away residual from fgStats and bgStats counted on sequences with a total weight of nFg and nBg, respectively.
getEAR(double[][][][], double[][][][], double, double) - Static method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.Measure: Computes the explaining away residual from fgStats and bgStats counted on sequences with a total weight of nFg and nBg, respectively.
getEdgeFromIndex(int, int) - Method in class de.jstacs.algorithms.graphs.tensor.SymmetricTensor: This method decodes an index in an edge.
getElapsedTime() - Method in class de.jstacs.utils.RealTime
getElapsedTime() - Method in class de.jstacs.utils.Time: Returns the elapsed time since invoking the constructor.
getElapsedTime() - Method in class de.jstacs.utils.UserTime
getElement(int) - Method in class de.jstacs.io.StringExtractor: Returns String no idx that has been extracted.
getElement() - Method in class de.jstacs.utils.ComparableElement: This method returns the element.
getElementAt(int) - Method in class de.jstacs.data.Sample: This method returns the element with index i.
getElementAt(int) - Method in class de.jstacs.data.Sample.WeightedSampleFactory: Returns the sequence with index index.
getElementLength() - Method in class de.jstacs.classifier.assessment.ClassifierAssessmentAssessParameterSet: Returns the length of elements (sequences) defined by this ClassifierAssessmentAssessParameterSet.
getElementLength() - Method in class de.jstacs.data.Sample: Returns the length of the elements in this Sample.
getElongateCosts() - Method in interface de.jstacs.algorithms.Alignment.Costs: Returns the costs to elongate a gap by one position
getElongateCosts() - Method in class de.jstacs.algorithms.Alignment.SimpleCosts
getEnd() - Method in class de.jstacs.data.sequences.annotation.LocatedSequenceAnnotationWithLength: Returns the end of this LocatedSequenceAnnotationWithLength, i.e.
getEndNode() - Method in class de.jstacs.algorithms.graphs.Edge: Returns the end node of the edge.
getEndValue() - Method in class de.jstacs.parameters.RangeParameter: Returns the last value of a range of parameter values or null if no range was specified.
getEntropy(Constraint) - Static method in class de.jstacs.models.discrete.ConstraintManager: Tries to compute the entropy as exact as possible.
getErrorMessage() - Method in class de.jstacs.parameters.CollectionParameter
getErrorMessage() - Method in class de.jstacs.parameters.FileParameter
getErrorMessage() - Method in class de.jstacs.parameters.Parameter: If a value could not be set successfully this method returns the corresponding error message.
getErrorMessage() - Method in class de.jstacs.parameters.ParameterSet: Returns the message of the last error that occurred.
getErrorMessage() - Method in class de.jstacs.parameters.ParameterSetContainer
getErrorMessage() - Method in class de.jstacs.parameters.RangeParameter
getErrorMessage() - Method in class de.jstacs.parameters.SimpleParameter
getErrorMessage() - Method in interface de.jstacs.parameters.validation.Constraint: Returns the message of the last error (missed constraint) or null if the constraint was fulfilled by the last checked value
getErrorMessage() - Method in class de.jstacs.parameters.validation.ConstraintValidator
getErrorMessage() - Method in class de.jstacs.parameters.validation.NumberValidator
getErrorMessage() - Method in interface de.jstacs.parameters.validation.ParameterValidator: Returns the error message if checkValue() returned false.
getErrorMessage() - Method in class de.jstacs.parameters.validation.ReferenceConstraint
getErrorMessage() - Method in class de.jstacs.parameters.validation.SimpleStaticConstraint
getErrorMessage() - Method in class de.jstacs.parameters.validation.StorableValidator
getESS() - Method in class de.jstacs.models.discrete.DiscreteGraphicalModel: This method return the ess that is used in this model.
getEss() - Method in class de.jstacs.models.discrete.inhomogeneous.StructureLearner: This method returns the ESS of the StructureLearner.
getEss() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
getEss() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunctionParameterSet: Returns the equivalent samples size defined in this set of parameters.
getEss() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures.BTExplainingAwayResidual.BTExplainingAwayResidualParameterSet: Returns the ess defined by this set of parameters.
getEss() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures.BTMutualInformation.BTMutualInformationParameterSet: Returns the ess defined by this set of parameters.
getEss() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMExplainingAwayResidual.PMMExplainingAwayResidualParameterSet: Returns the ess defined by this set of parameters.
getEss() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMMutualInformation.PMMMutualInformationParameterSet: Returns the ess defined by this set of parameters.
getEss() - Method in class de.jstacs.scoringFunctions.homogeneous.HMM0ScoringFunction
getEss() - Method in class de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction
getEss() - Method in class de.jstacs.scoringFunctions.homogeneous.UniformHomogeneousScoringFunction
getEss() - Method in class de.jstacs.scoringFunctions.IndependentProductScoringFunction
getEss() - Method in class de.jstacs.scoringFunctions.mix.MixtureScoringFunction
getEss() - Method in class de.jstacs.scoringFunctions.MRFScoringFunction
getEss() - Method in interface de.jstacs.scoringFunctions.NormalizableScoringFunction: Returns the equivalent sample size of this model, i.e. the equivalent sample size for the class or component that is represented by this model.
getEss() - Method in class de.jstacs.scoringFunctions.UniformScoringFunction
getExceptionIfMPNotComputable() - Method in class de.jstacs.classifier.assessment.ClassifierAssessmentAssessParameterSet: Returns the flag defined by this ClassifierAssessmentAssessParameterSet.
getExpLambda(int) - Method in class de.jstacs.models.discrete.inhomogeneous.MEMConstraint: Returns \exp(\lambda_{index}).
getExpValue() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.Parameter: Returns Math.exp(getValue()), which is pre-computed.
getExtremum() - Method in class de.jstacs.algorithms.optimization.QuadraticFunction: This method returns the extremum.
getFileContents() - Method in class de.jstacs.parameters.FileParameter: Returns the content of the file
getFilename() - Method in class de.jstacs.parameters.FileParameter.FileRepresentation: Returns the filename.
getFirst() - Method in class de.jstacs.algorithms.Alignment.StringAlignment: Returns the first string.
getFirstParent() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.ParameterTree: Returns the first parent of the random variable of this ParameterTree in the topological ordering of the network structure of the enclosing BayesianNetworkScoringFunction.
getFloatFromParameter(Parameter) - Static method in class de.jstacs.io.ParameterSetParser: Returns the float which is the value of the Parameter par.
getFPRForSensitivity(double[], double[], double) - Static method in class de.jstacs.classifier.ScoreBasedPerformanceMeasureDefinitions: This method computes the false positive rate (FPR) for a given sensitivity.
getFreq(int) - Method in class de.jstacs.models.discrete.Constraint: Returns the current frequency with index index
getFreq(Sequence, int) - Method in class de.jstacs.models.discrete.Constraint: This method determines the specific constraint that is fullfilled by the sequence beginning at start.
getFreq(int) - Method in class de.jstacs.models.discrete.inhomogeneous.MEMConstraint
getFunction(Sample[], double[][]) - Method in class de.jstacs.classifier.scoringFunctionBased.cll.CLLClassifier
getFunction(Sample[], double[][]) - Method in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier: Returns the function that should be optimized.
getFunction(int) - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction: This method returns a specific internal function
getFunctions() - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction: This method returns an array of clones of the internal used functions.
getFurtherClassifierInfos() - Method in class de.jstacs.classifier.AbstractClassifier: This method returns further information of a classifier as a StringBuffer.
getFurtherClassifierInfos() - Method in class de.jstacs.classifier.AbstractScoreBasedClassifier
getFurtherClassifierInfos() - Method in class de.jstacs.classifier.MappingClassifier
getFurtherClassifierInfos() - Method in class de.jstacs.classifier.modelBased.ModelBasedClassifier
getFurtherClassifierInfos() - Method in class de.jstacs.classifier.scoringFunctionBased.cll.CLLClassifier
getFurtherClassifierInfos() - Method in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier
getFurtherClassifierInfos() - Method in class de.jstacs.models.discrete.inhomogeneous.shared.SharedStructureClassifier
getFurtherInformation() - Method in class de.jstacs.models.mixture.AbstractMixtureModel: This method is used in the subclasses to append further information at the xml representation.
getFurtherInformation() - Method in class de.jstacs.models.mixture.gibbssampling.AbstractBurnInTest: This method returns further information for the AbstractBurnInTest.
getFurtherInformation() - Method in class de.jstacs.models.mixture.gibbssampling.VarianceRatioBurnInTest
getFurtherInformation() - Method in class de.jstacs.models.mixture.motif.HiddenMotifMixture
getFurtherInformation() - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction: This method is used to append further information of the instance to the xml representation.
getFurtherModelInfos() - Method in class de.jstacs.models.discrete.DiscreteGraphicalModel
getFurtherModelInfos() - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousMM
getFurtherModelInfos() - Method in class de.jstacs.models.discrete.inhomogeneous.DAGModel
getFurtherModelInfos() - Method in class de.jstacs.models.mixture.gibbssampling.FSDAGModelForGibbsSampling
getGapCostsFor(int) - Method in interface de.jstacs.algorithms.Alignment.Costs: Returns the costs for a gap of length length.
getGapCostsFor(int) - Method in class de.jstacs.algorithms.Alignment.SimpleCosts
getGeneralizedDivergence(double[][], double[][], double) - Static method in class de.jstacs.models.utils.StatisticalTest: Computes the generalized divergence for two given stochastic matrices over the same domain, i.e. the matrices have to have the same dimensionality.
getGeneralizedDivergence(double[][], double[], double[], double) - Static method in class de.jstacs.models.utils.StatisticalTest: Computes the generalized divergence for two stochastic matrices over the same domain, i.e. the matrices have to have the same dimensionality.
getGeneralizedDivergence(double[][], double) - Static method in class de.jstacs.models.utils.StatisticalTest: Computes the generalized divergence for two stochastic matrices over the same domain, i.e. the matrices have to have the same dimensionality.
getGlobalIndexOfMotifInComponent(int, int) - Method in class de.jstacs.models.mixture.motif.SingleHiddenMotifMixture
getGlobalIndexOfMotifInComponent(int, int) - Method in interface de.jstacs.motifDiscovery.MotifDiscoverer: Returns the global index of the motif used in component.
