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Sample
object containing artificial
sequence(s).
Sample
of a given number of Sequence
s from a
trained homogeneous model.
Sample
object containing artificial
sequence(s).
n sequences from the discrete inhomogeneous model m
.
- emitSampleUsingCurrentParameterSet(int, int...) -
Method in class de.jstacs.models.mixture.AbstractMixtureModel
- The method returns an array of sequences using the current parameter set.
- emitSampleUsingCurrentParameterSet(int, int...) -
Method in class de.jstacs.models.mixture.MixtureModel
-
- emitSampleUsingCurrentParameterSet(int, int...) -
Method in class de.jstacs.models.mixture.motif.HiddenMotifMixture
- Standard implementation throwing an
OperationNotSupportedException
.
- emitSampleUsingCurrentParameterSet(int, int...) -
Method in class de.jstacs.models.mixture.StrandModel
-
- EmptySampleException - Exception in de.jstacs.data
- An
EmptySampleException
will be thrown if no Sequence
is in a
Sample
(i.e. the Sample
is empty). - EmptySampleException() -
Constructor for exception de.jstacs.data.EmptySampleException
- This constructor creates an instance with default error message
("The created Sample is empty.
- encode(int[][]) -
Static method in class de.jstacs.models.discrete.inhomogeneous.parameters.FSDAGMParameterSet
- This method can be used to encode an adjacency list to a graph
description
String
(e.g. for the different constructors which
requires graph description String
s).
- enumerate(ScoringFunction[], int, int, RecyclableSequenceEnumerator, double, SFBasedOptimizableFunction, 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(ScoringFunction[], int[], int[], RecyclableSequenceEnumerator[], double, SFBasedOptimizableFunction, OutputStream) -
Static method in class de.jstacs.motifDiscovery.MutableMotifDiscovererToolbox
- This method allows to enumerate all possible seeds for a number of motifs in the
MutableMotifDiscoverer
s 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
CollectionParameter
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.
- 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.Sequence
-
- equals(Object) -
Method in class de.jstacs.parameters.CollectionParameter
-
- 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(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.CollectionParameter
- 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.
- errorMessage -
Variable in class de.jstacs.parameters.validation.ReferenceConstraint
- The message of the last error or
null
.
- ess -
Variable in class de.jstacs.models.hmm.models.DifferentiableHigherOrderHMM
- The equivalent sample size used for the prior
- ess -
Variable in class de.jstacs.models.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
- The equivalent sample sizes for each condition
- ess -
Variable in class de.jstacs.models.hmm.transitions.elements.ReferenceBasedTransitionElement
- The equivalent sample size (ess) used in the prior of this instance.
- ess -
Variable in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
- The equivalent sample size.
- ess -
Variable in class de.jstacs.scoringFunctions.mix.motifSearch.DurationScoringFunction
- The equivalent sample size.
- estimate(double) -
Method in class de.jstacs.models.discrete.Constraint
- Estimates the (smoothed) relative frequencies using the ess
(equivalent sample size).
- estimate(double) -
Method in class de.jstacs.models.discrete.homogeneous.HomogeneousModel.HomCondProb
-
- estimate(double) -
Method in class de.jstacs.models.discrete.inhomogeneous.InhCondProb
-
- estimate(double) -
Method in class de.jstacs.models.discrete.inhomogeneous.MEMConstraint
-
- estimateComponentProbs -
Variable in class de.jstacs.models.mixture.AbstractMixtureModel
- The switch for estimating the component probabilities or not.
- estimateFromStatistic() -
Method in class de.jstacs.models.hmm.states.emissions.continuous.GaussianEmission
-
- estimateFromStatistic() -
Method in class de.jstacs.models.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
-
- estimateFromStatistic() -
Method in class de.jstacs.models.hmm.states.emissions.discrete.PhyloDiscreteEmission
-
- estimateFromStatistic() -
Method in interface de.jstacs.models.hmm.states.emissions.Emission
- This method estimates the parameters from the internal sufficient statistic.
- estimateFromStatistic() -
Method in class de.jstacs.models.hmm.states.emissions.MixtureEmission
-
- estimateFromStatistic() -
Method in class de.jstacs.models.hmm.states.emissions.SilentEmission
-
- estimateFromStatistic() -
Method in class de.jstacs.models.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
- This method estimates the parameters from the sufficient statistic.
