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## D

DAG - Class in de.jstacs.algorithms.graphs
  This is the main class of the graph library.
DAG() - Constructor for class de.jstacs.algorithms.graphs.DAG
DAGTrainSM - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous
  The abstract class for directed acyclic graphical models (`DAGTrainSM`).
DAGTrainSM(IDGTrainSMParameterSet) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.DAGTrainSM
  This is the main constructor.
DAGTrainSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.DAGTrainSM
  The standard constructor for the interface `Storable`.
data - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractOptimizableFunction
  The data that is used to evaluate this function.
DataSet - Class in de.jstacs.data
  This is the class for any data set of `Sequence`s.
DataSet(AlphabetContainer, AbstractStringExtractor) - Constructor for class de.jstacs.data.DataSet
  Creates a new `DataSet` from a `StringExtractor` using the given `AlphabetContainer`.
DataSet(AlphabetContainer, AbstractStringExtractor, int) - Constructor for class de.jstacs.data.DataSet
  Creates a new `DataSet` from a `StringExtractor` using the given `AlphabetContainer` and all overlapping windows of length `subsequenceLength`.
DataSet(AlphabetContainer, AbstractStringExtractor, String) - Constructor for class de.jstacs.data.DataSet
  Creates a new `DataSet` from a `StringExtractor` using the given `AlphabetContainer` and a delimiter `delim`.
DataSet(AlphabetContainer, AbstractStringExtractor, String, int) - Constructor for class de.jstacs.data.DataSet
  Creates a new `DataSet` from a `StringExtractor` using the given `AlphabetContainer`, the given delimiter `delim` and all overlapping windows of length `subsequenceLength`.
DataSet(DataSet, int) - Constructor for class de.jstacs.data.DataSet
  Creates a new `DataSet` from a given `DataSet` and a given length `subsequenceLength`.
DataSet(String, Sequence...) - Constructor for class de.jstacs.data.DataSet
  Creates a new `DataSet` from an array of `Sequence`s and a given annotation.
DataSet.ElementEnumerator - Class in de.jstacs.data
  This class can be used to have a fast sequential access to a `DataSet`.
DataSet.ElementEnumerator(DataSet) - Constructor for class de.jstacs.data.DataSet.ElementEnumerator
  Creates a new `DataSet.ElementEnumerator` on the given `DataSet` `data`.
DataSet.PartitionMethod - Enum in de.jstacs.data
  This `enum` defines different partition methods for a `DataSet`.
DataSet.WeightedDataSetFactory - Class in de.jstacs.data
  This class enables you to eliminate `Sequence`s that occur more than once in one or more `DataSet`s.
DataSet.WeightedDataSetFactory(DataSet.WeightedDataSetFactory.SortOperation, DataSet...) - Constructor for class de.jstacs.data.DataSet.WeightedDataSetFactory
  Creates a new `DataSet.WeightedDataSetFactory` on the given `DataSet`(s) with `DataSet.WeightedDataSetFactory.SortOperation` `sort`.
DataSet.WeightedDataSetFactory(DataSet.WeightedDataSetFactory.SortOperation, DataSet, double[]) - Constructor for class de.jstacs.data.DataSet.WeightedDataSetFactory
  Creates a new `DataSet.WeightedDataSetFactory` on the given `DataSet` and an array of `weights` with `DataSet.WeightedDataSetFactory.SortOperation` `sort`.
DataSet.WeightedDataSetFactory(DataSet.WeightedDataSetFactory.SortOperation, DataSet, double[], int) - Constructor for class de.jstacs.data.DataSet.WeightedDataSetFactory
  Creates a new `DataSet.WeightedDataSetFactory` on the given `DataSet` and an array of `weights` with a given `length` and `DataSet.WeightedDataSetFactory.SortOperation` `sort`.
DataSet.WeightedDataSetFactory(DataSet.WeightedDataSetFactory.SortOperation, DataSet[], double[][], int) - Constructor for class de.jstacs.data.DataSet.WeightedDataSetFactory
  Creates a new `DataSet.WeightedDataSetFactory` on the given array of `DataSet`s and an array of `weights` with a given `length` and `DataSet.WeightedDataSetFactory.SortOperation` `sort`.
DataSet.WeightedDataSetFactory.SortOperation - Enum in de.jstacs.data
  This `enum` defines the different types of sort operations that can be performed while creating a `DataSet.WeightedDataSetFactory`.
DataSetKMerEnumerator - Class in de.jstacs.data
  Class for an `RecyclableSequenceEnumerator` of `Sequence`s that enumerates all k-mers that exist in a given `DataSet`, optionally ignoring reverse complements.
DataSetKMerEnumerator(DataSet, int, boolean) - Constructor for class de.jstacs.data.DataSetKMerEnumerator
  Constructs a new DataSetKMerEnumerator from a `DataSet` `data` by extracting all k-mers.
