L-Index (Cognitive Foundry)

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

L

L - Variable in class gov.sandia.cognition.math.matrix.custom.DenseMatrix.LU: The lower triangular matrix resulting from the factorization.
labelCollection(Collection<InputType>, OutputType) - Static method in class gov.sandia.cognition.learning.data.DefaultInputOutputPair: Takes a collection of input values and a single output value and creates a new collection of default input output pairs with each of the given inputs and the given output.
lambda - Variable in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineShiftingPerceptron: The lambda parameter for controlling how much shifting occurs.
LaplaceDistribution - Class in gov.sandia.cognition.statistics.distribution: A Laplace distribution, sometimes called a double exponential distribution.
LaplaceDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.LaplaceDistribution: Creates a new instance of LaplaceDistribution
LaplaceDistribution(double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.LaplaceDistribution: Creates a new instance of LaplaceDistribution
LaplaceDistribution(LaplaceDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.LaplaceDistribution: Copy Constructor
LaplaceDistribution.CDF - Class in gov.sandia.cognition.statistics.distribution: CDF of the Laplace distribution.
LaplaceDistribution.MaximumLikelihoodEstimator - Class in gov.sandia.cognition.statistics.distribution: Estimates the ML parameters of a Laplace distribution from a Collection of Numbers.
LaplaceDistribution.PDF - Class in gov.sandia.cognition.statistics.distribution: The PDF of a Laplace Distribution.
LaplaceDistribution.WeightedMaximumLikelihoodEstimator - Class in gov.sandia.cognition.statistics.distribution: Creates a UnivariateGaussian from weighted data
LAST_MODIFIED_DATE_FIELD_NAME - Static variable in class gov.sandia.cognition.text.document.AbstractDocument: The name of the last modified date field is "lastModifiedDate".
lastLogLikelihood - Variable in class gov.sandia.cognition.learning.algorithm.hmm.AbstractBaumWelchAlgorithm: Last Log Likelihood of the iterations
latentCount - Variable in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis: The number of latent variables.
latentCount - Variable in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis.Result: The number of latent variables.
LatentData() - Constructor for class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis.LatentData
LatentDirichletAllocationVectorGibbsSampler - Class in gov.sandia.cognition.text.topic: A Gibbs sampler for performing Latent Dirichlet Allocation (LDA).
LatentDirichletAllocationVectorGibbsSampler() - Constructor for class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler: Creates a new LatentDirichletAllocationVectorGibbsSampler with default parameters.
LatentDirichletAllocationVectorGibbsSampler(int, double, double, int, int, int, Random) - Constructor for class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler: Creates a new LatentDirichletAllocationVectorGibbsSampler with the given parameters.
LatentDirichletAllocationVectorGibbsSampler.Result - Class in gov.sandia.cognition.text.topic: Represents the result of performing Latent Dirichlet Allocation.
latents - Variable in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis: The information about each of the latent variables.
latents - Variable in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis.Result: The latent variable data.
LatentSemanticAnalysis - Class in gov.sandia.cognition.text.topic: Implements the Latent Semantic Analysis (LSA) algorithm using Singular Value Decomposition (SVD).
LatentSemanticAnalysis() - Constructor for class gov.sandia.cognition.text.topic.LatentSemanticAnalysis: Creates a new LatentSemanticAnalysis with default parameters.
LatentSemanticAnalysis(int) - Constructor for class gov.sandia.cognition.text.topic.LatentSemanticAnalysis: Creates a new LatentSemanticAnalysis with the given parameters.
LatentSemanticAnalysis.Transform - Class in gov.sandia.cognition.text.topic: The result from doing latent semantic analysis (LSA).
leafCountThreshold - Variable in class gov.sandia.cognition.learning.algorithm.tree.CategorizationTreeLearner: The threshold for making a node a leaf, determined by how many instances fall in the threshold.
leafCountThreshold - Variable in class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeLearner: The threshold for making a node a leaf, determined by how many instances fall in the threshold.
leakage - Variable in class gov.sandia.cognition.learning.function.scalar.LeakyRectifiedLinearFunction: The amount of leakage for when the value is less than zero.
LeakyRectifiedLinearFunction - Class in gov.sandia.cognition.learning.function.scalar: A leaky rectified linear unit.
LeakyRectifiedLinearFunction() - Constructor for class gov.sandia.cognition.learning.function.scalar.LeakyRectifiedLinearFunction: Creates a new LeakyRectifiedLinearFunction with the default amount of leakage.
LeakyRectifiedLinearFunction(double) - Constructor for class gov.sandia.cognition.learning.function.scalar.LeakyRectifiedLinearFunction: Creates a new LeakyRectifiedLinearFunction with the given leakage.
learn(CognitiveModel, Collection<? extends Collection<? extends CognitiveModelInput>>) - Method in interface gov.sandia.cognition.framework.learning.CognitiveModuleFactoryLearner: Learns a new CognitiveModuleFactory for the given CognitiveModuleFactory containing all of the modules that will be used before the created module factory along with the example data used to learn the factory from.
learn(CognitiveModel, Collection<? extends Collection<? extends CognitiveModelInput>>) - Method in class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleFactoryLearner: Learns a new EvaluatorBasedCognitiveModuleFactory based on the given existing factory plus the given collection of CognitiveModelInput objects.
learn(DataType) - Method in class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner
learn(Collection<? extends DataType>) - Method in class gov.sandia.cognition.learning.algorithm.AbstractBatchAndIncrementalLearner
learn(Iterable<? extends DataType>) - Method in class gov.sandia.cognition.learning.algorithm.AbstractBatchAndIncrementalLearner
learn(Object) - Method in class gov.sandia.cognition.learning.algorithm.baseline.ConstantLearner
learn(ValueType) - Method in class gov.sandia.cognition.learning.algorithm.baseline.IdentityLearner
learn(Collection<? extends InputOutputPair<?, Double>>) - Method in class gov.sandia.cognition.learning.algorithm.baseline.MeanLearner: Creates a constant evaluator that returns the mean output value of the given dataset.
learn(Collection<? extends InputOutputPair<? extends Object, OutputType>>) - Method in class gov.sandia.cognition.learning.algorithm.baseline.MostFrequentLearner: Creates a constant evaluator that for the most frequent output value of the given dataset.
learn(Collection<? extends InputOutputPair<?, Double>>) - Method in class gov.sandia.cognition.learning.algorithm.baseline.WeightedMeanLearner: Creates a constant evaluator for the weighted mean output value of the given dataset.
learn(Collection<? extends InputOutputPair<? extends Object, OutputType>>) - Method in class gov.sandia.cognition.learning.algorithm.baseline.WeightedMostFrequentLearner: Creates a constant evaluator based on the most frequent output in a given collection of input-output pairs, taking the weight into account.
learn(Iterable<? extends DataType>) - Method in interface gov.sandia.cognition.learning.algorithm.BatchAndIncrementalLearner: Creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.
learn(CostParametersType) - Method in interface gov.sandia.cognition.learning.algorithm.BatchCostMinimizationLearner: Invokes the minimization (learning) call using the given cost function parameters.
learn(DataType) - Method in interface gov.sandia.cognition.learning.algorithm.BatchLearner: The learn method creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.
learn(Collection<? extends InputOutputPair<? extends Collection<InputType>, CategoryType>>) - Method in class gov.sandia.cognition.learning.algorithm.bayes.DiscreteNaiveBayesCategorizer.Learner
learn(Collection<? extends InputOutputPair<? extends Vectorizable, CategoryType>>) - Method in class gov.sandia.cognition.learning.algorithm.bayes.VectorNaiveBayesCategorizer.BatchGaussianLearner
learn(Collection<? extends InputOutputPair<? extends Vectorizable, CategoryType>>) - Method in class gov.sandia.cognition.learning.algorithm.bayes.VectorNaiveBayesCategorizer.Learner
learn(Collection<? extends Vector>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.DirichletProcessClustering
learn(Collection<? extends InputType>) - Method in class gov.sandia.cognition.learning.algorithm.CompositeBatchLearnerPair: Learn by calling the first learner on all the input values.
learn(Collection<? extends InputOutputPair<? extends Vector, CategoryType>>) - Method in class gov.sandia.cognition.learning.algorithm.delta.AbstractDeltaCategorizer.AbstractLearner: Method that does the training.
learn(Collection<? extends InputOutputPair<? extends Vector, CategoryType>>) - Method in class gov.sandia.cognition.learning.algorithm.delta.BurrowsDeltaCategorizer.Learner
learn(Collection<? extends InputOutputPair<? extends Vector, CategoryType>>) - Method in class gov.sandia.cognition.learning.algorithm.delta.CosineDeltaCategorizer.Learner
learn(Collection<? extends InputOutputPair<? extends InputType, Boolean>>) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.BinaryCategorizerSelector: Selects the BinaryCategorizer from its list of categorizers that minimizes the weighted error on the given set of weighted input-output pairs.
learn(MultiCollection<ObservationType>) - Method in class gov.sandia.cognition.learning.algorithm.hmm.BaumWelchAlgorithm: Allows the algorithm to learn against multiple sequences of data.
learn(Collection<? extends InputOutputPair<? extends InputType, OutputType>>) - Method in class gov.sandia.cognition.learning.algorithm.InputOutputTransformedBatchLearner: Learn by first calling the input transformation learner on all the input values and the output transformation on the output values.
learn(EvaluatorType) - Method in interface gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizer: Finds the (local) minimum of the given function
learn(Operator) - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.IterativeMatrixSolver: Shell that solves for Ax = b (x0 and rhs passed in on initialization, A is contained in function).
learn(Collection<? extends InputOutputPair<? extends InputType, OutputType>>) - Method in class gov.sandia.cognition.learning.algorithm.nearest.KNearestNeighborExhaustive.Learner: Creates a new KNearestNeighborExhaustive from a Collection of InputType.
learn(Collection<? extends InputOutputPair<? extends InputType, OutputType>>) - Method in class gov.sandia.cognition.learning.algorithm.nearest.KNearestNeighborKDTree.Learner: Creates a new KNearestNeighbor from a Collection of InputType.
