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

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 Alphabets 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 IntLists are used during the parallel computation of the gradient.
iList - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
This array contains some IntLists that are used while computing the partial derivation.
IllegalValueException(String) - Constructor for exception de.jstacs.parameters.SimpleParameter.IllegalValueException
Creates a new SimpleParameter.IllegalValueException with the reason of the exception reason as error message.
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.
InconsistentCollectionException(String) - Constructor for exception de.jstacs.parameters.AbstractSelectionParameter.InconsistentCollectionException
Constructs a new AbstractSelectionParameter.InconsistentCollectionException with message message.
InconsistentResultNumberException() - Constructor for exception de.jstacs.results.MeanResultSet.InconsistentResultNumberException
Constructs a new MeanResultSet.InconsistentResultNumberException with an appropriate error message.
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 DifferentiableStatisticalModels.
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 DifferentiableStatisticalModels 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 DifferentiableSequenceScores.
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 DifferentiableSequenceScores 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
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.
InhomogeneousMarkovParameterSet() - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov.InhomogeneousMarkovParameterSet
Creates a new InhomogeneousMarkov.InhomogeneousMarkovParameterSet with empty parameter values.
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.
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.scoringFunctionss 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.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
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.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
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 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
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 Parameters, which is a ParameterSet.ParameterList.
initParameterList(int) - Method in class de.jstacs.parameters.ParameterSet
Initializes the internal set of Parameters, which is a ParameterSet.ParameterList, with an initial number of Parameters 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 Alphabets into an existing 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 Parameters 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
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
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 ints and can therefore be used for discrete AlphabetContainers 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.
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
Returns true if this ResultSaver is for storing atomic Results.
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
Indicates if all positions use DiscreteAlphabet.DiscreteAlphabetParameterSet, i.e.
isDiscrete() - Method in class de.jstacs.data.AlphabetContainer
Indicates if all positions use discrete Alphabets.
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.
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.
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
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.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
Indicates if the random variable of this BNDiffSMParameterTree is a leaf, i.e.
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
This method checks whether all given DifferentiableStatisticalModels 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
 
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
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.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
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.
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
The parameter set for a IUPACDNAAlphabet.
A B C D E F G H I J K L M N O P Q R S T U V W X Z 
Skip navigation links