DataSetResult - Class in de.jstacs.results
  `Result` that contains a `DataSet`.
DataSetResult(String, String, DataSet) - Constructor for class de.jstacs.results.DataSetResult
  Creates a new `DataSetResult` from a `DataSet` with the annotation `name` and `comment`.
DataSetResult(StringBuffer) - Constructor for class de.jstacs.results.DataSetResult
  The standard constructor for the interface `Storable`.
dataSetToSequenceIterator(DataSet, boolean) - Static method in class de.jstacs.data.bioJava.BioJavaAdapter
  Creates a `SequenceIterator` from the `DataSet` `sample` preserving as much annotation as possible.
datatype - Variable in class de.jstacs.AnnotatedEntity
  The data type of the entity.
DataType - Enum in de.jstacs
  This `enum` defines a number of data types that can be used for `Parameter`s and `Result` s.
DateFileFilter - Class in de.jstacs.io
  This class implements a `FileFilter` that accepts `File`s that were modified after the date that is given in the constructor.
DateFileFilter(int, int, int, int, int, int) - Constructor for class de.jstacs.io.DateFileFilter
  Creates an instance that accepts `File`s that were modified after the given year, month, ... .
DateFileFilter(Date) - Constructor for class de.jstacs.io.DateFileFilter
  Creates an instance that accepts `File`s that were modified after `d`.
de.jstacs - package de.jstacs
  This package is the root package for the most and important packages.
de.jstacs.algorithms.alignment - package de.jstacs.algorithms.alignment
  Provides classes for alignments
de.jstacs.algorithms.alignment.cost - package de.jstacs.algorithms.alignment.cost
  Provides classes for cost functions used in alignments
de.jstacs.algorithms.graphs - package de.jstacs.algorithms.graphs
  Provides classes for algorithms on graphs.
de.jstacs.algorithms.graphs.tensor - package de.jstacs.algorithms.graphs.tensor
  Provides classes to represent symmetric and asymmetric tensors in graphs
de.jstacs.algorithms.optimization - package de.jstacs.algorithms.optimization
  Provides classes for different types of algorithms that are not directly linked to the modelling components of Jstacs: Algorithms on graphs, algorithms for numerical optimization, and a basic alignment algorithm. de.jstacs.algorithms.optimization.termination - package de.jstacs.algorithms.optimization.termination
  Provides classes for termination conditions that can be used in algorithms
de.jstacs.classifiers - package de.jstacs.classifiers
  This package provides the framework for any classifier.
de.jstacs.classifiers.assessment - package de.jstacs.classifiers.assessment
  This package allows to assess classifiers.
de.jstacs.classifiers.differentiableSequenceScoreBased - package de.jstacs.classifiers.differentiableSequenceScoreBased
  Provides the classes for `Classifier`s that are based on `SequenceScore`s.
de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix - package de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix
  Provides an implementation of a classifier that allows to train the parameters of a set of `DifferentiableStatisticalModel`s by a unified generative-discriminative learning principle
de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior - package de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior
  Provides a general definition of a parameter log-prior and a number of implementations of Laplace and Gaussian priors
de.jstacs.classifiers.differentiableSequenceScoreBased.msp - package de.jstacs.classifiers.differentiableSequenceScoreBased.msp
  Provides an implementation of a classifier that allows to train the parameters of a set of `DifferentiableStatisticalModel`s either by maximum supervised posterior (MSP) or by maximum conditional likelihood (MCL)
de.jstacs.classifiers.differentiableSequenceScoreBased.sampling - package de.jstacs.classifiers.differentiableSequenceScoreBased.sampling
  Provides the classes for `AbstractScoreBasedClassifier`s that are based on `SamplingDifferentiableStatisticalModel`s and that sample parameters using the Metropolis-Hastings algorithm.
de.jstacs.classifiers.performanceMeasures - package de.jstacs.classifiers.performanceMeasures
  This package provides the implementations of performance measures that can be used to assess any classifier
de.jstacs.classifiers.trainSMBased - package de.jstacs.classifiers.trainSMBased
  Provides the classes for `Classifier`s that are based on `TrainableStatisticalModel`s
de.jstacs.classifiers.utils - package de.jstacs.classifiers.utils
  Provides some useful classes for working with classifiers
de.jstacs.data - package de.jstacs.data
  Provides classes for the representation of data.
de.jstacs.data.alphabets - package de.jstacs.data.alphabets
  Provides classes for the representation of discrete and continuous alphabets, including a `DNAAlphabet` for the most common case of DNA-sequences
de.jstacs.data.bioJava - package de.jstacs.data.bioJava
  Provides an adapter between the representation of data in BioJava and the representation used in Jstacs.
de.jstacs.data.sequences - package de.jstacs.data.sequences
  Provides classes for representing sequences.
