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## E

e - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleState
  The emission that is internally used for scoring subsequences.
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.
emission - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
  The emissions used in the states.
Emission - Interface in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions
  This interface declares all method for an emission of a state.
emissionIdx - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
  The index of the used emission of each state.
emit(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
  This method returns a `DataSet` object containing artificial sequence(s).
emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.UniformDiffSM
emitDataSet(int, int...) - Method in interface de.jstacs.sequenceScores.statisticalModels.StatisticalModel
  This method returns a `DataSet` object containing artificial sequence(s).
emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM
  Creates a `DataSet` of a given number of `Sequence`s from a trained homogeneous model.
emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.DAGTrainSM
emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.UniformTrainSM
emitDataSet(StatisticalModel, int) - Static method in class de.jstacs.utils.DiscreteInhomogenousDataSetEmitter
  This method emits a sample with `n`sequences from the discrete inhomogeneous model `m` .``
emitDataSetUsingCurrentParameterSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  The method returns an array of sequences using the current parameter set.
emitDataSetUsingCurrentParameterSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.MixtureTrainSM
emitDataSetUsingCurrentParameterSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.HiddenMotifMixture
  Standard implementation throwing an `OperationNotSupportedException`.
emitDataSetUsingCurrentParameterSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM
emitSymbol(int[]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
EmptyDataSetException - Exception in de.jstacs.data
  An `EmptyDataSetException` will be thrown if no `Sequence` is in a `DataSet` (i.e. the `DataSet` is empty).
EmptyDataSetException() - Constructor for exception de.jstacs.data.EmptyDataSetException
  This constructor creates an instance with default error message ("The created DataSet is empty.
encode(int[][]) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.FSDAGTrainSMParameterSet
  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(DifferentiableSequenceScore[], int, int, RecyclableSequenceEnumerator, double, DiffSSBasedOptimizableFunction, 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(DifferentiableSequenceScore[], int[], int[], RecyclableSequenceEnumerator[], double, DiffSSBasedOptimizableFunction, 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 `SelectionParameter` 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.sequences.Sequence
equals(Object) - Method in class de.jstacs.parameters.AbstractSelectionParameter
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
equals(Object) - Method in class de.jstacs.utils.IntList
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.AbstractSelectionParameter
  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.
ess - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
  The equivalent sample size.
ess - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.DurationDiffSM
  The equivalent sample size.
ess - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
  The equivalent sample size used for the prior
ess - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
  The equivalent sample sizes for each condition
ess - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ReferenceBasedTransitionElement
  The equivalent sample size (ess) used in the prior of this instance.
estimate(double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint
  Estimates the (smoothed) relative frequencies using the ess (equivalent sample size).
estimate(double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM.HomCondProb
estimate(double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb
estimate(double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint
estimateComponentProbs - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  The switch for estimating the component probabilities or not.
estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.PhyloDiscreteEmission
estimateFromStatistic() - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.Emission
  This method estimates the parameters from the internal sufficient statistic.
estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.MixtureEmission
estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.UniformEmission
estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
  This method estimates the parameters from the sufficient statistic.
estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.DistanceBasedScaledTransitionElement
estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ScaledTransitionElement
estimateFromStatistic() - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.TrainableTransition
  This method estimates the parameter of the transition using the internal sufficient statistic.
estimateFromStatistics() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
  This method estimates the parameters of all emissions and the transition using their sufficient statistics.
estimateParameters(DataSet, double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.DAGTrainSM
  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.sequenceScores.statisticalModels.trainable.discrete.Constraint
  Estimates unconditionally.
estimateUnConditional(int, int, double, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM.HomCondProb
estimateUnConditional(double, double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb
  Estimates the unconditional frequencies using the ess (equivalent sample size).
estimateUnConditional(int, int, double, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb
eval(String) - Method in class de.jstacs.utils.REnvironment
  Evaluates the `String` as R commands.
evaluate(PerformanceMeasureParameterSet, boolean, DataSet...) - Method in class de.jstacs.classifiers.AbstractClassifier
  This method evaluates the classifier and computes, for instance, the sensitivity for a given specificity, the area under the ROC curve and so on.
evaluateClassifier(NumericalPerformanceMeasureParameterSet, ClassifierAssessmentAssessParameterSet, DataSet[], ProgressUpdater) - Method in class de.jstacs.classifiers.assessment.ClassifierAssessment
  This method must be implemented in all subclasses.
evaluateClassifier(NumericalPerformanceMeasureParameterSet, ClassifierAssessmentAssessParameterSet, DataSet[], ProgressUpdater) - Method in class de.jstacs.classifiers.assessment.KFoldCrossValidation
  Evaluates a classifier.
evaluateClassifier(NumericalPerformanceMeasureParameterSet, ClassifierAssessmentAssessParameterSet, DataSet[], ProgressUpdater) - Method in class de.jstacs.classifiers.assessment.RepeatedHoldOutExperiment
  Evaluates the classifier.
evaluateClassifier(NumericalPerformanceMeasureParameterSet, ClassifierAssessmentAssessParameterSet, DataSet[], ProgressUpdater) - Method in class de.jstacs.classifiers.assessment.RepeatedSubSamplingExperiment
  Evaluates the classifier.
