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

C

Cache(Matrix, Matrix, Vector, double) - Constructor for class gov.sandia.cognition.learning.function.cost.SumSquaredErrorCostFunction.Cache

Creates a new instance of Cache

call() - Method in class gov.sandia.cognition.framework.concurrent.MultithreadedCognitiveModel.ModuleEvaluator

call() - Method in class gov.sandia.cognition.learning.algorithm.clustering.ParallelizedKMeansClusterer.AssignDataToCluster

call() - Method in class gov.sandia.cognition.learning.algorithm.clustering.ParallelizedKMeansClusterer.CreateClustersFromAssignments

call() - Method in class gov.sandia.cognition.learning.algorithm.genetic.ParallelizedGeneticAlgorithm.EvaluateGenome

call() - Method in class gov.sandia.cognition.learning.algorithm.hmm.ParallelBaumWelchAlgorithm.DistributionEstimatorTask

call() - Method in class gov.sandia.cognition.learning.algorithm.hmm.ParallelHiddenMarkovModel.ComputeTransitionsTask

call() - Method in class gov.sandia.cognition.learning.algorithm.hmm.ParallelHiddenMarkovModel.LogLikelihoodTask

call() - Method in class gov.sandia.cognition.learning.algorithm.hmm.ParallelHiddenMarkovModel.NormalizeTransitionTask

call() - Method in class gov.sandia.cognition.learning.algorithm.hmm.ParallelHiddenMarkovModel.ObservationLikelihoodTask

call() - Method in class gov.sandia.cognition.learning.algorithm.hmm.ParallelHiddenMarkovModel.StateObservationLikelihoodTask

call() - Method in class gov.sandia.cognition.learning.algorithm.hmm.ParallelHiddenMarkovModel.ViterbiTask

call() - Method in class gov.sandia.cognition.learning.function.cost.ParallelizedCostFunctionContainer.SubCostEvaluate

call() - Method in class gov.sandia.cognition.learning.function.cost.ParallelizedCostFunctionContainer.SubCostGradient

call() - Method in class gov.sandia.cognition.learning.function.cost.ParallelNegativeLogLikelihood.NegativeLogLikelihoodTask

call() - Method in class gov.sandia.cognition.statistics.bayesian.ParallelDirichletProcessMixtureModel.ClusterUpdaterTask

call() - Method in class gov.sandia.cognition.statistics.bayesian.ParallelDirichletProcessMixtureModel.ObservationAssignmentTask

call() - Method in class gov.sandia.cognition.statistics.distribution.StudentizedRangeDistribution.SampleRange

call() - Method in class gov.sandia.cognition.statistics.method.MaximumLikelihoodDistributionEstimator.DistributionEstimationTask

call() - Method in class gov.sandia.cognition.text.topic.ParallelLatentDirichletAllocationVectorGibbsSampler.DocumentSampleTask

canExtract(File) - Method in class gov.sandia.cognition.text.document.extractor.AbstractDocumentExtractor

canExtract(File) - Method in interface gov.sandia.cognition.text.document.extractor.DocumentExtractor

Determines if the given file can be extracted by this extractor.

canExtract(URI) - Method in interface gov.sandia.cognition.text.document.extractor.DocumentExtractor

Determines if the given file can be extracted by this extractor.

canExtract(URLConnection) - Method in interface gov.sandia.cognition.text.document.extractor.DocumentExtractor

Determines if the given file can be extracted by this extractor.

canExtract(URI) - Method in class gov.sandia.cognition.text.document.extractor.TextDocumentExtractor

canExtract(URLConnection) - Method in class gov.sandia.cognition.text.document.extractor.TextDocumentExtractor

capitalizeFirstCharacter(String) - Static method in class gov.sandia.cognition.util.StringUtil

Capitalizes the first character of the given string.

castOrCreateModel(Object) - Static method in class gov.sandia.cognition.framework.io.ModelFileHandler

Attempts to cast the given Object to a CognitiveModel.

CategoricalDistribution - Class in gov.sandia.cognition.statistics.distribution

The Categorical Distribution is the multivariate generalization of the Bernoulli distribution, where the outcome of an experiment is a one-of-N output, where the output is a selector Vector.

CategoricalDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.CategoricalDistribution

Creates a new instance of CategoricalDistribution

CategoricalDistribution(int) - Constructor for class gov.sandia.cognition.statistics.distribution.CategoricalDistribution

Creates a new instance of CategoricalDistribution

CategoricalDistribution(Vector) - Constructor for class gov.sandia.cognition.statistics.distribution.CategoricalDistribution

Creates a new instance of CategoricalDistribution

CategoricalDistribution(CategoricalDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.CategoricalDistribution

Copy constructor

CategoricalDistribution.PMF - Class in gov.sandia.cognition.statistics.distribution

PMF of the Categorical Distribution

categories - Variable in class gov.sandia.cognition.learning.algorithm.tree.CategorizationTree

The list of possible output categories.

categories - Variable in class gov.sandia.cognition.learning.algorithm.tree.VectorThresholdInformationGainLearner

The categories for the prior.

categories - Variable in class gov.sandia.cognition.learning.function.categorization.AbstractCategorizer

The set of categories that are the possible output values of the categorizer.

CategorizationTree<InputType,OutputType> - Class in gov.sandia.cognition.learning.algorithm.tree

The CategorizationTree class extends the DecisionTree class to implement a decision tree that does categorization.

CategorizationTree() - Constructor for class gov.sandia.cognition.learning.algorithm.tree.CategorizationTree

Creates a new instance of CategorizationTree.

CategorizationTree(DecisionTreeNode<InputType, OutputType>, Set<OutputType>) - Constructor for class gov.sandia.cognition.learning.algorithm.tree.CategorizationTree

Creates a new instance of CategorizationTree.

CategorizationTreeLearner<InputType,OutputType> - Class in gov.sandia.cognition.learning.algorithm.tree

The CategorizationTreeLearner class implements a supervised learning algorithm for learning a categorization tree.

CategorizationTreeLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.tree.CategorizationTreeLearner

Creates a new instance of CategorizationTreeLearner.

CategorizationTreeLearner(DeciderLearner<? super InputType, OutputType, ?, ?>) - Constructor for class gov.sandia.cognition.learning.algorithm.tree.CategorizationTreeLearner

Creates a new instance of CategorizationTreeLearner.

CategorizationTreeLearner(DeciderLearner<? super InputType, OutputType, ?, ?>, int, int) - Constructor for class gov.sandia.cognition.learning.algorithm.tree.CategorizationTreeLearner

Creates a new instance of CategorizationTreeLearner.

CategorizationTreeLearner(DeciderLearner<? super InputType, OutputType, ?, ?>, int, int, Map<OutputType, Double>) - Constructor for class gov.sandia.cognition.learning.algorithm.tree.CategorizationTreeLearner

Creates a new instance of CategorizationTreeLearner.

CategorizationTreeNode<InputType,OutputType,InteriorType> - Class in gov.sandia.cognition.learning.algorithm.tree

The CategorizationTreeNode implements a DecisionTreeNode for a tree that does categorization.

CategorizationTreeNode() - Constructor for class gov.sandia.cognition.learning.algorithm.tree.CategorizationTreeNode

Creates a new instance of CategorizationTreeNode.

CategorizationTreeNode(DecisionTreeNode<InputType, OutputType>, OutputType) - Constructor for class gov.sandia.cognition.learning.algorithm.tree.CategorizationTreeNode

Creates a new instance of CategorizationTreeNode.

CategorizationTreeNode(DecisionTreeNode<InputType, OutputType>, OutputType, Object) - Constructor for class gov.sandia.cognition.learning.algorithm.tree.CategorizationTreeNode

Creates a new instance of CategorizationTreeNode.

CategorizationTreeNode(DecisionTreeNode<InputType, OutputType>, Categorizer<? super InputType, ? extends InteriorType>, OutputType, Object) - Constructor for class gov.sandia.cognition.learning.algorithm.tree.CategorizationTreeNode

Creates a new instance of CategorizationTreeNode.

Categorizer<InputType,CategoryType> - Interface in gov.sandia.cognition.learning.function.categorization

The Categorizer interface defines the functionality of an object that can take an input and evaluate what category out of a fixed set of categories it belongs to.

categorizer - Variable in class gov.sandia.cognition.learning.function.categorization.CompositeCategorizer

The categorizer.

categorizers - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.BinaryCategorizerSelector

The collection of categorizers to evaluate and select from.

CategoryBalancedBaggingLearner<InputType,CategoryType> - Class in gov.sandia.cognition.learning.algorithm.ensemble

An extension of the basic bagging learner that attempts to sample bags that have equal numbers of examples from every category.

CategoryBalancedBaggingLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.CategoryBalancedBaggingLearner

Creates a new instance of CategoryBalancedBaggingLearner.

CategoryBalancedBaggingLearner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, CategoryType>>, ? extends Evaluator<? super InputType, ? extends CategoryType>>) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.CategoryBalancedBaggingLearner

Creates a new instance of CategoryBalancedBaggingLearner.

CategoryBalancedBaggingLearner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, CategoryType>>, ? extends Evaluator<? super InputType, ? extends CategoryType>>, int, double, Random) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.CategoryBalancedBaggingLearner

Creates a new instance of CategoryBalancedBaggingLearner.

CategoryBalancedIVotingLearner<InputType,CategoryType> - Class in gov.sandia.cognition.learning.algorithm.ensemble

An extension of IVoting for dealing with skew problems that makes sure that there are an equal number of examples from each category in each sample that an ensemble member is trained on.

CategoryBalancedIVotingLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.CategoryBalancedIVotingLearner

Creates a new CategoryBalancedIVotingLearner.

CategoryBalancedIVotingLearner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, CategoryType>>, ? extends Evaluator<? super InputType, ? extends CategoryType>>, int, double, Random) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.CategoryBalancedIVotingLearner

Creates a new CategoryBalancedIVotingLearner.

CategoryBalancedIVotingLearner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, CategoryType>>, ? extends Evaluator<? super InputType, ? extends CategoryType>>, int, double, double, boolean, Factory<? extends DataDistribution<CategoryType>>, Random) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.CategoryBalancedIVotingLearner

Creates a new CategoryBalancedIVotingLearner.

categoryCounts - Variable in class gov.sandia.cognition.learning.algorithm.tree.VectorThresholdInformationGainLearner

The counts for each category.

categoryList - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.CategoryBalancedBaggingLearner

The list of categories.

categoryPairsToEvaluatorMap - Variable in class gov.sandia.cognition.learning.function.categorization.BinaryVersusCategorizer

Maps false-true category pairs .

categoryPriors - Variable in class gov.sandia.cognition.learning.algorithm.tree.VectorThresholdInformationGainLearner

The priors for each category.

categoryProbabilities - Variable in class gov.sandia.cognition.learning.algorithm.tree.VectorThresholdInformationGainLearner

Following is scratch space used when computing weighted entropy.

CauchyDistribution - Class in gov.sandia.cognition.statistics.distribution

A Cauchy Distribution is the ratio of two Gaussian Distributions, sometimes known as the Lorentz distribution.

CauchyDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.CauchyDistribution

Creates a new instance of CauchyDistribution

CauchyDistribution(double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.CauchyDistribution

Creates a new instance of CauchyDistribution

CauchyDistribution(CauchyDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.CauchyDistribution

Copy constructor

CauchyDistribution.CDF - Class in gov.sandia.cognition.statistics.distribution

CDF of the CauchyDistribution.

CauchyDistribution.PDF - Class in gov.sandia.cognition.statistics.distribution

PDF of the CauchyDistribution.

cdf - Variable in class gov.sandia.cognition.learning.function.scalar.KolmogorovSmirnovEvaluator

The cumulative distribution function to base the evaluator on.

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.BernoulliDistribution.CDF

Default constructor

CDF(double) - Constructor for class gov.sandia.cognition.statistics.distribution.BernoulliDistribution.CDF

Creates a new instance of PMF

CDF(BernoulliDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.BernoulliDistribution.CDF

Copy constructor

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution.CDF

Creates a new instance of BetaBinomialDistribution

CDF(int, double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution.CDF

Creates a new instance of BetaBinomialDistribution

CDF(BetaBinomialDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution.CDF

Copy constructor

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.BetaDistribution.CDF

Default constructor.

CDF(double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.BetaDistribution.CDF

Creates a new CDF

CDF(BetaDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.BetaDistribution.CDF

Copy constructor

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.BinomialDistribution.CDF

Default constructor.

CDF(int, double) - Constructor for class gov.sandia.cognition.statistics.distribution.BinomialDistribution.CDF

Creates a new instance of BinomialDistribution

CDF(BinomialDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.BinomialDistribution.CDF

Creates a new instance of CDF

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.CauchyDistribution.CDF

Creates a new instance of CauchyDistribution

CDF(double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.CauchyDistribution.CDF

Creates a new instance of CauchyDistribution

CDF(CauchyDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.CauchyDistribution.CDF

Copy constructor

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution.CDF

Default constructor.

CDF(double) - Constructor for class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution.CDF

Creates a new instance of ChiSquareDistribution

CDF(ChiSquareDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution.CDF

Copy constructor

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.DeterministicDistribution.CDF

Creates a new instance of DeterministicDistribution

CDF(double) - Constructor for class gov.sandia.cognition.statistics.distribution.DeterministicDistribution.CDF

Creates a new instance of DeterministicDistribution

CDF(DeterministicDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.DeterministicDistribution.CDF

Copy Constructor

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.ExponentialDistribution.CDF

Default constructor.

CDF(double) - Constructor for class gov.sandia.cognition.statistics.distribution.ExponentialDistribution.CDF

Creates a new instance of CDF

CDF(ExponentialDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.ExponentialDistribution.CDF

Copy constructor

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.GammaDistribution.CDF

Default constructor.

CDF(double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.GammaDistribution.CDF

Creates a new instance of CDF

CDF(GammaDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.GammaDistribution.CDF

Copy constructor

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.GeometricDistribution.CDF

Creates a new instance of GeometricDistribution

CDF(double) - Constructor for class gov.sandia.cognition.statistics.distribution.GeometricDistribution.CDF

Creates a new instance of GeometricDistribution

CDF(GeometricDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.GeometricDistribution.CDF

Copy constructor

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.InverseGammaDistribution.CDF

Default constructor

CDF(double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.InverseGammaDistribution.CDF

Creates a new instance of InverseGammaDistribution

CDF(InverseGammaDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.InverseGammaDistribution.CDF

Copy constructor

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.KolmogorovDistribution.CDF

Creates a new instance of CDF

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.LaplaceDistribution.CDF

Creates a new instance of LaplaceDistribution.CDF

CDF(double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.LaplaceDistribution.CDF

Creates a new instance of LaplaceDistribution.CDF

CDF(LaplaceDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.LaplaceDistribution.CDF

Copy Constructor

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.LogisticDistribution.CDF

Default constructor

CDF(double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.LogisticDistribution.CDF

Creates a new instance of CDF

CDF(LogisticDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.LogisticDistribution.CDF

Copy constructor

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.LogNormalDistribution.CDF

Default constructor.

CDF(double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.LogNormalDistribution.CDF

Creates a new instance of LogNormalDistribution

CDF(LogNormalDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.LogNormalDistribution.CDF

Copy Constructor

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.NegativeBinomialDistribution.CDF

Creates a new instance of CDF

CDF(double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.NegativeBinomialDistribution.CDF

Creates a new instance of CDF

CDF(NegativeBinomialDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.NegativeBinomialDistribution.CDF

Copy constructor

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.ParetoDistribution.CDF

Creates a new instance of CDF

CDF(double, double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.ParetoDistribution.CDF

Creates a new instance of CDF

CDF(ParetoDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.ParetoDistribution.CDF

Copy constructor

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.PoissonDistribution.CDF

Default constructor.

CDF(double) - Constructor for class gov.sandia.cognition.statistics.distribution.PoissonDistribution.CDF

Creates a new CDF

CDF(PoissonDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.PoissonDistribution.CDF

Copy constructor

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.ScalarDataDistribution.CDF

Default constructor

CDF(ScalarDataDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.ScalarDataDistribution.CDF

Copy constructor

CDF(Iterable<? extends Number>) - Constructor for class gov.sandia.cognition.statistics.distribution.ScalarDataDistribution.CDF

Creates a new instance of PMF

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.CDF

Creates a new instance of ScalarMixtureDensityModel

CDF(SmoothUnivariateDistribution...) - Constructor for class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.CDF

Creates a new instance of ScalarMixtureDensityModel

CDF(Collection<? extends SmoothUnivariateDistribution>) - Constructor for class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.CDF

Creates a new instance of ScalarMixtureDensityModel

CDF(Collection<? extends SmoothUnivariateDistribution>, double[]) - Constructor for class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.CDF

Creates a new instance of ScalarMixtureDensityModel

CDF(ScalarMixtureDensityModel) - Constructor for class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.CDF

Copy constructor

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.SnedecorFDistribution.CDF

Default constructor

CDF(double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.SnedecorFDistribution.CDF

Creates a new instance of CumulativeDistribution

CDF(SnedecorFDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.SnedecorFDistribution.CDF

Copy Constructor

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.StudentizedRangeDistribution.CDF

Default constructor

CDF(int, double) - Constructor for class gov.sandia.cognition.statistics.distribution.StudentizedRangeDistribution.CDF

Creates a new instance of StudentizedRangeDistribution

CDF(StudentizedRangeDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.StudentizedRangeDistribution.CDF

Copy constructor

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.StudentTDistribution.CDF

Default constructor.

CDF(double) - Constructor for class gov.sandia.cognition.statistics.distribution.StudentTDistribution.CDF

Creates a new instance of CDF

CDF(double, double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.StudentTDistribution.CDF

Creates a new instance of PDF

CDF(StudentTDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.StudentTDistribution.CDF

Creates a new instance of CDF

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.UniformDistribution.CDF

Creates a new instance of CDF

CDF(double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.UniformDistribution.CDF

Creates a new instance of CDF

CDF(UniformDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.UniformDistribution.CDF

Copy constructor

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution.CDF

Creates a new UniformIntegerDistribution.PMF with min and max 0.

CDF(int, int) - Constructor for class gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution.CDF

Creates a new UniformIntegerDistribution.CDF with the given min and max.

CDF(UniformIntegerDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution.CDF

Creates a new UniformIntegerDistribution.CDF as a copy of the given other uniform distribution.

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.CDF

Creates a new instance of UnivariateGaussian with zero mean and unit variance

CDF(double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.CDF

Creates a new instance of UnivariateGaussian

CDF(UnivariateGaussian) - Constructor for class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.CDF

Copy constructor

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.WeibullDistribution.CDF

Creates a new instance of WeibullDistribution

CDF(double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.WeibullDistribution.CDF

Creates a new instance of WeibullDistribution

CDF(WeibullDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.WeibullDistribution.CDF

Copy constructor

CDF() - Constructor for class gov.sandia.cognition.statistics.distribution.YuleSimonDistribution.CDF

Creates a new instance of YuleSimonDistribution

CDF(double) - Constructor for class gov.sandia.cognition.statistics.distribution.YuleSimonDistribution.CDF

Creates a new instance of YuleSimonDistribution

CDF(YuleSimonDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.YuleSimonDistribution.CDF

Copy constructor

centerData - Variable in class gov.sandia.cognition.learning.algorithm.pca.KernelPrincipalComponentsAnalysis

Whether or not the data should be centered before doing KPCA.

centerData - Variable in class gov.sandia.cognition.learning.algorithm.pca.KernelPrincipalComponentsAnalysis.Function

A flag indicating if the incoming data needs to be centered or not.

centerWeightsRange() - Method in class gov.sandia.cognition.text.algorithm.ValenceSpreader

This algorithm only works when there are some negative scores and some positive scores.

centroid - Variable in class gov.sandia.cognition.learning.algorithm.clustering.cluster.CentroidCluster

The center of the cluster.

CentroidCluster<ClusterType> - Class in gov.sandia.cognition.learning.algorithm.clustering.cluster

The CentroidCluster class extends the default cluster to contain a central element.

CentroidCluster() - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.cluster.CentroidCluster

Creates a new instance of CentroidCluster.

CentroidCluster(ClusterType) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.cluster.CentroidCluster

Creates a new instance of CentroidCluster.

CentroidCluster(int, ClusterType) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.cluster.CentroidCluster

Creates a new instance of CentroidCluster.

CentroidCluster(ClusterType, Collection<? extends ClusterType>) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.cluster.CentroidCluster

Creates a new instance of CentroidCluster.

CentroidCluster(int, ClusterType, Collection<? extends ClusterType>) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.cluster.CentroidCluster

Creates a new instance of CentroidCluster.

CentroidClusterDivergenceFunction<DataType> - Class in gov.sandia.cognition.learning.algorithm.clustering.divergence

The CentroidClusterDivergenceFunction class implements a divergence function between a cluster and an object by computing the divergence between the center of the cluster and the object.

CentroidClusterDivergenceFunction(DivergenceFunction<? super DataType, ? super DataType>) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.divergence.CentroidClusterDivergenceFunction

Creates a new instance of CentroidClusterDivergenceFunction.

changedCount - Variable in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation

The number of examples that have changed assignments in the last iteration.

changeOfLogLikelihood - Variable in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis

The change in log-likelihood of the algorithm from the current iteration.

