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## I

IDGTrainSMParameterSet - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters
  This is the abstract container of parameters that is a root container for all inhomogeneous discrete graphical model parameter containers.
IDGTrainSMParameterSet(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.IDGTrainSMParameterSet
  The standard constructor for the interface `Storable`.
IDGTrainSMParameterSet(Class<? extends InhomogeneousDGTrainSM>) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.IDGTrainSMParameterSet
  This constructor creates an empty `IDGTrainSMParameterSet` instance from the class that can be instantiated using this `IDGTrainSMParameterSet`.
IDGTrainSMParameterSet(Class<? extends InhomogeneousDGTrainSM>, AlphabetContainer, int, double, String) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.IDGTrainSMParameterSet
  This constructor creates an `IDGTrainSMParameterSet` 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.classifiers.differentiableSequenceScoreBased.DiffSSBasedOptimizableFunction
  These `IntList`s are used during the parallel computation of the gradient.
iList - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
  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`.
ImageResult(StringBuffer) - Constructor for class de.jstacs.results.ImageResult
  The standard constructor for the interface `Storable`.
includePath - Static variable in class de.jstacs.utils.SubclassFinder
  This field can be set to include a path into the search performed in `SubclassFinder.findSubclasses(Class, String)` thereby enabling to find self-implemented classes not included in the Jstacs class hierarchy.
IndependentProductDiffSM - Class in de.jstacs.sequenceScores.statisticalModels.differentiable
  This class enables the user to model parts of a sequence independent of each other.
IndependentProductDiffSM(double, boolean, DifferentiableStatisticalModel...) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
  This constructor creates an instance of an `IndependentProductDiffSM` from a given series of independent `DifferentiableStatisticalModel`s.
IndependentProductDiffSM(double, boolean, DifferentiableStatisticalModel[], int[]) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
  This constructor creates an instance of an `IndependentProductDiffSM` from given series of independent `DifferentiableStatisticalModel`s and lengths.
IndependentProductDiffSM(double, boolean, DifferentiableStatisticalModel[], int[], int[], boolean[]) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
  This is the main constructor.
IndependentProductDiffSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
  This is the constructor for the interface `Storable`.
IndependentProductDiffSS - Class in de.jstacs.sequenceScores.differentiable
  This class enables the user to model parts of a sequence independent of each other.
IndependentProductDiffSS(boolean, DifferentiableSequenceScore...) - Constructor for class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
  This constructor creates an instance of an `IndependentProductDiffSS` from a given series of independent `DifferentiableSequenceScore`s.
IndependentProductDiffSS(boolean, DifferentiableSequenceScore[], int[]) - Constructor for class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
  This constructor creates an instance of an `IndependentProductDiffSS` from given series of independent `DifferentiableSequenceScore`s and lengths.
IndependentProductDiffSS(boolean, DifferentiableSequenceScore[], int[], int[], boolean[]) - Constructor for class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
  This is the main constructor.
IndependentProductDiffSS(StringBuffer) - Constructor for class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
  This is the constructor for the interface `Storable`.
index - Variable in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
  This index indicates which entry of the array `IndependentProductDiffSS.score` should be used for the specific parts.
index - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
  Index array used for computing the gradient
indicesState - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
  Help array for the indexes of the parameters of the states
indicesTransition - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
  Help array for the indexes of the parameters of the transition
InfixStringExtractor - Class in de.jstacs.io
  This class implements an `AbstractStringExtractor` that can be seen as a filter.
InfixStringExtractor(AbstractStringExtractor, int, int) - Constructor for class de.jstacs.io.InfixStringExtractor
  This constructor creates an instance that uses only a infix of the string returned by `se`.
InhCondProb - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous
  This class handles (conditional) probabilities of sequences for inhomogeneous models.
InhCondProb(int, int...) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb
  Creates a new `InhCondProb` instance.
InhCondProb(int[], int[], boolean) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb
  Creates a new `InhCondProb` instance.
InhCondProb(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb
  The standard constructor for the interface `Storable`.
