Overview | Package | Class | Use | Tree | Deprecated | Index | Help
PREV LETTER NEXT LETTER | FRAMES NO FRAMES

A B C D E F G H I K L M N O P Q R S T U V W X

## I

IDGMParameterSet - Class in de.jstacs.models.discrete.inhomogeneous.parameters
  This is the abstract container of parameters that is a root container for all inhomogeneous discrete graphical model parameter containers.
IDGMParameterSet(StringBuffer) - Constructor for class de.jstacs.models.discrete.inhomogeneous.parameters.IDGMParameterSet
  The standard constructor for the interface `Storable`.
IDGMParameterSet(Class<? extends InhomogeneousDGM>) - Constructor for class de.jstacs.models.discrete.inhomogeneous.parameters.IDGMParameterSet
  This constructor creates an empty `IDGMParameterSet` instance from the class that can be instantiated using this `IDGMParameterSet`.
IDGMParameterSet(Class<? extends InhomogeneousDGM>, AlphabetContainer, int, double, String) - Constructor for class de.jstacs.models.discrete.inhomogeneous.parameters.IDGMParameterSet
  This constructor creates an `IDGMParameterSet` instance for the specified class.
ignore - Variable in class de.jstacs.io.AbstractStringExtractor
  The internal comment character.
ignorePattern - Variable in class de.jstacs.io.AbstractStringExtractor
  The pattern for ignoring comment lines.
ignoresCase() - Method in class de.jstacs.data.AlphabetContainer
  Indicates if all used `Alphabet`s ignore the case.
ignoresCase() - Method in class de.jstacs.data.alphabets.DiscreteAlphabet
  Indicates if the alphabet ignores the case.
iList - Variable in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
  This array contains some `IntList`s that are used while computing the partial derivation.
ImageResult - Class in de.jstacs.results
  A class for results that are images of the PNG format.
ImageResult(String, String, BufferedImage) - Constructor for class de.jstacs.results.ImageResult
  Constructs a new `ImageResult` from a `BufferedImage`.
IndependentProductScoringFunction - Class in de.jstacs.scoringFunctions
  This class enables the user to model parts of a sequence independent of each other.
IndependentProductScoringFunction(NormalizableScoringFunction...) - Constructor for class de.jstacs.scoringFunctions.IndependentProductScoringFunction
  This constructor creates an instance of an `IndependentProductScoringFunction` from a given series of independent `NormalizableScoringFunction`s.
IndependentProductScoringFunction(NormalizableScoringFunction[], int[]) - Constructor for class de.jstacs.scoringFunctions.IndependentProductScoringFunction
  This constructor creates an instance of an `IndependentProductScoringFunction` from given series of independent `NormalizableScoringFunction`s and lengths.
IndependentProductScoringFunction(StringBuffer) - Constructor for class de.jstacs.scoringFunctions.IndependentProductScoringFunction
  This is the constructor for the interface `Storable`.
InhCondProb - Class in de.jstacs.models.discrete.inhomogeneous
  This class handles (conditional) probabilities of sequences for ihnhomogeneous models.
InhCondProb(int, int...) - Constructor for class de.jstacs.models.discrete.inhomogeneous.InhCondProb
  Creates a new `InhCondProb` instance.
InhCondProb(int[], int[], boolean) - Constructor for class de.jstacs.models.discrete.inhomogeneous.InhCondProb
  Creates a new `InhCondProb` instance.
InhCondProb(StringBuffer) - Constructor for class de.jstacs.models.discrete.inhomogeneous.InhCondProb
  The standard constructor for the interface `Storable`.
InhConstraint - Class in de.jstacs.models.discrete.inhomogeneous
  This class is the superclass for all inhomogeneous constraints.
InhConstraint(int[], int[]) - Constructor for class de.jstacs.models.discrete.inhomogeneous.InhConstraint
  Creates a new `InhConstraint` instance.
InhConstraint(StringBuffer) - Constructor for class de.jstacs.models.discrete.inhomogeneous.InhConstraint
  The standard constructor for the interface `Storable`.
InhomogeneousDGM - Class in de.jstacs.models.discrete.inhomogeneous
  This class is the main class for all inhomogeneous discrete graphical models (`InhomogeneousDGM`).
