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## L

L - Variable in class de.jstacs.algorithms.graphs.tensor.Tensor
  The number of nodes minus 1.
LearningPrinciple - Enum in de.jstacs.classifier.scoringFunctionBased.gendismix
  This enum can be used to obtain the weights for well-known optimization tasks.
length() - Method in class de.jstacs.data.Alphabet
  Returns the length of the `Alphabet`.
length() - Method in class de.jstacs.data.alphabets.ContinuousAlphabet
length() - Method in class de.jstacs.data.alphabets.DiscreteAlphabet
  Returns the number of symbols in the calling alphabet.
length - Variable in class de.jstacs.models.AbstractModel
  The length of the sequences the model can classify.
length - Variable in class de.jstacs.parameters.SequenceScoringParameterSet
  The length of sequences the model can work on or `0` for arbitrary length
length - Variable in class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
  The length of the modeled sequences.
length() - Method in class de.jstacs.utils.DoubleList
  Returns the number of inserted elements.
length() - Method in class de.jstacs.utils.IntList
  Returns the number of inserted elements.
lengths - Variable in class de.jstacs.models.CompositeModel
  The length for each component.
LEQ - Static variable in interface de.jstacs.parameters.validation.Constraint
  The condition is less or equal
LIKELIHOOD_INDEX - Static variable in enum de.jstacs.classifier.scoringFunctionBased.gendismix.LearningPrinciple
  This constant is the array index of the weighting factor for the likelihood.
LimitedMedianStartDistance - Class in de.jstacs.algorithms.optimization
  This class implements a `StartDistanceForecaster` that uses the median of a limited memory over the last values.
LimitedMedianStartDistance(int, double) - Constructor for class de.jstacs.algorithms.optimization.LimitedMedianStartDistance
  This constructor creates an instance with `slots` memory slots that will initially be filled with `value`.
limitedMemoryBFGS(DifferentiableFunction, double[], byte, TerminationCondition, double, StartDistanceForecaster, OutputStream, Time) - Static method in class de.jstacs.algorithms.optimization.Optimizer
  The Broyden-Fletcher-Goldfarb-Shanno version of limited memory quasi-Newton methods.
LimitedStringExtractor - Class in de.jstacs.io
  This `AbstractStringExtractor` allows to read only a limited amount of data.
LimitedStringExtractor(AbstractStringExtractor, int) - Constructor for class de.jstacs.io.LimitedStringExtractor
  This constructor creates a new instance based on a given `AbstractStringExtractor` and a maximal number of Strings to be read.
linearizeParameters() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.ParameterTree
  Extracts the `Parameter`s from the leaves of this tree in left-to-right order (as specified by the order of the alphabet) and returns them as a `LinkedList`.
list - Variable in class de.jstacs.results.ListResult
  The internal list of `ResultSet`s that are part of this `ListResult`
ListResult - Class in de.jstacs.results
  Class for a `Result` that contains a list or a matrix, respectively, of `ResultSet`s.
ListResult(String, String, ResultSet, ResultSet...) - Constructor for class de.jstacs.results.ListResult
  Creates a new `ListResult` from an array of `ResultSet`s and a `ResultSet` of annotations, which may provide additional information on this `ListResult`.
ListResult(StringBuffer) - Constructor for class de.jstacs.results.ListResult
  The standard constructor for the interface `Storable`.
loadParameters() - Method in class de.jstacs.classifier.assessment.ClassifierAssessmentAssessParameterSet
loadParameters() - Method in class de.jstacs.classifier.assessment.KFoldCVAssessParameterSet
loadParameters() - Method in class de.jstacs.classifier.assessment.RepeatedHoldOutAssessParameterSet
loadParameters() - Method in class de.jstacs.classifier.assessment.RepeatedSubSamplingAssessParameterSet
loadParameters() - Method in class de.jstacs.classifier.assessment.Sampled_RepeatedHoldOutAssessParameterSet
loadParameters() - Method in class de.jstacs.classifier.scoringFunctionBased.gendismix.GenDisMixClassifierParameterSet
loadParameters() - Method in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifierParameterSet
loadParameters() - Method in class de.jstacs.data.AlphabetContainerParameterSet
loadParameters() - Method in class de.jstacs.data.AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet
loadParameters() - Method in class de.jstacs.data.alphabets.ContinuousAlphabet.ContinuousAlphabetParameterSet
loadParameters() - Method in class de.jstacs.data.alphabets.DiscreteAlphabet.DiscreteAlphabetParameterSet
loadParameters() - Method in class de.jstacs.data.alphabets.DNAAlphabet.DNAAlphabetParameterSet
loadParameters() - Method in class de.jstacs.models.discrete.DGMParameterSet
loadParameters() - Method in class de.jstacs.models.discrete.homogeneous.parameters.HomogeneousModelParameterSet
loadParameters() - Method in class de.jstacs.models.discrete.inhomogeneous.parameters.BayesianNetworkModelParameterSet
loadParameters() - Method in class de.jstacs.models.discrete.inhomogeneous.parameters.FSDAGMParameterSet
loadParameters() - Method in class de.jstacs.parameters.ArrayParameterSet
loadParameters() - Method in class de.jstacs.parameters.ExpandableParameterSet
loadParameters() - Method in class de.jstacs.parameters.ParameterSet
  Loads the parameters for this `ParameterSet`.