InhConstraint - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous
  This class is the superclass for all inhomogeneous constraints.
InhConstraint(int[], int[]) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhConstraint
  Creates a new `InhConstraint` instance.
InhConstraint(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhConstraint
  The standard constructor for the interface `Storable`.
InhomogeneousDGTrainSM - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous
  This class is the main class for all inhomogeneous discrete graphical models (`InhomogeneousDGTrainSM`).
InhomogeneousDGTrainSM(IDGTrainSMParameterSet) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhomogeneousDGTrainSM
  Creates a new `InhomogeneousDGTrainSM` from a given `IDGTrainSMParameterSet`.
InhomogeneousDGTrainSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhomogeneousDGTrainSM
  The standard constructor for the interface `Storable`.
InhomogeneousMarkov - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures
  Class for a network structure of a `BayesianNetworkDiffSM` that is an inhomogeneous Markov model.
InhomogeneousMarkov(int) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov
  Creates the structure of an inhomogeneous Markov model of order `order`.
InhomogeneousMarkov(InhomogeneousMarkov.InhomogeneousMarkovParameterSet) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov
  Creates a new `InhomogeneousMarkov` from the corresponding `InstanceParameterSet` `parameters`.
InhomogeneousMarkov(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov
  The standard constructor for the interface `Storable`.
InhomogeneousMarkov.InhomogeneousMarkovParameterSet - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures
  Class for an `InstanceParameterSet` that defines the parameters of an `InhomogeneousMarkov` structure `Measure`.
InhomogeneousMarkov.InhomogeneousMarkovParameterSet() - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov.InhomogeneousMarkovParameterSet
  Creates a new `InhomogeneousMarkov.InhomogeneousMarkovParameterSet` with empty parameter values.
InhomogeneousMarkov.InhomogeneousMarkovParameterSet(int) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.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.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov.InhomogeneousMarkovParameterSet
  Creates a new `InhomogeneousMarkov.InhomogeneousMarkovParameterSet` from its XML representation as defined by the `Storable` interface.
init(int, boolean, String) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
  Initializes all internal fields and initializes the `SamplingScoreBasedClassifier.scoringFunctions`s randomly
init(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
  This method creates the underlying structure for the parameters.
init(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
init() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
  This method initializes internal fields.
init() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.DistanceBasedScaledTransitionElement
  Basic initialization.
init() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ReferenceBasedTransitionElement
init() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ScaledTransitionElement
init() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.TransitionElement
initForSampling(int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier.DiffSMSamplingComponent
initForSampling(int) - Method in interface de.jstacs.sampling.SamplingComponent
  This method initializes the instance for the sampling.
initForSampling(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGModelForGibbsSampling
initForSampling(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
initForSampling(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
initForSampling(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleSamplingState
initForSampling(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
initialIteration - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  The number of initial iterations.
initialize(DataSet, double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
  This method initializes all emissions and the transition.
initializeFunction(int, boolean, DataSet[], double[][]) - Method in interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore
  This method creates the underlying structure of the `DifferentiableSequenceScore`.
initializeFunction(int, boolean, DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
initializeFunction(int, boolean, DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.differentiable.logistic.LogisticDiffSS
initializeFunction(int, boolean, DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.differentiable.UniformDiffSS
initializeFunction(int, boolean, DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
initializeFunction(int, boolean, DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
initializeFunction(int, boolean, DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMM0DiffSM
initializeFunction(int, boolean, DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
initializeFunction(int, boolean, DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.UniformHomogeneousDiffSM
initializeFunction(int, boolean, DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
initializeFunction(int, boolean, DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MarkovRandomFieldDiffSM
initializeFunction(int, boolean, DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
initializeFunction(int, boolean, DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.MixtureDurationDiffSM
initializeFunction(int, boolean, DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
initializeFunction(int, boolean, DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.UniformDurationDiffSM
initializeFunction(int, boolean, DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MultiDimensionalSequenceWrapperDiffSM
initializeFunction(int, boolean, DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
initializeFunction(int, boolean, DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.UniformDiffSM
initializeFunction(int, boolean, DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
initializeFunctionRandomly(boolean) - Method in interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore
  This method initializes the `DifferentiableSequenceScore` randomly.
