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E

e - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleState
The emission that is internally used for scoring subsequences.
Edge - Class in de.jstacs.algorithms.graphs
This class is a representation of a weighted edge.
Edge(int, int, double) - Constructor for class de.jstacs.algorithms.graphs.Edge
Creates a new weighted edge.
ElementEnumerator(DataSet) - Constructor for class de.jstacs.data.DataSet.ElementEnumerator
Creates a new DataSet.ElementEnumerator on the given DataSet data.
emission - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
The emissions used in the states.
Emission - Interface in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions
This interface declares all method for an emission of a state.
emissionIdx - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
The index of the used emission of each state.
emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
 
emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
 
emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
This method returns a DataSet object containing artificial sequence(s).
emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
 
emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MarkovRandomFieldDiffSM
 
emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.MixtureDiffSM
 
emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.UniformDiffSM
 
emitDataSet(int, int...) - Method in interface de.jstacs.sequenceScores.statisticalModels.StatisticalModel
This method returns a DataSet object containing artificial sequence(s).
emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
 
emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM
Creates a DataSet of a given number of Sequences from a trained homogeneous model.
emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.DAGTrainSM
 
emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEManager
 
emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
 
emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.UniformTrainSM
 
emitDataSet(StatisticalModel, int) - Static method in class de.jstacs.utils.DiscreteInhomogenousDataSetEmitter
This method emits a data set with n sequences from the discrete inhomogeneous model m .
emitDataSetUsingCurrentParameterSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
The method returns an array of sequences using the current parameter set.
emitDataSetUsingCurrentParameterSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.MixtureTrainSM
 
emitDataSetUsingCurrentParameterSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.HiddenMotifMixture
Standard implementation throwing an OperationNotSupportedException.
emitDataSetUsingCurrentParameterSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM
 
EmptyDataSetException - Exception in de.jstacs.data
An EmptyDataSetException will be thrown if no Sequence is in a DataSet (i.e.
EmptyDataSetException() - Constructor for exception de.jstacs.data.EmptyDataSetException
This constructor creates an instance with default error message ("The created DataSet is empty.").
encode(int[][]) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.FSDAGTrainSMParameterSet
This method can be used to encode an adjacency list to a graph description String (e.g.
enumerate(DifferentiableSequenceScore[], int, int, RecyclableSequenceEnumerator, double, DiffSSBasedOptimizableFunction, OutputStream) - Static method in class de.jstacs.motifDiscovery.MutableMotifDiscovererToolbox
This method allows to enumerate all possible seeds for a motif in the MutableMotifDiscoverer of a specific class.
enumerate(DifferentiableSequenceScore[], int[], int[], RecyclableSequenceEnumerator[], double, DiffSSBasedOptimizableFunction, OutputStream) - Static method in class de.jstacs.motifDiscovery.MutableMotifDiscovererToolbox
This method allows to enumerate all possible seeds for a number of motifs in the MutableMotifDiscoverers of a specific classes.
enumerateHP(Tensor) - Static method in class de.jstacs.algorithms.graphs.DAG
The method computes the HP(k) (see DAG).
EnumParameter - Class in de.jstacs.parameters
This class implements a SelectionParameter based on an Enum.
EnumParameter(Class<? extends Enum>, String, boolean) - Constructor for class de.jstacs.parameters.EnumParameter
The main constructor.
EnumParameter(Class<? extends Enum>, String, boolean, String) - Constructor for class de.jstacs.parameters.EnumParameter
This constructor creates an instance and set the default value.
EnumParameter(StringBuffer) - Constructor for class de.jstacs.parameters.EnumParameter
The standard constructor for the interface Storable.
eps - Variable in class de.jstacs.algorithms.optimization.NumericalDifferentiableFunction
The constant used in the computation of the gradient.
EPSAdaptor - Class in de.jstacs.utils.graphics
GraphicsAdaptor for the EPS format.
EPSAdaptor() - Constructor for class de.jstacs.utils.graphics.EPSAdaptor
Creates a new adaptor for plotting to an EPS device.
EqualParts - Class in de.jstacs.utils.random
This class is no real random generator it just returns 1/n for all values.
EqualParts() - Constructor for class de.jstacs.utils.random.EqualParts
 