loadParameters() - Method in class de.jstacs.parameters.SimpleParameterSet
loadParameters() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunctionParameterSet
loadParameters() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures.BTExplainingAwayResidual.BTExplainingAwayResidualParameterSet
loadParameters() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures.BTMutualInformation.BTMutualInformationParameterSet
loadParameters() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov.InhomogeneousMarkovParameterSet
loadParameters() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMExplainingAwayResidual.PMMExplainingAwayResidualParameterSet
loadParameters() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMMutualInformation.PMMMutualInformationParameterSet
LocatedSequenceAnnotation - Class in de.jstacs.data.sequences.annotation
  Class for a `SequenceAnnotation` that has a position on the sequence, e.g for transcription start sites or intron-exon junctions.
LocatedSequenceAnnotation(int, String, String, Result...) - Constructor for class de.jstacs.data.sequences.annotation.LocatedSequenceAnnotation
  Creates a new `LocatedSequenceAnnotation` of type `type` with identifier `identifier` and additional annotation (that does not fit the `SequenceAnnotation` definitions) given as an array of `Result`s `result`.
LocatedSequenceAnnotation(int, String, String, Collection<Result>) - Constructor for class de.jstacs.data.sequences.annotation.LocatedSequenceAnnotation
  Creates a new `LocatedSequenceAnnotation` of type `type` with identifier `identifier` and additional annotation (that does not fit the `SequenceAnnotation` definitions) given as a `Collection` of `Result`s `result`.
LocatedSequenceAnnotation(int, String, String, SequenceAnnotation[], Result...) - Constructor for class de.jstacs.data.sequences.annotation.LocatedSequenceAnnotation
  Creates a new `LocatedSequenceAnnotation` of type `type` with identifier `identifier`, additional annotation (that does not fit the `SequenceAnnotation` definitions) given as an array of `Result`s `additionalAnnotation` and sub-annotations `annotations`.
LocatedSequenceAnnotation(String, String, LocatedSequenceAnnotation[], Result...) - Constructor for class de.jstacs.data.sequences.annotation.LocatedSequenceAnnotation
  Creates a new `LocatedSequenceAnnotation` of type `type` with identifier `identifier`, additional annotation (that does not fit the `SequenceAnnotation` definitions) given as an array of `Result`s `additionalAnnotation` and sub-annotations `annotations`.
LocatedSequenceAnnotation(StringBuffer) - Constructor for class de.jstacs.data.sequences.annotation.LocatedSequenceAnnotation
  The standard constructor for the interface `Storable`.
LocatedSequenceAnnotationWithLength - Class in de.jstacs.data.sequences.annotation
  Class for a `SequenceAnnotation` that has a position on the sequence and a length, e.g. for donor splice sites, exons or genes.
LocatedSequenceAnnotationWithLength(int, int, String, String, Result...) - Constructor for class de.jstacs.data.sequences.annotation.LocatedSequenceAnnotationWithLength
  Creates a new `LocatedSequenceAnnotationWithLength` of type `type` with identifier `identifier` and additional annotation (that does not fit the `SequenceAnnotation` definitions) given as an array of `Result`s `result`.
LocatedSequenceAnnotationWithLength(int, int, String, String, Collection<Result>) - Constructor for class de.jstacs.data.sequences.annotation.LocatedSequenceAnnotationWithLength
  Creates a new `LocatedSequenceAnnotationWithLength` of type `type` with identifier `identifier` and additional annotation (that does not fit the `SequenceAnnotation` definitions) given as a `Collection` of `Result`s `result`.
LocatedSequenceAnnotationWithLength(int, int, String, String, SequenceAnnotation[], Result...) - Constructor for class de.jstacs.data.sequences.annotation.LocatedSequenceAnnotationWithLength
  Creates a new `LocatedSequenceAnnotationWithLength` of type `type` with identifier `identifier`, additional annotation (that does not fit the `SequenceAnnotation` definitions) given as an array of `Result`s `additionalAnnotations` and sub-annotations `annotations`.