learn(Collection<? extends InputOutputPair<? extends InputType, OutputType>>) - Method in class gov.sandia.cognition.learning.algorithm.nearest.NearestNeighborExhaustive.Learner
learn(Collection<? extends InputOutputPair<? extends InputType, OutputType>>) - Method in class gov.sandia.cognition.learning.algorithm.nearest.NearestNeighborKDTree.Learner: Creates a new NearestNeighbor from a Collection of InputType.
learn(Collection<? extends DataType>) - Method in class gov.sandia.cognition.learning.algorithm.pca.KernelPrincipalComponentsAnalysis
learn(Collection<Vector>) - Method in class gov.sandia.cognition.learning.algorithm.pca.ThinSingularValueDecomposition: Creates a PrincipalComponentsAnalysisFunction based on the number of components and the given data.
learn(Collection<Vector>, int) - Static method in class gov.sandia.cognition.learning.algorithm.pca.ThinSingularValueDecomposition: Creates a PrincipalComponentsAnalysisFunction based on the number of components and the given data.
learn(Kernel<? super InputType>, Iterable<? extends InputOutputPair<? extends InputType, Boolean>>) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.AbstractKernelizableBinaryCategorizerOnlineLearner
learn(Kernel<? super InputType>, Iterable<? extends InputOutputPair<? extends InputType, Boolean>>) - Method in interface gov.sandia.cognition.learning.algorithm.perceptron.KernelizableBinaryCategorizerOnlineLearner: Run this algorithm on a batch of data using the given kernel function.
learn(Collection<? extends InputOutputPair<? extends Vector, Vector>>) - Method in class gov.sandia.cognition.learning.algorithm.regression.AbstractMinimizerBasedParameterCostMinimizer
learn(Collection<? extends InputOutputPair<? extends InputType, Double>>) - Method in class gov.sandia.cognition.learning.algorithm.regression.LinearBasisRegression: Computes the linear regression for the given Collection of InputOutputPairs.
learn(Collection<? extends InputOutputPair<? extends Vectorizable, Double>>) - Method in class gov.sandia.cognition.learning.algorithm.regression.LinearRegression: Computes the linear regression for the given Collection of InputOutputPairs.
learn(Collection<? extends InputOutputPair<? extends InputType, OutputType>>) - Method in class gov.sandia.cognition.learning.algorithm.regression.LocallyWeightedFunction.Learner
learn(Collection<? extends InputOutputPair<? extends Vector, Vector>>) - Method in class gov.sandia.cognition.learning.algorithm.regression.MultivariateLinearRegression
learn(Collection<? extends InputOutputPair<? extends Double, Double>>) - Method in class gov.sandia.cognition.learning.algorithm.regression.UnivariateLinearRegression
learn(Evaluator<Double, Double>) - Method in class gov.sandia.cognition.learning.algorithm.root.MinimizerBasedRootFinder
learn(Collection<? extends DataType>) - Method in class gov.sandia.cognition.learning.algorithm.SequencePredictionLearner
learn(MultiCollection<? extends DataType>) - Method in class gov.sandia.cognition.learning.algorithm.SequencePredictionLearner: Converts the given multi-collection of data sequences to create sequences of input-output pairs to learn over.
learn(Collection<? extends InputOutputPair<? extends InputType, OutputType>>) - Method in class gov.sandia.cognition.learning.algorithm.TimeSeriesPredictionLearner
learn(Collection<? extends InputOutputPair<? extends Vectorizable, OutputType>>) - Method in class gov.sandia.cognition.learning.algorithm.tree.AbstractVectorThresholdMaximumGainLearner
learn(Collection<? extends InputOutputPair<? extends InputType, OutputType>>) - Method in class gov.sandia.cognition.learning.algorithm.tree.CategorizationTreeLearner
learn(Collection<? extends InputOutputPair<? extends Vectorizable, OutputType>>) - Method in class gov.sandia.cognition.learning.algorithm.tree.RandomSubVectorThresholdLearner
learn(Collection<? extends InputOutputPair<? extends InputType, Double>>) - Method in class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeLearner
learn(Collection<? extends InputOutputPair<? extends Vectorizable, Double>>) - Method in class gov.sandia.cognition.learning.algorithm.tree.VectorThresholdVarianceLearner: Learns a VectorElementThresholdCategorizer from the given data by picking the vector element and threshold that best maximizes information gain.
learn(Collection<? extends Vectorizable>) - Method in class gov.sandia.cognition.learning.data.feature.MultivariateDecorrelator.DiagonalCovarianceLearner: Learns a MultivariateDecorrelator from the given values by computing the mean and variance for each dimension separately.
learn(Collection<? extends Vectorizable>) - Method in class gov.sandia.cognition.learning.data.feature.MultivariateDecorrelator.FullCovarianceLearner: Learns a MultivariateDecorrelator from the given values by computing the mean and covariance of the dimensions.
learn(Collection<? extends Vectorizable>) - Method in class gov.sandia.cognition.learning.data.feature.RandomSubspace
learn(Collection<? extends Number>) - Static method in class gov.sandia.cognition.learning.data.feature.StandardDistributionNormalizer: Builds a StandardDistributionNormalizer by computing the mean and variance of the given collection of values.
learn(Collection<? extends Number>, double) - Static method in class gov.sandia.cognition.learning.data.feature.StandardDistributionNormalizer: Builds a StandardDistributionNormalizer by computing the mean and variance of the given collection of values.
learn(Collection<Double>) - Method in class gov.sandia.cognition.learning.data.feature.StandardDistributionNormalizer.Learner: Learns a StandardDistributionNormalizer from the given values by computing the mean and standard deviation of the values.
learn(Collection<? extends InputOutputPair<? extends InputType, CategoryType>>) - Method in class gov.sandia.cognition.learning.function.categorization.BinaryVersusCategorizer.Learner
learn(Collection<? extends InputOutputPair<? extends InputType, CategoryType>>) - Method in class gov.sandia.cognition.learning.function.categorization.EvaluatorToCategorizerAdapter.Learner
learn(Collection<? extends InputOutputPair<? extends Vector, Boolean>>) - Method in class gov.sandia.cognition.learning.function.categorization.FisherLinearDiscriminantBinaryCategorizer.ClosedFormSolver
learn(Collection<? extends InputOutputPair<? extends Vector, Boolean>>, double) - Static method in class gov.sandia.cognition.learning.function.categorization.FisherLinearDiscriminantBinaryCategorizer.ClosedFormSolver: Closed-form learning algorithm for a Fisher Linear Discriminant.
learn(Collection<? extends InputOutputPair<? extends ObservationType, CategoryType>>) - Method in class gov.sandia.cognition.learning.function.categorization.MaximumAPosterioriCategorizer.Learner
learn(Collection<? extends InputOutputPair<? extends InputType, CategoryType>>) - Method in class gov.sandia.cognition.learning.function.categorization.WinnerTakeAllCategorizer.Learner
learn(DataType) - Method in class gov.sandia.cognition.learning.function.distance.DivergencesEvaluator.Learner
learn(int, Collection<? extends InputOutputPair<Double, Double>>) - Static method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Regression: Performs LinearRegression using all integer-exponent polynomials less than or equal to the maxOrder
learn(Collection<? extends InputOutputPair<? extends Double, Double>>) - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Regression
learn(Collection<? extends InputOutputPair<? extends InputType, Double>>) - Method in class gov.sandia.cognition.learning.function.scalar.VectorFunctionToScalarFunction.Learner
learn(Collection<? extends Vector>) - Method in class gov.sandia.cognition.learning.function.vector.GaussianContextRecognizer.Learner
learn(DataType) - Method in class gov.sandia.cognition.learning.parameter.ParameterAdaptableBatchLearnerWrapper
learn(Collection<? extends InputOutputPair<? extends Vectorizable, Double>>) - Method in class gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression.IncrementalEstimator
learn(Collection<? extends InputOutputPair<? extends Vectorizable, Double>>) - Method in class gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression
learn(Collection<? extends InputOutputPair<? extends Vectorizable, Double>>) - Method in class gov.sandia.cognition.statistics.bayesian.BayesianRobustLinearRegression.IncrementalEstimator
learn(Collection<? extends InputOutputPair<? extends Vectorizable, Double>>) - Method in class gov.sandia.cognition.statistics.bayesian.BayesianRobustLinearRegression
learn(Collection<? extends InputOutputPair<? extends InputType, Double>>) - Method in class gov.sandia.cognition.statistics.bayesian.GaussianProcessRegression
learn(Collection<? extends ObservationType>) - Method in class gov.sandia.cognition.statistics.bayesian.ImportanceSampling
learn(Collection<? extends ObservationType>) - Method in class gov.sandia.cognition.statistics.bayesian.RejectionSampling
learn(Collection<? extends Number>) - Method in class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution.MomentMatchingEstimator
learn(int, double, double) - Static method in class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution.MomentMatchingEstimator: Computes the Beta-Binomial distribution describes by the given moments
learn(Collection<? extends Double>) - Method in class gov.sandia.cognition.statistics.distribution.BetaDistribution.MomentMatchingEstimator
learn(double, double) - Static method in class gov.sandia.cognition.statistics.distribution.BetaDistribution.MomentMatchingEstimator: Computes the Beta distribution describes by the given moments
learn(Collection<? extends WeightedValue<? extends Double>>) - Method in class gov.sandia.cognition.statistics.distribution.BetaDistribution.WeightedMomentMatchingEstimator
learn(Collection<? extends Number>) - Method in class gov.sandia.cognition.statistics.distribution.BinomialDistribution.MaximumLikelihoodEstimator
learn(Collection<? extends Double>) - Method in class gov.sandia.cognition.statistics.distribution.ExponentialDistribution.MaximumLikelihoodEstimator
learn(Collection<? extends WeightedValue<? extends Double>>) - Method in class gov.sandia.cognition.statistics.distribution.ExponentialDistribution.WeightedMaximumLikelihoodEstimator
learn(Collection<? extends Double>) - Method in class gov.sandia.cognition.statistics.distribution.GammaDistribution.MomentMatchingEstimator
learn(double, double) - Static method in class gov.sandia.cognition.statistics.distribution.GammaDistribution.MomentMatchingEstimator: Computes the Gamma distribution describes by the given moments
learn(Collection<? extends WeightedValue<? extends Double>>) - Method in class gov.sandia.cognition.statistics.distribution.GammaDistribution.WeightedMomentMatchingEstimator
learn(Collection<? extends Number>) - Method in class gov.sandia.cognition.statistics.distribution.GeometricDistribution.MaximumLikelihoodEstimator
learn(Collection<? extends Double>) - Method in class gov.sandia.cognition.statistics.distribution.LaplaceDistribution.MaximumLikelihoodEstimator
learn(Collection<? extends WeightedValue<? extends Double>>) - Method in class gov.sandia.cognition.statistics.distribution.LaplaceDistribution.WeightedMaximumLikelihoodEstimator: Creates a new instance of LaplaceDistribution using a weighted Maximum Likelihood estimate based on the given data
learn(Collection<? extends Double>) - Method in class gov.sandia.cognition.statistics.distribution.LogNormalDistribution.MaximumLikelihoodEstimator
learn(Collection<? extends WeightedValue<? extends Double>>) - Method in class gov.sandia.cognition.statistics.distribution.LogNormalDistribution.WeightedMaximumLikelihoodEstimator
learn(Collection<? extends Vector>) - Method in class gov.sandia.cognition.statistics.distribution.MixtureOfGaussians.Learner
learn(Collection<? extends Vector>, double) - Static method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.MaximumLikelihoodEstimator: Computes the Gaussian that estimates the maximum likelihood of generating the given set of samples.