initializeFunctionRandomly(boolean) - Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
initializeFunctionRandomly(boolean) - Method in class de.jstacs.sequenceScores.differentiable.logistic.LogisticDiffSS
initializeFunctionRandomly(boolean) - Method in class de.jstacs.sequenceScores.differentiable.UniformDiffSS
initializeFunctionRandomly(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
initializeFunctionRandomly(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
initializeFunctionRandomly(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMM0DiffSM
initializeFunctionRandomly(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
initializeFunctionRandomly(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.UniformHomogeneousDiffSM
initializeFunctionRandomly(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
initializeFunctionRandomly(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MarkovRandomFieldDiffSM
initializeFunctionRandomly(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
initializeFunctionRandomly(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
initializeFunctionRandomly(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.MixtureDurationDiffSM
initializeFunctionRandomly(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
initializeFunctionRandomly(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.UniformDurationDiffSM
initializeFunctionRandomly(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MultiDimensionalSequenceWrapperDiffSM
initializeFunctionRandomly(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
initializeFunctionRandomly(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
initializeFunctionRandomly() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
initializeFunctionRandomly() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.PluginGaussianEmission
initializeFunctionRandomly() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
initializeFunctionRandomly() - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.Emission
  This method initializes the emission randomly.
initializeFunctionRandomly() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.MixtureEmission
initializeFunctionRandomly() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
initializeFunctionRandomly() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.UniformEmission
initializeHiddenPotentialRandomly() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
  This method initializes the hidden potential (and the corresponding parameters) randomly.
initializeHiddenUniformly() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
  This method initializes the hidden parameters of the instance uniformly.
initializeHiddenUniformly() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
  This method initializes the hidden parameters of the internal `DifferentiableStatisticalModel` uniformly if it is a `AbstractMixtureDiffSM`.
initializeMotif(int, DataSet, 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, DataSet, double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
initializeMotif(int, DataSet, double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
initializeMotif(int, DataSet, double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.MixtureDiffSM
initializeMotif(int, DataSet, double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
initializeMotifRandomly(int) - Method in interface de.jstacs.motifDiscovery.MutableMotifDiscoverer
  This method initializes the motif with index `motif` randomly using for instance `DifferentiableSequenceScore.initializeFunctionRandomly(boolean)`.
initializeMotifRandomly(int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
initializeMotifRandomly(int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
initializeMotifRandomly(int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.MixtureDiffSM
initializeMotifRandomly(int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
initializeRandomly(double) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
  Initializes the parameters of this `BNDiffSMParameterTree` randomly.
initializeRandomly() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
  This method initializes all emissions and the transition randomly.
initializeRandomly() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
  This method draws new parameters from the prior.
initializeRandomly() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
initializeRandomly() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.BasicPluginTransitionElement
initializeRandomly() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ReferenceBasedTransitionElement
initializeRandomly() - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.Transition
  This method randomly initializes the parameters of the transition.
initializeUniformly(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousDiffSM
  This method allows to initialize the instance with an uniform distribution.
initializeUniformly(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMM0DiffSM
initializeUniformly(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
initializeUniformly(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.UniformHomogeneousDiffSM
initializeUniformly() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.DurationDiffSM
  This method set special parameters that lead to an uniform distribution.
initializeUniformly() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.MixtureDurationDiffSM
initializeUniformly() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
initializeUniformly() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.UniformDurationDiffSM
initializeUsingPlugIn(int, boolean, DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
  This method initializes the functions using the data in some way.
initializeUsingPlugIn(int, boolean, DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.MixtureDiffSM
initializeUsingPlugIn(int, boolean, DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
initializeUsingPlugIn(int, boolean, DataSet[], double[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
initModelForSampling(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  This method initializes the model for the sampling.
initMotif(int, int[], int[], DataSet[], double[][], boolean[], MutableMotifDiscoverer[], int[], DataSet[], double[][]) - Static method in class de.jstacs.motifDiscovery.MutableMotifDiscovererToolbox
  This method allows to initialize a number of motifs.
