D-Index

A B C D E F G H I J K L M N O P Q R S T U V W X Z

D

d - Variable in class de.jstacs.algorithms.alignment.Alignment: The matrices holding the edit distances
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.
DataColumnParameter - Class in de.jstacs.tools: SimpleParameter that represents a data column parameter in Galaxy and JstacsFX.
DataColumnParameter(String, String, String, boolean, Integer) - Constructor for class de.jstacs.tools.DataColumnParameter: Creates a new DataColumnParameter with given name, comment, and reference.
DataColumnParameter(String, String, String, boolean, ParameterValidator, Integer) - Constructor for class de.jstacs.tools.DataColumnParameter: Creates a new DataColumnParameter with given name, comment, and reference.
DataColumnParameter(String, String, String, boolean, ParameterValidator) - Constructor for class de.jstacs.tools.DataColumnParameter: Creates a new DataColumnParameter with given name, comment, and reference.
DataColumnParameter(String, String, String, boolean) - Constructor for class de.jstacs.tools.DataColumnParameter: Creates a new DataColumnParameter with given name, comment, and reference.
DataColumnParameter(StringBuffer) - Constructor for class de.jstacs.tools.DataColumnParameter: The standard constructor for the interface Storable.
DataSet - Class in de.jstacs.data: This is the class for any data set of Sequences.
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(AlphabetContainer, AbstractStringExtractor, String, int, double) - 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.
This constructor enables you to use subsequences of the elements of a DataSet.
DataSet(String, Sequence...) - Constructor for class de.jstacs.data.DataSet: Creates a new DataSet from an array of Sequences and a given annotation.
This constructor is specially designed for the method StatisticalModel.emitDataSet(int, int...)
DataSet(String, Collection<Sequence>) - Constructor for class de.jstacs.data.DataSet: Creates a new DataSet from a Collection of Sequences 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.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 Sequences that occur more than once in one or more DataSets.
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 Sequences 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.
DataSetResultSaver - Class in de.jstacs.results.savers: Class for a ResultSaver working on DataSetResult.
dataSetToSequenceIterator(DataSet, boolean, 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 Parameters and Result s.
DatatypeNotValidException(String) - Constructor for exception de.jstacs.parameters.SimpleParameter.DatatypeNotValidException: Creates a new SimpleParameter.DatatypeNotValidException with an error message.
dataTypeToGalaxy() - Method in class de.jstacs.parameters.SimpleParameter: Returns the Galaxy identifier for the DataType of this parameter
dataTypeToGalaxy() - Method in class de.jstacs.tools.DataColumnParameter
DateFileFilter - Class in de.jstacs.io: This class implements a FileFilter that accepts Files 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 Files that were modified after the given year, month, ...
DateFileFilter(Date) - Constructor for class de.jstacs.io.DateFileFilter: Creates an instance that accepts Files 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.

