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## F

FALSE - Static variable in class de.jstacs.results.StorableResult
  The model/classifier has not been trained.
FalsePositiveRateForFixedSensitivity - Class in de.jstacs.classifiers.performanceMeasures
  This class implements the false positive rate for a fixed sensitivity.
FalsePositiveRateForFixedSensitivity() - Constructor for class de.jstacs.classifiers.performanceMeasures.FalsePositiveRateForFixedSensitivity
  Constructs a new instance of the performance measure `FalsePositiveRateForFixedSensitivity` with empty parameter values.
FalsePositiveRateForFixedSensitivity(double) - Constructor for class de.jstacs.classifiers.performanceMeasures.FalsePositiveRateForFixedSensitivity
  Constructs a new instance of the performance measure `FalsePositiveRateForFixedSensitivity` with given `sensitivity`.
FalsePositiveRateForFixedSensitivity(StringBuffer) - Constructor for class de.jstacs.classifiers.performanceMeasures.FalsePositiveRateForFixedSensitivity
  The standard constructor for the interface `Storable`.
FASTA - Static variable in class de.jstacs.io.AbstractStringExtractor
  The comment character for FastA-formatted files is ">".
FastDirichletMRGParams - Class in de.jstacs.utils.random
  The container for parameters of a Dirichlet random generator that uses the same hyperparameter at all positions.
FastDirichletMRGParams(double) - Constructor for class de.jstacs.utils.random.FastDirichletMRGParams
  Creates the hyperparameter for a Dirichlet random generator which is used at all positions for the hyperparameter vector of the underlying Dirichlet distribution.
file - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  The file in which the component probabilities are stored.
FileManager - Class in de.jstacs.io
  This class is for handling `File`s.
FileParameter - Class in de.jstacs.parameters
  Class for a `Parameter` that represents a local file.
FileParameter(StringBuffer) - Constructor for class de.jstacs.parameters.FileParameter
  The standard constructor for the interface `Storable`.
FileParameter(String, String, String, boolean) - Constructor for class de.jstacs.parameters.FileParameter
  Creates a `FileParameter`.
FileParameter(String, String, String, boolean, ParameterValidator) - Constructor for class de.jstacs.parameters.FileParameter
  Constructs a `FileParameter`.
FileParameter.FileRepresentation - Class in de.jstacs.parameters
  Class that represents a file.
FileParameter.FileRepresentation(String, String) - Constructor for class de.jstacs.parameters.FileParameter.FileRepresentation
  Creates a `FileParameter.FileRepresentation` out of the filename and the file's contents.
FileParameter.FileRepresentation(String) - Constructor for class de.jstacs.parameters.FileParameter.FileRepresentation
  Creates a new `FileParameter.FileRepresentation` from a filename.
FileParameter.FileRepresentation(StringBuffer) - Constructor for class de.jstacs.parameters.FileParameter.FileRepresentation
  The standard constructor for the interface `Storable` .
filereader - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  Reading component probabilities from a file.
filewriter - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  Saving component probabilities in a file.
fill(double[], double[][][][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
  Fills all parameters with the probabilities given in `distribution`.
fillBwdMatrix(int, int, Sequence) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
  This method fills the backward-matrix for a given sequence.
fillBwdMatrix(int, int, Sequence) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
fillBwdOrViterbiMatrix(HigherOrderHMM.Type, int, int, double, Sequence) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
  This method computes the entries of the backward or the viterbi matrix.
fillComponentScoreOf(int, Sequence, int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
  This method fills an internal array with the partial scores.
fillComponentScores(Sequence, int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
  Fills the internal array `AbstractMixtureDiffSM.componentScore` with the logarithmic scores of the components given a `Sequence`.
fillComponentScores(Sequence, int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.MixtureDiffSM
fillComponentScores(Sequence, int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
fillComponentScores(Sequence, int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
fillContainer(float[], int) - Method in class de.jstacs.data.sequences.ArbitraryFloatSequence
fillContainer(double[], int) - Method in class de.jstacs.data.sequences.ArbitrarySequence
fillContainer(int[], int) - Method in class de.jstacs.data.sequences.MappedDiscreteSequence
fillContainer(double[], int) - Method in class de.jstacs.data.sequences.MultiDimensionalArbitrarySequence
fillContainer(int[], int) - Method in class de.jstacs.data.sequences.MultiDimensionalDiscreteSequence
fillContainer(T, int) - Method in class de.jstacs.data.sequences.Sequence
  The method fills the content of a specific position in to the container.