de.jstacs.data.sequences.annotation - package de.jstacs.data.sequences.annotation
  Provides the facilities to annotate `Sequence`s using a number of pre-defined annotation types, or additional implementations of the `SequenceAnnotation` class
de.jstacs.io - package de.jstacs.io
  Provides classes for reading data from and writing to a file and storing a number of datatypes, including all primitives, arrays of primitives, and `Storable`s to an XML-representation
de.jstacs.motifDiscovery - package de.jstacs.motifDiscovery
  This package provides the framework including the interface for any de novo motif discoverer
de.jstacs.motifDiscovery.history - package de.jstacs.motifDiscovery.history
de.jstacs.parameters - package de.jstacs.parameters
  This package provides classes for parameters that establish a general convention for the description of parameters as defined in the `Parameter`-interface.
de.jstacs.parameters.validation - package de.jstacs.parameters.validation
  Provides classes for the validation of `Parameter` values
de.jstacs.results - package de.jstacs.results
  This package provides classes for results and sets of results.
de.jstacs.sampling - package de.jstacs.sampling
  This package contains many classes that can be used while a sampling.
de.jstacs.sequenceScores - package de.jstacs.sequenceScores
  Provides all `SequenceScore`s, which can be used to score a `Sequence`, typically using some model assumptions.
de.jstacs.sequenceScores.differentiable - package de.jstacs.sequenceScores.differentiable
de.jstacs.sequenceScores.differentiable.logistic - package de.jstacs.sequenceScores.differentiable.logistic
de.jstacs.sequenceScores.statisticalModels - package de.jstacs.sequenceScores.statisticalModels
  Provides all `StatisticalModel`s, which can compute a proper (i.e., normalized) likelihood over the input space of sequences.
de.jstacs.sequenceScores.statisticalModels.differentiable - package de.jstacs.sequenceScores.statisticalModels.differentiable
  Provides all `DifferentiableStatisticalModel`s, which can compute the gradient with respect to their parameters for a given input `Sequence`.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels - package de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels
  Provides `DifferentiableStatisticalModel`s that are directed graphical models.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures - package de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures
  Provides the facilities to learn the structure of a `BayesianNetworkDiffSM`.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures - package de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures
  Provides the facilities to learn the structure of a `BayesianNetworkDiffSM` as a Bayesian tree using a number of measures to define a rating of structures
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures - package de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures
  Provides the facilities to learn the structure of a `BayesianNetworkDiffSM` as a permuted Markov model using a number of measures to define a rating of structures
de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous - package de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous
  Provides `DifferentiableStatisticalModel`s that are homogeneous, i.e. model probabilities or scores independent of the position within a sequence
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture - package de.jstacs.sequenceScores.statisticalModels.differentiable.mixture
  Provides `DifferentiableSequenceScore`s that are mixtures of other `DifferentiableSequenceScore`s.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif - package de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif
de.jstacs.sequenceScores.statisticalModels.trainable - package de.jstacs.sequenceScores.statisticalModels.trainable
  Provides all `TrainableStatisticalModel`s, which can be learned from a single `DataSet`.
de.jstacs.sequenceScores.statisticalModels.trainable.discrete - package de.jstacs.sequenceScores.statisticalModels.trainable.discrete
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous - package de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.parameters - package de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.parameters
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous - package de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous
  This package contains various inhomogeneous models.
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters - package de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared - package de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared
de.jstacs.sequenceScores.statisticalModels.trainable.hmm - package de.jstacs.sequenceScores.statisticalModels.trainable.hmm
  The package provides all interfaces and classes for a hidden Markov model (HMM).
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models - package de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models
  The package provides different implementations of hidden Markov models based on `AbstractHMM`
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states - package de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states
  The package provides all interfaces and classes for states used in hidden Markov models.
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions - package de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous - package de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete - package de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training - package de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training
  The package provides all classes used to determine the training algorithm of a hidden Markov model
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions - package de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions
  The package provides all interfaces and classes for transitions used in hidden Markov models.
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements - package de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements
de.jstacs.sequenceScores.statisticalModels.trainable.mixture - package de.jstacs.sequenceScores.statisticalModels.trainable.mixture
  This package is the super package for any mixture model.
de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif - package de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif
de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior - package de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior
de.jstacs.sequenceScores.statisticalModels.trainable.phylo - package de.jstacs.sequenceScores.statisticalModels.trainable.phylo
de.jstacs.sequenceScores.statisticalModels.trainable.phylo.parser - package de.jstacs.sequenceScores.statisticalModels.trainable.phylo.parser
de.jstacs.utils - package de.jstacs.utils
  This package contains a bundle of useful classes and interfaces like ...
de.jstacs.utils.galaxy - package de.jstacs.utils.galaxy
de.jstacs.utils.random - package de.jstacs.utils.random
  This package contains some classes for generating random numbers
decodePath(IntList) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
  This method decodes any path of the HMM, i.e. it converts the integer representation of the path in a String representation.
decodeStatePosterior(double[][]...) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
  The method returns the decoded state posterior, i.e. a sequence of states.
