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C

canBeCastedFromTo(DataType, DataType) - Static method in enum de.jstacs.DataType
Checks if the DataType from can be casted to the DataType to without losing information.
CappedHistory - Class in de.jstacs.motifDiscovery.history
This class combines a threshold on the number of steps which can be performed with any other History.
CappedHistory(int, History) - Constructor for class de.jstacs.motifDiscovery.history.CappedHistory
This constructor creates an instance that allows at most t steps using the history h.
CappedHistory(StringBuffer) - Constructor for class de.jstacs.motifDiscovery.history.CappedHistory
This is the constructor for the interface Storable.
caseInsensitive - Variable in class de.jstacs.data.alphabets.DiscreteAlphabet
Switch to decide whether the input should be treated case sensitive or insensitive.
cast(Object[]) - Static method in class de.jstacs.io.ArrayHandler
This method creates a new array of the superclass of all elements of the given array and copies the elements.
cast(Class<? extends S>, Object[]) - Static method in class de.jstacs.io.ArrayHandler
This method returns an array of a user-specified class with all elements in the given array o.
CategoricalResult - Class in de.jstacs.results
A class for categorical results (i.e.
CategoricalResult(StringBuffer) - Constructor for class de.jstacs.results.CategoricalResult
The standard constructor for the interface Storable.
CategoricalResult(DataType, String, String, Comparable) - Constructor for class de.jstacs.results.CategoricalResult
Creates a result of a primitive categorical data type or a String .
CategoricalResult(String, String, String) - Constructor for class de.jstacs.results.CategoricalResult
Creates a result of a String.
CategoricalResult(String, String, boolean) - Constructor for class de.jstacs.results.CategoricalResult
Creates a result of a boolean.
CDFOfNormal - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif
This class enables to compute the

The code is a simplified version of pnorm.c from R (version 2.8.0 downloaded at 07.12.2008 about 19:30).
CDFOfNormal() - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.CDFOfNormal
 
check(DataSet) - Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
This method checks if the given DataSet can be used.
check(Sequence) - Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
This method checks if the given Sequence can be used.
check(Object) - Method in class de.jstacs.parameters.AbstractSelectionParameter
MSPDTest whether a given value can be used in Parameter.setValue(Object).
check(Object) - Method in interface de.jstacs.parameters.validation.Constraint
Checks value for the constraint defined in the Constraint.
check(Object) - Method in class de.jstacs.parameters.validation.SimpleStaticConstraint
 
check(Sequence, int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
This method checks all parameters before a probability can be computed for a sequence.
check(Sequence, int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DiscreteGraphicalTrainSM
Checks some conditions on a Sequence.
check(Sequence, int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM
Checks some constraints, these are in general conditions on the AlphabetContainer of a (sub)Sequence between startpos und endpos.
check(Sequence, int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhomogeneousDGTrainSM
 
checkAcyclic(int, int[][]) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.DAGTrainSM
This method checks whether a given graph is acyclic.
checkConsistency(AlphabetContainer) - Method in class de.jstacs.data.AlphabetContainer
Checks if this AlphabetContainer is consistent consistent with another AlphabetContainer.
checkConsistency(Alphabet) - Method in class de.jstacs.data.alphabets.Alphabet
Checks if this Alphabet is consistent consistent with another Alphabet, i.e.
checkDatatype(DataType, Object) - Static method in class de.jstacs.results.Result
This method provides the possibility to check the compliance of some result value with one of the pre-defined DataTypes before creating a new Result and possibly running into an Exception.
checkLength(int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
This method checks if the length l of the model with index index is capable for the current instance.
checkLength(int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.HiddenMotifMixture
 
checkModelsForGibbsSampling() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
This method can be used to check whether the necessary models have implemented the SamplingComponent.
checkValue(Object) - Method in class de.jstacs.parameters.AbstractSelectionParameter
Returns true if the key specified by value is in the set of keys of this AbstractSelectionParameter.
checkValue(Object) - Method in class de.jstacs.parameters.FileParameter
 
checkValue(Object) - Method in class de.jstacs.parameters.MultiSelectionParameter
 
checkValue(Object) - Method in class de.jstacs.parameters.Parameter
Checks the value for correctness, e.g.
checkValue(Object) - Method in class de.jstacs.parameters.ParameterSetContainer
 
checkValue(Object) - Method in class de.jstacs.parameters.RangeParameter
 
checkValue(Object) - Method in class de.jstacs.parameters.SimpleParameter
 
checkValue(Object) - Method in class de.jstacs.parameters.validation.ConstraintValidator
 
checkValue(Object) - Method in class de.jstacs.parameters.validation.NumberValidator
 
checkValue(Object) - Method in interface de.jstacs.parameters.validation.ParameterValidator
Returns true if the value is valid and false otherwise.
checkValue(Object) - Method in class de.jstacs.parameters.validation.RegExpValidator
 
