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## C

canBeCastedFromTo(DataType, DataType) - Static method in enum de.jstacs.DataType
  Checks if the `DataType` `from` can be casted to the `DataType` `to` without losing information.
CappedHistory - Class in de.jstacs.motifDiscovery.history
  This class combines a threshold on the number of steps which can be performed with any other `History`.
CappedHistory(int, History) - Constructor for class de.jstacs.motifDiscovery.history.CappedHistory
  This constructor creates an instance that allows at most `t` steps using the history `h`.
CappedHistory(StringBuffer) - Constructor for class de.jstacs.motifDiscovery.history.CappedHistory
  This is the constructor for the interface `Storable`.
caseInsensitive - Variable in class de.jstacs.data.alphabets.DiscreteAlphabet
  Switch to decide whether the input should be treated case sensitive or insensitive.
cast(Object[]) - Static method in class de.jstacs.io.ArrayHandler
  This method creates a new array of the superclass of all elements of the given array and copies the elements.
cast(Class<? extends S>, Object[]) - Static method in class de.jstacs.io.ArrayHandler
  This method returns an array of a user-specified class with all elements in the given array `o`.
CategoricalResult - Class in de.jstacs.results
  A class for categorical results (i.e. non-numerical results) for primitives and `String`s.
CategoricalResult(StringBuffer) - Constructor for class de.jstacs.results.CategoricalResult
  The standard constructor for the interface `Storable`.
CategoricalResult(DataType, String, String, Comparable) - Constructor for class de.jstacs.results.CategoricalResult
  Creates a result of a primitive categorical data type or a `String` .
CategoricalResult(String, String, String) - Constructor for class de.jstacs.results.CategoricalResult
  Creates a result of a `String`.
CategoricalResult(String, String, boolean) - Constructor for class de.jstacs.results.CategoricalResult
  Creates a result of a `boolean`.
CDFOfNormal - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif
  This class enables to compute the
  The code is a simplified version of pnorm.c from R (version 2.8.0 downloaded at 07.12.2008 about 19:30).
CDFOfNormal() - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.CDFOfNormal
check(DataSet) - Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
  This method checks if the given `DataSet` can be used.
check(Sequence) - Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
  This method checks if the given `Sequence` can be used.
check(Object) - Method in class de.jstacs.parameters.AbstractSelectionParameter
  Test whether a given `value` can be used in `Parameter.setValue(Object)`.
check(Object) - Method in interface de.jstacs.parameters.validation.Constraint
  Checks `value` for the constraint defined in the `Constraint`.
check(Object) - Method in class de.jstacs.parameters.validation.SimpleStaticConstraint
check(Sequence, int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
  This method checks all parameters before a probability can be computed for a sequence.
check(Sequence, int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DiscreteGraphicalTrainSM
  Checks some conditions on a `Sequence`.
check(Sequence, int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM
  Checks some constraints, these are in general conditions on the `AlphabetContainer` of a (sub)`Sequence` between `startpos` und `endpos`.
check(Sequence, int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhomogeneousDGTrainSM
checkAcyclic(int, int[][]) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.DAGTrainSM
  This method checks whether a given graph is acyclic.
checkConsistency(AlphabetContainer) - Method in class de.jstacs.data.AlphabetContainer
  Checks if this `AlphabetContainer` is consistent consistent with another `AlphabetContainer`.
checkConsistency(Alphabet) - Method in class de.jstacs.data.alphabets.Alphabet
  Checks if this `Alphabet` is consistent consistent with another `Alphabet`, i.e. both `Alphabet`s use the same symbols.
checkDatatype(DataType, Object) - Static method in class de.jstacs.results.Result
  This method provides the possibility to check the compliance of some result `value` with one of the pre-defined `DataType`s before creating a new `Result` and possibly running into an `Exception`.
checkLength(int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  This method checks if the length `l` of the model with index `index` is capable for the current instance.
checkLength(int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.HiddenMotifMixture
checkModelsForGibbsSampling() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  This method can be used to check whether the necessary models have implemented the `SamplingComponent`.
checkValue(Object) - Method in class de.jstacs.parameters.AbstractSelectionParameter
  Returns `true` if the key specified by `value` is in the set of keys of this `AbstractSelectionParameter`.
checkValue(Object) - Method in class de.jstacs.parameters.FileParameter
checkValue(Object) - Method in class de.jstacs.parameters.MultiSelectionParameter
checkValue(Object) - Method in class de.jstacs.parameters.Parameter
  Checks the value for correctness, e.g. for numerical parameters this might be checking if the value is within specified bounds.
checkValue(Object) - Method in class de.jstacs.parameters.ParameterSetContainer
checkValue(Object) - Method in class de.jstacs.parameters.RangeParameter
checkValue(Object) - Method in class de.jstacs.parameters.SimpleParameter
checkValue(Object) - Method in class de.jstacs.parameters.validation.ConstraintValidator
checkValue(Object) - Method in class de.jstacs.parameters.validation.NumberValidator
checkValue(Object) - Method in interface de.jstacs.parameters.validation.ParameterValidator
  Returns `true` if the value is valid and `false` otherwise.
checkValue(Object) - Method in class de.jstacs.parameters.validation.StorableValidator
  Checks the value of `value`.
checkWeights(double[]) - Static method in enum de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.LearningPrinciple
  This method checks the values of the `weights` array.
chooseFromDistr(Constraint, int, int, double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM
  Chooses a value in `[0,end-start]` according to the distribution encoded in the frequencies of `distr` between the indices `start` and `end`.
Chu_Liu_Edmonds - Class in de.jstacs.algorithms.graphs
  This class implements the algorithm of Chu_Liu_Edmonds to determine an optimal branching (optimal directed graph of order 1).
CisRegulatoryModuleAnnotation - Class in de.jstacs.data.sequences.annotation
  Annotation for a cis-regulatory module as defined by a set of `MotifAnnotation`s of the motifs in the module.
