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## E

Edge - Class in de.jstacs.algorithms.graphs
  This class is a representation of a weighted edge.
Edge(int, int, double) - Constructor for class de.jstacs.algorithms.graphs.Edge
  Creates a new weighted edge.
emit(int, int...) - Method in class de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction
  This method returns a `Sample` object containing artificial sequence(s).
emitSample(int, int...) - Method in class de.jstacs.models.AbstractModel
emitSample(int, int...) - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousModel
  Creates a `Sample` of a given number of `Sequence`s from a trained homogeneous model.
emitSample(int, int...) - Method in class de.jstacs.models.discrete.inhomogeneous.DAGModel
emitSample(int, int...) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
emitSample(int, int...) - Method in interface de.jstacs.models.Model
  This method returns a `Sample` object containing artificial sequence(s).
emitSample(int, int...) - Method in class de.jstacs.models.UniformModel
emitSample(Model, int) - Static method in class de.jstacs.models.utils.DiscreteInhomogenousSampleEmitter
  This method emits a sample with `n`sequences from the discrete inhomogeneous model `m` .``
emitSampleUsingCurrentParameterSet(int, int...) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
  The method returns an array of sequences using the current parameter set.
emitSampleUsingCurrentParameterSet(int, int...) - Method in class de.jstacs.models.mixture.MixtureModel
emitSampleUsingCurrentParameterSet(int, int...) - Method in class de.jstacs.models.mixture.motif.HiddenMotifMixture
  Standard implementation throwing an `OperationNotSupportedException`.
emitSampleUsingCurrentParameterSet(int, int...) - Method in class de.jstacs.models.mixture.StrandModel
EmptySampleException - Exception in de.jstacs.data
  An `EmptySampleException` will be thrown if no `Sequence` is in a `Sample` (i.e. the `Sample` is empty).
EmptySampleException() - Constructor for exception de.jstacs.data.EmptySampleException
  This constructor creates an instance with default error message ("The created Sample is empty.
encode(int[][]) - Static method in class de.jstacs.models.discrete.inhomogeneous.parameters.FSDAGMParameterSet
  This method can be used to encode an adjacency list to a graph description `String` (e.g. for the different constructors which requires graph description `String`s).
enumerate(ScoringFunction[], int, int, RecyclableSequenceEnumerator, double, SFBasedOptimizableFunction, OutputStream) - Static method in class de.jstacs.motifDiscovery.MutableMotifDiscovererToolbox
  This method allows to enumerate all possible seeds for a motif in the `MutableMotifDiscoverer` of a specific class.
enumerate(ScoringFunction[], int[], int[], RecyclableSequenceEnumerator[], double, SFBasedOptimizableFunction, OutputStream) - Static method in class de.jstacs.motifDiscovery.MutableMotifDiscovererToolbox
  This method allows to enumerate all possible seeds for a number of motifs in the `MutableMotifDiscoverer`s of a specific classes.
enumerateHP(Tensor) - Static method in class de.jstacs.algorithms.graphs.DAG
  The method computes the HP(k) (see `DAG`).
EnumParameter - Class in de.jstacs.parameters
  This class implements a `CollectionParameter` based on an `Enum`.
EnumParameter(Class<? extends Enum>, String, boolean) - Constructor for class de.jstacs.parameters.EnumParameter
  The main constructor.
EnumParameter(Class<? extends Enum>, String, boolean, String) - Constructor for class de.jstacs.parameters.EnumParameter
  This constructor creates an instance and set the default value.
EnumParameter(StringBuffer) - Constructor for class de.jstacs.parameters.EnumParameter
  The standard constructor for the interface `Storable`.
eps - Variable in class de.jstacs.algorithms.optimization.NumericalDifferentiableFunction
  The constant used in the computation of the gradient.
EqualParts - Class in de.jstacs.utils.random
  This class is no real random generator it just returns 1/n for all values.
EqualParts() - Constructor for class de.jstacs.utils.random.EqualParts
equals(Object) - Method in class de.jstacs.data.Sequence
equals(Object) - Method in class de.jstacs.parameters.CollectionParameter
equals(Object) - Method in class de.jstacs.parameters.SequenceScoringParameterSet
equals(Object) - Method in class de.jstacs.parameters.SimpleParameter
EQUALS - Static variable in interface de.jstacs.parameters.validation.Constraint
  The condition is equality
equals(Object) - Method in class de.jstacs.results.SimpleResult
ErlangMRG - Class in de.jstacs.utils.random
  This class is a multivariate random generator based on a Dirichlet distribution for `alpha_i \in N`.
ErlangMRG() - Constructor for class de.jstacs.utils.random.ErlangMRG
  Constructor that creates a new multivariate random generator with underlying Erlang distribution.