checkValue(Object) - Method in class de.jstacs.parameters.validation.StorableValidator
Checks the value of value.
checkWeights(double[]) - Static method in enum de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.LearningPrinciple
This method checks the values of the weights array.
chooseFromDistr(Constraint, int, int, double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM
Chooses a value in [0,end-start] according to the distribution encoded in the frequencies of distr between the indices start and end.
Chu_Liu_Edmonds - Class in de.jstacs.algorithms.graphs
This class implements the algorithm of Chu_Liu_Edmonds to determine an optimal branching (optimal directed graph of order 1).
CisRegulatoryModuleAnnotation - Class in de.jstacs.data.sequences.annotation
Annotation for a cis-regulatory module as defined by a set of MotifAnnotations of the motifs in the module.
CisRegulatoryModuleAnnotation(String, MotifAnnotation[], Result...) - Constructor for class de.jstacs.data.sequences.annotation.CisRegulatoryModuleAnnotation
Creates a new CisRegulatoryModuleAnnotation from a set of motifs and possibly additional annotations.
CisRegulatoryModuleAnnotation(StringBuffer) - Constructor for class de.jstacs.data.sequences.annotation.CisRegulatoryModuleAnnotation
The standard constructor for the interface Storable.
cl - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractOptimizableFunction
The number of different classes.
ClassDimensionException - Exception in de.jstacs.classifiers
This class indicates that a classifier is defined for less than 2 classes or is defined over a different number of classes than given (e.g.
ClassDimensionException() - Constructor for exception de.jstacs.classifiers.ClassDimensionException
This constructor creates a ClassDimensionException with the default error message ("The number of classes in the classfier differs from the given number.").
ClassDimensionException(String) - Constructor for exception de.jstacs.classifiers.ClassDimensionException
This constructor creates a ClassDimensionException with given error message.
ClassificationRate - Class in de.jstacs.classifiers.performanceMeasures
This class implements the classification rate, i.e.
ClassificationRate() - Constructor for class de.jstacs.classifiers.performanceMeasures.ClassificationRate
Constructs a new instance of the performance measure ClassificationRate.
ClassificationRate(StringBuffer) - Constructor for class de.jstacs.classifiers.performanceMeasures.ClassificationRate
The standard constructor for the interface Storable.
ClassificationVisualizer - Class in de.jstacs.classifiers.utils
This class enables you to visualize some classifier results.
ClassifierAssessment<T extends ClassifierAssessmentAssessParameterSet> - Class in de.jstacs.classifiers.assessment
Class defining an assessment of classifiers.
ClassifierAssessment(AbstractClassifier[], TrainableStatisticalModel[][], boolean, boolean) - Constructor for class de.jstacs.classifiers.assessment.ClassifierAssessment
Creates a new ClassifierAssessment from an array of AbstractClassifiers and a two-dimensional array of TrainableStatisticalModel s, which are combined to additional classifiers.
ClassifierAssessment(AbstractClassifier...) - Constructor for class de.jstacs.classifiers.assessment.ClassifierAssessment
Creates a new ClassifierAssessment from a set of AbstractClassifiers.
ClassifierAssessment(boolean, TrainableStatisticalModel[]...) - Constructor for class de.jstacs.classifiers.assessment.ClassifierAssessment
Creates a new ClassifierAssessment from a set of TrainableStatisticalModels.
ClassifierAssessment(AbstractClassifier[], boolean, TrainableStatisticalModel[]...) - Constructor for class de.jstacs.classifiers.assessment.ClassifierAssessment
This constructor allows to assess a collection of given AbstractClassifiers and, in addition, classifiers that will be constructed using the given TrainableStatisticalModels.
ClassifierAssessmentAssessParameterSet - Class in de.jstacs.classifiers.assessment
This class is the superclass used by all ClassifierAssessmentAssessParameterSets.
ClassifierAssessmentAssessParameterSet() - Constructor for class de.jstacs.classifiers.assessment.ClassifierAssessmentAssessParameterSet
Constructs a new ClassifierAssessmentAssessParameterSet with empty parameter values.
ClassifierAssessmentAssessParameterSet(StringBuffer) - Constructor for class de.jstacs.classifiers.assessment.ClassifierAssessmentAssessParameterSet
The standard constructor for the interface Storable.
ClassifierAssessmentAssessParameterSet(int, boolean) - Constructor for class de.jstacs.classifiers.assessment.ClassifierAssessmentAssessParameterSet
Constructs a new ClassifierAssessmentAssessParameterSet with given parameter values.
ClassifierFactory - Class in de.jstacs.classifiers
This class allows to easily create classifiers from TrainableStatisticalModels, DifferentiableStatisticalModels, and DifferentiableSequenceScores.
ClassifierFactory() - Constructor for class de.jstacs.classifiers.ClassifierFactory
 
classify(Sequence) - Method in class de.jstacs.classifiers.AbstractClassifier
This method classifies a sequence and returns the index i of the class to which the sequence is assigned with 0 < i < getNumberOfClasses().
classify(DataSet) - Method in class de.jstacs.classifiers.AbstractClassifier
This method classifies all sequences of a data set and returns an array of indices of the classes to which the respective sequences are assigned with for each index i in the array 0 < i < getNumberOfClasses().
classify(Sequence) - Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
 
classify(Sequence, boolean) - Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
This method classifies a Sequence.
classify(DataSet) - Method in class de.jstacs.classifiers.trainSMBased.TrainSMBasedClassifier
 
classMus - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateLogPrior
The means for the class parameters, as specified by the user.
classVars - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateLogPrior
The variances for the class parameters, as specified by the user.
clazz - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractOptimizableFunction
The class parameters.
clear() - Method in class de.jstacs.motifDiscovery.history.CappedHistory
 
clear() - Method in interface de.jstacs.motifDiscovery.history.History
This method clears the history, i.e.
clear() - Method in class de.jstacs.motifDiscovery.history.NoRevertHistory
 
clear() - Method in class de.jstacs.motifDiscovery.history.RestrictedRepeatHistory
 
clear() - Method in class de.jstacs.motifDiscovery.history.SimpleHistory
 
clear() - Method in class de.jstacs.utils.DoubleList
Removes all elements from the list.
clear() - Method in class de.jstacs.utils.IntList
Removes all elements from the list.
clearAnnotation() - Method in class de.jstacs.data.sequences.annotation.NullSequenceAnnotationParser
 
clearAnnotation() - Method in interface de.jstacs.data.sequences.annotation.SequenceAnnotationParser
This method reset the current SequenceAnnotation.
clearAnnotation() - Method in class de.jstacs.data.sequences.annotation.SimpleSequenceAnnotationParser
 
clearAnnotation() - Method in class de.jstacs.data.sequences.annotation.SplitSequenceAnnotationParser
 
clearHistoryArray(History[][]) - Static method in class de.jstacs.motifDiscovery.MutableMotifDiscovererToolbox
This method clears all elements of an History-array, so that it can be used again.
CLI - Class in de.jstacs.tools.ui.cli
Class that allows for building generic command line interface (CLI) applications based on the JstacsTool interface.
CLI(JstacsTool...) - Constructor for class de.jstacs.tools.ui.cli.CLI
Creates a new command line interface for the tools provided.
CLI(boolean[], JstacsTool...) - Constructor for class de.jstacs.tools.ui.cli.CLI
Creates a new command line interface from a set of Jstacs tools.
CLI(String, boolean[], JstacsTool...) - Constructor for class de.jstacs.tools.ui.cli.CLI
Creates a new command line interface for the tools provided, where for each tool multi-threading may be configured.
CLI(String, String, boolean[], JstacsTool...) - Constructor for class de.jstacs.tools.ui.cli.CLI
Creates a new command line interface for the tools provided, where for each tool multi-threading may be configured.
CLI.SysProtocol - Class in de.jstacs.tools.ui.cli
Class for a Protocol that prints messages to System.out and warnings to System.err and additionally hold a log of all messages in a local StringBuffer that may be, e.g., stored to a file later.
cllGrad - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.LogGenDisMixFunction
Array for the gradient of the conditional log-likelihood
clone() - Method in class de.jstacs.algorithms.graphs.Edge
 
clone() - Method in class de.jstacs.algorithms.optimization.termination.AbstractTerminationCondition.AbstractTerminationConditionParameterSet
 
clone() - Method in class de.jstacs.algorithms.optimization.termination.AbstractTerminationCondition
 
clone() - Method in class de.jstacs.classifiers.AbstractClassifier
 
clone() - Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
 
clone() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier
 
clone() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifierParameterSet
 
clone() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
 
clone() - Method in class de.jstacs.classifiers.trainSMBased.TrainSMBasedClassifier
 
clone() - Method in class de.jstacs.data.AlphabetContainer.AbstractAlphabetContainerParameterSet
 
clone() - Method in class de.jstacs.data.AlphabetContainerParameterSet.AlphabetArrayParameterSet
 
clone() - Method in class de.jstacs.data.AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet
 
clone() - Method in class de.jstacs.data.alphabets.Alphabet.AlphabetParameterSet
 
clone() - Method in class de.jstacs.data.alphabets.DNAAlphabetContainer.DNAAlphabetContainerParameterSet
 
