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## I

IDGMParameterSet - Class in de.jstacs.models.discrete.inhomogeneous.parameters
  This is the abstract container of parameters that is root container for all inhomogeneous discrete graphical model parameter containers.
IDGMParameterSet(StringBuffer) - Constructor for class de.jstacs.models.discrete.inhomogeneous.parameters.IDGMParameterSet
  The constructor for the `Storable` interface.
IDGMParameterSet(Class<? extends InhomogeneousDGM>) - Constructor for class de.jstacs.models.discrete.inhomogeneous.parameters.IDGMParameterSet
  This constructor creates an empty parameter set instance for the specified class.
IDGMParameterSet(Class<? extends InhomogeneousDGM>, AlphabetContainer, int, double, String) - Constructor for class de.jstacs.models.discrete.inhomogeneous.parameters.IDGMParameterSet
  This constructor creates a parameter set 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
  If this method returns `true` all used alphabets ignore the case.
ignoresCase() - Method in class de.jstacs.data.alphabets.DiscreteAlphabet
  If this method returns `true` 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 the sequence independent of each other.
IndependentProductScoringFunction(NormalizableScoringFunction...) - Constructor for class de.jstacs.scoringFunctions.IndependentProductScoringFunction
  This constructor creates an instance of a given series of independent NormalizableScoringFunctions.
IndependentProductScoringFunction(NormalizableScoringFunction[], int[]) - Constructor for class de.jstacs.scoringFunctions.IndependentProductScoringFunction
  This constructor creates an instance of a given series of independent NormalizableScoringFunctions and lengths.
IndependentProductScoringFunction(StringBuffer) - Constructor for class de.jstacs.scoringFunctions.IndependentProductScoringFunction
  This is the constructor for `Storable`.
InhCondProb - Class in de.jstacs.models.discrete.inhomogeneous
  This class handles the (conditional) probabilities.
InhCondProb(int, int...) - Constructor for class de.jstacs.models.discrete.inhomogeneous.InhCondProb
InhCondProb(int[], int[], boolean) - Constructor for class de.jstacs.models.discrete.inhomogeneous.InhCondProb
InhCondProb(StringBuffer) - Constructor for class de.jstacs.models.discrete.inhomogeneous.InhCondProb
  The constructor for saved objects.
InhConstraint - Class in de.jstacs.models.discrete.inhomogeneous
  This class is the super class for all inhomogeneous constraints.
InhConstraint(int[], int[]) - Constructor for class de.jstacs.models.discrete.inhomogeneous.InhConstraint
InhConstraint(StringBuffer) - Constructor for class de.jstacs.models.discrete.inhomogeneous.InhConstraint
  The constructor for saved objects.
InhomogeneousDGM - Class in de.jstacs.models.discrete.inhomogeneous
  This class is the main class for all inhomogeneous discrete graphical models (IDGM).
InhomogeneousDGM(IDGMParameterSet) - Constructor for class de.jstacs.models.discrete.inhomogeneous.InhomogeneousDGM
  The default constructor.
InhomogeneousDGM(StringBuffer) - Constructor for class de.jstacs.models.discrete.inhomogeneous.InhomogeneousDGM
  This is the constructor for `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
  Re-creates an `InhomogeneousMarkov` structure from its XML-representation as returned by `InhomogeneousMarkov.toXML()`.
InhomogeneousMarkov.InhomogeneousMarkovParameterSet - Class in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures
  Class for an `InstanceParameterSet` that defines the parameters of a `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.
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.MRFScoringFunction
initializeFunction(int, boolean, Sample[], double[][]) - Method in interface de.jstacs.scoringFunctions.ScoringFunction
  This method creates the underlying structure of the scoring function.
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.MRFScoringFunction
initializeFunctionRandomly(boolean) - Method in interface de.jstacs.scoringFunctions.ScoringFunction
  This method initializes the scoring function 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.
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
initializeUsingPlugIn(int, boolean, Sample[], double[][]) - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
  This method initializes the function using the data in some way.
initializeUsingPlugIn(int, boolean, Sample[], double[][]) - Method in class de.jstacs.scoringFunctions.mix.MixtureScoringFunction
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 alphabets 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 a `InstanceParameterSet` from the class that can be instantiated using this `InstanceParameterSet`.