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`.
initParameters - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
  The initial parameters if set by `SamplingScoreBasedClassifier.setInitParameters(double[])`, `null` otherwise
initTraining(DataSet, double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM
  This methods initialize the training procedure with the given training data
initTransition(BasicHigherOrderTransition.AbstractTransitionElement...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
  This method creates the internal transition.
initUsingParameters(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
  Sets the parameters of this classifier and the contained scoring functions to the supplied parameters.
initWithLength(boolean, int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
  This method is used to create the underlying structure, e.g.
initWithPrior(double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  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.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
  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<T extends InstantiableFromParameterSet> - Class in de.jstacs.parameters
  Container class for a set of `Parameter`s that can be used to instantiate another class.
InstanceParameterSet(Class<? extends T>) - 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`.
internal - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.PositionDiffSM
  This array is used for some method of `DurationDiffSM` that use an internal memory
intersection(DataSet...) - Static method in class de.jstacs.data.DataSet
  This method computes the intersection between all elements/`DataSet` s of the array, i.e. it returns a `DataSet` containing only `Sequence`s that are contained in all `DataSet`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.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter
  Resets all internal normalization constants to `null`.
invalidateNormalizers() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
  Resets all pre-computed normalization constants.
isAbsoring() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
isAbsoring() - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.Transition
  This method returns for each state whether it is absorbing or not.
isAtomic() - Method in class de.jstacs.parameters.AbstractSelectionParameter
isAtomic() - Method in class de.jstacs.parameters.FileParameter
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`.
isComparable(Parameter) - Method in class de.jstacs.parameters.Parameter
  This method checks whether the given `Parameter` is comparable to the current instance, i.e. whether the `Class`, the `DataType`, the name and the comment are identical.
isComparable(ParameterSet) - Method in class de.jstacs.parameters.ParameterSet
  This method checks whether the given `ParameterSet` is comparable to the current instance, i.e. whether the `Class` and the number of parameters are identical, and the individual `Parameter`s are comparable.
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.AbstractAlphabetContainerParameterSet
  Indicates if all positions use `DiscreteAlphabet.DiscreteAlphabetParameterSet`, i.e.
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.
isDiscrete() - Method in class de.jstacs.data.alphabets.DNAAlphabetContainer.DNAAlphabetContainerParameterSet
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.
isDiscreteDataSet() - Method in class de.jstacs.data.DataSet
  This method indicates if all positions use discrete values.
isDoubleStrand() - Method in enum de.jstacs.data.DinucleotideProperty
  Returns `true` if this property has been determined for a double-stranded nucleic acid.
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.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter
  Indicates if this parameter is free.
isInitialized() - Method in class de.jstacs.classifiers.AbstractClassifier
  This method gives information about the state of the classifier.
isInitialized() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
isInitialized() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
isInitialized() - Method in class de.jstacs.classifiers.MappingClassifier
isInitialized() - Method in class de.jstacs.classifiers.trainSMBased.TrainSMBasedClassifier
isInitialized() - 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.
isInitialized() - Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
isInitialized() - Method in class de.jstacs.sequenceScores.differentiable.logistic.LogisticDiffSS
isInitialized() - Method in class de.jstacs.sequenceScores.differentiable.UniformDiffSS
isInitialized() - Method in interface de.jstacs.sequenceScores.SequenceScore
  This method can be used to determine whether the instance is initialized.