clone(T...) - Static method in class de.jstacs.io.ArrayHandler
This method returns a deep copy of a (multi-dimensional) array of Cloneables or primitives.
clone() - Method in class de.jstacs.motifDiscovery.history.CappedHistory
 
clone() - Method in interface de.jstacs.motifDiscovery.history.History
This method returns a deep copy of the instance
clone() - Method in class de.jstacs.motifDiscovery.history.NoRevertHistory
 
clone() - Method in class de.jstacs.motifDiscovery.history.RestrictedRepeatHistory
 
clone() - Method in class de.jstacs.motifDiscovery.history.SimpleHistory
 
clone() - Method in interface de.jstacs.motifDiscovery.MotifDiscoverer
This method returns a deep clone of the instance.
clone() - Method in class de.jstacs.parameters.AbstractSelectionParameter
 
clone() - Method in class de.jstacs.parameters.ExpandableParameterSet
 
clone() - Method in class de.jstacs.parameters.FileParameter
 
clone() - Method in class de.jstacs.parameters.FileParameter.FileRepresentation
 
clone() - Method in class de.jstacs.parameters.MultiSelectionParameter
 
clone() - Method in class de.jstacs.parameters.Parameter
 
clone() - Method in class de.jstacs.parameters.ParameterSet
Creates a full clone (deep copy) of this ParameterSet.
clone() - Method in class de.jstacs.parameters.ParameterSetContainer
 
clone() - Method in class de.jstacs.parameters.RangeParameter
 
clone() - Method in class de.jstacs.parameters.SequenceScoringParameterSet
 
clone() - Method in class de.jstacs.parameters.SimpleParameter
 
clone() - Method in class de.jstacs.parameters.SimpleParameterSet
 
clone() - Method in interface de.jstacs.parameters.validation.Constraint
This method returns a deep copy of the current instance.
clone() - Method in class de.jstacs.parameters.validation.ConstraintValidator
 
clone() - Method in class de.jstacs.parameters.validation.NumberValidator
 
clone() - Method in interface de.jstacs.parameters.validation.ParameterValidator
This method returns a deep copy of the current instance.
clone() - Method in class de.jstacs.parameters.validation.RegExpValidator
 
clone() - Method in class de.jstacs.parameters.validation.SimpleStaticConstraint
 
clone() - Method in class de.jstacs.parameters.validation.StorableValidator
 
clone() - Method in class de.jstacs.sampling.AbstractBurnInTest
 
clone() - Method in class de.jstacs.sampling.AbstractBurnInTestParameterSet
 
clone() - Method in interface de.jstacs.sampling.BurnInTest
Return a deep copy of this object.
clone() - Method in class de.jstacs.sampling.SimpleBurnInTest
Deprecated.
 
clone() - Method in class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
 
clone() - Method in interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore
Creates a clone (deep copy) of the current DifferentiableSequenceScore instance.
clone() - Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
 
clone() - Method in class de.jstacs.sequenceScores.differentiable.logistic.LogisticDiffSS
 
clone() - Method in class de.jstacs.sequenceScores.differentiable.logistic.ProductConstraint
 
clone() - Method in class de.jstacs.sequenceScores.differentiable.MultiDimensionalSequenceWrapperDiffSS
 
clone() - Method in interface de.jstacs.sequenceScores.SequenceScore
Creates a clone (deep copy) of the current SequenceScore instance.
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMM0DiffSM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MarkovRandomFieldDiffSM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.MixtureDiffSM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.MixtureDurationDiffSM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.PositionDiffSM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
Follows the conventions of Object's clone()-method.
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.CompositeTrainSM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.DifferentiableStatisticalModelWrapperTrainSM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DGTrainSMParameterSet
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DiscreteGraphicalTrainSM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousMM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.BayesianNetworkTrainSM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.DAGTrainSM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGModelForGibbsSampling
In this method the reader is set to null and the paramsFile is cloned.
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhConstraint
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhomogeneousDGTrainSM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEManager
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.SequenceIterator
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureClassifier
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureMixture
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.MultivariateGaussianEmission
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.PhyloDiscreteEmission
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.MixtureEmission
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.UniformEmission
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.DistanceBasedScaledTransitionElement
Clones the object.
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ReferenceBasedTransitionElement
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ScaledTransitionElement
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.TransitionElement
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
 
clone() - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.Transition
This method returns a deep clone of the current instance.
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.HiddenMotifMixture
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.PositionPrior
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.phylo.PhyloNode
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.phylo.PhyloTree
 
clone() - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.TrainableStatisticalModel
Creates a clone (deep copy) of the current TrainableStatisticalModel instance.
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.UniformTrainSM
 
clone() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.VariableLengthWrapperTrainSM
 