ChebyshevDistanceMetric - Class in gov.sandia.cognition.learning.function.distance

An implementation of the Chebyshev distance, which is the absolute value of the largest difference between two vectors in a single dimension.

ChebyshevDistanceMetric() - Constructor for class gov.sandia.cognition.learning.function.distance.ChebyshevDistanceMetric

Creates a new ChebyshevDistanceMetric.

ChebyshevInequality - Class in gov.sandia.cognition.statistics.method

Computes the Chebyshev Inequality for the given level of confidence.

ChebyshevInequality() - Constructor for class gov.sandia.cognition.statistics.method.ChebyshevInequality

Creates a new instance of ChebyshevInequality

checkConfidence(double) - Static method in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrixConfidenceInterval

Checks to make sure that confidence is between 0.0 and 1.0.

checkedAdd(int, int) - Static method in class gov.sandia.cognition.math.MathUtil

Safely checks for underflow/overflow before adding two integers.

checkedMultiply(int, int) - Static method in class gov.sandia.cognition.math.MathUtil

Safely checks for overflow before multiplying two integers.

checkMultiplicationDimensions(Matrix) - Method in class gov.sandia.cognition.math.matrix.AbstractMatrix

checkMultiplicationDimensions(Matrix) - Method in interface gov.sandia.cognition.math.matrix.Matrix

Checks to see if the dimensions are appropriate for: this.times( postMultiplicationMatrix )

checkNumFolds(int) - Static method in class gov.sandia.cognition.learning.experiment.CrossFoldCreator

Checks the given number of folds to make sure that it is greater than 1.

checkSameDimensionality(Vector) - Method in class gov.sandia.cognition.math.matrix.AbstractVector

checkSameDimensionality(Vector) - Method in interface gov.sandia.cognition.math.matrix.Vector

Determines if this and other have the same number of dimensions (size)

checkSameDimensions(Matrix) - Method in class gov.sandia.cognition.math.matrix.AbstractMatrix

checkSameDimensions(Matrix) - Method in interface gov.sandia.cognition.math.matrix.Matrix

Checks to see if the dimensions are the same between this and otherMatrix

checkTrainingPercent(double) - Static method in class gov.sandia.cognition.learning.data.RandomDataPartitioner

Checks to make sure the training percent greater than 0.0 and less than 1.0.

child - Variable in class gov.sandia.cognition.math.Combinations.AbstractCombinationsIterator

Child iterator for recursion

childMap - Variable in class gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeNode

The mapping of decider decision values to child nodes.

children - Variable in class gov.sandia.cognition.learning.algorithm.clustering.hierarchy.DefaultClusterHierarchyNode

The list of children.

children - Variable in class gov.sandia.cognition.math.geometry.Quadtree.Node

The list of children for this node.

childrenDivergence - Variable in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer.HierarchyNode

The divergence between the two children, if they exist.

ChineseRestaurantProcess - Class in gov.sandia.cognition.statistics.distribution

A Chinese Restaurant Process is a discrete stochastic processes that partitions data points to clusters.

ChineseRestaurantProcess() - Constructor for class gov.sandia.cognition.statistics.distribution.ChineseRestaurantProcess

Creates a new instance of ChineseRestaurantProcess

ChineseRestaurantProcess(double, int) - Constructor for class gov.sandia.cognition.statistics.distribution.ChineseRestaurantProcess

Creates a new instance of ChineseRestaurantProcess

ChineseRestaurantProcess(ChineseRestaurantProcess) - Constructor for class gov.sandia.cognition.statistics.distribution.ChineseRestaurantProcess

Default constructor

ChineseRestaurantProcess.PMF - Class in gov.sandia.cognition.statistics.distribution

PMF of the Chinese Restaurant Process

ChiSquareConfidence - Class in gov.sandia.cognition.statistics.method

This is the chi-square goodness-of-fit test.

ChiSquareConfidence() - Constructor for class gov.sandia.cognition.statistics.method.ChiSquareConfidence

Creates a new instance of ChiSquareConfidence

ChiSquareConfidence.Statistic - Class in gov.sandia.cognition.statistics.method

Confidence Statistic for a chi-square test

ChiSquareDistribution - Class in gov.sandia.cognition.statistics.distribution

Describes a Chi-Square Distribution.

ChiSquareDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution

Default constructor.

ChiSquareDistribution(double) - Constructor for class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution

Creates a new instance of ChiSquareDistribution

ChiSquareDistribution(ChiSquareDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution

Copy constructor

ChiSquareDistribution.CDF - Class in gov.sandia.cognition.statistics.distribution

Cumulative Distribution Function (CDF) of a Chi-Square Distribution

ChiSquareDistribution.PDF - Class in gov.sandia.cognition.statistics.distribution

PDF of the Chi-Square distribution

ChiSquaredSimilarity - Class in gov.sandia.cognition.statistics

A class for computing the chi-squared similarity between two vectors.

ChiSquaredSimilarity(Vector, Vector) - Constructor for class gov.sandia.cognition.statistics.ChiSquaredSimilarity

Basic constructor.

CholeskyDecompositionMTJ - Class in gov.sandia.cognition.math.matrix.mtj.decomposition

Computes the Cholesky decomposition of the symmetric positive definite matrix.

chooseChild(InputType) - Method in class gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeNode

chooseChild(InputType) - Method in interface gov.sandia.cognition.learning.algorithm.tree.DecisionTreeNode

Chooses the child node corresponding to the given input.

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner

Called to clean up the learning algorithm's state after learning has finished.

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.annealing.SimulatedAnnealer

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.clustering.DBSCANClusterer

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.clustering.KMeansClusterer

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.clustering.MiniBatchKMeansClusterer

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractBaggingLearner

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AdaBoost

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.ensemble.CategoryBalancedBaggingLearner

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.ensemble.MultiCategoryAdaBoost

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachineAlternatingLeastSquares

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachineStochasticGradient

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.genetic.GeneticAlgorithm

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.hmm.BaumWelchAlgorithm

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerConjugateGradient

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerDirectionSetPowell

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerGradientDescent

Called to clean up the learning algorithm's state after learning has finished.

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerNelderMead

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerQuasiNewton

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.AbstractAnytimeLineMinimizer

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.pca.GeneralizedHebbianAlgorithm

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.perceptron.BatchMultiPerceptron

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelAdatron

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelPerceptron

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.perceptron.Perceptron

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.regression.FletcherXuHybridEstimation

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.regression.GaussNewtonAlgorithm

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.regression.KernelBasedIterativeRegression

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.regression.KernelWeightedRobustRegression

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.regression.LevenbergMarquardtEstimation

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.regression.LogisticRegression

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.root.AbstractBracketedRootFinder

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.root.RootBracketExpander

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.root.RootFinderNewtonsMethod

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.svm.PrimalEstimatedSubGradient

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.svm.SequentialMinimalOptimization

cleanupAlgorithm() - Method in class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation

cleanupAlgorithm() - Method in class gov.sandia.cognition.statistics.bayesian.AbstractMarkovChainMonteCarlo

cleanupAlgorithm() - Method in class gov.sandia.cognition.statistics.distribution.MixtureOfGaussians.EMLearner

cleanupAlgorithm() - Method in class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.EMLearner

cleanupAlgorithm() - Method in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler

cleanupAlgorithm() - Method in class gov.sandia.cognition.text.topic.ParallelLatentDirichletAllocationVectorGibbsSampler

cleanupAlgorithm() - Method in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis

clear() - Method in class gov.sandia.cognition.collection.AbstractScalarMap

clear() - Method in class gov.sandia.cognition.collection.DefaultIndexer

clear() - Method in class gov.sandia.cognition.collection.DynamicArrayMap

Runs in O(n).

clear() - Method in class gov.sandia.cognition.collection.FiniteCapacityBuffer

clear() - Method in interface gov.sandia.cognition.collection.Indexer

Clears the contents of this index.

clear() - Method in interface gov.sandia.cognition.collection.NumericMap

Removes all elements from the map.

clear() - Method in class gov.sandia.cognition.framework.lite.CognitiveModelLiteState

Clears this CognitiveModelLite state, resetting it to being uninitialized.

clear() - Method in class gov.sandia.cognition.framework.lite.CogxelStateLite

Clears all of the Cogxels in this CogxelState.

clear() - Method in class gov.sandia.cognition.graph.DenseMemoryGraph

clear() - Method in interface gov.sandia.cognition.graph.DirectedNodeEdgeGraph

Clears the graph back to the original, empty state

clear() - Method in class gov.sandia.cognition.graph.GraphMetrics

Clears all cached metrics for the originally input graph.

clear() - Method in class gov.sandia.cognition.graph.WeightedDenseMemoryGraph

clear() - Method in interface gov.sandia.cognition.learning.performance.categorization.ConfusionMatrix

Empties out all the data in this confusion matrix.

clear() - Method in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrix

clear() - Method in class gov.sandia.cognition.learning.performance.categorization.DefaultConfusionMatrix

clear() - Method in class gov.sandia.cognition.math.RingAccumulator

Clears the accumulator.

clear() - Method in class gov.sandia.cognition.statistics.distribution.DefaultDataDistribution

clear() - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.SufficientStatistic

Resets this set of sufficient statistics to its empty state.

clear() - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.SufficientStatisticCovarianceInverse

Resets this set of sufficient statistics to its empty state.

clear() - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.SufficientStatistic

Resets this set of sufficient statistics to its empty state.

clearLabels() - Method in class gov.sandia.cognition.graph.inference.GraphWrappingEnergyFunction

Clears labels previously set.

clearStoredCosts() - Method in class gov.sandia.cognition.graph.inference.CostSpeedupEnergyFunction

Clears the pre-computed costs that were stored to keep from calling log and potential each time.

clone() - Method in class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

clone() - Method in class gov.sandia.cognition.algorithm.AbstractParallelAlgorithm

clone() - Method in class gov.sandia.cognition.algorithm.AnytimeAlgorithmWrapper

clone() - Method in class gov.sandia.cognition.collection.AbstractLogNumberMap

clone() - Method in class gov.sandia.cognition.collection.AbstractMutableDoubleMap

clone() - Method in class gov.sandia.cognition.collection.AbstractScalarMap

clone() - Method in class gov.sandia.cognition.collection.DefaultIndexer

clone() - Method in class gov.sandia.cognition.collection.DynamicArrayMap

Creates a new clone (shallow copy) of this object.

clone() - Method in class gov.sandia.cognition.collection.FiniteCapacityBuffer

clone() - Method in class gov.sandia.cognition.collection.IntegerSpan

clone() - Method in class gov.sandia.cognition.data.convert.IdentityDataConverter

clone() - Method in class gov.sandia.cognition.evaluator.AbstractStatefulEvaluator

clone() - Method in class gov.sandia.cognition.evaluator.CompositeEvaluatorList

clone() - Method in class gov.sandia.cognition.evaluator.IdentityEvaluator

clone() - Method in class gov.sandia.cognition.evaluator.ValueClamper

clone() - Method in class gov.sandia.cognition.evaluator.ValueMapper

clone() - Method in class gov.sandia.cognition.factory.ConstructorBasedFactory

clone() - Method in class gov.sandia.cognition.factory.PrototypeFactory

Clones this PrototypeFactory.

clone() - Method in interface gov.sandia.cognition.framework.CognitiveModelState

A deep copy clone of this state.

clone() - Method in interface gov.sandia.cognition.framework.CognitiveModuleState

Performs a deep copy of the state.

clone() - Method in interface gov.sandia.cognition.framework.Cogxel

Clones the Cogxel.

clone() - Method in interface gov.sandia.cognition.framework.CogxelState

Clones this Cogxel state, returning a deep copy of the Cogxels.

clone() - Method in class gov.sandia.cognition.framework.DefaultCogxel

This makes public the clone method on the Object class and removes the exception that it throws.

clone() - Method in class gov.sandia.cognition.framework.DefaultSemanticIdentifierMap

clone() - Method in class gov.sandia.cognition.framework.learning.converter.AbstractCogxelConverter

clone() - Method in class gov.sandia.cognition.framework.learning.converter.AbstractCogxelPairConverter

clone() - Method in class gov.sandia.cognition.framework.learning.converter.CogxelBooleanConverter

clone() - Method in interface gov.sandia.cognition.framework.learning.converter.CogxelConverter

Creates a new clone (shallow copy) of this object.

clone() - Method in class gov.sandia.cognition.framework.learning.converter.CogxelDoubleConverter

Creates a new clone (shallow copy) of this object.

clone() - Method in class gov.sandia.cognition.framework.learning.converter.CogxelMatrixConverter

Creates a new clone (shallow copy) of this object.

clone() - Method in class gov.sandia.cognition.framework.learning.converter.CogxelVectorCollectionConverter

Creates a new clone (shallow copy) of this object.

clone() - Method in class gov.sandia.cognition.framework.learning.converter.CogxelVectorConverter

This makes public the clone method on the Object class and removes the exception that it throws.

clone() - Method in class gov.sandia.cognition.framework.learning.converter.CogxelWeightedInputOutputPairConverter

Creates a new clone (shallow copy) of this object.

clone() - Method in class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleFactory

Creates a clone of this EvaluatorBasedCognitiveModuleFactory.

clone() - Method in class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleFactoryLearner

Creates a copy of this EvaluatorBasedCognitiveModuleFactoryLearner.

clone() - Method in class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleSettings

Creates a clone of this EvaluatorBasedCognitiveModuleSettings.

clone() - Method in class gov.sandia.cognition.framework.lite.CognitiveModelLiteState

This makes public the clone method on the Object class and removes the exception that it throws.

clone() - Method in class gov.sandia.cognition.framework.lite.CognitiveModuleStateWrapper

This makes public the clone method on the Object class and removes the exception that it throws.

clone() - Method in class gov.sandia.cognition.framework.lite.CogxelStateLite

This makes public the clone method on the Object class and removes the exception that it throws.

clone() - Method in interface gov.sandia.cognition.framework.lite.PatternRecognizerLite

Creates a deep copy of the pattern recognizer.

clone() - Method in class gov.sandia.cognition.framework.lite.SharedSemanticMemoryLiteFactory

This makes public the clone method on the Object class and removes the exception that it throws.

clone() - Method in class gov.sandia.cognition.framework.lite.SharedSemanticMemoryLiteSettings

This makes public the clone method on the Object class and removes the exception that it throws.

clone() - Method in class gov.sandia.cognition.framework.lite.SimplePatternRecognizer

This makes public the clone method on the Object class and removes the exception that it throws.

clone() - Method in class gov.sandia.cognition.framework.lite.SimplePatternRecognizerState

This makes public the clone method on the Object class and removes the exception that it throws.

clone() - Method in class gov.sandia.cognition.framework.lite.VectorBasedCognitiveModelInput

This makes public the clone method on the Object class and removes the exception that it throws.

clone() - Method in class gov.sandia.cognition.hash.AbstractHashFunction

clone() - Method in class gov.sandia.cognition.hash.Eva32Hash

clone() - Method in class gov.sandia.cognition.hash.Eva64Hash

clone() - Method in class gov.sandia.cognition.hash.FNV1a32Hash

clone() - Method in class gov.sandia.cognition.hash.FNV1a64Hash

clone() - Method in class gov.sandia.cognition.hash.Murmur32Hash

clone() - Method in class gov.sandia.cognition.hash.Prime32Hash

clone() - Method in class gov.sandia.cognition.hash.Prime64Hash

clone() - Method in class gov.sandia.cognition.hash.SHA256Hash

clone() - Method in class gov.sandia.cognition.hash.SHA512Hash

clone() - Method in class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner

clone() - Method in class gov.sandia.cognition.learning.algorithm.AbstractBatchAndIncrementalLearner

clone() - Method in class gov.sandia.cognition.learning.algorithm.AbstractBatchLearnerContainer

clone() - Method in class gov.sandia.cognition.learning.algorithm.annealing.SimulatedAnnealer

clone() - Method in class gov.sandia.cognition.learning.algorithm.annealing.VectorizablePerturber

clone() - Method in class gov.sandia.cognition.learning.algorithm.baseline.IdentityLearner

clone() - Method in class gov.sandia.cognition.learning.algorithm.bayes.DiscreteNaiveBayesCategorizer

clone() - Method in class gov.sandia.cognition.learning.algorithm.bayes.VectorNaiveBayesCategorizer

clone() - Method in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation

clone() - Method in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer

clone() - Method in class gov.sandia.cognition.learning.algorithm.clustering.cluster.DefaultCluster

clone() - Method in class gov.sandia.cognition.learning.algorithm.clustering.DBSCANClusterer

clone() - Method in class gov.sandia.cognition.learning.algorithm.clustering.DirichletProcessClustering

clone() - Method in class gov.sandia.cognition.learning.algorithm.clustering.divergence.CentroidClusterDivergenceFunction

clone() - Method in class gov.sandia.cognition.learning.algorithm.clustering.divergence.WithinClusterDivergenceWrapper

clone() - Method in class gov.sandia.cognition.learning.algorithm.clustering.divergence.WithinNormalizedCentroidClusterCosineDivergence

clone() - Method in class gov.sandia.cognition.learning.algorithm.clustering.initializer.AbstractMinDistanceFixedClusterInitializer

clone() - Method in class gov.sandia.cognition.learning.algorithm.clustering.initializer.DistanceSamplingClusterInitializer

clone() - Method in class gov.sandia.cognition.learning.algorithm.clustering.initializer.GreedyClusterInitializer

clone() - Method in class gov.sandia.cognition.learning.algorithm.clustering.initializer.RandomClusterInitializer

clone() - Method in class gov.sandia.cognition.learning.algorithm.clustering.KMeansClusterer

clone() - Method in class gov.sandia.cognition.learning.algorithm.clustering.MiniBatchKMeansClusterer

clone() - Method in class gov.sandia.cognition.learning.algorithm.clustering.OptimizedKMeansClusterer

clone() - Method in class gov.sandia.cognition.learning.algorithm.clustering.ParallelizedKMeansClusterer

clone() - Method in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer

clone() - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractUnweightedEnsemble

clone() - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractWeightedEnsemble

clone() - Method in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachine

clone() - Method in interface gov.sandia.cognition.learning.algorithm.gradient.GradientDescendable

Creates a new clone (shallow copy) of this object.

clone() - Method in class gov.sandia.cognition.learning.algorithm.gradient.GradientDescendableApproximator

clone() - Method in class gov.sandia.cognition.learning.algorithm.hmm.AbstractBaumWelchAlgorithm

clone() - Method in class gov.sandia.cognition.learning.algorithm.hmm.BaumWelchAlgorithm

clone() - Method in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel

clone() - Method in class gov.sandia.cognition.learning.algorithm.hmm.MarkovChain

clone() - Method in class gov.sandia.cognition.learning.algorithm.InputOutputTransformedBatchLearner

clone() - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.DirectionalVectorToDifferentiableScalarFunction

clone() - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.DirectionalVectorToScalarFunction

clone() - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.InputOutputSlopeTriplet

clone() - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.LineBracket

clone() - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.WolfeConditions

clone() - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.ConjugateGradientMatrixSolver

clone() - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.ConjugateGradientWithPreconditionerMatrixSolver

clone() - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.IterativeMatrixSolver

clone() - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.OverconstrainedConjugateGradientMatrixMinimizer

clone() - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.SteepestDescentMatrixSolver

clone() - Method in class gov.sandia.cognition.learning.algorithm.nearest.AbstractKNearestNeighbor

clone() - Method in class gov.sandia.cognition.learning.algorithm.nearest.KNearestNeighborExhaustive

clone() - Method in class gov.sandia.cognition.learning.algorithm.nearest.KNearestNeighborKDTree

clone() - Method in class gov.sandia.cognition.learning.algorithm.nearest.NearestNeighborExhaustive

clone() - Method in class gov.sandia.cognition.learning.algorithm.nearest.NearestNeighborKDTree

clone() - Method in class gov.sandia.cognition.learning.algorithm.pca.AbstractPrincipalComponentsAnalysis

clone() - Method in class gov.sandia.cognition.learning.algorithm.pca.GeneralizedHebbianAlgorithm

clone() - Method in class gov.sandia.cognition.learning.algorithm.pca.PrincipalComponentsAnalysisFunction

clone() - Method in class gov.sandia.cognition.learning.algorithm.perceptron.Perceptron

clone() - Method in class gov.sandia.cognition.learning.algorithm.perceptron.Winnow

clone() - Method in class gov.sandia.cognition.learning.algorithm.regression.AbstractLogisticRegression

clone() - Method in class gov.sandia.cognition.learning.algorithm.regression.AbstractMinimizerBasedParameterCostMinimizer

clone() - Method in class gov.sandia.cognition.learning.algorithm.regression.KernelBasedIterativeRegression

clone() - Method in class gov.sandia.cognition.learning.algorithm.regression.LinearBasisRegression

clone() - Method in class gov.sandia.cognition.learning.algorithm.regression.LinearRegression

clone() - Method in class gov.sandia.cognition.learning.algorithm.regression.LinearRegression.Statistic

clone() - Method in class gov.sandia.cognition.learning.algorithm.regression.LogisticRegression

clone() - Method in class gov.sandia.cognition.learning.algorithm.regression.LogisticRegression.Function

clone() - Method in class gov.sandia.cognition.learning.algorithm.regression.MultivariateLinearRegression

clone() - Method in class gov.sandia.cognition.learning.algorithm.regression.ParameterDerivativeFreeCostMinimizer

clone() - Method in class gov.sandia.cognition.learning.algorithm.regression.ParameterDerivativeFreeCostMinimizer.ParameterCostEvaluatorDerivativeFree

clone() - Method in class gov.sandia.cognition.learning.algorithm.regression.ParameterDifferentiableCostMinimizer

clone() - Method in class gov.sandia.cognition.learning.algorithm.regression.ParameterDifferentiableCostMinimizer.ParameterCostEvaluatorDerivativeBased

clone() - Method in class gov.sandia.cognition.learning.algorithm.root.AbstractRootFinder

clone() - Method in class gov.sandia.cognition.learning.algorithm.root.RootFinderNewtonsMethod

clone() - Method in class gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeLearner

clone() - Method in class gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeNode

clone() - Method in class gov.sandia.cognition.learning.algorithm.tree.AbstractVectorThresholdMaximumGainLearner

clone() - Method in class gov.sandia.cognition.learning.algorithm.tree.CategorizationTree

clone() - Method in class gov.sandia.cognition.learning.algorithm.tree.CategorizationTreeLearner

clone() - Method in class gov.sandia.cognition.learning.algorithm.tree.CategorizationTreeNode

clone() - Method in class gov.sandia.cognition.learning.algorithm.tree.DecisionTree

clone() - Method in class gov.sandia.cognition.learning.algorithm.tree.RandomSubVectorThresholdLearner

clone() - Method in class gov.sandia.cognition.learning.algorithm.tree.RegressionTree

clone() - Method in class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeLearner

clone() - Method in class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeNode

clone() - Method in class gov.sandia.cognition.learning.algorithm.tree.VectorThresholdGiniImpurityLearner

clone() - Method in class gov.sandia.cognition.learning.algorithm.tree.VectorThresholdHellingerDistanceLearner

clone() - Method in class gov.sandia.cognition.learning.algorithm.tree.VectorThresholdInformationGainLearner

clone() - Method in class gov.sandia.cognition.learning.data.DefaultTargetEstimatePair

clone() - Method in class gov.sandia.cognition.learning.data.DefaultValueDiscriminantPair

clone() - Method in class gov.sandia.cognition.learning.data.feature.DelayFunction

clone() - Method in class gov.sandia.cognition.learning.data.feature.LinearRegressionCoefficientExtractor

clone() - Method in class gov.sandia.cognition.learning.data.feature.MultivariateDecorrelator

