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).
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.
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.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.utils.random.DirichletMRG
This instance shall be used, since quite often two instance of this class return the same values.
DEFAULT_SIGN - Static variable in class de.jstacs.scoringFunctions.mix.motifSearch.HiddenMotifsMixture
The default significance level that is used in Mutable.determineNotSignificantPositions(double, double[], double[], double[][][][], double[][][][], double).
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.
determineNotSignificantPositions(double, double[], double[], double[][][][], double[][][][], double) - Method in interface de.jstacs.motifDiscovery.Mutable
This method determines the number of not significant positions from each side of the motif using the the significance level sign and the contrast distributions of the left or right side, contrastLeft and contrastRight, respectively.
determineNotSignificantPositions(double, double[], double[], double[][][][], double[][][][], double) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.MutableMarkovModelScoringFunction
 
determineNotSignificantPositions(double, double[], double[], double[][][][], double[][][][], double) - Method in class de.jstacs.scoringFunctions.mix.StrandScoringFunction
 
determineNotSignificantPositions(double, double[], double[], double[][][][], double[][][][], double) - Method in class de.jstacs.scoringFunctions.NormalizedScoringFunction
 
determineNotSignificantPositionsFor(int, Sample[], double[][], int) - Method in interface de.jstacs.motifDiscovery.MutableMotifDiscoverer
This method determines the number of not significant positions from each side of the motif with index motif.
determineNotSignificantPositionsFor(int, Sample[], double[][], int) - Method in class de.jstacs.scoringFunctions.IndependentProductScoringFunction
 
determineNotSignificantPositionsFor(int, Sample[], double[][], int) - Method in class de.jstacs.scoringFunctions.mix.motifSearch.HiddenMotifsMixture
 
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.
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 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.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 .
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[][], OptimizableFunction, SafeOutputStream, boolean, History[][], int[][]) - 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.
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 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 K L M N O P Q R S T U V W X