getHyperparameter(int) - Method in class de.jstacs.utils.random.DiMRGParams: Returns the value at position i of the hyperparameter vector of the underlying Dirichlet distribution.
getHyperparameter(int) - Method in class de.jstacs.utils.random.DirichletMRGParams
getHyperparameter(int) - Method in class de.jstacs.utils.random.ErlangMRGParams: Returns the value at position i of the hyperparameter vector of the underlying Erlang distribution.
getHyperparameter(int) - Method in class de.jstacs.utils.random.FastDirichletMRGParams
getHyperparameterForHiddenParameter(int) - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction: This method returns the hyperparameter for the hidden parameter with index index.
getHyperparameterForHiddenParameter(int) - Method in class de.jstacs.scoringFunctions.mix.MixtureScoringFunction
getId() - Method in class de.jstacs.parameters.Parameter: Returns the id of this Parameter.
getId() - Method in class de.jstacs.parameters.ParameterSet: Returns the id of this ParameterSet.
getIdentifier() - Method in class de.jstacs.data.sequences.annotation.SequenceAnnotation: Returns the identifier of this SequenceAnnotation as given in the constructor.
getImage(double[][], REnvironment) - Static method in class de.jstacs.models.discrete.inhomogeneous.TwoPointEvaluater: This method can be used to create an image of mutual information matrix.
getImmutableInstance() - Static method in class de.jstacs.utils.NullProgressUpdater: Returns a reference to the same NullProgressUpdater that is immutable.
getIndex(double[], double) - Static method in class de.jstacs.classifier.utils.PValueComputation: This method searches in sortedScores for the index i so that sortedScores[i-1] < myScore <= sortedScores[i].
getIndex(double[], double, int) - Static method in class de.jstacs.classifier.utils.PValueComputation: This method searches in sortedScores beginning at start for the index i so that sortedScores[i-1] < myScore <= sortedScores[i].
getIndex(int) - Method in class de.jstacs.data.Sequence.CompositeSequence
getIndex(int) - Method in class de.jstacs.data.Sequence.SubSequence
getIndex(int) - Method in class de.jstacs.data.sequences.PermutedSequence
getIndex(int) - Method in class de.jstacs.data.sequences.RecursiveSequence: Returns the index in the internal sequence.
getIndex(int[]) - Method in class de.jstacs.models.discrete.inhomogeneous.CombinationIterator: The combi has to be sorted.
getIndex(String[], Object[], Comparable, boolean) - Static method in class de.jstacs.parameters.SequenceScoringParameterSet: This method tries to find the correct name (String) for your choice.
getIndex() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.Parameter: Returns the index of this parameter as defined in the constructor.
getIndexOfMaximalComponentFor(Sequence) - Method in class de.jstacs.models.mixture.AbstractMixtureModel: Returns the index i of the component with P(i|s) maximal.
getIndexOfMaximalComponentFor(Sequence) - Method in interface de.jstacs.motifDiscovery.MotifDiscoverer: Returns the index of the component with the maximal score for the sequence sequence.
getIndexOfMaximalComponentFor(Sequence, int) - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction: Returns the index of the component that has the greatest impact on the complete score
getIndices(int) - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction: This array is used to compute the relative indices of a parameter index.
getInfixSample(int, int) - Method in class de.jstacs.data.Sample: This method enables you to use only an infix of all elements in the current sample.
getInfos() - Method in class de.jstacs.results.MeanResultSet: Returns some information for this MeanResultSet.
getInitialClassParam(double) - Method in class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
getInitialClassParam(double) - Method in interface de.jstacs.scoringFunctions.NormalizableScoringFunction
getInitialClassParam(double) - Method in interface de.jstacs.scoringFunctions.ScoringFunction: Returns the initial class parameter for the class this ScoringFunction is responsible for, based on the probability classProb.
getInstance() - Method in class de.jstacs.parameters.InstanceParameterSet: Returns a new instance of the class of getInstanceClass() that was created using this ParameterSet.
getInstanceClass() - Method in class de.jstacs.parameters.InstanceParameterSet: Returns the class of the instances that can be constructed using this set.
getInstanceComment() - Method in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifierParameterSet
getInstanceComment() - Method in class de.jstacs.data.AlphabetContainerParameterSet.AlphabetArrayParameterSet: Returns a descriptive comment on this ParameterSet.
getInstanceComment() - Method in class de.jstacs.data.AlphabetContainerParameterSet
getInstanceComment() - Method in class de.jstacs.data.AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet: Returns a descriptive comment on this ParameterSet.
getInstanceComment() - Method in class de.jstacs.data.alphabets.ContinuousAlphabet.ContinuousAlphabetParameterSet
getInstanceComment() - Method in class de.jstacs.data.alphabets.DiscreteAlphabet.DiscreteAlphabetParameterSet
getInstanceComment() - Method in class de.jstacs.data.alphabets.DNAAlphabet.DNAAlphabetParameterSet
getInstanceComment() - Method in class de.jstacs.models.discrete.homogeneous.parameters.HomMMParameterSet
getInstanceComment() - Method in class de.jstacs.models.discrete.inhomogeneous.parameters.BayesianNetworkModelParameterSet
getInstanceComment() - Method in class de.jstacs.models.discrete.inhomogeneous.parameters.FSDAGMParameterSet
getInstanceComment() - Method in class de.jstacs.parameters.InstanceParameterSet: Returns a comment (a textual description) of the class that can be constructed using this ParameterSet.
getInstanceComment() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunctionParameterSet
getInstanceComment() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures.BTExplainingAwayResidual.BTExplainingAwayResidualParameterSet
getInstanceComment() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures.BTMutualInformation.BTMutualInformationParameterSet
getInstanceComment() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov.InhomogeneousMarkovParameterSet
getInstanceComment() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMExplainingAwayResidual.PMMExplainingAwayResidualParameterSet
getInstanceComment() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMMutualInformation.PMMMutualInformationParameterSet
getInstanceFromParameterSet(InstanceParameterSet) - Static method in class de.jstacs.io.ParameterSetParser: Returns an instance of a subclass of InstantiableFromParameterSet that can be instantiated by the InstanceParameterSet pars.
getInstanceFromParameterSet(ParameterSet, Class) - Static method in class de.jstacs.io.ParameterSetParser: Returns an instance of a subclass of InstantiableFromParameterSet that can be instantiated by the ParameterSet pars.
getInstanceName() - Method in class de.jstacs.classifier.AbstractClassifier: Returns a short description of the classifier.
getInstanceName() - Method in class de.jstacs.classifier.MappingClassifier
getInstanceName() - Method in class de.jstacs.classifier.modelBased.ModelBasedClassifier
getInstanceName() - Method in class de.jstacs.classifier.scoringFunctionBased.cll.CLLClassifier
getInstanceName() - Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.DoesNothingLogPrior
getInstanceName() - Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.LogPrior: Returns a short instance name.
getInstanceName() - Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.SeparateGaussianLogPrior
getInstanceName() - Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.SeparateLaplaceLogPrior
getInstanceName() - Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.SimpleGaussianSumLogPrior
getInstanceName() - Method in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier
getInstanceName() - Method in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifierParameterSet
getInstanceName() - Method in class de.jstacs.data.Alphabet.AlphabetParameterSet
getInstanceName() - Method in class de.jstacs.data.AlphabetContainerParameterSet.AlphabetArrayParameterSet: Returns a descriptive name for this ParameterSet.
getInstanceName() - Method in class de.jstacs.data.AlphabetContainerParameterSet
getInstanceName() - Method in class de.jstacs.data.AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet: Returns a descriptive name for this ParameterSet.
getInstanceName() - Method in class de.jstacs.models.CompositeModel
getInstanceName() - Method in class de.jstacs.models.discrete.DGMParameterSet
getInstanceName() - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousMM
getInstanceName() - Method in class de.jstacs.models.discrete.inhomogeneous.BayesianNetworkModel
getInstanceName() - Method in class de.jstacs.models.discrete.inhomogeneous.FSDAGModel
getInstanceName() - Method in class de.jstacs.models.discrete.inhomogeneous.shared.SharedStructureClassifier
getInstanceName() - Method in class de.jstacs.models.discrete.inhomogeneous.shared.SharedStructureMixture
getInstanceName() - Method in class de.jstacs.models.mixture.AbstractMixtureModel
getInstanceName() - Method in class de.jstacs.models.mixture.gibbssampling.BurnInTest: Returns a short description of the burn-in test.
getInstanceName() - Method in class de.jstacs.models.mixture.gibbssampling.SimpleBurnInTest
getInstanceName() - Method in class de.jstacs.models.mixture.gibbssampling.VarianceRatioBurnInTest
getInstanceName() - Method in class de.jstacs.models.mixture.motif.HiddenMotifMixture
getInstanceName() - Method in class de.jstacs.models.mixture.motif.positionprior.GaussianLikePositionPrior
getInstanceName() - Method in class de.jstacs.models.mixture.motif.positionprior.PositionPrior: Returns the instance name
getInstanceName() - Method in class de.jstacs.models.mixture.motif.positionprior.UniformPositionPrior
getInstanceName() - Method in interface de.jstacs.models.Model: Should return a short instance name such as iMM(0), BN(2), ...
getInstanceName() - Method in class de.jstacs.models.UniformModel
getInstanceName() - Method in class de.jstacs.parameters.InstanceParameterSet: Returns the name of an instance of the class that can be constructed using this ParameterSet.
getInstanceName() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
getInstanceName() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunctionParameterSet
getInstanceName() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures.BTExplainingAwayResidual.BTExplainingAwayResidualParameterSet
getInstanceName() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures.BTExplainingAwayResidual
getInstanceName() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures.BTMutualInformation.BTMutualInformationParameterSet
getInstanceName() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures.BTMutualInformation
getInstanceName() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov
getInstanceName() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov.InhomogeneousMarkovParameterSet
getInstanceName() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.Measure: Returns the name of the Measure and possibly some additional information about the current instance.