Creates a new copy of this MultivariateDecorrelator.

clone() - Method in class gov.sandia.cognition.learning.data.feature.StandardDistributionNormalizer

Creates a new copy of this StandardDistributionNormalizer.

clone() - Method in class gov.sandia.cognition.learning.function.categorization.AbstractCategorizer

clone() - Method in class gov.sandia.cognition.learning.function.categorization.BinaryVersusCategorizer

clone() - Method in class gov.sandia.cognition.learning.function.categorization.CompositeCategorizer

clone() - Method in class gov.sandia.cognition.learning.function.categorization.EvaluatorToCategorizerAdapter.Learner

clone() - Method in class gov.sandia.cognition.learning.function.categorization.FisherLinearDiscriminantBinaryCategorizer

clone() - Method in class gov.sandia.cognition.learning.function.categorization.LinearBinaryCategorizer

clone() - Method in class gov.sandia.cognition.learning.function.categorization.LinearMultiCategorizer

clone() - Method in class gov.sandia.cognition.learning.function.categorization.MaximumAPosterioriCategorizer

clone() - Method in class gov.sandia.cognition.learning.function.categorization.MaximumAPosterioriCategorizer.Learner

clone() - Method in class gov.sandia.cognition.learning.function.categorization.ScalarFunctionToBinaryCategorizerAdapter

clone() - Method in class gov.sandia.cognition.learning.function.categorization.ScalarThresholdBinaryCategorizer

clone() - Method in class gov.sandia.cognition.learning.function.categorization.VectorElementThresholdCategorizer

clone() - Method in class gov.sandia.cognition.learning.function.cost.AbstractCostFunction

clone() - Method in class gov.sandia.cognition.learning.function.cost.AbstractSupervisedCostFunction

clone() - Method in class gov.sandia.cognition.learning.function.cost.ClusterDistortionMeasure

clone() - Method in interface gov.sandia.cognition.learning.function.cost.CostFunction

clone() - Method in class gov.sandia.cognition.learning.function.cost.EuclideanDistanceCostFunction

clone() - Method in class gov.sandia.cognition.learning.function.cost.MeanL1CostFunction

clone() - Method in class gov.sandia.cognition.learning.function.cost.MeanSquaredErrorCostFunction

clone() - Method in class gov.sandia.cognition.learning.function.cost.ParallelizedCostFunctionContainer

clone() - Method in class gov.sandia.cognition.learning.function.cost.SumSquaredErrorCostFunction

clone() - Method in class gov.sandia.cognition.learning.function.distance.DefaultDivergenceFunctionContainer

clone() - Method in class gov.sandia.cognition.learning.function.distance.DivergencesEvaluator

clone() - Method in class gov.sandia.cognition.learning.function.distance.DivergencesEvaluator.Learner

clone() - Method in class gov.sandia.cognition.learning.function.distance.WeightedEuclideanDistanceMetric

clone() - Method in class gov.sandia.cognition.learning.function.kernel.DefaultKernelContainer

clone() - Method in class gov.sandia.cognition.learning.function.kernel.DefaultKernelsContainer

clone() - Method in class gov.sandia.cognition.learning.function.kernel.ExponentialKernel

clone() - Method in class gov.sandia.cognition.learning.function.kernel.KernelDistanceMetric

clone() - Method in class gov.sandia.cognition.learning.function.kernel.LinearKernel

clone() - Method in class gov.sandia.cognition.learning.function.kernel.NormalizedKernel

clone() - Method in class gov.sandia.cognition.learning.function.kernel.PolynomialKernel

clone() - Method in class gov.sandia.cognition.learning.function.kernel.ProductKernel

clone() - Method in class gov.sandia.cognition.learning.function.kernel.RadialBasisKernel

clone() - Method in class gov.sandia.cognition.learning.function.kernel.ScalarFunctionKernel

clone() - Method in class gov.sandia.cognition.learning.function.kernel.SigmoidKernel

clone() - Method in class gov.sandia.cognition.learning.function.kernel.SumKernel

clone() - Method in class gov.sandia.cognition.learning.function.kernel.VectorFunctionKernel

clone() - Method in class gov.sandia.cognition.learning.function.kernel.WeightedKernel

Creates a copy of this kernel.

clone() - Method in class gov.sandia.cognition.learning.function.kernel.ZeroKernel

clone() - Method in class gov.sandia.cognition.learning.function.LinearCombinationFunction

clone() - Method in class gov.sandia.cognition.learning.function.scalar.AtanFunction

clone() - Method in class gov.sandia.cognition.learning.function.scalar.CosineFunction

clone() - Method in class gov.sandia.cognition.learning.function.scalar.HardSigmoidFunction

clone() - Method in class gov.sandia.cognition.learning.function.scalar.HardTanHFunction

clone() - Method in class gov.sandia.cognition.learning.function.scalar.IdentityScalarFunction

clone() - Method in class gov.sandia.cognition.learning.function.scalar.KolmogorovSmirnovEvaluator

clone() - Method in class gov.sandia.cognition.learning.function.scalar.LeakyRectifiedLinearFunction

clone() - Method in class gov.sandia.cognition.learning.function.scalar.LinearCombinationScalarFunction

clone() - Method in class gov.sandia.cognition.learning.function.scalar.LinearDiscriminant

clone() - Method in class gov.sandia.cognition.learning.function.scalar.LinearDiscriminantWithBias

clone() - Method in class gov.sandia.cognition.learning.function.scalar.LinearFunction

clone() - Method in class gov.sandia.cognition.learning.function.scalar.LinearVectorScalarFunction

clone() - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction

clone() - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Cubic

clone() - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Linear

clone() - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Quadratic

clone() - Method in class gov.sandia.cognition.learning.function.scalar.RectifiedLinearFunction

clone() - Method in class gov.sandia.cognition.learning.function.scalar.SigmoidFunction

clone() - Method in class gov.sandia.cognition.learning.function.scalar.SoftPlusFunction

clone() - Method in class gov.sandia.cognition.learning.function.scalar.TanHFunction

clone() - Method in class gov.sandia.cognition.learning.function.scalar.ThresholdFunction

clone() - Method in class gov.sandia.cognition.learning.function.scalar.VectorEntryFunction

clone() - Method in class gov.sandia.cognition.learning.function.scalar.VectorFunctionLinearDiscriminant

clone() - Method in class gov.sandia.cognition.learning.function.scalar.VectorFunctionToScalarFunction

clone() - Method in class gov.sandia.cognition.learning.function.scalar.VectorFunctionToScalarFunction.Learner

clone() - Method in class gov.sandia.cognition.learning.function.vector.DifferentiableFeedforwardNeuralNetwork

clone() - Method in class gov.sandia.cognition.learning.function.vector.DifferentiableGeneralizedLinearModel

clone() - Method in class gov.sandia.cognition.learning.function.vector.ElementWiseVectorFunction

clone() - Method in class gov.sandia.cognition.learning.function.vector.FeedforwardNeuralNetwork

clone() - Method in class gov.sandia.cognition.learning.function.vector.GaussianContextRecognizer

clone() - Method in class gov.sandia.cognition.learning.function.vector.GeneralizedLinearModel

clone() - Method in class gov.sandia.cognition.learning.function.vector.LinearCombinationVectorFunction

clone() - Method in class gov.sandia.cognition.learning.function.vector.MultivariateDiscriminant

clone() - Method in class gov.sandia.cognition.learning.function.vector.MultivariateDiscriminantWithBias

clone() - Method in class gov.sandia.cognition.learning.function.vector.ThreeLayerFeedforwardNeuralNetwork

clone() - Method in class gov.sandia.cognition.learning.function.vector.VectorizableVectorConverter

clone() - Method in class gov.sandia.cognition.learning.function.vector.VectorizableVectorConverterWithBias

This makes public the clone method on the Object class and removes the exception that it throws.

clone() - Method in class gov.sandia.cognition.learning.parameter.ParameterAdaptableBatchLearnerWrapper

clone() - Method in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrix

clone() - Method in class gov.sandia.cognition.learning.performance.categorization.DefaultConfusionMatrix

clone() - Method in class gov.sandia.cognition.math.AbstractRing

clone() - Method in class gov.sandia.cognition.math.Combinations

clone() - Method in class gov.sandia.cognition.math.ComplexNumber

Returns a deep copy of this

clone() - Method in class gov.sandia.cognition.math.geometry.KDTree

clone() - Method in class gov.sandia.cognition.math.LogNumber

clone() - Method in class gov.sandia.cognition.math.matrix.custom.DenseMatrix

Returns a deep copy of this.

clone() - Method in class gov.sandia.cognition.math.matrix.custom.DenseVector

clone() - Method in class gov.sandia.cognition.math.matrix.custom.DiagonalMatrix

clone() - Method in class gov.sandia.cognition.math.matrix.custom.ParallelSparseMatrix

This makes public the clone method on the Object class and removes the exception that it throws.

clone() - Method in class gov.sandia.cognition.math.matrix.custom.SparseMatrix

This makes public the clone method on the Object class and removes the exception that it throws.

clone() - Method in class gov.sandia.cognition.math.matrix.custom.SparseVector

clone() - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector

clone() - Method in interface gov.sandia.cognition.math.matrix.Matrix

clone() - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix

clone() - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJVector

clone() - Method in class gov.sandia.cognition.math.matrix.mtj.DenseMatrix

clone() - Method in class gov.sandia.cognition.math.matrix.mtj.Vector1

clone() - Method in class gov.sandia.cognition.math.matrix.mtj.Vector2

clone() - Method in class gov.sandia.cognition.math.matrix.mtj.Vector3

clone() - Method in class gov.sandia.cognition.math.matrix.NumericalDifferentiator

clone() - Method in interface gov.sandia.cognition.math.matrix.Quaternion

Clones this object.

clone() - Method in interface gov.sandia.cognition.math.matrix.Vector

clone() - Method in interface gov.sandia.cognition.math.matrix.Vectorizable

clone() - Method in class gov.sandia.cognition.math.matrix.VectorizableIndexComparator

clone() - Method in interface gov.sandia.cognition.math.matrix.VectorizableVectorFunction

Creates a new clone (shallow copy) of this object.

clone() - Method in class gov.sandia.cognition.math.MutableDouble

clone() - Method in class gov.sandia.cognition.math.MutableInteger

clone() - Method in class gov.sandia.cognition.math.MutableLong

clone() - Method in interface gov.sandia.cognition.math.Ring

Returns a smart copy of this, such that changing the values of the return class will not effect this

clone() - Method in class gov.sandia.cognition.math.signals.AutoRegressiveMovingAverageFilter

clone() - Method in class gov.sandia.cognition.math.signals.LinearDynamicalSystem

clone() - Method in class gov.sandia.cognition.math.signals.MovingAverageFilter

clone() - Method in class gov.sandia.cognition.math.UnivariateSummaryStatistics

clone() - Method in class gov.sandia.cognition.math.UnsignedLogNumber

clone() - Method in class gov.sandia.cognition.statistics.AbstractClosedFormUnivariateDistribution

clone() - Method in class gov.sandia.cognition.statistics.AbstractDataDistribution

clone() - Method in class gov.sandia.cognition.statistics.AbstractIncrementalEstimator

clone() - Method in class gov.sandia.cognition.statistics.AbstractSufficientStatistic

clone() - Method in class gov.sandia.cognition.statistics.bayesian.AbstractBayesianParameter

clone() - Method in class gov.sandia.cognition.statistics.bayesian.AbstractKalmanFilter

clone() - Method in class gov.sandia.cognition.statistics.bayesian.AbstractMarkovChainMonteCarlo

clone() - Method in class gov.sandia.cognition.statistics.bayesian.AbstractParticleFilter

clone() - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.AbstractEnvelope

clone() - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling

clone() - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.LogEvaluator

clone() - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.UpperEnvelope

clone() - Method in class gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression

clone() - Method in class gov.sandia.cognition.statistics.bayesian.BayesianRobustLinearRegression

clone() - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.AbstractConjugatePriorBayesianEstimator

clone() - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.BinomialBayesianEstimator

clone() - Method in class gov.sandia.cognition.statistics.bayesian.DefaultBayesianParameter

clone() - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel

clone() - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.DPMMCluster

clone() - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.MultivariateMeanCovarianceUpdater

clone() - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.MultivariateMeanUpdater

clone() - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.Sample

clone() - Method in class gov.sandia.cognition.statistics.bayesian.ExtendedKalmanFilter

clone() - Method in class gov.sandia.cognition.statistics.bayesian.GaussianProcessRegression

clone() - Method in class gov.sandia.cognition.statistics.bayesian.ImportanceSampling

clone() - Method in class gov.sandia.cognition.statistics.bayesian.KalmanFilter

clone() - Method in class gov.sandia.cognition.statistics.bayesian.MetropolisHastingsAlgorithm

clone() - Method in class gov.sandia.cognition.statistics.bayesian.RejectionSampling

clone() - Method in interface gov.sandia.cognition.statistics.DataDistribution

clone() - Method in class gov.sandia.cognition.statistics.DefaultDistributionParameter

clone() - Method in class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.BetaDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.BinomialDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.CategoricalDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.CauchyDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.ChineseRestaurantProcess

clone() - Method in class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.DefaultDataDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.DirichletDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.ExponentialDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.GammaDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.GeometricDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.InverseGammaDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.InverseWishartDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.LaplaceDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.LinearMixtureModel

clone() - Method in class gov.sandia.cognition.statistics.distribution.LogisticDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.MixtureOfGaussians.PDF

clone() - Method in class gov.sandia.cognition.statistics.distribution.MultinomialDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian

clone() - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.SufficientStatistic

clone() - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.SufficientStatisticCovarianceInverse

clone() - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussianInverseGammaDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.MultivariateMixtureDensityModel

clone() - Method in class gov.sandia.cognition.statistics.distribution.MultivariatePolyaDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.NegativeBinomialDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.NormalInverseGammaDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.NormalInverseWishartDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.ParetoDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.PoissonDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.ScalarDataDistribution.CDF

clone() - Method in class gov.sandia.cognition.statistics.distribution.ScalarDataDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel

clone() - Method in class gov.sandia.cognition.statistics.distribution.SnedecorFDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.StudentizedRangeDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.StudentTDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.UniformDistribution.CDF

clone() - Method in class gov.sandia.cognition.statistics.distribution.UniformDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian

clone() - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.SufficientStatistic

clone() - Method in class gov.sandia.cognition.statistics.distribution.WeibullDistribution

clone() - Method in class gov.sandia.cognition.statistics.distribution.YuleSimonDistribution

clone() - Method in class gov.sandia.cognition.statistics.method.AbstractMultipleHypothesisComparison.Statistic

clone() - Method in class gov.sandia.cognition.statistics.method.AbstractPairwiseMultipleHypothesisComparison

clone() - Method in class gov.sandia.cognition.statistics.method.AbstractPairwiseMultipleHypothesisComparison.Statistic

clone() - Method in class gov.sandia.cognition.statistics.method.AdjustedPValueStatistic

clone() - Method in class gov.sandia.cognition.statistics.method.AnalysisOfVarianceOneWay.Statistic

clone() - Method in class gov.sandia.cognition.statistics.method.BonferroniCorrection

clone() - Method in class gov.sandia.cognition.statistics.method.ConvexReceiverOperatingCharacteristic

clone() - Method in class gov.sandia.cognition.statistics.method.DistributionParameterEstimator

clone() - Method in class gov.sandia.cognition.statistics.method.DistributionParameterEstimator.DistributionWrapper

clone() - Method in class gov.sandia.cognition.statistics.method.FriedmanConfidence.Statistic

clone() - Method in class gov.sandia.cognition.statistics.method.HolmCorrection

clone() - Method in class gov.sandia.cognition.statistics.method.MaximumLikelihoodDistributionEstimator

clone() - Method in class gov.sandia.cognition.statistics.method.MultipleComparisonExperiment

clone() - Method in class gov.sandia.cognition.statistics.method.NemenyiConfidence.Statistic

clone() - Method in class gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic

clone() - Method in class gov.sandia.cognition.statistics.method.ShafferStaticCorrection

clone() - Method in class gov.sandia.cognition.statistics.method.SidakCorrection

clone() - Method in class gov.sandia.cognition.statistics.method.StudentTConfidence.Statistic

clone() - Method in class gov.sandia.cognition.statistics.method.TukeyKramerConfidence

clone() - Method in class gov.sandia.cognition.statistics.method.TukeyKramerConfidence.Statistic

clone() - Method in class gov.sandia.cognition.statistics.montecarlo.ImportanceSampler

clone() - Method in class gov.sandia.cognition.statistics.UnivariateRandomVariable

clone() - Method in class gov.sandia.cognition.text.DefaultTextual

clone() - Method in class gov.sandia.cognition.text.term.DefaultIndexedTerm

clone() - Method in class gov.sandia.cognition.text.term.DefaultTerm

clone() - Method in class gov.sandia.cognition.text.term.DefaultTermIndex

clone() - Method in class gov.sandia.cognition.text.term.DefaultTermNGram

clone() - Method in class gov.sandia.cognition.text.term.filter.DefaultStopList

clone() - Method in class gov.sandia.cognition.text.term.filter.NGramFilter

clone() - Method in class gov.sandia.cognition.text.term.vector.weighter.CompositeLocalGlobalTermWeighter

clone() - Method in class gov.sandia.cognition.text.term.vector.weighter.global.AbstractEntropyBasedGlobalTermWeighter

clone() - Method in class gov.sandia.cognition.text.term.vector.weighter.global.AbstractFrequencyBasedGlobalTermWeighter

clone() - Method in class gov.sandia.cognition.text.term.vector.weighter.global.DominanceGlobalTermWeighter

clone() - Method in class gov.sandia.cognition.text.term.vector.weighter.global.EntropyGlobalTermWeighter

clone() - Method in class gov.sandia.cognition.text.term.vector.weighter.global.InverseDocumentFrequencyGlobalTermWeighter

clone() - Method in class gov.sandia.cognition.util.AbstractCloneableSerializable

This makes public the clone method on the Object class and removes the exception that it throws.

clone() - Method in class gov.sandia.cognition.util.AbstractNamed

clone() - Method in class gov.sandia.cognition.util.AbstractRandomized

clone() - Method in class gov.sandia.cognition.util.AbstractTemporal

clone() - Method in interface gov.sandia.cognition.util.CloneableSerializable

Creates a new clone (shallow copy) of this object.

clone() - Method in class gov.sandia.cognition.util.DefaultNamedValue

clone() - Method in class gov.sandia.cognition.util.DefaultPair

clone() - Method in class gov.sandia.cognition.util.DefaultTriple

clone() - Method in class gov.sandia.cognition.util.DefaultWeightedValue

Creates a shallow copy of the WeightedValue.