- estimateFromStatistic() -
Method in class de.jstacs.models.hmm.transitions.BasicHigherOrderTransition
-
- estimateFromStatistic() -
Method in class de.jstacs.models.hmm.transitions.elements.DistanceBasedScaledTransitionElement
-
- estimateFromStatistic() -
Method in class de.jstacs.models.hmm.transitions.elements.ScaledTransitionElement
-
- estimateFromStatistic() -
Method in interface de.jstacs.models.hmm.transitions.TrainableTransition
- This method estimates the parameter of the transition using the internal sufficient statistic.
- estimateFromStatistics() -
Method in class de.jstacs.models.hmm.models.HigherOrderHMM
- This method estimates the parameters of all emissions and the transition using their sufficient statistics.
- estimateParameters(Sample, double[]) -
Method in class de.jstacs.models.discrete.inhomogeneous.DAGModel
- 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.models.discrete.Constraint
- Estimates unconditionally.
- estimateUnConditional(int, int, double, boolean) -
Method in class de.jstacs.models.discrete.homogeneous.HomogeneousModel.HomCondProb
-
- estimateUnConditional(double, double) -
Method in class de.jstacs.models.discrete.inhomogeneous.InhCondProb
- Estimates the unconditional frequencies using the ess (equivalent
sample size).
- estimateUnConditional(int, int, double, boolean) -
Method in class de.jstacs.models.discrete.inhomogeneous.InhCondProb
-
- eval(String) -
Method in class de.jstacs.utils.REnvironment
- Evaluates the
String
as R commands.
- evaluate(MeasureParameters, boolean, Sample...) -
Method in class de.jstacs.classifier.AbstractClassifier
- This method evaluates the classifier and computes all numerical
results as, for instance, the sensitivity for a given specificity, the
area under the ROC curve and so on.
- evaluateAll(MeasureParameters, boolean, Sample...) -
Method in class de.jstacs.classifier.AbstractClassifier
- This method evaluates the classifier and computes all results.
- evaluateClassifier(MeasureParameters, ClassifierAssessmentAssessParameterSet, Sample[], ProgressUpdater) -
Method in class de.jstacs.classifier.assessment.ClassifierAssessment
- This method must be implemented in all subclasses.
- evaluateClassifier(MeasureParameters, ClassifierAssessmentAssessParameterSet, Sample[], ProgressUpdater) -
Method in class de.jstacs.classifier.assessment.KFoldCrossValidation
- Evaluates a classifier.
- evaluateClassifier(MeasureParameters, ClassifierAssessmentAssessParameterSet, Sample[], ProgressUpdater) -
Method in class de.jstacs.classifier.assessment.RepeatedHoldOutExperiment
- Evaluates the classifier.
- evaluateClassifier(MeasureParameters, ClassifierAssessmentAssessParameterSet, Sample[], ProgressUpdater) -
Method in class de.jstacs.classifier.assessment.RepeatedSubSamplingExperiment
- Evaluates the classifier.
- evaluateClassifier(MeasureParameters, ClassifierAssessmentAssessParameterSet, Sample[], ProgressUpdater) -
Method in class de.jstacs.classifier.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.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.classifier.scoringFunctionBased.AbstractMultiThreadedOptimizableFunction
-
- evaluateFunction(int, int, int, int, int) -
Method in class de.jstacs.classifier.scoringFunctionBased.AbstractMultiThreadedOptimizableFunction
- This method evaluates the function for a part of the data.
- evaluateFunction(int, int, int, int, int) -
Method in class de.jstacs.classifier.scoringFunctionBased.gendismix.LogGenDisMixFunction
-
- evaluateFunction(int, int, int, int, int) -
Method in class de.jstacs.classifier.scoringFunctionBased.gendismix.OneSampleLogGenDisMixFunction
-
- evaluateFunction(double[]) -
Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.CompositeLogPrior
-
- evaluateFunction(double[]) -
Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.DoesNothingLogPrior
-
- evaluateFunction(double[]) -
Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.SeparateGaussianLogPrior
-
- evaluateFunction(double[]) -
Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.SeparateLaplaceLogPrior
-
- evaluateFunction(double[]) -
Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.SimpleGaussianSumLogPrior
-
- 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.,
.
