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

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
 
DAGModel - Class in de.jstacs.models.discrete.inhomogeneous
The abstract class for directed acyclic graphical models (DAGModel).
DAGModel(IDGMParameterSet) - Constructor for class de.jstacs.models.discrete.inhomogeneous.DAGModel
This is the main constructor.
DAGModel(StringBuffer) - Constructor for class de.jstacs.models.discrete.inhomogeneous.DAGModel
The standard constructor for the interface Storable.
data - Variable in class de.jstacs.classifier.scoringFunctionBased.AbstractOptimizableFunction
The data that is used to evaluate this function.
DataType - Enum in de.jstacs
This enum defines a number of data types that can be used for Parameters and Result s.
datatype - Variable in class de.jstacs.parameters.SimpleParameter
The data type of the parameter value
datatype - Variable in class de.jstacs.results.Result
The data type of the result.
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 - package de.jstacs.algorithms
Provides classes for algorithms on graphs.
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
 
de.jstacs.classifier - package de.jstacs.classifier
This package provides the framework for any classifier.
de.jstacs.classifier.assessment - package de.jstacs.classifier.assessment
This package allows to assess classifiers.
de.jstacs.classifier.modelBased - package de.jstacs.classifier.modelBased
Provides the classes for Classifiers that are based on Models
de.jstacs.classifier.scoringFunctionBased - package de.jstacs.classifier.scoringFunctionBased
Provides the classes for Classifiers that are based on ScoringFunctions.
de.jstacs.classifier.scoringFunctionBased.gendismix - package de.jstacs.classifier.scoringFunctionBased.gendismix
Provides an implementation of a classifier that allows to train the parameters of a set of NormalizableScoringFunctions by a unified generative-discriminative learning principle
de.jstacs.classifier.scoringFunctionBased.logPrior - package de.jstacs.classifier.scoringFunctionBased.logPrior
Provides a general definition of a parameter log-prior and a number of implementations of Laplace and Gaussian priors
de.jstacs.classifier.scoringFunctionBased.msp - package de.jstacs.classifier.scoringFunctionBased.msp
Provides an implementation of a classifier that allows to train the parameters of a set of ScoringFunctions either by maximum supervised posterior (MSP) or by maximum conditional likelihood (MCL)
de.jstacs.classifier.utils - package de.jstacs.classifier.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 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.models - package de.jstacs.models
Provides the interface Model and its abstract implementation AbstractModel, which is the super class of all other models.
de.jstacs.models.discrete - package de.jstacs.models.discrete
 
de.jstacs.models.discrete.homogeneous - package de.jstacs.models.discrete.homogeneous
 
de.jstacs.models.discrete.homogeneous.parameters - package de.jstacs.models.discrete.homogeneous.parameters
 
de.jstacs.models.discrete.inhomogeneous - package de.jstacs.models.discrete.inhomogeneous
This package contains various inhomogeneous models.
de.jstacs.models.discrete.inhomogeneous.parameters - package de.jstacs.models.discrete.inhomogeneous.parameters
 
de.jstacs.models.discrete.inhomogeneous.shared - package de.jstacs.models.discrete.inhomogeneous.shared
 
de.jstacs.models.mixture - package de.jstacs.models.mixture
This package is the super package for any mixture model.
de.jstacs.models.mixture.gibbssampling - package de.jstacs.models.mixture.gibbssampling
This package contains many classes that can be used while a Gibbs sampling.
de.jstacs.models.mixture.motif - package de.jstacs.models.mixture.motif
 
de.jstacs.models.mixture.motif.positionprior - package de.jstacs.models.mixture.motif.positionprior
 
de.jstacs.models.utils - package de.jstacs.models.utils
 
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.scoringFunctions - package de.jstacs.scoringFunctions
Provides ScoringFunctions that can be used in a ScoreClassifier.
de.jstacs.scoringFunctions.directedGraphicalModels - package de.jstacs.scoringFunctions.directedGraphicalModels
Provides ScoringFunctions that are equivalent to directed graphical models.
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures - package de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures
Provides the facilities to learn the structure of a BayesianNetworkScoringFunction.
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures - package de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures
Provides the facilities to learn the structure of a BayesianNetworkScoringFunction as a Bayesian tree using a number of measures to define a rating of structures
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures - package de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures
Provides the facilities to learn the structure of a BayesianNetworkScoringFunction as a permuted Markov model using a number of measures to define a rating of structures
de.jstacs.scoringFunctions.homogeneous - package de.jstacs.scoringFunctions.homogeneous
Provides ScoringFunctions that are homogeneous, i.e. model probabilities or scores independent of the position within a sequence
de.jstacs.scoringFunctions.mix - package de.jstacs.scoringFunctions.mix
Provides ScoringFunctions that are mixtures of other ScoringFunctions.
de.jstacs.scoringFunctions.mix.motifSearch - package de.jstacs.scoringFunctions.mix.motifSearch
 