getInstanceName() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMExplainingAwayResidual
getInstanceName() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMExplainingAwayResidual.PMMExplainingAwayResidualParameterSet
getInstanceName() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMMutualInformation
getInstanceName() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMMutualInformation.PMMMutualInformationParameterSet
getInstanceName() - Method in class de.jstacs.scoringFunctions.homogeneous.HMM0ScoringFunction
getInstanceName() - Method in class de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction
getInstanceName() - Method in class de.jstacs.scoringFunctions.homogeneous.UniformHomogeneousScoringFunction
getInstanceName() - Method in class de.jstacs.scoringFunctions.IndependentProductScoringFunction
getInstanceName() - Method in class de.jstacs.scoringFunctions.mix.MixtureScoringFunction
getInstanceName() - Method in class de.jstacs.scoringFunctions.MRFScoringFunction
getInstanceName() - Method in interface de.jstacs.scoringFunctions.ScoringFunction: Returns a short instance name.
getInstanceName() - Method in class de.jstacs.scoringFunctions.UniformScoringFunction
getIntFromParameter(Parameter) - Static method in class de.jstacs.io.ParameterSetParser: Returns the int which is the value of the Parameter par.
getK() - Method in class de.jstacs.classifier.assessment.KFoldCVAssessParameterSet: Returns the number of mutually exclusive random-splits of user supplied data defined by this KFoldCVAssessParameterSet
getKLDivergence(Model, Model, int) - Static method in class de.jstacs.models.utils.ModelTester: Returns the Kullback-Leibler-divergence D(p_m1||p_m2).
getKLDivergence(double[]) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.ParameterTree: Returns the KL-divergence of the parameters of a PWM to the reference distribution q.
getLambda(int) - Method in class de.jstacs.models.discrete.inhomogeneous.MEMConstraint: Returns \lambda_{index}.
getLastScore() - Method in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier: Returns the score that was computed in the last optimization of the parameters.
getLength() - Method in class de.jstacs.classifier.AbstractClassifier: Returns the length of the sequences this classifier can handle or 0 for sequences of arbitrary length.
getLength() - Method in class de.jstacs.data.Sequence.CompositeSequence
getLength() - Method in class de.jstacs.data.Sequence: Returns the length of the sequence
getLength() - Method in class de.jstacs.data.Sequence.SubSequence
getLength() - Method in class de.jstacs.data.sequences.annotation.LocatedSequenceAnnotationWithLength: Returns the length of this LocatedSequenceAnnotationWithLength as given in the constructor.
getLength() - Method in class de.jstacs.data.sequences.ArbitrarySequence
getLength() - Method in class de.jstacs.data.sequences.ByteSequence
getLength() - Method in class de.jstacs.data.sequences.IntSequence
getLength() - Method in class de.jstacs.data.sequences.PermutedSequence
getLength() - Method in class de.jstacs.data.sequences.ShortSequence
getLength() - Method in class de.jstacs.data.sequences.SparseSequence
getLength() - Method in class de.jstacs.models.AbstractModel
getLength() - Method in class de.jstacs.models.mixture.motif.positionprior.GaussianLikePositionPrior
getLength() - Method in class de.jstacs.models.mixture.motif.positionprior.PositionPrior: Returns the length that is supported by this prior.
getLength() - Method in class de.jstacs.models.mixture.motif.positionprior.UniformPositionPrior
getLength() - Method in interface de.jstacs.models.Model: Returns the length of sequence this model can classify.
getLength() - Method in class de.jstacs.parameters.SequenceScoringParameterSet: Returns the length of sequences the model can work on
getLength() - Method in class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
getLength() - Method in interface de.jstacs.scoringFunctions.ScoringFunction: Returns the length of this ScoringFunction. i.e. the length of the Sequences this ScoringFunction can handle.
getLengthOfBurnIn() - Method in class de.jstacs.models.mixture.gibbssampling.AbstractBurnInTest
getLengthOfBurnIn() - Method in class de.jstacs.models.mixture.gibbssampling.BurnInTest: Computes and returns the length of the burn-in phase using the values from BurnInTest.setValue(double).
getLengthOfBurnIn() - Method in class de.jstacs.models.mixture.gibbssampling.SimpleBurnInTest
getLengthOfModels() - Method in class de.jstacs.models.CompositeModel: This method returns the length of the models in the CompositeModel
getLine(int) - Method in class de.jstacs.classifier.AbstractScoreBasedClassifier.DoubleTableResult: Return the line with index index from the table.
getList() - Method in class de.jstacs.parameters.RangeParameter: Returns a list of all parameter values as a String or null if no parameter values have been set.
getLnFreq(int) - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousModel.HomCondProb: Returns the logarithmic frequency.
getLnFreq(int) - Method in class de.jstacs.models.discrete.inhomogeneous.InhCondProb: Returns the logarithmic frequency.
getLnFreq(Sequence, int) - Method in class de.jstacs.models.discrete.inhomogeneous.InhCondProb: Returns the logarithmic frequency.
getLogGammaSum(Constraint, double) - Static method in class de.jstacs.models.discrete.ConstraintManager: Computes the sum of differences of the logarithmic values of the prior knowlegde and all counts.
getLogLikelihood(Model, Sample) - Static method in class de.jstacs.models.utils.ModelTester: Returns the loglikelihood of a sample data for a given model m.
getLogLikelihood(Model, Sample, double[]) - Static method in class de.jstacs.models.utils.ModelTester: Returns the loglikelihood of a sample data for a given model m.
getLogPriorForPositions(int, int...) - Method in class de.jstacs.models.mixture.motif.positionprior.GaussianLikePositionPrior: Returns only the important part and leaving the logarithm of the normalization constant out.
getLogPriorForPositions(int, int...) - Method in class de.jstacs.models.mixture.motif.positionprior.PositionPrior: The logarithmic value of the prior for specified start positions of the part motifs.
getLogPriorForPositions(int, int...) - Method in class de.jstacs.models.mixture.motif.positionprior.UniformPositionPrior
getLogPriorTerm() - Method in class de.jstacs.models.CompositeModel
getLogPriorTerm() - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousMM
getLogPriorTerm() - Method in class de.jstacs.models.discrete.inhomogeneous.BayesianNetworkModel
getLogPriorTerm() - Method in class de.jstacs.models.discrete.inhomogeneous.DAGModel
getLogPriorTerm() - Method in class de.jstacs.models.mixture.AbstractMixtureModel
getLogPriorTerm() - Method in class de.jstacs.models.mixture.motif.HiddenMotifMixture
getLogPriorTerm() - Method in interface de.jstacs.models.Model: Returns a value that is proportional to the log of the prior.
getLogPriorTerm() - Method in class de.jstacs.models.UniformModel
getLogPriorTerm() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
getLogPriorTerm() - Method in class de.jstacs.scoringFunctions.homogeneous.HMM0ScoringFunction
getLogPriorTerm() - Method in class de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction
getLogPriorTerm() - Method in class de.jstacs.scoringFunctions.homogeneous.UniformHomogeneousScoringFunction
getLogPriorTerm() - Method in class de.jstacs.scoringFunctions.IndependentProductScoringFunction
getLogPriorTerm() - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
getLogPriorTerm() - Method in class de.jstacs.scoringFunctions.MRFScoringFunction
getLogPriorTerm() - Method in interface de.jstacs.scoringFunctions.NormalizableScoringFunction: This method computes a value that is proportional to getESS()*Math.log( getNormalizationConstant() ) + Math.log( prior ).
getLogPriorTerm() - Method in class de.jstacs.scoringFunctions.UniformScoringFunction
getLogPriorTermForComponentProbs() - Method in class de.jstacs.models.mixture.AbstractMixtureModel: This method computes the part of the prior that comes from the component probabilities.
getLogProbFor(Sequence, int, int) - Method in class de.jstacs.models.AbstractModel
getLogProbFor(Sequence, int) - Method in class de.jstacs.models.AbstractModel
getLogProbFor(Sequence) - Method in class de.jstacs.models.AbstractModel
getLogProbFor(Sample) - Method in class de.jstacs.models.AbstractModel
getLogProbFor(Sample, double[]) - Method in class de.jstacs.models.AbstractModel
getLogProbFor(Sequence, int, int) - Method in class de.jstacs.models.CompositeModel
getLogProbFor(Sequence, int, int) - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousModel
getLogProbFor(Sequence, int, int) - Method in class de.jstacs.models.discrete.inhomogeneous.DAGModel
getLogProbFor(int, Sequence) - Method in class de.jstacs.models.mixture.AbstractMixtureModel: Returns the log probability for the sequence and the given component.
getLogProbFor(Sequence, int, int) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
getLogProbFor(Sample) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
getLogProbFor(Sequence, int, int) - Method in interface de.jstacs.models.Model: Returns the logarithm of the probability of the given sequence given the model.
getLogProbFor(Sequence, int) - Method in interface de.jstacs.models.Model: Returns the logarithm of the probability of the given sequence given the model.
getLogProbFor(Sequence) - Method in interface de.jstacs.models.Model: Returns the logarithm of the probability of the given sequence given the model.
getLogProbFor(Sample) - Method in interface de.jstacs.models.Model: This method computes the logarithm of the probabilities of all sequences in the given sample.
getLogProbFor(Sample, double[]) - Method in interface de.jstacs.models.Model: This method computes and stores the logarithm of the probabilities for any sequence in the sample in the given double array.
getLogProbUsingCurrentParameterSetFor(int, Sequence, int, int) - Method in class de.jstacs.models.mixture.AbstractMixtureModel: Returns the log probability for the sequence and the given component using the current parameter set.
