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## L

L - Variable in class de.jstacs.algorithms.graphs.tensor.Tensor
  The number of nodes minus 1.
lastParameters - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
  The last accepted parameters for all samplings, backup for iterative sampling when checking for `BurnInTest`
lastScore - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
  The scores yielded for the parameters in `lastParameters`
LearningPrinciple - Enum in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix
  This enum can be used to obtain the weights for well-known optimization tasks.
length() - Method in class de.jstacs.data.alphabets.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.parameters.SequenceScoringParameterSet
  The length of sequences the model can work on or `0` for arbitrary length
length - Variable in class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
  The length of the modeled sequences.
length - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
  The length of the sequences the model can classify.
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.sequenceScores.statisticalModels.trainable.CompositeTrainSM
  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.classifiers.differentiableSequenceScoreBased.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.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
  Extracts the `BNDiffSMParameter`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`.
llGrad - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.LogGenDisMixFunction
  Array for the gradient of the log-likelihood
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.classifiers.differentiableSequenceScoreBased.AbstractOptimizableFunction
  The logarithm of the class parameters.
logEmission - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
  Helper variable = only for internal use.
logGammaSum - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
  This `double` contains the sum of the logarithm of the gamma functions used in the prior.
LogGenDisMixFunction - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix
  This class implements the the following function
  The weights have to sum to 1.
LogGenDisMixFunction(int, DifferentiableSequenceScore[], DataSet[], double[][], LogPrior, double[], boolean, boolean) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.LogGenDisMixFunction
  The constructor for creating an instance that can be used in an `Optimizer`.
logHiddenNorm - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
  This `double` contains the logarithm of the normalization constant of hidden parameters of the instance.
logHiddenPotential - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
  This array contains the logarithm of the hidden potentials of the instance.
LogisticConstraint - Interface in de.jstacs.sequenceScores.differentiable.logistic
  This interface defines the function for sequence which can be used in `LogisticDiffSS`.
LogisticDiffSS - Class in de.jstacs.sequenceScores.differentiable.logistic
  This class implements a logistic function.
LogisticDiffSS(AlphabetContainer, int, LogisticConstraint...) - Constructor for class de.jstacs.sequenceScores.differentiable.logistic.LogisticDiffSS
  This is the main constructor to create `LogisticDiffSS` instance.
LogisticDiffSS(StringBuffer) - Constructor for class de.jstacs.sequenceScores.differentiable.logistic.LogisticDiffSS
  This is the constructor for `Storable`.
logNorm - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
  The log-normalization constants for each condition
logNorm - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
  The log normalization constant based on the parameters.
logNormalizationConstant - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
  Normalization constant to obtain normalized probabilities.
LogPrior - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior
  The abstract class for any log-prior used e.g. for maximum supervised posterior optimization.
LogPrior() - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.LogPrior
logProb(int, int, Sequence) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
  This method computes the logarithm of the probability of the corresponding subsequences.
logProb(int, int, Sequence) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
logProb(int, int, Sequence) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM
logProbFor(Sequence, int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousMM
logProbFor(Sequence, int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM
  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 the array `d`, where the values of `d` are assumed to be logarithmised.
logSumNormalisation(double[], int, int) - Static method in class de.jstacs.utils.Normalisation
  The method does a log-sum-normalisation on the values of the array `d` between start index `startD` and end index `endD`, where the values of `d` are assumed to be logarithmised.
logSumNormalisation(double[], int, int, double[]) - Static method in class de.jstacs.utils.Normalisation
  The method does a log-sum-normalisation on the values of the array `d` between start index `startD` and end index `endD`, where the values of `d` are assumed to be logarithmised.
logSumNormalisation(double[], int, int, double[], int) - Static method in class de.jstacs.utils.Normalisation
  The method does a log-sum-normalisation on the values of the array `d` between start index `startD` and end index `endD`, where the values of `d` are assumed to be logarithmised.
logSumNormalisation(double[], int, int, double[], double[], int) - Static method in class de.jstacs.utils.Normalisation
  The method does a log-sum-normalisation on the values of the array `d` between start index `startD` and end index `endD`, where the values of `d` are assumed to be logarithmised.
logSumNormalisation(double[], double) - Static method in class de.jstacs.utils.Normalisation
  The method does a log-sum-normalisation on the array `d`, where the values of `d` are assumed to be logarithmised.
logSumNormalisation(double[], int, int, double, double[], int) - Static method in class de.jstacs.utils.Normalisation
  The method does a log-sum-normalisation on the values of the array `d` between start index `startD` and end index `endD`, where the values of `d` are assumed to be logarithmised.
logSumNormalisation(double[], int, int, double, double[], double[], int) - Static method in class de.jstacs.utils.Normalisation
  The method does a log-sum-normalisation on the values of the array `d` between start index `startD` and end index `endD`, where the values of `d` are assumed to be logarithmised.
logWeights - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  The log probabilities for each component.
lookup - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
  The lookup table for spare context en- and decoding.
LT - Static variable in interface de.jstacs.parameters.validation.Constraint
  The condition is less than

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