DEFAULT_INSTANCE - Static variable in class de.jstacs.data.sequences.annotation.NullSequenceAnnotationParser
  The only instance of this class which is publicly available.
DEFAULT_INSTANCE - Static variable in class de.jstacs.utils.random.DirichletMRG
  This instance shall be used, since quite often two instance of this class return the same values.
DEFAULT_STREAM - Static variable in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhomogeneousDGTrainSM
  The default `OutputStream`.
DEFAULT_STREAM - Static variable in class de.jstacs.utils.SafeOutputStream
  This stream can be used as default stream.
defaultInstance - Static variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.DoesNothingLogPrior
  As this prior does not penalize parameters and does not have any parameters itself, this class does not have a constructor, but provides a default instance in order to reduce memory consumption.
DefaultProgressUpdater - Class in de.jstacs.utils
  Simple class that implements `ProgressUpdater` and prints the percentage of iterations that is already done on the screen.
DefaultProgressUpdater() - Constructor for class de.jstacs.utils.DefaultProgressUpdater
  Creates a `DefaultProgressUpdater`.
defaultValue - Variable in class de.jstacs.parameters.SimpleParameter
  The default value of the parameter
deleteAllFilesAtTheServer() - Method in class de.jstacs.utils.REnvironment
  Deletes all files that have been copied to the server or created on the server.
delta - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.DurationDiffSM
  The difference of maximal and minimal value.
descendants - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
  The indices for the descendant transition elements that can be visited following the states.
determineDiagonalElement() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ReferenceBasedTransitionElement
  This method determines the self transition.
determineFinalStates() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
  This method determines the final states of the HMM.
determineIsNormalized() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
  This method is used to determine the value that is returned by the method `AbstractMixtureDiffSM.isNormalized()`.
DGTrainSMParameterSet<T extends DiscreteGraphicalTrainSM> - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete
  The super `ParameterSet` for any parameter set of a `DiscreteGraphicalTrainSM`.
DGTrainSMParameterSet(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DGTrainSMParameterSet
  The standard constructor for the interface `Storable`.
DGTrainSMParameterSet(Class<T>, boolean, boolean) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DGTrainSMParameterSet
  An empty constructor.
DGTrainSMParameterSet(Class<T>, AlphabetContainer, double, String) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DGTrainSMParameterSet
  The constructor for models that can handle variable lengths.
DGTrainSMParameterSet(Class<T>, AlphabetContainer, int, double, String) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DGTrainSMParameterSet
  The constructor for models that can handle only sequences of fixed length given by `length`.
diagElement - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ReferenceBasedTransitionElement
  The index of the self transition.
diagonalWeights - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.DistanceBasedScaledTransitionElement
  Contains the single epsilons of the diagonal elements required for estimating the self-transition probability.
diff(DataSet, DataSet...) - Static method in class de.jstacs.data.DataSet
  This method computes the difference between the `DataSet` `data` and the `DataSet`s `samples`.
DifferentiableEmission - Interface in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions
  This interface declares all methods needed in an emission during a numerical optimization of HMM.
DifferentiableFunction - Class in de.jstacs.algorithms.optimization
  This class is the framework for any (at least) one time differentiable function .
DifferentiableFunction() - Constructor for class de.jstacs.algorithms.optimization.DifferentiableFunction
DifferentiableHigherOrderHMM - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models
  This class combines an `HigherOrderHMM` and a `DifferentiableStatisticalModel` by implementing some of the declared methods.
DifferentiableHigherOrderHMM(MaxHMMTrainingParameterSet, String[], int[], boolean[], DifferentiableEmission[], boolean, double, TransitionElement...) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
  This is the main constructor.
DifferentiableHigherOrderHMM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
  The standard constructor for the interface `Storable`.
DifferentiableSequenceScore - Interface in de.jstacs.sequenceScores.differentiable
  This interface is the main part of any `ScoreClassifier`.
DifferentiableState - Interface in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states
  This interface declares a method that allows to evaluate the gradient which is essential for numerical optimization.
DifferentiableStatisticalModel - Interface in de.jstacs.sequenceScores.statisticalModels.differentiable
  The interface for normalizable `DifferentiableSequenceScore`s.
DifferentiableStatisticalModelFactory - Class in de.jstacs.sequenceScores.statisticalModels.differentiable
  This class allows to easily create some frequently used models.
DifferentiableStatisticalModelFactory() - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModelFactory
DifferentiableStatisticalModelWrapperTrainSM - Class in de.jstacs.sequenceScores.statisticalModels.trainable
  This model can be used to use a DifferentiableStatisticalModel as model.