CisRegulatoryModuleAnnotation(String, MotifAnnotation[], Result...) - Constructor for class de.jstacs.data.sequences.annotation.CisRegulatoryModuleAnnotation
  Creates a new `CisRegulatoryModuleAnnotation` from a set of motifs and possibly additional annotations.
CisRegulatoryModuleAnnotation(StringBuffer) - Constructor for class de.jstacs.data.sequences.annotation.CisRegulatoryModuleAnnotation
  The standard constructor for the interface `Storable`.
cl - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractOptimizableFunction
  The number of different classes.
ClassDimensionException - Exception in de.jstacs.classifiers
  This class indicates that a classifier is defined for less than 2 classes or is defined over a different number of classes than given (e.g. by an array of `DataSet`s).
ClassDimensionException() - Constructor for exception de.jstacs.classifiers.ClassDimensionException
  This constructor creates a `ClassDimensionException` with the default error message ("The number of classes in the classfier differs from the given number.
ClassDimensionException(String) - Constructor for exception de.jstacs.classifiers.ClassDimensionException
  This constructor creates a `ClassDimensionException` with given error message.
ClassificationRate - Class in de.jstacs.classifiers.performanceMeasures
  This class implements the classification rate, i.e.
ClassificationRate() - Constructor for class de.jstacs.classifiers.performanceMeasures.ClassificationRate
  Constructs a new instance of the performance measure `ClassificationRate`.
ClassificationRate(StringBuffer) - Constructor for class de.jstacs.classifiers.performanceMeasures.ClassificationRate
  The standard constructor for the interface `Storable`.
ClassificationVisualizer - Class in de.jstacs.classifiers.utils
  This class enables you to visualize some classifier results.
ClassifierAssessment - Class in de.jstacs.classifiers.assessment
  Class defining an assessment of classifiers.
ClassifierAssessment(AbstractClassifier[], TrainableStatisticalModel[][], boolean, boolean) - Constructor for class de.jstacs.classifiers.assessment.ClassifierAssessment
  Creates a new `ClassifierAssessment` from an array of `AbstractClassifier`s and a two-dimensional array of `TrainableStatisticalModel` s, which are combined to additional classifiers.
ClassifierAssessment(AbstractClassifier...) - Constructor for class de.jstacs.classifiers.assessment.ClassifierAssessment
  Creates a new `ClassifierAssessment` from a set of `AbstractClassifier`s.
ClassifierAssessment(boolean, TrainableStatisticalModel[]...) - Constructor for class de.jstacs.classifiers.assessment.ClassifierAssessment
  Creates a new `ClassifierAssessment` from a set of `TrainableStatisticalModel`s.
ClassifierAssessment(AbstractClassifier[], boolean, TrainableStatisticalModel[]...) - Constructor for class de.jstacs.classifiers.assessment.ClassifierAssessment
  This constructor allows to assess a collection of given `AbstractClassifier`s and, in addition, classifiers that will be constructed using the given `TrainableStatisticalModel`s.
ClassifierAssessmentAssessParameterSet - Class in de.jstacs.classifiers.assessment
  This class is the superclass used by all `ClassifierAssessmentAssessParameterSet`s.
ClassifierAssessmentAssessParameterSet() - Constructor for class de.jstacs.classifiers.assessment.ClassifierAssessmentAssessParameterSet
  Constructs a new `ClassifierAssessmentAssessParameterSet` with empty parameter values.
ClassifierAssessmentAssessParameterSet(StringBuffer) - Constructor for class de.jstacs.classifiers.assessment.ClassifierAssessmentAssessParameterSet
  The standard constructor for the interface `Storable`.
ClassifierAssessmentAssessParameterSet(int, boolean) - Constructor for class de.jstacs.classifiers.assessment.ClassifierAssessmentAssessParameterSet
  Constructs a new `ClassifierAssessmentAssessParameterSet` with given parameter values.
classify(Sequence) - Method in class de.jstacs.classifiers.AbstractClassifier
  This method classifies a sequence and returns the index `i` of the class to which the sequence is assigned with `0 < i < getNumberOfClasses()`.
classify(DataSet) - Method in class de.jstacs.classifiers.AbstractClassifier
  This method classifies all sequences of a sample and returns an array of indices of the classes to which the respective sequences are assigned with for each index `i` in the array `0 < i < getNumberOfClasses()`.
classify(Sequence) - Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
classify(Sequence, boolean) - Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
  This method classifies a `Sequence`.
classify(DataSet) - Method in class de.jstacs.classifiers.trainSMBased.TrainSMBasedClassifier
classMus - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateLogPrior
  The means for the class parameters, as specified by the user.
classVars - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateLogPrior
  The variances for the class parameters, as specified by the user.
clazz - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractOptimizableFunction
  The class parameters.
clear() - Method in class de.jstacs.motifDiscovery.history.CappedHistory
clear() - Method in interface de.jstacs.motifDiscovery.history.History
  This method clears the history, i.e. it removes all operations from the history.
clear() - Method in class de.jstacs.motifDiscovery.history.NoRevertHistory
clear() - Method in class de.jstacs.motifDiscovery.history.RestrictedRepeatHistory
clear() - Method in class de.jstacs.motifDiscovery.history.SimpleHistory
clear() - Method in class de.jstacs.utils.DoubleList
  Removes all elements from the list.
clear() - Method in class de.jstacs.utils.IntList
  Removes all elements from the list.
clearAnnotation() - Method in class de.jstacs.data.sequences.annotation.NullSequenceAnnotationParser
clearAnnotation() - Method in interface de.jstacs.data.sequences.annotation.SequenceAnnotationParser
  This method reset the current `SequenceAnnotation`.
clearAnnotation() - Method in class de.jstacs.data.sequences.annotation.SimpleSequenceAnnotationParser
clearAnnotation() - Method in class de.jstacs.data.sequences.annotation.SplitSequenceAnnotationParser
clearHistoryArray(History[][]) - Static method in class de.jstacs.motifDiscovery.MutableMotifDiscovererToolbox
  This method clears all elements of an History-array, so that it can be used again.