getLogProbUsingCurrentParameterSetFor(int, Sequence, int, int) - Method in class de.jstacs.models.mixture.MixtureModel
getLogProbUsingCurrentParameterSetFor(int, Sequence, int, int) - Method in class de.jstacs.models.mixture.motif.SingleHiddenMotifMixture
getLogProbUsingCurrentParameterSetFor(int, Sequence, int, int) - Method in class de.jstacs.models.mixture.StrandModel
getLogScore(Sequence) - Method in class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction: Returns the log score for the sequence
getLogScore(Sequence, int) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
getLogScore(Sequence, int, int) - Method in class de.jstacs.scoringFunctions.homogeneous.HMM0ScoringFunction
getLogScore(Sequence, int, int) - Method in class de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction
getLogScore(Sequence, int, int) - Method in class de.jstacs.scoringFunctions.homogeneous.UniformHomogeneousScoringFunction
getLogScore(Sequence, int) - Method in class de.jstacs.scoringFunctions.IndependentProductScoringFunction
getLogScore(Sequence, int) - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
getLogScore(Sequence, int) - Method in class de.jstacs.scoringFunctions.MRFScoringFunction
getLogScore(Sequence) - Method in interface de.jstacs.scoringFunctions.ScoringFunction: Returns the log score for the sequence
getLogScore(Sequence, int) - Method in interface de.jstacs.scoringFunctions.ScoringFunction: Returns the log score for the sequence
getLogScore(Sequence, int) - Method in class de.jstacs.scoringFunctions.UniformScoringFunction
getLogScore(Sequence, int) - Method in class de.jstacs.scoringFunctions.VariableLengthScoringFunction
getLogScore(Sequence, int, int) - Method in class de.jstacs.scoringFunctions.VariableLengthScoringFunction: This method computes the logarithm of the score for a given subsequence.
getLogScoreAndPartialDerivation(Sequence, IntList, DoubleList) - Method in class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
getLogScoreAndPartialDerivation(Sequence, int, int, IntList, DoubleList) - Method in class de.jstacs.scoringFunctions.homogeneous.HMM0ScoringFunction
getLogScoreAndPartialDerivation(Sequence, int, int, IntList, DoubleList) - Method in class de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction
getLogScoreAndPartialDerivation(Sequence, int, int, IntList, DoubleList) - Method in class de.jstacs.scoringFunctions.homogeneous.UniformHomogeneousScoringFunction
getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList) - Method in class de.jstacs.scoringFunctions.IndependentProductScoringFunction
getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList) - Method in class de.jstacs.scoringFunctions.mix.MixtureScoringFunction
getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList) - Method in class de.jstacs.scoringFunctions.MRFScoringFunction
getLogScoreAndPartialDerivation(Sequence, IntList, DoubleList) - Method in interface de.jstacs.scoringFunctions.ScoringFunction: Returns the log score for the sequence and fills the list with the indices and the partial derivations.
getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList) - Method in interface de.jstacs.scoringFunctions.ScoringFunction: Returns the log score for the sequence and fills the list with the indices and the partial derivations.
getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList) - Method in class de.jstacs.scoringFunctions.UniformScoringFunction
getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList) - Method in class de.jstacs.scoringFunctions.VariableLengthScoringFunction
getLogScoreAndPartialDerivation(Sequence, int, int, IntList, DoubleList) - Method in class de.jstacs.scoringFunctions.VariableLengthScoringFunction: This method computes the logarithm of the score and the partial derivations for a given subsequence.
getLogSum(double...) - Static method in class de.jstacs.utils.Normalisation: Returns the logarithm of the sum of values given as lnVal[i] = Math.log( val[i] ).
getLogSum(int, int, double...) - Static method in class de.jstacs.utils.Normalisation: Returns the logarithm of the sum of values given as lnVal[i] = Math.log( val[i] ) between a start and end index.
getLongFromParameter(Parameter) - Static method in class de.jstacs.io.ParameterSetParser: Returns the long which is the value of the Parameter par.
getLowerBound() - Method in class de.jstacs.parameters.validation.NumberValidator: Returns the lower bound of the NumberValidator
getMarginalDistribution(Model, int[]) - Static method in class de.jstacs.models.utils.ModelTester: This method computes the marginal distribution for any discrete model m and all sequences that fulfil the constraint, if possible.
getMarginalOrder() - Method in class de.jstacs.models.discrete.Constraint: Returns the marginal order i.e. the number of used random variables.
getMatrix() - Method in class de.jstacs.classifier.ConfusionMatrix: This method returns the confusion matrix as a 2D-int-array.
getMax() - Method in class de.jstacs.data.alphabets.ContinuousAlphabet: Returns the maximal value of this alphabet.
getMax(double[][]) - Static method in class de.jstacs.models.discrete.inhomogeneous.TwoPointEvaluater: This method can be used to determine the maximal value of the matrix.
getMaximalAlphabetLength() - Method in class de.jstacs.data.AlphabetContainer: Returns the maximal alphabet length of this container.
getMaximalEdgeFor(byte, int, int...) - Method in class de.jstacs.algorithms.graphs.tensor.AsymmetricTensor
getMaximalEdgeFor(byte, int, int...) - Method in class de.jstacs.algorithms.graphs.tensor.SymmetricTensor
getMaximalEdgeFor(byte, int, int...) - Method in class de.jstacs.algorithms.graphs.tensor.Tensor: Returns the edge permute(parents[0],...
getMaximalElementLength() - Method in class de.jstacs.data.Sample: Returns the maximal length of an element in this Sample.
getMaximalMarkovOrder() - Method in class de.jstacs.models.AbstractModel
getMaximalMarkovOrder() - Method in class de.jstacs.models.CompositeModel
getMaximalMarkovOrder() - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousModel
getMaximalMarkovOrder() - Method in class de.jstacs.models.discrete.inhomogeneous.BayesianNetworkModel
getMaximalMarkovOrder() - Method in class de.jstacs.models.discrete.inhomogeneous.FSDAGModel
getMaximalMarkovOrder() - Method in interface de.jstacs.models.Model: This method returns the maximal used markov order if possible.
getMaximalMarkovOrder() - Method in class de.jstacs.models.UniformModel
getMaximalMarkovOrder() - Method in class de.jstacs.scoringFunctions.homogeneous.HMM0ScoringFunction
getMaximalMarkovOrder() - Method in class de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction
getMaximalMarkovOrder() - Method in class de.jstacs.scoringFunctions.homogeneous.HomogeneousScoringFunction: Returns the maximal used markov oder.
getMaximalMarkovOrder() - Method in class de.jstacs.scoringFunctions.homogeneous.UniformHomogeneousScoringFunction
getMaximalSymbolLength() - Method in class de.jstacs.data.alphabets.DiscreteAlphabet: Returns the length of the longest "symbol" in the alphabet.
getMaxIndex(double[]) - Static method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction: Returns the index with maximal value in the array.
getMaxOfCC(double[], double[]) - Static method in class de.jstacs.classifier.ScoreBasedPerformanceMeasureDefinitions: This method computes the maximal correlation coefficient (CC_max).
getMaxOfDeviation(Model, Model, int) - Static method in class de.jstacs.models.utils.ModelTester: This method computes the maximum deviation between the probabilties for the all sequences of length for discrete models m1 and m2.
getMeasure() - Method in class de.jstacs.classifier.ScoreBasedPerformanceMeasureDefinitions.ThresholdMeasurePair: This method returns the value of the measure.
getMeasure() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunctionParameterSet: Returns the structure Measure defined by this set of parameters.
getMeasuresForEvaluate() - Static method in class de.jstacs.classifier.AbstractClassifier: Returns an object of the parameters for the evaluate-method.
getMeasuresForEvaluateAll() - Static method in class de.jstacs.classifier.AbstractClassifier: Returns an object of the parameters for the evaluateAll -method.
getMI(double[][][][][][], double) - Static method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.Measure: Computes the mutual information from counts counted on sequences with a total weight of n.
getMI(double[][][][], double) - Static method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.Measure: Computes the mutual information from counts counted on sequences with a total weight of n.
getMIInBits(Sample, double[]) - Static method in class de.jstacs.models.discrete.inhomogeneous.TwoPointEvaluater: This method computes the pairwise mutual information (in bits) between the sequence positions.
getMin() - Method in class de.jstacs.data.Alphabet: Returns the minimal value.
getMin(int) - Method in class de.jstacs.data.AlphabetContainer: Returns the minimal value of the underlying alphabet of position pos.
getMin() - Method in class de.jstacs.data.alphabets.ContinuousAlphabet
getMin() - Method in class de.jstacs.data.alphabets.DiscreteAlphabet
getMinimalAlphabetLength() - Method in class de.jstacs.data.AlphabetContainer: Returns the minimal alphabet length of this container.
getMinimalElementLength() - Method in class de.jstacs.data.Sample: Returns the minimal length of an element in this Sample.
getMinimalSequenceLength() - Method in class de.jstacs.models.mixture.motif.HiddenMotifMixture: Returns the minimal length a sequence respectively a sample has to have.
getMinimalSequenceLength() - Method in class de.jstacs.models.mixture.motif.SingleHiddenMotifMixture
getMisclassificationRate() - Method in class de.jstacs.classifier.ConfusionMatrix: This method returns the misclassification rate.
getModel(int) - Method in class de.jstacs.classifier.modelBased.ModelBasedClassifier: Returns a clone of the model for a specified class.
getModel(int) - Method in class de.jstacs.models.mixture.AbstractMixtureModel: Returns the a deep copy of the i-th model.
getModelInstanceName() - Method in class de.jstacs.models.discrete.inhomogeneous.parameters.BayesianNetworkModelParameterSet: This method returns a short description of the model.
getModelInstanceName(StructureLearner.ModelType, byte, StructureLearner.LearningType, double) - Static method in class de.jstacs.models.discrete.inhomogeneous.parameters.IDGMParameterSet: This method returns a short textual representation of the model instance.
getModels() - Method in class de.jstacs.models.CompositeModel: Returns the a deep copy of the models.
getModels() - Method in class de.jstacs.models.mixture.AbstractMixtureModel: Returns the a deep copy of the models.
getMostProbableSequence(Model, int) - Static method in class de.jstacs.models.utils.ModelTester: Returns one most probable sequence for the discrete model m.
getMotifLength(int) - Method in class de.jstacs.models.mixture.motif.SingleHiddenMotifMixture
getMotifLength(int) - Method in interface de.jstacs.motifDiscovery.MotifDiscoverer: This method returns the length of the motif with index motif.
getMRG() - Method in class de.jstacs.models.mixture.AbstractMixtureModel: This method creates the multivariate random generator that will be used while initialization.