fillContainer(T, int) - Method in class de.jstacs.data.sequences.Sequence.RecursiveSequence
fillContainer(int[], int) - Method in class de.jstacs.data.sequences.SimpleDiscreteSequence
fillCurrentParameter(double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
fillCurrentParameter(double[]) - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.DifferentiableEmission
  Fills the current parameters in the global code>params array using the internal offset.
fillCurrentParameter(double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
fillCurrentParameter(double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
fillCurrentParameter(double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.UniformEmission
fillFwdMatrix(int, int, Sequence) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
  This method fills the forward-matrix for a given sequence.
fillFwdMatrix(int, int, Sequence) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
fillLogStatePosteriorMatrix(double[][], int, int, Sequence, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
  This method fills the log state posterior of Sequence `seq` in a given matrix.
fillLogStatePosteriorMatrix(double[][], int, int, Sequence, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
fillParameters(String, String...) - Method in class de.jstacs.parameters.ParameterSetTagger
fillParameters(double[]) - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.DifferentiableTransition
  This method allows to fill the parameters of the transition in a given array.
fillParameters(double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.TransitionElement
  This method fills the current parameters of this `TransitionElement` into the given array `params` starting at position `offset`.
fillParameters(double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
fillParameters(double[], int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
  This method allows to fill the current parameters using a specific offset.
fillSamplingGroups(int, LinkedList<int[]>) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
fillSamplingGroups(int, LinkedList<int[]>) - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.DifferentiableEmission
  Adds the groups of indexes of those parameters of this emission that should be sampled together in one step of a grouped sampling procedure, each as an `int[]`, into `list`.
fillSamplingGroups(int, LinkedList<int[]>) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
fillSamplingGroups(int, LinkedList<int[]>) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
fillSamplingGroups(int, LinkedList<int[]>) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.UniformEmission
fillSamplingGroups(int, LinkedList<int[]>) - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.DifferentiableTransition
  Adds the groups of indexes of those parameters of this transition that should be sampled together in one step of a grouped sampling procedure, each as an `int[]`, into `list`.
fillSamplingGroups(int, LinkedList<int[]>) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
fillTensor(Tensor, double[][]) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure
  Fills a `Tensor` `t` with the weights defined in `weights`.
fillTensor(Tensor, double[][][]) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure
  Fills a `Tensor` `t` with the weights defined in `weights`.
fillTransitionInformation(int, int, int, int[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
fillTransitionInformation(int, int, int, int[]) - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.Transition
  This method fills all relevant information for a specific edge in a given container.
filter(String, char, AlphabetContainer, int, String) - Static method in class de.jstacs.io.SymbolExtractor
  This method allows the user to filter the content of a `File` using a given `AlphabetContainer` and a minimal sequence length.
filterBySuperclass(Class<S>, LinkedList<Class<? extends T>>) - Static method in class de.jstacs.utils.SubclassFinder
  Returns a `LinkedList` of the `Class` objects for all classes in `listToFilter` that are sub-classes of `superClass`.
filterProperties(String, Boolean, DinucleotideProperty.HowCreated, DinucleotideProperty.Type, String) - Static method in enum de.jstacs.data.DinucleotideProperty
  Filters all `DinucleotideProperty`s by some of their annotations.
finalize() - Method in class de.jstacs.io.SparseStringExtractor
finalize() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGModelForGibbsSampling
finalize() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
finalize() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
finalize() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
finalize() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
finalize() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
finalize() - Method in class de.jstacs.utils.GUIProgressUpdater
finalize() - Method in class de.jstacs.utils.REnvironment
finalize() - Method in class de.jstacs.utils.SafeOutputStream
finalState - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
  An array of switches that contains for each state whether is is a final state or not (cf.
find(int) - Method in class de.jstacs.algorithms.graphs.UnionFind
  Finds the root of the tree with node `n` and does path contraction.