CloneableSerializable - Interface in gov.sandia.cognition.util

An object that is both cloneable and serializable, because Java's Cloneable interface mistakenly doesn't have a clone() method (search on the Web, it's funny "lost in the mists of time..." )

cloneSafe(T) - Static method in class gov.sandia.cognition.util.ObjectUtil

Calls the Clone method on the given object of some type that extends CloneableSerializable.

cloneSmart(T) - Static method in class gov.sandia.cognition.util.ObjectUtil

Attempts to clone a given object.

cloneSmartArrayAndElements(T[]) - Static method in class gov.sandia.cognition.util.ObjectUtil

Clones an array and its elements.

cloneSmartElementsAsArrayList(Collection<T>) - Static method in class gov.sandia.cognition.util.ObjectUtil

Creates a new ArrayList and attempts to copy all of the elements from the given collection into it by calling the cloneSmart method on each of them.

cloneSmartElementsAsLinkedList(Iterable<T>) - Static method in class gov.sandia.cognition.util.ObjectUtil

Creates a new LinkedList and attempts to copy all of the elements from the given collection into it by calling the cloneSmart method on each of them.

ClosedFormComputableDiscreteDistribution<DataType> - Interface in gov.sandia.cognition.statistics

A discrete, closed-form Distribution with a PMF.

ClosedFormComputableDistribution<DataType> - Interface in gov.sandia.cognition.statistics

A closed-form Distribution that also has an associated distribution function.

ClosedFormCumulativeDistributionFunction<DomainType extends java.lang.Number> - Interface in gov.sandia.cognition.statistics

Functionality of a cumulative distribution function that's defined with closed-form parameters.

ClosedFormDifferentiableEvaluator<InputType,OutputType,DerivativeType> - Interface in gov.sandia.cognition.math

A differentiable function that has a closed-form derivative.

ClosedFormDiscreteUnivariateDistribution<DomainType extends java.lang.Number> - Interface in gov.sandia.cognition.statistics

A ClosedFormUnivariateDistribution that is also a DiscreteDistribution

ClosedFormDistribution<DataType> - Interface in gov.sandia.cognition.statistics

Defines a distribution that is described a parameterized mathematical equation.

ClosedFormSolver() - Constructor for class gov.sandia.cognition.learning.function.categorization.FisherLinearDiscriminantBinaryCategorizer.ClosedFormSolver

Default constructor.

ClosedFormSolver(double) - Constructor for class gov.sandia.cognition.learning.function.categorization.FisherLinearDiscriminantBinaryCategorizer.ClosedFormSolver

Creates a new ClosedFormSolver.

ClosedFormUnivariateDistribution<NumberType extends java.lang.Number> - Interface in gov.sandia.cognition.statistics

Defines the functionality associated with a closed-form scalar distribution.

Cluster<ClusterType> - Interface in gov.sandia.cognition.learning.algorithm.clustering.cluster

The Cluster interface defines the general functionality of a cluster, which is just the ability to get the members of the cluster.

cluster - Variable in class gov.sandia.cognition.learning.algorithm.clustering.hierarchy.AbstractClusterHierarchyNode

The cluster associated with the node.

ClusterCentroidDivergenceFunction<DataType> - Class in gov.sandia.cognition.learning.algorithm.clustering.divergence

The ClusterCentroidDivergenceFunction class implements the distance between two clusters by computing the distance between the cluster's centroid.

ClusterCentroidDivergenceFunction() - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.divergence.ClusterCentroidDivergenceFunction

Creates a new instance of ClusterCompleteLinkDivergenceFunction.

ClusterCentroidDivergenceFunction(DivergenceFunction<? super DataType, ? super DataType>) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.divergence.ClusterCentroidDivergenceFunction

Creates a new instance of ClusterCompleteLinkDivergenceFunction using the given divergence function for elements.

ClusterCompleteLinkDivergenceFunction<ClusterType extends Cluster<DataType>,DataType> - Class in gov.sandia.cognition.learning.algorithm.clustering.divergence

The ClusterCompleteLinkDivergenceFunction class implements the complete linkage distance metric between two clusters.

ClusterCompleteLinkDivergenceFunction() - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.divergence.ClusterCompleteLinkDivergenceFunction

Creates a new instance of ClusterCompleteLinkDivergenceFunction.

ClusterCompleteLinkDivergenceFunction(DivergenceFunction<? super DataType, ? super DataType>) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.divergence.ClusterCompleteLinkDivergenceFunction

Creates a new instance of ClusterCompleteLinkDivergenceFunction using the given divergence function for elements.

clusterCounts - Variable in class gov.sandia.cognition.learning.algorithm.clustering.KMeansClusterer

The current number of elements assigned to each cluster.

ClusterCreator<ClusterType extends Cluster<DataType>,DataType> - Interface in gov.sandia.cognition.learning.algorithm.clustering.cluster

The ClusterCreator defines the functionality of a class that can create a new cluster from a given collection of members of that cluster.

clusterDistances - Variable in class gov.sandia.cognition.learning.algorithm.clustering.OptimizedKMeansClusterer

The distances between clusters.

ClusterDistortionMeasure<DataType,ClusterType extends Cluster<DataType>> - Class in gov.sandia.cognition.learning.function.cost

Computes the objective measure for a clustering algorithm, based on the internal "distortion" of each cluster.

ClusterDistortionMeasure() - Constructor for class gov.sandia.cognition.learning.function.cost.ClusterDistortionMeasure

Creates a new instance of ClusterDistortionMeasure

ClusterDistortionMeasure(ClusterDivergenceFunction<ClusterType, DataType>) - Constructor for class gov.sandia.cognition.learning.function.cost.ClusterDistortionMeasure

Creates a new instance of ClusterDistortionMeasure

ClusterDivergenceFunction<ClusterType extends Cluster<DataType>,DataType> - Interface in gov.sandia.cognition.learning.algorithm.clustering.divergence

The ClusterDivergenceFunction interface defines a function that computes the divergence between a cluster and some other object.

clusterDivergenceFunction - Variable in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer

The divergence function used to find the distance between two clusters.

clusterHierarchically(Collection<? extends DataType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer

clusterHierarchically(Collection<? extends DataType>) - Method in interface gov.sandia.cognition.learning.algorithm.clustering.hierarchy.BatchHierarchicalClusterer

Performs hierarchical clustering on the given elements.

clusterHierarchically(Collection<? extends DataType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer

ClusterHierarchyNode<DataType,ClusterType extends Cluster<DataType>> - Interface in gov.sandia.cognition.learning.algorithm.clustering.hierarchy

Defines a node in a hierarchy of clusters.

ClusterMeanLinkDivergenceFunction<ClusterType extends Cluster<DataType>,DataType> - Class in gov.sandia.cognition.learning.algorithm.clustering.divergence

The ClusterMeanLinkDivergenceFunction class implements the mean linkage distance metric between two clusters.

ClusterMeanLinkDivergenceFunction() - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.divergence.ClusterMeanLinkDivergenceFunction

Creates a new instance of ClusterMeanLinkDivergenceFunction.

ClusterMeanLinkDivergenceFunction(DivergenceFunction<? super DataType, ? super DataType>) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.divergence.ClusterMeanLinkDivergenceFunction

Creates a new instance of ClusterMeanLinkDivergenceFunction using the given divergence function for elements.

clusters - Variable in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation

The clusters that have been found so far.

clusters - Variable in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer

The current set of clusters.

clusters - Variable in class gov.sandia.cognition.learning.algorithm.clustering.KMeansClusterer

The current set of clusters.

clusters - Variable in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer

The current set of clusters all clusters created.

clusters - Variable in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.Sample

Point mass realizations from the base distribution.

clustersHierarchy - Variable in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer

The current set of hierarchical clusters.

clustersHierarchy - Variable in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer

The current set of hierarchical clusters created.

ClusterSingleLinkDivergenceFunction<ClusterType extends Cluster<DataType>,DataType> - Class in gov.sandia.cognition.learning.algorithm.clustering.divergence

The ClusterSingleLinkDivergenceFunction class implements the complete linkage distance metric between two clusters.

ClusterSingleLinkDivergenceFunction() - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.divergence.ClusterSingleLinkDivergenceFunction

Creates a new instance of ClusterSingleLinkDivergenceFunction.

ClusterSingleLinkDivergenceFunction(DivergenceFunction<? super DataType, ? super DataType>) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.divergence.ClusterSingleLinkDivergenceFunction

Creates a new instance of ClusterSingleLinkDivergenceFunction using the given divergence function for elements.

ClusterToClusterDivergenceFunction<ClusterType extends Cluster<DataType>,DataType> - Interface in gov.sandia.cognition.learning.algorithm.clustering.divergence

The ClusterToClusterDivergenceFunction defines a DivergenceFunction between two clusters of the same data type.

ClusterUpdaterTask() - Constructor for class gov.sandia.cognition.statistics.bayesian.ParallelDirichletProcessMixtureModel.ClusterUpdaterTask

Creates a new instance of ClusterUpdaterTask

clusterUpdaterTasks - Variable in class gov.sandia.cognition.statistics.bayesian.ParallelDirichletProcessMixtureModel

Tasks that update the values of the clusters for Gibbs sampling

clusterWeights - Variable in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel

Holds the cluster weights so that we don't have to re-allocate them each mcmcUpdate step.

CodeReview - Annotation Type in gov.sandia.cognition.annotation

The CodeReview annotation describes information about the last code review for a piece of code.

CodeReviewResponse - Annotation Type in gov.sandia.cognition.annotation

The CodeReviewResponse annotation contains information regarding a response to a CodeReview annotation.

CodeReviews - Annotation Type in gov.sandia.cognition.annotation

The CodeReviews annotation defines a container for one or more CodeReview annotations.

CognitiveModel - Interface in gov.sandia.cognition.framework

The CognitiveModel interface defines the basic functionality of a cognitive model.

CognitiveModelFactory - Interface in gov.sandia.cognition.framework

The CognitiveModelFactory interface defines an interface for creating a new CognitiveModel using a predefined set of CognitiveModules, as created by CognitiveModuleFactories.

CognitiveModelInput - Interface in gov.sandia.cognition.framework

The CognitiveModelInput defines the inteface for an input to a CognitiveModel.

CognitiveModelListener - Interface in gov.sandia.cognition.framework

The CognitiveModelListener interface is an event listener that listens for events on a CognitiveModel.

CognitiveModelLite - Class in gov.sandia.cognition.framework.lite

This class provides a lite implementation of the CognitiveModel interface.

CognitiveModelLite(CognitiveModuleFactory...) - Constructor for class gov.sandia.cognition.framework.lite.CognitiveModelLite

Creates a new instance of CognitiveModelLite.

CognitiveModelLite(Iterable<? extends CognitiveModuleFactory>) - Constructor for class gov.sandia.cognition.framework.lite.CognitiveModelLite

Creates a new instance of CognitiveModelLite.

CognitiveModelLiteFactory - Class in gov.sandia.cognition.framework.lite

The CognitiveModelLiteFactory defines a CognitiveModelFactory for creating CognitiveModelLite objects.

CognitiveModelLiteFactory() - Constructor for class gov.sandia.cognition.framework.lite.CognitiveModelLiteFactory

Creates a new instance of CognitiveModelLiteFactory.

CognitiveModelLiteFactory(Collection<CognitiveModuleFactory>) - Constructor for class gov.sandia.cognition.framework.lite.CognitiveModelLiteFactory

Creates a new instance of CognitiveModelLiteFactory.

CognitiveModelLiteState - Class in gov.sandia.cognition.framework.lite

The CognitiveModelLiteState class implements a CognitiveModelState object for the CognitiveModelLite.

CognitiveModelLiteState(int) - Constructor for class gov.sandia.cognition.framework.lite.CognitiveModelLiteState

Creates a new instance of CognitiveModelState.

CognitiveModelLiteState(int, int) - Constructor for class gov.sandia.cognition.framework.lite.CognitiveModelLiteState

Creates a new instance of CognitiveModelState.

CognitiveModelLiteState(CognitiveModelLiteState) - Constructor for class gov.sandia.cognition.framework.lite.CognitiveModelLiteState

Creates a new copy of a CognitiveModelLiteState.

CognitiveModelState - Interface in gov.sandia.cognition.framework

The CognitiveModelState interface defines the general functionality required of an object that represents the state of a CognitiveModel.

CognitiveModelStateChangeEvent - Class in gov.sandia.cognition.framework

The CognitiveModelStateChangeEvent class is an EventObject that contains the data pertaining to a change in the state of a CognitiveModel.

CognitiveModelStateChangeEvent(CognitiveModel, CognitiveModelState) - Constructor for class gov.sandia.cognition.framework.CognitiveModelStateChangeEvent

Creates a new instance of CognitiveModelStateChangeEvent.

CognitiveModule - Interface in gov.sandia.cognition.framework

The CognitiveModule interface defines the functionality of a general module that can be used in a CognitiveModel.

CognitiveModuleFactory - Interface in gov.sandia.cognition.framework

The CognitiveModuleFactory interface defines the functionality required by something that creates new CognitiveModules for a CognitiveModel.

CognitiveModuleFactoryLearner - Interface in gov.sandia.cognition.framework.learning

The CognitiveModuleFactoryLearner is an interface defining the functionality of an Object that can learn a CognitiveModuleFactory from a collection of input data.

CognitiveModuleSettings - Interface in gov.sandia.cognition.framework

The CogntiviteModuleSettings class defines the functionality required for the settings of a CognitiveModule.

CognitiveModuleState - Interface in gov.sandia.cognition.framework

The CognitiveModuleState defines the interface for the state of a CognitiveModule.

CognitiveModuleStateWrapper - Class in gov.sandia.cognition.framework.lite

The CognitiveModuleStateWrapper wraps some other object as a CognitiveModuleState object.

CognitiveModuleStateWrapper() - Constructor for class gov.sandia.cognition.framework.lite.CognitiveModuleStateWrapper

Creates a new instance of CognitiveModuleStateWrapper.

CognitiveModuleStateWrapper(CloneableSerializable) - Constructor for class gov.sandia.cognition.framework.lite.CognitiveModuleStateWrapper

Creates a new instance of CognitiveModuleStateWrapper.

Cogxel - Interface in gov.sandia.cognition.framework

The interface for the fundamental unit of operation inside a CognitiveModel.

CogxelBooleanConverter - Class in gov.sandia.cognition.framework.learning.converter

Implements a CogxelConverter that encodes booleans as positive and negative values (+1/-1).

CogxelBooleanConverter() - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelBooleanConverter

Creates a new instance of CogxelBooleanConverter.

CogxelBooleanConverter(SemanticLabel) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelBooleanConverter

Creates a new instance of CogxelDoubleConverter.

CogxelBooleanConverter(SemanticLabel, SemanticIdentifierMap) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelBooleanConverter

Creates a new instance of CogxelBooleanConverter.

CogxelBooleanConverter(SemanticLabel, SemanticIdentifierMap, CogxelFactory) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelBooleanConverter

Creates a new instance of CogxelBooleanConverter.

CogxelConverter<DataType> - Interface in gov.sandia.cognition.framework.learning.converter

The CogxelConverter interface defines the functionality required for an object to act as a converter from some DataType to and from a CogxelState object.

CogxelDoubleConverter - Class in gov.sandia.cognition.framework.learning.converter

The CogxelDoubleConverter class converts a Cogxel to and from a double value by using its activation.

CogxelDoubleConverter() - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelDoubleConverter

Creates a new instance of CogxelDoubleConverter.

CogxelDoubleConverter(SemanticLabel) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelDoubleConverter

Creates a new instance of CogxelDoubleConverter.

CogxelDoubleConverter(SemanticLabel, SemanticIdentifierMap) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelDoubleConverter

Creates a new instance of CogxelDoubleConverter.

CogxelDoubleConverter(SemanticLabel, SemanticIdentifierMap, CogxelFactory) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelDoubleConverter

Creates a new instance of CogxelDoubleConverter.

CogxelDoubleConverter(CogxelDoubleConverter) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelDoubleConverter

Creates a new instance of CogxelDoubleConverter.

CogxelFactory - Interface in gov.sandia.cognition.framework

The CogxelFactory interface defines the functionality required for an object to be used to create a Cogxel for a CognitiveModel.

CogxelInputOutputPairConverter<InputType,OutputType> - Class in gov.sandia.cognition.framework.learning.converter

The InputOutputPairCogxelConverter class implements a converter to and from Cogxels to InputOutputPair objects.

CogxelInputOutputPairConverter() - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelInputOutputPairConverter

Default constructor

CogxelInputOutputPairConverter(CogxelConverter<InputType>, CogxelConverter<OutputType>) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelInputOutputPairConverter

Creates a new instance of InputOutputCogxelConverter.

CogxelInputOutputPairConverter(CogxelConverter<InputType>, CogxelConverter<OutputType>, SemanticIdentifierMap) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelInputOutputPairConverter

Creates a new instance of InputOutputCogxelConverter.

CogxelMatrixConverter - Class in gov.sandia.cognition.framework.learning.converter

The CogxelVectorConverter implements a converter to convert Cogxels to and from Matrix objects.

CogxelMatrixConverter(Collection<? extends Iterable<SemanticLabel>>) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelMatrixConverter

Creates a new instance of CogxelMatrixConverter

CogxelMatrixConverter(ArrayList<CogxelVectorConverter>) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelMatrixConverter

Creates a new instance of CogxelMatrixConverter

CogxelMatrixConverter(ArrayList<CogxelVectorConverter>, SemanticIdentifierMap) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelMatrixConverter

Creates a new instance of CogxelMatrixConverter

CogxelMatrixConverter(CogxelMatrixConverter) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelMatrixConverter

Copy constructor

CogxelState - Interface in gov.sandia.cognition.framework

Keeps a collection of Cogxels and some accessor methods.

CogxelStateLite - Class in gov.sandia.cognition.framework.lite

The CogxelStateLite class implements a CogxelState to be used with the CognitiveModelLite.

CogxelStateLite() - Constructor for class gov.sandia.cognition.framework.lite.CogxelStateLite

Creates a new instance of CogxelStateLite

CogxelStateLite(int) - Constructor for class gov.sandia.cognition.framework.lite.CogxelStateLite

Creates a new instance of CogxelStateLite with an expected initial capacity for the number of cogxels contained in it.

CogxelStateLite(CogxelState) - Constructor for class gov.sandia.cognition.framework.lite.CogxelStateLite

Creates a copy of a CogxelStateLite

CogxelTargetEstimatePairConverter<TargetType,EstimateType> - Class in gov.sandia.cognition.framework.learning.converter

CogxelConverter based on a TargetEstimatePair.

CogxelTargetEstimatePairConverter() - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelTargetEstimatePairConverter

Creates a new CogxelTargetEstimatePairConverter.

CogxelTargetEstimatePairConverter(CogxelConverter<TargetType>, CogxelConverter<EstimateType>) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelTargetEstimatePairConverter

Creates a new CogxelTargetEstimatePairConverter with the given converters for each element of the pair.

CogxelTargetEstimatePairConverter(CogxelConverter<TargetType>, CogxelConverter<EstimateType>, SemanticIdentifierMap) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelTargetEstimatePairConverter

Creates a new CogxelTargetEstimatePairConverter with the given converters for each element of the pair.

CogxelVectorCollectionConverter - Class in gov.sandia.cognition.framework.learning.converter

Converts a Collection of Vectors to and from a CogxelState

CogxelVectorCollectionConverter(Collection<CogxelVectorConverter>) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelVectorCollectionConverter

Creates a new instance of CogxelVectorCollectionConverter

CogxelVectorCollectionConverter(CogxelVectorConverter...) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelVectorCollectionConverter

Creates a new instance of CogxelVectorCollectionConverter

CogxelVectorCollectionConverter(CogxelVectorCollectionConverter) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelVectorCollectionConverter

Copy Constructor

CogxelVectorConverter - Class in gov.sandia.cognition.framework.learning.converter

The CogxelVectorConverter implements a converter to convert Cogxels to and from Vector objects.

CogxelVectorConverter() - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelVectorConverter

Creates a new, empty instance of VectorCogxelMap.

CogxelVectorConverter(SemanticLabel...) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelVectorConverter

Creates a new CogxelVectorConverter from the given labels.

CogxelVectorConverter(Iterable<SemanticLabel>) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelVectorConverter

Creates a new CogxelVectorConverter from the given labels.

CogxelVectorConverter(SemanticLabel[], SemanticIdentifierMap) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelVectorConverter

Creates a new CogxelVectorConverter from the given SemanticIdentifierMap and SemanticLabels.

CogxelVectorConverter(Iterable<SemanticLabel>, SemanticIdentifierMap) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelVectorConverter

Creates a new CogxelVectorConverter from the given SemanticIdentifierMap and SemanticLabels.

CogxelVectorConverter(SemanticLabel[], SemanticIdentifierMap, VectorFactory<?>, CogxelFactory) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelVectorConverter

Creates a new CogxelVectorConverter

CogxelVectorConverter(Iterable<SemanticLabel>, SemanticIdentifierMap, VectorFactory<?>, CogxelFactory) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelVectorConverter

Creates a new CogxelVectorConverter.

CogxelVectorConverter(CogxelVectorConverter) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelVectorConverter

Creates a new copy of the given CogxelVectorConverter.