learn(Collection<? extends Vector>) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.MaximumLikelihoodEstimator: Computes the Gaussian that estimates the maximum likelihood of generating the given set of samples.
learn(Collection<? extends WeightedValue<? extends Vector>>) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.WeightedMaximumLikelihoodEstimator: Computes the Gaussian that estimates the maximum likelihood of generating the given set of weighted samples.
learn(Collection<? extends WeightedValue<? extends Vector>>, double) - Static method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.WeightedMaximumLikelihoodEstimator: Computes the Gaussian that estimates the maximum likelihood of generating the given set of weighted samples.
learn(Collection<? extends Number>) - Method in class gov.sandia.cognition.statistics.distribution.NegativeBinomialDistribution.MaximumLikelihoodEstimator
learn(Collection<? extends WeightedValue<? extends Number>>) - Method in class gov.sandia.cognition.statistics.distribution.NegativeBinomialDistribution.WeightedMaximumLikelihoodEstimator
learn(Collection<? extends Number>) - Method in class gov.sandia.cognition.statistics.distribution.PoissonDistribution.MaximumLikelihoodEstimator
learn(Collection<? extends WeightedValue<? extends Number>>) - Method in class gov.sandia.cognition.statistics.distribution.PoissonDistribution.WeightedMaximumLikelihoodEstimator: Creates a new instance of PoissonDistribution using a weighted Maximum Likelihood estimate based on the given data.
learn(Collection<? extends Double>) - Method in class gov.sandia.cognition.statistics.distribution.StudentTDistribution.MaximumLikelihoodEstimator: Creates a new instance of UnivariateGaussian from the given data
learn(Collection<? extends Double>, double) - Static method in class gov.sandia.cognition.statistics.distribution.StudentTDistribution.MaximumLikelihoodEstimator: Creates a new instance of UnivariateGaussian from the given data
learn(Collection<? extends WeightedValue<? extends Double>>) - Method in class gov.sandia.cognition.statistics.distribution.StudentTDistribution.WeightedMaximumLikelihoodEstimator: Creates a new instance of UnivariateGaussian using a weighted Maximum Likelihood estimate based on the given data
learn(Collection<? extends WeightedValue<? extends Double>>, double) - Static method in class gov.sandia.cognition.statistics.distribution.StudentTDistribution.WeightedMaximumLikelihoodEstimator: Creates a new instance of UnivariateGaussian using a weighted Maximum Likelihood estimate based on the given data
learn(Collection<? extends Double>) - Method in class gov.sandia.cognition.statistics.distribution.UniformDistribution.MaximumLikelihoodEstimator
learn(Collection<? extends Number>) - Method in class gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution.MaximumLikelihoodEstimator
learn(Collection<? extends Double>) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.MaximumLikelihoodEstimator: Creates a new instance of UnivariateGaussian from the given data
learn(Collection<? extends Number>, double) - Static method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.MaximumLikelihoodEstimator: Creates a new instance of UnivariateGaussian from the given data
learn(Collection<? extends WeightedValue<? extends Double>>) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.WeightedMaximumLikelihoodEstimator: Creates a new instance of UnivariateGaussian using a weighted Maximum Likelihood estimate based on the given data
learn(Collection<? extends WeightedValue<? extends Number>>, double) - Static method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.WeightedMaximumLikelihoodEstimator: Creates a new instance of UnivariateGaussian using a weighted Maximum Likelihood estimate based on the given data
learn(Collection<? extends DataType>) - Method in class gov.sandia.cognition.statistics.method.DistributionParameterEstimator
learn(Collection<? extends DataType>) - Method in class gov.sandia.cognition.statistics.method.MaximumLikelihoodDistributionEstimator
learn(Collection<? extends Vectorizable>) - Method in class gov.sandia.cognition.text.topic.LatentSemanticAnalysis
learnChildNodes(AbstractDecisionTreeNode<InputType, OutputType, DecisionType>, Collection<? extends InputOutputPair<? extends InputType, OutputType>>, Categorizer<? super InputType, ? extends DecisionType>) - Method in class gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeLearner: Learns the child nodes for a node using the given data at the node plus the decision function for the node.
learnDiagonalCovariance(Collection<? extends Vectorizable>, double) - Static method in class gov.sandia.cognition.learning.data.feature.MultivariateDecorrelator: Learns a normalization based on a mean and covariance where the covariance matrix is diagonal.
learner - Variable in class gov.sandia.cognition.learning.algorithm.AbstractBatchLearnerContainer: The wrapped learner.
Learner() - Constructor for class gov.sandia.cognition.learning.algorithm.bayes.DiscreteNaiveBayesCategorizer.Learner: Default constructor.
Learner() - Constructor for class gov.sandia.cognition.learning.algorithm.bayes.VectorNaiveBayesCategorizer.Learner: Creates a new BatchLearner with a null estimator.
Learner(DistributionEstimator<? super Double, ? extends DistributionType>) - Constructor for class gov.sandia.cognition.learning.algorithm.bayes.VectorNaiveBayesCategorizer.Learner: Creates a new BatchLearner with the given distribution estimator.
learner - Variable in class gov.sandia.cognition.learning.algorithm.delta.AbstractDeltaCategorizer: The learner that was used to train this categorizer.
Learner() - Constructor for class gov.sandia.cognition.learning.algorithm.delta.BurrowsDeltaCategorizer.Learner: Default constructor.
Learner() - Constructor for class gov.sandia.cognition.learning.algorithm.delta.CosineDeltaCategorizer.Learner: Default constructor.
learner - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractBaggingLearner: The learner to use to create the categorizer for each iteration.
learner - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractCategorizerOutOfBagStoppingCriteria: The learner the stopping criteria is for.
learner - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner: The learner used to produce each ensemble member.
learner - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner.OutOfBagErrorStoppingCriteria: The learner the stopping criteria is for.
learner - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.OnlineBaggingCategorizerLearner: The base learner used for each ensemble member.
Learner() - Constructor for class gov.sandia.cognition.learning.algorithm.nearest.KNearestNeighborExhaustive.Learner: Default constructor.
Learner(int, DivergenceFunction<? super InputType, ? super InputType>, Summarizer<? super OutputType, OutputType>) - Constructor for class gov.sandia.cognition.learning.algorithm.nearest.KNearestNeighborExhaustive.Learner: Creates a new instance of Learner
Learner() - Constructor for class gov.sandia.cognition.learning.algorithm.nearest.KNearestNeighborKDTree.Learner: Default constructor.
Learner(Summarizer<? super OutputType, ? extends OutputType>) - Constructor for class gov.sandia.cognition.learning.algorithm.nearest.KNearestNeighborKDTree.Learner: Creates a new instance of Learner.
Learner(int, Metric<? super Vectorizable>, Summarizer<? super OutputType, ? extends OutputType>) - Constructor for class gov.sandia.cognition.learning.algorithm.nearest.KNearestNeighborKDTree.Learner: Creates a new instance of Learner
Learner() - Constructor for class gov.sandia.cognition.learning.algorithm.nearest.NearestNeighborExhaustive.Learner: Creates a new instance of NearestNeighborExhaustive.Learner.
Learner(DivergenceFunction<? super InputType, ? super InputType>) - Constructor for class gov.sandia.cognition.learning.algorithm.nearest.NearestNeighborExhaustive.Learner: Creates a new instance of NearestNeighborExhaustive.Learner.
Learner() - Constructor for class gov.sandia.cognition.learning.algorithm.nearest.NearestNeighborKDTree.Learner: Default constructor.
Learner(Metric<? super Vectorizable>) - Constructor for class gov.sandia.cognition.learning.algorithm.nearest.NearestNeighborKDTree.Learner: Creates a new instance of Learner
learner - Variable in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelBinaryCategorizerOnlineLearnerAdapter: The wrapped kernelizable learner.
Learner(Kernel<? super InputType>, SupervisedBatchLearner<InputType, OutputType, ?>) - Constructor for class gov.sandia.cognition.learning.algorithm.regression.LocallyWeightedFunction.Learner: Creates a new instance of LocallyWeightedFunction
Learner() - Constructor for class gov.sandia.cognition.learning.data.feature.StandardDistributionNormalizer.Learner: Creates a new StandardDistributionNormalizer.Learner.
Learner(double) - Constructor for class gov.sandia.cognition.learning.data.feature.StandardDistributionNormalizer.Learner: Creates a new StandardDistributionNormalizer.Learner.
Learner() - Constructor for class gov.sandia.cognition.learning.function.categorization.BinaryVersusCategorizer.Learner: Creates a new BinaryVersusCategorizer.Learner with no initial binary categorizer learner.
Learner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, Boolean>>, ? extends Evaluator<? super InputType, Boolean>>) - Constructor for class gov.sandia.cognition.learning.function.categorization.BinaryVersusCategorizer.Learner: Creates a new BinaryVersusCategorizer.Learner with an binary categorizer learner to learn category versus category.