InhomogeneousDGM(IDGMParameterSet) - Constructor for class de.jstacs.models.discrete.inhomogeneous.InhomogeneousDGM
  Creates a new `InhomogeneousDGM` from a given `IDGMParameterSet`.
InhomogeneousDGM(StringBuffer) - Constructor for class de.jstacs.models.discrete.inhomogeneous.InhomogeneousDGM
  The standard constructor for the interface `Storable`.
InhomogeneousMarkov - Class in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures
  Class for a network structure of a `BayesianNetworkScoringFunction` that is an inhomogeneous Markov model.
InhomogeneousMarkov(int) - Constructor for class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov
  Creates the structure of an inhomogeneous Markov model of order `order`.
InhomogeneousMarkov(InhomogeneousMarkov.InhomogeneousMarkovParameterSet) - Constructor for class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov
  Creates a new `InhomogeneousMarkov` from the corresponding `InstanceParameterSet` `parameters`.
InhomogeneousMarkov(StringBuffer) - Constructor for class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov
  The standard constructor for the interface `Storable`.
InhomogeneousMarkov.InhomogeneousMarkovParameterSet - Class in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures
  Class for an `InstanceParameterSet` that defines the parameters of an `InhomogeneousMarkov` structure `Measure`.
InhomogeneousMarkov.InhomogeneousMarkovParameterSet() - Constructor for class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov.InhomogeneousMarkovParameterSet
  Creates a new `InhomogeneousMarkov.InhomogeneousMarkovParameterSet` with empty parameter values.
InhomogeneousMarkov.InhomogeneousMarkovParameterSet(int) - Constructor for class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov.InhomogeneousMarkovParameterSet
  Creates a new `InhomogeneousMarkov.InhomogeneousMarkovParameterSet` with the parameter for the order set to `order`.
InhomogeneousMarkov.InhomogeneousMarkovParameterSet(StringBuffer) - Constructor for class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov.InhomogeneousMarkovParameterSet
  Creates a new `InhomogeneousMarkov.InhomogeneousMarkovParameterSet` from its XML representation as defined by the `Storable` interface.
init(boolean) - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
  This method creates the underlying structure for the parameters.
init(boolean) - Method in class de.jstacs.scoringFunctions.mix.StrandScoringFunction
initialIteration - Variable in class de.jstacs.models.mixture.AbstractMixtureModel
  The number of initial iterations.
initializeFunction(int, boolean, Sample[], double[][]) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
initializeFunction(int, boolean, Sample[], double[][]) - Method in class de.jstacs.scoringFunctions.homogeneous.HMM0ScoringFunction
initializeFunction(int, boolean, Sample[], double[][]) - Method in class de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction
initializeFunction(int, boolean, Sample[], double[][]) - Method in class de.jstacs.scoringFunctions.homogeneous.UniformHomogeneousScoringFunction
initializeFunction(int, boolean, Sample[], double[][]) - Method in class de.jstacs.scoringFunctions.IndependentProductScoringFunction
initializeFunction(int, boolean, Sample[], double[][]) - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
initializeFunction(int, boolean, Sample[], double[][]) - Method in class de.jstacs.scoringFunctions.mix.motifSearch.SkewNormalLikeScoringFunction
initializeFunction(int, boolean, Sample[], double[][]) - Method in class de.jstacs.scoringFunctions.mix.motifSearch.UniformDurationScoringFunction
initializeFunction(int, boolean, Sample[], double[][]) - Method in class de.jstacs.scoringFunctions.MRFScoringFunction
initializeFunction(int, boolean, Sample[], double[][]) - Method in class de.jstacs.scoringFunctions.NormalizedScoringFunction
initializeFunction(int, boolean, Sample[], double[][]) - Method in interface de.jstacs.scoringFunctions.ScoringFunction
  This method creates the underlying structure of the `ScoringFunction`.