CogxelWeightedInputOutputPairConverter<InputType,OutputType> - Class in gov.sandia.cognition.framework.learning.converter

A CogxelConverter for creating WeightedInputOutputPairs

CogxelWeightedInputOutputPairConverter() - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelWeightedInputOutputPairConverter

Creates a new instance of CogxelWeightedInputOutputPairConverter

CogxelWeightedInputOutputPairConverter(CogxelConverter<InputType>, CogxelConverter<OutputType>, CogxelConverter<Double>) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelWeightedInputOutputPairConverter

Creates a new instance of CogxelWeightedInputOutputPairConverter

CogxelWeightedInputOutputPairConverter(CogxelWeightedInputOutputPairConverter<InputType, OutputType>) - Constructor for class gov.sandia.cognition.framework.learning.converter.CogxelWeightedInputOutputPairConverter

Copy constructor

CollectionUtil - Class in gov.sandia.cognition.collection

The CollectionUtil class implements static methods for dealing with Collection and Iterable objects.

CollectionUtil() - Constructor for class gov.sandia.cognition.collection.CollectionUtil

COLLINEAR_TOLERANCE - Static variable in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Linear

Tolerance below which to consider something zero, 0.0

columnIndices - Variable in class gov.sandia.cognition.math.matrix.custom.SparseMatrix

Part of the compressed Yale format.

Combinations - Class in gov.sandia.cognition.math

Enumerates all the combinations on a given number of items sampled from a larger set without considering order, that is, (1,2) is the same as (2,1).

Combinations(int, int) - Constructor for class gov.sandia.cognition.math.Combinations

Creates a new instance of Combinations

Combinations.AbstractCombinationsIterator<IteratorType extends Combinations.AbstractCombinationsIterator<IteratorType,ClassType>,ClassType> - Class in gov.sandia.cognition.math

Partial implementation of a CombinationsIterator.

Combinations.IndexIterator - Class in gov.sandia.cognition.math

Iterator that returns the index into a set: 0, 1, 2, ...

Combinations.SubsetIterator<ClassType> - Class in gov.sandia.cognition.math

Creates a new instance of SubsetIterator, one that returns the elements from a given set

combineHash(HashFunction, boolean, byte[]...) - Static method in class gov.sandia.cognition.hash.HashFunctionUtil

Cascades the hash codes

CombineSummarizer() - Constructor for class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrix.CombineSummarizer

Creates a new CombineSummarizer.

CombineSummarizer() - Constructor for class gov.sandia.cognition.learning.performance.categorization.DefaultConfusionMatrix.CombineSummarizer

Creates a new CombineSummarizer.

COMMENT_LINE_PREFIX - Static variable in class gov.sandia.cognition.math.matrix.VectorReader

The start of a comment line has the "#" string at the beginning.

CommonDocumentTextualConverterFactory - Class in gov.sandia.cognition.text.convert

A utility class for creating common document-text converters.

CommonDocumentTextualConverterFactory() - Constructor for class gov.sandia.cognition.text.convert.CommonDocumentTextualConverterFactory

CommonTermWeighterFactory - Class in gov.sandia.cognition.text.term.vector.weighter

A factory for well-known weighting schemes.

CommunityComparisons<NodeNameType> - Class in gov.sandia.cognition.graph.community

This class implements various comparisons of partitionings of a graph (or the resulting communities)

CommunityComparisons(NodePartitioning<NodeNameType>, NodePartitioning<NodeNameType>) - Constructor for class gov.sandia.cognition.graph.community.CommunityComparisons

Initializes this with the input partitionings of the same underlying set

CommunityMetrics - Class in gov.sandia.cognition.graph.community

This class stores several static methods for computing metrics specific to a graph and a set of communities.

CommunityMetrics() - Constructor for class gov.sandia.cognition.graph.community.CommunityMetrics

compact() - Method in class gov.sandia.cognition.collection.AbstractMutableDoubleMap

Removes entries from the map with value of 0.0

compact() - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector

compact() - Method in interface gov.sandia.cognition.math.matrix.InfiniteVector

Removes the zero elements from the vector.

compact() - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractSparseMatrix

Compact the memory used by the matrix, getting rid of any zero elements

compact() - Method in class gov.sandia.cognition.math.matrix.mtj.SparseColumnMatrix

compact() - Method in class gov.sandia.cognition.math.matrix.mtj.SparseRowMatrix

compact() - Method in class gov.sandia.cognition.math.matrix.mtj.SparseVector

Compacts the SparseVector, getting rid of any zero'ed elements.

comparator - Variable in class gov.sandia.cognition.math.geometry.KDTree

Comparator of this node to determine less than, greater than, or equality.

comparator - Variable in class gov.sandia.cognition.math.geometry.KDTree.PairFirstVectorizableIndexComparator

Embedded comparator for the Vectorizable argument.

compare(T, T) - Method in class gov.sandia.cognition.collection.DefaultComparator

compare(Number, Number) - Method in class gov.sandia.cognition.collection.NumberComparator

compare(Pair<? extends Vectorizable, ?>, Pair<? extends Vectorizable, ?>) - Method in class gov.sandia.cognition.math.geometry.KDTree.PairFirstVectorizableIndexComparator

compare(Vectorizable, Vectorizable) - Method in class gov.sandia.cognition.math.matrix.VectorizableIndexComparator

compare(ReceiverOperatingCharacteristic.DataPoint, ReceiverOperatingCharacteristic.DataPoint) - Method in class gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic.DataPoint.Sorter

Sorts ROCDataPoints in ascending order according to their falsePositiveRate (x-axis), used in Arrays.sort() method

compare(WeightedValue<?>, WeightedValue<?>) - Method in class gov.sandia.cognition.util.DefaultWeightedValue.WeightComparator

compareTimes(Collection<? extends Callable<ResultType>>) - Static method in class gov.sandia.cognition.algorithm.ParallelUtil

Compares the times needed by running the tasks sequentially versus parallel.

compareTimes(Collection<? extends Callable<ResultType>>, ThreadPoolExecutor) - Static method in class gov.sandia.cognition.algorithm.ParallelUtil

Compares the times needed by running the tasks sequentially versus parallel.

compareTo(SemanticIdentifier) - Method in class gov.sandia.cognition.framework.AbstractSemanticIdentifier

compareTo(DefaultSemanticLabel) - Method in class gov.sandia.cognition.framework.DefaultSemanticLabel

Takes a label and compares that label to this one.

compareTo(SemanticIdentifier) - Method in interface gov.sandia.cognition.framework.SemanticIdentifier

compareTo(KNearestNeighborExhaustive<InputType, OutputType>.Neighbor) - Method in class gov.sandia.cognition.learning.algorithm.nearest.KNearestNeighborExhaustive.Neighbor

compareTo(SuccessiveOverrelaxation<InputType>.Entry) - Method in class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation.Entry

Compares this entry to another one by comparing the weights.

compareTo(KDTree.Neighborhood<VectorType, DataType, PairType>.Neighbor<VectorType, DataType, PairType>) - Method in class gov.sandia.cognition.math.geometry.KDTree.Neighborhood.Neighbor

compareTo(LogNumber) - Method in class gov.sandia.cognition.math.LogNumber

compareTo(MutableDouble) - Method in class gov.sandia.cognition.math.MutableDouble

compareTo(MutableInteger) - Method in class gov.sandia.cognition.math.MutableInteger

compareTo(MutableLong) - Method in class gov.sandia.cognition.math.MutableLong

compareTo(UnsignedLogNumber) - Method in class gov.sandia.cognition.math.UnsignedLogNumber

compareTo(Double) - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.LineSegment

compareTo(AdaptiveRejectionSampling.Point) - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.Point

compareTo(Duration) - Method in class gov.sandia.cognition.time.DefaultDuration

compareTo(Temporal) - Method in class gov.sandia.cognition.util.AbstractTemporal

completeSolver() - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.ConjugateGradientMatrixSolver

completeSolver() - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.ConjugateGradientWithPreconditionerMatrixSolver

completeSolver() - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.IterativeMatrixSolver

Called after the final iteration.

completeSolver() - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.OverconstrainedConjugateGradientMatrixMinimizer

completeSolver() - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.SteepestDescentMatrixSolver

ComplexNumber - Class in gov.sandia.cognition.math

Represents a complex number in a rectangular manner, explicitly storing the real and imaginary portions: real + j*imaginary

ComplexNumber() - Constructor for class gov.sandia.cognition.math.ComplexNumber

Creates a new instance of ComplexNumber with zero magnitude.

ComplexNumber(double, double) - Constructor for class gov.sandia.cognition.math.ComplexNumber

Creates a new instance of ComplexNumber using the specified complex parts

ComplexNumber(ComplexNumber) - Constructor for class gov.sandia.cognition.math.ComplexNumber

Copy constructor

componentCount - Variable in class gov.sandia.cognition.learning.algorithm.pca.KernelPrincipalComponentsAnalysis

The number of components to create from the analysis.

components - Variable in class gov.sandia.cognition.learning.algorithm.pca.KernelPrincipalComponentsAnalysis.Function

The matrix of components for the function.

compose(Quaternion) - Method in interface gov.sandia.cognition.math.matrix.Quaternion

Multiplies this * other and returns the result.

composeEquals(Quaternion) - Method in interface gov.sandia.cognition.math.matrix.Quaternion

Multiplies this = this * other, thus modifying this object.

CompositeBatchLearnerPair<InputType,IntermediateType,OutputType> - Class in gov.sandia.cognition.learning.algorithm

Composes together a pair of batch (typically unsupervised) learners.

CompositeBatchLearnerPair() - Constructor for class gov.sandia.cognition.learning.algorithm.CompositeBatchLearnerPair

Creates a new, empty CompositeBatchLearnerPair.

CompositeBatchLearnerPair(BatchLearner<? super Collection<? extends InputType>, ? extends Evaluator<? super InputType, ? extends IntermediateType>>, BatchLearner<? super Collection<? extends IntermediateType>, ? extends Evaluator<? super IntermediateType, ? extends OutputType>>) - Constructor for class gov.sandia.cognition.learning.algorithm.CompositeBatchLearnerPair

Creates a new CompositeBatchLearnerPair with the given two learner.

CompositeCategorizer<InputType,IntermediateType,CategoryType> - Class in gov.sandia.cognition.learning.function.categorization

Composes a preprocessor function with a categorizer.

CompositeCategorizer() - Constructor for class gov.sandia.cognition.learning.function.categorization.CompositeCategorizer

Creates a new CompositeCategorizer with the preprocessor and categorizer set to null.

CompositeCategorizer(Evaluator<? super InputType, ? extends IntermediateType>, Categorizer<? super IntermediateType, ? extends CategoryType>) - Constructor for class gov.sandia.cognition.learning.function.categorization.CompositeCategorizer

Creates a new CompositeCategorizer.

CompositeEvaluatorList<InputType,OutputType> - Class in gov.sandia.cognition.evaluator

Implements the composition of a list of evaluators.

CompositeEvaluatorList() - Constructor for class gov.sandia.cognition.evaluator.CompositeEvaluatorList

Creates a new CompositeEvaluatorList with an empty list of evaluators.

CompositeEvaluatorList(Evaluator<?, ?>...) - Constructor for class gov.sandia.cognition.evaluator.CompositeEvaluatorList

Creates a new CompositeEvaluatorList from the given array of evaluators.

CompositeEvaluatorList(Collection<? extends Evaluator<?, ?>>) - Constructor for class gov.sandia.cognition.evaluator.CompositeEvaluatorList

Creates a new CompositeEvaluatorList from the given collection of evaluators.

CompositeEvaluatorPair<InputType,IntermediateType,OutputType> - Class in gov.sandia.cognition.evaluator

Implements a composition of two evaluators.

CompositeEvaluatorPair() - Constructor for class gov.sandia.cognition.evaluator.CompositeEvaluatorPair

Creates a new CompositeEvalutor.

CompositeEvaluatorPair(Evaluator<? super InputType, ? extends IntermediateType>, Evaluator<? super IntermediateType, ? extends OutputType>) - Constructor for class gov.sandia.cognition.evaluator.CompositeEvaluatorPair

Creates a new CompositeEvaluatorPair from the two given evaluators.

CompositeEvaluatorTriple<InputType,FirstIntermediateType,SecondIntermediateType,OutputType> - Class in gov.sandia.cognition.evaluator

Implements a composition of three evaluators.

CompositeEvaluatorTriple() - Constructor for class gov.sandia.cognition.evaluator.CompositeEvaluatorTriple

Creates a new CompositeEvalutorTriple.

CompositeEvaluatorTriple(Evaluator<? super InputType, ? extends FirstIntermediateType>, Evaluator<? super FirstIntermediateType, ? extends SecondIntermediateType>, Evaluator<? super SecondIntermediateType, ? extends OutputType>) - Constructor for class gov.sandia.cognition.evaluator.CompositeEvaluatorTriple

Creates a new CompositeEvaluatorTriple from the three given evaluators.

CompositeLocalGlobalTermWeighter - Class in gov.sandia.cognition.text.term.vector.weighter

Composes together local and global term weighters along with a normalizer.

CompositeLocalGlobalTermWeighter() - Constructor for class gov.sandia.cognition.text.term.vector.weighter.CompositeLocalGlobalTermWeighter

Creates a new CompositeLocalGlobalTermWeighter.

CompositeLocalGlobalTermWeighter(LocalTermWeighter, GlobalTermWeighter, TermWeightNormalizer) - Constructor for class gov.sandia.cognition.text.term.vector.weighter.CompositeLocalGlobalTermWeighter

Creates a new CompositeLocalGlobalTermWeighter with the given weighting schemes.

compress() - Method in class gov.sandia.cognition.math.matrix.custom.SparseMatrix

This method is provided so that the calling programmer can explicitly declare when a matrix should be compressed to the compressed Yale format.

compress() - Method in class gov.sandia.cognition.math.matrix.custom.SparseVector

The compressed representation should allow for quicker mathematical operations, but does not permit editing the values in the vector.

ComputableDistribution<DomainType> - Interface in gov.sandia.cognition.statistics

A type of Distribution that has an associated distribution function, either a PDF or PMF.

compute(GradientDescendable, Collection<? extends InputOutputPair<? extends Vector, Vector>>) - Static method in class gov.sandia.cognition.learning.function.cost.SumSquaredErrorCostFunction.Cache

Computes often-used parameters of a sum-squared error term

compute(Collection<? extends DefaultBinaryConfusionMatrix>, double) - Static method in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrixConfidenceInterval

Computes the ConfidenceIntervals for the given Collection of ConfusionMatrices

compute(Collection<? extends TargetEstimatePair<? extends Double, ? extends Double>>) - Static method in class gov.sandia.cognition.learning.performance.MeanAbsoluteErrorEvaluator

Computes the mean absolute error for the given pairs of values.

compute(Collection<? extends TargetEstimatePair<? extends Double, ? extends Double>>) - Static method in class gov.sandia.cognition.learning.performance.MeanSquaredErrorEvaluator

Computes the mean squared error for the given pairs of values.

compute(Collection<? extends TargetEstimatePair<? extends DataType, ? extends DataType>>) - Static method in class gov.sandia.cognition.learning.performance.MeanZeroOneErrorEvaluator

Computes the mean zero-one loss for the given pairs of values.

compute(Collection<? extends TargetEstimatePair<? extends Double, ? extends Double>>) - Static method in class gov.sandia.cognition.learning.performance.RootMeanSquaredErrorEvaluator

Computes the mean squared error for the given pairs of values.

compute(UnivariateDistribution<NumberType>, double) - Static method in class gov.sandia.cognition.statistics.bayesian.BayesianCredibleInterval

Creates a Bayesian credible interval by inverting the given CDF.

compute() - Method in class gov.sandia.cognition.statistics.ChiSquaredSimilarity

Computes the chi-squared statistic of the two vectors.

compute() - Method in class gov.sandia.cognition.statistics.KullbackLeiblerDivergence

Computes the Kullback--Leibler Divergence.

compute(Vector, Distribution<Vector>, Random, double) - Static method in class gov.sandia.cognition.statistics.montecarlo.MultivariateCumulativeDistributionFunction

Computes a multi-variate cumulative distribution for a given input according to the given distribution.

compute() - Method in class gov.sandia.cognition.statistics.TransferEntropy

Computes the transfer entropy value.

computeAcceptanceProbability(ParameterType, Iterable<? extends ObservationType>) - Method in class gov.sandia.cognition.statistics.bayesian.RejectionSampling.DefaultUpdater

computeAcceptanceProbability(ParameterType, Iterable<? extends ObservationType>) - Method in interface gov.sandia.cognition.statistics.bayesian.RejectionSampling.Updater

Computes the probability of accepting the parameter for the given data.

computeAllDistancesForNode(int) - Method in class gov.sandia.cognition.graph.GraphMetrics

Helper which computes Dijkstra's Algorithm for the input node and returns all of the distances to all other nodes from this node.

computeAreaUnderConvexHull() - Method in class gov.sandia.cognition.statistics.method.ConvexReceiverOperatingCharacteristic

Computes the area under the convex hull

computeAreaUnderCurve(ReceiverOperatingCharacteristic) - Static method in class gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic.Statistic

Computes the "pessimistic" area under the ROC curve using the top-left rectangle method for numerical integration.

computeAreaUnderCurveTopLeft(Collection<ReceiverOperatingCharacteristic.DataPoint>) - Static method in class gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic.Statistic

Computes the Area Under Curve for an x-axis sorted Collection of ROC points using the top-left rectangle method for numerical integration.

computeAreaUnderCurveTrapezoid(Collection<ReceiverOperatingCharacteristic.DataPoint>) - Static method in class gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic.Statistic

Computes the Area Under Curve for an x-axis sorted Collection of ROC points using the top-left rectangle method for numerical integration.

computeAscendingArray(Collection<? extends Number>) - Static method in class gov.sandia.cognition.statistics.method.KolmogorovSmirnovConfidence

Returns an array of ascending sorted values from the given Collection

computeBackwardProbabilities(Vector, Vector, double) - Method in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel

Computes the backward probability recursion.

computeBackwardProbabilities(ArrayList<Vector>, ArrayList<WeightedValue<Vector>>) - Method in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel

Computes the backward-probabilities for the given observation likelihoods and the weights from the alphas.

computeBestGainAndThreshold(Collection<? extends InputOutputPair<? extends Vectorizable, OutputType>>, int, DefaultDataDistribution<OutputType>) - Method in class gov.sandia.cognition.learning.algorithm.tree.AbstractVectorThresholdMaximumGainLearner

Computes the best gain and threshold for a given dimension using the computeSplitGain method for each potential split point of values for the given dimension.

computeBestGainAndThreshold(Collection<? extends InputOutputPair<? extends Vectorizable, OutputType>>, int, DefaultDataDistribution<OutputType>, ArrayList<DefaultWeightedValue<OutputType>>) - Method in class gov.sandia.cognition.learning.algorithm.tree.AbstractVectorThresholdMaximumGainLearner

Computes the best gain and threshold for a given dimension using the computeSplitGain method for each potential split point of values for the given dimension.

computeBestGainThreshold(Collection<? extends InputOutputPair<? extends Vectorizable, Double>>, int, double) - Method in class gov.sandia.cognition.learning.algorithm.tree.VectorThresholdVarianceLearner

Computes the best information gain-threshold pair for the given dimension on the given data.

computeBounds(Collection<? extends DataType>) - Method in class gov.sandia.cognition.math.geometry.Quadtree

Computes the bounding rectangle of a given collection of points.

computeCentralMoment(Iterable<? extends Number>, double, int) - Static method in class gov.sandia.cognition.math.UnivariateStatisticsUtil

Computes the desired biased estimate central moment of the given dataset.

computeChiSquare(int, int, ArrayList<Double>) - Static method in class gov.sandia.cognition.statistics.method.FriedmanConfidence.Statistic

Computes the chi-square error for the rank means

computeClusterDistances() - Method in class gov.sandia.cognition.learning.algorithm.clustering.OptimizedKMeansClusterer

Computes the distances between the clusters.

computeConditionalProbability(Collection<InputType>, CategoryType) - Method in class gov.sandia.cognition.learning.algorithm.bayes.DiscreteNaiveBayesCategorizer

Computes the class conditional for the given inputs at the given category assuming that each input feature is conditionally independent of all other features.

computeConditionalProbability(Collection<DataType>, DataType, ComputableDistribution<Collection<DataType>>, ComputableDistribution<Collection<DataType>>) - Method in class gov.sandia.cognition.statistics.bayesian.ConditionalProbability

Computes the conditional probability between a collection of objects and a new object.

computeConditionalProbability(DataType, DataType, ComputableDistribution<DataType>, ComputableDistribution<Collection<DataType>>) - Method in class gov.sandia.cognition.statistics.bayesian.ConditionalProbability

Computes the conditional probability between two objects.

computeConditionalProbabilityWhenDataTypeHasHistoricalData(DataType, DataType, ComputableDistribution<DataType>, ComputableDistribution<DataType>) - Method in class gov.sandia.cognition.statistics.bayesian.ConditionalProbability

Computes the conditional probability between two objects.

computeConductance(DirectedNodeEdgeGraph<NodeNameType>, Set<NodeNameType>) - Static method in class gov.sandia.cognition.graph.community.CommunityMetrics

computeConfidenceInterval(Collection<Boolean>, double) - Method in class gov.sandia.cognition.statistics.method.BernoulliConfidence

Computes the ConfidenceInterval for the Bernoulli parameter based on the given data and the desired level of confidence.

computeConfidenceInterval(double, int, double) - Static method in class gov.sandia.cognition.statistics.method.BernoulliConfidence

