- 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
-
- IDGTrainSMParameterSet(Class<? extends InhomogeneousDGTrainSM>, AlphabetContainer, int, double, String) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.IDGTrainSMParameterSet
-
- 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.
- IllegalValueException(String) - Constructor for exception de.jstacs.parameters.SimpleParameter.IllegalValueException
-
- 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
-
- 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
-
- InconsistentCollectionException(String) - Constructor for exception de.jstacs.parameters.AbstractSelectionParameter.InconsistentCollectionException
-
- InconsistentResultNumberException() - Constructor for exception de.jstacs.results.MeanResultSet.InconsistentResultNumberException
-
- 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
-
- IndependentProductDiffSM(double, boolean, DifferentiableStatisticalModel[], int[]) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
-
- 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
-
- IndependentProductDiffSS(boolean, DifferentiableSequenceScore[], int[]) - Constructor for class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
-
- 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
-
- 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
-
- 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
-
- InhCondProb(int[], int[], boolean) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb
-
- 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
-
- 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
-
- InhomogeneousDGTrainSM(IDGTrainSMParameterSet) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhomogeneousDGTrainSM
-
- 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
-
- 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
-
- 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
-
- InhomogeneousMarkovParameterSet() - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov.InhomogeneousMarkovParameterSet
-
- InhomogeneousMarkovParameterSet(int) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov.InhomogeneousMarkovParameterSet
-
- InhomogeneousMarkovParameterSet(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov.InhomogeneousMarkovParameterSet
-
- init(int, boolean, String) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
-
- 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
-
- 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.MultiDimensionalSequenceWrapperDiffSS
-
- 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.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
- 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.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
-
- 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.MultiDimensionalSequenceWrapperDiffSS
-
- 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.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
- 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.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.MultivariateGaussianEmission
-
- 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
-
- 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 data set.
- 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
-
- 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
-
- 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
-
- initParameterList(int) - Method in class de.jstacs.parameters.ParameterSet
-
- initParameters - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
-
- 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
-
- insertProbs(double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
-
Computes the probabilities for a PWM, i.e.
- 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
-
- InstanceParameterSet(StringBuffer) - Constructor for class de.jstacs.parameters.InstanceParameterSet
-
The standard constructor for the interface
Storable
.
- InstantiableFromParameterSet - Interface in de.jstacs
-
- 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
- interpolationSearch(int, int, int) - Method in class de.jstacs.utils.IntList
-
Performs an interpolation search for element key
on the internal array.
- 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.
- 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
-
- 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
-
- IntSequence(AlphabetContainer, SequenceAnnotation[], String, String) - Constructor for class de.jstacs.data.sequences.IntSequence
-
- IntSequence(AlphabetContainer, SequenceAnnotation[], SymbolExtractor) - Constructor for class de.jstacs.data.sequences.IntSequence
-
- 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.
- isAtomic() - Method in class de.jstacs.parameters.ParameterSetContainer
-
- isAtomic() - Method in class de.jstacs.parameters.RangeParameter
-
- isAtomic() - Method in class de.jstacs.parameters.SimpleParameter
-
- isAtomic() - Method in class de.jstacs.results.savers.DataSetResultSaver
-
- isAtomic() - Method in class de.jstacs.results.savers.ListResultSaver
-
- isAtomic() - Method in class de.jstacs.results.savers.PlotGeneratorResultSaver
-
- isAtomic() - Method in interface de.jstacs.results.savers.ResultSaver
-
- isAtomic() - Method in class de.jstacs.results.savers.ResultSetResultSaver
-
- isAtomic() - Method in class de.jstacs.results.savers.StorableResultSaver
-
- isAtomic() - Method in class de.jstacs.results.savers.TextResultSaver
-
- 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
-
Deprecated.
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.AbstractSelectionParameter
-
- isComparable(Parameter) - Method in class de.jstacs.parameters.Parameter
-
This method checks whether the given
Parameter
is comparable to the current instance, i.e.
- isComparable(ParameterSet) - Method in class de.jstacs.parameters.ParameterSet
-
This method checks whether the given
ParameterSet
is comparable to the current instance, i.e.
- 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
-
- isDiscrete() - Method in class de.jstacs.data.AlphabetContainer
-
Indicates if all positions use discrete
Alphabet
s.
- isDiscrete() - Method in class de.jstacs.data.AlphabetContainerParameterSet
-
- 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.
- 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
-
- 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.
- isIndeterminate() - Method in class de.jstacs.tools.ProgressUpdater
-
Checks if this progress is indeterminate.
- 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
-
- 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.MultiDimensionalSequenceWrapperDiffSS
-
- 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.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
- 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.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.PFMWrapperTrainSM
-
- 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.clustering.hierachical.ClusterTree
-
Returns true
if this cluster tree comprises just a leaf.
- isLeaf() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
-
- 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.CyclicSequenceAdaptor
-
- 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 false
.
- 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
-
- 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
-
- isPart(String, String) - Method in class de.jstacs.data.alphabets.IUPACDNAAlphabet
-
Indicates if query
is contained in code
according to the IUPAC DNA alphabet.
- isPart(int, int) - Method in class de.jstacs.data.alphabets.IUPACDNAAlphabet
-
Indicates if query
is contained in code
according to the IUPAC DNA alphabet.
- 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
-
- 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.MultipleIterationsCondition
-
- 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.
- isSimple() - Method in class de.jstacs.data.AlphabetContainer
-
Indicates whether all random variables are defined over the same range,
i.e.
- 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.
- isStatic() - Method in class de.jstacs.results.PlotGeneratorResult
-
Returns true
if the plot is considered static and may be cached.
- isStrandModel(DifferentiableStatisticalModel) - Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
-
- isStrandModel() - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
-
- 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
-
- 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
-
- IterationConditionParameterSet() - Constructor for class de.jstacs.algorithms.optimization.termination.IterationCondition.IterationConditionParameterSet
-
This constructor creates an empty parameter set.
- IterationConditionParameterSet(StringBuffer) - Constructor for class de.jstacs.algorithms.optimization.termination.IterationCondition.IterationConditionParameterSet
-
The standard constructor for the interface
Storable
.
- 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
-
- IUPACDNAAlphabet - Class in de.jstacs.data.alphabets
-
This class implements a discrete alphabet for the IUPAC DNA code.
- IUPACDNAAlphabet.IUPACDNAAlphabetParameterSet - Class in de.jstacs.data.alphabets
-