Learner() - Constructor for class gov.sandia.cognition.learning.function.categorization.EvaluatorToCategorizerAdapter.Learner: Creates a new EvaluatorToCategorizerAdapter.
Learner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, CategoryType>>, ? extends Evaluator<? super InputType, ? extends CategoryType>>) - Constructor for class gov.sandia.cognition.learning.function.categorization.EvaluatorToCategorizerAdapter.Learner: Creates a new EvaluatorToCategorizerAdapter.
Learner() - Constructor for class gov.sandia.cognition.learning.function.categorization.MaximumAPosterioriCategorizer.Learner: Default constructor
Learner(BatchLearner<Collection<? extends ObservationType>, ? extends ComputableDistribution<ObservationType>>) - Constructor for class gov.sandia.cognition.learning.function.categorization.MaximumAPosterioriCategorizer.Learner: Creates a new instance of Learner
Learner() - Constructor for class gov.sandia.cognition.learning.function.categorization.WinnerTakeAllCategorizer.Learner: Creates a new learner adapter.
Learner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, Vector>>, Evaluator<? super InputType, ? extends Vectorizable>>) - Constructor for class gov.sandia.cognition.learning.function.categorization.WinnerTakeAllCategorizer.Learner: Creates a new learner adapter with the given internal learner.
Learner() - Constructor for class gov.sandia.cognition.learning.function.distance.DivergencesEvaluator.Learner: Creates a new DivergenceFunction.Learner with null base learner and divergence functions.
Learner(BatchLearner<DataType, ? extends Collection<ValueType>>, DivergenceFunction<? super ValueType, ? super InputType>) - Constructor for class gov.sandia.cognition.learning.function.distance.DivergencesEvaluator.Learner: Creates a new DivergenceFunction.Learner with the given properties.
Learner(BatchLearner<DataType, ? extends Collection<ValueType>>, DivergenceFunction<? super ValueType, ? super InputType>, VectorFactory<?>) - Constructor for class gov.sandia.cognition.learning.function.distance.DivergencesEvaluator.Learner: Creates a new DivergenceFunction.Learner with the given properties.
Learner() - Constructor for class gov.sandia.cognition.learning.function.scalar.VectorFunctionToScalarFunction.Learner: Creates a new VectorFunctionToScalarFunction.Learner.
Learner(BatchLearner<Collection<? extends InputOutputPair<? extends InputType, Vector>>, ? extends Evaluator<? super InputType, ? extends Vectorizable>>) - Constructor for class gov.sandia.cognition.learning.function.scalar.VectorFunctionToScalarFunction.Learner: Creates a new VectorFunctionToScalarFunction.Learner.
Learner() - Constructor for class gov.sandia.cognition.learning.function.vector.GaussianContextRecognizer.Learner: Creates a new Learner.
Learner(KMeansClusterer<Vector, GaussianCluster>) - Constructor for class gov.sandia.cognition.learning.function.vector.GaussianContextRecognizer.Learner: Creates a new instance of Learner
Learner(KMeansClusterer<Vector, GaussianCluster>) - Constructor for class gov.sandia.cognition.statistics.distribution.MixtureOfGaussians.Learner: Creates a new Learner
Learner() - Constructor for class gov.sandia.cognition.text.spelling.SimpleStatisticalSpellingCorrector.Learner: Creates a new simple statistical spelling corrector learner with the default alphabet.
Learner(char[]) - Constructor for class gov.sandia.cognition.text.spelling.SimpleStatisticalSpellingCorrector.Learner: Creates a new simple statistical spelling corrector learner with the default alphabet.
LearnerComparisonExperiment<InputDataType,FoldDataType,LearnedType,StatisticType,SummaryType> - Class in gov.sandia.cognition.learning.experiment: The LearnerComparisonExperiment compares the performance of two machine learning algorithms to determine (using a statistical test) if the two algorithms have significantly different performance.
LearnerComparisonExperiment() - Constructor for class gov.sandia.cognition.learning.experiment.LearnerComparisonExperiment: Creates a new instance of LearnerComparisonExperiment.
LearnerComparisonExperiment(ValidationFoldCreator<InputDataType, FoldDataType>, PerformanceEvaluator<? super LearnedType, ? super Collection<? extends FoldDataType>, ? extends StatisticType>, NullHypothesisEvaluator<Collection<? extends StatisticType>>, Summarizer<? super StatisticType, ? extends SummaryType>) - Constructor for class gov.sandia.cognition.learning.experiment.LearnerComparisonExperiment: Creates a new instance of LearnerComparisonExperiment.
LearnerComparisonExperiment.Result<SummaryType> - Class in gov.sandia.cognition.learning.experiment: Encapsulates the results of the comparison experiment.
LearnerRepeatExperiment<InputDataType,LearnedType,StatisticType,SummaryType> - Class in gov.sandia.cognition.learning.experiment: Runs an experiment where the same learner is evaluated multiple times on the same data.
LearnerRepeatExperiment() - Constructor for class gov.sandia.cognition.learning.experiment.LearnerRepeatExperiment: Creates a new instance of LearnerRepeatExperiment.
LearnerRepeatExperiment(int, PerformanceEvaluator<? super LearnedType, ? super Collection<? extends InputDataType>, ? extends StatisticType>, Summarizer<? super StatisticType, ? extends SummaryType>) - Constructor for class gov.sandia.cognition.learning.experiment.LearnerRepeatExperiment: Creates a new instance of LearnerRepeatExperiment.
learners - Variable in class gov.sandia.cognition.learning.experiment.LearnerComparisonExperiment: The learners that the experiment is being performed on.
LearnerValidationExperiment<InputDataType,FoldDataType,LearnedType,StatisticType,SummaryType> - Class in gov.sandia.cognition.learning.experiment: The LearnerValidationExperiment class implements an experiment where a supervised machine learning algorithm is evaluated by applying it to a set of folds created from a given set of data.
LearnerValidationExperiment() - Constructor for class gov.sandia.cognition.learning.experiment.LearnerValidationExperiment: Creates a new instance of SupervisedLearnerExperiment.
LearnerValidationExperiment(ValidationFoldCreator<InputDataType, FoldDataType>, PerformanceEvaluator<? super LearnedType, ? super Collection<? extends FoldDataType>, ? extends StatisticType>, Summarizer<? super StatisticType, ? extends SummaryType>) - Constructor for class gov.sandia.cognition.learning.experiment.LearnerValidationExperiment: Creates a new instance of SupervisedLearnerExperiment.
learnFullCovariance(Collection<? extends Vectorizable>, double) - Static method in class gov.sandia.cognition.learning.data.feature.MultivariateDecorrelator: Learns a normalization based on a mean and full covariance matrix from the given data.
LearningExperiment - Interface in gov.sandia.cognition.learning.experiment: The LearningExperiment interface defines the general functionality of an object that implements an experiment regarding machine learning algorithms.
LearningExperimentListener - Interface in gov.sandia.cognition.learning.experiment: The LearningExperimentListener interface defines the functionality of an object that listens to events from a LearningExperiment.
learningRate - Variable in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachineStochasticGradient: The learning rate for the algorithm.
learnNode(Collection<? extends InputOutputPair<? extends InputType, OutputType>>, AbstractDecisionTreeNode<InputType, OutputType, ?>) - Method in class gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeLearner: Recursively learns the decision tree using the given collection of data, returning the created node.
learnNode(Collection<? extends InputOutputPair<? extends InputType, OutputType>>, AbstractDecisionTreeNode<InputType, OutputType, ?>) - Method in class gov.sandia.cognition.learning.algorithm.tree.CategorizationTreeLearner: Recursively learns the categorization tree using the given collection of data, returning the created node.
learnNode(Collection<? extends InputOutputPair<? extends InputType, Double>>, AbstractDecisionTreeNode<InputType, Double, ?>) - Method in class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeLearner: Recursively learns the regression tree using the given collection of data, returning the created node.
learnUsingCachedClusters(Collection<? extends DataType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer: Perform clustering by extending the existing cluster hierarchy, if one exists.
LeastSquaresEstimator - Class in gov.sandia.cognition.learning.algorithm.regression: Abstract implementation of iterative least-squares estimators.
LeastSquaresEstimator(int, double) - Constructor for class gov.sandia.cognition.learning.algorithm.regression.LeastSquaresEstimator: Creates a new instance of LeastSquaresEstimator
LeaveOneOutFoldCreator<DataType> - Class in gov.sandia.cognition.learning.experiment: The LeaveOneOutFoldCreator class implements the leave-one-out method for creating training-testing folds for a cross-validation experiment.
LeaveOneOutFoldCreator() - Constructor for class gov.sandia.cognition.learning.experiment.LeaveOneOutFoldCreator: Creates a new instance of LeaveOneOutCreator
leftChild - Variable in class gov.sandia.cognition.math.geometry.KDTree: Left child of this subtree
leftIterator - Variable in class gov.sandia.cognition.math.geometry.KDTree.InOrderKDTreeIterator: Iterator for the left subtree.
length() - Method in class gov.sandia.cognition.collection.DoubleArrayList
length() - Method in class gov.sandia.cognition.collection.IntArrayList: Returns the number of elements in the vector
LENGTH - Static variable in class gov.sandia.cognition.hash.Eva32Hash: A 32-bit, 4-byte hash, 4.
length() - Method in class gov.sandia.cognition.hash.Eva32Hash
LENGTH - Static variable in class gov.sandia.cognition.hash.Eva64Hash: Length of the hash in bytes, 8.
length() - Method in class gov.sandia.cognition.hash.Eva64Hash
LENGTH - Static variable in class gov.sandia.cognition.hash.FNV1a32Hash: Length of the hash is 32-bits (4-bytes), 4.
length() - Method in class gov.sandia.cognition.hash.FNV1a32Hash
LENGTH - Static variable in class gov.sandia.cognition.hash.FNV1a64Hash: Length of the hash is 64-bits (8-bytes), 8.
length() - Method in class gov.sandia.cognition.hash.FNV1a64Hash
length() - Method in interface gov.sandia.cognition.hash.HashFunction: Returns the number of bytes in the output hash code.