equals(Object) - Method in class de.jstacs.data.sequences.Sequence
 
equals(Object) - Method in class de.jstacs.parameters.AbstractSelectionParameter
 
equals(Object) - Method in class de.jstacs.parameters.SequenceScoringParameterSet
 
equals(Object) - Method in class de.jstacs.parameters.SimpleParameter
 
EQUALS - Static variable in interface de.jstacs.parameters.validation.Constraint
The condition is equality
equals(Object) - Method in class de.jstacs.results.SimpleResult
 
equals(String[], String) - Static method in class de.jstacs.results.TextResult
Checks if the list of mime types given in p1 contains an element that is equal to one of the mime types given in mime2 (may be multiple, separated by commas).
equals(String, String) - Static method in class de.jstacs.results.TextResult
Checks if the list of mime types given in mime1 (may be multiple, separated by commas) contains an element that is equal to one of the mime types given in mime2.
equals(Object) - Method in class de.jstacs.utils.IntList
 
ErlangMRG - Class in de.jstacs.utils.random
This class is a multivariate random generator based on a Dirichlet distribution for alpha_i \in N.
ErlangMRG() - Constructor for class de.jstacs.utils.random.ErlangMRG
Constructor that creates a new multivariate random generator with underlying Erlang distribution.
ErlangMRGParams - Class in de.jstacs.utils.random
The container for parameters of an Erlang multivariate random generator.
ErlangMRGParams(int, int) - Constructor for class de.jstacs.utils.random.ErlangMRGParams
Constructor which creates a new hyperparameter vector for an Erlang random generator.
ErlangMRGParams(int[]) - Constructor for class de.jstacs.utils.random.ErlangMRGParams
Constructor which creates a new hyperparameter vector for an Erlang random generator.
errorMessage - Variable in class de.jstacs.parameters.AbstractSelectionParameter
If a value was illegal for the collection parameter, this field holds the error message.
errorMessage - Variable in class de.jstacs.parameters.ParameterSet
The error message of the last error or null
errorMessage - Variable in class de.jstacs.parameters.ParameterSetContainer
The message of the last error or null if no error occurred.
ess - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
The equivalent sample size.
ess - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.DurationDiffSM
The equivalent sample size.
ess - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
The equivalent sample size used for the prior
ess - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
The equivalent sample sizes for each condition
ess - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ReferenceBasedTransitionElement
The equivalent sample size (ess) used in the prior of this instance.
estimate(double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint
Estimates the (smoothed) relative frequencies using the ess (equivalent sample size).
estimate(double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM.HomCondProb
 
estimate(double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb
 
estimate(double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint
 
estimateComponentProbs - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
The switch for estimating the component probabilities or not.
estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
 
estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.MultivariateGaussianEmission
 
estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
 
estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.PhyloDiscreteEmission
 
estimateFromStatistic() - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.Emission
This method estimates the parameters from the internal sufficient statistic.
estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.MixtureEmission
 
estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
 
estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.UniformEmission
 
estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
This method estimates the parameters from the sufficient statistic.
estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
 
estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.DistanceBasedScaledTransitionElement
 
estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ScaledTransitionElement
 
estimateFromStatistic() - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.TrainableTransition
This method estimates the parameter of the transition using the internal sufficient statistic.
estimateFromStatistics() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
This method estimates the parameters of all emissions and the transition using their sufficient statistics.
estimateParameters(DataSet, double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.DAGTrainSM
This method estimates the parameter of the model from the likelihood or the posterior, respectively.
estimateUnConditional(int, int, double, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint
Estimates unconditionally.
estimateUnConditional(int, int, double, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM.HomCondProb
 
estimateUnConditional(double, double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb
Estimates the unconditional frequencies using the ess (equivalent sample size).
estimateUnConditional(int, int, double, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb
 