ErlangMRGParams - Class in de.jstacs.utils.random
  The container for parameters of an Erlang multivariate random generator.
ErlangMRGParams(int, int) - Constructor for class de.jstacs.utils.random.ErlangMRGParams
  Constructor which creates a new hyperparameter vector for an Erlang random generator.
ErlangMRGParams(int[]) - Constructor for class de.jstacs.utils.random.ErlangMRGParams
  Constructor which creates a new hyperparameter vector for an Erlang random generator.
errorMessage - Variable in class de.jstacs.parameters.CollectionParameter
  If a value was illegal for the collection parameter, this field holds the error message.
errorMessage - Variable in class de.jstacs.parameters.ParameterSet
  The error message of the last error or `null`
errorMessage - Variable in class de.jstacs.parameters.ParameterSetContainer
  The message of the last error or `null` if no error occurred.
errorMessage - Variable in class de.jstacs.parameters.validation.ReferenceConstraint
  The message of the last error or `null`.
ess - Variable in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
  The equivalent sample size.
ess - Variable in class de.jstacs.scoringFunctions.mix.motifSearch.DurationScoringFunction
  The equivalent sample size.
estimate(double) - Method in class de.jstacs.models.discrete.Constraint
  Estimates the (smoothed) relative frequencies using the ess (equivalent sample size).
estimate(double) - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousModel.HomCondProb
estimate(double) - Method in class de.jstacs.models.discrete.inhomogeneous.InhCondProb
estimate(double) - Method in class de.jstacs.models.discrete.inhomogeneous.MEMConstraint
estimateComponentProbs - Variable in class de.jstacs.models.mixture.AbstractMixtureModel
  The switch for estimating the component probabilities or not.
estimateParameters(Sample, double[]) - Method in class de.jstacs.models.discrete.inhomogeneous.DAGModel
  This method estimates the parameter of the model from the likelihood or the posterior, respectively.
estimateUnConditional(int, int, double, boolean) - Method in class de.jstacs.models.discrete.Constraint
  Estimates unconditionally.
estimateUnConditional(int, int, double, boolean) - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousModel.HomCondProb
estimateUnConditional(double, double) - Method in class de.jstacs.models.discrete.inhomogeneous.InhCondProb
  Estimates the unconditional frequencies using the ess (equivalent sample size).
estimateUnConditional(int, int, double, boolean) - Method in class de.jstacs.models.discrete.inhomogeneous.InhCondProb
eval(String) - Method in class de.jstacs.utils.REnvironment
  Evaluates the `String` as R commands.
evaluate(MeasureParameters, boolean, Sample...) - Method in class de.jstacs.classifier.AbstractClassifier
  This method evaluates the classifier and computes all numerical results as, for instance, the sensitivity for a given specificity, the area under the ROC curve and so on.
evaluateAll(MeasureParameters, boolean, Sample...) - Method in class de.jstacs.classifier.AbstractClassifier
  This method evaluates the classifier and computes all results.
evaluateClassifier(MeasureParameters, ClassifierAssessmentAssessParameterSet, Sample[], ProgressUpdater) - Method in class de.jstacs.classifier.assessment.ClassifierAssessment
  This method must be implemented in all subclasses.
evaluateClassifier(MeasureParameters, ClassifierAssessmentAssessParameterSet, Sample[], ProgressUpdater) - Method in class de.jstacs.classifier.assessment.KFoldCrossValidation
  Evaluates a classifier.
evaluateClassifier(MeasureParameters, ClassifierAssessmentAssessParameterSet, Sample[], ProgressUpdater) - Method in class de.jstacs.classifier.assessment.RepeatedHoldOutExperiment
  Evaluates the classifier.
evaluateClassifier(MeasureParameters, ClassifierAssessmentAssessParameterSet, Sample[], ProgressUpdater) - Method in class de.jstacs.classifier.assessment.RepeatedSubSamplingExperiment
  Evaluates the classifier.
evaluateClassifier(MeasureParameters, ClassifierAssessmentAssessParameterSet, Sample[], ProgressUpdater) - Method in class de.jstacs.classifier.assessment.Sampled_RepeatedHoldOutExperiment
evaluateFunction(double[]) - Method in interface de.jstacs.algorithms.optimization.Function
  Evaluates the function at a certain vector (in mathematical sense) `x`.
