A B C D E F G H I 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(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

This is the constructor for 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 datatypes that can be used for Parameter and Result.

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.

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.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.cll - package de.jstacs.classifier.scoringFunctionBased.cll

Provides the implementation of the log conditional likelihood as an OptimizableFunction and a classifier that uses log conditional likelihood or supervised posterior to learn the parameters of a set of ScoringFunctions

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.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.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.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.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.

deselectAll() - Method in class de.jstacs.classifier.MeasureParameters

De-selects all measures, i.e. calls MeasureParameters.setSelected(Measure, boolean) for all MeasureParameters.Measures.

DGMParameterSet - Class in de.jstacs.models.discrete

The super ParameterSet for any ParameterSet of a DiscreteGraphicalModel.

DGMParameterSet(StringBuffer) - Constructor for class de.jstacs.models.discrete.DGMParameterSet

The constructor for the Storable interface.

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.

DifferentiableFunction - Class in de.jstacs.algorithms.optimization

This class is the framework for any (at least) one time differentiable function f: R^n -> 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 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 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

Extracts the alphabet from the StringBuffer.

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

Constructor for a discrete alphabet in [min,max].

DiscreteAlphabet(String[], boolean) - Constructor for class de.jstacs.data.alphabets.DiscreteAlphabet

Constructor for a discrete alphabet.

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 array of symbols.

DiscreteAlphabet.DiscreteAlphabetParameterSet(char[], boolean) - Constructor for class de.jstacs.data.alphabets.DiscreteAlphabet.DiscreteAlphabetParameterSet

Creates a new DiscreteAlphabet.DiscreteAlphabetParameterSet from an array of symbols.

DiscreteAlphabet.DiscreteAlphabetParameterSet(StringBuffer) - Constructor for class de.jstacs.data.alphabets.DiscreteAlphabet.DiscreteAlphabetParameterSet

Creates a new DiscreteAlphabet.DiscreteAlphabetParameterSet from its XML representation.

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 constructor for a model in xml format.

DiscreteInhomogenousSampleEmitter - Class in de.jstacs.models.utils

Emits Samples for discrete, inhomogenous models by a naive implementation.

DiscreteInhomogenousSampleEmitter() - Constructor for class de.jstacs.models.utils.DiscreteInhomogenousSampleEmitter

DiscreteSequence - Class in de.jstacs.data.sequences

The main class for any discrete sequence.

DiscreteSequence(AlphabetContainer, SequenceAnnotation[]) - Constructor for class de.jstacs.data.sequences.DiscreteSequence

This constructor creates an instance with the AlphabetContainer and the 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

The constructor.

discreteVal(int) - Method in class de.jstacs.data.Sequence

Returns the discrete value of position pos.

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.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 alphabet that is used for DNA.

DNAAlphabet(StringBuffer) - Constructor for class de.jstacs.data.alphabets.DNAAlphabet

The constructor for the Storable interface.

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

Creates a new DNAAlphabet.DNAAlphabetParameterSet from its XML representation.

doesApplyFor(Sequence) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.Parameter

Returns true if seq fulfills all requirements defined in the context (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

doOptimization(Sample[], double[][]) - Method in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier

This method does the optimization of the train-method

DoubleArrayWithTags(double[]) - Static method in class de.jstacs.io.XMLParser

Encodes a double array.

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 the alphabet.

DoubleSymbolException(String) - Constructor for exception de.jstacs.data.alphabets.DoubleSymbolException

Constructor for a DoubleSymbolException that takes the symbol that occurs more than once.

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.

drawParameters(Sample, double[]) - Method in class de.jstacs.models.discrete.inhomogeneous.DAGModel

This method draws the parameter of the model from the likelihood respectively posterior.

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 using the counts and the given ESS 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.

drawUnConditional(int, int, double) - Method in class de.jstacs.models.discrete.inhomogeneous.InhCondProb

This method draws the parameters for a part of this constraint.