cllGrad - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.LogGenDisMixFunction
  Array for the gradient of the conditional log-likelihood
clone() - Method in class de.jstacs.algorithms.graphs.Edge
clone() - Method in class de.jstacs.algorithms.optimization.termination.AbstractTerminationCondition.AbstractTerminationConditionParameterSet
clone() - Method in class de.jstacs.algorithms.optimization.termination.AbstractTerminationCondition
clone() - Method in class de.jstacs.classifiers.AbstractClassifier
clone() - Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
clone() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier
clone() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifierParameterSet
clone() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
clone() - Method in class de.jstacs.classifiers.trainSMBased.TrainSMBasedClassifier
clone() - Method in class de.jstacs.data.AlphabetContainer.AbstractAlphabetContainerParameterSet
clone() - Method in class de.jstacs.data.AlphabetContainerParameterSet.AlphabetArrayParameterSet
clone() - Method in class de.jstacs.data.AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet
clone() - Method in class de.jstacs.data.alphabets.Alphabet.AlphabetParameterSet
clone() - Method in class de.jstacs.data.alphabets.DNAAlphabetContainer.DNAAlphabetContainerParameterSet
clone(T...) - Static method in class de.jstacs.io.ArrayHandler
  This method returns a deep copy of a (multi-dimensional) array of `Cloneable`s or primitives.
clone() - Method in class de.jstacs.motifDiscovery.history.CappedHistory
clone() - Method in interface de.jstacs.motifDiscovery.history.History
  This method returns a deep copy of the instance
clone() - Method in class de.jstacs.motifDiscovery.history.NoRevertHistory
clone() - Method in class de.jstacs.motifDiscovery.history.RestrictedRepeatHistory
clone() - Method in class de.jstacs.motifDiscovery.history.SimpleHistory
clone() - Method in interface de.jstacs.motifDiscovery.MotifDiscoverer
  This method returns a deep clone of the instance.
clone() - Method in class de.jstacs.parameters.AbstractSelectionParameter
clone() - Method in class de.jstacs.parameters.ExpandableParameterSet
clone() - Method in class de.jstacs.parameters.FileParameter
clone() - Method in class de.jstacs.parameters.FileParameter.FileRepresentation
clone() - Method in class de.jstacs.parameters.MultiSelectionParameter
clone() - Method in class de.jstacs.parameters.Parameter
clone() - Method in class de.jstacs.parameters.ParameterSet
  Creates a full clone (deep copy) of this `ParameterSet`.
clone() - Method in class de.jstacs.parameters.ParameterSetContainer
clone() - Method in class de.jstacs.parameters.RangeParameter
clone() - Method in class de.jstacs.parameters.SequenceScoringParameterSet
clone() - Method in class de.jstacs.parameters.SimpleParameter
clone() - Method in class de.jstacs.parameters.SimpleParameterSet
clone() - Method in interface de.jstacs.parameters.validation.Constraint
  This method returns a deep copy of the current instance.
clone() - Method in class de.jstacs.parameters.validation.ConstraintValidator
clone() - Method in class de.jstacs.parameters.validation.NumberValidator
clone() - Method in interface de.jstacs.parameters.validation.ParameterValidator
  This method returns a deep copy of the current instance.
clone() - Method in class de.jstacs.parameters.validation.SimpleStaticConstraint
clone() - Method in class de.jstacs.parameters.validation.StorableValidator
clone() - Method in class de.jstacs.sampling.AbstractBurnInTest
clone() - Method in class de.jstacs.sampling.AbstractBurnInTestParameterSet
clone() - Method in interface de.jstacs.sampling.BurnInTest
  Return a deep copy of this object.
clone() - Method in class de.jstacs.sampling.SimpleBurnInTest
  Deprecated.
clone() - Method in class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
clone() - Method in interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore
  Creates a clone (deep copy) of the current `DifferentiableSequenceScore` instance.
clone() - Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
clone() - Method in class de.jstacs.sequenceScores.differentiable.logistic.LogisticDiffSS
clone() - Method in class de.jstacs.sequenceScores.differentiable.logistic.ProductConstraint
clone() - Method in interface de.jstacs.sequenceScores.SequenceScore
  Creates a clone (deep copy) of the current `SequenceScore` instance.
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMM0DiffSM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MarkovRandomFieldDiffSM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.MixtureDiffSM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.MixtureDurationDiffSM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.PositionDiffSM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MultiDimensionalSequenceWrapperDiffSM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
  Follows the conventions of `Object`'s `clone()`-method.
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.CompositeTrainSM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.DifferentiableStatisticalModelWrapperTrainSM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DGTrainSMParameterSet
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DiscreteGraphicalTrainSM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousMM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.BayesianNetworkTrainSM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.DAGTrainSM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGModelForGibbsSampling
  In this method the `reader` is set to `null` and the `paramsFile` is cloned.
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhConstraint
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhomogeneousDGTrainSM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.SequenceIterator
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureClassifier
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureMixture
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.PhyloDiscreteEmission
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.MixtureEmission
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.UniformEmission
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.DistanceBasedScaledTransitionElement
  Clones the object.
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ReferenceBasedTransitionElement
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ScaledTransitionElement
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.TransitionElement
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
clone() - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.Transition
  This method returns a deep clone of the current instance.
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.HiddenMotifMixture
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.PositionPrior
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.phylo.PhyloNode
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.phylo.PhyloTree
clone() - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.TrainableStatisticalModel
  Creates a clone (deep copy) of the current `TrainableStatisticalModel` instance.
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.UniformTrainSM
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.VariableLengthWrapperTrainSM
clone() - Method in class de.jstacs.utils.DoubleList
clone() - Method in class de.jstacs.utils.IntList
cloneFunctions(DifferentiableStatisticalModel[]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
  This method clones the given array of functions and enables the user to do some post-processing.
cloneHomProb(HomogeneousTrainSM.HomCondProb[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM
  Clones the given array of conditional probabilities.
close() - Method in class de.jstacs.utils.NullProgressUpdater
  Closes the supervision.
close() - Method in class de.jstacs.utils.REnvironment
  Closes the `REnvironment` and removes all files from the server.
close() - Method in class de.jstacs.utils.SafeOutputStream
  Closes the `SafeOutputStream` by closing the `OutputStream` this stream was constructed of.