getMRGParams() - Method in class de.jstacs.models.mixture.AbstractMixtureModel: This method creates the parameters used in a multivariate random generator while initialisation.
getName() - Method in class de.jstacs.parameters.CollectionParameter
getName() - Method in class de.jstacs.parameters.FileParameter
getName() - Method in class de.jstacs.parameters.Parameter: Returns the name of the parameter
getName() - Method in class de.jstacs.parameters.ParameterSetContainer
getName() - Method in class de.jstacs.parameters.RangeParameter
getName() - Method in class de.jstacs.parameters.SimpleParameter
getName() - Method in class de.jstacs.results.Result: Returns the name of the result.
getNameOfAlgorithm() - Method in class de.jstacs.models.mixture.AbstractMixtureModel: Returns the name of the used algorithm.
getNameOfAssessment() - Method in class de.jstacs.classifier.assessment.ClassifierAssessment: Returns the name of this class.
getNameString() - Method in enum de.jstacs.classifier.MeasureParameters.Measure: Returns the name of the MeasureParameters.Measure.
getNeededReference() - Method in class de.jstacs.parameters.Parameter: Returns a reference to a ParameterSet whose ParameterSet.hasDefaultOrIsSet() method depends on the value of this Parameter.
getNeededReference() - Method in class de.jstacs.parameters.RangeParameter
getNeededReferenceId() - Method in class de.jstacs.parameters.Parameter: Returns the id of the ParameterSet that would be returned by Parameter.getNeededReference().
getNewComponentProbs(double[]) - Method in class de.jstacs.models.mixture.AbstractMixtureModel: Estimates the weights.
getNewInstance() - Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.DoesNothingLogPrior
getNewInstance() - Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.LogPrior: This method returns an empty new instance of the current prior.
getNewInstance() - Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.SeparateLogPrior
getNewInstance() - Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.SimpleGaussianSumLogPrior
getNewParameters(int, double[][], double[]) - Method in class de.jstacs.models.discrete.inhomogeneous.shared.SharedStructureMixture
getNewParameters(int, double[][], double[]) - Method in class de.jstacs.models.mixture.AbstractMixtureModel: This method trains the internal models on the internal sample and the given weights.
getNewParameters(int, double[][], double[]) - Method in class de.jstacs.models.mixture.motif.HiddenMotifMixture
getNewParametersForModel(int, int, int, double[]) - Method in class de.jstacs.models.mixture.AbstractMixtureModel: This method trains the internal model with index modelIndex on the internal sample and the given weights.
getNewStartDistance() - Method in class de.jstacs.algorithms.optimization.ConstantStartDistance
getNewStartDistance() - Method in class de.jstacs.algorithms.optimization.LimitedMedianStartDistance
getNewStartDistance() - Method in interface de.jstacs.algorithms.optimization.StartDistanceForecaster: This method returns the new positive start distance.
getNewWeights(double[], double[], double[][]) - Method in class de.jstacs.models.mixture.AbstractMixtureModel: Computes sequence weights and returns the score.
getNewWeights(double[], double[], double[][]) - Method in class de.jstacs.models.mixture.MixtureModel: Computes sequence weights and returns the score.
getNewWeights(double[], double[], double[][]) - Method in class de.jstacs.models.mixture.motif.SingleHiddenMotifMixture
getNewWeights(double[], double[], double[][]) - Method in class de.jstacs.models.mixture.StrandModel: Computes sequence weights and returns the score.
getNormalizationConstant() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
getNormalizationConstant(int) - Method in class de.jstacs.scoringFunctions.homogeneous.HMM0ScoringFunction
getNormalizationConstant(int) - Method in class de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction
getNormalizationConstant(int) - Method in class de.jstacs.scoringFunctions.homogeneous.UniformHomogeneousScoringFunction
getNormalizationConstant() - Method in class de.jstacs.scoringFunctions.IndependentProductScoringFunction
getNormalizationConstant() - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
getNormalizationConstant() - Method in class de.jstacs.scoringFunctions.MRFScoringFunction
getNormalizationConstant() - Method in interface de.jstacs.scoringFunctions.NormalizableScoringFunction: Returns the sum of the scores over all sequences of the event space.
getNormalizationConstant() - Method in class de.jstacs.scoringFunctions.UniformScoringFunction
getNormalizationConstant() - Method in class de.jstacs.scoringFunctions.VariableLengthScoringFunction
getNormalizationConstant(int) - Method in class de.jstacs.scoringFunctions.VariableLengthScoringFunction: This method returns the normalization constant for a given sequence length.
getNormalizationConstantForComponent(int) - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction: Computes the normalization constant for the component i
getNormalizationConstantForComponent(int) - Method in class de.jstacs.scoringFunctions.mix.MixtureScoringFunction
getNumberOfClasses() - Method in class de.jstacs.classifier.AbstractClassifier: Returns the number of classes that can be distinguished.
getNumberOfClasses() - Method in class de.jstacs.classifier.AbstractScoreBasedClassifier
getNumberOfCombinations(int) - Method in class de.jstacs.models.discrete.inhomogeneous.CombinationIterator: Returns the number of possible combinations
getNumberOfComponents() - Method in class de.jstacs.models.mixture.AbstractMixtureModel: Returns the number of components the are modeled by this AbstractMixtureModel.
getNumberOfComponents() - Method in interface de.jstacs.motifDiscovery.MotifDiscoverer: Returns the number of components in this MotifDiscoverer.
getNumberOfComponents() - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction: Returns the number of different components.
getNumberOfElements() - Method in class de.jstacs.data.Sample: Returns the number of elements in this Sample.
getNumberOfElements() - Method in class de.jstacs.data.Sample.WeightedSampleFactory: Returns the number of elements in the internal Sample.
getNumberOfElements() - Method in class de.jstacs.io.StringExtractor: Returns the number of Strings that have been read.
getNumberOfElementsWithLength(int) - Method in class de.jstacs.data.Sample: Returns the number of overlapping elements that can be extracted.
getNumberOfLines() - Method in class de.jstacs.classifier.AbstractScoreBasedClassifier.DoubleTableResult: Returns the number of lines in this table.
getNumberOfModels() - Method in class de.jstacs.models.CompositeModel: This method returns the number of models in the CompositeModel
getNumberOfMotifs() - Method in class de.jstacs.models.mixture.motif.SingleHiddenMotifMixture
getNumberOfMotifs() - Method in interface de.jstacs.motifDiscovery.MotifDiscoverer: Returns the number of motifs for this MotifDiscoverer
getNumberOfMotifsInComponent(int) - Method in class de.jstacs.models.mixture.motif.SingleHiddenMotifMixture
getNumberOfMotifsInComponent(int) - Method in interface de.jstacs.motifDiscovery.MotifDiscoverer: Returns the number of motifs that are used in the component component of this motif discoverer.
getNumberOfNexts(int) - Method in class de.jstacs.parameters.MultiSelectionCollectionParameter: Returns the number of calls to MultiSelectionCollectionParameter.next() that can be called before false is returned.
getNumberOfNexts(int) - Method in class de.jstacs.parameters.RangeParameter: Returns the number of calls to RangeParameter.next() that can be done, before obtaining false.
getNumberOfNodes() - Method in class de.jstacs.algorithms.graphs.tensor.Tensor: Returns the number of nodes.
getNumberOfParameters() - Method in class de.jstacs.parameters.ArrayParameterSet
getNumberOfParameters() - Method in class de.jstacs.parameters.ParameterSet: Returns the number of parameters in set
getNumberOfParameters() - Method in class de.jstacs.parameters.SequenceScoringParameterSet
getNumberOfParameters() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
getNumberOfParameters() - Method in class de.jstacs.scoringFunctions.homogeneous.HMM0ScoringFunction
getNumberOfParameters() - Method in class de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction
getNumberOfParameters() - Method in class de.jstacs.scoringFunctions.homogeneous.UniformHomogeneousScoringFunction
getNumberOfParameters() - Method in class de.jstacs.scoringFunctions.IndependentProductScoringFunction
getNumberOfParameters() - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
getNumberOfParameters() - Method in class de.jstacs.scoringFunctions.MRFScoringFunction
getNumberOfParameters() - Method in interface de.jstacs.scoringFunctions.ScoringFunction: The number of parameters in this scoring function.
getNumberOfParameters() - Method in class de.jstacs.scoringFunctions.UniformScoringFunction
getNumberOfParents() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.ParameterTree: Returns the number of parents for the random variable of this ParameterTree in the network structure of the enclosing BayesianNetworkScoringFunction.
getNumberOfRecommendedStarts() - Method in class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
getNumberOfRecommendedStarts() - Method in class de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction
getNumberOfRecommendedStarts() - Method in class de.jstacs.scoringFunctions.IndependentProductScoringFunction
getNumberOfRecommendedStarts() - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
getNumberOfRecommendedStarts() - Method in interface de.jstacs.scoringFunctions.ScoringFunction: This method return the number of recommended optimization starts.
getNumberOfResults() - Method in class de.jstacs.results.ResultSet: Returns the number of Results in this ResultSet
getNumberOfSpecificConstraints() - Method in class de.jstacs.models.discrete.Constraint: Returns the number of specific constraint.
getNumberOfStarts(ScoringFunction[]) - Method in class de.jstacs.classifier.scoringFunctionBased.AbstractOptimizableFunction: Returns the number of recommended starts.
getNumberOfStarts() - Method in class de.jstacs.classifier.scoringFunctionBased.cll.NormConditionalLogLikelihood
getNumberOfStarts() - Method in class de.jstacs.classifier.scoringFunctionBased.OptimizableFunction: Returns the number of starts that should be done for a good optimum.
getNumberOfValues() - Method in class de.jstacs.parameters.MultiSelectionCollectionParameter
getNumberOfValues() - Method in class de.jstacs.parameters.ParameterSet
getNumberOfValues() - Method in class de.jstacs.parameters.ParameterSetContainer
getNumberOfValues() - Method in interface de.jstacs.parameters.RangeIterator: Returns the number of values in the collection.
getNumberOfValues() - Method in class de.jstacs.parameters.RangeParameter: Returns the number of values in a list or range of parameter values
getNumericalCharacteristics() - Method in class de.jstacs.classifier.AbstractClassifier: Returns the subset of numerical values that are also returned by AbstractClassifier.getCharacteristics().