isInitialized() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
isInitialized() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
isInitialized() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMM0DiffSM
isInitialized() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
isInitialized() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.UniformHomogeneousDiffSM
isInitialized() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
isInitialized() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MarkovRandomFieldDiffSM
isInitialized() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
isInitialized() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.MixtureDurationDiffSM
isInitialized() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
isInitialized() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.UniformDurationDiffSM
isInitialized() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MultiDimensionalSequenceWrapperDiffSM
isInitialized() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
isInitialized() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.CompositeTrainSM
isInitialized() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.DifferentiableStatisticalModelWrapperTrainSM
isInitialized() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DiscreteGraphicalTrainSM
isInitialized() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
isInitialized() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
isInitialized() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM
isInitialized() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
isInitialized() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.UniformTrainSM
  Returns `true` if the model is trained, `false` otherwise.
isInitialized() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.VariableLengthWrapperTrainSM
isInSamplingMode() - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier.DiffSMSamplingComponent
isInSamplingMode() - Method in interface de.jstacs.sampling.SamplingComponent
  This method returns `true` if the object is currently used in a sampling, otherwise `false`.
isInSamplingMode() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGModelForGibbsSampling
isInSamplingMode() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
isInSamplingMode() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
isInSamplingMode() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleSamplingState
isInSamplingMode() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
isInSamplingMode() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  This method returns `true` if the object is currently used in a sampling, otherwise `false`.
isLeaf() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
  Indicates if the random variable of this `BNDiffSMParameterTree` is a leaf, i.e. it has no children in the network structure of the enclosing `BayesianNetworkDiffSM`.
isMultiDimensional() - Method in class de.jstacs.data.sequences.ArbitraryFloatSequence
isMultiDimensional() - Method in class de.jstacs.data.sequences.ArbitrarySequence
isMultiDimensional() - Method in class de.jstacs.data.sequences.MultiDimensionalSequence
isMultiDimensional() - Method in class de.jstacs.data.sequences.Sequence
  The method returns `true` if the sequence is multidimensional, otherwise .
isMultiDimensional() - Method in class de.jstacs.data.sequences.Sequence.RecursiveSequence
isMultiDimensional() - Method in class de.jstacs.data.sequences.SimpleDiscreteSequence
isNormalized() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
isNormalized(DifferentiableSequenceScore...) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
  This method checks whether all given `DifferentiableStatisticalModel`s are normalized.
isNormalized() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
isNormalized() - Method in interface de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModel
  This method indicates whether the implemented score is already normalized to 1 or not.
isNormalized() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
isNormalized() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.UniformHomogeneousDiffSM
isNormalized() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
isNormalized() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
isNormalized() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.MixtureDurationDiffSM
isNormalized() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
isNormalized() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.UniformDurationDiffSM
isNormalized() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
isNormalized() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.UniformDiffSM
isNormalized() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
isPossible(int...) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.DurationDiffSM
isPossible(int...) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.PositionDiffSM
  This method returns `true` if the given `positions` are in the domain of the PositionDiffSM.
isRangeable() - Method in class de.jstacs.parameters.AbstractSelectionParameter
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.MultiSelectionParameter
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.AbstractSelectionParameter
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(int) - Method in class de.jstacs.parameters.AbstractSelectionParameter
  Returns `true` if the option at position `idx` is selected.
isSelected(String) - Method in class de.jstacs.parameters.MultiSelectionParameter
  Returns the selection value of the option with key `key`.
isSelected(int) - Method in class de.jstacs.parameters.MultiSelectionParameter
isSelected(int) - Method in class de.jstacs.parameters.SelectionParameter
  Returns `true` if the option at position `idx` is selected.
isSet() - Method in class de.jstacs.parameters.AbstractSelectionParameter
isSet() - Method in class de.jstacs.parameters.FileParameter
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(String) - Method in class de.jstacs.parameters.ParameterSetTagger
isSet() - Method in class de.jstacs.parameters.RangeParameter
isSet() - Method in class de.jstacs.parameters.SimpleParameter
isShiftable() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov
isShiftable() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure
  Indicates if `Measure` supports shifts.
isSilent() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleState
isSilent() - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.State
  This method returns whether a state is silent or not.
isSilent - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
  A vector indicating for each state whether it is silent or not.
isSimple() - Method in class de.jstacs.algorithms.optimization.termination.AbsoluteValueCondition
  Deprecated.