evaluateFunction(double[]) - Method in class de.jstacs.algorithms.optimization.NegativeDifferentiableFunction
evaluateFunction(double[]) - Method in class de.jstacs.algorithms.optimization.NegativeFunction
evaluateFunction(double[]) - Method in class de.jstacs.algorithms.optimization.NegativeOneDimensionalFunction
evaluateFunction(double) - Method in class de.jstacs.algorithms.optimization.NegativeOneDimensionalFunction
evaluateFunction(double[]) - Method in class de.jstacs.algorithms.optimization.OneDimensionalFunction
evaluateFunction(double) - Method in class de.jstacs.algorithms.optimization.OneDimensionalFunction
  Evaluates the function at position `x`.
evaluateFunction(double) - Method in class de.jstacs.algorithms.optimization.OneDimensionalSubFunction
evaluateFunction(double) - Method in class de.jstacs.algorithms.optimization.QuadraticFunction
evaluateFunction(double[]) - Method in class de.jstacs.classifier.scoringFunctionBased.AbstractMultiThreadedOptimizableFunction
evaluateFunction(int, int, int, int, int) - Method in class de.jstacs.classifier.scoringFunctionBased.AbstractMultiThreadedOptimizableFunction
  This method evaluates the function for a part of the data.
evaluateFunction(int, int, int, int, int) - Method in class de.jstacs.classifier.scoringFunctionBased.gendismix.LogGenDisMixFunction
evaluateFunction(int, int, int, int, int) - Method in class de.jstacs.classifier.scoringFunctionBased.gendismix.OneSampleLogGenDisMixFunction
evaluateFunction(double[]) - Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.CompositeLogPrior
evaluateFunction(double[]) - Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.DoesNothingLogPrior
evaluateFunction(double[]) - Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.SeparateGaussianLogPrior
evaluateFunction(double[]) - Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.SeparateLaplaceLogPrior
evaluateFunction(double[]) - Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.SimpleGaussianSumLogPrior
evaluateGradientOfFunction(double[]) - Method in class de.jstacs.algorithms.optimization.DifferentiableFunction
  Evaluates the gradient of a function at a certain vector (in mathematical sense) `x`, i.e., .
evaluateGradientOfFunction(double[]) - Method in class de.jstacs.algorithms.optimization.NegativeDifferentiableFunction
evaluateGradientOfFunction(double[]) - Method in class de.jstacs.algorithms.optimization.NumericalDifferentiableFunction
  Evaluates the gradient of a function at a certain vector (in mathematical sense) `x` numerically.
evaluateGradientOfFunction(double[]) - Method in class de.jstacs.classifier.scoringFunctionBased.AbstractMultiThreadedOptimizableFunction
evaluateGradientOfFunction(int, int, int, int, int) - Method in class de.jstacs.classifier.scoringFunctionBased.AbstractMultiThreadedOptimizableFunction
  This method evaluates the gradient of the function for a part of the data.
evaluateGradientOfFunction(int, int, int, int, int) - Method in class de.jstacs.classifier.scoringFunctionBased.gendismix.LogGenDisMixFunction
evaluateGradientOfFunction(int, int, int, int, int) - Method in class de.jstacs.classifier.scoringFunctionBased.gendismix.OneSampleLogGenDisMixFunction
evaluateGradientOfFunction(double[]) - Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.LogPrior
EvaluationException - Exception in de.jstacs.algorithms.optimization
  This class indicates that there was a problem to evaluate a function or the gradient of the function.
EvaluationException(String) - Constructor for exception de.jstacs.algorithms.optimization.EvaluationException
  This constructor creates an `EvaluationException` with given error `message`.
ExpandableParameterSet - Class in de.jstacs.parameters
  A class for a `ParameterSet` that can be expanded by additional `Parameter`s at runtime.
ExpandableParameterSet(ParameterSet, String, String) - Constructor for class de.jstacs.parameters.ExpandableParameterSet
  Creates a new `ExpandableParameterSet` from a `Class` that can be instantiated using this `ExpandableParameterSet` and templates for the `ParameterSet` in each element of the array, the name and the comment that are displayed for the `ParameterSetContainer`s enclosing the `ParameterSet`s.
ExpandableParameterSet(ParameterSet, String, String, int) - Constructor for class de.jstacs.parameters.ExpandableParameterSet
  Creates a new `ExpandableParameterSet` from a `Class` that can be instantiated using this `ExpandableParameterSet` and templates for the `ParameterSet` in each element of the array, the name and the comment that are displayed for the `ParameterSetContainer`s enclosing the `ParameterSet`s.
ExpandableParameterSet(StringBuffer) - Constructor for class de.jstacs.parameters.ExpandableParameterSet
  The standard constructor for the interface `Storable`.