Computes the ConfidenceInterval for the Bernoulli parameter based on the given data and the desired level of confidence.

computeConfidenceInterval(double, double, int, double) - Method in class gov.sandia.cognition.statistics.method.BernoulliConfidence

computeConfidenceInterval(Collection<Double>, double) - Method in class gov.sandia.cognition.statistics.method.ChebyshevInequality

Computes the Chebyshev Inequality for the given level of confidence.

computeConfidenceInterval(double, double, int, double) - Method in class gov.sandia.cognition.statistics.method.ChebyshevInequality

Computes the Chebyshev Inequality for the given level of confidence.

computeConfidenceInterval(DataType, double) - Method in interface gov.sandia.cognition.statistics.method.ConfidenceIntervalEvaluator

Computes a confidence interval for a given dataset and confidence (power) level

computeConfidenceInterval(double, double, int, double) - Method in interface gov.sandia.cognition.statistics.method.ConfidenceIntervalEvaluator

Computes the confidence interval given the mean and variance of the samples, number of samples, and corresponding confidence interval

computeConfidenceInterval(Collection<DataType>, ConfidenceIntervalEvaluator<Collection<? extends Number>>, double) - Static method in class gov.sandia.cognition.statistics.method.FieldConfidenceInterval

Computes a FieldConfidenceInterval for each Double/double Field in the given data.

computeConfidenceInterval(Collection<DataType>, ArrayList<Field>, ConfidenceIntervalEvaluator<Collection<? extends Number>>, double) - Static method in class gov.sandia.cognition.statistics.method.FieldConfidenceInterval

Computes a FieldConfidenceInterval for the given Fields in the given data

computeConfidenceInterval(Collection<? extends Number>, double) - Method in class gov.sandia.cognition.statistics.method.GaussianConfidence

computeConfidenceInterval(UnivariateDistribution<?>, int, double) - Static method in class gov.sandia.cognition.statistics.method.GaussianConfidence

Computes the Gaussian confidence interval given a distribution of data, number of samples, and corresponding confidence interval

computeConfidenceInterval(double, double, int, double) - Method in class gov.sandia.cognition.statistics.method.GaussianConfidence

computeConfidenceInterval(Collection<Double>, double) - Method in class gov.sandia.cognition.statistics.method.MarkovInequality

Computes the Markov Inequality Bound for the given data at the given confidence level.

computeConfidenceInterval(double, int, double) - Static method in class gov.sandia.cognition.statistics.method.MarkovInequality

Computes the Markov Inequality Bound for the given data at the given confidence level.

computeConfidenceInterval(double, double, int, double) - Method in class gov.sandia.cognition.statistics.method.MarkovInequality

computeConfidenceInterval(Collection<? extends Number>, double) - Method in class gov.sandia.cognition.statistics.method.StudentTConfidence

computeConfidenceInterval(double, double, int, double) - Method in class gov.sandia.cognition.statistics.method.StudentTConfidence

computeConjuctiveProbability(Collection<InputType>, CategoryType) - Method in class gov.sandia.cognition.learning.algorithm.bayes.DiscreteNaiveBayesCategorizer

Computes the conjunctive probability of the inputs and the category.

computeConvexNull(ReceiverOperatingCharacteristic) - Static method in class gov.sandia.cognition.statistics.method.ConvexReceiverOperatingCharacteristic

Computes the convex hull of a ROC curve

computeCorrelation(Collection<? extends Number>, Collection<? extends Number>) - Static method in class gov.sandia.cognition.math.UnivariateStatisticsUtil

Computes the correlation coefficient in a single pass.

computeCumulativeProbabilityValue() - Method in class gov.sandia.cognition.statistics.ChiSquaredSimilarity

Computes the chi-squared similarity statistic, then uses that to compute a cumulative probability.

computeCumulativeValue(int, ClosedFormDiscreteUnivariateDistribution<? super Integer>) - Static method in class gov.sandia.cognition.statistics.ProbabilityMassFunctionUtil

Computes the CDF value for the given PMF for the input.

computeDecay(LinearBinaryCategorizer, Vector, boolean, double, double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.AbstractLinearCombinationOnlineLearner

Computes the decay scalar for the existing weight vector.

computeDecay(DefaultKernelBinaryCategorizer<InputType>, InputType, boolean, double, double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.AbstractLinearCombinationOnlineLearner

Computes the decay scalar for the existing weights.

computeDecay(LinearBinaryCategorizer, Vector, boolean, double, double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineShiftingPerceptron

computeDecay(DefaultKernelBinaryCategorizer<InputType>, InputType, boolean, double, double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineShiftingPerceptron

computeDPrime(ReceiverOperatingCharacteristic.DataPoint) - Static method in class gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic.Statistic

Computes the value of d-prime given a datapoint

computeEffectiveParticles(DataDistribution<ParameterType>) - Method in class gov.sandia.cognition.statistics.bayesian.AbstractParticleFilter

computeEffectiveParticles(DataDistribution<ParameterType>) - Method in interface gov.sandia.cognition.statistics.bayesian.ParticleFilter

Computes the effective number of particles.

computeEntropy(Iterable<? extends Number>) - Static method in class gov.sandia.cognition.math.UnivariateStatisticsUtil

Computes the information-theoretic entropy of the PMF in bits (base 2).

computeEquivalentSampleSize(BetaDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.BernoulliBayesianEstimator

computeEquivalentSampleSize(BetaDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.BinomialBayesianEstimator

computeEquivalentSampleSize(BeliefType) - Method in interface gov.sandia.cognition.statistics.bayesian.conjugate.ConjugatePriorBayesianEstimator

Computes the equivalent sample size of using the given prior.

computeEquivalentSampleSize(GammaDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.ExponentialBayesianEstimator

computeEquivalentSampleSize(GammaDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.GammaInverseScaleBayesianEstimator

computeEquivalentSampleSize(DirichletDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.MultinomialBayesianEstimator

computeEquivalentSampleSize(MultivariateGaussian) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanBayesianEstimator

computeEquivalentSampleSize(NormalInverseWishartDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanCovarianceBayesianEstimator

computeEquivalentSampleSize(GammaDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.PoissonBayesianEstimator

computeEquivalentSampleSize(ParetoDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.UniformDistributionBayesianEstimator

computeEquivalentSampleSize(UnivariateGaussian) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.UnivariateGaussianMeanBayesianEstimator

computeEquivalentSampleSize(NormalInverseGammaDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.UnivariateGaussianMeanVarianceBayesianEstimator

computeEvidenceProbabilty(Collection<InputType>) - Method in class gov.sandia.cognition.learning.algorithm.bayes.DiscreteNaiveBayesCategorizer

Computes the probability of the given inputs.

computeExponent() - Method in class gov.sandia.cognition.math.ComplexNumber

Computes the natural-base exponent of the complex number, such that this = log(exp(this)) = exp(log(this))

computeForwardProbabilities(Vector, Vector, boolean) - Method in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel

Computes the recursive solution to the forward probabilities of the HMM.

computeForwardProbabilities(ArrayList<Vector>, boolean) - Method in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel

Computes the forward probabilities for the given observation likelihood sequence.

computeGaussianSampler(Iterable<? extends ObservationType>, Random, int) - Method in class gov.sandia.cognition.statistics.bayesian.RejectionSampling.DefaultUpdater

Computes a Gaussian sample for the parameter, assuming it has is a Double, using importance sampling.

computeGraphPermanance(DirectedNodeEdgeGraph<NodeNameType>, GraphMetrics<NodeNameType>, NodePartitioning<NodeNameType>) - Static method in class gov.sandia.cognition.graph.community.CommunityMetrics

Computes the average permanence for the partitioning of the entire graph

computeKurtosis(Collection<? extends Number>) - Static method in class gov.sandia.cognition.math.UnivariateStatisticsUtil

Computes the biased excess kurtosis of the given dataset.

computeLines() - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.AbstractEnvelope

Computes the line segments comprising this Envelope

computeLines() - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.LowerEnvelope

Recomputes the line segments that comprise the upper envelope

computeLines() - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.UpperEnvelope

Recomputes the line segments that comprise the upper envelope

computeLocalWeights(Vectorizable) - Method in class gov.sandia.cognition.text.term.vector.weighter.local.AbstractLocalTermWeighter

computeLocalWeights(Vector) - Method in class gov.sandia.cognition.text.term.vector.weighter.local.BinaryLocalTermWeighter

computeLocalWeights(Vectorizable) - Method in interface gov.sandia.cognition.text.term.vector.weighter.local.LocalTermWeighter

Computes the new local weights for a given document.

computeLocalWeights(Vector) - Method in interface gov.sandia.cognition.text.term.vector.weighter.local.LocalTermWeighter

Computes the new local weights for a given document.

computeLocalWeights(Vector) - Method in class gov.sandia.cognition.text.term.vector.weighter.local.LogLocalTermWeighter

computeLocalWeights(Vector) - Method in class gov.sandia.cognition.text.term.vector.weighter.local.NormalizedLogLocalTermWeighter

computeLocalWeights(Vector) - Method in class gov.sandia.cognition.text.term.vector.weighter.local.TermFrequencyLocalTermWeighter

computeLogImportanceValue(ParameterType) - Method in class gov.sandia.cognition.statistics.bayesian.ImportanceSampling.DefaultUpdater

computeLogImportanceValue(ParameterType) - Method in interface gov.sandia.cognition.statistics.bayesian.ImportanceSampling.Updater

Computes the parameter's importance weight.

computeLogLikelihood(ParameterType, Iterable<? extends ObservationType>) - Method in class gov.sandia.cognition.statistics.bayesian.ImportanceSampling.DefaultUpdater

computeLogLikelihood(ParameterType, Iterable<? extends ObservationType>) - Method in interface gov.sandia.cognition.statistics.bayesian.ImportanceSampling.Updater

Computes the log likelihood of the data given the parameter

computeLogLikelihood(ParameterType, Iterable<? extends ObservationType>) - Method in interface gov.sandia.cognition.statistics.bayesian.MetropolisHastingsAlgorithm.Updater

Computes the log likelihood of the data given the parameter

computeLogLikelihood(ParameterType, ObservationType) - Method in interface gov.sandia.cognition.statistics.bayesian.ParticleFilter.Updater

Computes the log likelihood of the parameter and the observation.

computeLogPosterior(Vector, CategoryType) - Method in class gov.sandia.cognition.learning.algorithm.bayes.VectorNaiveBayesCategorizer

Computes the log-posterior probability that the input belongs to the given category.

computeMaximum(Iterable<? extends Number>) - Static method in class gov.sandia.cognition.math.UnivariateStatisticsUtil

Finds the maximum value of a data set.

computeMean(Iterable<? extends RingType>) - Static method in class gov.sandia.cognition.math.MultivariateStatisticsUtil

Computes the arithmetic mean (average, expectation, first central moment) of a dataset

computeMean(Iterable<? extends Number>) - Static method in class gov.sandia.cognition.math.UnivariateStatisticsUtil

Computes the arithmetic mean (average, expectation, first central moment) of a dataset

computeMeanAndCovariance(Iterable<? extends Vectorizable>) - Static method in class gov.sandia.cognition.math.MultivariateStatisticsUtil

Computes the mean and unbiased covariance Matrix of a multivariate data set.

computeMeanAndVariance(Iterable<? extends Number>) - Static method in class gov.sandia.cognition.math.UnivariateStatisticsUtil

Computes the mean and unbiased variance of a Collection of data using the one-pass approach.

computeMeasurementBelief(MultivariateGaussian, Vector, Matrix) - Method in class gov.sandia.cognition.statistics.bayesian.AbstractKalmanFilter

Updates the measurement belief by computing the Kalman gain and incorporating the innovation into the estimate

computeMedian(Collection<? extends Number>) - Static method in class gov.sandia.cognition.math.UnivariateStatisticsUtil

Computes the median of the given data.

computeMinAndMax(Iterable<? extends Number>) - Static method in class gov.sandia.cognition.math.UnivariateStatisticsUtil

Computes the minimum and maximum of a set of data in a single pass.

computeMinimum(Iterable<? extends Number>) - Static method in class gov.sandia.cognition.math.UnivariateStatisticsUtil

Finds the minimum value of a data set.

computeMinimumDifference(VectorType) - Method in class gov.sandia.cognition.math.geometry.KDTree

Computes the minimum absolute difference between the given key and the "first" value stored in this subtree for the index given by the embedded comparator.

computeModularity(DirectedNodeEdgeGraph<NodeNameType>, Set<Set<NodeNameType>>) - Static method in class gov.sandia.cognition.graph.community.CommunityMetrics

Computes the modularity of the input graph into the input set of communities.

computeModularity(Set<Set<NodeNameType>>, GraphMetrics<NodeNameType>) - Static method in class gov.sandia.cognition.graph.community.CommunityMetrics

Computes the modularity of the graph (whose metrics were passed in) into the input set of communities.

computeModularity(DirectedNodeEdgeGraph<NodeNameType>, NodePartitioning<NodeNameType>) - Static method in class gov.sandia.cognition.graph.community.CommunityMetrics

Computes the modularity of the input graph into the input set of communities.

computeModularity(NodePartitioning<NodeNameType>, GraphMetrics<NodeNameType>) - Static method in class gov.sandia.cognition.graph.community.CommunityMetrics

computeMultipleObservationLogLikelihood(Collection<? extends Collection<? extends ObservationType>>) - Method in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel

Computes the log-likelihood of the observation sequences, given the current HMM's parameterization.

computeMultipleObservationLogLikelihood(Collection<? extends Collection<? extends ObservationType>>) - Method in class gov.sandia.cognition.learning.algorithm.hmm.ParallelHiddenMarkovModel

computeNaturalLogarithm() - Method in class gov.sandia.cognition.math.ComplexNumber

Computes the natural-base logarithm of the complex number, such that this = log(exp(this)) = exp(log(this))

computeNeighborhood(InputType) - Method in class gov.sandia.cognition.learning.algorithm.nearest.AbstractKNearestNeighbor

Computes the neighbors to the input key.

computeNeighborhood(InputType) - Method in class gov.sandia.cognition.learning.algorithm.nearest.KNearestNeighborExhaustive

computeNeighborhood(InputType) - Method in class gov.sandia.cognition.learning.algorithm.nearest.KNearestNeighborKDTree

computeNullHypothesisProbabilities(int, Matrix) - Method in class gov.sandia.cognition.statistics.method.NemenyiConfidence.Statistic

Computes null-hypothesis probability for the (i,j) treatment comparison

computeNullHypothesisProbabilities(ArrayList<Integer>, Matrix) - Method in class gov.sandia.cognition.statistics.method.TukeyKramerConfidence.Statistic

Computes null-hypothesis probability for the (i,j) treatment comparison

computeNullHypothesisProbability(double) - Static method in class gov.sandia.cognition.statistics.method.MannWhitneyUConfidence.Statistic

Computes the p-value for the test, given the z-value

computeNullHypothesisProbability(double) - Static method in class gov.sandia.cognition.statistics.method.WilcoxonSignedRankConfidence.Statistic

Computes the p-value given the z-value

computeObjective() - Method in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachineAlternatingLeastSquares

Gets the total objective, which is the squared error plus the regularization terms.

computeObservationLikelihoods(ObservationType) - Method in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel

Computes the conditionally independent likelihoods for each state given the observation.

computeObservationLikelihoods(ObservationType, Vector) - Method in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel

Computes the conditionally independent likelihoods for each state given the observation.

computeObservationLikelihoods(Collection<? extends ObservationType>) - Method in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel

Computes the conditionally independent likelihoods for each state given the observation sequence.

computeObservationLogLikelihood(Collection<? extends ObservationType>) - Method in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel

Computes the log-likelihood of the observation sequence, given the current HMM's parameterization.

computeObservationLogLikelihood(Collection<? extends ObservationType>, Collection<Integer>) - Method in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel

Computes the log-likelihood that the given observation sequence was generated by the given sequence of state indices.

computeOneNodePermanence(GraphMetrics<NodeNameType>, NodePartitioning<NodeNameType>, NodeNameType, DirectedNodeEdgeGraph<NodeNameType>) - Static method in class gov.sandia.cognition.graph.community.CommunityMetrics

computeOneNodePermanenceById(GraphMetrics<NodeNameType>, NodePartitioning<NodeNameType>, int, DirectedNodeEdgeGraph<NodeNameType>) - Static method in class gov.sandia.cognition.graph.community.CommunityMetrics

Computes the permanence for one node in the graph.

computeOptimalScale(Iterable<? extends ObservationType>) - Method in class gov.sandia.cognition.statistics.bayesian.RejectionSampling.DefaultUpdater

Computes the optimal scale factor for enveloping the conjunctive distribution with the sampler function given the data

computeOptimalThreshold(ReceiverOperatingCharacteristic) - Static method in class gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic.Statistic

Determines the DataPoint, and associated threshold, that simultaneously maximizes the value of Area=TruePositiveRate+TrueNegativeRate, usually the upper-left "knee" on the ROC curve.

computeOptimalThreshold(ReceiverOperatingCharacteristic, double, double) - Static method in class gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic.Statistic

Determines the DataPoint, and associated threshold, that simultaneously maximizes the value of Area=TruePositiveRate+TrueNegativeRate, usually the upper-left "knee" on the ROC curve.

computeOuterProductDataMatrix(ArrayList<? extends Vector>) - Static method in class gov.sandia.cognition.learning.data.DatasetUtil

Computes the outer-product Matrix of the given set of data: XXt = [ x1 x2 ...

computeOutputMean(Collection<? extends InputOutputPair<?, ? extends Number>>) - Static method in class gov.sandia.cognition.learning.data.DatasetUtil

Computes the mean of the output data.

computeOutputVariance(Collection<? extends InputOutputPair<?, ? extends Number>>) - Static method in class gov.sandia.cognition.learning.data.DatasetUtil

Computes the variance of the output of a given set of input-output pairs.

computePairwiseTestResults(Collection<? extends Collection<? extends Number>>, NullHypothesisEvaluator<Collection<? extends Number>>) - Method in class gov.sandia.cognition.statistics.method.AbstractPairwiseMultipleHypothesisComparison.Statistic

Computes the pair-wise confidence test results

computeParameterGradient(Vector) - Method in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachine

computeParameterGradient(Vector) - Method in interface gov.sandia.cognition.learning.algorithm.gradient.GradientDescendable

Computes the derivative of the function about the input with respect to the parameters of the function.

computeParameterGradient(VectorizableVectorFunction, Vector, double) - Static method in class gov.sandia.cognition.learning.algorithm.gradient.GradientDescendableApproximator

Computes a forward-differences approximation to the parameter Jacobian

computeParameterGradient(Vector) - Method in class gov.sandia.cognition.learning.algorithm.gradient.GradientDescendableApproximator

Computes a forward-differences approximation to the parameter Jacobian

computeParameterGradient(InputType) - Method in interface gov.sandia.cognition.learning.algorithm.gradient.ParameterGradientEvaluator

Computes the derivative of the output with respect to the parameters for a particular input.

computeParameterGradient(GradientDescendable) - Method in class gov.sandia.cognition.learning.function.cost.AbstractParallelizableCostFunction

computeParameterGradient(GradientDescendable) - Method in interface gov.sandia.cognition.learning.function.cost.DifferentiableCostFunction

Differentiates function with respect to its parameters.

computeParameterGradient(GradientDescendable) - Method in class gov.sandia.cognition.learning.function.cost.MeanSquaredErrorCostFunction

computeParameterGradient(GradientDescendable) - Method in class gov.sandia.cognition.learning.function.cost.ParallelizedCostFunctionContainer

computeParameterGradient(Double) - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction

computeParameterGradient(Vector) - Method in class gov.sandia.cognition.learning.function.vector.DifferentiableFeedforwardNeuralNetwork

computeParameterGradient(Vector) - Method in class gov.sandia.cognition.learning.function.vector.DifferentiableGeneralizedLinearModel

computeParameterGradient(Vector) - Method in class gov.sandia.cognition.learning.function.vector.MultivariateDiscriminant

computeParameterGradient(Matrix, Vector) - Static method in class gov.sandia.cognition.learning.function.vector.MultivariateDiscriminant

Computes the parameter gradient of the given matrix post-multiplied by the input Vector

computeParameterGradient(Vector) - Method in class gov.sandia.cognition.learning.function.vector.MultivariateDiscriminantWithBias

computeParameterGradient(Vector) - Method in class gov.sandia.cognition.learning.function.vector.ThreeLayerFeedforwardNeuralNetwork

computeParameterGradientAmalgamate(Collection<Object>) - Method in interface gov.sandia.cognition.learning.function.cost.ParallelizableCostFunction

Amalgamates the linear components of the cost gradient function into a single Vector.

computeParameterGradientAmalgamate(Collection<Object>) - Method in class gov.sandia.cognition.learning.function.cost.SumSquaredErrorCostFunction

computeParameterGradientPartial(GradientDescendable) - Method in interface gov.sandia.cognition.learning.function.cost.ParallelizableCostFunction

Computes the partial (linear) component of the cost function gradient.

computeParameterGradientPartial(GradientDescendable) - Method in class gov.sandia.cognition.learning.function.cost.SumSquaredErrorCostFunction

computePercentile(Collection<? extends Number>, double) - Static method in class gov.sandia.cognition.math.UnivariateStatisticsUtil

