- 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
-
- 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
-
- 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
-
- 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
-
- checkConsistency(Alphabet) - Method in class de.jstacs.data.alphabets.Alphabet
-
- 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
DataType
s
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
-
- 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
MotifAnnotation
s of the motifs in the module.
- CisRegulatoryModuleAnnotation(String, MotifAnnotation[], Result...) - Constructor for class de.jstacs.data.sequences.annotation.CisRegulatoryModuleAnnotation
-
- 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
-
- ClassificationRate - Class in de.jstacs.classifiers.performanceMeasures
-
This class implements the classification rate, i.e.
- ClassificationRate() - Constructor for class de.jstacs.classifiers.performanceMeasures.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
-
- ClassifierAssessment(AbstractClassifier...) - Constructor for class de.jstacs.classifiers.assessment.ClassifierAssessment
-
- ClassifierAssessment(boolean, TrainableStatisticalModel[]...) - Constructor for class de.jstacs.classifiers.assessment.ClassifierAssessment
-
- ClassifierAssessment(AbstractClassifier[], boolean, TrainableStatisticalModel[]...) - Constructor for class de.jstacs.classifiers.assessment.ClassifierAssessment
-
- ClassifierAssessmentAssessParameterSet - Class in de.jstacs.classifiers.assessment
-
- ClassifierAssessmentAssessParameterSet() - Constructor for class de.jstacs.classifiers.assessment.ClassifierAssessmentAssessParameterSet
-
- 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
-
- ClassifierFactory - Class in de.jstacs.classifiers
-
- 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
-
- 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
-
- 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
-
- 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
Cloneable
s 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
-
- 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
-
- 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
-
- 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
-
- 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
-
- CombinedCondition - Class in de.jstacs.algorithms.optimization.termination
-
- CombinedCondition(int, AbstractTerminationCondition...) - Constructor for class de.jstacs.algorithms.optimization.termination.CombinedCondition
-
- 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
-
- 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
-
- 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
-
- 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
-
- 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
-
- 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
-
- 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
-
- 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
-
- CompositeSequence(AlphabetContainer, Sequence<T>, int[], int[]) - Constructor for class de.jstacs.data.sequences.Sequence.CompositeSequence
-
- 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
-
- 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
String
s
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
-
- 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
-
- 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
-
- ConstantStartDistance(double) - Constructor for class de.jstacs.algorithms.optimization.ConstantStartDistance
-
- Constraint - Interface in de.jstacs.parameters.validation
-
- 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
-
- ConstraintValidator() - Constructor for class de.jstacs.parameters.validation.ConstraintValidator
-
- 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
-
- ContinuousAlphabet(double, double) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet
-
- ContinuousAlphabet(double, double, boolean) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet
-
- ContinuousAlphabet() - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet
-
- ContinuousAlphabet(boolean) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet
-
- 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
-
- ContinuousAlphabetParameterSet() - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet.ContinuousAlphabetParameterSet
-
- ContinuousAlphabetParameterSet(double, double) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet.ContinuousAlphabetParameterSet
-
- ContinuousAlphabetParameterSet(double, double, boolean) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet.ContinuousAlphabetParameterSet
-
- 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
File
s 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
-
- 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
-
- 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
-
- 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
-
- create(AlphabetContainer, String) - Static method in class de.jstacs.data.sequences.Sequence
-
- create(AlphabetContainer, String, String) - Static method in class de.jstacs.data.sequences.Sequence
-
- create(AlphabetContainer, SequenceAnnotation[], String, String) - Static method in class de.jstacs.data.sequences.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
-
- createClassifier(double[], DifferentiableStatisticalModel...) - Static method in class de.jstacs.classifiers.ClassifierFactory
-
- createClassifier(DifferentiableSequenceScore...) - Static method in class de.jstacs.classifiers.ClassifierFactory
-
- 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
-
- 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
-
- createMatrix(String, int[][]) - Method in class de.jstacs.utils.REnvironment
-
Creates a matrix of int
s.
- createMatrix(String, double[][]) - Method in class de.jstacs.utils.REnvironment
-
Creates a matrix of double
s.
- 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
-
- 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
-
- 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
-
- createVector(String, String[]) - Method in class de.jstacs.utils.REnvironment
-
- createVector(String, int[]) - Method in class de.jstacs.utils.REnvironment
-
Creates a vector of int
s.
- createVector(String, long[]) - Method in class de.jstacs.utils.REnvironment
-
Creates a vector of long
s.
- createVector(String, double[]) - Method in class de.jstacs.utils.REnvironment
-
Creates a vector of double
s.
- 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
-
- 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
-
- CyclicSequenceAdaptor(Sequence<T>) - Constructor for class de.jstacs.data.sequences.CyclicSequenceAdaptor
-
Creates a new cyclic sequence of the length of the original sequence.