LocatedSequenceAnnotationWithLength(String, String, LocatedSequenceAnnotation[], Result...) - Constructor for class de.jstacs.data.sequences.annotation.LocatedSequenceAnnotationWithLength
  Creates a new `LocatedSequenceAnnotationWithLength` of type `type` with identifier `identifier`, additional annotation (that does not fit the `SequenceAnnotation` definitions) given as an array of `Result`s `additionalAnnotations` and sub-annotations `annotations`.
LocatedSequenceAnnotationWithLength(StringBuffer) - Constructor for class de.jstacs.data.sequences.annotation.LocatedSequenceAnnotationWithLength
  The standard constructor for the interface `Storable`.
logClazz - Variable in class de.jstacs.classifier.scoringFunctionBased.AbstractOptimizableFunction
  The logarithm of the class parameters.
logGammaSum - Variable in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
  This `double` contains the sum of the logarithm of the gamma functions used in the prior.
LogGenDisMixFunction - Class in de.jstacs.classifier.scoringFunctionBased.gendismix
  This class implements the the following function
  The weights have to sum to 1.
LogGenDisMixFunction(int, ScoringFunction[], Sample[], double[][], LogPrior, double[], boolean, boolean) - Constructor for class de.jstacs.classifier.scoringFunctionBased.gendismix.LogGenDisMixFunction
  The constructor for creating an instance that can be used in an `Optimizer`.
logHiddenNorm - Variable in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
  This `double` contains the logarithm of the normalization constant of hidden parameters of the instance.
logHiddenPotential - Variable in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
  This array contains the logarithm of the hidden potentials of the instance.
logNormalizationConstant - Variable in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
  Normalization constant to obtain normalized probabilities.
LogPrior - Class in de.jstacs.classifier.scoringFunctionBased.logPrior
  The abstract class for any log-prior used e.g. for maximum supervised posterior optimization.
LogPrior() - Constructor for class de.jstacs.classifier.scoringFunctionBased.logPrior.LogPrior
logProbFor(Sequence, int, int) - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousMM
logProbFor(Sequence, int, int) - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousModel
  This method computes the logarithm of the probability of the given `Sequence` in the given interval.
logSumNormalisation(double[]) - Static method in class de.jstacs.utils.Normalisation
  The method does a log-sum-normalisation on `d` with the values of `d` given as `d[i] = Math.log( val[i] )`.
logSumNormalisation(double[], int, int) - Static method in class de.jstacs.utils.Normalisation
  The method does a log-sum-normalisation on `d` between start index `startD` and end index `endD` with the values of `d` given as `d[i] = Math.log( val[i] )`.
logSumNormalisation(double[], int, int, double[]) - Static method in class de.jstacs.utils.Normalisation
  The method does a log-sum-normalisation on `d` within start index `startD` and end index `endD` with the values of `d` given as `d[i] = Math.log( val[i] )`.
logSumNormalisation(double[], int, int, double[], int) - Static method in class de.jstacs.utils.Normalisation
  The method does a log-sum-normalisation on `d` between start index `startD` and end index `endD` with the values of `d` given as `d[i] = Math.log( val[i] )`.
logSumNormalisation(double[], int, int, double[], double[], int) - Static method in class de.jstacs.utils.Normalisation
  The method does a log-sum-normalisation on `d` within start index `startD` and end index `endD` with the values of `d` given logarithmised: `d[i] = Math.log( val[i] )`.
logSumNormalisation(double[], double) - Static method in class de.jstacs.utils.Normalisation
  The method does a log-sum-normalisation on `d` with the values of `d` given as `d[i] = Math.log( val[i] )` using offset `offset`.
logSumNormalisation(double[], int, int, double, double[], int) - Static method in class de.jstacs.utils.Normalisation
  The method does a log-sum-normalisation on `d` between start index `startD` and end index `endD` with the values of `d` given as `d[i] = Math.log( val[i] )` using offset `offset`.
logSumNormalisation(double[], int, int, double, double[], double[], int) - Static method in class de.jstacs.utils.Normalisation
  The method does a log-sum-normalisation on `d` between start index `startD` and end index `endD` with the values of `d` given as `d[i] = Math.log( val[i] )` using offset `offset`.
logWeights - Variable in class de.jstacs.models.mixture.AbstractMixtureModel
  The log probabilities for each component.
LT - Static variable in interface de.jstacs.parameters.validation.Constraint
  The condition is less than

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