de.jstacs.utils - package de.jstacs.utils
This package contains a bundle of useful classes and interfaces like ...
de.jstacs.utils.random - package de.jstacs.utils.random
This package contains some classes for generating random numbers
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.models.discrete.inhomogeneous.InhomogeneousDGM
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.classifier.scoringFunctionBased.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.scoringFunctions.mix.motifSearch.DurationScoringFunction
The difference of maximal and minimal value.
deselectAll() - Method in class de.jstacs.classifier.MeasureParameters
Deselects all measures, i.e. calls MeasureParameters.setSelected(Measure, boolean) for all MeasureParameters.Measures.
determineIsNormalized() - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
This method is used to determine the value that is returned by the method AbstractMixtureScoringFunction.isNormalized().
DGMParameterSet - Class in de.jstacs.models.discrete
The super ParameterSet for any parameter set of a DiscreteGraphicalModel.
DGMParameterSet(StringBuffer) - Constructor for class de.jstacs.models.discrete.DGMParameterSet
The standard constructor for the interface Storable.
DGMParameterSet(Class<? extends DiscreteGraphicalModel>, boolean, boolean) - Constructor for class de.jstacs.models.discrete.DGMParameterSet
An empty constructor.
DGMParameterSet(Class<? extends DiscreteGraphicalModel>, AlphabetContainer, double, String) - Constructor for class de.jstacs.models.discrete.DGMParameterSet
The constructor for models that can handle variable lengths.
DGMParameterSet(Class<? extends DiscreteGraphicalModel>, AlphabetContainer, int, double, String) - Constructor for class de.jstacs.models.discrete.DGMParameterSet
The constructor for models that can handle only sequences of fixed length given by length.
diff(Sample, Sample...) - Static method in class de.jstacs.data.Sample
This method computes the difference between the Sample data and the Samples samples.
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
 
dimension - Variable in class de.jstacs.models.mixture.AbstractMixtureModel
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
 
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.
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. 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() - 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 .
DiscreteGraphicalModel - Class in de.jstacs.models.discrete
This is the main class for all discrete graphical models (DGM).
DiscreteGraphicalModel(DGMParameterSet) - Constructor for class de.jstacs.models.discrete.DiscreteGraphicalModel
The default constructor.
DiscreteGraphicalModel(StringBuffer) - Constructor for class de.jstacs.models.discrete.DiscreteGraphicalModel
The standard constructor for the interface Storable.
DiscreteInhomogenousSampleEmitter - Class in de.jstacs.models.utils
Emits Samples for discrete inhomogeneous models by a naive implementation.
DiscreteInhomogenousSampleEmitter() - Constructor for class de.jstacs.models.utils.DiscreteInhomogenousSampleEmitter
 
DiscreteSequence - Class in de.jstacs.data.sequences
This is the main class for any discrete sequence.
DiscreteSequence(AlphabetContainer, SequenceAnnotation[]) - Constructor for class de.jstacs.data.sequences.DiscreteSequence
This constructor creates a new DiscreteSequence with the AlphabetContainer container and the annotation annotation but without the content.
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.Sequence
Returns the discrete value at position pos of the Sequence.CompositeSequence.
discreteVal(int) - Method in class de.jstacs.data.Sequence.SubSequence
 