ExpandableParameterSet(ParameterSet[], String, String) - Constructor for class de.jstacs.parameters.ExpandableParameterSet
  Creates a new `ExpandableParameterSet` from a `ParameterSet` -array.
extendSampling(int) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
  This method prepares the model to extend an existing sampling.
extendSampling(int, boolean) - Method in class de.jstacs.models.mixture.gibbssampling.FSDAGModelForGibbsSampling
extendSampling(int, boolean) - Method in interface de.jstacs.models.mixture.gibbssampling.GibbsSamplingComponent
  This method allows to extend a sampling.
extract(int, String) - Static method in class de.jstacs.models.discrete.ConstraintManager
  Extracts the constraints of a `String` and returns an `ArrayList` of `int[]`.
extractAdditionalInfo(StringBuffer) - Method in class de.jstacs.models.discrete.Constraint
  This method parses additional information from the `StringBuffer` that is not parsed in the base class.
extractAdditionalInfo(StringBuffer) - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousModel.HomCondProb
extractAdditionalInfo(StringBuffer) - Method in class de.jstacs.models.discrete.inhomogeneous.InhCondProb
extractAdditionalInfo(StringBuffer) - Method in class de.jstacs.models.discrete.inhomogeneous.InhConstraint
extractAdditionalInfo(StringBuffer) - Method in class de.jstacs.models.discrete.inhomogeneous.MEMConstraint
extractAdditionalInformation(StringBuffer) - Method in class de.jstacs.models.mixture.motif.positionprior.GaussianLikePositionPrior
extractAdditionalInformation(StringBuffer) - Method in class de.jstacs.models.mixture.motif.positionprior.PositionPrior
  This method extracts additional information from a `StringBuffer`.
extractAdditionalInformation(StringBuffer) - Method in class de.jstacs.models.mixture.motif.positionprior.UniformPositionPrior
extractCollection(StringBuffer) - Method in class de.jstacs.parameters.CollectionParameter
  Extracts the internal `ParameterSet` from its XML representation ( `ParameterSet.toXML()`).
extractCollection(StringBuffer) - Method in class de.jstacs.parameters.EnumParameter
extractForTag(StringBuffer, String) - Static method in class de.jstacs.io.XMLParser
  Extracts the contents of `source` between `tag` start and end tags.
extractForTag(StringBuffer, String, Map<String, String>, Map<String, String>) - Static method in class de.jstacs.io.XMLParser
  Extracts the contents of `source` between `tag` start and end tags.
extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifier.AbstractClassifier
  Extracts further information of a classifier from an XML representation.
extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifier.AbstractScoreBasedClassifier
extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifier.MappingClassifier
extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifier.modelBased.ModelBasedClassifier
extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifier.scoringFunctionBased.gendismix.GenDisMixClassifier
extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier
extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.models.discrete.inhomogeneous.shared.SharedStructureClassifier
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
  This method is used in the subclasses to extract further information from the XML representation and to set these as values of the instance.
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.models.mixture.motif.HiddenMotifMixture
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
  This method is the opposite of `AbstractMixtureScoringFunction.getFurtherInformation()`.
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.scoringFunctions.mix.motifSearch.HiddenMotifsMixture
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.scoringFunctions.mix.StrandScoringFunction
extractMainInfo(StringBuffer) - Method in class de.jstacs.results.Result
  This method can be used in the method `Result.fromXML(StringBuffer)` to extract the main information (name, comment, data type).
extractObjectAndAttributesForTags(StringBuffer, String, Map<String, String>, Map<String, String>) - Static method in class de.jstacs.io.XMLParser
  Returns the parsed value between the tags.
extractObjectAndAttributesForTags(StringBuffer, String, Map<String, String>, Map<String, String>, Class<T>) - Static method in class de.jstacs.io.XMLParser
  Returns the parsed value between the tags.
extractObjectAndAttributesForTags(StringBuffer, String, Map<String, String>, Map<String, String>, Class<T>, Class<S>, S) - Static method in class de.jstacs.io.XMLParser
  Returns the parsed value between the tags as an inner instance of the object `outerInstance`.
extractObjectForTags(StringBuffer, String) - Static method in class de.jstacs.io.XMLParser
  Returns the parsed value between the tags.
extractObjectForTags(StringBuffer, String, Class<T>) - Static method in class de.jstacs.io.XMLParser
  Returns the parsed value between the tags.
extractSequenceParts(int, Sample[], Sample[]) - Method in class de.jstacs.scoringFunctions.IndependentProductScoringFunction
  This method extracts the corresponding `Sequence` parts for a specific `ScoringFunction`.
extractWeights(int, double[][]) - Method in class de.jstacs.scoringFunctions.IndependentProductScoringFunction
  This method creates the weights for `IndependentProductScoringFunction.extractSequenceParts(int, Sample[], Sample[])`.

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