clone() - Method in class de.jstacs.utils.DoubleList
 
clone() - Method in class de.jstacs.utils.IntList
 
cloneFunctions(DifferentiableStatisticalModel[]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
This method clones the given array of functions and enables the user to do some post-processing.
cloneHomProb(HomogeneousTrainSM.HomCondProb[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM
Clones the given array of conditional probabilities.
close() - Method in class de.jstacs.utils.NullProgressUpdater
Closes the supervision.
close() - Method in class de.jstacs.utils.REnvironment
Closes the REnvironment and removes all files from the server.
close() - Method in class de.jstacs.utils.SafeOutputStream
Closes the SafeOutputStream by closing the OutputStream this stream was constructed of.
cluster(T...) - Method in class de.jstacs.clustering.hierachical.Hclust
Clusters the supplied objects and return the resulting cluster tree.
cluster(double[][], LinkedList<ClusterTree<Integer>>, int) - Method in class de.jstacs.clustering.hierachical.Hclust
Further clusters the supplied cluster trees using the given distance matrix and creating original indexes for the inner node starting at -indexOff-1 in descending order
cluster(int, double[][], ClusterTree<T>[]) - Method in class de.jstacs.clustering.hierachical.Hclust
Clusters the given leaf trees using the supplied distance matrix
cluster(double[][], T...) - Method in class de.jstacs.clustering.hierachical.Hclust
Clusters the given objects using the supplied distance matrix, which must be in the same order as the elements provided in objects.
ClusterTree<T> - Class in de.jstacs.clustering.hierachical
Class for a generic cluster tree with leaves of type T.
ClusterTree(T, int) - Constructor for class de.jstacs.clustering.hierachical.ClusterTree
Creates a new cluster tree for a given leaf element (i.e., the tree comprises just this leaf) with the supplied index in the set of cluster elements
ClusterTree(double, int, ClusterTree<T>...) - Constructor for class de.jstacs.clustering.hierachical.ClusterTree
Creates a new cluster tree with supplied sub-trees and given distance.
ClusterTree(StringBuffer) - Constructor for class de.jstacs.clustering.hierachical.ClusterTree
Creates a cluster tree from its XML representation
CombinationIterator - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous
This class can be used for iterating over all possible combinations (in the sense of combinatorics).
CombinationIterator(int, int) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.CombinationIterator
Creates a new CombinationIterator with n elements and at most max selected elements.
CombinedCondition - Class in de.jstacs.algorithms.optimization.termination
This class allows to use many TerminationConditions at once.
CombinedCondition(int, AbstractTerminationCondition...) - Constructor for class de.jstacs.algorithms.optimization.termination.CombinedCondition
This constructor creates an instance that allows to use many TerminationConditions at once.
CombinedCondition(CombinedCondition.CombinedConditionParameterSet) - Constructor for class de.jstacs.algorithms.optimization.termination.CombinedCondition
This is the main constructor creating an instance from a given parameter set.
CombinedCondition(StringBuffer) - Constructor for class de.jstacs.algorithms.optimization.termination.CombinedCondition
The standard constructor for the interface Storable.
CombinedCondition.CombinedConditionParameterSet - Class in de.jstacs.algorithms.optimization.termination
This class implements the parameter set for a CombinedCondition.
CombinedConditionParameterSet() - Constructor for class de.jstacs.algorithms.optimization.termination.CombinedCondition.CombinedConditionParameterSet
This constructor creates an empty parameter set.
CombinedConditionParameterSet(StringBuffer) - Constructor for class de.jstacs.algorithms.optimization.termination.CombinedCondition.CombinedConditionParameterSet
The standard constructor for the interface Storable.
CombinedConditionParameterSet(int, AbstractTerminationCondition[]) - Constructor for class de.jstacs.algorithms.optimization.termination.CombinedCondition.CombinedConditionParameterSet
This constructor creates a filled instance of a parameters set.
CombinedFileFilter - Class in de.jstacs.io
This class allows to combine several FileFilters.
CombinedFileFilter(int, FileFilter...) - Constructor for class de.jstacs.io.CombinedFileFilter
Creates an instance that accepts a File if at least minAccepted filters accept the File.
comment - Variable in class de.jstacs.AnnotatedEntity
The comment for the entity.
commentTemplate - Variable in class de.jstacs.parameters.ExpandableParameterSet
A template for the comment of the enclosing ParameterSetContainer
ComparableElement<E,C extends Comparable> - Class in de.jstacs.utils
This class is a container for any objects that have to be compared.
ComparableElement(E, C) - Constructor for class de.jstacs.utils.ComparableElement
Creates a new ComparableElement.
compare(double[], double[], int) - Static method in class de.jstacs.clustering.distances.DeBruijnMotifComparison
Computes the correlation of the two score profiles with relative shifts of the profiles of up to maxShift.
compare(Map.Entry<K, V>, Map.Entry<K, V>) - Method in class de.jstacs.parameters.ParameterSetTagger.KeyEntryComparator
 
compare(Map.Entry<K, ComparableElement<V, Integer>>, Map.Entry<K, ComparableElement<V, Integer>>) - Method in class de.jstacs.parameters.ParameterSetTagger.RankEntryComparator
 
compare(double[], double[]) - Method in class de.jstacs.utils.DoubleArrayComparator
 
compare(double[][], double[][], int) - Method in class de.jstacs.utils.PFMComparator.PFMDistance
This method compares two PFMs, pfm1 and pfm2.
comparePosition(int, MEMConstraint) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint
This method computes the number of overlapping positions between the current and the given constraints.
compareTo(StringAlignment) - Method in class de.jstacs.algorithms.alignment.StringAlignment
 
compareTo(Edge) - Method in class de.jstacs.algorithms.graphs.Edge
 
compareTo(AlphabetContainer) - Method in class de.jstacs.data.AlphabetContainer
 
compareTo(Alphabet) - Method in class de.jstacs.data.alphabets.ContinuousAlphabet
 
compareTo(Alphabet) - Method in class de.jstacs.data.alphabets.DiscreteAlphabet
 
compareTo(float[], float[]) - Method in class de.jstacs.data.sequences.ArbitraryFloatSequence
 
compareTo(double[], double[]) - Method in class de.jstacs.data.sequences.ArbitrarySequence
 
compareTo(Sequence<T>) - Method in class de.jstacs.data.sequences.CyclicSequenceAdaptor
 
compareTo(T, T) - Method in class de.jstacs.data.sequences.CyclicSequenceAdaptor
 
compareTo(double[], double[]) - Method in class de.jstacs.data.sequences.MultiDimensionalArbitrarySequence
 
compareTo(int[], int[]) - Method in class de.jstacs.data.sequences.MultiDimensionalDiscreteSequence
 
compareTo(Sequence<T>) - Method in class de.jstacs.data.sequences.Sequence
 
compareTo(T, T) - Method in class de.jstacs.data.sequences.Sequence
This method compares to container and is used in Sequence.compareTo(Sequence).
compareTo(T, T) - Method in class de.jstacs.data.sequences.Sequence.RecursiveSequence
 
compareTo(int[], int[]) - Method in class de.jstacs.data.sequences.SimpleDiscreteSequence
 
compareTo(SimpleResult) - Method in class de.jstacs.results.SimpleResult
 
compareTo(ComparableElement<E, C>) - Method in class de.jstacs.utils.ComparableElement
 
complement() - Method in class de.jstacs.data.sequences.CyclicSequenceAdaptor
 
complement(int, int) - Method in class de.jstacs.data.sequences.CyclicSequenceAdaptor
 
complement(int, int) - Method in class de.jstacs.data.sequences.MultiDimensionalSequence
 
complement() - Method in class de.jstacs.data.sequences.Sequence
This method returns a new instance of Sequence containing the complementary current Sequence.
complement(int, int) - Method in class de.jstacs.data.sequences.Sequence
This method returns a new instance of Sequence containing a part of the complementary current Sequence.
complement(int, int) - Method in class de.jstacs.data.sequences.SparseSequence
 
ComplementableDiscreteAlphabet - Class in de.jstacs.data.alphabets
This abstract class indicates that an alphabet can be used to compute the complement.
ComplementableDiscreteAlphabet(DiscreteAlphabet.DiscreteAlphabetParameterSet) - Constructor for class de.jstacs.data.alphabets.ComplementableDiscreteAlphabet
The constructor for the InstantiableFromParameterSet interface.
ComplementableDiscreteAlphabet(StringBuffer) - Constructor for class de.jstacs.data.alphabets.ComplementableDiscreteAlphabet
The standard constructor for the interface Storable.
ComplementableDiscreteAlphabet(boolean, String...) - Constructor for class de.jstacs.data.alphabets.ComplementableDiscreteAlphabet
Creates a new ComplementableDiscreteAlphabet from a given array of symbols.
componentHyperParams - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
The hyperparameters for estimating the probabilities of the components.
componentScore - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
This array is used while computing the score.
CompositeLogPrior - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior
This class implements a composite prior that can be used for DifferentiableStatisticalModel.
CompositeLogPrior() - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.CompositeLogPrior
The main constructor.
CompositeLogPrior(StringBuffer) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.CompositeLogPrior
The constructor for the Storable interface.
CompositeSequence(Sequence, int[], int[]) - Constructor for class de.jstacs.data.sequences.Sequence.CompositeSequence
This is a very efficient way to create a Sequence.CompositeSequence for Sequences with a simple AlphabetContainer.
CompositeSequence(AlphabetContainer, Sequence<T>, int[], int[]) - Constructor for class de.jstacs.data.sequences.Sequence.CompositeSequence
This constructor should be used if one wants to create a DataSet of Sequence.CompositeSequences.
CompositeTrainSM - Class in de.jstacs.sequenceScores.statisticalModels.trainable
This class is for modelling sequences by modelling the different positions of the each sequence by different models.
CompositeTrainSM(AlphabetContainer, int[], TrainableStatisticalModel...) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.CompositeTrainSM
Creates a new CompositeTrainSM.
CompositeTrainSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.CompositeTrainSM
The standard constructor for the interface Storable.
compProb - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
This array is used while training to avoid creating many new objects.
Compression - Class in de.jstacs.utils
Class for compressing and de-compressing Strings using ZIP.
Compression() - Constructor for class de.jstacs.utils.Compression
 