- 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.classifier.scoringFunctionBased.AbstractMultiThreadedOptimizableFunction
-
- evaluateGradientOfFunction(int, int, int, int, int) -
Method in class de.jstacs.classifier.scoringFunctionBased.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.classifier.scoringFunctionBased.gendismix.LogGenDisMixFunction
-
- evaluateGradientOfFunction(int, int, int, int, int) -
Method in class de.jstacs.classifier.scoringFunctionBased.gendismix.OneSampleLogGenDisMixFunction
-
- evaluateGradientOfFunction(double[]) -
Method in class de.jstacs.classifier.scoringFunctionBased.logPrior.LogPrior
-
- 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
.
- ExpandableParameterSet - Class in de.jstacs.parameters
- A class for a
ParameterSet
that can be expanded by additional
Parameter
s 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
ParameterSetContainer
s enclosing the ParameterSet
s.
- 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
ParameterSetContainer
s enclosing the ParameterSet
s.
- 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) -
Method in class de.jstacs.utils.galaxy.GalaxyAdaptor
- Exports a specified
GalaxyAdaptor.LinkedImageResult
of a program execution
to a file provided by filename
and returns the
corresponding Galaxy data type.
- extendSampling(int, boolean) -
Method in class de.jstacs.classifier.scoringFunctionBased.sampling.SamplingScoreBasedClassifier.ScoringFunctionSamplingComponent
-
- extendSampling(int, boolean) -
Method in class de.jstacs.models.discrete.inhomogeneous.FSDAGModelForGibbsSampling
-
- extendSampling(int, boolean) -
Method in class de.jstacs.models.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
-
- extendSampling(int, boolean) -
Method in class de.jstacs.models.hmm.states.emissions.SilentEmission
-
- extendSampling(int, boolean) -
Method in class de.jstacs.models.hmm.states.SimpleSamplingState
-
- extendSampling(int, boolean) -
Method in class de.jstacs.models.hmm.transitions.HigherOrderTransition
-
- extendSampling(int) -
Method in class de.jstacs.models.mixture.AbstractMixtureModel
- This method prepares the model to extend an existing sampling.
- extendSampling(int, boolean) -
Method in interface de.jstacs.sampling.SamplingComponent
- This method allows to extend a sampling.
- extract(int, String) -
Static method in class de.jstacs.models.discrete.ConstraintManager
- Extracts the constraints of a
String
and returns an
ArrayList
of int[]
.
- extractAdditionalInfo(StringBuffer) -
Method in class de.jstacs.models.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.models.discrete.homogeneous.HomogeneousModel.HomCondProb
-
- extractAdditionalInfo(StringBuffer) -
Method in class de.jstacs.models.discrete.inhomogeneous.InhCondProb
-
- extractAdditionalInfo(StringBuffer) -
Method in class de.jstacs.models.discrete.inhomogeneous.InhConstraint
-
- extractAdditionalInfo(StringBuffer) -
Method in class de.jstacs.models.discrete.inhomogeneous.MEMConstraint
-
- extractAdditionalInformation(StringBuffer) -
Method in class de.jstacs.models.mixture.motif.positionprior.GaussianLikePositionPrior
-
- extractAdditionalInformation(StringBuffer) -
Method in class de.jstacs.models.mixture.motif.positionprior.PositionPrior
- This method extracts additional information from a
StringBuffer
.
- extractAdditionalInformation(StringBuffer) -
Method in class de.jstacs.models.mixture.motif.positionprior.UniformPositionPrior
-
- extractCollection(StringBuffer) -
Method in class de.jstacs.parameters.CollectionParameter
- Extracts the internal
ParameterSet
from its XML representation (
ParameterSet.toXML()
).
- extractCollection(StringBuffer) -
Method in class de.jstacs.parameters.EnumParameter
-
- 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.classifier.AbstractClassifier
- Extracts further information of a classifier from an XML representation.