CombinationIterator - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous
  This class can be used for iterating over all possible combinations (in the sense of combinatorics).
CombinationIterator(int, int) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.CombinationIterator
  Creates a new `CombinationIterator` with `n` elements and at most `max` selected elements.
CombinedCondition - Class in de.jstacs.algorithms.optimization.termination
  This class allows to use many `TerminationCondition`s at once.
CombinedCondition(int, AbstractTerminationCondition...) - Constructor for class de.jstacs.algorithms.optimization.termination.CombinedCondition
  This constructor creates an instance that allows to use many `TerminationCondition`s at once.
CombinedCondition(CombinedCondition.CombinedConditionParameterSet) - Constructor for class de.jstacs.algorithms.optimization.termination.CombinedCondition
  This is the main constructor creating an instance from a given parameter set.
CombinedCondition(StringBuffer) - Constructor for class de.jstacs.algorithms.optimization.termination.CombinedCondition
  The standard constructor for the interface `Storable`.
CombinedCondition.CombinedConditionParameterSet - Class in de.jstacs.algorithms.optimization.termination
  This class implements the parameter set for a `CombinedCondition`.
CombinedCondition.CombinedConditionParameterSet() - Constructor for class de.jstacs.algorithms.optimization.termination.CombinedCondition.CombinedConditionParameterSet
  This constructor creates an empty parameter set.
CombinedCondition.CombinedConditionParameterSet(StringBuffer) - Constructor for class de.jstacs.algorithms.optimization.termination.CombinedCondition.CombinedConditionParameterSet
  The standard constructor for the interface `Storable`.
CombinedCondition.CombinedConditionParameterSet(int, AbstractTerminationCondition[]) - Constructor for class de.jstacs.algorithms.optimization.termination.CombinedCondition.CombinedConditionParameterSet
  This constructor creates a filled instance of a parameters set.
CombinedFileFilter - Class in de.jstacs.io
  This class allows to combine several `FileFilter`s.
CombinedFileFilter(int, FileFilter...) - Constructor for class de.jstacs.io.CombinedFileFilter
  Creates an instance that accepts a `File` if at least `minAccepted` filters accept the `File`.
comment - Variable in class de.jstacs.AnnotatedEntity
  The comment for the entity.
commentTemplate - Variable in class de.jstacs.parameters.ExpandableParameterSet
  A template for the comment of the enclosing `ParameterSetContainer`
ComparableElement<E,C extends Comparable> - Class in de.jstacs.utils
  This class is a container for any objects that have to be compared.
ComparableElement(E, C) - Constructor for class de.jstacs.utils.ComparableElement
  Creates a new `ComparableElement`.
compare(Map.Entry<K, V>, Map.Entry<K, V>) - Method in class de.jstacs.parameters.ParameterSetTagger.KeyEntryComparator
compare(Map.Entry<K, ComparableElement<V, Integer>>, Map.Entry<K, ComparableElement<V, Integer>>) - Method in class de.jstacs.parameters.ParameterSetTagger.RankEntryComparator
comparePosition(int, MEMConstraint) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint
compareTo(Edge) - Method in class de.jstacs.algorithms.graphs.Edge
compareTo(AlphabetContainer) - Method in class de.jstacs.data.AlphabetContainer
compareTo(Alphabet) - Method in class de.jstacs.data.alphabets.ContinuousAlphabet
compareTo(Alphabet) - Method in class de.jstacs.data.alphabets.DiscreteAlphabet
compareTo(float[], float[]) - Method in class de.jstacs.data.sequences.ArbitraryFloatSequence
compareTo(double[], double[]) - Method in class de.jstacs.data.sequences.ArbitrarySequence
compareTo(double[], double[]) - Method in class de.jstacs.data.sequences.MultiDimensionalArbitrarySequence
compareTo(int[], int[]) - Method in class de.jstacs.data.sequences.MultiDimensionalDiscreteSequence
compareTo(Sequence<T>) - Method in class de.jstacs.data.sequences.Sequence
compareTo(T, T) - Method in class de.jstacs.data.sequences.Sequence
  This method compares to container and is used in `Sequence.compareTo(Sequence)`.
compareTo(T, T) - Method in class de.jstacs.data.sequences.Sequence.RecursiveSequence
compareTo(int[], int[]) - Method in class de.jstacs.data.sequences.SimpleDiscreteSequence
compareTo(SimpleResult) - Method in class de.jstacs.results.SimpleResult
compareTo(ComparableElement<E, C>) - Method in class de.jstacs.utils.ComparableElement
complement(int, int) - Method in class de.jstacs.data.sequences.MultiDimensionalSequence
complement() - Method in class de.jstacs.data.sequences.Sequence
  This method returns a new instance of `Sequence` containing the complementary current `Sequence`.
complement(int, int) - Method in class de.jstacs.data.sequences.Sequence
  This method returns a new instance of `Sequence` containing a part of the complementary current `Sequence`.
complement(int, int) - Method in class de.jstacs.data.sequences.SparseSequence
ComplementableDiscreteAlphabet - Class in de.jstacs.data.alphabets
  This abstract class indicates that an alphabet can be used to compute the complement.
ComplementableDiscreteAlphabet(DiscreteAlphabet.DiscreteAlphabetParameterSet) - Constructor for class de.jstacs.data.alphabets.ComplementableDiscreteAlphabet
  The constructor for the `InstantiableFromParameterSet` interface.
ComplementableDiscreteAlphabet(StringBuffer) - Constructor for class de.jstacs.data.alphabets.ComplementableDiscreteAlphabet
  The standard constructor for the interface `Storable`.