InstanceParameterSet(StringBuffer) - Constructor for class de.jstacs.parameters.InstanceParameterSet
  Constructs a `InstanceParameterSet` out of an XML representation
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.
intersection(Sample...) - Static method in class de.jstacs.data.Sample
  This method computes the intersection between all elements of the array, i.e. it returns a `Sample` containing only sequences 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
  Re-creates an `IntronAnnotation` from its XML representation as returned by `LocatedSequenceAnnotationWithLength.toXML()`.
IntSequence - Class in de.jstacs.data.sequences
  This class can be used for discrete `AlphabetContainer` with alphabets that use a huge number of symbols.
IntSequence(AlphabetContainer, int[]) - Constructor for class de.jstacs.data.sequences.IntSequence
  This constructor is designed for `Model.emitSample(int, int...)`.
IntSequence(AlphabetContainer, int[], int, int) - Constructor for class de.jstacs.data.sequences.IntSequence
  This constructor creates an instance from a part of the content.
IntSequence(AlphabetContainer, String) - Constructor for class de.jstacs.data.sequences.IntSequence
  Creates a new sequence from a string representation using the default delimiter.
IntSequence(AlphabetContainer, SequenceAnnotation[], String, String) - Constructor for class de.jstacs.data.sequences.IntSequence
  Creates a new sequence from a string representation using the delimiter `delim`.
IntSequence(AlphabetContainer, SequenceAnnotation[], SymbolExtractor) - Constructor for class de.jstacs.data.sequences.IntSequence
  Creates a new sequence from a `SymbolExtractor`.
invalidateNormalizers() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.Parameter
  Resets all internal normalization constants
invalidateNormalizers() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.ParameterTree
  Resets all pre-computed normalization constants.
isAtomic() - Method in class de.jstacs.parameters.CollectionParameter
  Returns true if the collection is of an atomic data type
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
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 datatype of `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 `test` and the current object have the same datatype, name and comment for the result.
isDiscrete() - Method in class de.jstacs.data.AlphabetContainer
  If this method returns `true` all positions use discrete values.
isDiscrete() - Method in class de.jstacs.data.AlphabetContainerParameterSet
  If this method returns `true` all positions use `DiscreteAlphabet.DiscreteAlphabetParameterSet`s.
isDiscreteAt(int) - Method in class de.jstacs.data.AlphabetContainer
  Returns `true` if position `pos` is a discrete random variable.
isDiscreteSample() - Method in class de.jstacs.data.Sample
  This method returns `true` if all positions use discrete values.
isEncodedSymbol(int, double) - Method in class de.jstacs.data.AlphabetContainer
  Returns `true` if `continuous` is a symbol of the alphabet used in position `pos`.
isEncodedSymbol(double) - Method in class de.jstacs.data.alphabets.ContinuousAlphabet
  Returns `true` if `candidat` is an element of the internal interval.
isEncodedSymbol(int) - Method in class de.jstacs.data.alphabets.DiscreteAlphabet
  Returns `true` 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.MRFScoringFunction
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
  Returns true 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 NormalizableScoringFunctions 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 interface de.jstacs.scoringFunctions.NormalizableScoringFunction
  This method returns whether the implemented score is already normalized to 1.
isNormalized() - Method in class de.jstacs.scoringFunctions.UniformScoringFunction
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 range iterator 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
  Returns `true` if the option `sel` is selected.
isSelected(String) - Method in class de.jstacs.parameters.MultiSelectionCollectionParameter
  Returns the selection value of the option `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
  Returns true if this collection parameter was selected by the user.
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.
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
  This method answers the question whether all random variables are defined over the same range, i.e. all positions use the same (fixed) alphabet.
isSimple() - Method in class de.jstacs.data.AlphabetContainerParameterSet
  If this method returns `true` all positions use the same alphabet.
isSimpleSample() - Method in class de.jstacs.data.Sample
  This method answers the question whether all random variables are defined over the same range, i.e. all positions use the same (fixed) alphabet.
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`.
isTrained() - Method in class de.jstacs.results.StorableResult
  Returns `TRUE` if the model or classifier was trained when obtaining its XML-representation stored in this `ObjectResult`, `FALSE` if it was not, and `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.

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