initializeFunction(int, boolean, Sample[], double[][]) - Method in class de.jstacs.scoringFunctions.UniformScoringFunction
initializeFunctionRandomly(boolean) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
initializeFunctionRandomly(boolean) - Method in class de.jstacs.scoringFunctions.homogeneous.HMM0ScoringFunction
initializeFunctionRandomly(boolean) - Method in class de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction
initializeFunctionRandomly(boolean) - Method in class de.jstacs.scoringFunctions.homogeneous.UniformHomogeneousScoringFunction
initializeFunctionRandomly(boolean) - Method in class de.jstacs.scoringFunctions.IndependentProductScoringFunction
initializeFunctionRandomly(boolean) - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
initializeFunctionRandomly(boolean) - Method in class de.jstacs.scoringFunctions.mix.motifSearch.HiddenMotifsMixture
initializeFunctionRandomly(boolean) - Method in class de.jstacs.scoringFunctions.mix.motifSearch.SkewNormalLikeScoringFunction
initializeFunctionRandomly(boolean) - Method in class de.jstacs.scoringFunctions.mix.motifSearch.UniformDurationScoringFunction
initializeFunctionRandomly(boolean) - Method in class de.jstacs.scoringFunctions.MRFScoringFunction
initializeFunctionRandomly(boolean) - Method in class de.jstacs.scoringFunctions.NormalizedScoringFunction
initializeFunctionRandomly(boolean) - Method in interface de.jstacs.scoringFunctions.ScoringFunction
  This method initializes the `ScoringFunction` randomly.
initializeFunctionRandomly(boolean) - Method in class de.jstacs.scoringFunctions.UniformScoringFunction
initializeHiddenPotentialRandomly() - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
  This method initializes the hidden potential (and the corresponding parameters) randomly.
initializeHiddenUniformly() - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
  This method initializes the hidden parameters of the instance uniformly.
initializeHiddenUniformly() - Method in class de.jstacs.scoringFunctions.NormalizedScoringFunction
  This method initializes the hidden parameters of the internal `NormalizableScoringFunction` uniformly if it is a `AbstractMixtureScoringFunction`.
initializeMotif(int, Sample, double[]) - Method in interface de.jstacs.motifDiscovery.MutableMotifDiscoverer
  This method allows to initialize the model of a motif manually using a weighted sample.
initializeMotif(int, Sample, double[]) - Method in class de.jstacs.scoringFunctions.IndependentProductScoringFunction
initializeMotif(int, Sample, double[]) - Method in class de.jstacs.scoringFunctions.mix.motifSearch.HiddenMotifsMixture
initializeMotifRandomly(int) - Method in class de.jstacs.scoringFunctions.mix.motifSearch.HiddenMotifsMixture
  This method initializes the motif with index `motif` randomly using `ScoringFunction.initializeFunctionRandomly(boolean)`.
initializeMyParametersArrayList() - Method in class de.jstacs.classifier.assessment.ClassifierAssessmentAssessParameterSet
  Initializes the list of `Parameter`s in this `ClassifierAssessmentAssessParameterSet`.
initializeMyParametersArrayList() - Method in class de.jstacs.classifier.assessment.KFoldCVAssessParameterSet
initializeMyParametersArrayList() - Method in class de.jstacs.classifier.assessment.RepeatedHoldOutAssessParameterSet
initializeMyParametersArrayList() - Method in class de.jstacs.classifier.assessment.RepeatedSubSamplingAssessParameterSet
initializeMyParametersArrayList() - Method in class de.jstacs.classifier.assessment.Sampled_RepeatedHoldOutAssessParameterSet
initializeRandomly(double) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.ParameterTree
  Initializes the parameters of this `ParameterTree` randomly.
initializeUniformly(boolean) - Method in class de.jstacs.scoringFunctions.homogeneous.HMM0ScoringFunction
initializeUniformly(boolean) - Method in class de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction
initializeUniformly(boolean) - Method in class de.jstacs.scoringFunctions.homogeneous.HomogeneousScoringFunction
  This method allows to initialize the instance with an uniform distribution.
initializeUniformly(boolean) - Method in class de.jstacs.scoringFunctions.homogeneous.UniformHomogeneousScoringFunction
initializeUniformly() - Method in class de.jstacs.scoringFunctions.mix.motifSearch.DurationScoringFunction
  This method set special parameters that lead to an uniform distribution.
initializeUniformly() - Method in class de.jstacs.scoringFunctions.mix.motifSearch.SkewNormalLikeScoringFunction
initializeUniformly() - Method in class de.jstacs.scoringFunctions.mix.motifSearch.UniformDurationScoringFunction
initializeUsingPlugIn(int, boolean, Sample[], double[][]) - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
  This method initializes the functions using the data in some way.