eval(CharSequence) - Method in class de.jstacs.utils.REnvironment
Evaluates the String as R commands.
evaluate(AbstractPerformanceMeasureParameterSet<? extends PerformanceMeasure>, boolean, DataSet...) - Method in class de.jstacs.classifiers.AbstractClassifier
This method evaluates the classifier and computes, for instance, the sensitivity for a given specificity, the area under the ROC curve and so on.
evaluate(AbstractPerformanceMeasureParameterSet<? extends PerformanceMeasure>, boolean, DataSet[], double[][]) - Method in class de.jstacs.classifiers.AbstractClassifier
This method evaluates the classifier and computes, for instance, the sensitivity for a given specificity, the area under the ROC curve and so on.
evaluateClassifier(NumericalPerformanceMeasureParameterSet, T, DataSet[], double[][], ProgressUpdater) - Method in class de.jstacs.classifiers.assessment.ClassifierAssessment
This method must be implemented in all subclasses.
evaluateClassifier(NumericalPerformanceMeasureParameterSet, KFoldCrossValidationAssessParameterSet, DataSet[], double[][], ProgressUpdater) - Method in class de.jstacs.classifiers.assessment.KFoldCrossValidation
Evaluates a classifier.
evaluateClassifier(NumericalPerformanceMeasureParameterSet, RepeatedHoldOutAssessParameterSet, DataSet[], double[][], ProgressUpdater) - Method in class de.jstacs.classifiers.assessment.RepeatedHoldOutExperiment
Evaluates the classifier.
evaluateClassifier(NumericalPerformanceMeasureParameterSet, RepeatedSubSamplingAssessParameterSet, DataSet[], double[][], ProgressUpdater) - Method in class de.jstacs.classifiers.assessment.RepeatedSubSamplingExperiment
Evaluates the classifier.
evaluateClassifier(NumericalPerformanceMeasureParameterSet, Sampled_RepeatedHoldOutAssessParameterSet, DataSet[], double[][], ProgressUpdater) - Method in class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutExperiment
 
evaluateFunction(double[]) - Method in interface de.jstacs.algorithms.optimization.Function
Evaluates the function at a certain vector (in mathematical sense) x.
evaluateFunction(double[]) - Method in class de.jstacs.algorithms.optimization.NegativeDifferentiableFunction
 
evaluateFunction(double[]) - Method in class de.jstacs.algorithms.optimization.NegativeFunction
 
evaluateFunction(double[]) - Method in class de.jstacs.algorithms.optimization.NegativeOneDimensionalFunction
 
evaluateFunction(double) - Method in class de.jstacs.algorithms.optimization.NegativeOneDimensionalFunction
 
evaluateFunction(double[]) - Method in class de.jstacs.algorithms.optimization.NumericalDifferentiableFunction
 
evaluateFunction(double[]) - Method in class de.jstacs.algorithms.optimization.OneDimensionalFunction
 
evaluateFunction(double) - Method in class de.jstacs.algorithms.optimization.OneDimensionalFunction
Evaluates the function at position x.
evaluateFunction(double) - Method in class de.jstacs.algorithms.optimization.OneDimensionalSubFunction
 
evaluateFunction(double) - Method in class de.jstacs.algorithms.optimization.QuadraticFunction
 
evaluateFunction(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractMultiThreadedOptimizableFunction
 
evaluateFunction(int, int, int, int, int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractMultiThreadedOptimizableFunction
This method evaluates the function for a part of the data.
evaluateFunction(int, int, int, int, int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.LogGenDisMixFunction
 
evaluateFunction(int, int, int, int, int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.OneDataSetLogGenDisMixFunction
 
evaluateFunction(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.CompositeLogPrior
 
evaluateFunction(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.DoesNothingLogPrior
 
evaluateFunction(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateGaussianLogPrior
 
evaluateFunction(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateLaplaceLogPrior
 
evaluateFunction(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SimpleGaussianSumLogPrior
 
evaluateFunction(double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMTools.DualFunction
 
evaluateGradientOfFunction(double[]) - Method in class de.jstacs.algorithms.optimization.DifferentiableFunction
Evaluates the gradient of a function at a certain vector (in mathematical sense) x, i.e., $\nabla f(\underline{x}) = \left(\frac{\partial f(\underline{x})}{\partial x_1},\ldots,\frac{\partial f(\underline{x})}{\partial x_n}\right)$.
evaluateGradientOfFunction(double[]) - Method in class de.jstacs.algorithms.optimization.NegativeDifferentiableFunction
 