getNumericalCharacteristics() - Method in class de.jstacs.classifier.MappingClassifier
getNumericalCharacteristics() - Method in class de.jstacs.classifier.modelBased.ModelBasedClassifier
getNumericalCharacteristics() - Method in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier
getNumericalCharacteristics() - Method in class de.jstacs.models.CompositeModel
getNumericalCharacteristics() - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousModel
getNumericalCharacteristics() - Method in class de.jstacs.models.discrete.inhomogeneous.DAGModel
getNumericalCharacteristics() - Method in class de.jstacs.models.mixture.AbstractMixtureModel
getNumericalCharacteristics() - Method in interface de.jstacs.models.Model: Returns the subset of numerical values that are also returned by getCharacteristsics.
getNumericalCharacteristics() - Method in class de.jstacs.models.UniformModel
getOptimalBranching(double[][], double[][], byte) - Static method in class de.jstacs.algorithms.graphs.Chu_Liu_Edmonds: Returns an optimal branching.
getOrder() - Method in class de.jstacs.algorithms.graphs.tensor.Tensor: Returns the order.
getOrder() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov: Returns the order of the Markov model as defined in the constructor
getOrder() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov.InhomogeneousMarkovParameterSet: Returns the order of the InhomogeneousMarkov structure measure as defined by this set of parameters
getOrder() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMExplainingAwayResidual.PMMExplainingAwayResidualParameterSet: Returns the order defined by this set of parameters.
getOrder() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMMutualInformation.PMMMutualInformationParameterSet: Returns the order defined by this set of parameters.
getOutput(byte[], double) - Method in class de.jstacs.models.discrete.inhomogeneous.InhCondProb: This method is used to create random sequences.
getOutputStream() - Method in class de.jstacs.utils.SafeOutputStream: Returns the internal used OutputStream.
getParameterAt(int) - Method in class de.jstacs.parameters.ArrayParameterSet
getParameterAt(int) - Method in class de.jstacs.parameters.ParameterSet: Returns the parameter at position i
getParameterAt(int) - Method in class de.jstacs.parameters.SequenceScoringParameterSet
getParameterFor(Sequence, int) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.ParameterTree: Returns the Parameter that is responsible for the suffix of sequence seq starting at position start.
getParameters(OptimizableFunction.KindOfParameter, double[]) - Method in class de.jstacs.classifier.scoringFunctionBased.AbstractOptimizableFunction: This method enables the user to get the parameters without creating a new array.
getParameters(OptimizableFunction.KindOfParameter) - Method in class de.jstacs.classifier.scoringFunctionBased.AbstractOptimizableFunction
getParameters(OptimizableFunction.KindOfParameter, double[]) - Method in class de.jstacs.classifier.scoringFunctionBased.cll.NormConditionalLogLikelihood
getParameters(OptimizableFunction.KindOfParameter) - Method in class de.jstacs.classifier.scoringFunctionBased.OptimizableFunction: Returns some parameters that can be used for instance as start parameters.
getParameterSetFor(Class<? extends InstantiableFromParameterSet>) - Static method in class de.jstacs.utils.SubclassFinder: Returns a LinkedList of the classes of the InstanceParameterSets that can be used to instantiate the sub-class of InstantiableFromParameterSet that is given by clazz
getParametersInCollection() - Method in class de.jstacs.parameters.CollectionParameter: Returns the possible values in this collection
getParent() - Method in class de.jstacs.parameters.Parameter: Returns a reference to the ParameterSet enclosing this Parameter.
getParent() - Method in class de.jstacs.parameters.ParameterSet: Returns the enclosing ParameterSetContainer of this ParameterSet or null if none exists.
getParents(Sample, Sample, double[], double[], int) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures.BTExplainingAwayResidual
getParents(Sample, Sample, double[], double[], int) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures.BTMutualInformation
getParents(Sample, Sample, double[], double[], int) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov
getParents(Sample, Sample, double[], double[], int) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.Measure: Returns the optimal parents for the given data and weights.
getParents(Sample, Sample, double[], double[], int) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMExplainingAwayResidual
getParents(Sample, Sample, double[], double[], int) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMMutualInformation
getPartialNormalizationConstant(int) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
getPartialNormalizationConstant(int, int) - Method in class de.jstacs.scoringFunctions.homogeneous.HMM0ScoringFunction
getPartialNormalizationConstant(int, int) - Method in class de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction
getPartialNormalizationConstant(int, int) - Method in class de.jstacs.scoringFunctions.homogeneous.UniformHomogeneousScoringFunction
getPartialNormalizationConstant(int) - Method in class de.jstacs.scoringFunctions.IndependentProductScoringFunction
getPartialNormalizationConstant(int) - Method in class de.jstacs.scoringFunctions.mix.MixtureScoringFunction
getPartialNormalizationConstant(int) - Method in class de.jstacs.scoringFunctions.MRFScoringFunction
getPartialNormalizationConstant(int) - Method in interface de.jstacs.scoringFunctions.NormalizableScoringFunction: Returns the partial normalization constant for the parameter with index parameterIndex.
getPartialNormalizationConstant(int) - Method in class de.jstacs.scoringFunctions.UniformScoringFunction
getPartialNormalizationConstant(int) - Method in class de.jstacs.scoringFunctions.VariableLengthScoringFunction
getPartialNormalizationConstant(int, int) - Method in class de.jstacs.scoringFunctions.VariableLengthScoringFunction: This method returns the partial normalization constant for a given parameter index and sequence length.
getPartialNormalizer() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.Parameter: Returns the partial derivative of the normalization constant with respect to this parameter.
getPartialROC(double[], double[], RangeParameter) - Static method in class de.jstacs.classifier.ScoreBasedPerformanceMeasureDefinitions: This method allows to compute a partial ROC curve.
getPercent() - Method in class de.jstacs.classifier.assessment.Sampled_RepeatedHoldOutAssessParameterSet: Returns the percentage of user supplied data that is used in each iteration as test dataset.
getPercents() - Method in class de.jstacs.classifier.assessment.RepeatedHoldOutAssessParameterSet: Returns an array containing for each class the percentage of user supplied data that is used in each iteration as test dataset.
getPlotCommands(REnvironment, String, AbstractScoreBasedClassifier.DoubleTableResult...) - Static method in class de.jstacs.classifier.AbstractScoreBasedClassifier.DoubleTableResult: This method copies the data to the server side and creates a StringBuffer containing the plot commands.
getPlugInParameters() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunctionParameterSet: Returns true if plug-in parameters shall be used when creating a BayesianNetworkScoringFunction from this set of parameters.
getPosition() - Method in class de.jstacs.data.sequences.annotation.LocatedSequenceAnnotation: Returns the position of this LocatedSequenceAnnotation on the sequence.
getPosition(int) - Method in class de.jstacs.models.discrete.Constraint: Returns the position with index index
getPosition() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.Parameter: Returns the position of this parameter as defined in the constructor.
getPositionForParameter(int) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction: Returns the position in the sequence, the parameter index is responsible for.
getPositions() - Method in class de.jstacs.models.discrete.Constraint: Returns a clone of array of used positions.
getPossibleLength(Model...) - Static method in class de.jstacs.classifier.modelBased.ModelBasedClassifier: This method returns the possible length of a classifier that would use the given models.
getPossibleLength() - Method in class de.jstacs.data.AlphabetContainer: Returns the possible length for sequences using this container.
getPossibleLength() - Method in class de.jstacs.data.AlphabetContainerParameterSet: Returns the length of the alphabet that can be instantiated using this set.
getPPVForSensitivity(double[], double[], double) - Static method in class de.jstacs.classifier.ScoreBasedPerformanceMeasureDefinitions: This method computes the positive predictive value (PPV) for a given sensitivity.
getPriorTerm() - Method in class de.jstacs.models.AbstractModel
getPriorTerm() - Method in interface de.jstacs.models.Model: Returns a value that is proportional to the prior.
getProbFor(Sequence) - Method in class de.jstacs.models.AbstractModel
getProbFor(Sequence, int) - Method in class de.jstacs.models.AbstractModel
getProbFor(Sequence, int, int) - Method in class de.jstacs.models.CompositeModel
getProbFor(Sequence, int, int) - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousModel
getProbFor(Sequence, int, int) - Method in class de.jstacs.models.discrete.inhomogeneous.DAGModel
getProbFor(Sequence, int, int) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
getProbFor(Sequence) - Method in interface de.jstacs.models.Model: Returns the probability of the given sequence given the model.
getProbFor(Sequence, int) - Method in interface de.jstacs.models.Model: Returns the probability of the given sequence given the model.
getProbFor(Sequence, int, int) - Method in interface de.jstacs.models.Model: Returns the probability of the given sequence given the model.
getProbFor(Sequence, int, int) - Method in class de.jstacs.models.UniformModel
getProbsForComponent(Sequence) - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction: Returns the probabilities for each component
getProfileOfScoresFor(int, int, Sequence, int, MotifDiscoverer.KindOfProfile) - Method in class de.jstacs.models.mixture.motif.SingleHiddenMotifMixture
getProfileOfScoresFor(int, int, Sequence, int, MotifDiscoverer.KindOfProfile) - Method in interface de.jstacs.motifDiscovery.MotifDiscoverer: Returns the profile of the scores for component component and motif motif at all possible start-positions of the motif in the sequence sequence.
getPseudoCount() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.Parameter: Returns the pseudo count as given in the constructor.
getPValue(Sequence, Sample) - Method in class de.jstacs.classifier.AbstractScoreBasedClassifier: Returns the p-value for a sequence candidate with respect to a given background sample.
getPValue(Sample, Sample) - Method in class de.jstacs.classifier.AbstractScoreBasedClassifier: Returns the p-values for all sequence in candidates with respect to a given background sample.
getPValue(double[], double) - Static method in class de.jstacs.classifier.utils.PValueComputation: This method searches for the insertion point of the score in a given sorted array of scores and returns the p-value for this score.