initializeUsingPlugIn(int, boolean, Sample[], double[][]) - Method in class de.jstacs.scoringFunctions.mix.MixtureScoringFunction
initializeUsingPlugIn(int, boolean, Sample[], double[][]) - Method in class de.jstacs.scoringFunctions.mix.motifSearch.HiddenMotifsMixture
initializeUsingPlugIn(int, boolean, Sample[], double[][]) - Method in class de.jstacs.scoringFunctions.mix.StrandScoringFunction
initModelForSampling(int) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
  This method initializes the model for the sampling.
initModelForSampling(int) - Method in class de.jstacs.models.mixture.gibbssampling.FSDAGModelForGibbsSampling
initModelForSampling(int) - Method in interface de.jstacs.models.mixture.gibbssampling.GibbsSamplingComponent
  This method initializes the model for the sampling.
initParameterList() - Method in class de.jstacs.parameters.ParameterSet
  Initializes the internal set of `Parameter`s, which is a `ParameterSet.ParameterList`.
initParameterList(int) - Method in class de.jstacs.parameters.ParameterSet
  Initializes the internal set of `Parameter`s, which is a `ParameterSet.ParameterList`, with an initial number of `Parameter`s of `initCapacity`.
initWithLength(boolean, int) - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
  This method is used to create the underlying structure, e.g.
initWithPrior(double[]) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
  This method sets the initial weights before counting the usage of each component.
insertAlphabet(AlphabetContainer, Alphabet, boolean[]) - Static method in class de.jstacs.data.AlphabetContainer
  This method may be used to construct a new `AlphabetContainer` by incorporating additional `Alphabet`s into an existing `AlphabetContainer`.
insertProbs(double[]) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.ParameterTree
  Computes the probabilities for a PWM, i.e. the parameters in the tree have an empty context, and inserts them into `probs`.
installRScript(String, String, RConnection) - Static method in class de.jstacs.utils.RUtils
  Installs an R script on the Rserve server.
installScript(String, String, boolean) - Method in class de.jstacs.utils.REnvironment
  Installs a script on the server.
InstanceParameterSet - Class in de.jstacs.parameters
  Container class for a set of `Parameter`s that can be used to instantiate another class.
InstanceParameterSet(Class) - Constructor for class de.jstacs.parameters.InstanceParameterSet
  Constructs an `InstanceParameterSet` from the class that can be instantiated using this `InstanceParameterSet`.
InstanceParameterSet(StringBuffer) - Constructor for class de.jstacs.parameters.InstanceParameterSet
  The standard constructor for the interface `Storable`.
InstantiableFromParameterSet - Interface in de.jstacs
  Interface for all classes that can be instantiated from a `InstanceParameterSet`.
IntArrayWithTags(int[]) - Static method in class de.jstacs.io.XMLParser
  Encodes an `int` array.
internal - Variable in class de.jstacs.scoringFunctions.mix.motifSearch.PositionScoringFunction
  This array is used for some method of `DurationScoringFunction` that use an internal memory
intersection(Sample...) - Static method in class de.jstacs.data.Sample
  This method computes the intersection between all elements/`Sample` s of the array, i.e. it returns a `Sample` containing only `Sequence`s that are contained in all `Sample`s of the array.
IntList - Class in de.jstacs.utils
  A simple list of primitive type `int`.
IntList() - Constructor for class de.jstacs.utils.IntList
  This is the default constructor that creates an `IntList` with initial length 10.
IntList(int) - Constructor for class de.jstacs.utils.IntList
  This is the default constructor that creates an `IntList` with initial length `size`.
IntronAnnotation - Class in de.jstacs.data.sequences.annotation
  Annotation class for an intron as defined by a donor and an acceptor splice site.
IntronAnnotation(String, SinglePositionSequenceAnnotation, SinglePositionSequenceAnnotation, Result...) - Constructor for class de.jstacs.data.sequences.annotation.IntronAnnotation
  Creates a new `IntronAnnotation` from a donor `SinglePositionSequenceAnnotation` and an acceptor `SinglePositionSequenceAnnotation` and a set of additional annotations.
IntronAnnotation(StringBuffer) - Constructor for class de.jstacs.data.sequences.annotation.IntronAnnotation
  The standard constructor for the interface `Storable`.