DifferentiableStatisticalModelWrapperTrainSM(DifferentiableStatisticalModel, int, byte, AbstractTerminationCondition, double, double) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.DifferentiableStatisticalModelWrapperTrainSM
  The main constructor that creates an instance with the user given parameters.
DifferentiableStatisticalModelWrapperTrainSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.DifferentiableStatisticalModelWrapperTrainSM
  The standard constructor for the interface `Storable`.
DifferentiableTransition - Interface in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions
  This class declares methods that allow for optimizing the parameters numerically using the `Optimizer`.
DiffSSBasedOptimizableFunction - Class in de.jstacs.classifiers.differentiableSequenceScoreBased
  This abstract class is the basis of all multi-threaded `OptimizableFunction`s that are based on `DifferentiableSequenceScore`s.
DiffSSBasedOptimizableFunction(int, DifferentiableSequenceScore[], DataSet[], double[][], LogPrior, boolean, boolean) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.DiffSSBasedOptimizableFunction
  Creates an instance with the underlying infrastructure.
dimension - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  The number of dimensions.
DimensionException - Exception in de.jstacs.algorithms.optimization
  This class is for `Exception`s depending on wrong dimensions of vectors for a given function.
DimensionException() - Constructor for exception de.jstacs.algorithms.optimization.DimensionException
  Creates a new `DimensionException` with standard error message ("The vector has wrong dimension for this function.
DimensionException(int, int) - Constructor for exception de.jstacs.algorithms.optimization.DimensionException
  Creates a new `DimensionException` with a more detailed error message.
DiMRGParams - Class in de.jstacs.utils.random
  The super container for parameters of Dirichlet multivariate random generators.
DiMRGParams() - Constructor for class de.jstacs.utils.random.DiMRGParams
DinucleotideProperty - Enum in de.jstacs.data
  This `enum` defines physicochemical, conformational, and letter-based dinucleotide properties of nucleotide sequences.
DinucleotideProperty.HowCreated - Enum in de.jstacs.data
  This enum defines the origins of nucleotide properties
DinucleotideProperty.MeanSmoothing - Class in de.jstacs.data
  Smoothing by mean using a pre-defined window width.
DinucleotideProperty.MeanSmoothing(int) - Constructor for class de.jstacs.data.DinucleotideProperty.MeanSmoothing
  Creates a new `DinucleotideProperty.MeanSmoothing` that averages over windows of width `width`.
DinucleotideProperty.MedianSmoothing - Class in de.jstacs.data
  Smoothing by median using a pre-defined window width.
DinucleotideProperty.MedianSmoothing(int) - Constructor for class de.jstacs.data.DinucleotideProperty.MedianSmoothing
  Creates a new `DinucleotideProperty.MedianSmoothing` that computes the median over windows of width `width`.
DinucleotideProperty.NoSmoothing - Class in de.jstacs.data
  Implementation of `DinucleotideProperty.Smoothing` that conducts no smoothing.
DinucleotideProperty.Smoothing - Class in de.jstacs.data
  Abstract class for methods that smooth a series of real values.
DinucleotideProperty.Smoothing() - Constructor for class de.jstacs.data.DinucleotideProperty.Smoothing
DinucleotideProperty.Type - Enum in de.jstacs.data
  This enum defines the types of dinucleotide properties.
DirichletMRG - Class in de.jstacs.utils.random
  This class is a multivariate random generator based on a Dirichlet distribution.
DirichletMRGParams - Class in de.jstacs.utils.random
  The container for parameters of a Dirichlet random generator.
DirichletMRGParams(double, int) - Constructor for class de.jstacs.utils.random.DirichletMRGParams
  Constructor which creates a new hyperparameter vector for a Dirichlet random generator.
DirichletMRGParams(double...) - Constructor for class de.jstacs.utils.random.DirichletMRGParams
  Constructor which creates a new hyperparameter vector for a Dirichlet random generator.
DirichletMRGParams(int, int, double...) - Constructor for class de.jstacs.utils.random.DirichletMRGParams
  Constructor which creates a new hyperparameter vector for a Dirichlet random generator.
DiscreteAlphabet - Class in de.jstacs.data.alphabets
  Class for an alphabet that consists of arbitrary `String`s.
DiscreteAlphabet(StringBuffer) - Constructor for class de.jstacs.data.alphabets.DiscreteAlphabet
  The standard constructor for the interface `Storable`.
DiscreteAlphabet(DiscreteAlphabet.DiscreteAlphabetParameterSet) - Constructor for class de.jstacs.data.alphabets.DiscreteAlphabet
  The constructor for the `InstantiableFromParameterSet` interface.
DiscreteAlphabet(int, int) - Constructor for class de.jstacs.data.alphabets.DiscreteAlphabet
  Creates a new `DiscreteAlphabet` from a minimal and a maximal value, i.e. in `[min,max]`.