isSimple() - Method in class de.jstacs.algorithms.optimization.termination.CombinedCondition
isSimple() - Method in class de.jstacs.algorithms.optimization.termination.IterationCondition
isSimple() - Method in class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition
isSimple() - Method in class de.jstacs.algorithms.optimization.termination.SmallGradientConditon
isSimple() - Method in class de.jstacs.algorithms.optimization.termination.SmallStepCondition
isSimple() - Method in interface de.jstacs.algorithms.optimization.termination.TerminationCondition
  This method returns `false` if the `TerminationCondition` uses either the gradient or the direction for the decision, otherwise it returns `true`.
isSimple() - Method in class de.jstacs.algorithms.optimization.termination.TimeCondition
isSimple() - Method in class de.jstacs.data.AlphabetContainer.AbstractAlphabetContainerParameterSet
  Indicates if all positions use the same `Alphabet`, i.e. if the corresponding `AlphabetContainer` is simple.
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
isSimple() - Method in class de.jstacs.data.alphabets.DNAAlphabetContainer.DNAAlphabetContainerParameterSet
isSimpleDataSet() - Method in class de.jstacs.data.DataSet
  This method indicates whether all random variables are defined over the same range, i.e. all positions use the same (fixed) alphabet.
isStrandModel(DifferentiableStatisticalModel) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
  Check whether a `DifferentiableStatisticalModel` is a `StrandDiffSM`.
isStrandModel() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
  This method returns `true` if the internal `DifferentiableStatisticalModel` is a `StrandDiffSM` otherwise `false`.
isSymbol(String) - Method in class de.jstacs.data.alphabets.DiscreteAlphabet
  Tests if a given symbol is contained in the alphabet.
isTrained - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
  Indicates if the instance has been trained.
isUserSelected() - Method in class de.jstacs.parameters.AbstractSelectionParameter
  Returns `true` if the value was selected by the user.
isVariable - Variable in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
  This array specifies for each entry of `IndependentProductDiffSS.score` whether it is able to score sequences of variable length.
iterate(DataSet, double[], MultivariateRandomGenerator, MRGParams[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  This method runs the train algorithm for the current model.
iterate(int, double[], MultivariateRandomGenerator, MRGParams[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  This method runs the train algorithm for the current model and the internal data set.
iterate(int, double[], MultivariateRandomGenerator, MRGParams[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.ZOOPSTrainSM
IterationCondition - Class in de.jstacs.algorithms.optimization.termination
  This class will stop an optimization if the number of iteration reaches a given number.
IterationCondition(int) - Constructor for class de.jstacs.algorithms.optimization.termination.IterationCondition
  This constructor creates an instance that does not allow any further iteration after `maxIter` iterations.
IterationCondition(IterationCondition.IterationConditionParameterSet) - Constructor for class de.jstacs.algorithms.optimization.termination.IterationCondition
  This is the main constructor creating an instance from a given parameter set.
IterationCondition(StringBuffer) - Constructor for class de.jstacs.algorithms.optimization.termination.IterationCondition
  The standard constructor for the interface `Storable`.
IterationCondition.IterationConditionParameterSet - Class in de.jstacs.algorithms.optimization.termination
  This class implements the parameter set for a `IterationCondition`.
IterationCondition.IterationConditionParameterSet() - Constructor for class de.jstacs.algorithms.optimization.termination.IterationCondition.IterationConditionParameterSet
  This constructor creates an empty parameter set.
IterationCondition.IterationConditionParameterSet(StringBuffer) - Constructor for class de.jstacs.algorithms.optimization.termination.IterationCondition.IterationConditionParameterSet
  The standard constructor for the interface `Storable`.
IterationCondition.IterationConditionParameterSet(int) - Constructor for class de.jstacs.algorithms.optimization.termination.IterationCondition.IterationConditionParameterSet
  This constructor creates a filled instance of a parameters set.
iterator() - Method in class de.jstacs.data.DataSet

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