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d
beginning at start
.
index
.
index
.
s1
and
s2
(Alignment.Alignment(Sequence, Sequence, de.jstacs.algorithms.Alignment.Costs)
)
pos
.
AlphabetContainer
for this ScoringFunction
.
pos
.
ListResult
SequenceAnnotation
as given in the constructor.
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
getNumberOfClasses
that contains information 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.cll.NormConditionalLogLikelihood
-
- 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
- Retuns the specific class weights of a AbstractScoreBasedClassifier
- 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
-
- 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 the 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 an 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
ParameterSet
that has been used to instantiate the current instance of the implementing
class.
- getCurrentParameterSet() -
Method in class de.jstacs.models.discrete.DiscreteGraphicalModel
-
- 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
-
- getDataSplitMethod() -
Method in class de.jstacs.classifier.assessment.RepeatedHoldOutAssessParameterSet
-
- getDataSplitMethod() -
Method in class de.jstacs.classifier.assessment.Sampled_RepeatedHoldOutAssessParameterSet
-
- 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
-
- getDimension() -
Method in class de.jstacs.utils.random.DirichletMRGParams
-
- getDimension() -
Method in class de.jstacs.utils.random.ErlangMRGParams
-
- 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.cll.NormConditionalLogLikelihood
-
- 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
-
- 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() -
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
-
- 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
-
- 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.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
-
- 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 the 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.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.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.
- getHyperparameter(int) -
Method in class de.jstacs.utils.random.DiMRGParams
-
- getHyperparameter(int) -
Method in class de.jstacs.utils.random.DirichletMRGParams
-
- getHyperparameter(int) -
Method in class de.jstacs.utils.random.ErlangMRGParams
-
- 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
-
- 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
- Return 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.InstanceParameterSet
- 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, 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.ParameterSet
- Returns a new instance of the class of
getInstanceClass()
that was created using this ParameterSet
.
- getInstanceClass() -
Method in class de.jstacs.parameters.ParameterSet
- Returns the class of the instances that can be constructed using this
set.
- getInstanceComment() -
Method in class de.jstacs.classifier.assessment.ClassifierAssessmentAssessParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.classifier.assessment.KFoldCVAssessParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.classifier.assessment.RepeatedHoldOutAssessParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.classifier.assessment.RepeatedSubSamplingAssessParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.classifier.assessment.Sampled_RepeatedHoldOutAssessParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifierParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.data.AlphabetContainerParameterSet.AlphabetArrayParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.data.AlphabetContainerParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.data.AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet
-
- 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.inhomogeneous.parameters.BayesianNetworkModelParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.models.discrete.inhomogeneous.parameters.FSDAGMParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.parameters.ExpandableParameterSet
-
- getInstanceComment() -
Method in class de.jstacs.parameters.ParameterSet
- Returns a comment (a textual description) of the class that can be
constructed using this
ParameterSet
.
- getInstanceComment() -
Method in class de.jstacs.parameters.SimpleParameterSet
-
- getInstanceFromParameterSet(ParameterSet) -
Static method in class de.jstacs.io.ParameterSetParser
- Returns an instance of a subclass of
InstantiableFromParameterSet
that can be instantiated by
the ParameterSet 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.assessment.ClassifierAssessmentAssessParameterSet
-
- getInstanceName() -
Method in class de.jstacs.classifier.assessment.KFoldCVAssessParameterSet
-
- getInstanceName() -
Method in class de.jstacs.classifier.assessment.RepeatedHoldOutAssessParameterSet
-
- getInstanceName() -
Method in class de.jstacs.classifier.assessment.RepeatedSubSamplingAssessParameterSet
-
- getInstanceName() -
Method in class de.jstacs.classifier.assessment.Sampled_RepeatedHoldOutAssessParameterSet
-
- 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
-
- getInstanceName() -
Method in class de.jstacs.data.AlphabetContainerParameterSet
-
- getInstanceName() -
Method in class de.jstacs.data.AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet
-
- 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.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 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.ExpandableParameterSet
-
- getInstanceName() -
Method in class de.jstacs.parameters.ParameterSet
- Returns the name of an instance of the class that can be constructed
using this
ParameterSet
.