It contains the class ClassifierAssessment that is used as a super-class of all implemented methodologies of an assessment to assess classifiers.
de.jstacs.classifiers.differentiableSequenceScoreBased - package de.jstacs.classifiers.differentiableSequenceScoreBased: Provides the classes for Classifiers that are based on SequenceScores.
It includes a sub-package for discriminative objective functions, namely conditional likelihood and supervised posterior, and a separate sub-package for the parameter priors, that can be used for the supervised posterior.
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 DifferentiableStatisticalModels 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 DifferentiableStatisticalModels 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 AbstractScoreBasedClassifiers that are based on SamplingDifferentiableStatisticalModels 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 Classifiers that are based on TrainableStatisticalModels.
de.jstacs.classifiers.utils - package de.jstacs.classifiers.utils: Provides some useful classes for working with classifiers.
de.jstacs.clustering.distances - package de.jstacs.clustering.distances
de.jstacs.clustering.hierachical - package de.jstacs.clustering.hierachical
de.jstacs.data - package de.jstacs.data: Provides classes for the representation of data.
The base classes to represent data are Alphabet and AlphabetContainer for representing alphabets, Sequence and its sub-classes to represent continuous and discrete sequences, and DataSet to represent data sets comprising a set of sequences.
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.
The implementations of sequences currently include DiscreteSequences prepared for alphabets of different sizes, and ArbitrarySequences that may contain continuous values as well.
As sub-package provides the facilities to annotate Sequences.
de.jstacs.data.sequences.annotation - package de.jstacs.data.sequences.annotation: Provides the facilities to annotate Sequences 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 Storables 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.results.savers - package de.jstacs.results.savers
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 SequenceScores, 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 StatisticalModels, which can compute a proper (i.e., normalized) likelihood over the input space of sequences.
StatisticalModels can be further differentiated into TrainableStatisticalModels, which can be learned from a single input DataSet, and DifferentiableStatisticalModels, which define a proper likelihood but can also compute gradients like DifferentiableSequenceScores.
de.jstacs.sequenceScores.statisticalModels.differentiable - package de.jstacs.sequenceScores.statisticalModels.differentiable: Provides all DifferentiableStatisticalModels, 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 DifferentiableStatisticalModels 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 DifferentiableStatisticalModels that are homogeneous, i.e.
de.jstacs.sequenceScores.statisticalModels.differentiable.localMixture - package de.jstacs.sequenceScores.statisticalModels.differentiable.localMixture
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture - package de.jstacs.sequenceScores.statisticalModels.differentiable.mixture: Provides DifferentiableSequenceScores that are mixtures of other DifferentiableSequenceScores.
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 TrainableStatisticalModels, 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.tools - package de.jstacs.tools
de.jstacs.tools.ui.cli - package de.jstacs.tools.ui.cli
de.jstacs.tools.ui.galaxy - package de.jstacs.tools.ui.galaxy
de.jstacs.utils - package de.jstacs.utils: This package contains a bundle of useful classes and interfaces like ...
de.jstacs.utils.graphics - package de.jstacs.utils.graphics
de.jstacs.utils.random - package de.jstacs.utils.random: This package contains some classes for generating random numbers.
DeBruijnGraphSequenceGenerator - Class in de.jstacs.data: Class for creating De Bruin sequences using explicit De Bruijn graphs.
DeBruijnGraphSequenceGenerator() - Constructor for class de.jstacs.data.DeBruijnGraphSequenceGenerator
DeBruijnMotifComparison - Class in de.jstacs.clustering.distances: Helper class for comparisons of motif models based on De Bruijn sequences.
DeBruijnMotifComparison() - Constructor for class de.jstacs.clustering.distances.DeBruijnMotifComparison
DeBruijnSequenceGenerator - Class in de.jstacs.data: Generates De Buijn sequences using the algorithm from Frank Ruskey's Combinatorial Generation.
DeBruijnSequenceGenerator() - Constructor for class de.jstacs.data.DeBruijnSequenceGenerator
decodePath(IntList) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM: This method decodes any path of the HMM, i.e.
decodeStatePosterior(double[][]...) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM: The method returns the decoded state posterior, i.e.
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 DataSets 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 $f: \mathbb{R}^n \to \mathbb{R}$ .
DifferentiableFunction() - Constructor for class de.jstacs.algorithms.optimization.DifferentiableFunction: Default constructor, automatically sets the internal function for line search to a OneDimensionalSubFunction of this 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 DifferentiableSequenceScores.
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 and CompositeLogPrior.
DifferentiableStatisticalModelWrapperTrainSM(DifferentiableStatisticalModel, int, byte, AbstractTerminationCondition, double, double, LogPrior) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.DifferentiableStatisticalModelWrapperTrainSM: 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.
DiffSMSamplingComponent(String) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier.DiffSMSamplingComponent: Creates a new SamplingScoreBasedClassifier.DiffSMSamplingComponent that uses temporary files with name prefix outfilePrefix to store sampled parameters.
DiffSSBasedOptimizableFunction - Class in de.jstacs.classifiers.differentiableSequenceScoreBased: This abstract class is the basis of all multi-threaded OptimizableFunctions that are based on DifferentiableSequenceScores.
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 Exceptions 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.MedianSmoothing - Class in de.jstacs.data: Smoothing by median using a pre-defined window 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.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.
disconnect(AbstractList<int[]>, int[], ConstraintManager.Decomposition) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.ConstraintManager: This method tries to disconnect the constraints and create the models.
DiscreteAlphabet - Class in de.jstacs.data.alphabets: Class for an alphabet that consists of arbitrary Strings.
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.
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.
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.
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.
DiscreteAlphabetParameterSet() - Constructor for class de.jstacs.data.alphabets.DiscreteAlphabet.DiscreteAlphabetParameterSet: Creates a new DiscreteAlphabet.DiscreteAlphabetParameterSet with empty values.
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.
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.
DiscreteAlphabetParameterSet(StringBuffer) - Constructor for class de.jstacs.data.alphabets.DiscreteAlphabet.DiscreteAlphabetParameterSet: 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 DataSets 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 Sequences 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.CyclicSequenceAdaptor
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.
DistanceMetric<T> - Class in de.jstacs.clustering.distances: This abstract class defined a DistanceMetric (which may be used for clustering) on a generic type T.
DistanceMetric() - Constructor for class de.jstacs.clustering.distances.DistanceMetric
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 DoubleLists are used during the parallel computation of the gradient.
dList - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM: This array contains some DoubleLists 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 DataSets of DNA Sequences.
DNADataSet(String) - Constructor for class de.jstacs.data.DNADataSet: Creates a new data set of DNA sequence from a FASTA file with file name fName.
DNADataSet(String, char) - Constructor for class de.jstacs.data.DNADataSet: Creates a new data set of DNA sequence from a file with file name fName.
DNADataSet(String, char, SequenceAnnotationParser) - Constructor for class de.jstacs.data.DNADataSet: Creates a new data set of DNA sequence from a file with file name fName using the given parser.
document - Variable in class de.jstacs.utils.graphics.SVGAdaptor: The SVG document representation, may be used in sub-classes for different Transcoders
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 data set.
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.MultipleIterationsCondition
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(Function, 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.
DoubleArrayComparator - Class in de.jstacs.utils: This class implements a Comparator of double arrays.
DoubleArrayComparator(int) - Constructor for class de.jstacs.utils.DoubleArrayComparator: Creates a new instance with a specific index for comparing double arrays.
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.
DoubleTableResult(String, String, AbstractList<double[]>) - Constructor for class de.jstacs.classifiers.AbstractScoreBasedClassifier.DoubleTableResult: This is the default constructor that creates an instance based on the results given in list
DoubleTableResult(StringBuffer) - Constructor for class de.jstacs.classifiers.AbstractScoreBasedClassifier.DoubleTableResult: The standard constructor for the interface Storable .
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.
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.
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.
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.
drawParameters(double[][], boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission: Draws the parameters of this AbstractConditionalDiscreteEmission from a Dirichlet distribution with given hyper-parameters.
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.
dropBelow(IntList, S[]) - Method in class de.jstacs.clustering.hierachical.ClusterTree: Removes all sub-trees below the inner nodes identified by the original indexes supplied and creates new leaf nodes instead, which obtain the supplied leaf elements.
DualFunction(SequenceIterator, MEMConstraint[]) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMTools.DualFunction: The constructor of a dual function.
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.

A B C D E F G H I J K L M N O P Q R S T U V W X Z