getPValue(double[], double, int) - Static method in class de.jstacs.classifier.utils.PValueComputation: This method searches for the insertion point of the score in a given sorted array of scores and returns the p-value for this score.
getPWM() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction: If this BayesianNetworkScoringFunction is a PWM, i.e.
getRandomSequence(Random, int) - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousMM
getRandomSequence(Random, int) - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousModel: This method creates a sequence from a trained model.
getRangedInstance() - Method in class de.jstacs.parameters.CollectionParameter
getRangedInstance() - Method in class de.jstacs.parameters.ParameterSetContainer
getRangedInstance() - Method in interface de.jstacs.parameters.Rangeable: Returns an instance of RangeIterator that has the same properties as the current instance, but accepts a range or list of values.
getRangedInstance() - Method in class de.jstacs.parameters.SimpleParameter
getRawResult() - Method in class de.jstacs.results.ListResult: Returns a copy of the internal list of ResultSet.
getReferenceClass() - Method in class de.jstacs.classifier.assessment.Sampled_RepeatedHoldOutAssessParameterSet: Returns the index of the reference class.
getRepeats() - Method in class de.jstacs.classifier.assessment.RepeatedHoldOutAssessParameterSet: Returns the repeats defined by this RepeatedHoldOutAssessParameterSet (repeats define how many iterations (train and test classifiers) of that RepeatedHoldOutExperiment this RepeatedHoldOutAssessParameterSet is used with are performed).
getRepeats() - Method in class de.jstacs.classifier.assessment.RepeatedSubSamplingAssessParameterSet: Returns the repeats defined by this RepeatedSubSamplingAssessParameterSet (repeats defines how many iterations (train and test classifiers) of that RepeatedSubSamplingExperiment this RepeatedSubSamplingAssessParameterSet is used with are performed).
getRepeats() - Method in class de.jstacs.classifier.assessment.Sampled_RepeatedHoldOutAssessParameterSet: Returns the repeats defined by this Sampled_RepeatedHoldOutAssessParameterSet (repeats defines how many iterations (train and test classifiers) of that Sampled_RepeatedHoldOutExperiment this Sampled_RepeatedHoldOutAssessParameterSet is used with are performed).
getResult() - Method in class de.jstacs.classifier.AbstractScoreBasedClassifier.DoubleTableResult
getResult() - Method in class de.jstacs.results.ImageResult
getResult() - Method in class de.jstacs.results.ListResult
getResult() - Method in class de.jstacs.results.Result: Returns the value of the result.
getResult() - Method in class de.jstacs.results.SampleResult
getResult() - Method in class de.jstacs.results.SimpleResult
getResult() - Method in class de.jstacs.results.StorableResult
getResultAt(int) - Method in class de.jstacs.results.NumericalResultSet: Returns the NumericalResult number index.
getResultAt(int) - Method in class de.jstacs.results.ResultSet: Returns Result number index in this ResultSet.
getResultInstance() - Method in class de.jstacs.results.StorableResult: Returns the instance of the Storable that is the result of this ObjectResult
getResults(Sample[], MeasureParameters, boolean, boolean) - Method in class de.jstacs.classifier.AbstractClassifier: This method computes the results for any evaluation of the classifier.
getResults(Sample[], MeasureParameters, boolean, boolean) - Method in class de.jstacs.classifier.AbstractScoreBasedClassifier
getResults() - Method in class de.jstacs.results.ResultSet: Returns all internal results.
getRootValue(int) - Method in class de.jstacs.algorithms.graphs.tensor.AsymmetricTensor
getRootValue(int) - Method in class de.jstacs.algorithms.graphs.tensor.SymmetricTensor
getRootValue(int) - Method in class de.jstacs.algorithms.graphs.tensor.Tensor: Returns the value for child as root.
getSample() - Method in class de.jstacs.data.Sample.WeightedSampleFactory: Returns the sample, where each sequence occurs only once.
getScale() - Method in class de.jstacs.parameters.RangeParameter: Returns a description of the the scale of a range of parameter values.
getScatterplot(AbstractScoreBasedClassifier, AbstractScoreBasedClassifier, Sample, Sample, REnvironment, boolean) - Static method in class de.jstacs.classifier.utils.ClassificationVisualizer: This method returns an ImageResult containing a scatter plot of the scores for the given classifiers cl1,cl2.
getScore(Tensor, int[][]) - Static method in class de.jstacs.algorithms.graphs.DAG: Returns the score for any graph.
getScore(Sequence, int) - Method in class de.jstacs.classifier.AbstractScoreBasedClassifier: This method returns the score for a given sequence and a given class.
getScore(Sequence, int, boolean) - Method in class de.jstacs.classifier.AbstractScoreBasedClassifier: This method returns the score for a given sequence and a given class.
getScore(Sequence, int, boolean) - Method in class de.jstacs.classifier.MappingClassifier
getScore(Sequence, int, boolean) - Method in class de.jstacs.classifier.modelBased.ModelBasedClassifier
getScore(Sequence, int, boolean) - Method in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier
getScoreForBestRun() - Method in class de.jstacs.models.mixture.AbstractMixtureModel: Returns the value of optimized function from the best run of the last training
getScoreForPath(Tensor, int, byte, int[]) - Static method in class de.jstacs.algorithms.graphs.DAG: Returns the score for a given path path using the first l nodes and dependencies of order k.
getScores(Sample) - Method in class de.jstacs.classifier.AbstractScoreBasedClassifier: This method returns the scores of the classifier for any sequence in the sample.
getScores(Sample) - Method in class de.jstacs.classifier.modelBased.ModelBasedClassifier
getScoringFunction(int) - Method in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier: Returns the internally used ScoringFunction with index i.
getScoringFunctions() - Method in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier: Returns all internally used ScoringFunctions in the internal order.
getScoringFunctions() - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction: Returns a deep copy of all internal used ScoringFunctions
getSecond() - Method in class de.jstacs.algorithms.Alignment.StringAlignment: Returns the second string.
getSelected() - Method in class de.jstacs.parameters.CollectionParameter: Returns the index of the selected value.
getSelected() - Method in class de.jstacs.parameters.MultiSelectionCollectionParameter
getSensitivityForSpecificity(double[], double[], double) - Static method in class de.jstacs.classifier.ScoreBasedPerformanceMeasureDefinitions: This method computes the sensitivity for a given specificity.
getSequence() - Method in class de.jstacs.models.utils.ModelTester.SeqIterator
getShannonEntropy(Model, int) - Static method in class de.jstacs.models.utils.ModelTester: This method computes the Shannon Entropy for any discrete model m and all sequences of length, if possible.
getShannonEntropyInBits(Model, int) - Static method in class de.jstacs.models.utils.ModelTester: This method computes the Shannon Entropy in bits for any discrete model m and all sequences of length, if possible.
getShortFromParameter(Parameter) - Static method in class de.jstacs.io.ParameterSetParser: Returns the short which is the value of the Parameter par.
getSimplifiedAlphabetContainer(Alphabet[], int[]) - Static method in class de.jstacs.data.AlphabetContainer: This method creates a new AlphabetContainer that uses as less as possible alphabets to describe the container.
getSizeOfEventSpaceForRandomVariablesOfParameter(int) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
getSizeOfEventSpaceForRandomVariablesOfParameter(int) - Method in class de.jstacs.scoringFunctions.homogeneous.HMM0ScoringFunction
getSizeOfEventSpaceForRandomVariablesOfParameter(int) - Method in class de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction
getSizeOfEventSpaceForRandomVariablesOfParameter(int) - Method in class de.jstacs.scoringFunctions.homogeneous.UniformHomogeneousScoringFunction
getSizeOfEventSpaceForRandomVariablesOfParameter(int) - Method in class de.jstacs.scoringFunctions.IndependentProductScoringFunction
getSizeOfEventSpaceForRandomVariablesOfParameter(int) - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
getSizeOfEventSpaceForRandomVariablesOfParameter(int) - Method in class de.jstacs.scoringFunctions.MRFScoringFunction
getSizeOfEventSpaceForRandomVariablesOfParameter(int) - Method in interface de.jstacs.scoringFunctions.NormalizableScoringFunction: Returns the size of the event space of the random variables that are affected by parameter no.
getSizeOfEventSpaceForRandomVariablesOfParameter(int) - Method in class de.jstacs.scoringFunctions.UniformScoringFunction
getStartNode() - Method in class de.jstacs.algorithms.graphs.Edge: Returns the start node of the edge.
getStartValue() - Method in class de.jstacs.parameters.RangeParameter: Returns the start value of a range of parameter values or null if no range was specified.
getStationaryDistribution(double[], int) - Static method in class de.jstacs.models.utils.StationaryDistribution: This method return the stationary distribution.
getStatistics() - Method in class de.jstacs.results.MeanResultSet: Returns the means and (if possible the) standard errors of the results in this MeanResultSet as a new NumericalResultSet.
getStatistics(Sample, double[], int, double) - Static method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.Measure: Counts the occurrences of symbols of the AlphabetContainer of s using weights.
getStatisticsOrderTwo(Sample, double[], int, double) - Static method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.Measure: Counts the occurrences of symbols of the AlphabetContainer of s using weights.
getSteps() - Method in class de.jstacs.parameters.RangeParameter: Returns the number of steps of a range of parameter values or 0 if no range was specified.
getStrandedness() - Method in class de.jstacs.data.sequences.annotation.StrandedLocatedSequenceAnnotationWithLength: Returns the orientation/strandedness of this annotation.
getStrandFor(int, int, Sequence, int) - Method in class de.jstacs.models.mixture.motif.SingleHiddenMotifMixture
getStrandFor(int, int, Sequence, int) - Method in interface de.jstacs.motifDiscovery.MotifDiscoverer: This method returns the strand for a given subsequence if it is consider as site of the motif model in a specific component.
getStringFromParameter(Parameter) - Static method in class de.jstacs.io.ParameterSetParser: Returns the String which is the value of the Parameter par.
getStructure() - Method in class de.jstacs.models.discrete.inhomogeneous.DAGModel
getStructure() - Method in class de.jstacs.models.discrete.inhomogeneous.FSDAGModel
getStructure() - Method in class de.jstacs.models.discrete.inhomogeneous.InhomogeneousDGM: Returns a string representation of the graph.