compute(double[][][]) - Method in class de.jstacs.classifiers.performanceMeasures.AbstractNumericalTwoClassPerformanceMeasure
 
compute(double[][][], double[][]) - Method in class de.jstacs.classifiers.performanceMeasures.AbstractNumericalTwoClassPerformanceMeasure
 
compute(double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.AbstractNumericalTwoClassPerformanceMeasure
 
compute(double[], double[], double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.AbstractNumericalTwoClassPerformanceMeasure
 
compute(double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.AbstractPerformanceMeasure
 
compute(double[][][]) - Method in class de.jstacs.classifiers.performanceMeasures.AbstractPerformanceMeasure
 
compute(double[][][], double[][]) - Method in class de.jstacs.classifiers.performanceMeasures.AbstractTwoClassPerformanceMeasure
 
compute(double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.AucPR
 
compute(double[][][]) - Method in class de.jstacs.classifiers.performanceMeasures.AucPR
 
compute(double[], double[], double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.AucPR
 
compute(double[][][], double[][]) - Method in class de.jstacs.classifiers.performanceMeasures.AucPR
 
compute(double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.AucROC
 
compute(double[][][]) - Method in class de.jstacs.classifiers.performanceMeasures.AucROC
 
compute(double[], double[], double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.AucROC
 
compute(double[][][], double[][]) - Method in class de.jstacs.classifiers.performanceMeasures.AucROC
 
compute(double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.ClassificationRate
 
compute(double[][][]) - Method in class de.jstacs.classifiers.performanceMeasures.ClassificationRate
 
compute(double[], double[], double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.ClassificationRate
 
compute(double[][][], double[][]) - Method in class de.jstacs.classifiers.performanceMeasures.ClassificationRate
 
compute(double[], double[], double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.ConfusionMatrix
 
compute(double[][][], double[][]) - Method in class de.jstacs.classifiers.performanceMeasures.ConfusionMatrix
 
compute(double[], double[], double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.CorrelationCoefficient
 
compute(double[], double[], double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.FalsePositiveRateForFixedSensitivity
 
compute(double[], double[], double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.MaximumNumericalTwoClassMeasure
 
compute(double[], double[]) - Method in interface de.jstacs.classifiers.performanceMeasures.NumericalPerformanceMeasure
This method allows to compute the performance measure of given sorted score ratios.
compute(double[][][]) - Method in interface de.jstacs.classifiers.performanceMeasures.NumericalPerformanceMeasure
This method allows to compute the performance measure of given class specific scores.
compute(double[], double[], double[], double[]) - Method in interface de.jstacs.classifiers.performanceMeasures.NumericalPerformanceMeasure
This method allows to compute the performance measure of given sorted score ratios.
compute(double[][][], double[][]) - Method in interface de.jstacs.classifiers.performanceMeasures.NumericalPerformanceMeasure
This method allows to compute the performance measure of given class specific scores.
compute(double[], double[]) - Method in interface de.jstacs.classifiers.performanceMeasures.PerformanceMeasure
This method allows to compute the performance measure of given sorted score ratios.
compute(double[][][]) - Method in interface de.jstacs.classifiers.performanceMeasures.PerformanceMeasure
This method allows to compute the performance measure of given class specific scores.
compute(double[], double[], double[], double[]) - Method in interface de.jstacs.classifiers.performanceMeasures.PerformanceMeasure
This method allows to compute the performance measure of given sorted score ratios.
compute(double[][][], double[][]) - Method in interface de.jstacs.classifiers.performanceMeasures.PerformanceMeasure
This method allows to compute the performance measure of given class specific scores.
compute(double[], double[], double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.PositivePredictiveValueForFixedSensitivity
 
compute(double[], double[], double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.PRCurve
 
compute(double[], double[], double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.ROCCurve
 
compute(double[], double[], double[], double[]) - Method in class de.jstacs.classifiers.performanceMeasures.SensitivityForFixedSpecificity
 