LENGTH - Static variable in class gov.sandia.cognition.hash.MD5Hash: MD5 is a 128-bit (16-byte) length hash.
length() - Method in class gov.sandia.cognition.hash.MD5Hash
LENGTH - Static variable in class gov.sandia.cognition.hash.Murmur32Hash: Length of the hash function is 32-bits or 4 bytes, 4.
length() - Method in class gov.sandia.cognition.hash.Murmur32Hash
LENGTH - Static variable in class gov.sandia.cognition.hash.Prime32Hash: Length of the hash is 32 bits (4 bytes), 4.
length() - Method in class gov.sandia.cognition.hash.Prime32Hash
LENGTH - Static variable in class gov.sandia.cognition.hash.Prime64Hash: Length of the hash is 64 bits (8 bytes), 8.
length() - Method in class gov.sandia.cognition.hash.Prime64Hash
LENGTH - Static variable in class gov.sandia.cognition.hash.SHA1Hash: SHA-1 hash function output is 160-bits or 20-bytes, 20.
length() - Method in class gov.sandia.cognition.hash.SHA1Hash
LENGTH - Static variable in class gov.sandia.cognition.hash.SHA256Hash: Length of the hash is 256 bits (32 bytes), 32.
length() - Method in class gov.sandia.cognition.hash.SHA256Hash
LENGTH - Static variable in class gov.sandia.cognition.hash.SHA512Hash: Length of the hash is 512 bits (64 bytes), 64.
length() - Method in class gov.sandia.cognition.hash.SHA512Hash
length - Variable in class gov.sandia.cognition.text.AbstractOccurrenceInText: The length of the occurrence.
LentzMethod - Class in gov.sandia.cognition.math: This class implements Lentz's method for evaluating continued fractions.
LentzMethod() - Constructor for class gov.sandia.cognition.math.LentzMethod: Creates a new instance of LentzMethod
LentzMethod(int, double, double) - Constructor for class gov.sandia.cognition.math.LentzMethod: Creates a new instance of LentzMethod
LetterNumberTokenizer - Class in gov.sandia.cognition.text.token: A tokenizer that creates tokens from sequences of letters and numbers, treating everything else as a delimiter.
LetterNumberTokenizer() - Constructor for class gov.sandia.cognition.text.token.LetterNumberTokenizer: Creates a new LetterNumberTokenizer.
LevenbergMarquardtEstimation - Class in gov.sandia.cognition.learning.algorithm.regression: Implementation of the nonlinear regression algorithm, known as Levenberg-Marquardt Estimation (or LMA).
LevenbergMarquardtEstimation() - Constructor for class gov.sandia.cognition.learning.algorithm.regression.LevenbergMarquardtEstimation: Creates a new instance of LevenbergMarquardtEstimation
LevenbergMarquardtEstimation(double, double, int, int, double) - Constructor for class gov.sandia.cognition.learning.algorithm.regression.LevenbergMarquardtEstimation: Creates a new instance of LevenbergMarquardtEstimation
line(int, ArrayList<AdaptiveRejectionSampling.Point>) - Static method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.Point: Connects the points at index and index + 1 with a straight line.
Linear(double, double) - Constructor for class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Linear: Creates a new instance of Linear
LinearBasisRegression<InputType> - Class in gov.sandia.cognition.learning.algorithm.regression: Computes the least-squares regression for a LinearCombinationFunction given a dataset.
LinearBasisRegression(Collection<? extends Evaluator<? super InputType, Double>>) - Constructor for class gov.sandia.cognition.learning.algorithm.regression.LinearBasisRegression: Creates a new instance of LinearRegression
LinearBasisRegression(ScalarBasisSet<InputType>) - Constructor for class gov.sandia.cognition.learning.algorithm.regression.LinearBasisRegression: Creates a new instance of LinearRegression
LinearBasisRegression(Evaluator<? super InputType, Vector>) - Constructor for class gov.sandia.cognition.learning.algorithm.regression.LinearBasisRegression: Creates a new instance of LinearRegression
LinearBinaryCategorizer - Class in gov.sandia.cognition.learning.function.categorization: The LinearBinaryCategorizer class implements a binary categorizer that is implemented by a linear function.
LinearBinaryCategorizer() - Constructor for class gov.sandia.cognition.learning.function.categorization.LinearBinaryCategorizer: Creates a new instance of LinearBinaryCategorizer.
LinearBinaryCategorizer(Vector, double) - Constructor for class gov.sandia.cognition.learning.function.categorization.LinearBinaryCategorizer: Creates a new instance of LinearBinaryCategorizer with the given weights and bias.
LinearBinaryCategorizer(LinearBinaryCategorizer) - Constructor for class gov.sandia.cognition.learning.function.categorization.LinearBinaryCategorizer: Creates a new copy of a LinearBinaryCategorizer.
LinearCombinationFunction<InputType,OutputType> - Class in gov.sandia.cognition.learning.function: A function whose output is a weighted linear combination of (potentially) nonlinear basis function.
LinearCombinationFunction(ArrayList<? extends Evaluator<? super InputType, ? extends OutputType>>, Vector) - Constructor for class gov.sandia.cognition.learning.function.LinearCombinationFunction: Creates a new instance of LinearCombinationFunction
LinearCombinationScalarFunction<InputType> - Class in gov.sandia.cognition.learning.function.scalar: A weighted linear combination of scalar functions.
LinearCombinationScalarFunction(Collection<? extends Evaluator<InputType, Double>>) - Constructor for class gov.sandia.cognition.learning.function.scalar.LinearCombinationScalarFunction: Creates a new instance of LinearCombinationFunction
LinearCombinationScalarFunction(ArrayList<? extends Evaluator<InputType, Double>>, Vector) - Constructor for class gov.sandia.cognition.learning.function.scalar.LinearCombinationScalarFunction: Creates a new instance of LinearCombinationFunction
LinearCombinationVectorFunction - Class in gov.sandia.cognition.learning.function.vector: A weighted linear combination of scalar functions.
LinearCombinationVectorFunction(VectorFunction...) - Constructor for class gov.sandia.cognition.learning.function.vector.LinearCombinationVectorFunction: Creates a new instance of LinearCombinationFunction
LinearCombinationVectorFunction(Collection<? extends Evaluator<? super Vector, ? extends Vector>>) - Constructor for class gov.sandia.cognition.learning.function.vector.LinearCombinationVectorFunction: Creates a new instance of LinearCombinationFunction
LinearCombinationVectorFunction(ArrayList<? extends Evaluator<? super Vector, ? extends Vector>>, Vector) - Constructor for class gov.sandia.cognition.learning.function.vector.LinearCombinationVectorFunction: Creates a new instance of LinearCombinationFunction
LinearDiscriminant - Class in gov.sandia.cognition.learning.function.scalar: LinearDiscriminant takes the dot product between the weight Vector and the input Vector.
LinearDiscriminant() - Constructor for class gov.sandia.cognition.learning.function.scalar.LinearDiscriminant: Creates a new instance of LinearClassifier
LinearDiscriminant(Vector) - Constructor for class gov.sandia.cognition.learning.function.scalar.LinearDiscriminant: Creates a new instance of LinearClassifier
LinearDiscriminantWithBias - Class in gov.sandia.cognition.learning.function.scalar: A LinearDiscriminant with an additional bias term that gets added to the output of the dot product.
LinearDiscriminantWithBias() - Constructor for class gov.sandia.cognition.learning.function.scalar.LinearDiscriminantWithBias: Creates a new instance of LinearDiscriminantWithBias
LinearDiscriminantWithBias(Vector) - Constructor for class gov.sandia.cognition.learning.function.scalar.LinearDiscriminantWithBias: Creates a new instance of LinearClassifier
LinearDiscriminantWithBias(Vector, double) - Constructor for class gov.sandia.cognition.learning.function.scalar.LinearDiscriminantWithBias: Creates a new instance of LinearClassifier
LinearDynamicalSystem - Class in gov.sandia.cognition.math.signals: A generic Linear Dynamical System of the form
x_n = A*x_(n-1) + B*u_n
y_n = C*x_n,
where x_(n-1) is the previous state, x_n is the current state, u_n is the current input, y_n is the current output, A is the system matrix, B is the input-gain matrix, and C is the output-selector matrix
LinearDynamicalSystem() - Constructor for class gov.sandia.cognition.math.signals.LinearDynamicalSystem: Default constructor.
LinearDynamicalSystem(int, int) - Constructor for class gov.sandia.cognition.math.signals.LinearDynamicalSystem: Creates a new instance of LinearDynamicalSystem.
LinearDynamicalSystem(int, int, int) - Constructor for class gov.sandia.cognition.math.signals.LinearDynamicalSystem: Creates a new instance of LinearDynamicalSystem.
LinearDynamicalSystem(Matrix, Matrix) - Constructor for class gov.sandia.cognition.math.signals.LinearDynamicalSystem: Creates a new instance of LinearDynamicalSystem
LinearDynamicalSystem(Matrix, Matrix, Matrix) - Constructor for class gov.sandia.cognition.math.signals.LinearDynamicalSystem: Creates a new instance of LinearDynamicalSystem
LinearFunction - Class in gov.sandia.cognition.learning.function.scalar: This function acts as a simple linear function of the form f(x) = m*x + b.
LinearFunction() - Constructor for class gov.sandia.cognition.learning.function.scalar.LinearFunction: Creates a new LinearFunction with a slope of 1 and offset of 0.
LinearFunction(double, double) - Constructor for class gov.sandia.cognition.learning.function.scalar.LinearFunction: Creates a new LinearFunction with the given slope and offset.
LinearFunction(LinearFunction) - Constructor for class gov.sandia.cognition.learning.function.scalar.LinearFunction: Creates a copy of a given LinearFunction.
LinearizableBinaryCategorizerOnlineLearner<InputType> - Interface in gov.sandia.cognition.learning.algorithm.perceptron: Interface for an online learner of a kernel binary categorizer that can also be used for learning a linear categorizer.
LinearKernel - Class in gov.sandia.cognition.learning.function.kernel: The LinearKernel class implements the most basic kernel: it just does the actual inner product between two vectors.
LinearKernel() - Constructor for class gov.sandia.cognition.learning.function.kernel.LinearKernel: Creates a new instance of LinearKernel.