evaluateClassifier(NumericalPerformanceMeasureParameterSet, ClassifierAssessmentAssessParameterSet, DataSet[], ProgressUpdater) - Method in class de.jstacs.classifiers.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.classifiers.differentiableSequenceScoreBased.AbstractMultiThreadedOptimizableFunction
evaluateFunction(int, int, int, int, int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractMultiThreadedOptimizableFunction
  This method evaluates the function for a part of the data.
evaluateFunction(int, int, int, int, int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.LogGenDisMixFunction
evaluateFunction(int, int, int, int, int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.OneDataSetLogGenDisMixFunction
evaluateFunction(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.CompositeLogPrior
evaluateFunction(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.DoesNothingLogPrior
evaluateFunction(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateGaussianLogPrior
evaluateFunction(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateLaplaceLogPrior
evaluateFunction(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.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.classifiers.differentiableSequenceScoreBased.AbstractMultiThreadedOptimizableFunction
evaluateGradientOfFunction(int, int, int, int, int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.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.classifiers.differentiableSequenceScoreBased.gendismix.LogGenDisMixFunction
evaluateGradientOfFunction(int, int, int, int, int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.OneDataSetLogGenDisMixFunction
evaluateGradientOfFunction(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.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.
export(String, Result) - Method in class de.jstacs.utils.galaxy.GalaxyAdaptor
  Exports a specified `GalaxyAdaptor.LinkedImageResult` of a program execution to a file provided by `filename` and returns the corresponding Galaxy data type.
ExtendedZOOPSDiffSM - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif
  This class handles mixtures with at least one hidden motif.
ExtendedZOOPSDiffSM(boolean, int, int, boolean, HomogeneousDiffSM, DifferentiableStatisticalModel, DurationDiffSM, boolean) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
  This constructor creates an instance of `ExtendedZOOPSDiffSM` that is either an OOPS or a ZOOPS model depending on the chosen `type`.
ExtendedZOOPSDiffSM(boolean, int, int, boolean, HomogeneousDiffSM, DifferentiableStatisticalModel[], DurationDiffSM[], boolean) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
  This constructor creates an instance of `ExtendedZOOPSDiffSM` that allows to have one site of the specified motifs in a `Sequence`.
ExtendedZOOPSDiffSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
  This is the constructor for the interface `Storable`.
extendSampling(int, boolean) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier.DiffSMSamplingComponent
extendSampling(int, boolean) - Method in interface de.jstacs.sampling.SamplingComponent
  This method allows to extend a sampling.
extendSampling(int, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGModelForGibbsSampling
extendSampling(int, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
extendSampling(int, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
extendSampling(int, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleSamplingState
extendSampling(int, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
extendSampling(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  This method prepares the model to extend an existing sampling.
extract(int, String) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.ConstraintManager
  Extracts the constraints of a `String` and returns an `ArrayList` of `int[]`.
extractAdditionalInfo(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.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.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM.HomCondProb
extractAdditionalInfo(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb
extractAdditionalInfo(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhConstraint
extractAdditionalInfo(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint
extractAdditionalInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.GaussianLikePositionPrior
extractAdditionalInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.PositionPrior
  This method extracts additional information from a `StringBuffer`.
extractAdditionalInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.UniformPositionPrior
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.classifiers.AbstractClassifier
  Extracts further information of a classifier from an XML representation.
extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier
extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifier
extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifiers.MappingClassifier
extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifiers.trainSMBased.TrainSMBasedClassifier
extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureClassifier
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
  This method is the opposite of `IndependentProductDiffSS.getFurtherInformation()`.
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.differentiable.UniformDiffSS
  This method is the opposite of `UniformDiffSS.getFurtherInformation()`.
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
  This method is the opposite of `AbstractMixtureDiffSM.getFurtherInformation()`.
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.UniformDiffSM
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
  This method extracts further information from the XML representation.
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
  This method extracts further information from the XML representation.
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
  This method extracts further information from the XML representation.
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.PhyloDiscreteEmission
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.ReferenceSequenceDiscreteEmission
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
  This method extracts further information from the XML representation.
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
  This method extracts further information from the XML representation.
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.BasicPluginTransitionElement
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.BasicTransitionElement
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.DistanceBasedScaledTransitionElement
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ReferenceBasedTransitionElement
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ScaledTransitionElement
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  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.sequenceScores.statisticalModels.trainable.mixture.motif.HiddenMotifMixture
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.AnnotatedEntity
  This method can be used in the constructor with parameter `StringBuffer` to extract the further information.
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier.DoubleTableResult
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.AbstractSelectionParameter
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.EnumParameter
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.FileParameter
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.MultiSelectionParameter
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.Parameter
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.ParameterSetContainer
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.RangeParameter
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.SelectionParameter
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.SimpleParameter
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.results.DataSetResult
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.results.ImageResult
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.results.ListResult
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.results.SimpleResult
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.results.StorableResult
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.utils.galaxy.GalaxyAdaptor.FileResult
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.utils.galaxy.GalaxyAdaptor.LinkedImageResult
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, DataSet[], DataSet[]) - Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
  This method extracts the corresponding `Sequence` parts for a specific `DifferentiableSequenceScore`.
extractWeights(int, double[][]) - Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
  This method creates the weights for `IndependentProductDiffSS.extractSequenceParts(int, DataSet[], DataSet[])`.

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