- getInstanceName() -
Method in class de.jstacs.parameters.SimpleParameterSet
-
- getInstanceName() -
Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
-
- getInstanceName() -
Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures.BTExplainingAwayResidual
-
- 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.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.PMMMutualInformation
-
- 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
-
- 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
- Return 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 interface de.jstacs.models.Model
- Returns the length of sequence this model can classify.
- getLength() -
Method in class de.jstacs.parameters.InstanceParameterSet
- 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 Sequence
s this ScoringFunction
can handle.
- getLengthOfBurnIn(int) -
Method in class de.jstacs.models.mixture.gibbssampling.BurnInTest
- Return the length of the burn in phase of sampling
index
.
- getLengthOfBurnIn(int) -
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.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
.
- getLogPriorTerm() -
Method in class de.jstacs.models.CompositeModel
-
- 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 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.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.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 log of the sum of values.
- getLogSum(int, int, double...) -
Static method in class de.jstacs.utils.Normalisation
- Returns the log of the sum of values.
- 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.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
-
- 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.
- 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 min 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.
- 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
.
- 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
-
- 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.
- 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.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 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.
- 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
- 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.InstanceParameterSet
-
- getNumberOfParameters() -
Method in class de.jstacs.parameters.ParameterSet
- Returns the number of parameters in set
- 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
Result
s in this ResultSet
- getNumberOfSpecificConstraints() -
Method in class de.jstacs.models.discrete.Constraint
- Returns the number of specific constraint.
- 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.
- getNumberOfStrings() -
Method in class de.jstacs.io.StringExtractor
- Returns the number of strings
- 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
getCharacteristsics
.
- 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.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
-
- 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
- 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.InstanceParameterSet
-
- getParameterAt(int) -
Method in class de.jstacs.parameters.ParameterSet
- Returns the parameter at position
i
- 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
.
- 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 are partial ROC curve.
- getPercent() -
Method in class de.jstacs.classifier.assessment.Sampled_RepeatedHoldOutAssessParameterSet
-
- getPercents() -
Method in class de.jstacs.classifier.assessment.RepeatedHoldOutAssessParameterSet
-
- 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.
- 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.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
- 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.
- 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
-
- getRepeats() -
Method in class de.jstacs.classifier.assessment.RepeatedHoldOutAssessParameterSet
-
- getRepeats() -
Method in class de.jstacs.classifier.assessment.RepeatedSubSamplingAssessParameterSet
-
- getRepeats() -
Method in class de.jstacs.classifier.assessment.Sampled_RepeatedHoldOutAssessParameterSet
-
- 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.cll.CLLClassifier
-
- 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
and 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 used 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
-
- getStartParams(boolean, double[]) -
Method in class de.jstacs.classifier.scoringFunctionBased.cll.NormConditionalLogLikelihood
- This method enables the user to get the start parameters without creating a new array.
- getStartParams(boolean) -
Method in class de.jstacs.classifier.scoringFunctionBased.cll.NormConditionalLogLikelihood
-
- getStartParams(boolean) -
Method in class de.jstacs.classifier.scoringFunctionBased.OptimizableFunction
- Returns some start parameters.
- 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.
- getStationarySymbolDistribution(double[], int) -
Static method in class de.jstacs.models.utils.StationaryDistribution
- This method return the stationary symbol distribution.
- getStationarySymbolDistribution() -
Method in class de.jstacs.scoringFunctions.homogeneous.HMM0ScoringFunction
-
- getStationarySymbolDistribution() -
Method in class de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction
-
- getStationarySymbolDistribution() -
Method in class de.jstacs.scoringFunctions.homogeneous.UniformHomogeneousScoringFunction
-
- getStationarySymbolDistribution() -
Method in class de.jstacs.scoringFunctions.VariableLengthScoringFunction
- This method returns the stationary symbol 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.
- getString(int) -
Method in class de.jstacs.io.StringExtractor
- Returns the string with index
i
.
- 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 an 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
-
- getSumOfHyperparameter() -
Method in class de.jstacs.utils.random.ErlangMRGParams
-
- 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 repsresentation 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.
- 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
-
- getTrueIndexForLastGetBest() -
Method in class de.jstacs.algorithms.graphs.tensor.SymmetricTensor
- Returns the edge from getBest in an endcoded index.
- 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
-
- getWeight() -
Method in class de.jstacs.algorithms.graphs.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.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.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 GUIProgressUpdaterBar.
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