LinearMixtureModel<DataType,DistributionType extends Distribution<DataType>> - Class in gov.sandia.cognition.statistics.distribution: A linear mixture of RandomVariables, with a prior probability distribution.
LinearMixtureModel(Collection<? extends DistributionType>) - Constructor for class gov.sandia.cognition.statistics.distribution.LinearMixtureModel: Creates a new instance of LinearMixtureModel
LinearMixtureModel(Collection<? extends DistributionType>, double[]) - Constructor for class gov.sandia.cognition.statistics.distribution.LinearMixtureModel: Creates a new instance of LinearMixtureModel
LinearMultiCategorizer<CategoryType> - Class in gov.sandia.cognition.learning.function.categorization: A multi-category version of the LinearBinaryCategorizer that keeps a separate LinearBinaryCategorizer for each category.
LinearMultiCategorizer() - Constructor for class gov.sandia.cognition.learning.function.categorization.LinearMultiCategorizer: Creates a new, empty LinearMultiCategorizer.
LinearMultiCategorizer(Map<CategoryType, LinearBinaryCategorizer>) - Constructor for class gov.sandia.cognition.learning.function.categorization.LinearMultiCategorizer: Creates a new LinearMultiCategorizer with the given prototypes.
LinearRegression - Class in gov.sandia.cognition.learning.algorithm.regression: Computes the least-squares regression for a LinearCombinationFunction given a dataset.
LinearRegression() - Constructor for class gov.sandia.cognition.learning.algorithm.regression.LinearRegression: Creates a new instance of LinearRegression
LinearRegression(double, boolean) - Constructor for class gov.sandia.cognition.learning.algorithm.regression.LinearRegression: Creates a new instance of LinearRegression
LinearRegression.Statistic - Class in gov.sandia.cognition.learning.algorithm.regression: Computes regression statistics using a chi-square measure of the statistical significance of the learned approximator
LinearRegressionCoefficientExtractor - Class in gov.sandia.cognition.learning.data.feature: Takes a sampled sequence of equal-dimension vectors as input and computes the linear regression coefficients for each dimension in the vectors.
LinearRegressionCoefficientExtractor() - Constructor for class gov.sandia.cognition.learning.data.feature.LinearRegressionCoefficientExtractor: Default constructor.
LinearRegressionCoefficientExtractor(int) - Constructor for class gov.sandia.cognition.learning.data.feature.LinearRegressionCoefficientExtractor: Creates new instance of LinearRegressionEvaluator
LinearResult() - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.OnlineShiftingPerceptron.LinearResult: Creates a new, empty LinearResult.
LinearSoftMargin() - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Projectron.LinearSoftMargin: Creates a new Projectron.LinearSoftMargin with a null kernel and default parameters.
LinearSoftMargin(Kernel<? super InputType>) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Projectron.LinearSoftMargin: Creates a new Projectron.LinearSoftMargin with the given kernel and default parameters.
LinearSoftMargin(Kernel<? super InputType>, double) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Projectron.LinearSoftMargin: Creates a new Projectron.LinearSoftMargin with the given parameters.
LinearSoftMargin() - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.OnlinePassiveAggressivePerceptron.LinearSoftMargin: Creates a new LinearSoftMargin with default parameters.
LinearSoftMargin(double) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.OnlinePassiveAggressivePerceptron.LinearSoftMargin: Creates a new LinearSoftMargin with the given aggressiveness.
LinearSoftMargin(double, VectorFactory<?>) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.OnlinePassiveAggressivePerceptron.LinearSoftMargin: Creates a new LinearSoftMargin with the given parameters.
LinearVectorFunction - Class in gov.sandia.cognition.learning.function.vector: The LinearFunction class is a simple VectorFunction that just scales the given input vector by a scalar value.
LinearVectorFunction() - Constructor for class gov.sandia.cognition.learning.function.vector.LinearVectorFunction: Creates a new instance of LinearVectorFunction with the default scale factor.
LinearVectorFunction(double) - Constructor for class gov.sandia.cognition.learning.function.vector.LinearVectorFunction: Creates a new instance of LinearFunction.
LinearVectorScalarFunction - Class in gov.sandia.cognition.learning.function.scalar: The LinearVectorScalarFunction class implements a scalar function that is implemented by a linear function.
LinearVectorScalarFunction() - Constructor for class gov.sandia.cognition.learning.function.scalar.LinearVectorScalarFunction: Creates a new instance of LinearVectorScalarFunction.
LinearVectorScalarFunction(Vector) - Constructor for class gov.sandia.cognition.learning.function.scalar.LinearVectorScalarFunction: Creates a new instance of LinearVectorScalarFunction.
LinearVectorScalarFunction(Vector, double) - Constructor for class gov.sandia.cognition.learning.function.scalar.LinearVectorScalarFunction: Creates a new instance of LinearVectorScalarFunction with the given weights and bias.
LinearVectorScalarFunction(LinearVectorScalarFunction) - Constructor for class gov.sandia.cognition.learning.function.scalar.LinearVectorScalarFunction: Creates a new copy of a LinearVectorScalarFunction.
LineBracket - Class in gov.sandia.cognition.learning.algorithm.minimization.line: Class that defines a bracket for a scalar function.
LineBracket() - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.LineBracket: Creates a new instance of LineBracket
LineBracket(InputOutputSlopeTriplet, InputOutputSlopeTriplet, InputOutputSlopeTriplet) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.LineBracket: Creates a new instance of LineBracket
LineBracket(LineBracket) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.LineBracket: Copy Constructor
LineBracketInterpolator<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>> - Interface in gov.sandia.cognition.learning.algorithm.minimization.line.interpolator: Definition of an interpolator/extrapolator for a LineBracket.
LineBracketInterpolatorBrent - Class in gov.sandia.cognition.learning.algorithm.minimization.line.interpolator: Implements Brent's method of function interpolation to find a minimum.
LineBracketInterpolatorBrent() - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.interpolator.LineBracketInterpolatorBrent: Creates a new instance of LineBracketInterpolatorBrent
LineBracketInterpolatorGoldenSection - Class in gov.sandia.cognition.learning.algorithm.minimization.line.interpolator: Interpolates between the two bound points of a LineBracket using the golden-section step rule, if that step fails, then the interpolator uses a linear (secant) interpolation.
LineBracketInterpolatorGoldenSection() - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.interpolator.LineBracketInterpolatorGoldenSection: Creates a new instance of LineBracketInterpolatorGoldenSection
LineBracketInterpolatorHermiteCubic - Class in gov.sandia.cognition.learning.algorithm.minimization.line.interpolator: Interpolates using a cubic with two points, both of which must have slope information.
LineBracketInterpolatorHermiteCubic() - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.interpolator.LineBracketInterpolatorHermiteCubic: Creates a new instance of LineBracketInterpolatorHermiteCubic
LineBracketInterpolatorHermiteParabola - Class in gov.sandia.cognition.learning.algorithm.minimization.line.interpolator: Interpolates using a parabola with two points, at least one of which must have slope information.
LineBracketInterpolatorHermiteParabola() - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.interpolator.LineBracketInterpolatorHermiteParabola: Creates a new instance of LineBracketInterpolatorHermiteParabola
LineBracketInterpolatorLinear - Class in gov.sandia.cognition.learning.algorithm.minimization.line.interpolator: Interpolates using a linear (stright-line) curve between two points, neither of which need slope information.
LineBracketInterpolatorLinear() - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.interpolator.LineBracketInterpolatorLinear: Creates a new instance of LineBracketInterpolatorLinear
LineBracketInterpolatorParabola - Class in gov.sandia.cognition.learning.algorithm.minimization.line.interpolator: Interpolates using a parabola based on three points without slope information.
LineBracketInterpolatorParabola() - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.interpolator.LineBracketInterpolatorParabola: Creates a new instance of LineBracketInterpolatorParabola
lineFunction - Variable in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerConjugateGradient: Function that maps a Evaluator onto a Evaluator using a set point, direction and scale factor
LineMinimizer<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>> - Interface in gov.sandia.cognition.learning.algorithm.minimization.line: Defines the functionality of a line-minimization algorithm, often called a "line search" algorithm.
LineMinimizerBacktracking - Class in gov.sandia.cognition.learning.algorithm.minimization.line: Implementation of the backtracking line-minimization algorithm.
LineMinimizerBacktracking() - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerBacktracking: Creates a new instance of LineMinimizerBacktracking
LineMinimizerBacktracking(double) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerBacktracking: Creates a new instance of LineMinimizerBacktracking
LineMinimizerBacktracking(double, boolean) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerBacktracking: Creates a new instance of LineMinimizerBacktracking
LineMinimizerBacktracking(double, boolean, double) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerBacktracking: Creates a new instance of LineMinimizerBacktracking
LineMinimizerDerivativeBased - Class in gov.sandia.cognition.learning.algorithm.minimization.line: This is an implementation of a line-minimization algorithm proposed by Fletcher that makes extensive use of first-order derivative information.
LineMinimizerDerivativeBased() - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerDerivativeBased: Default constructor
LineMinimizerDerivativeBased(double) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerDerivativeBased: Creates a new instance of LineMinimizerDerivativeBased
LineMinimizerDerivativeBased(LineBracketInterpolator<? super DifferentiableUnivariateScalarFunction>, double) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerDerivativeBased: Creates a new instance of LineMinimizerDerivativeBased
LineMinimizerDerivativeBased.InternalFunction - Class in gov.sandia.cognition.learning.algorithm.minimization.line: Internal function used to map/remap/unmap the search direction.
LineMinimizerDerivativeFree - Class in gov.sandia.cognition.learning.algorithm.minimization.line: This is an implementation of a LineMinimizer that does not require derivative information.