DiscreteAlphabet(boolean, String...) - Constructor for class de.jstacs.data.alphabets.DiscreteAlphabet
  Creates a new `DiscreteAlphabet` from a given alphabet as a `String` array.
DiscreteAlphabet.DiscreteAlphabetParameterSet - Class in de.jstacs.data.alphabets
  Class for the `ParameterSet` of a `DiscreteAlphabet`.
DiscreteAlphabet.DiscreteAlphabetParameterSet(Class<? extends DiscreteAlphabet>) - Constructor for class de.jstacs.data.alphabets.DiscreteAlphabet.DiscreteAlphabetParameterSet
  This constructor should only be used for parameter sets that are intended to created subclasses of `DiscreteAlphabet`.
DiscreteAlphabet.DiscreteAlphabetParameterSet() - Constructor for class de.jstacs.data.alphabets.DiscreteAlphabet.DiscreteAlphabetParameterSet
  Creates a new `DiscreteAlphabet.DiscreteAlphabetParameterSet` with empty values.
DiscreteAlphabet.DiscreteAlphabetParameterSet(String[], boolean) - Constructor for class de.jstacs.data.alphabets.DiscreteAlphabet.DiscreteAlphabetParameterSet
  Creates a new `DiscreteAlphabet.DiscreteAlphabetParameterSet` from an alphabet given as a `String` array.
DiscreteAlphabet.DiscreteAlphabetParameterSet(char[], boolean) - Constructor for class de.jstacs.data.alphabets.DiscreteAlphabet.DiscreteAlphabetParameterSet
  Creates a new `DiscreteAlphabet.DiscreteAlphabetParameterSet` from an alphabet of symbols given as a `char` array.
DiscreteAlphabet.DiscreteAlphabetParameterSet(StringBuffer) - Constructor for class de.jstacs.data.alphabets.DiscreteAlphabet.DiscreteAlphabetParameterSet
  The standard constructor for the interface `Storable` .
DiscreteAlphabetMapping - Class in de.jstacs.data.alphabets
  This class implements the transformation of discrete values to other discrete values which define a `DiscreteAlphabet`.
DiscreteAlphabetMapping(int[], DiscreteAlphabet) - Constructor for class de.jstacs.data.alphabets.DiscreteAlphabetMapping
  The main constructor creating a `DiscreteAlphabetMapping`.
DiscreteAlphabetMapping(StringBuffer) - Constructor for class de.jstacs.data.alphabets.DiscreteAlphabetMapping
  The standard constructor for the interface `Storable`.
DiscreteEmission - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete
  This class implements a simple discrete emission without any condition.
DiscreteEmission(AlphabetContainer, double) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.DiscreteEmission
  This is a simple constructor for a `DiscreteEmission` based on the equivalent sample size.
DiscreteEmission(AlphabetContainer, double[]) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.DiscreteEmission
  This is a simple constructor for a `DiscreteEmission` defining the individual hyper parameters.
DiscreteEmission(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.DiscreteEmission
  Creates a `DiscreteEmission` from its XML representation.
DiscreteGraphicalTrainSM - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete
  This is the main class for all discrete graphical models (DGM).
DiscreteGraphicalTrainSM(DGTrainSMParameterSet) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DiscreteGraphicalTrainSM
  The default constructor.
DiscreteGraphicalTrainSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DiscreteGraphicalTrainSM
  The standard constructor for the interface `Storable`.
DiscreteInhomogenousDataSetEmitter - Class in de.jstacs.utils
  Emits `DataSet`s for discrete inhomogeneous models by a naive implementation.
DiscreteInhomogenousDataSetEmitter() - Constructor for class de.jstacs.utils.DiscreteInhomogenousDataSetEmitter
DiscreteSequenceEnumerator - Class in de.jstacs.data
  This class enumerates over all `Sequence`s of a specific `AlphabetContainer` and length.
DiscreteSequenceEnumerator(AlphabetContainer, int, boolean) - Constructor for class de.jstacs.data.DiscreteSequenceEnumerator
  Creates a new `DiscreteSequenceEnumerator` from a given `AlphabetContainer` and a length.
discreteVal(int) - Method in class de.jstacs.data.sequences.ArbitraryFloatSequence
discreteVal(int) - Method in class de.jstacs.data.sequences.ArbitrarySequence
discreteVal(int) - Method in class de.jstacs.data.sequences.ByteSequence
discreteVal(int) - Method in class de.jstacs.data.sequences.IntSequence
discreteVal(int) - Method in class de.jstacs.data.sequences.MappedDiscreteSequence
discreteVal(int) - Method in class de.jstacs.data.sequences.MultiDimensionalSequence
discreteVal(int) - Method in class de.jstacs.data.sequences.Sequence
  Returns the discrete value at position `pos` of the `Sequence`.
discreteVal(int) - Method in class de.jstacs.data.sequences.Sequence.RecursiveSequence
discreteVal(int) - Method in class de.jstacs.data.sequences.Sequence.SubSequence
discreteVal(int) - Method in class de.jstacs.data.sequences.ShortSequence
discreteVal(int) - Method in class de.jstacs.data.sequences.SparseSequence
discreteValAt(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.SequenceIterator
  This method returns the discrete value for a specific position.