computeAlignment(Alignment.AlignmentType, Sequence, Sequence) - Method in class de.jstacs.algorithms.alignment.Alignment
Computes the alignment between s1 and s2.
computeAlignment(Alignment.AlignmentType, Sequence, int, int, Sequence, int, int) - Method in class de.jstacs.algorithms.alignment.Alignment
Computes the alignment between s1 and s2 starting from startS1 and startS2 until endS1 and endS2, respectively.
computeFreqs(double, Constraint...) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.ConstraintManager
This method computes the (smoothed) relative frequencies.
computeGammaNorm() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
Computes the Gamma-normalization for the prior.
computeHiddenParameter(double[], boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
This method has to be invoked during an initialization.
computeLengthOfBurnIn() - Method in class de.jstacs.sampling.AbstractBurnInTest
Computes and returns the length of the burn-in phase using the values from BurnInTest.setValue(double).
computeLengthOfBurnIn() - Method in class de.jstacs.sampling.VarianceRatioBurnInTest
Computes and returns the length of the burn-in phase given by the Variance-Ratio burn-in test.
computeLogGammaSum() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
This method is used to pre-compute the sum of the logarithm of the gamma functions that is used in the prior.
computeMaximalHP(Tensor) - Static method in class de.jstacs.algorithms.graphs.DAG
The method computes the HP(k) (see DAG).
computeMaximalKDAG(Tensor) - Static method in class de.jstacs.algorithms.graphs.DAG
Computes the maximal k-DAG (see DAG), i.e.
computeMaxKDAG(SymmetricTensor) - Static method in class de.jstacs.algorithms.graphs.DAG
Computes the maximal k-DAG (see DAG), i.e.
con - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
The alphabet of the emissions
CONDITIONAL_LIKELIHOOD_INDEX - Static variable in enum de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.LearningPrinciple
This constant is the array index of the weighting factor for the conditional likelihood.
ConfusionMatrix - Class in de.jstacs.classifiers.performanceMeasures
This class implements the performance measure confusion matrix.
ConfusionMatrix() - Constructor for class de.jstacs.classifiers.performanceMeasures.ConfusionMatrix
Constructs a new instance of the performance measure ConfusionMatrix.
ConfusionMatrix(StringBuffer) - Constructor for class de.jstacs.classifiers.performanceMeasures.ConfusionMatrix
The standard constructor for the interface Storable.
CONJUGATE_GRADIENTS_FR - Static variable in class de.jstacs.algorithms.optimization.Optimizer
This constant can be used to specify that conjugate gradients (by Fletcher and Reeves) should be used in the optimize-method.
CONJUGATE_GRADIENTS_PRP - Static variable in class de.jstacs.algorithms.optimization.Optimizer
This constant can be used to specify that conjugate gradients (by Polak and Ribière should be used in the optimize-method.
conjugateGradientsFR(DifferentiableFunction, double[], TerminationCondition, double, StartDistanceForecaster, OutputStream, Time) - Static method in class de.jstacs.algorithms.optimization.Optimizer
The conjugate gradient algorithm by Fletcher and Reeves.
conjugateGradientsPR(DifferentiableFunction, double[], TerminationCondition, double, StartDistanceForecaster, OutputStream, Time) - Static method in class de.jstacs.algorithms.optimization.Optimizer
The conjugate gradient algorithm by Polak and Ribière.
conjugateGradientsPRP(DifferentiableFunction, double[], TerminationCondition, double, StartDistanceForecaster, OutputStream, Time) - Static method in class de.jstacs.algorithms.optimization.Optimizer
The conjugate gradient algorithm by Polak and Ribière called "Polak-Ribière-Positive".
ConstantStartDistance - Class in de.jstacs.algorithms.optimization
The most simple StartDistanceForecaster that returns always the same value.
ConstantStartDistance(double) - Constructor for class de.jstacs.algorithms.optimization.ConstantStartDistance
This constructor creates an instance of ConstantStartDistance that returns always the given value.
Constraint - Interface in de.jstacs.parameters.validation
Interface for a constraint that must be fulfilled in a ConstraintValidator.
Constraint - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete
The main class for all constraints on models.
Constraint(int[], int) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint
The main constructor.
Constraint(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint
The standard constructor for the interface Storable.
ConstraintManager - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete
The class manipulate some constraints.
ConstraintManager.Decomposition - Enum in de.jstacs.sequenceScores.statisticalModels.trainable.discrete
This enum defines the different possible types of decomposition of a model.
ConstraintParameterSet - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters
This class enables you to input your own structure defined by some constraints.
ConstraintParameterSet() - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.ConstraintParameterSet
The main constructor.
ConstraintParameterSet(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.ConstraintParameterSet
The constructor for the Storable interface.
constraints - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.DAGTrainSM
The constraints for the model.
constraints - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEM
The specific constraints of this MEM.
ConstraintValidator - Class in de.jstacs.parameters.validation
Class for a ParameterValidator that is based on Constraints.
ConstraintValidator() - Constructor for class de.jstacs.parameters.validation.ConstraintValidator
Creates a new ConstraintValidator having an empty list of Constraints, i.e.
ConstraintValidator(StringBuffer) - Constructor for class de.jstacs.parameters.validation.ConstraintValidator
The standard constructor for the interface Storable.
container - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
Helper variable = only for internal use.
contains(int) - Method in class de.jstacs.utils.IntList
Checks if val is already returned in the list.
CONTAINS_ALWAYS_A_MOTIF - Static variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
This constant indicates that in each sequence has one binding site of a motif instance (similar to OOPS).
CONTAINS_SOMETIMES_A_MOTIF - Static variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
This constant indicates that a sequence possibly has one binding site of a motif instance (similar to ZOOPS).
content - Variable in class de.jstacs.data.sequences.MultiDimensionalSequence
The internally used sequences.
content - Variable in class de.jstacs.data.sequences.Sequence.RecursiveSequence
The internal sequence.
context - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter
The context of this parameter.
context - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
The context, i.e.
continueIterations(double[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
This method will run the train algorithm for the current model on the internal data set.
continueIterations(double[], double[][], int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
This method will run the train algorithm for the current model on the internal sample.
ContinuousAlphabet - Class in de.jstacs.data.alphabets
Class for a continuous alphabet.
ContinuousAlphabet(ContinuousAlphabet.ContinuousAlphabetParameterSet) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet
The constructor for the InstantiableFromParameterSet interface.
ContinuousAlphabet(double, double) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet
Creates a new ContinuousAlphabet from a minimal and a maximal value.
ContinuousAlphabet(double, double, boolean) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet
Creates a new ContinuousAlphabet from a minimal and a maximal value.
ContinuousAlphabet() - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet
Creates a new ContinuousAlphabet with minimum and maximum value being -Double.MAX_VALUE and Double.MAX_VALUE, respectively.
ContinuousAlphabet(boolean) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet
Creates a new ContinuousAlphabet with minimum and maximum value being -Double.MAX_VALUE and Double.MAX_VALUE, respectively.
ContinuousAlphabet(StringBuffer) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet
The standard constructor for the interface Storable.
continuousAlphabet - Static variable in enum de.jstacs.data.DinucleotideProperty
 
ContinuousAlphabet.ContinuousAlphabetParameterSet - Class in de.jstacs.data.alphabets
Class for the ParameterSet of a ContinuousAlphabet.
ContinuousAlphabetParameterSet() - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet.ContinuousAlphabetParameterSet
Creates a new ContinuousAlphabet.ContinuousAlphabetParameterSet with empty values.
ContinuousAlphabetParameterSet(double, double) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet.ContinuousAlphabetParameterSet
Creates a new ContinuousAlphabet.ContinuousAlphabetParameterSet from a minimum and a maximum value.
ContinuousAlphabetParameterSet(double, double, boolean) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet.ContinuousAlphabetParameterSet
Creates a new ContinuousAlphabet.ContinuousAlphabetParameterSet from a minimum and a maximum value.
ContinuousAlphabetParameterSet(StringBuffer) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet.ContinuousAlphabetParameterSet
The standard constructor for the interface Storable .
continuousVal(int) - Method in class de.jstacs.data.sequences.ArbitraryFloatSequence
 
continuousVal(int) - Method in class de.jstacs.data.sequences.ArbitrarySequence
 
continuousVal(int) - Method in class de.jstacs.data.sequences.CyclicSequenceAdaptor
 
continuousVal(int) - Method in class de.jstacs.data.sequences.MultiDimensionalSequence
 
continuousVal(int) - Method in class de.jstacs.data.sequences.Sequence
Returns the continuous value at position pos of the Sequence.
continuousVal(int) - Method in class de.jstacs.data.sequences.Sequence.RecursiveSequence
 
continuousVal(int) - Method in class de.jstacs.data.sequences.SimpleDiscreteSequence
 