LineMinimizerDerivativeFree() - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerDerivativeFree: Creates a new instance of LineMinimizerDerivativeFree
LineMinimizerDerivativeFree(LineBracketInterpolator<? super Evaluator<Double, Double>>) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerDerivativeFree: Creates a new instance of LineMinimizerDerivativeFree
lines - Variable in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.AbstractEnvelope: Line segments that comprise the envelope
LineSegment(PolynomialFunction.Linear, double, double) - Constructor for class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.LineSegment: Creates a new instance of LineSegment
listeners - Variable in class gov.sandia.cognition.learning.algorithm.minimization.matrix.IterativeMatrixSolver: Listeners to the algorithms progress have the opportunity to stop the algorithm after a specified number of iterations.
listeners - Variable in class gov.sandia.cognition.learning.experiment.AbstractLearningExperiment: The listeners for the experiment.
loadFromText(File) - Static method in class gov.sandia.cognition.text.term.filter.DefaultStopList: Loads a stop list by reading in a given file and treating each line as a word.
loadFromText(URI) - Static method in class gov.sandia.cognition.text.term.filter.DefaultStopList: Loads a stop list by reading in a given file and treating each line as a word.
loadFromText(URL) - Static method in class gov.sandia.cognition.text.term.filter.DefaultStopList: Loads a stop list by reading in a given file and treating each line as a word.
loadFromText(URLConnection) - Static method in class gov.sandia.cognition.text.term.filter.DefaultStopList: Loads a stop list by reading in a given file and treating each line as a word.
loadFromText(BufferedReader) - Static method in class gov.sandia.cognition.text.term.filter.DefaultStopList: Loads a stop list by reading in from the given reader and treating each line as a word.
localItems - Variable in class gov.sandia.cognition.math.geometry.Quadtree.Node: The local items stored at this node.
LocallyWeightedFunction<InputType,OutputType> - Class in gov.sandia.cognition.learning.algorithm.regression: LocallyWeightedFunction is a generalization of the k-nearest neighbor concept, also known as "Instance-Based Learning", "Memory-Based Learning", "Nonparametric Regression", "Case-Based Regression", or "Kernel-Based Regression".
LocallyWeightedFunction(Kernel<? super InputType>, Collection<? extends InputOutputPair<? extends InputType, OutputType>>, SupervisedBatchLearner<InputType, OutputType, ?>) - Constructor for class gov.sandia.cognition.learning.algorithm.regression.LocallyWeightedFunction: Evaluator that implements the concept of LocallyWeightedLearning.
LocallyWeightedFunction.Learner<InputType,OutputType> - Class in gov.sandia.cognition.learning.algorithm.regression: Learning algorithm for creating LocallyWeightedFunctions.
LocallyWeightedKernelScalarFunction<InputType> - Class in gov.sandia.cognition.learning.function.scalar: The LocallyWeightedKernelScalarFunction class implements a scalar function that uses kernels and does local weighting on them to get the result value.
LocallyWeightedKernelScalarFunction() - Constructor for class gov.sandia.cognition.learning.function.scalar.LocallyWeightedKernelScalarFunction: Creates a new instance of LocallyWeightedKernelScalarFunction.
LocallyWeightedKernelScalarFunction(Kernel<? super InputType>) - Constructor for class gov.sandia.cognition.learning.function.scalar.LocallyWeightedKernelScalarFunction: Creates a new instance of LocallyWeightedKernelScalarFunction with the given kernel.
LocallyWeightedKernelScalarFunction(Kernel<? super InputType>, Collection<? extends WeightedValue<? extends InputType>>) - Constructor for class gov.sandia.cognition.learning.function.scalar.LocallyWeightedKernelScalarFunction: Creates a new instance of LocallyWeightedKernelScalarFunction with the given kernel and weighted examples.
LocallyWeightedKernelScalarFunction(Kernel<? super InputType>, Collection<? extends WeightedValue<? extends InputType>>, double) - Constructor for class gov.sandia.cognition.learning.function.scalar.LocallyWeightedKernelScalarFunction: Creates a new instance of LocallyWeightedKernelScalarFunction with the given kernel, weighted examples, and bias.
LocallyWeightedKernelScalarFunction(Kernel<? super InputType>, Collection<? extends WeightedValue<? extends InputType>>, double, double, double) - Constructor for class gov.sandia.cognition.learning.function.scalar.LocallyWeightedKernelScalarFunction: Creates a new instance of LocallyWeightedKernelScalarFunction with the given kernel, weighted examples, and bias.
LocallyWeightedKernelScalarFunction(LocallyWeightedKernelScalarFunction<InputType>) - Constructor for class gov.sandia.cognition.learning.function.scalar.LocallyWeightedKernelScalarFunction: Creates a new instance copy of LocallyWeightedKernelScalarFunction.
LocalTermWeighter - Interface in gov.sandia.cognition.text.term.vector.weighter.local: Defines the functionality of a local term weighting scheme.
localWeighter - Variable in class gov.sandia.cognition.text.term.vector.weighter.CompositeLocalGlobalTermWeighter: The weighting scheme for the local weights.
location - Variable in class gov.sandia.cognition.statistics.distribution.CauchyDistribution: Central location (also the median and mode) of the distribution.
log(double, double) - Static method in class gov.sandia.cognition.math.MathUtil: Returns the log of the given base of the given value, y=log_b(x) such that x=b^y
log1MinusExp(double) - Static method in class gov.sandia.cognition.math.MathUtil: Computes log(1 - exp(x)).
log1Plus(double) - Static method in class gov.sandia.cognition.math.MathUtil: Computes log(1 + x).
log1PlusExp(double) - Static method in class gov.sandia.cognition.math.MathUtil: Computes log(1 + exp(x)).
log2(double) - Static method in class gov.sandia.cognition.math.MathUtil: Returns the base-2 logarithm of the given value.
LOG_0 - Static variable in class gov.sandia.cognition.math.LogMath: The natural logarithm of 0 (log(0)), which is negative infinity.
LOG_1 - Static variable in class gov.sandia.cognition.math.LogMath: The natural logarithm of 1 (log(1)), which is 0.
LOG_10 - Static variable in class gov.sandia.cognition.math.LogMath: The natural logarithm of 10 (log(10)).
LOG_2 - Static variable in class gov.sandia.cognition.math.LogMath: The natural logarithm of 2 (log(2)).
LOG_E - Static variable in class gov.sandia.cognition.math.LogMath: The natural logarithm of e (log(e)), which is 1.
LOG_OF_2 - Static variable in class gov.sandia.cognition.statistics.distribution.InverseWishartDistribution.PDF: The natural logarithm of 2.0.
LOG_PI - Static variable in class gov.sandia.cognition.statistics.distribution.InverseWishartDistribution.MultivariateGammaFunction: Natural logarithm of pi.
LOG_TWO_PI - Static variable in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian: Natural logarithm of 2pi.
logBetaFunction(double, double) - Static method in class gov.sandia.cognition.math.MathUtil: Compute the natural logarithm of the Beta Function.
logBinomialCoefficient(int, int) - Static method in class gov.sandia.cognition.math.MathUtil: Computes the natural logarithm of the binomial coefficient.
logConditional - Variable in class gov.sandia.cognition.statistics.bayesian.ParallelDirichletProcessMixtureModel.DPMMAssignments: Log conditional likelihood of the previous sample
logConjunctive(Double) - Method in class gov.sandia.cognition.statistics.bayesian.RejectionSampling.ScalarEstimator: Computes the logarithm of the conjunctive likelihood for the given parameter
logDeterminant() - Method in class gov.sandia.cognition.math.matrix.custom.DenseMatrix
logDeterminant() - Method in class gov.sandia.cognition.math.matrix.custom.DiagonalMatrix
logDeterminant() - Method in class gov.sandia.cognition.math.matrix.custom.SparseMatrix: Computes the natural logarithm of the determinant of this.
logDeterminant() - Method in interface gov.sandia.cognition.math.matrix.Matrix: Computes the natural logarithm of the determinant of this.
logDeterminant() - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix
logDeterminant() - Method in class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixMTJ
logEvaluate(Double) - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.AbstractEnvelope: Evaluates the logarithm of the Envelope
logEvaluate(Number) - Method in class gov.sandia.cognition.statistics.distribution.BernoulliDistribution.PMF
logEvaluate(Number) - Method in class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution.PMF
logEvaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.BetaDistribution.PDF
logEvaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.BetaDistribution.PDF
logEvaluate(double, double, double) - Static method in class gov.sandia.cognition.statistics.distribution.BetaDistribution.PDF: Evaluate the Beta-distribution PDF for beta(x;alpha,beta)
logEvaluate(Number) - Method in class gov.sandia.cognition.statistics.distribution.BinomialDistribution.PMF
logEvaluate(int, int, double) - Static method in class gov.sandia.cognition.statistics.distribution.BinomialDistribution.PMF: Computes the natural logarithm of the PMF.
logEvaluate(Vector) - Method in class gov.sandia.cognition.statistics.distribution.CategoricalDistribution.PMF
logEvaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.CauchyDistribution.PDF
logEvaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.CauchyDistribution.PDF
logEvaluate(Vector) - Method in class gov.sandia.cognition.statistics.distribution.ChineseRestaurantProcess.PMF
logEvaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution.PDF
logEvaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution.PDF
logEvaluate(double, double) - Static method in class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution.PDF: Computes the natural logarithm of the PDF.
logEvaluate(KeyType) - Method in class gov.sandia.cognition.statistics.distribution.DefaultDataDistribution.PMF
logEvaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.DeterministicDistribution.PMF
logEvaluate(Vector) - Method in class gov.sandia.cognition.statistics.distribution.DirichletDistribution.PDF
logEvaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.ExponentialDistribution.PDF
logEvaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.ExponentialDistribution.PDF
logEvaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.GammaDistribution.PDF
logEvaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.GammaDistribution.PDF
logEvaluate(double, double, double) - Static method in class gov.sandia.cognition.statistics.distribution.GammaDistribution.PDF: Evaluates the natural logarithm of the PDF.
logEvaluate(Number) - Method in class gov.sandia.cognition.statistics.distribution.GeometricDistribution.PMF
logEvaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.InverseGammaDistribution.PDF
logEvaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.InverseGammaDistribution.PDF
logEvaluate(double, int) - Static method in class gov.sandia.cognition.statistics.distribution.InverseWishartDistribution.MultivariateGammaFunction: Evaluates the logarithm of the Multivariate Gamma Function
logEvaluate(Matrix) - Method in class gov.sandia.cognition.statistics.distribution.InverseWishartDistribution.PDF
logEvaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.LaplaceDistribution.PDF
logEvaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.LaplaceDistribution.PDF
logEvaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.LogisticDistribution.PDF
logEvaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.LogisticDistribution.PDF
logEvaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.LogNormalDistribution.PDF
logEvaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.LogNormalDistribution.PDF
logEvaluate(double, double, double) - Static method in class gov.sandia.cognition.statistics.distribution.LogNormalDistribution.PDF: Computes the natural logarithm of the PDF.