ComplementableDiscreteAlphabet(boolean, String...) - Constructor for class de.jstacs.data.alphabets.ComplementableDiscreteAlphabet
  Creates a new `ComplementableDiscreteAlphabet` from a given array of symbols.
componentHyperParams - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  The hyperparameters for estimating the probabilities of the components.
componentScore - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
  This array is used while computing the score.
CompositeLogPrior - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior
  This class implements a composite prior that can be used for DifferentiableStatisticalModel.
CompositeLogPrior() - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.CompositeLogPrior
  The main constructor.
CompositeLogPrior(StringBuffer) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.CompositeLogPrior
  The constructor for the `Storable` interface.
CompositeTrainSM - Class in de.jstacs.sequenceScores.statisticalModels.trainable
  This class is for modelling sequences by modelling the different positions of the each sequence by different models.
CompositeTrainSM(AlphabetContainer, int[], TrainableStatisticalModel...) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.CompositeTrainSM
  Creates a new `CompositeTrainSM`.
CompositeTrainSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.CompositeTrainSM
  The standard constructor for the interface `Storable`.
compProb - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  This array is used while training to avoid creating many new objects.
compute(double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.AbstractPerformanceMeasure
  This method allows to compute the performance measure of given sorted score ratios.
compute(double[][][]) - Method in class de.jstacs.classifiers.performanceMeasures.AbstractPerformanceMeasure
  This method allows to compute the performance measure of given class specific scores.
compute(double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.AucPR
compute(double[][][]) - Method in class de.jstacs.classifiers.performanceMeasures.AucPR
compute(double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.AucROC
compute(double[][][]) - Method in class de.jstacs.classifiers.performanceMeasures.AucROC
compute(double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.ClassificationRate
compute(double[][][]) - Method in class de.jstacs.classifiers.performanceMeasures.ClassificationRate
compute(double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.ConfusionMatrix
compute(double[][][]) - Method in class de.jstacs.classifiers.performanceMeasures.ConfusionMatrix
compute(double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.FalsePositiveRateForFixedSensitivity
compute(double[][][]) - Method in class de.jstacs.classifiers.performanceMeasures.FalsePositiveRateForFixedSensitivity
compute(double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.MaximumNumericalTwoClassMeasure
compute(double[][][]) - Method in class de.jstacs.classifiers.performanceMeasures.MaximumNumericalTwoClassMeasure
compute(double[], double[]) - Method in interface de.jstacs.classifiers.performanceMeasures.NumericalPerformanceMeasure
  This method allows to compute the performance measure of given sorted score ratios.
compute(double[][][]) - Method in interface de.jstacs.classifiers.performanceMeasures.NumericalPerformanceMeasure
  This method allows to compute the performance measure of given class specific scores.
compute(double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.PositivePredictiveValueForFixedSensitivity
compute(double[][][]) - Method in class de.jstacs.classifiers.performanceMeasures.PositivePredictiveValueForFixedSensitivity
compute(double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.PRCurve
compute(double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.ROCCurve
compute(double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.SensitivityForFixedSpecificity
compute(double[][][]) - Method in class de.jstacs.classifiers.performanceMeasures.SensitivityForFixedSpecificity
compute(double[][][]) - Method in class de.jstacs.classifiers.performanceMeasures.TwoClassAbstractPerformanceMeasure
computeFreqs(double, Constraint...) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.ConstraintManager
  This method computes the (smoothed) relative frequencies.
computeGammaNorm() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
  Computes the Gamma-normalization for the prior.
computeHiddenParameter(double[], boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
  This method has to be invoked during an initialization.
computeLengthOfBurnIn() - Method in class de.jstacs.sampling.AbstractBurnInTest
  Computes and returns the length of the burn-in phase using the values from `BurnInTest.setValue(double)`.
computeLengthOfBurnIn() - Method in class de.jstacs.sampling.VarianceRatioBurnInTest
  Computes and returns the length of the burn-in phase given by the Variance-Ratio burn-in test.
computeLogGammaSum() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
  This method is used to pre-compute the sum of the logarithm of the gamma functions that is used in the prior.
computeMaximalHP(Tensor) - Static method in class de.jstacs.algorithms.graphs.DAG
  The method computes the HP(k) (see `DAG`).
computeMaximalKDAG(Tensor) - Static method in class de.jstacs.algorithms.graphs.DAG
  Computes the maximal k-DAG (see `DAG`), i.e. the k-DAG that maximizes the score given by a `Tensor`.
computeMaxKDAG(SymmetricTensor) - Static method in class de.jstacs.algorithms.graphs.DAG
  Computes the maximal k-DAG (see `DAG`), i.e. the k-DAG that maximizes the score given by a `SymmetricTensor`.
con - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
  The alphabet of the emissions
CONDITIONAL_LIKELIHOOD_INDEX - Static variable in enum de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.LearningPrinciple
  This constant is the array index of the weighting factor for the conditional likelihood.
ConfusionMatrix - Class in de.jstacs.classifiers.performanceMeasures
  This class implements the performance measure confusion matrix.
ConfusionMatrix() - Constructor for class de.jstacs.classifiers.performanceMeasures.ConfusionMatrix
  Constructs a new instance of the performance measure `ConfusionMatrix`.
ConfusionMatrix(StringBuffer) - Constructor for class de.jstacs.classifiers.performanceMeasures.ConfusionMatrix
  The standard constructor for the interface `Storable`.
CONJUGATE_GRADIENTS_FR - Static variable in class de.jstacs.algorithms.optimization.Optimizer
  This constant can be used to specify that conjugate gradients (by Fletcher and Reeves) should be used in the `optimize`-method.