findBracket(OneDimensionalFunction, double, double, double) - Static method in class de.jstacs.algorithms.optimization.Optimizer
  This method returns a bracket containing a minimum.
findBracket(OneDimensionalFunction, double, double) - Static method in class de.jstacs.algorithms.optimization.Optimizer
  This method returns a bracket containing a minimum.
findColumn(String) - Method in class de.jstacs.results.ResultSet
  This method enables you to search for a column.
findInstantiableSubclasses(Class<T>, String) - Static method in class de.jstacs.utils.SubclassFinder
  Returns all sub-classes of `T` that can be instantiated, i.e.
findMax() - Method in class de.jstacs.algorithms.optimization.QuadraticFunction
  This method returns the maximum of the `QuadraticFunction`.
findMin(double, double, double, double) - Method in class de.jstacs.algorithms.optimization.OneDimensionalFunction
  This method returns a minimum `x` and the value `f(x)`, starting the search at `lower`.
findMin() - Method in class de.jstacs.algorithms.optimization.QuadraticFunction
  This method returns the minimum of the `QuadraticFunction`.
findModification(int, int, MutableMotifDiscoverer, DifferentiableSequenceScore[], DataSet[], double[][], DiffSSBasedOptimizableFunction, DifferentiableFunction, byte, double, StartDistanceForecaster, SafeOutputStream, History, int, boolean) - Static method in class de.jstacs.motifDiscovery.MutableMotifDiscovererToolbox
  This method tries to find a modification, i.e. shifting, shrinking, or expanding a motif, that is promising.
findOneDimensionalMin(double[], double[], double, double, double, double) - Method in class de.jstacs.algorithms.optimization.DifferentiableFunction
  This method is used to find an approximation of an one-dimensional subfunction.
findSignificantMotifOccurrences(int, Sequence, int) - Method in class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
  This method finds the significant motif occurrences in the sequence.
findSignificantMotifOccurrences(int, Sequence, int, int) - Method in class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
  This method finds the significant motif occurrences in the sequence.
findSubclasses(Class<T>, String) - Static method in class de.jstacs.utils.SubclassFinder
  Returns all sub-classes of `T` including interfaces and abstract classes that are located in a package below `startPackage`.
flatCloneWithoutAnnotation() - Method in class de.jstacs.data.sequences.ArbitraryFloatSequence
flatCloneWithoutAnnotation() - Method in class de.jstacs.data.sequences.ArbitrarySequence
flatCloneWithoutAnnotation() - Method in class de.jstacs.data.sequences.ByteSequence
flatCloneWithoutAnnotation() - Method in class de.jstacs.data.sequences.IntSequence
flatCloneWithoutAnnotation() - Method in class de.jstacs.data.sequences.MappedDiscreteSequence
flatCloneWithoutAnnotation() - Method in class de.jstacs.data.sequences.MultiDimensionalSequence
flatCloneWithoutAnnotation() - Method in class de.jstacs.data.sequences.PermutedSequence
flatCloneWithoutAnnotation() - Method in class de.jstacs.data.sequences.Sequence.CompositeSequence
flatCloneWithoutAnnotation() - Method in class de.jstacs.data.sequences.Sequence
  Works in analogy to `Object.clone()`, but does not clone the annotation.
flatCloneWithoutAnnotation() - Method in class de.jstacs.data.sequences.Sequence.SubSequence
flatCloneWithoutAnnotation() - Method in class de.jstacs.data.sequences.ShortSequence
flatCloneWithoutAnnotation() - Method in class de.jstacs.data.sequences.SparseSequence
flush() - Method in class de.jstacs.utils.SafeOutputStream
forward(BNDiffSMParameterTree[]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
  Computes the forward-part of the normalization constant starting from this `BNDiffSMParameterTree`.
forward - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
  An array of switches that contains for each state whether the emission is forward or the reverse strand.
forward - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleState
  A switch that decides whether to use the forward or the reverse complementary strand of a sequence.
freeParameters - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateLogPrior
  Indicates, if only free parameters shall be used and hence penalized.
freeParams - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractOptimizableFunction
  Indicates whether only the free parameters or all should be used.
freeParams - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
  This `boolean` indicates whether free parameterization or all parameters are used.
freq - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint
  The frequencies estimated from the counts.