Computes the percentile value of the given data.

computePercentiles(Collection<? extends Number>, double...) - Static method in class gov.sandia.cognition.math.UnivariateStatisticsUtil

Computes the given percentiles of the given data.

computePolynomial(LineBracket, EvaluatorType) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.interpolator.AbstractLineBracketInterpolatorPolynomial

Fits the interpolating polynomial to the given LineBracket

computePolynomial(LineBracket, DifferentiableUnivariateScalarFunction) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.interpolator.LineBracketInterpolatorHermiteCubic

computePolynomial(LineBracket, DifferentiableUnivariateScalarFunction) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.interpolator.LineBracketInterpolatorHermiteParabola

computePolynomial(LineBracket, Evaluator<Double, Double>) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.interpolator.LineBracketInterpolatorLinear

computePolynomial(LineBracket, Evaluator<Double, Double>) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.interpolator.LineBracketInterpolatorParabola

computePosterior(Collection<InputType>, CategoryType) - Method in class gov.sandia.cognition.learning.algorithm.bayes.DiscreteNaiveBayesCategorizer

Computes the posterior probability of the inputs for the given category.

computePosterior(Vector, CategoryType) - Method in class gov.sandia.cognition.learning.algorithm.bayes.VectorNaiveBayesCategorizer

Computes the posterior probability that the input belongs to the given category.

computePosterior(ObservationType, CategoryType) - Method in class gov.sandia.cognition.learning.function.categorization.MaximumAPosterioriCategorizer

Computes the posterior of the observation given the category.

computePosteriorLogLikelihood(Iterable<? extends ObservationType>) - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.Sample

Computes the posterior log likelihood of the data given the clusters and the prior probability of the clustering from a Chinese Restaurant Process

computePosteriorLogLikelihood(int, double) - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.Sample

Computes the posterior log likelihood of the Sample

computePredictionCovariance(Matrix, Matrix) - Method in class gov.sandia.cognition.statistics.bayesian.AbstractKalmanFilter

Computes the prediction covariance from the Jacobian and believe covariance

computeRandomVariableLikelihoods(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateMixtureDensityModel.PDF

Computes the likelihoods of the underlying distributions

computeRandomVariableProbabilities(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateMixtureDensityModel.PDF

Computes the probability distribution that the input was generated by the underlying distributions

computeRescaling(LinearBinaryCategorizer, Vector, boolean, double, double, double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.AbstractLinearCombinationOnlineLearner

Computes the rescaling for the new weight vector.

computeRescaling(DefaultKernelBinaryCategorizer<InputType>, InputType, boolean, double, double, double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.AbstractLinearCombinationOnlineLearner

Computes the rescaling for the new weights.

computeRootMeanSquaredError(Collection<? extends Number>) - Static method in class gov.sandia.cognition.math.UnivariateStatisticsUtil

Computes the Root mean-squared (RMS) error between the data and its mean.

computeRootMeanSquaredError(Collection<? extends Number>, double) - Static method in class gov.sandia.cognition.math.UnivariateStatisticsUtil

Computes the Root mean-squared (RMS) error between the data and its mean

computeSampleSize(double, double) - Static method in class gov.sandia.cognition.statistics.method.BernoulliConfidence

Computes the number of samples needed to estimate the Bernoulli parameter "p" (mean) within "accuracy" with probability at least "confidence".

computeScaleFactor(Vector, Vector) - Method in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerConjugateGradient

Computes the conjugate gradient parameter for the particular update scheme.

computeScaleFactor(Vector, Vector) - Method in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerFletcherReeves

computeScaleFactor(Vector, Vector) - Method in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerLiuStorey

computeScaleFactor(Vector, Vector) - Method in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerPolakRibiere

computeSequenceParameters() - Method in class gov.sandia.cognition.learning.algorithm.hmm.BaumWelchAlgorithm

Computes the gammas and A matrices for each sequence.

computeSimplexInputSum() - Method in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerNelderMead

Computes the sum of input values in the simplex

computeSkewness(Collection<? extends Number>) - Static method in class gov.sandia.cognition.math.UnivariateStatisticsUtil

Computes the unbiased skewness of the dataset.

computeSplitGain(DefaultDataDistribution<OutputType>, DefaultDataDistribution<OutputType>, DefaultDataDistribution<OutputType>) - Method in class gov.sandia.cognition.learning.algorithm.tree.AbstractVectorThresholdMaximumGainLearner

Computes the gain of a given split.

computeSplitGain(DefaultDataDistribution<OutputType>, DefaultDataDistribution<OutputType>, DefaultDataDistribution<OutputType>) - Method in class gov.sandia.cognition.learning.algorithm.tree.VectorThresholdGiniImpurityLearner

Computes the split gain by computing the Gini impurity for the given split.

computeSplitGain(DefaultDataDistribution<OutputType>, DefaultDataDistribution<OutputType>, DefaultDataDistribution<OutputType>) - Method in class gov.sandia.cognition.learning.algorithm.tree.VectorThresholdHellingerDistanceLearner

Computes the split gain by computing the mean Hellinger distance for the given split.

computeSplitGain(DefaultDataDistribution<OutputType>, DefaultDataDistribution<OutputType>, DefaultDataDistribution<OutputType>) - Method in class gov.sandia.cognition.learning.algorithm.tree.VectorThresholdInformationGainLearner

computeStandardDeviation(Collection<? extends Number>) - Static method in class gov.sandia.cognition.math.UnivariateStatisticsUtil

Computes the standard deviation of a dataset, which is the square root of the unbiased variance.

computeStandardDeviation(Collection<? extends Number>, double) - Static method in class gov.sandia.cognition.math.UnivariateStatisticsUtil

Computes the standard deviation of a dataset, which is the square root of the unbiased variance.

computeStateObservationLikelihood(Vector, Vector, double) - Static method in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel

Computes the probability of the various states at a time instance given the observation sequence.

computeStateObservationLikelihood(ArrayList<WeightedValue<Vector>>, ArrayList<WeightedValue<Vector>>, double) - Method in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel

Computes the probabilities of the various states over time given the observation sequence.

computeStateObservationLikelihood(ArrayList<WeightedValue<Vector>>, ArrayList<WeightedValue<Vector>>, double) - Method in class gov.sandia.cognition.learning.algorithm.hmm.ParallelHiddenMarkovModel

computeStatistics() - Method in class gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic

Computes useful statistical information associated with the ROC curve

computeSum(Iterable<? extends RingType>) - Static method in class gov.sandia.cognition.math.MultivariateStatisticsUtil

Computes the arithmetic sum of the dataset

computeSum(Iterable<? extends Number>) - Static method in class gov.sandia.cognition.math.UnivariateStatisticsUtil

Computes the arithmetic sum of the dataset

computeSumSquaredDifference(Iterable<? extends Number>, double) - Static method in class gov.sandia.cognition.math.UnivariateStatisticsUtil

Computes the sum-squared difference between the data and a target

computeTemporaryMessage(int) - Method in class gov.sandia.cognition.graph.inference.SumProductBeliefPropagation

computeTemporaryMessage(int) - Method in class gov.sandia.cognition.graph.inference.SumProductDirectedPropagation

Private helper that computes the temporary message for the specified edge for the current iteration going in the specified direction

computeTemporaryMessage(int) - Method in class gov.sandia.cognition.graph.inference.SumProductInferencingAlgorithm

Private helper that computes the temporary message for the specified edge for the current iteration going in the specified direction (a different message for each direction)

computeTestStatistics(int, ArrayList<Double>, double) - Method in class gov.sandia.cognition.statistics.method.NemenyiConfidence.Statistic

Computes the test statistic for all treatments

computeTestStatistics(ArrayList<Integer>, ArrayList<Double>, double) - Method in class gov.sandia.cognition.statistics.method.TukeyKramerConfidence.Statistic

Computes the test statistic for all treatments

computeTransitions(Vector, Vector, Vector) - Static method in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel

Computes the stochastic transition-probability matrix from the given probabilities.

computeTransitions(ArrayList<WeightedValue<Vector>>, ArrayList<WeightedValue<Vector>>, ArrayList<Vector>) - Method in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel

Computes the stochastic transition-probability matrix from the given probabilities.

computeTransitions(ArrayList<WeightedValue<Vector>>, ArrayList<WeightedValue<Vector>>, ArrayList<Vector>) - Method in class gov.sandia.cognition.learning.algorithm.hmm.ParallelHiddenMarkovModel

ComputeTransitionsTask() - Constructor for class gov.sandia.cognition.learning.algorithm.hmm.ParallelHiddenMarkovModel.ComputeTransitionsTask

Default constructor.

computeTransitionTasks - Variable in class gov.sandia.cognition.learning.algorithm.hmm.ParallelHiddenMarkovModel

ComputeTransitionsTasks.

computeTreatmentRankMeans(Collection<? extends Collection<? extends Number>>) - Static method in class gov.sandia.cognition.statistics.method.FriedmanConfidence

Computes the mean rank of the treatments

computeU(double, int, double, int) - Static method in class gov.sandia.cognition.statistics.method.MannWhitneyUConfidence.Statistic

Computes the U-statistic, the minimum rank sum above "chance"

computeUpdate(LinearBinaryCategorizer, Vector, boolean, double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.AbstractLinearCombinationOnlineLearner

Compute the update weight in the linear case.

computeUpdate(DefaultKernelBinaryCategorizer<InputType>, InputType, boolean, double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.AbstractLinearCombinationOnlineLearner

Compute the update weight in the linear case.

computeUpdate(LinearBinaryCategorizer, Vector, boolean, double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineBinaryMarginInfusedRelaxedAlgorithm

computeUpdate(DefaultKernelBinaryCategorizer<InputType>, InputType, boolean, double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineBinaryMarginInfusedRelaxedAlgorithm

computeUpdate(double, double, double, double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlinePassiveAggressivePerceptron

Compute the update value (tau) for the algorithm.

computeUpdate(LinearBinaryCategorizer, Vector, boolean, double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlinePassiveAggressivePerceptron

computeUpdate(DefaultKernelBinaryCategorizer<InputType>, InputType, boolean, double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlinePassiveAggressivePerceptron

computeUpdate(double, double, double, double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlinePassiveAggressivePerceptron.LinearSoftMargin

computeUpdate(double, double, double, double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlinePassiveAggressivePerceptron.QuadraticSoftMargin

computeUpdate(LinearBinaryCategorizer, Vector, boolean, double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlinePerceptron

computeUpdate(DefaultKernelBinaryCategorizer<InputType>, InputType, boolean, double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlinePerceptron

computeUpdate(boolean, double) - Static method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlinePerceptron

Computes the update weight for the given actual category and predicted value according to the Perceptron update rule.

computeUpdate(LinearBinaryCategorizer, Vector, boolean, double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineRampPassiveAggressivePerceptron

computeUpdate(DefaultKernelBinaryCategorizer<InputType>, InputType, boolean, double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineRampPassiveAggressivePerceptron

computeUpdate(LinearBinaryCategorizer, Vector, boolean, double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineShiftingPerceptron

computeUpdate(DefaultKernelBinaryCategorizer<InputType>, InputType, boolean, double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineShiftingPerceptron

computeVariance(Collection<? extends Vector>) - Static method in class gov.sandia.cognition.math.MultivariateStatisticsUtil

Computes the variance (second central moment, squared standard deviation) of a dataset.

computeVariance(Collection<? extends Vector>, Vector) - Static method in class gov.sandia.cognition.math.MultivariateStatisticsUtil

Computes the variance (second central moment, squared standard deviation) of a dataset

computeVariance(Collection<? extends Number>) - Static method in class gov.sandia.cognition.math.UnivariateStatisticsUtil

Computes the unbiased variance (second central moment, squared standard deviation) of a dataset.

computeVariance(Collection<? extends Number>, double) - Static method in class gov.sandia.cognition.math.UnivariateStatisticsUtil

Computes the unbiased variance (second central moment, squared standard deviation) of a dataset

computeVector(double) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.DirectionalVectorToScalarFunction

Transforms the scaleFactor into a multidimensional Vector using the direction

computeViterbiRecursion(Vector, Vector) - Method in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel

Computes the Viterbi recursion for a given "delta" and "b"

computeViterbiRecursion(Vector, Vector) - Method in class gov.sandia.cognition.learning.algorithm.hmm.ParallelHiddenMarkovModel

computeWeightedCentralMoment(Iterable<? extends WeightedValue<? extends Number>>, double, int) - Static method in class gov.sandia.cognition.math.UnivariateStatisticsUtil

Computes the desired biased estimate central moment of the given dataset.

computeWeightedKurtosis(Collection<? extends WeightedValue<? extends Number>>) - Static method in class gov.sandia.cognition.math.UnivariateStatisticsUtil

Computes the biased excess kurtosis of the given dataset.

computeWeightedMean(Iterable<? extends WeightedValue<? extends Number>>) - Static method in class gov.sandia.cognition.math.UnivariateStatisticsUtil

Computes the arithmetic mean (average, expectation, first central moment) of a dataset.

computeWeightedMeanAndCovariance(Iterable<? extends WeightedValue<? extends Vectorizable>>) - Static method in class gov.sandia.cognition.math.MultivariateStatisticsUtil

Computes the mean and biased covariance Matrix of a multivariate weighted data set.

computeWeightedMeanAndVariance(Iterable<? extends WeightedValue<? extends Number>>) - Static method in class gov.sandia.cognition.math.UnivariateStatisticsUtil

Computes the mean and unbiased variance of a Collection of data using the one-pass approach.

computeWeightedOutputMean(Collection<? extends InputOutputPair<?, ? extends Number>>) - Static method in class gov.sandia.cognition.learning.data.DatasetUtil

Computes the mean of the output data.

computeWeightedZSquared(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MixtureOfGaussians.PDF

Computes the weighted z-value (deviate) of the given input.

computeWithPartialSums() - Method in class gov.sandia.cognition.statistics.TransferEntropy

Computes the transfer entropy value and returns a map of the states with their corresponding partial sums.

computeZ(double, int, int) - Static method in class gov.sandia.cognition.statistics.method.MannWhitneyUConfidence.Statistic

Computes the z-value, used in the UnivariateGaussian CDF

computeZ(double, int) - Static method in class gov.sandia.cognition.statistics.method.WilcoxonSignedRankConfidence.Statistic

Computes the z-value from the T-statistic and numNonZero value

computeZSquared(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian

Computes the z value squared, such that p(x) = coefficient * exp{-0.5*z^2}

ConcurrentCognitiveModule - Interface in gov.sandia.cognition.framework.concurrent

The ConcurrentCognitiveModule interface extends the functionality of CognitiveModule to support concurrent module evaluation within a CognitiveModel.

conditionalDistribution - Variable in class gov.sandia.cognition.statistics.bayesian.AbstractBayesianParameter

Distribution from which to pull the parameters.

conditionalDistribution - Variable in class gov.sandia.cognition.statistics.DefaultDistributionParameter

Distribution from which to pull the parameters.

conditionalPriorPredictive - Variable in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel

Base predictive distribution that determines the value of the new cluster weighting during the Gibbs sampling.

ConditionalProbability<DataType> - Class in gov.sandia.cognition.statistics.bayesian

A class for finding the conditional probability of two elements, or one element and a collection of other elements.

ConditionalProbability() - Constructor for class gov.sandia.cognition.statistics.bayesian.ConditionalProbability

Constructor for new Conditional Probability object.

conditionals - Variable in class gov.sandia.cognition.learning.algorithm.bayes.VectorNaiveBayesCategorizer

The mapping of category to the conditional distribution for the category with one probability density function for each dimension.

conditionNumber() - Method in class gov.sandia.cognition.math.matrix.decomposition.AbstractSingularValueDecomposition

conditionNumber() - Method in interface gov.sandia.cognition.math.matrix.decomposition.SingularValueDecomposition

Returns the condition number of the underlying matrix, which is simply the ratio of the largest to smallest singular value

confidence - Variable in class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalDeviation

The confidence to use for updating.

confidence - Variable in class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalVariance

The confidence to use for updating.

confidence - Variable in class gov.sandia.cognition.learning.experiment.LearnerComparisonExperiment

The confidence statistic generated from the underlying performance statistics.

CONFIDENCE_REGION - Static variable in class gov.sandia.cognition.math.UnivariateSummaryStatistics

Region of the confidence interval, 0.95.

ConfidenceInterval - Class in gov.sandia.cognition.statistics.method

Contains a specification for a confidence interval, that is, the solution of Pr{ lowerBound <= x(centralValue) <= upperBound } >= confidence

ConfidenceInterval(double, double, double, double, int) - Constructor for class gov.sandia.cognition.statistics.method.ConfidenceInterval

Creates a new instance of ConfidenceInterval

ConfidenceInterval(ConfidenceInterval) - Constructor for class gov.sandia.cognition.statistics.method.ConfidenceInterval

Copy constructor

ConfidenceIntervalEvaluator<DataType> - Interface in gov.sandia.cognition.statistics.method

Computes a confidence interval for a given dataset and confidence (power) level

ConfidenceStatistic - Interface in gov.sandia.cognition.statistics.method

An interface that describes the result of a statistical confidence test.

ConfidenceTestAssumptions - Annotation Type in gov.sandia.cognition.statistics.method

Describes the assumptions and other information of a statistical confidence test.

ConfidenceWeightedBinaryCategorizer - Interface in gov.sandia.cognition.learning.function.categorization

Interface for a confidence-weighted binary categorizer, which defines a distribution over linear binary categorizers.

ConfidenceWeightedDiagonalDeviation - Class in gov.sandia.cognition.learning.algorithm.confidence

An implementation of the Standard Deviation (Stdev) algorithm for learning a confidence-weighted categorizer.

ConfidenceWeightedDiagonalDeviation() - Constructor for class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalDeviation

Creates a new ConfidenceWeightedDiagonalVariance with default parameters.

ConfidenceWeightedDiagonalDeviation(double, double) - Constructor for class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalDeviation

Creates a new ConfidenceWeightedDiagonalVariance with the given parameters.

ConfidenceWeightedDiagonalDeviationProject - Class in gov.sandia.cognition.learning.algorithm.confidence

An implementation of the Standard Deviation (Stdev) algorithm for learning a confidence-weighted categorizer.

ConfidenceWeightedDiagonalDeviationProject() - Constructor for class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalDeviationProject

Creates a new ConfidenceWeightedDiagonalDeviationProject with default parameters.

ConfidenceWeightedDiagonalDeviationProject(double, double) - Constructor for class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalDeviationProject

Creates a new ConfidenceWeightedDiagonalDeviationProject with the given parameters.

ConfidenceWeightedDiagonalVariance - Class in gov.sandia.cognition.learning.algorithm.confidence

An implementation of the Variance algorithm for learning a confidence-weighted linear categorizer.

ConfidenceWeightedDiagonalVariance() - Constructor for class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalVariance

Creates a new ConfidenceWeightedDiagonalVariance with default parameters.

ConfidenceWeightedDiagonalVariance(double, double) - Constructor for class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalVariance

Creates a new ConfidenceWeightedDiagonalVariance with the given parameters.

ConfidenceWeightedDiagonalVarianceProject - Class in gov.sandia.cognition.learning.algorithm.confidence

An implementation of the Variance algorithm for learning a confidence-weighted linear categorizer.

ConfidenceWeightedDiagonalVarianceProject() - Constructor for class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalVarianceProject

Creates a new ConfidenceWeightedDiagonalVarianceProject with default parameters.

ConfidenceWeightedDiagonalVarianceProject(double, double) - Constructor for class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalVarianceProject

Creates a new ConfidenceWeightedDiagonalVarianceProject with the given parameters.

configure(Map<OutputType, Double>, Map<OutputType, Integer>) - Method in interface gov.sandia.cognition.learning.algorithm.tree.PriorWeightedNodeLearner

Configure the node learner with prior weights and training counts.

configure(Map<OutputType, Double>, Map<OutputType, Integer>) - Method in class gov.sandia.cognition.learning.algorithm.tree.VectorThresholdInformationGainLearner

ConfusionMatrix<CategoryType> - Interface in gov.sandia.cognition.learning.performance.categorization

An interface for a general confusion matrix, which is used to tabulate a set of actual category values against the values predicted for those categories.

ConfusionMatrixPerformanceEvaluator<InputType,CategoryType> - Class in gov.sandia.cognition.learning.performance.categorization

A performance evaluator that builds a confusion matrix.

ConfusionMatrixPerformanceEvaluator() - Constructor for class gov.sandia.cognition.learning.performance.categorization.ConfusionMatrixPerformanceEvaluator

Creates a new ConfusionMatrixPerformanceEvaluator with a default factory behind it.

ConfusionMatrixPerformanceEvaluator(Factory<? extends ConfusionMatrix<CategoryType>>) - Constructor for class gov.sandia.cognition.learning.performance.categorization.ConfusionMatrixPerformanceEvaluator

Creates a new ConfusionMatrixPerformanceEvaluator using the given factory.

confusions - Variable in class gov.sandia.cognition.learning.performance.categorization.DefaultConfusionMatrix

The backing map of confusion matrix entries.

conjuctive - Variable in class gov.sandia.cognition.statistics.bayesian.ImportanceSampling.DefaultUpdater

Defines the parameter that connects the conditional and prior distributions.

conjuctive - Variable in class gov.sandia.cognition.statistics.bayesian.RejectionSampling.DefaultUpdater

Defines the parameter that connects the conditional and prior distributions.

conjugate() - Method in class gov.sandia.cognition.math.ComplexNumber

Switches the sign of the imaginary part of this complex number.

conjugateEquals() - Method in class gov.sandia.cognition.math.ComplexNumber

Switches the sign of the imaginary part of this complex number.