CONJUGATE_GRADIENTS_PRP - Static variable in class de.jstacs.algorithms.optimization.Optimizer
  This constant can be used to specify that conjugate gradients (by Polak and Ribière should be used in the `optimize`-method.
conjugateGradientsFR(DifferentiableFunction, double[], TerminationCondition, double, StartDistanceForecaster, OutputStream, Time) - Static method in class de.jstacs.algorithms.optimization.Optimizer
  The conjugate gradient algorithm by Fletcher and Reeves.
conjugateGradientsPR(DifferentiableFunction, double[], TerminationCondition, double, StartDistanceForecaster, OutputStream, Time) - Static method in class de.jstacs.algorithms.optimization.Optimizer
  The conjugate gradient algorithm by Polak and Ribière.
conjugateGradientsPRP(DifferentiableFunction, double[], TerminationCondition, double, StartDistanceForecaster, OutputStream, Time) - Static method in class de.jstacs.algorithms.optimization.Optimizer
  The conjugate gradient algorithm by Polak and Ribière called "Polak-Ribière-Positive".
ConstantStartDistance - Class in de.jstacs.algorithms.optimization
  The most simple `StartDistanceForecaster` that returns always the same value.
ConstantStartDistance(double) - Constructor for class de.jstacs.algorithms.optimization.ConstantStartDistance
  This constructor creates an instance of `ConstantStartDistance` that returns always the given `value`.
Constraint - Interface in de.jstacs.parameters.validation
  Interface for a constraint that must be fulfilled in a `ConstraintValidator`.
Constraint - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete
  The main class for all constraints on models.
Constraint(int[], int) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint
  The main constructor.
Constraint(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint
  The standard constructor for the interface `Storable`.
ConstraintManager - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete
  This class manipulates and manages some constraints.
constraints - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.DAGTrainSM
  The constraints for the model.
ConstraintValidator - Class in de.jstacs.parameters.validation
  Class for a `ParameterValidator` that is based on `Constraint`s.
ConstraintValidator() - Constructor for class de.jstacs.parameters.validation.ConstraintValidator
  Creates a new `ConstraintValidator` having an empty list of `Constraint`s, i.e. the constraints of this `ConstraintValidator` are always fulfilled before additional `Constraint`s are added using `ConstraintValidator.addConstraint(Constraint)`.
ConstraintValidator(StringBuffer) - Constructor for class de.jstacs.parameters.validation.ConstraintValidator
  The standard constructor for the interface `Storable`.
container - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
  Helper variable = only for internal use.
contains(int) - Method in class de.jstacs.utils.IntList
  Checks if `val` is already returned in the list.
CONTAINS_ALWAYS_A_MOTIF - Static variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
  This constant indicates that in each sequence has one binding site of a motif instance (similar to OOPS).
CONTAINS_SOMETIMES_A_MOTIF - Static variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
  This constant indicates that a sequence possibly has one binding site of a motif instance (similar to ZOOPS).
content - Variable in class de.jstacs.data.sequences.MultiDimensionalSequence
  The internally used sequences.
content - Variable in class de.jstacs.data.sequences.Sequence.RecursiveSequence
  The internal sequence.
context - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter
  The context of this parameter.
context - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
  The context, i.e. the visited states, of the `BasicHigherOrderTransition.AbstractTransitionElement`
continueIterations(double[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  This method will run the train algorithm for the current model on the internal sample.
continueIterations(double[], double[][], int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  This method will run the train algorithm for the current model on the internal sample.
ContinuousAlphabet - Class in de.jstacs.data.alphabets
  Class for a continuous alphabet.
ContinuousAlphabet(ContinuousAlphabet.ContinuousAlphabetParameterSet) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet
  The constructor for the `InstantiableFromParameterSet` interface.
ContinuousAlphabet(double, double) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet
  Creates a new `ContinuousAlphabet` from a minimal and a maximal value.
ContinuousAlphabet(double, double, boolean) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet
  Creates a new `ContinuousAlphabet` from a minimal and a maximal value.
ContinuousAlphabet() - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet
  Creates a new `ContinuousAlphabet` with minimum and maximum value being -`Double.MAX_VALUE` and `Double.MAX_VALUE`, respectively.
ContinuousAlphabet(boolean) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet
  Creates a new `ContinuousAlphabet` with minimum and maximum value being -`Double.MAX_VALUE` and `Double.MAX_VALUE`, respectively.
ContinuousAlphabet(StringBuffer) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet
  The standard constructor for the interface `Storable`.
ContinuousAlphabet.ContinuousAlphabetParameterSet - Class in de.jstacs.data.alphabets
  Class for the `ParameterSet` of a `ContinuousAlphabet`.
ContinuousAlphabet.ContinuousAlphabetParameterSet() - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet.ContinuousAlphabetParameterSet
  Creates a new `ContinuousAlphabet.ContinuousAlphabetParameterSet` with empty values.
ContinuousAlphabet.ContinuousAlphabetParameterSet(double, double) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet.ContinuousAlphabetParameterSet
  Creates a new `ContinuousAlphabet.ContinuousAlphabetParameterSet` from a minimum and a maximum value.
ContinuousAlphabet.ContinuousAlphabetParameterSet(double, double, boolean) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet.ContinuousAlphabetParameterSet
  Creates a new `ContinuousAlphabet.ContinuousAlphabetParameterSet` from a minimum and a maximum value.
ContinuousAlphabet.ContinuousAlphabetParameterSet(StringBuffer) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet.ContinuousAlphabetParameterSet
  The standard constructor for the interface `Storable` .
continuousVal(int) - Method in class de.jstacs.data.sequences.ArbitraryFloatSequence
continuousVal(int) - Method in class de.jstacs.data.sequences.ArbitrarySequence
continuousVal(int) - Method in class de.jstacs.data.sequences.MultiDimensionalSequence
continuousVal(int) - Method in class de.jstacs.data.sequences.Sequence
  Returns the continuous value at position `pos` of the `Sequence`.
continuousVal(int) - Method in class de.jstacs.data.sequences.Sequence.RecursiveSequence
continuousVal(int) - Method in class de.jstacs.data.sequences.SimpleDiscreteSequence
copy(File, File, FileFilter) - Static method in class de.jstacs.io.FileManager
  This method copies all `File`s and directories, if selected, from a `source` `File`, i.e. directory, to a `target` `File`, i.e. directory, that are accepted by the f `FileFilter` `filter`.
copy(String, String) - Static method in class de.jstacs.io.FileManager
  This method copies a `File` in a faster manner.