getStructure() - Method in class de.jstacs.models.discrete.inhomogeneous.shared.SharedStructureMixture
getStructure(Sample, double[], StructureLearner.ModelType, byte, StructureLearner.LearningType) - Method in class de.jstacs.models.discrete.inhomogeneous.StructureLearner: This method finds the optimal structure (in some sense).
getStructure(Tensor, StructureLearner.ModelType, byte) - Static method in class de.jstacs.models.discrete.inhomogeneous.StructureLearner: This method can be used to determine the optimal structure.
getStructureFromPath(int[], Tensor) - Static method in class de.jstacs.algorithms.graphs.DAG: Extracts the structure from a given path and score-"function".
getSubAnnotations() - Method in class de.jstacs.data.sequences.annotation.SequenceAnnotation: Returns the sub-annotations of this SequenceAnnotation as given in the constructor.
getSubContainer(int, int) - Method in class de.jstacs.data.AlphabetContainer: This method returns a subcontainer for the positions starting at start and with length length.
getSubSequence(AlphabetContainer, int) - Method in class de.jstacs.data.Sequence: This method should be used if one wants to create a sample of subsequences of defined length.
getSubSequence(AlphabetContainer, int, int) - Method in class de.jstacs.data.Sequence: This method should be used if one wants to create a sample of subsequences of defined length.
getSubSequence(int) - Method in class de.jstacs.data.Sequence: This is an very efficient way to create a subsequence/suffix for sequences with a simple AlphabetContainer.
getSubSequence(int, int) - Method in class de.jstacs.data.Sequence: This is an very efficient way to create a subsequence of defined length for sequences with a simple AlphabetContainer.
getSuffixSample(int) - Method in class de.jstacs.data.Sample: This method enables you to use only a suffix of all elements in the current sample.
getSumOfDeviation(Model, Model, int) - Static method in class de.jstacs.models.utils.ModelTester: This method computes the sum of deviations between the probabilties for the all sequences of length for discrete models m1 and m2.
getSumOfDistribution(Model, int) - Static method in class de.jstacs.models.utils.ModelTester: This method computes the marginal distribution for any discrete model m and all sequences of length, if possible.
getSumOfHyperparameter() - Method in class de.jstacs.utils.random.DirichletMRGParams: Returns the sum of the hyperparameters (entries of the hyperparameter vector) of the underlying Dirichlet distribution.
getSumOfHyperparameter() - Method in class de.jstacs.utils.random.ErlangMRGParams: Returns the sum of the hyperparameters (entries of the hyperparameter vector) of the underlying Erlang distribution.
getSumOfWeights() - Method in class de.jstacs.data.Sample.WeightedSampleFactory: Returns the sum of all weights.
getSuperClassOf(T...) - Static method in class de.jstacs.io.ArrayHandler: This method returns the deepest class in the class hierarchy that is the class or a super class of all instances in the array.
getSymbol(int, double) - Method in class de.jstacs.data.AlphabetContainer: This method returns a String representation of val.
getSymbolAt(int) - Method in class de.jstacs.data.alphabets.DiscreteAlphabet: Returns the symbol at position i in the alphabet.
getSymKLDivergence(Model, Model, int) - Static method in class de.jstacs.models.utils.ModelTester: Returns the difference of the Kullback-Leibler-divergences, i.e.
getT() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.Parameter: Returns the part of the normalization constant of parameters before this parameter in the structure of the network.
getTensor(Sample, double[], byte, StructureLearner.LearningType) - Method in class de.jstacs.models.discrete.inhomogeneous.StructureLearner: This method can be used to compute a tensor that can be used to determine the optimal structure.
getThreshold() - Method in class de.jstacs.classifier.ScoreBasedPerformanceMeasureDefinitions.ThresholdMeasurePair: This method returns the value of threshold.
getThreshold(double[], int) - Static method in class de.jstacs.classifier.utils.PValueComputation: This method returns the threshold t that determines if an observed score is significant.
getTopologicalOrder(int[][]) - Static method in class de.jstacs.algorithms.graphs.TopSort: Returns the topological order of indexes according to parents2.
getTopologicalOrder2(byte[][]) - Static method in class de.jstacs.algorithms.graphs.TopSort: Method to compute a topological ordering for a given graph.
getTrain_TestNumbers(boolean) - Method in class de.jstacs.classifier.assessment.RepeatedSubSamplingAssessParameterSet: Returns an array containing the number of elements the subsampled (train | test) datasets should consist of.
getTrueIndexForLastGetBest() - Method in class de.jstacs.algorithms.graphs.tensor.SymmetricTensor: Returns the edge from SymmetricTensor.getBest(int, int[], byte) in an encoded index.
getType() - Method in class de.jstacs.data.AlphabetContainer: Returns the type of this AlphabetContainer.
getType() - Method in class de.jstacs.data.sequences.annotation.SequenceAnnotation: Returns the type of this SequenceAnnotation as given in the constructor.
getValidator() - Method in class de.jstacs.parameters.SimpleParameter: Returns the ParameterValidator used in this SimpleParameter.
getValue(byte, int, int...) - Method in class de.jstacs.algorithms.graphs.tensor.AsymmetricTensor
getValue(byte, int, int...) - Method in class de.jstacs.algorithms.graphs.tensor.SymmetricTensor
getValue(byte, int, int...) - Method in class de.jstacs.algorithms.graphs.tensor.Tensor: Returns the value for the edge parents[0],...
getValue() - Method in class de.jstacs.parameters.CollectionParameter
getValue() - Method in class de.jstacs.parameters.EnumParameter
getValue() - Method in class de.jstacs.parameters.FileParameter
getValue() - Method in class de.jstacs.parameters.MultiSelectionCollectionParameter
getValue() - Method in class de.jstacs.parameters.Parameter: Returns the current value of this Parameter
getValue() - Method in class de.jstacs.parameters.ParameterSetContainer
getValue() - Method in class de.jstacs.parameters.RangeParameter
getValue() - Method in class de.jstacs.parameters.SimpleParameter
getValue() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.Parameter: Returns the current value of this parameter.
getValueFor(String) - Method in class de.jstacs.parameters.MultiSelectionCollectionParameter: Returns the value for the option with key key.
getValueFor(int) - Method in class de.jstacs.parameters.MultiSelectionCollectionParameter: Returns the value of the option no.
getValueOfAIC(Model, Sample, int) - Static method in class de.jstacs.models.utils.ModelTester: This method computes the value of Akaikes Information Criterion (AIC).
getValueOfBIC(Model, Sample, int) - Static method in class de.jstacs.models.utils.ModelTester: This method computes the value of Bayesian Information Criterion (BIC).
getValues() - Method in class de.jstacs.parameters.MultiSelectionCollectionParameter: Returns the values of all selected options as an array.
getVersionInformation() - Method in class de.jstacs.utils.REnvironment: Returns information about the version of R that is used.
getWeight() - Method in class de.jstacs.algorithms.graphs.Edge: Returns the weight of the edge.
getWeight(int) - Method in class de.jstacs.data.Sample.WeightedSampleFactory: Returns the weight for the sequence with index index.
getWeight() - Method in class de.jstacs.utils.ComparableElement: This method returns the weight of the element.
getWeights() - Method in class de.jstacs.data.Sample.WeightedSampleFactory: Returns a copy of the weights for the sample.
getWeights() - Method in class de.jstacs.models.mixture.AbstractMixtureModel: This method returns the a deep copy of the weights for each component.
getXMLTag() - Method in class de.jstacs.classifier.AbstractClassifier: Returns the String that is used as tag for the XML representation.
getXMLTag() - Method in class de.jstacs.classifier.MappingClassifier
getXMLTag() - Method in class de.jstacs.classifier.modelBased.ModelBasedClassifier
getXMLTag() - Method in class de.jstacs.classifier.scoringFunctionBased.cll.CLLClassifier
getXMLTag() - Method in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier
getXMLTag() - Method in class de.jstacs.models.discrete.Constraint: Returns the XML-tag that is used for the class to en- or decode.
getXMLTag() - Method in class de.jstacs.models.discrete.DiscreteGraphicalModel
getXMLTag() - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousMM
getXMLTag() - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousModel.HomCondProb
getXMLTag() - Method in class de.jstacs.models.discrete.inhomogeneous.BayesianNetworkModel
getXMLTag() - Method in class de.jstacs.models.discrete.inhomogeneous.FSDAGModel
getXMLTag() - Method in class de.jstacs.models.discrete.inhomogeneous.InhCondProb
getXMLTag() - Method in class de.jstacs.models.discrete.inhomogeneous.MEMConstraint
getXMLTag() - Method in class de.jstacs.models.mixture.gibbssampling.AbstractBurnInTest: This method return the XML tag that is used in AbstractBurnInTest.toXML() and AbstractBurnInTest.AbstractBurnInTest(StringBuffer).
getXMLTag() - Method in class de.jstacs.models.mixture.gibbssampling.VarianceRatioBurnInTest
getXMLTag() - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction: This method returns the XML tag of the instance that is used to build and XML representation
getZ() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.Parameter: Returns the part of the normalization constant of parameters after this parameter in the structure of the network.
GibbsSamplingComponent - Interface in de.jstacs.models.mixture.gibbssampling: This is the interface that any AbstractModel has to implement if it should be used in a Gibbs Sampling.
goldenRatio(OneDimensionalFunction, double, double, double, double, double) - Static method in class de.jstacs.algorithms.optimization.Optimizer: Approximates a minimum (not necessary the global) in the interval [lower,upper].
goldenRatio(OneDimensionalFunction, double, double, double) - Static method in class de.jstacs.algorithms.optimization.Optimizer: Approximates a minimum (not necessary the global) in the interval [lower,upper].
GT - Static variable in interface de.jstacs.parameters.validation.Constraint: The condition is greater than
GUIProgressUpdater - Class in de.jstacs.utils: This class implements a ProgressUpdater with a GUI.
GUIProgressUpdater(boolean) - Constructor for class de.jstacs.utils.GUIProgressUpdater: This is the constructor for a GUIProgressUpdater.













  
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