evaluateGradientOfFunction(double[]) - Method in class de.jstacs.algorithms.optimization.NumericalDifferentiableFunction
Evaluates the gradient of a function at a certain vector (in mathematical sense) x numerically.
evaluateGradientOfFunction(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractMultiThreadedOptimizableFunction
 
evaluateGradientOfFunction(int, int, int, int, int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractMultiThreadedOptimizableFunction
This method evaluates the gradient of the function for a part of the data.
evaluateGradientOfFunction(int, int, int, int, int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.LogGenDisMixFunction
 
evaluateGradientOfFunction(int, int, int, int, int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.OneDataSetLogGenDisMixFunction
 
evaluateGradientOfFunction(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.LogPrior
 
evaluateGradientOfFunction(double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMTools.DualFunction
 
EvaluationException - Exception in de.jstacs.algorithms.optimization
This class indicates that there was a problem to evaluate a function or the gradient of the function.
EvaluationException(String) - Constructor for exception de.jstacs.algorithms.optimization.EvaluationException
This constructor creates an EvaluationException with given error message.
exp - Variable in class de.jstacs.clustering.distances.SequenceScoreDistance
if exponential scores should be used
ExpandableParameterSet - Class in de.jstacs.parameters
A class for a ParameterSet that can be expanded by additional Parameters at runtime.
ExpandableParameterSet(ParameterSet, String, String) - Constructor for class de.jstacs.parameters.ExpandableParameterSet
Creates a new ExpandableParameterSet from a Class that can be instantiated using this ExpandableParameterSet and templates for the ParameterSet in each element of the array, the name and the comment that are displayed for the ParameterSetContainers enclosing the ParameterSets.
ExpandableParameterSet(ParameterSet, String, String, int) - Constructor for class de.jstacs.parameters.ExpandableParameterSet
Creates a new ExpandableParameterSet from a Class that can be instantiated using this ExpandableParameterSet and templates for the ParameterSet in each element of the array, the name and the comment that are displayed for the ParameterSetContainers enclosing the ParameterSets.
ExpandableParameterSet(StringBuffer) - Constructor for class de.jstacs.parameters.ExpandableParameterSet
The standard constructor for the interface Storable.
ExpandableParameterSet(ParameterSet[], String, String) - Constructor for class de.jstacs.parameters.ExpandableParameterSet
Creates a new ExpandableParameterSet from a ParameterSet -array.
export(String, Result, String) - Method in class de.jstacs.tools.ui.galaxy.GalaxyAdaptor
Exports a specified Result of a program execution to a file provided by filename and returns the corresponding Galaxy data type.
ExtendedZOOPSDiffSM - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif
This class handles mixtures with at least one hidden motif.
ExtendedZOOPSDiffSM(boolean, int, int, boolean, HomogeneousDiffSM, DifferentiableStatisticalModel, DurationDiffSM, boolean) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
This constructor creates an instance of ExtendedZOOPSDiffSM that is either an OOPS or a ZOOPS model depending on the chosen type.
ExtendedZOOPSDiffSM(boolean, int, int, boolean, HomogeneousDiffSM, DifferentiableStatisticalModel[], DurationDiffSM[], boolean) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
This constructor creates an instance of ExtendedZOOPSDiffSM that allows to have one site of the specified motifs in a Sequence.
ExtendedZOOPSDiffSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
This is the constructor for the interface Storable.
extendSampling(int, boolean) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier.DiffSMSamplingComponent
 
extendSampling(int, boolean) - Method in interface de.jstacs.sampling.SamplingComponent
This method allows to extend a sampling.
extendSampling(int, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGModelForGibbsSampling
 
extendSampling(int, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
 
extendSampling(int, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
 
extendSampling(int, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleSamplingState
 
extendSampling(int, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
 
extendSampling(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
This method prepares the model to extend an existing sampling.
extract(int, String) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.ConstraintManager
Extracts the constraint of a String and returns an ArrayList of int[].
extractAdditionalInfo(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint
This method parses additional information from the StringBuffer that is not parsed in the base class.
extractAdditionalInfo(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM.HomCondProb
 
extractAdditionalInfo(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb
 
extractAdditionalInfo(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhConstraint
 