fromGalaxy(String, StringBuffer) - Method in class de.jstacs.parameters.ExpandableParameterSet
fromGalaxy(String, StringBuffer) - Method in class de.jstacs.parameters.FileParameter
fromGalaxy(String, StringBuffer) - Method in interface de.jstacs.parameters.GalaxyConvertible
  Parses the contents of `command` in the format defined by `configBuffer` of `GalaxyConvertible.toGalaxy(String, String, int, StringBuffer, StringBuffer)` and sets the values of the `Parameter` or `ParameterSet` accordingly.
fromGalaxy(String, StringBuffer) - Method in class de.jstacs.parameters.MultiSelectionParameter
fromGalaxy(String, StringBuffer) - Method in class de.jstacs.parameters.ParameterSet
fromGalaxy(String, StringBuffer) - Method in class de.jstacs.parameters.ParameterSetContainer
fromGalaxy(String, StringBuffer) - Method in class de.jstacs.parameters.RangeParameter
fromGalaxy(String, StringBuffer) - Method in class de.jstacs.parameters.SelectionParameter
fromGalaxy(String, StringBuffer) - Method in class de.jstacs.parameters.SimpleParameter
fromGalaxy(String, StringBuffer) - Method in class de.jstacs.parameters.validation.NumberValidator
fromGalaxyConfig(String) - Method in class de.jstacs.utils.galaxy.GalaxyAdaptor
  Parses the values of the parameters from a galaxy script file
fromXML(StringBuffer) - Method in class de.jstacs.data.AlphabetContainerParameterSet.AlphabetArrayParameterSet
fromXML(StringBuffer) - Method in class de.jstacs.data.AlphabetContainerParameterSet
fromXML(StringBuffer) - Method in class de.jstacs.data.AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet
fromXML(StringBuffer) - Method in class de.jstacs.data.sequences.annotation.LocatedSequenceAnnotation
fromXML(StringBuffer) - Method in class de.jstacs.data.sequences.annotation.LocatedSequenceAnnotationWithLength
fromXML(StringBuffer) - Method in class de.jstacs.data.sequences.annotation.SequenceAnnotation
fromXML(StringBuffer) - Method in class de.jstacs.data.sequences.annotation.StrandedLocatedSequenceAnnotationWithLength
fromXML(StringBuffer) - Method in class de.jstacs.parameters.ArrayParameterSet
fromXML(StringBuffer) - Method in class de.jstacs.parameters.ExpandableParameterSet
fromXML(StringBuffer) - Method in class de.jstacs.parameters.InstanceParameterSet
fromXML(StringBuffer) - Method in class de.jstacs.parameters.ParameterSet
  Parses the instance fields of a `ParameterSet` from the XML representation as returned by `ParameterSet.toXML()`.
fromXML(StringBuffer) - Method in class de.jstacs.parameters.SequenceScoringParameterSet
fromXML(StringBuffer) - Method in class de.jstacs.parameters.validation.ConstraintValidator
  Parses a `ConstraintValidator` from the XML representation as returned by `ConstraintValidator.toXML()`.
fromXML(StringBuffer) - Method in class de.jstacs.parameters.validation.NumberValidator
  Parses a `NumberValidator` from the XML representation as returned by `NumberValidator.toXML()`.
fromXML(StringBuffer) - Method in class de.jstacs.parameters.validation.SimpleStaticConstraint
  Parses a `SimpleStaticConstraint` from the XML representation as returned by `SimpleStaticConstraint.toXML()`.
fromXML(StringBuffer) - Method in class de.jstacs.parameters.validation.StorableValidator
  Parses a `StorableValidator` from the XML representation as returned by `StorableValidator.toXML()`.
fromXML(StringBuffer) - Method in class de.jstacs.results.MeanResultSet
fromXML(StringBuffer) - Method in class de.jstacs.results.ResultSet
  Parses the contents of a `ResultSet` from its XML representation as returned by `ResultSet.toXML()`.
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
  This method is called in the constructor for the `Storable` interface to create a scoring function from a `StringBuffer`.