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.RecursiveSequence
 
discreteVal(int) - Method in class de.jstacs.data.sequences.ShortSequence
 
discreteVal(int) - Method in class de.jstacs.data.sequences.SparseSequence
 
divideByUnfree() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.ParameterTree
Divides each of the normalized parameters on a simplex by the last Parameter, which is defined not to be free.
dList - Variable in class de.jstacs.classifier.scoringFunctionBased.SFBasedOptimizableFunction
These DoubleLists are used during the parallel computation of the gradient.
dList - Variable in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
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(StringBuffer) - Constructor for class de.jstacs.data.alphabets.DNAAlphabet
The standard constructor for the interface Storable.
DNAAlphabet(DNAAlphabet.DNAAlphabetParameterSet) - Constructor for class de.jstacs.data.alphabets.DNAAlphabet
The constructor for the InstantiableFromParameterSet interface.
DNAAlphabet() - Constructor for class de.jstacs.data.alphabets.DNAAlphabet
The main constructor.
DNAAlphabet.DNAAlphabetParameterSet - Class in de.jstacs.data.alphabets
The parameter set for a DNAAlphabet.
DNAAlphabet.DNAAlphabetParameterSet() - Constructor for class de.jstacs.data.alphabets.DNAAlphabet.DNAAlphabetParameterSet
Creates a new DNAAlphabet.DNAAlphabetParameterSet.
DNAAlphabet.DNAAlphabetParameterSet(StringBuffer) - Constructor for class de.jstacs.data.alphabets.DNAAlphabet.DNAAlphabetParameterSet
The standard constructor for the interface Storable .
DNASample - Class in de.jstacs.data
This class exist for convenience to allow the user an easy creation of Samples of DNA Sequences.
DNASample(String) - Constructor for class de.jstacs.data.DNASample
Creates a new sample of DNA sequence from a FASTA file with file name fName.
DNASample(String, char) - Constructor for class de.jstacs.data.DNASample
Creates a new sample of DNA sequence from a file with file name fName.
DNASample(String, char, SequenceAnnotationParser) - Constructor for class de.jstacs.data.DNASample
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.scoringFunctions.directedGraphicalModels.Parameter
Indicates if the Sequence seq fulfills all requirements defined in the Parameter.context.
doesNothing() - Method in class de.jstacs.utils.SafeOutputStream
Indicates whether the instance is doing something or not.
DoesNothingLogPrior - Class in de.jstacs.classifier.scoringFunctionBased.logPrior
This class defines a LogPrior that does not penalize any parameter.
doFirstIteration(Sample, double[]) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
This method will do the first step in the train algorithm for the current model.
doFirstIteration(Sample, double[], MultivariateRandomGenerator, MRGParams[]) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
This method will do the first step in the train algorithm for the current model.
doFirstIteration(double[], MultivariateRandomGenerator, MRGParams[]) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
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.models.mixture.MixtureModel
 
doFirstIteration(Sample, double[], double[][]) - Method in class de.jstacs.models.mixture.MixtureModel
This method enables you to train a mixture model with a fixed start partitioning.
doFirstIteration(double[], MultivariateRandomGenerator, MRGParams[]) - Method in class de.jstacs.models.mixture.motif.SingleHiddenMotifMixture
 
doFirstIteration(double[], MultivariateRandomGenerator, MRGParams[]) - Method in class de.jstacs.models.mixture.StrandModel
 
doHeuristicSteps(ScoringFunction[], Sample[], double[][], SFBasedOptimizableFunction, 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 MutableMotifDiscovererToolbox.InitMethodForScoringFunction 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.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
 
doOptimization(Sample[], double[][]) - Method in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier
This method does the optimization of the train-method
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[], int) - Static method in class de.jstacs.models.mixture.AbstractMixtureModel
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.models.discrete.ConstraintManager
This method draws relative frequencies for the constraints in constr.
drawKLDivergences(double, double[], int, int, double[][][], double) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.ParameterTree
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.scoringFunctions.directedGraphicalModels.ParameterTree
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(Sample, double[]) - Method in class de.jstacs.models.discrete.inhomogeneous.DAGModel
This method draws the parameter of the model from the likelihood or the posterior, respectively.
drawParameters(Sample, double[], int[][]) - Method in class de.jstacs.models.discrete.inhomogeneous.FSDAGModel
This method draws the parameters of the model from the a posteriori density.
drawParameters(double) - Method in class de.jstacs.models.discrete.inhomogeneous.InhCondProb
Draws the parameters from a Dirichlet distribution using the counts and the given ess (equivalent sample size) as hyperparameters.
drawParameters(Sample, double[]) - Method in class de.jstacs.models.mixture.gibbssampling.FSDAGModelForGibbsSampling
 
drawParameters(Sample, double[], int[][]) - Method in class de.jstacs.models.mixture.gibbssampling.FSDAGModelForGibbsSampling
 
drawParameters(Sample, double[]) - Method in interface de.jstacs.models.mixture.gibbssampling.GibbsSamplingComponent
This method draws the parameters of the model from the a posteriori density.
drawPosition(int[]) - Method in class de.jstacs.scoringFunctions.mix.motifSearch.UniformDurationScoringFunction
This method draws from the distribution and returns the result in the given array.
drawUnConditional(int, int, double) - Method in class de.jstacs.models.discrete.inhomogeneous.InhCondProb
This method draws the parameters for a part of this constraint.
DurationScoringFunction - Class in de.jstacs.scoringFunctions.mix.motifSearch
This class is the super class for all one dimensional position scoring functions that can be used as durations for semi Markov models.
DurationScoringFunction(int, int, double) - Constructor for class de.jstacs.scoringFunctions.mix.motifSearch.DurationScoringFunction
The default constructor.
DurationScoringFunction(StringBuffer) - Constructor for class de.jstacs.scoringFunctions.mix.motifSearch.DurationScoringFunction
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