copy(File, File, FileFilter, boolean) - Static method in class de.jstacs.io.FileManager
This method copies all Files and directories, if selected, from a source File, i.e.
copy(String, String) - Static method in class de.jstacs.io.FileManager
This method copies a File in a faster manner.
copy(String, String, byte[]) - Static method in class de.jstacs.io.FileManager
This method copies a File in a faster manner using a specified buffer.
copy(BNDiffSMParameterTree) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
Copies the values of the parameters from another BNDiffSMParameterTree.
copyFileFromServer(String, String, boolean) - Method in class de.jstacs.utils.REnvironment
Copies a file from the server to the local machine.
copyFileFromServer(String, String, RConnection) - Static method in class de.jstacs.utils.RUtils
This method copies a file from the server to the client.
copyFileFromServer(String, OutputStream, RConnection) - Static method in class de.jstacs.utils.RUtils
Pipes a RFileInputStream of the given sourcePath into the given OutputStream out
copyFileToServer(String, String, boolean) - Method in class de.jstacs.utils.REnvironment
Copies a file from the local machine to the server.
copyFileToServer(File, String, RConnection) - Static method in class de.jstacs.utils.RUtils
Copies a file to the R side.
copyFileToServer(String, String, RConnection) - Static method in class de.jstacs.utils.RUtils
Copies a file to the R side.
CorrelationCoefficient - Class in de.jstacs.classifiers.performanceMeasures
PerformanceMeasure using Pearson or Spearman correlation between prediction scores and weighted class labels.
CorrelationCoefficient() - Constructor for class de.jstacs.classifiers.performanceMeasures.CorrelationCoefficient
Creates a new CorrelationCoefficient using Spearman correlation and the raw weighted labels.
CorrelationCoefficient(CorrelationCoefficient.Method, boolean) - Constructor for class de.jstacs.classifiers.performanceMeasures.CorrelationCoefficient
Creates a new CorrelationCoefficient using the suppled type of correlation and, optionally, logit transformation of weighted labels.
CorrelationCoefficient.Method - Enum in de.jstacs.classifiers.performanceMeasures
The type of correlation used.
costs - Variable in class de.jstacs.algorithms.alignment.Alignment
The alignment costs
Costs - Interface in de.jstacs.algorithms.alignment.cost
The general interface for the costs of an alignment.
count - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter
The counts for this parameter.
count(int[][], byte) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.BayesianNetworkTrainSM
Counts the occurrence of the different indegrees and checks if the conventions are met.
countElements() - Method in class de.jstacs.io.SymbolExtractor
Counts the number of elements (symbols) that can be received initially.
counter - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGModelForGibbsSampling
The counter for the sampling steps of each sampling.
counter - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
The counter for the sampling steps of each sampling.
counter - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
The counter for the sampling steps of each sampling.
counter - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
The current index of the parameter set while adjustment (optimization).
countInhomogeneous(AlphabetContainer, int, DataSet, double[], boolean, Constraint...) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.ConstraintManager
Fills the (inhomogeneous) constr with the weighted absolute frequency of the DataSet data and computes the frequencies will not be computed.
counts - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint
The counts for each specific constraint.
create(GenDisMixClassifierParameterSet, LogPrior, double[], DifferentiableStatisticalModel[]...) - Static method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier
This method creates an array of GenDisMixClassifiers by using the cross-product of the given DifferentiableStatisticalModels.
create(AlphabetContainer, String) - Static method in class de.jstacs.data.sequences.Sequence
Creates a Sequence from a String based on the given AlphabetContainer using the standard delimiter for this AlphabetContainer.
create(AlphabetContainer, String, String) - Static method in class de.jstacs.data.sequences.Sequence
Creates a Sequence from a String based on the given AlphabetContainer using the given delimiter delim.
create(AlphabetContainer, SequenceAnnotation[], String, String) - Static method in class de.jstacs.data.sequences.Sequence
Creates a Sequence from a String based on the given AlphabetContainer using the given delimiter delim and some annotation for the Sequence.
createArrayOf(T, int) - Static method in class de.jstacs.io.ArrayHandler
This method returns an array of length l that has at each position a clone of t.
createBayesianNetworkModel(AlphabetContainer, int, double, byte) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.TrainableStatisticalModelFactory
This method returns a Bayesian network model (BN) with user-specified order.
createClassifier(LearningPrinciple, DifferentiableStatisticalModel...) - Static method in class de.jstacs.classifiers.ClassifierFactory
Creates a classifier that is based on at least two DifferentiableStatisticalModels.
createClassifier(double[], DifferentiableStatisticalModel...) - Static method in class de.jstacs.classifiers.ClassifierFactory
Creates a classifier that is based on at least two DifferentiableStatisticalModels.
createClassifier(DifferentiableSequenceScore...) - Static method in class de.jstacs.classifiers.ClassifierFactory
Creates a classifier that is based on at least two DifferentiableSequenceScores.
createConstraints(AbstractList<int[]>, int[], int[]) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.ConstraintManager
Creates the constraints for a part of a model
createConstraints(AbstractList<int[]>, int[]) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.ConstraintManager
Creates the constraints of a model
createConstraints(int[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.DAGTrainSM
This method creates the constraints for a given structure.
createDefaultClassWeights(int, double) - Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
This method creates new class weights.
createErgodicHMM(HMMTrainingParameterSet, int, double, double, double, Emission...) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.HMMFactory
This method creates an ergodic, i.e.
createFiles(StringBuffer) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier.DiffSMSamplingComponent
Creates files out of file contents saved as XML.
createFilledParameters() - Static method in class de.jstacs.classifiers.performanceMeasures.AbstractPerformanceMeasureParameterSet
createFilledParameters(boolean, double, double, double, double) - Static method in class de.jstacs.classifiers.performanceMeasures.AbstractPerformanceMeasureParameterSet
createGenerativeClassifier(TrainableStatisticalModel...) - Static method in class de.jstacs.classifiers.ClassifierFactory
Creates a classifier that is based on at least two TrainableStatisticalModels.
createHelperVariables() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
This method instantiates all helper variables that are need inside the model for instance for filling forward and backward matrix, ...
createHelperVariables() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
 