copy(String, String, byte[]) - Static method in class de.jstacs.io.FileManager
  This method copies a `File` in a faster manner using a specified buffer.
copy(BNDiffSMParameterTree) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
  Copies the values of the parameters from another `BNDiffSMParameterTree`.
copyFileFromServer(String, String, boolean) - Method in class de.jstacs.utils.REnvironment
  Copies a file from the server to the local machine.
copyFileFromServer(String, String, RConnection) - Static method in class de.jstacs.utils.RUtils
  This method copies a file from the server to the client.
copyFileFromServer(String, OutputStream, RConnection) - Static method in class de.jstacs.utils.RUtils
  Pipes a `RFileInputStream` of the given `sourcePath` into the given `OutputStream` `out`
copyFileToServer(String, String, boolean) - Method in class de.jstacs.utils.REnvironment
  Copies a file from the local machine to the server.
copyFileToServer(File, String, RConnection) - Static method in class de.jstacs.utils.RUtils
  Copies a file to the R side.
copyFileToServer(String, String, RConnection) - Static method in class de.jstacs.utils.RUtils
  Copies a file to the R side.
Costs - Interface in de.jstacs.algorithms.alignment.cost
  The general interface for the costs of an alignment.
count - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter
  The counts for this parameter.
count(int[][], byte) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.BayesianNetworkTrainSM
  Counts the occurrence of the different indegrees and checks if the conventions are met.
countElements() - Method in class de.jstacs.io.SymbolExtractor
  Counts the number of elements (symbols) that can be received initially.
counter - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGModelForGibbsSampling
  The counter for the sampling steps of each sampling.
counter - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
  The counter for the sampling steps of each sampling.
counter - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
  The counter for the sampling steps of each sampling.
counter - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  The current index of the parameter set while adjustment (optimization).
countInhomogeneous(AlphabetContainer, int, DataSet, double[], boolean, Constraint...) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.ConstraintManager
  Fills the (inhomogeneous) `Constraint` `constr` with the weighted absolute frequencies of the `DataSet` `data`.
counts - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint
  The counts for each specific constraint.
create(GenDisMixClassifierParameterSet, LogPrior, double[], DifferentiableStatisticalModel[]...) - Static method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier
  This method creates an array of GenDisMixClassifiers by using the cross-product of the given `DifferentiableStatisticalModel`s.
create(AlphabetContainer, String) - Static method in class de.jstacs.data.sequences.Sequence
  Creates a `Sequence` from a `String` based on the given `AlphabetContainer` using the standard delimiter for this `AlphabetContainer`.
create(AlphabetContainer, String, String) - Static method in class de.jstacs.data.sequences.Sequence
  Creates a `Sequence` from a `String` based on the given `AlphabetContainer` using the given delimiter `delim`.
create(AlphabetContainer, SequenceAnnotation[], String, String) - Static method in class de.jstacs.data.sequences.Sequence
  Creates a `Sequence` from a `String` based on the given `AlphabetContainer` using the given delimiter `delim` and some `annotation` for the `Sequence`.
createArrayOf(T, int) - Static method in class de.jstacs.io.ArrayHandler
  This method returns an array of length `l` that has at each position a clone of `t`.
createBayesianNetworkModel(AlphabetContainer, int, double, byte) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.TrainableStatisticalModelFactory
  This method returns a Bayesian network model (BN) with user-specified order.
createConstraints(AbstractList<int[]>, int[], int[]) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.ConstraintManager
  Creates the constraints for a part of a model.
createConstraints(AbstractList<int[]>, int[]) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.ConstraintManager
  Creates the constraints of a model.
createConstraints(int[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.DAGTrainSM
  This method creates the constraints for a given structure.
createDefaultClassWeights(int, double) - Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
  This method creates new class weights.
createErgodicHMM(HMMTrainingParameterSet, int, double, double, double, Emission...) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.HMMFactory
  This method creates an ergodic, i.e. a completely connected, HMM using the given emissions.
createFiles(StringBuffer) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier.DiffSMSamplingComponent
  Creates files out of file contents saved as XML.
createFilledParameters() - Static method in class de.jstacs.classifiers.performanceMeasures.PerformanceMeasureParameterSet
  Creates a filled `NumericalPerformanceMeasureParameterSet` that can be used in `AbstractClassifier.evaluate(PerformanceMeasureParameterSet, boolean, de.jstacs.data.DataSet...)`
createFilledParameters(boolean, double, double, double, double) - Static method in class de.jstacs.classifiers.performanceMeasures.PerformanceMeasureParameterSet
  Creates a filled `PerformanceMeasureParameterSet` that can be used in `AbstractClassifier.evaluate(PerformanceMeasureParameterSet, boolean, de.jstacs.data.DataSet...)`.
createHelperVariables() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
  This method instantiates all helper variables that are need inside the model for instance for filling forward and backward matrix, ...
createHelperVariables() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
createHelperVariables() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
createHistoryArray(DifferentiableSequenceScore[], History) - Static method in class de.jstacs.motifDiscovery.MutableMotifDiscovererToolbox
  This method creates a History-array that can be used in an optimization.
createHomogeneousMarkovModel(AlphabetContainer, double, int, int) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModelFactory
  This method returns a homogeneous Markov model with user-specified order.
createHomogeneousMarkovModel(AlphabetContainer, double, byte) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.TrainableStatisticalModelFactory
  This method returns a homogeneous Markov model with user-specified order.
createInhomogeneousMarkovModel(AlphabetContainer, int, double, int) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModelFactory
  This method returns a inhomogeneous Markov model (IMM) with user-specified order.
createInhomogeneousMarkovModel(AlphabetContainer, int, double, byte) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.TrainableStatisticalModelFactory
  This method returns a inhomogeneous Markov model (IMM) with user-specified order.
createMarkovRandomField(AlphabetContainer, int, String) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModelFactory
  This method allows to create a `MarkovRandomFieldDiffSM` of the specified length and with the given constraint type.