DistanceBasedScaledTransitionElement - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements
  Distance-based scaled transition element for an HMM with distance-scaled transition matrices (DSHMM).
DistanceBasedScaledTransitionElement(int[], int[], double[], double, double, String) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.DistanceBasedScaledTransitionElement
  Creates an object representing the transition probabilities of a Hidden Markov TrainableStatisticalModel with scaled transition matrices (SHMM) for the given context.
DistanceBasedScaledTransitionElement(int[], int[], double[], double, double, String, double[]) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.DistanceBasedScaledTransitionElement
  Creates an object representing the transition probabilities of a Hidden Markov TrainableStatisticalModel with scaled transition matrices (SHMM) for the given context.
DistanceBasedScaledTransitionElement(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.DistanceBasedScaledTransitionElement
  Extracts a distance-base scaled transition element from XML.
divideByUnfree() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
  Divides each of the normalized parameters on a simplex by the last `BNDiffSMParameter`, which is defined not to be free.
dList - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.DiffSSBasedOptimizableFunction
  These `DoubleList`s are used during the parallel computation of the gradient.
dList - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
  This array contains some `DoubleList`s that are used while computing the partial derivation.
DNAAlphabet - Class in de.jstacs.data.alphabets
  This class implements the discrete alphabet that is used for DNA.
DNAAlphabet.DNAAlphabetParameterSet - Class in de.jstacs.data.alphabets
  The parameter set for a `DNAAlphabet`.
DNAAlphabetContainer - Class in de.jstacs.data.alphabets
  This class implements a singleton for an `AlphabetContainer` that can be used for DNA.
DNAAlphabetContainer.DNAAlphabetContainerParameterSet - Class in de.jstacs.data.alphabets
  This class implements a singleton for the `ParameterSet` of a `DNAAlphabetContainer`.
DNADataSet - Class in de.jstacs.data
  This class exist for convenience to allow the user an easy creation of `DataSet`s of DNA `Sequence`s.
DNADataSet(String) - Constructor for class de.jstacs.data.DNADataSet
  Creates a new sample of DNA sequence from a FASTA file with file name `fName`.
DNADataSet(String, char) - Constructor for class de.jstacs.data.DNADataSet
  Creates a new sample of DNA sequence from a file with file name `fName`.
DNADataSet(String, char, SequenceAnnotationParser) - Constructor for class de.jstacs.data.DNADataSet
  Creates a new sample of DNA sequence from a file with file name `fName` using the given `parser`.
doesApplyFor(Sequence) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter
  Indicates if the `Sequence` `seq` fulfills all requirements defined in the `BNDiffSMParameter.context`.
doesNothing() - Method in class de.jstacs.utils.SafeOutputStream
  Indicates whether the instance is doing something or not.
DoesNothingLogPrior - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior
  This class defines a `LogPrior` that does not penalize any parameter.
doFirstIteration(DataSet, double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  This method will do the first step in the train algorithm for the current model.
doFirstIteration(DataSet, double[], MultivariateRandomGenerator, MRGParams[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  This method will do the first step in the train algorithm for the current model.
doFirstIteration(double[], MultivariateRandomGenerator, MRGParams[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  This method will do the first step in the train algorithm for the current model on the internal sample.
doFirstIteration(double[], MultivariateRandomGenerator, MRGParams[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.MixtureTrainSM
doFirstIteration(DataSet, double[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.MixtureTrainSM
  This method enables you to train a mixture model with a fixed start partitioning.
doFirstIteration(double[], MultivariateRandomGenerator, MRGParams[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.ZOOPSTrainSM
doFirstIteration(double[], MultivariateRandomGenerator, MRGParams[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM
doHeuristicSteps(DifferentiableSequenceScore[], DataSet[], double[][], DiffSSBasedOptimizableFunction, DifferentiableFunction, byte, double, StartDistanceForecaster, SafeOutputStream, boolean, History[][], int[][], boolean) - Static method in class de.jstacs.motifDiscovery.MutableMotifDiscovererToolbox
  This method tries to make some heuristic step if at least one `DifferentiableSequenceScore` is a `MutableMotifDiscoverer`.
doNextIteration(int, double, double, double[], double[], double, Time) - Method in class de.jstacs.algorithms.optimization.termination.AbsoluteValueCondition
  Deprecated.