IntSequence - Class in de.jstacs.data.sequences
  This class is for sequences with the alphabet symbols encoded as `int`s and can therefore be used for discrete `AlphabetContainer`s with alphabets that use a huge number of symbols.
IntSequence(AlphabetContainer, int...) - Constructor for class de.jstacs.data.sequences.IntSequence
  Creates a new `IntSequence` from an array of `int`- encoded alphabet symbols.
IntSequence(AlphabetContainer, int[], int, int) - Constructor for class de.jstacs.data.sequences.IntSequence
  Creates a new `IntSequence` from a part of the array of `int`- encoded alphabet symbols.
IntSequence(AlphabetContainer, String) - Constructor for class de.jstacs.data.sequences.IntSequence
  Creates a new `IntSequence` from a `String` representation using the default delimiter.
IntSequence(AlphabetContainer, SequenceAnnotation[], String, String) - Constructor for class de.jstacs.data.sequences.IntSequence
  Creates a new `IntSequence` from a `String` representation using the delimiter `delim`.
IntSequence(AlphabetContainer, SequenceAnnotation[], SymbolExtractor) - Constructor for class de.jstacs.data.sequences.IntSequence
  Creates a new `IntSequence` from a `SymbolExtractor`.
invalidateNormalizers() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.Parameter
  Resets all internal normalization constants to `null`.
invalidateNormalizers() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.ParameterTree
  Resets all pre-computed normalization constants.
isAtomic() - Method in class de.jstacs.parameters.CollectionParameter
isAtomic() - Method in class de.jstacs.parameters.FileParameter
isAtomic() - Method in class de.jstacs.parameters.MultiSelectionCollectionParameter
isAtomic() - Method in class de.jstacs.parameters.Parameter
  Returns `true` if the parameter is of an atomic data type, `false` otherwise.
isAtomic() - Method in class de.jstacs.parameters.ParameterSet
  Returns `true` if this `ParameterSet` contains only atomic parameters, i.e. the parameters do not contain `ParameterSet`s themselves.
isAtomic() - Method in class de.jstacs.parameters.ParameterSetContainer
isAtomic() - Method in class de.jstacs.parameters.RangeParameter
isAtomic() - Method in class de.jstacs.parameters.SimpleParameter
isCancelled() - Method in class de.jstacs.utils.DefaultProgressUpdater
isCancelled() - Method in class de.jstacs.utils.GUIProgressUpdater
isCancelled() - Method in class de.jstacs.utils.NullProgressUpdater
isCancelled() - Method in interface de.jstacs.utils.ProgressUpdater
  Specifies if the process is cancelled by the user.
isCancelled() - Method in class de.jstacs.utils.TimeLimitedProgressUpdater
isCastableResult(Result) - Method in class de.jstacs.results.Result
  Returns `true` if the data type of the `Result` `test` can be casted to that of this instance and both have the same name and comment for the `Result`.
isComparableResult(Result) - Method in class de.jstacs.results.Result
  Returns `true` if the `Result` `test` and the current object have the same data type, name and comment for the result.
isDiscrete() - Method in class de.jstacs.data.AlphabetContainer
  Indicates if all positions use discrete `Alphabet`s.
isDiscrete() - Method in class de.jstacs.data.AlphabetContainerParameterSet
  Indicates if all positions use `DiscreteAlphabet.DiscreteAlphabetParameterSet`, i.e.
isDiscreteAt(int) - Method in class de.jstacs.data.AlphabetContainer
  Indicates if position `pos` is a discrete random variable, i.e. if the `Alphabet` of position `pos` is discrete.
isDiscreteSample() - Method in class de.jstacs.data.Sample
  This method indicates if all positions use discrete values.
isEncodedSymbol(int, double) - Method in class de.jstacs.data.AlphabetContainer
  Indicates if `continuous` is a symbol of the `Alphabet` used at position `pos` of the `AlphabetContainer`.
isEncodedSymbol(double) - Method in class de.jstacs.data.alphabets.ContinuousAlphabet
  Indicates if `candidat` is an element of the internal interval.
isEncodedSymbol(int) - Method in class de.jstacs.data.alphabets.DiscreteAlphabet
  Indicates if `candidate` is an element of the internal interval.