createHelperVariables() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
 
createHistoryArray(DifferentiableSequenceScore[], History) - Static method in class de.jstacs.motifDiscovery.MutableMotifDiscovererToolbox
This method creates a History-array that can be used in an optimization.
createHomogeneousMarkovModel(AlphabetContainer, double, int, int) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModelFactory
This method returns a homogeneous Markov model with user-specified order.
createHomogeneousMarkovModel(AlphabetContainer, double, byte) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.TrainableStatisticalModelFactory
This method returns a homogeneous Markov model with user-specified order.
createInhomogeneousMarkovModel(AlphabetContainer, int, double, int) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModelFactory
This method returns a inhomogeneous Markov model (IMM) with user-specified order.
createInhomogeneousMarkovModel(AlphabetContainer, int, double, byte) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.TrainableStatisticalModelFactory
This method returns a inhomogeneous Markov model (IMM) with user-specified order.
createMarkovRandomField(AlphabetContainer, int, String) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModelFactory
This method allows to create a MarkovRandomFieldDiffSM of the specified length and with the given constraint type.
createMatrix(String, int[][]) - Method in class de.jstacs.utils.REnvironment
Creates a matrix of ints.
createMatrix(String, double[][]) - Method in class de.jstacs.utils.REnvironment
Creates a matrix of doubles.
createMatrixForStatePosterior(int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
This method creates an empty matrix for the log state posterior.
createMinimalNewLengthArray(DifferentiableSequenceScore[]) - Static method in class de.jstacs.motifDiscovery.MutableMotifDiscovererToolbox
This method creates a minimalNewLength-array that can be used in an optimization.
createMixtureModel(DifferentiableStatisticalModel[]) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModelFactory
This method allows to create a MixtureDiffSM that models a mixture of individual component DifferentiableStatisticalModels.
createMixtureModel(double[], TrainableStatisticalModel[]) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.TrainableStatisticalModelFactory
This method allows to create a MixtureTrainSM that allows to model a DataSet as a mixture of individual components.
createParameterSet(Object[], String[], String[]) - Method in class de.jstacs.parameters.AbstractSelectionParameter
Creates a new ParameterSet from an array of values, an array of names and an array of comments.
createPermutedMarkovModel(AlphabetContainer, int, double, byte) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.TrainableStatisticalModelFactory
This method returns a permuted Markov model (PMM) with user-specified order.
createProfileHMM(MaxHMMTrainingParameterSet, HMMFactory.HMMType, boolean, int, int, AlphabetContainer, double, boolean, boolean, double[][]) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.HMMFactory
Creates a new profile HMM for a given architecture and number of layers.
createProfileHMM(MaxHMMTrainingParameterSet, double[][], boolean, int, int, AlphabetContainer, double, boolean, boolean, double[][], boolean) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.HMMFactory
Creates a new profile HMM for a given architecture and number of layers.
createProfileHMM(MaxHMMTrainingParameterSet, double[][], boolean, int, int, AlphabetContainer, double, boolean, int, double[][], boolean) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.HMMFactory
Creates a new profile HMM for a given architecture and number of layers.
createPseudoErgodicHMM(HMMTrainingParameterSet, double, double, double, AlphabetContainer, int, boolean) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.HMMFactory
Creates an HMM with numStates+1 states, where numStates emitting build a clique and each of those states is connected to the absorbing silent final state.
createPWM(AlphabetContainer, int, double) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModelFactory
This method returns a position weight matrix (PWM).
createPWM(AlphabetContainer, int, double) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.TrainableStatisticalModelFactory
This method returns a position weight matrix (PWM).
createResult(String, String, DataType, Object) - Static method in class de.jstacs.results.Result
Factory method to create a new Result.
createSequences(DiscreteAlphabet, int) - Static method in class de.jstacs.clustering.distances.RandomSequenceScoreDistance
Creates a new random sequence of the given length and alphabet.
createSeqWeightsArray() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
Creates an array that can be used for weighting sequences in the algorithm.
createSeqWeightsArray() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.ZOOPSTrainSM
 
createStates() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
This method creates states for the internal usage.
createStates() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
 
createStates() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
 
createStates() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM
 
createStrandModel(DifferentiableStatisticalModel) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModelFactory
This method allows to create a StrandDiffSM that allows to score binding sites on both strand of DNA.
createStrandModel(TrainableStatisticalModel) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.TrainableStatisticalModelFactory
This method allows to create a StrandTrainSM that allows to score binding sites on both strand of DNA.
createStructure(DataSet[], double[][], boolean) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
Creates the structure that will be used in the optimization.
createStructure(DataSet[], double[][]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
Creates the structure that will be used in the optimization.
createSunflowerHMM(HMMTrainingParameterSet, AlphabetContainer, double, int, boolean, int...) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.HMMFactory
This method creates a first order sunflower HMM.
createSunflowerHMM(HMMTrainingParameterSet, AlphabetContainer, double, int, boolean, PhyloTree[], double[], int[]) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.HMMFactory
This method creates a first order sunflower HMM allowing phylogenetic emissions.
createTransition(double[][], ArrayList<HMMFactory.PseudoTransitionElement>) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.HMMFactory
Creates the real TransitionElements that can be used to create the HMM.
createTree(ClusterTree<Integer>, T...) - Method in class de.jstacs.clustering.hierachical.Hclust
Creates a cluster tree given an index tree using the original indexes refering to the indexes of elements in objects.
createTrees(DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
Creates the tree structures that represent the context (array BayesianNetworkDiffSM.trees) and the parameter objects BayesianNetworkDiffSM.parameters using the given Measure BayesianNetworkDiffSM.structureMeasure.
createVector(String, String[]) - Method in class de.jstacs.utils.REnvironment
Creates a vector of Strings.
createVector(String, int[]) - Method in class de.jstacs.utils.REnvironment
Creates a vector of ints.
createVector(String, long[]) - Method in class de.jstacs.utils.REnvironment
Creates a vector of longs.
createVector(String, double[]) - Method in class de.jstacs.utils.REnvironment
Creates a vector of doubles.
createZOOPS(int, DifferentiableStatisticalModel, HomogeneousDiffSM) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModelFactory
This method allows to create a "zero or one occurrence per sequence" (ZOOPS) model that allows to discover binding sites in a DataSet.
createZOOPS(TrainableStatisticalModel, TrainableStatisticalModel, double[], boolean) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.TrainableStatisticalModelFactory
This method allows to create a "zero or one occurrence per sequence" (ZOOPS) model that allows to discover binding sites in a DataSet.
currentParameters - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
the currently accepted parameters
currentScore - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
cutTree(ClusterTree<T>, double) - Static method in class de.jstacs.clustering.hierachical.Hclust
Cuts the cluster tree at the specified distance and returns the leaf elements grouped by their origin in the sub-trees below the cut
cutTree(double, ClusterTree<T>) - Static method in class de.jstacs.clustering.hierachical.Hclust
Cuts the cluster tree at the given distance and returns the sub-trees below the cut.
CyclicMarkovModelDiffSM - Class in de.jstacs.sequenceScores.statisticalModels.differentiable
This scoring function implements a cyclic Markov model of arbitrary order and periodicity for any sequence length.
CyclicMarkovModelDiffSM(AlphabetContainer, int, int, double, double[], boolean, boolean, int, int) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
The main constructor.
CyclicMarkovModelDiffSM(AlphabetContainer, double[], double[][][], boolean, boolean, int, int) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
This constructor allows to create an instance with specific hyper-parameters for all conditional distributions.
CyclicMarkovModelDiffSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
This is the constructor for Storable.
CyclicSequenceAdaptor<T> - Class in de.jstacs.data.sequences
This class is an adapter for sequence to mimic cyclic sequences.
CyclicSequenceAdaptor(Sequence<T>, int) - Constructor for class de.jstacs.data.sequences.CyclicSequenceAdaptor
Creates a new cyclic sequence of given virtual length (i.e., the length reported by CyclicSequenceAdaptor.getLength()).
CyclicSequenceAdaptor(Sequence<T>) - Constructor for class de.jstacs.data.sequences.CyclicSequenceAdaptor
Creates a new cyclic sequence of the length of the original sequence.
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