createMatrix(String, int[][]) - Method in class de.jstacs.utils.REnvironment
  Creates a matrix of `int`s.
createMatrix(String, double[][]) - Method in class de.jstacs.utils.REnvironment
  Creates a matrix of `double`s.
createMatrixForStatePosterior(int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
  This method creates an empty matrix for the log state posterior.
createMinimalNewLengthArray(DifferentiableSequenceScore[]) - Static method in class de.jstacs.motifDiscovery.MutableMotifDiscovererToolbox
  This method creates a minimalNewLength-array that can be used in an optimization.
createMixtureModel(DifferentiableStatisticalModel[]) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModelFactory
  This method allows to create a `MixtureDiffSM` that models a mixture of individual component `DifferentiableStatisticalModel`s.
createMixtureModel(double[], TrainableStatisticalModel[]) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.TrainableStatisticalModelFactory
  This method allows to create a `MixtureTrainSM` that allows to model a `DataSet` as a mixture of individual components.
createParameterSet(Object[], String[], String[]) - Method in class de.jstacs.parameters.AbstractSelectionParameter
  Creates a new `ParameterSet` from an array of values, an array of names and an array of comments.
createPermutedMarkovModel(AlphabetContainer, int, double, byte) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.TrainableStatisticalModelFactory
  This method returns a permuted Markov model (PMM) with user-specified order.
createProfileHMM(MaxHMMTrainingParameterSet, HMMFactory.HMMType, boolean, int, int, AlphabetContainer, double, boolean, boolean, double[][]) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.HMMFactory
  Creates a new profile HMM for a given architecture and number of layers.
createProfileHMM(MaxHMMTrainingParameterSet, double[][], boolean, int, int, AlphabetContainer, double, boolean, boolean, double[][], boolean) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.HMMFactory
  Creates a new profile HMM for a given architecture and number of layers.
createProfileHMM(MaxHMMTrainingParameterSet, double[][], boolean, int, int, AlphabetContainer, double, boolean, int, double[][], boolean) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.HMMFactory
  Creates a new profile HMM for a given architecture and number of layers.
createPseudoErgodicHMM(HMMTrainingParameterSet, double, double, double, AlphabetContainer, int, boolean) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.HMMFactory
  Creates an HMM with `numStates+1` states, where `numStates` emitting build a clique and each of those states is connected to the absorbing silent final state.
createPWM(AlphabetContainer, int, double) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModelFactory
  This method returns a position weight matrix (PWM).
createPWM(AlphabetContainer, int, double) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.TrainableStatisticalModelFactory
  This method returns a position weight matrix (PWM).
createResult(String, String, DataType, Object) - Static method in class de.jstacs.results.Result
  Factory method to create a new `Result`.
createSeqWeightsArray() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  Creates an array that can be used for weighting sequences in the algorithm.
createSeqWeightsArray() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.ZOOPSTrainSM
createStates() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
  This method creates states for the internal usage.
createStates() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
createStates() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
createStates() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM
createStrandModel(DifferentiableStatisticalModel) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModelFactory
  This method allows to create a `StrandDiffSM` that allows to score binding sites on both strand of DNA.
createStrandModel(TrainableStatisticalModel) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.TrainableStatisticalModelFactory
  This method allows to create a `StrandTrainSM` that allows to score binding sites on both strand of DNA.
createStructure(DataSet[], double[][], boolean) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
  Creates the structure that will be used in the optimization.
createStructure(DataSet[], double[][]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
  Creates the structure that will be used in the optimization.
createSunflowerHMM(HMMTrainingParameterSet, AlphabetContainer, double, int, boolean, int...) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.HMMFactory
  This method creates a first order sunflower HMM.
createSunflowerHMM(HMMTrainingParameterSet, AlphabetContainer, double, int, boolean, PhyloTree[], double[], int[]) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.HMMFactory
  This method creates a first order sunflower HMM allowing phylogenetic emissions.
createTransition(double[][], ArrayList<HMMFactory.PseudoTransitionElement>) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.HMMFactory
  Creates the real `TransitionElement`s that can be used to create the HMM.
createTrees(DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
  Creates the tree structures that represent the context (array `BayesianNetworkDiffSM.trees`) and the parameter objects `BayesianNetworkDiffSM.parameters` using the given `Measure` `BayesianNetworkDiffSM.structureMeasure`.
createVector(String, String[]) - Method in class de.jstacs.utils.REnvironment
  Creates a vector of `String`s.
createVector(String, int[]) - Method in class de.jstacs.utils.REnvironment
  Creates a vector of `int`s.
createVector(String, long[]) - Method in class de.jstacs.utils.REnvironment
  Creates a vector of `long`s.
createVector(String, double[]) - Method in class de.jstacs.utils.REnvironment
  Creates a vector of `double`s.
createZOOPS(int, DifferentiableStatisticalModel, HomogeneousDiffSM) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModelFactory
  This method allows to create a "zero or one occurrence per sequence" (ZOOPS) model that allows to discover binding sites in a `DataSet`.
createZOOPS(TrainableStatisticalModel, TrainableStatisticalModel, double[], boolean) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.TrainableStatisticalModelFactory
  This method allows to create a "zero or one occurrence per sequence" (ZOOPS) model that allows to discover binding sites in a `DataSet`.
currentParameters - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
  the currently accepted parameters
currentScore - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
  The score achieved using `SamplingScoreBasedClassifier.currentParameters`
CyclicMarkovModelDiffSM - Class in de.jstacs.sequenceScores.statisticalModels.differentiable
  This scoring function implements a cyclic Markov model of arbitrary order and periodicity for any sequence length.
CyclicMarkovModelDiffSM(AlphabetContainer, int, int, double, double[], boolean, boolean, int, int) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
  The main constructor.
CyclicMarkovModelDiffSM(AlphabetContainer, double[], double[][][], boolean, boolean, int, int) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
  This constructor allows to create an instance with specific hyper-parameters for all conditional distributions.
CyclicMarkovModelDiffSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
  This is the constructor for `Storable`.

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