isFree() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.Parameter
  Indicates if this parameter is free.
isInitialized() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
isInitialized() - Method in class de.jstacs.scoringFunctions.homogeneous.HMM0ScoringFunction
isInitialized() - Method in class de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction
isInitialized() - Method in class de.jstacs.scoringFunctions.homogeneous.UniformHomogeneousScoringFunction
isInitialized() - Method in class de.jstacs.scoringFunctions.IndependentProductScoringFunction
isInitialized() - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
isInitialized() - Method in class de.jstacs.scoringFunctions.mix.motifSearch.SkewNormalLikeScoringFunction
isInitialized() - Method in class de.jstacs.scoringFunctions.mix.motifSearch.UniformDurationScoringFunction
isInitialized() - Method in class de.jstacs.scoringFunctions.MRFScoringFunction
isInitialized() - Method in class de.jstacs.scoringFunctions.NormalizedScoringFunction
isInitialized() - Method in interface de.jstacs.scoringFunctions.ScoringFunction
  This method can be used to determine whether the model is initialized.
isInitialized() - Method in class de.jstacs.scoringFunctions.UniformScoringFunction
isInSamplingMode() - Method in class de.jstacs.models.mixture.AbstractMixtureModel
  This method returns `true` if the object is currently used in a sampling, otherwise `false`.
isInSamplingMode() - Method in class de.jstacs.models.mixture.gibbssampling.FSDAGModelForGibbsSampling
isInSamplingMode() - Method in interface de.jstacs.models.mixture.gibbssampling.GibbsSamplingComponent
  This method returns `true` if the object is currently used in a sampling, otherwise `false`.
isLeaf() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.ParameterTree
  Indicates if the random variable of this `ParameterTree` is a leaf, i.e. it has no children in the network structure of the enclosing `BayesianNetworkScoringFunction`.
isNormalized() - Method in class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
isNormalized(NormalizableScoringFunction...) - Static method in class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
  This method checks whether all given `NormalizableScoringFunction`s are normalized.
isNormalized() - Method in class de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction
isNormalized() - Method in class de.jstacs.scoringFunctions.homogeneous.UniformHomogeneousScoringFunction
isNormalized - Variable in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
  This boolean indicates whether this instance is a normalized one or not.
isNormalized() - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
isNormalized() - Method in class de.jstacs.scoringFunctions.mix.motifSearch.SkewNormalLikeScoringFunction
isNormalized() - Method in class de.jstacs.scoringFunctions.mix.motifSearch.UniformDurationScoringFunction
isNormalized() - Method in interface de.jstacs.scoringFunctions.NormalizableScoringFunction
  This method indicates whether the implemented score is already normalized to 1 or not.
isNormalized() - Method in class de.jstacs.scoringFunctions.NormalizedScoringFunction
isNormalized() - Method in class de.jstacs.scoringFunctions.UniformScoringFunction
isPossible(int...) - Method in class de.jstacs.scoringFunctions.mix.motifSearch.DurationScoringFunction
isPossible(int...) - Method in class de.jstacs.scoringFunctions.mix.motifSearch.PositionScoringFunction
  This method returns `true` if the given `positions` are in the domain of the PositionScoringFunction.
isRangeable() - Method in class de.jstacs.parameters.CollectionParameter
isRangeable() - Method in class de.jstacs.parameters.ParameterSetContainer
isRangeable() - Method in interface de.jstacs.parameters.Rangeable
  Returns `true` if the parameters can be varied over a range of values.
isRangeable() - Method in class de.jstacs.parameters.SimpleParameter
isRanged() - Method in class de.jstacs.parameters.MultiSelectionCollectionParameter
isRanged() - Method in class de.jstacs.parameters.ParameterSet
isRanged() - Method in class de.jstacs.parameters.ParameterSetContainer
isRanged() - Method in interface de.jstacs.parameters.RangeIterator
  Returns `true` if this `RangeIterator` is ranging over a set of values.
isRanged() - Method in class de.jstacs.parameters.RangeParameter
isRequired() - Method in class de.jstacs.parameters.CollectionParameter
isRequired() - Method in class de.jstacs.parameters.FileParameter
isRequired() - Method in class de.jstacs.parameters.Parameter
  Returns `true` if the `Parameter` is required, `false` otherwise.