- extractFurtherClassifierInfosFromXML(StringBuffer) -
Method in class de.jstacs.classifier.AbstractScoreBasedClassifier
-
- extractFurtherClassifierInfosFromXML(StringBuffer) -
Method in class de.jstacs.classifier.MappingClassifier
-
- extractFurtherClassifierInfosFromXML(StringBuffer) -
Method in class de.jstacs.classifier.modelBased.ModelBasedClassifier
-
- extractFurtherClassifierInfosFromXML(StringBuffer) -
Method in class de.jstacs.classifier.scoringFunctionBased.gendismix.GenDisMixClassifier
-
- extractFurtherClassifierInfosFromXML(StringBuffer) -
Method in class de.jstacs.classifier.scoringFunctionBased.sampling.SamplingGenDisMixClassifier
-
- extractFurtherClassifierInfosFromXML(StringBuffer) -
Method in class de.jstacs.classifier.scoringFunctionBased.sampling.SamplingScoreBasedClassifier
-
- extractFurtherClassifierInfosFromXML(StringBuffer) -
Method in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier
-
- extractFurtherClassifierInfosFromXML(StringBuffer) -
Method in class de.jstacs.models.discrete.inhomogeneous.shared.SharedStructureClassifier
-
- extractFurtherInformation(StringBuffer) -
Method in class de.jstacs.models.hmm.AbstractHMM
- This method extracts further information from the XML representation.
- extractFurtherInformation(StringBuffer) -
Method in class de.jstacs.models.hmm.models.DifferentiableHigherOrderHMM
-
- extractFurtherInformation(StringBuffer) -
Method in class de.jstacs.models.hmm.models.HigherOrderHMM
- This method extracts further information from the XML representation.
- extractFurtherInformation(StringBuffer) -
Method in class de.jstacs.models.hmm.models.SamplingHigherOrderHMM
-
- extractFurtherInformation(StringBuffer) -
Method in class de.jstacs.models.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
- This method extracts further information from the XML representation.
- extractFurtherInformation(StringBuffer) -
Method in class de.jstacs.models.hmm.states.emissions.discrete.PhyloDiscreteEmission
-
- extractFurtherInformation(StringBuffer) -
Method in class de.jstacs.models.hmm.states.emissions.discrete.ReferenceSequenceDiscreteEmission
-
- extractFurtherInformation(StringBuffer) -
Method in class de.jstacs.models.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
- This method extracts further information from the XML representation.
- extractFurtherInformation(StringBuffer) -
Method in class de.jstacs.models.hmm.transitions.BasicHigherOrderTransition
- This method extracts further information from the XML representation.
- extractFurtherInformation(StringBuffer) -
Method in class de.jstacs.models.hmm.transitions.elements.BasicPluginTransitionElement
-
- extractFurtherInformation(StringBuffer) -
Method in class de.jstacs.models.hmm.transitions.elements.BasicTransitionElement
-
- extractFurtherInformation(StringBuffer) -
Method in class de.jstacs.models.hmm.transitions.elements.DistanceBasedScaledTransitionElement
-
- extractFurtherInformation(StringBuffer) -
Method in class de.jstacs.models.hmm.transitions.elements.ReferenceBasedTransitionElement
-
- extractFurtherInformation(StringBuffer) -
Method in class de.jstacs.models.hmm.transitions.elements.ScaledTransitionElement
-
- extractFurtherInformation(StringBuffer) -
Method in class de.jstacs.models.hmm.transitions.HigherOrderTransition
-
- extractFurtherInformation(StringBuffer) -
Method in class de.jstacs.models.mixture.AbstractMixtureModel
- 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.models.mixture.motif.HiddenMotifMixture
-
- extractFurtherInformation(StringBuffer) -
Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
- This method is the opposite of
AbstractMixtureScoringFunction.getFurtherInformation()
.
- extractFurtherInformation(StringBuffer) -
Method in class de.jstacs.scoringFunctions.mix.motifSearch.HiddenMotifsMixture
-
- extractFurtherInformation(StringBuffer) -
Method in class de.jstacs.scoringFunctions.mix.StrandScoringFunction
-
- extractMainInfo(StringBuffer) -
Method in class de.jstacs.results.Result
- This method can be used in the method
Result.fromXML(StringBuffer)
to
extract the main information (name, comment, data type).
- 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, Sample[], Sample[]) -
Method in class de.jstacs.scoringFunctions.IndependentProductScoringFunction
- This method extracts the corresponding
Sequence
parts for a specific ScoringFunction
.
- extractWeights(int, double[][]) -
Method in class de.jstacs.scoringFunctions.IndependentProductScoringFunction
- This method creates the weights for
IndependentProductScoringFunction.extractSequenceParts(int, Sample[], Sample[])
.
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