logEvaluate(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MultinomialDistribution.PMF
logEvaluate(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.PDF
logEvaluate(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateMixtureDensityModel.PDF
logEvaluate(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MultivariatePolyaDistribution.PMF
logEvaluate(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution.PDF
logEvaluate(Number) - Method in class gov.sandia.cognition.statistics.distribution.NegativeBinomialDistribution.PMF
logEvaluate(Vector) - Method in class gov.sandia.cognition.statistics.distribution.NormalInverseGammaDistribution.PDF
logEvaluate(Matrix) - Method in class gov.sandia.cognition.statistics.distribution.NormalInverseWishartDistribution.PDF
logEvaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.ParetoDistribution.PDF
logEvaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.ParetoDistribution.PDF
logEvaluate(Number) - Method in class gov.sandia.cognition.statistics.distribution.PoissonDistribution.PMF
logEvaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.ScalarDataDistribution.PMF
logEvaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.PDF
logEvaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.PDF
logEvaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.StudentTDistribution.PDF
logEvaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.StudentTDistribution.PDF
logEvaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.UniformDistribution.PDF
logEvaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.UniformDistribution.PDF
logEvaluate(Number) - Method in class gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution.PMF
logEvaluate(int, int, int) - Static method in class gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution.PMF: Evaluates the log of the probability mass function of the discrete uniform distribution.
logEvaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.PDF
logEvaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.PDF
logEvaluate(double, double, double) - Static method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.PDF: Computes the natural logarithm of the pdf.
logEvaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.WeibullDistribution.PDF
logEvaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.WeibullDistribution.PDF
logEvaluate(Number) - Method in class gov.sandia.cognition.statistics.distribution.YuleSimonDistribution.PMF
logEvaluate(DataType) - Method in interface gov.sandia.cognition.statistics.ProbabilityFunction: Evaluate the natural logarithm of the distribution function.
logEvaluate(double) - Method in interface gov.sandia.cognition.statistics.UnivariateProbabilityDensityFunction: Evaluate the natural logarithm of the distribution function.
LogEvaluator(EvaluatorType) - Constructor for class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.LogEvaluator: Creates a new instance of LogEvaluator
logFactorial(int) - Static method in class gov.sandia.cognition.math.MathUtil: Returns the natural logarithm of n factorial log(n!) = log(n*(n-1)*...*3*2*1)
logGammaFunction(double) - Static method in class gov.sandia.cognition.math.MathUtil: Computes the logarithm of the Gamma function.
logistic(double) - Static method in class gov.sandia.cognition.learning.function.scalar.SigmoidFunction: Utility method to evaluate a logistic sigmoid.
LogisticDistribution - Class in gov.sandia.cognition.statistics.distribution: A implementation of the scalar logistic distribution, which measures the log-odds of a binary event.
LogisticDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.LogisticDistribution: Default constructor
LogisticDistribution(double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.LogisticDistribution: Creates a new instance of LogisticDistribution
LogisticDistribution(LogisticDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.LogisticDistribution: Copy constructor
LogisticDistribution.CDF - Class in gov.sandia.cognition.statistics.distribution: CDF of the LogisticDistribution
LogisticDistribution.PDF - Class in gov.sandia.cognition.statistics.distribution: PDF of the LogisticDistribution
LogisticRegression - Class in gov.sandia.cognition.learning.algorithm.regression: Performs Logistic Regression by means of the iterative reweighted least squares (IRLS) algorithm, where the logistic function has an explicit bias term, and a diagonal L2 regularization term.
LogisticRegression() - Constructor for class gov.sandia.cognition.learning.algorithm.regression.LogisticRegression: Default constructor, with no regularization.
LogisticRegression(double) - Constructor for class gov.sandia.cognition.learning.algorithm.regression.LogisticRegression: Creates a new instance of LogisticRegression
LogisticRegression(double, double, int) - Constructor for class gov.sandia.cognition.learning.algorithm.regression.LogisticRegression: Creates a new instance of LogisticRegression
LogisticRegression.Function - Class in gov.sandia.cognition.learning.algorithm.regression: Class that is a linear discriminant, followed by a sigmoid function.
logLikelihood(ComputableDistribution<? super ObservationType>, Iterable<? extends ObservationType>) - Static method in class gov.sandia.cognition.statistics.bayesian.BayesianUtil: Computes the log likelihood of the i.i.d.
logLikelihood - Variable in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis: The current log-likelihood of the algorithm.
LogLikelihoodTask(Collection<? extends ObservationType>) - Constructor for class gov.sandia.cognition.learning.algorithm.hmm.ParallelHiddenMarkovModel.LogLikelihoodTask: Creates a new instance of LogLikelihoodTask
LogLocalTermWeighter - Class in gov.sandia.cognition.text.term.vector.weighter.local: Implements the log-based local term weighting scheme.
LogLocalTermWeighter() - Constructor for class gov.sandia.cognition.text.term.vector.weighter.local.LogLocalTermWeighter: Creates a new LogLocalTermWeighter.
LogLocalTermWeighter(VectorFactory<? extends Vector>) - Constructor for class gov.sandia.cognition.text.term.vector.weighter.local.LogLocalTermWeighter: Creates a new LogLocalTermWeighter.
LogMath - Class in gov.sandia.cognition.math: A utility class for doing math with numbers represented as logarithms.
LogMath() - Constructor for class gov.sandia.cognition.math.LogMath
logMultinomialBetaFunction(Vector) - Static method in class gov.sandia.cognition.math.MathUtil: Evaluates the natural logarithm of the multinomial beta function for the given input vector.
LogNormalDistribution - Class in gov.sandia.cognition.statistics.distribution: Log-Normal distribution PDF and CDF implementations.
LogNormalDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.LogNormalDistribution: Default constructor.
LogNormalDistribution(double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.LogNormalDistribution: Creates a new instance of LogNormalDistribution
LogNormalDistribution(LogNormalDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.LogNormalDistribution: Copy Constructor
LogNormalDistribution.CDF - Class in gov.sandia.cognition.statistics.distribution: CDF of the Log-Normal Distribution
LogNormalDistribution.MaximumLikelihoodEstimator - Class in gov.sandia.cognition.statistics.distribution: Maximum Likelihood Estimator of a log-normal distribution.
LogNormalDistribution.PDF - Class in gov.sandia.cognition.statistics.distribution: PDF of a Log-normal distribution
LogNormalDistribution.WeightedMaximumLikelihoodEstimator - Class in gov.sandia.cognition.statistics.distribution: Maximum Likelihood Estimator from weighted data
LogNumber - Class in gov.sandia.cognition.math: Represents a number in log-space, storing the log of the absolute value log(|value|) and the sign of the value sign(value).
LogNumber() - Constructor for class gov.sandia.cognition.math.LogNumber: Creates the LogNumber representing zero.
LogNumber(boolean, double) - Constructor for class gov.sandia.cognition.math.LogNumber: Creates a new LogNumber from the given value in log-space.
LogNumber(LogNumber) - Constructor for class gov.sandia.cognition.math.LogNumber: Copies a given LogNumber.
logSize() - Method in class gov.sandia.cognition.statistics.distribution.MultinomialDistribution.Domain: The log of the size.
logValue - Variable in class gov.sandia.cognition.math.LogNumber: The log of the absolute value represented by this object, log(|value|).
logValue - Variable in class gov.sandia.cognition.math.UnsignedLogNumber: The log of the value represented by this object, log(value).
longValue() - Method in class gov.sandia.cognition.math.LogNumber
longValue() - Method in class gov.sandia.cognition.math.MutableDouble
longValue() - Method in class gov.sandia.cognition.math.MutableInteger
longValue() - Method in class gov.sandia.cognition.math.MutableLong
longValue() - Method in class gov.sandia.cognition.math.UnsignedLogNumber
Louvain<NodeNameType> - Class in gov.sandia.cognition.graph.community: This class performs community detection using the Louvain method.
Louvain(DirectedNodeEdgeGraph<NodeNameType>) - Constructor for class gov.sandia.cognition.graph.community.Louvain: Initializes the internal Louvain datatypes that store the necessaries to run Louvain on the input graph.
Louvain(DirectedNodeEdgeGraph<NodeNameType>, int, double) - Constructor for class gov.sandia.cognition.graph.community.Louvain: Initializes the internal Louvain datatypes that store the necessaries to run Louvain on the input graph.
Louvain.LouvainHierarchy<NodeNameType> - Class in gov.sandia.cognition.graph.community: The return type from running Louvain.
lowerBounds - Variable in class gov.sandia.cognition.learning.algorithm.clustering.OptimizedKMeansClusterer: The lower bounds on the distances to the clusters.
LowerCaseTermFilter - Class in gov.sandia.cognition.text.term.filter: A term filter that converts all terms to lower case.
LowerCaseTermFilter() - Constructor for class gov.sandia.cognition.text.term.filter.LowerCaseTermFilter: Creates a new LowerCaseTermFilter.
LowerEnvelope() - Constructor for class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.LowerEnvelope: Default constructor
lowerIncompleteGammaFunction(double, double) - Static method in class gov.sandia.cognition.math.MathUtil: Computes the Lower incomplete gamma function.
lowerLeft - Variable in class gov.sandia.cognition.math.geometry.Quadtree.Node: The child for the lower-left quadrant of this node.
lowerRight - Variable in class gov.sandia.cognition.math.geometry.Quadtree.Node: The child for the lower-right quadrant of this node.
lowestIndex(EntryType, EntryType) - Method in interface gov.sandia.cognition.math.matrix.EntryIndexComparator: Determines which iterator has the lowest index
lowestIndex(MatrixEntry, MatrixEntry) - Method in class gov.sandia.cognition.math.matrix.mtj.MatrixEntryIndexComparatorMTJ: Determines which iterator has the lowest index
lowestIndex(VectorEntry, VectorEntry) - Method in class gov.sandia.cognition.math.matrix.VectorEntryIndexComparator: Determines which iterator has the lowest index
luDecompose() - Method in class gov.sandia.cognition.math.matrix.custom.DenseMatrix: Leverages LAPACK to create the LU decomposition of this.

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