isRequired() - Method in class de.jstacs.parameters.ParameterSetContainer
isRequired() - Method in class de.jstacs.parameters.RangeParameter
isRequired() - Method in class de.jstacs.parameters.SimpleParameter
isReverseComplementable() - Method in class de.jstacs.data.AlphabetContainer
  This method helps to determine if the `AlphabetContainer` also computes the reverse complement of a `Sequence`.
isSelected(MeasureParameters.Measure) - Method in class de.jstacs.classifier.MeasureParameters
  Indicates if the option `sel` is selected.
isSelected(String) - Method in class de.jstacs.parameters.MultiSelectionCollectionParameter
  Returns the selection value of the option with key `key`.
isSelected(int) - Method in class de.jstacs.parameters.MultiSelectionCollectionParameter
  Returns `true` if the option at position `idx` is selected.
isSet() - Method in class de.jstacs.parameters.CollectionParameter
isSet() - Method in class de.jstacs.parameters.FileParameter
isSet() - Method in class de.jstacs.parameters.MultiSelectionCollectionParameter
isSet() - Method in class de.jstacs.parameters.Parameter
  Returns `true` if the parameter was set by the user, `false` otherwise.
isSet() - Method in class de.jstacs.parameters.ParameterSetContainer
isSet() - Method in class de.jstacs.parameters.RangeParameter
isSet() - Method in class de.jstacs.parameters.SimpleParameter
isShiftable() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov
isShiftable() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.Measure
  Indicates if `Measure` supports shifts.
isSimple() - Method in class de.jstacs.data.AlphabetContainer
  Indicates whether all random variables are defined over the same range, i.e. if the `AlphabetContainer` is simple and all positions use the same (fixed) `Alphabet`.
isSimple() - Method in class de.jstacs.data.AlphabetContainerParameterSet
  Indicates if all positions use the same `Alphabet`, i.e. if the corresponding `AlphabetContainer` is simple.
isSimpleSample() - Method in class de.jstacs.data.Sample
  This method indicates whether all random variables are defined over the same range, i.e. all positions use the same (fixed) alphabet.
isStrandScoringFunction() - Method in class de.jstacs.scoringFunctions.NormalizedScoringFunction
  This method returns `true` if the internal `NormalizableScoringFunction` is a `StrandScoringFunction` otherwise `false`.
isSymbol(String) - Method in class de.jstacs.data.alphabets.DiscreteAlphabet
  Tests if a given symbol is contained in the alphabet.
isTrained() - Method in class de.jstacs.classifier.AbstractClassifier
  This method gives information about the state of the classifier.
isTrained() - Method in class de.jstacs.classifier.MappingClassifier
isTrained() - Method in class de.jstacs.classifier.modelBased.ModelBasedClassifier
isTrained() - Method in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier
isTrained() - Method in class de.jstacs.models.CompositeModel
isTrained() - Method in class de.jstacs.models.discrete.DiscreteGraphicalModel
isTrained() - Method in class de.jstacs.models.mixture.AbstractMixtureModel
isTrained() - Method in interface de.jstacs.models.Model
  Returns `true` if the model has been trained successfully, `false` otherwise.
isTrained() - Method in class de.jstacs.models.UniformModel
  Returns `true` if the model is trained, `false` otherwise.
isTrained() - Method in class de.jstacs.results.StorableResult
  Returns `StorableResult.TRUE` if the model or classifier was trained when obtaining its XML representation stored in this `StorableResult`, `StorableResult.FALSE` if it was not, and `StorableResult.NA` if the object could not be trained anyway.
isTrained - Variable in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
  Indicates if the instance has been trained.
isUserSelected() - Method in class de.jstacs.parameters.CollectionParameter
  Returns `true` if the value was selected by the user.
iterate(Sample, double[], MultivariateRandomGenerator, MRGParams[]) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
  This method runs the train algorithm for the current model.
iterate(int, double[], MultivariateRandomGenerator, MRGParams[]) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
  This method runs the train algorithm for the current model and the internal data set.

Overview | Package | Class | Use | Tree | Deprecated | Index | Help
PREV LETTER NEXT LETTER | FRAMES NO FRAMES

A B C D E F G H I K L M N O P Q R S T U V W X