extractAdditionalInfo(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint
 
extractAdditionalInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.GaussianLikePositionPrior
 
extractAdditionalInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.PositionPrior
This method extracts additional information from a StringBuffer.
extractAdditionalInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.UniformPositionPrior
 
extractForTag(StringBuffer, String) - Static method in class de.jstacs.io.XMLParser
Extracts the contents of source between tag start and end tags.
extractForTag(StringBuffer, String, Map<String, String>, Map<String, String>) - Static method in class de.jstacs.io.XMLParser
Extracts the contents of source between tag start and end tags.
extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifiers.AbstractClassifier
Extracts further information of a classifier from an XML representation.
extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
 
extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier
 
extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifier
 
extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
 
extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
 
extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifiers.MappingClassifier
 
extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifiers.trainSMBased.TrainSMBasedClassifier
 
extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureClassifier
 
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
This method is the opposite of IndependentProductDiffSS.getFurtherInformation().
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.differentiable.UniformDiffSS
This method is the opposite of UniformDiffSS.getFurtherInformation().
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
 
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
This method is the opposite of AbstractMixtureDiffSM.getFurtherInformation().
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
 
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
 
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.UniformDiffSM
 
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
This method extracts further information from the XML representation.
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
 
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
This method extracts further information from the XML representation.
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM
 
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
This method extracts further information from the XML representation.
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.PhyloDiscreteEmission
 
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.ReferenceSequenceDiscreteEmission
 
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
This method extracts further information from the XML representation.
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
This method extracts further information from the XML representation.
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.BasicPluginTransitionElement
 
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.BasicTransitionElement
 
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.DistanceBasedScaledTransitionElement
 
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ReferenceBasedTransitionElement
 
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ScaledTransitionElement
 
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
 
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
This method is used in the subclasses to extract further information from the XML representation and to set these as values of the instance.
extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.HiddenMotifMixture
 
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.AnnotatedEntity
This method can be used in the constructor with parameter StringBuffer to extract the further information.
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier.DoubleTableResult
 
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.AbstractSelectionParameter
 
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.EnumParameter
 
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.FileParameter
 
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.MultiSelectionParameter
 
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.Parameter
 
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.ParameterSetContainer
 
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.RangeParameter
 
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.SelectionParameter
 
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.SimpleParameter
 
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.results.DataSetResult
 
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.results.ImageResult
 
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.results.ListResult
 
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.results.PlotGeneratorResult
 
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.results.Result
 
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.results.SimpleResult
 
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.results.StorableResult
 
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.results.TextResult
 
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.tools.DataColumnParameter
 
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.tools.ToolResult
 
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.tools.ui.galaxy.GalaxyAdaptor.FileResult
 
extractFurtherInfos(StringBuffer) - Method in class de.jstacs.tools.ui.galaxy.GalaxyAdaptor.LinkedImageResult
 
extractObjectAndAttributesForTags(StringBuffer, String, Map<String, String>, Map<String, String>) - Static method in class de.jstacs.io.XMLParser
Returns the parsed value between the tags.
extractObjectAndAttributesForTags(StringBuffer, String, Map<String, String>, Map<String, String>, Class<T>) - Static method in class de.jstacs.io.XMLParser
Returns the parsed value between the tags.
extractObjectAndAttributesForTags(StringBuffer, String, Map<String, String>, Map<String, String>, Class<T>, Class<S>, S) - Static method in class de.jstacs.io.XMLParser
Returns the parsed value between the tags as an inner instance of the object outerInstance.
extractObjectForTags(StringBuffer, String) - Static method in class de.jstacs.io.XMLParser
Returns the parsed value between the tags.
extractObjectForTags(StringBuffer, String, Class<T>) - Static method in class de.jstacs.io.XMLParser
Returns the parsed value between the tags.
extractSequenceParts(int, DataSet[], DataSet[]) - Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
This method extracts the corresponding Sequence parts for a specific DifferentiableSequenceScore.
extractSequencesWithTags(StringBuffer, String) - Static method in class de.jstacs.io.XMLParser
Extracts a set of sequences from their XML representation.
extractWeights(int, double[][]) - Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
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