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.differentiable.logistic.LogisticDiffSS
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.differentiable.UniformDiffSS
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.MarkovModelDiffSM
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMM0DiffSM
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.UniformHomogeneousDiffSM
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MarkovRandomFieldDiffSM
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.DurationDiffSM
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.MixtureDurationDiffSM
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.PositionDiffSM
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MultiDimensionalSequenceWrapperDiffSM
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
  This method should only be used by the constructor that works on a `StringBuffer`.
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.CompositeTrainSM
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.DifferentiableStatisticalModelWrapperTrainSM
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DiscreteGraphicalTrainSM
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureMixture
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
  This method is used by the `AbstractHMM.AbstractHMM(StringBuffer)` constructor for creating an instance from an XML representation.
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
  This method is internally used by the constructor `GaussianEmission.GaussianEmission(StringBuffer)`.
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.PluginGaussianEmission
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
  This method is internally used by the constructor `AbstractConditionalDiscreteEmission.AbstractConditionalDiscreteEmission(StringBuffer)`.
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.UniformTrainSM
fromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.VariableLengthWrapperTrainSM
FSDAGModelForGibbsSampling - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous
  This is the class for a fixed structure directed acyclic graphical model (see `FSDAGTrainSM`) that can be used in a Gibbs sampling.
FSDAGModelForGibbsSampling(FSDAGModelForGibbsSamplingParameterSet) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGModelForGibbsSampling
  The default constructor.
FSDAGModelForGibbsSampling(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGModelForGibbsSampling
  This is the constructor for the `Storable` interface.
FSDAGModelForGibbsSamplingParameterSet - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters
  The class for the parameters of a `FSDAGModelForGibbsSampling`.
FSDAGModelForGibbsSamplingParameterSet(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.FSDAGModelForGibbsSamplingParameterSet
  The constructor for the `Storable` interface.
FSDAGModelForGibbsSamplingParameterSet() - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.FSDAGModelForGibbsSamplingParameterSet
  This is the constructor that creates an empty parameter set for a `FSDAGModelForGibbsSampling`.
FSDAGModelForGibbsSamplingParameterSet(AlphabetContainer, int, double, String, String) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.FSDAGModelForGibbsSamplingParameterSet
  This is the constructor that creates a filled parameter set.
FSDAGTrainSM - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous
  This class can be used for any discrete fixed structure directed acyclic graphical model ( `FSDAGTrainSM`).
FSDAGTrainSM(FSDAGTrainSMParameterSet) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGTrainSM
  This is the main constructor.
FSDAGTrainSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGTrainSM
  The standard constructor for the interface `Storable`.
FSDAGTrainSMParameterSet - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters
  The class for the parameters of a `FSDAGTrainSM` (fixed structure directed acyclic graphical model).
FSDAGTrainSMParameterSet(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.FSDAGTrainSMParameterSet
  The standard constructor for the interface `Storable`.
FSDAGTrainSMParameterSet() - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.FSDAGTrainSMParameterSet
  This constructor creates an empty `FSDAGTrainSMParameterSet` set for a `FSDAGTrainSM`.
FSDAGTrainSMParameterSet(AlphabetContainer, int, double, String, String) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.FSDAGTrainSMParameterSet
  This constructor creates an `FSDAGTrainSMParameterSet` instance.
FSDAGTrainSMParameterSet(Class<? extends FSDAGTrainSM>) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.FSDAGTrainSMParameterSet
  This the constructor creates an empty `FSDAGTrainSMParameterSet` from the class that can be instantiated using this `FSDAGTrainSMParameterSet`.
FSDAGTrainSMParameterSet(Class<? extends FSDAGTrainSM>, AlphabetContainer, int, double, String, String) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.FSDAGTrainSMParameterSet
  This constructor creates an `FSDAGTrainSMParameterSet` instance for the specified class.
Function - Interface in de.jstacs.algorithms.optimization
  This interface is the framework for any mathematical function .
function - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier
  The function that is optimized in this classifier.
function - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
  This array contains the internal `DifferentiableStatisticalModel`s that are used to determine the score.
funs - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateLogPrior
  The `DifferentiableSequenceScore`s using the parameters that shall be penalized.
furtherInits(DataSet, double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM
  This method allows the implementation of further initialisations
fwdMatrix - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
  matrix for all forward-computed variables; fwdMatrix[l][c] = log P(x_1,...

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