doNextIteration(int, double, double, double[], double[], double, Time) - Method in class de.jstacs.algorithms.optimization.termination.CombinedCondition
doNextIteration(int, double, double, double[], double[], double, Time) - Method in class de.jstacs.algorithms.optimization.termination.IterationCondition
doNextIteration(int, double, double, double[], double[], double, Time) - Method in class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition
doNextIteration(int, double, double, double[], double[], double, Time) - Method in class de.jstacs.algorithms.optimization.termination.SmallGradientConditon
doNextIteration(int, double, double, double[], double[], double, Time) - Method in class de.jstacs.algorithms.optimization.termination.SmallStepCondition
doNextIteration(int, double, double, double[], double[], double, Time) - Method in interface de.jstacs.algorithms.optimization.termination.TerminationCondition
  This method allows to decide whether to do another iteration in an optimization or not.
doNextIteration(int, double, double, double[], double[], double, Time) - Method in class de.jstacs.algorithms.optimization.termination.TimeCondition
doOneSamplingStep(DiffSSBasedOptimizableFunction, SamplingScoreBasedClassifier.SamplingScheme, double) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
  Performs one sampling step, i.e., one sampling of all parameter values.
doOptimization(DataSet[], double[][]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
  This method does the optimization of the `train`-method
doSingleSampling(DataSet[], double[][], int, String) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
  Does a single sampling run for a predefined number of steps.
DoubleList - Class in de.jstacs.utils
  A simple list of primitive type `double`.
DoubleList() - Constructor for class de.jstacs.utils.DoubleList
  This is the default constructor that creates a `DoubleList` with initial length 10.
DoubleList(int) - Constructor for class de.jstacs.utils.DoubleList
  This is the default constructor that creates a `DoubleList` with initial length `size`.
DoubleList(StringBuffer) - Constructor for class de.jstacs.utils.DoubleList
  This is the constructor for the interface `Storable`.
DoubleSymbolException - Exception in de.jstacs.data.alphabets
  A `DoubleSymbolException` is thrown if a symbol occurred more than once in an alphabet.
DoubleSymbolException(String) - Constructor for exception de.jstacs.data.alphabets.DoubleSymbolException
  Constructor for a `DoubleSymbolException` that takes the symbol that occurs more than once in the error message.
draw(double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint
  Draws the parameters from a Dirichlet.
draw(double[], int) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  This method draws an index of an array corresponding to the probabilities encoded in the entries of the array.
drawFreqs(double, InhCondProb...) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.ConstraintManager
  This method draws relative frequencies for the constraints in `constr`.
drawFromStatistics() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM
  This method draws all parameters for the current statistics
drawKLDivergences(double, double[], int, int, double[][][], double) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
  Draws KL-divergences between the distribution given by `contrast` and `endIdx-startIdx` distributions drawn from a Dirichlet density centered around `contrast`, i.e. the hyper-parameters of the Dirichlet density are the probabilities of `contrast` weighted by `samples`.
drawKLDivergences(double[], double[], double[][][][], double) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
  Draws KL-divergences between the distributions given by `contrast[i]` each weighted by `weights[i]` `kls.length` distributions drawn from a Dirichlet density centered around `contrast`, i.e. the hyper-parameters of the Dirichlet density are the probabilities of `contrast` weighted by `samples`.
drawParameters(DataSet, double[]) - Method in interface de.jstacs.sampling.GibbsSamplingModel
  This method draws the parameters of the model from the a posteriori density.
drawParameters(DataSet, double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.DAGTrainSM
  This method draws the parameter of the model from the likelihood or the posterior, respectively.
drawParameters(DataSet, double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGModelForGibbsSampling
drawParameters(DataSet, double[], int[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGModelForGibbsSampling
drawParameters(DataSet, double[], int[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGTrainSM
  This method draws the parameters of the model from the a posteriori density.
drawParameters(double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb
  Draws the parameters from a Dirichlet distribution using the counts and the given `ess` (equivalent sample size) as hyperparameters.
drawParametersFromStatistic() - Method in interface de.jstacs.sampling.SamplingFromStatistic
  This method draws the parameters using a sufficient statistic representing a posteriori density.
drawParametersFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
drawParametersFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
drawParametersFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleSamplingState
drawParametersFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
  This method draws new parameters from the sufficient statistics.
drawParametersFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
  This method allows to draw parameters from the sufficient statistic, i.e., to draw from the posterior.
drawPosition(int[]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.UniformDurationDiffSM
  This method draws from the distribution and returns the result in the given array.
drawUnConditional(int, int, double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb
  This method draws the parameters for a part of this constraint.
DurationDiffSM - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif
  This class is the super class for all one dimensional position scoring functions that can be used as durations for semi Markov models.
DurationDiffSM(int, int, double) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.DurationDiffSM
  The default constructor.
DurationDiffSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.DurationDiffSM
  This is the constructor for `Storable`.

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