ConjugateGradientMatrixSolver - Class in gov.sandia.cognition.learning.algorithm.minimization.matrix

Implements a matrix solver using Conjugate Gradient.

ConjugateGradientMatrixSolver(Vector, Vector) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.matrix.ConjugateGradientMatrixSolver

Initializes a conjugate gradient solver with the minimum values

ConjugateGradientMatrixSolver(Vector, Vector, double) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.matrix.ConjugateGradientMatrixSolver

Initializes a conjugate gradient solver with some additional parameters

ConjugateGradientMatrixSolver(Vector, Vector, double, int) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.matrix.ConjugateGradientMatrixSolver

Initializes a conjugate gradient solver with all user-definable parameters

ConjugateGradientWithPreconditionerMatrixSolver - Class in gov.sandia.cognition.learning.algorithm.minimization.matrix

Implements a matrix solver using Conjugate Gradient with a preconditioner.

ConjugateGradientWithPreconditionerMatrixSolver(Vector, Vector) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.matrix.ConjugateGradientWithPreconditionerMatrixSolver

Initializes a steepest-descent solver with the minimum values

ConjugateGradientWithPreconditionerMatrixSolver(Vector, Vector, double) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.matrix.ConjugateGradientWithPreconditionerMatrixSolver

Initializes a steepest-descent solver with some additional parameters

ConjugateGradientWithPreconditionerMatrixSolver(Vector, Vector, double, int) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.matrix.ConjugateGradientWithPreconditionerMatrixSolver

Initializes a steepest-descent solver with all user-definable parameters

ConjugatePriorBayesianEstimator<ObservationType,ParameterType,ConditionalType extends ClosedFormDistribution<ObservationType>,BeliefType extends ClosedFormDistribution<ParameterType>> - Interface in gov.sandia.cognition.statistics.bayesian.conjugate

A Bayesian Estimator that makes use of conjugate priors, which is a mathematical trick when the conditional and the prior result a posterior that is the same type as the prior.

ConjugatePriorBayesianEstimatorPredictor<ObservationType,ParameterType,ConditionalType extends ClosedFormDistribution<ObservationType>,BeliefType extends ClosedFormDistribution<ParameterType>> - Interface in gov.sandia.cognition.statistics.bayesian.conjugate

A conjugate prior estimator that also has a closed-form predictive posterior.

constant - Variable in class gov.sandia.cognition.learning.function.kernel.PolynomialKernel

The constant for the polynomial.

constant - Variable in class gov.sandia.cognition.learning.function.kernel.SigmoidKernel

The constant used in the sigmoid.

ConstantEvaluator<OutputType> - Class in gov.sandia.cognition.learning.function

The ConstantEvaluator class implements an Evaluator that always returns the same output value.

ConstantEvaluator() - Constructor for class gov.sandia.cognition.learning.function.ConstantEvaluator

Creates a new ConstantEvaluator.

ConstantEvaluator(OutputType) - Constructor for class gov.sandia.cognition.learning.function.ConstantEvaluator

Creates a new ConstantEvaluator.

ConstantLearner<ValueType> - Class in gov.sandia.cognition.learning.algorithm.baseline

A learner that always returns the same value as the result.

ConstantLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.baseline.ConstantLearner

Creates a new ConstantLearner with a null value.

ConstantLearner(ValueType) - Constructor for class gov.sandia.cognition.learning.algorithm.baseline.ConstantLearner

Creates a new ConstantLearner with the given value;

constantValue - Variable in class gov.sandia.cognition.learning.function.scalar.LocallyWeightedKernelScalarFunction

The constant value is what the constant weight biases the function toward when there is near zero weight.

constantWeight - Variable in class gov.sandia.cognition.learning.function.scalar.LocallyWeightedKernelScalarFunction

The constant weight is used as a weight for the constant value that is added to the result to bias the function to the constant value.

constructor - Variable in class gov.sandia.cognition.factory.ConstructorBasedFactory

The constructor to use to create new objects.

ConstructorBasedFactory<CreatedType> - Class in gov.sandia.cognition.factory

The ConstructorBasedFactory class implements a Factory that takes a constructor and parameters to that constructor used to create new objects.

ConstructorBasedFactory(Constructor<? extends CreatedType>, Object...) - Constructor for class gov.sandia.cognition.factory.ConstructorBasedFactory

Creates a new ConstructorBasedFactory from the given constructor and parameters.

consume(KeyType, double) - Method in interface gov.sandia.cognition.math.matrix.InfiniteVector.KeyValueConsumer

Consumes one entry in the infinite vector.

consume(int, double) - Method in interface gov.sandia.cognition.math.matrix.Vector.IndexValueConsumer

Consumes one entry in the vector.

consumeClusters(Collection<GaussianCluster>) - Method in class gov.sandia.cognition.learning.function.vector.GaussianContextRecognizer

Uses the given clusters to populate the internal clusters of this

contains(Object) - Method in class gov.sandia.cognition.collection.DefaultMultiCollection

contains(int) - Method in class gov.sandia.cognition.collection.IntegerSpan

Determines if the given value is within the inclusive bounds

contains(Object) - Method in class gov.sandia.cognition.collection.IntegerSpan

contains(Termable) - Method in class gov.sandia.cognition.text.term.filter.DefaultStopList

contains(Term) - Method in class gov.sandia.cognition.text.term.filter.DefaultStopList

Returns true if the given term is in the stop list.

contains(String) - Method in class gov.sandia.cognition.text.term.filter.DefaultStopList

Returns true if the given word is in the stop list.

contains(Termable) - Method in interface gov.sandia.cognition.text.term.filter.StopList

Determines if the given term is contained in this stop list.

containsKey(KeyType) - Method in class gov.sandia.cognition.collection.AbstractScalarMap

containsKey(Object) - Method in class gov.sandia.cognition.collection.DynamicArrayMap

Runs in O(1).

containsKey(int) - Method in class gov.sandia.cognition.collection.DynamicArrayMap

Returns true if this is a valid key in the mapping.

containsKey(KeyType) - Method in interface gov.sandia.cognition.collection.NumericMap

Determines if this map contains the given key.

containsNode(NodeNameType) - Method in class gov.sandia.cognition.graph.DenseMemoryGraph

containsNode(NodeNameType) - Method in interface gov.sandia.cognition.graph.DirectedNodeEdgeGraph

Returns whether or not the graph contains the specified vertex.

containsValue(Object) - Method in class gov.sandia.cognition.collection.DynamicArrayMap

Runs in O(n).

CONTENT_TYPE - Static variable in class gov.sandia.cognition.text.document.extractor.TextDocumentExtractor

The content type is "text/plain".

convergence(Vector, Double, Vector, Vector, double) - Static method in class gov.sandia.cognition.learning.algorithm.minimization.MinimizationStoppingCriterion

Tests for convergence on approximately zero slope and nonmovement along the x-axis

convert(int) - Method in class gov.sandia.cognition.collection.FiniteCapacityBuffer

Converts the given index from zero-based world to circular world

convert(InputType) - Method in class gov.sandia.cognition.text.convert.AbstractMultiTextualConverter

convert(InputType) - Method in class gov.sandia.cognition.text.convert.AbstractSingleTextualConverter

convert(InputType) - Method in interface gov.sandia.cognition.text.convert.MultiTextualConverter

Convert the input object into zero or more textual objects.

convert(InputType) - Method in interface gov.sandia.cognition.text.convert.SingleTextualConverter

Convert an input into its single textual form.

convertAll(Iterable<? extends InputType>) - Method in class gov.sandia.cognition.text.convert.AbstractMultiTextualConverter

convertAll(Iterable<? extends InputType>) - Method in class gov.sandia.cognition.text.convert.AbstractSingleTextualConverter

convertAll(Iterable<? extends InputType>) - Method in interface gov.sandia.cognition.text.convert.TextualConverter

Convert the given input objects into zero or more textual objects.

converter - Variable in class gov.sandia.cognition.data.convert.vector.NumberConverterToVectorAdapter

The converter to adapt for use with Vectors.

converter - Variable in class gov.sandia.cognition.text.convert.SingleToMultiTextualConverterAdapter

The single text converter being wrapped.

convertFromBytes(byte[]) - Static method in class gov.sandia.cognition.io.ObjectSerializationHandler

Takes a byte array produced by convertToBytes and returns the Object from the serialized byte array.

convertFromBytes(byte[]) - Method in class gov.sandia.cognition.io.serialization.AbstractStreamSerializationHandler

convertFromBytes(byte[]) - Method in interface gov.sandia.cognition.io.serialization.StreamSerializationHandler

Converts the first given object in the given byte array.

convertFromRotationMatrix(Matrix) - Method in interface gov.sandia.cognition.math.matrix.Quaternion

Sets the quaternion to be equivalent to the given a 3-by-3 rotation matrix.

convertFromString(String) - Method in class gov.sandia.cognition.io.serialization.AbstractTextSerializationHandler

convertFromString(String) - Method in interface gov.sandia.cognition.io.serialization.TextSerializationHandler

Converts an object from its serialized string representation.

convertFromString(String) - Static method in class gov.sandia.cognition.io.XStreamSerializationHandler

Attempts to read an Object from the given string.

convertFromVector(Vector) - Method in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachine

convertFromVector(Vector) - Method in class gov.sandia.cognition.learning.algorithm.gradient.GradientDescendableApproximator

convertFromVector(Vector) - Method in class gov.sandia.cognition.learning.algorithm.regression.LogisticRegression.Function

convertFromVector(Vector) - Method in class gov.sandia.cognition.learning.function.categorization.ScalarThresholdBinaryCategorizer

convertFromVector(Vector) - Method in class gov.sandia.cognition.learning.function.LinearCombinationFunction

convertFromVector(Vector) - Method in class gov.sandia.cognition.learning.function.scalar.AtanFunction

convertFromVector(Vector) - Method in class gov.sandia.cognition.learning.function.scalar.CosineFunction

convertFromVector(Vector) - Method in class gov.sandia.cognition.learning.function.scalar.LinearDiscriminant

convertFromVector(Vector) - Method in class gov.sandia.cognition.learning.function.scalar.LinearDiscriminantWithBias

convertFromVector(Vector) - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction

Sets the value of the exponent

convertFromVector(Vector) - Method in class gov.sandia.cognition.learning.function.scalar.ThresholdFunction

Converts this function from its parameters, which consists of the threshold value

convertFromVector(Vector) - Method in class gov.sandia.cognition.learning.function.scalar.VectorEntryFunction

Converts a vector into the index to select from the vector.

convertFromVector(Vector) - Method in class gov.sandia.cognition.learning.function.scalar.VectorFunctionLinearDiscriminant

convertFromVector(Vector) - Method in class gov.sandia.cognition.learning.function.vector.FeedforwardNeuralNetwork

convertFromVector(Vector) - Method in class gov.sandia.cognition.learning.function.vector.GeneralizedLinearModel

convertFromVector(Vector) - Method in class gov.sandia.cognition.learning.function.vector.MultivariateDiscriminant

Uploads a matrix from a row-stacked vector of parameters.

convertFromVector(Vector) - Method in class gov.sandia.cognition.learning.function.vector.MultivariateDiscriminantWithBias

convertFromVector(Vector) - Method in class gov.sandia.cognition.learning.function.vector.ThreeLayerFeedforwardNeuralNetwork

convertFromVector(Vector) - Method in class gov.sandia.cognition.math.matrix.AbstractVector

Assigns the values in the provided vector into this.

convertFromVector(Vector) - Method in class gov.sandia.cognition.math.matrix.custom.DenseMatrix

convertFromVector(Vector) - Method in class gov.sandia.cognition.math.matrix.custom.DiagonalMatrix

uploads a matrix from a column-stacked vector of parameters, so that v(k) = A(i,j) = A( k%M, k/M )

convertFromVector(Vector) - Method in class gov.sandia.cognition.math.matrix.custom.SparseMatrix

uploads a matrix from a column-stacked vector of parameters, so that v(k) = A(i,j) = A( k%M, k/M )

convertFromVector(Vector) - Method in interface gov.sandia.cognition.math.matrix.Matrix

uploads a matrix from a column-stacked vector of parameters, so that v(k) = A(i,j) = A( k%M, k/M )

convertFromVector(Vector) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix

convertFromVector(Vector) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseMatrix

convertFromVector(DenseVector) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseMatrix

Incorporates the parameters in the given vector back into the object.

convertFromVector(Vector) - Method in class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixMTJ

convertFromVector(Vector) - Method in interface gov.sandia.cognition.math.matrix.Vectorizable

Converts the object from a Vector of parameters.

convertFromVector(Vector) - Method in class gov.sandia.cognition.math.MutableDouble

convertFromVector(Vector) - Method in class gov.sandia.cognition.math.MutableInteger

convertFromVector(Vector) - Method in class gov.sandia.cognition.math.MutableLong

convertFromVector(Vector) - Method in class gov.sandia.cognition.math.signals.AutoRegressiveMovingAverageFilter

convertFromVector(Vector) - Method in class gov.sandia.cognition.math.signals.LinearDynamicalSystem

convertFromVector(Vector) - Method in class gov.sandia.cognition.math.signals.MovingAverageFilter

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.BernoulliDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.BetaDistribution

Sets the parameters of the distribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.BinomialDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.CategoricalDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.CauchyDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.ChineseRestaurantProcess

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution

Sets the parameter of the chi-square PDF

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.DeterministicDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.DirichletDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.ExponentialDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.GammaDistribution

Sets the parameters of the distribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.GeometricDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.InverseGammaDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.InverseWishartDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.KolmogorovDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.LaplaceDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.LogisticDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.LogNormalDistribution

Sets the parameters of the distribution from a 2-dimensional Vector with ( logNormalMean logNormalVariance )

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MultinomialDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussianInverseGammaDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateMixtureDensityModel

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MultivariatePolyaDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.NegativeBinomialDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.NormalInverseGammaDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.NormalInverseWishartDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.ParetoDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.PoissonDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.SnedecorFDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.StudentizedRangeDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.StudentTDistribution

Sets the parameters of this PDF, which must be a 1-dimensional Vector containing the degrees of freedom

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.UniformDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.WeibullDistribution

convertFromVector(Vector) - Method in class gov.sandia.cognition.statistics.distribution.YuleSimonDistribution

convertInputFromInternal(double) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerDerivativeBased.InternalFunction

Converts the internal x-axis value to real-world x-axis value

convertInputFromInternal(InputOutputSlopeTriplet) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerDerivativeBased.InternalFunction

Converts an InternalFunction InputOutputSlopeTriplet to a real-world InputOutputSlopeTriplet by unreflection and flipping the sign of the slope (if the direction of search was backward).

convertInputToInternal(double) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerDerivativeBased.InternalFunction

Converts a real-world "x" value to the internal values used inside the search algorithm.

convertTo2D(DataType) - Method in class gov.sandia.cognition.math.geometry.Quadtree

Converts the given item into a two-dimensional vector.

convertToBoolean(Number) - Method in class gov.sandia.cognition.data.convert.number.DefaultBooleanToNumberConverter

Converts the given number to a boolean value by determining if it is closer to the number representing true or the number representing false.

convertToBytes(Serializable) - Static method in class gov.sandia.cognition.io.ObjectSerializationHandler

Converts the given Object into an array of bytes.

convertToBytes(SerializedType) - Method in class gov.sandia.cognition.io.serialization.AbstractStreamSerializationHandler

convertToBytes(SerializedType) - Method in interface gov.sandia.cognition.io.serialization.StreamSerializationHandler

Converts the given object to bytes.

convertToComplex(Collection<Double>) - Static method in class gov.sandia.cognition.math.signals.FourierTransform

Converts the Collection of real data to complex numbers

convertToNumber(Boolean) - Method in class gov.sandia.cognition.data.convert.number.DefaultBooleanToNumberConverter

Converts the given boolean to a number using the

convertToRotationMatrix() - Method in interface gov.sandia.cognition.math.matrix.Quaternion

Converts the quaternion to a 3-by-3 rotation matrix.

convertToString(SerializedType) - Method in class gov.sandia.cognition.io.serialization.AbstractTextSerializationHandler

convertToString(SerializedType) - Method in interface gov.sandia.cognition.io.serialization.TextSerializationHandler

Converts a given object to its serialized string representation.

convertToString(Serializable) - Static method in class gov.sandia.cognition.io.XStreamSerializationHandler

Writes the given object to a String.

convertToVector() - Method in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachine

convertToVector() - Method in class gov.sandia.cognition.learning.algorithm.gradient.GradientDescendableApproximator

convertToVector() - Method in class gov.sandia.cognition.learning.algorithm.regression.LogisticRegression.Function

convertToVector() - Method in class gov.sandia.cognition.learning.function.categorization.ScalarThresholdBinaryCategorizer

convertToVector() - Method in class gov.sandia.cognition.learning.function.LinearCombinationFunction

convertToVector() - Method in class gov.sandia.cognition.learning.function.scalar.AtanFunction

convertToVector() - Method in class gov.sandia.cognition.learning.function.scalar.CosineFunction

convertToVector() - Method in class gov.sandia.cognition.learning.function.scalar.LinearDiscriminant

convertToVector() - Method in class gov.sandia.cognition.learning.function.scalar.LinearDiscriminantWithBias

convertToVector() - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction

Returns the value of the exponent

convertToVector() - Method in class gov.sandia.cognition.learning.function.scalar.ThresholdFunction

Converts this function into its parameters, which consists of the threshold value

convertToVector() - Method in class gov.sandia.cognition.learning.function.scalar.VectorEntryFunction

Converts the index to select into a vector (of length 1).

convertToVector() - Method in class gov.sandia.cognition.learning.function.scalar.VectorFunctionLinearDiscriminant

convertToVector() - Method in class gov.sandia.cognition.learning.function.vector.FeedforwardNeuralNetwork

convertToVector() - Method in class gov.sandia.cognition.learning.function.vector.GeneralizedLinearModel

convertToVector() - Method in class gov.sandia.cognition.learning.function.vector.MultivariateDiscriminant

Creates a row-stacked version of the discriminant.

convertToVector() - Method in class gov.sandia.cognition.learning.function.vector.MultivariateDiscriminantWithBias

convertToVector() - Method in class gov.sandia.cognition.learning.function.vector.ThreeLayerFeedforwardNeuralNetwork

convertToVector() - Method in class gov.sandia.cognition.math.matrix.AbstractVector

Returns this.

convertToVector() - Method in class gov.sandia.cognition.math.matrix.custom.DenseMatrix

convertToVector() - Method in class gov.sandia.cognition.math.matrix.custom.DiagonalMatrix

convertToVector() - Method in class gov.sandia.cognition.math.matrix.custom.SparseMatrix

Creates a column-stacked version of this, so that v(k) = A(i,j) = v(j*M+i)

convertToVector() - Method in interface gov.sandia.cognition.math.matrix.Matrix

Creates a column-stacked version of this, so that v(k) = A(i,j) = v(j*M+i)

convertToVector() - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix

convertToVector() - Method in class gov.sandia.cognition.math.matrix.mtj.DenseMatrix

convertToVector() - Method in class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixMTJ

convertToVector() - Method in interface gov.sandia.cognition.math.matrix.Vectorizable

Converts the object to a vector.

convertToVector() - Method in class gov.sandia.cognition.math.MutableDouble

convertToVector() - Method in class gov.sandia.cognition.math.MutableInteger

convertToVector() - Method in class gov.sandia.cognition.math.MutableLong

convertToVector() - Method in class gov.sandia.cognition.math.signals.AutoRegressiveMovingAverageFilter

convertToVector() - Method in class gov.sandia.cognition.math.signals.LinearDynamicalSystem

convertToVector() - Method in class gov.sandia.cognition.math.signals.MovingAverageFilter

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.BernoulliDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.BetaDistribution

Gets the parameters of the distribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.BinomialDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.CategoricalDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.CauchyDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.ChineseRestaurantProcess

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution

Returns the parameter of the chi-square PDF

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.DeterministicDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.DirichletDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.ExponentialDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.GammaDistribution

Gets the parameters of the distribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.GeometricDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.InverseGammaDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.InverseWishartDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.KolmogorovDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.LaplaceDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.LogisticDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.LogNormalDistribution

Returns a 2-dimensional Vector with ( logNormalMean logNormalVariance )

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.MultinomialDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussianInverseGammaDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.MultivariateMixtureDensityModel

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.MultivariatePolyaDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.NegativeBinomialDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.NormalInverseGammaDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.NormalInverseWishartDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.ParetoDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.PoissonDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.SnedecorFDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.StudentizedRangeDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.StudentTDistribution

Returns the parameters of this PDF, which is a 1-dimensional Vector containing the degrees of freedom

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.UniformDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.WeibullDistribution

convertToVector() - Method in class gov.sandia.cognition.statistics.distribution.YuleSimonDistribution

convertToVector(Iterable<? extends Termable>) - Method in class gov.sandia.cognition.text.term.vector.BagOfWordsTransform

Converts a given list of terms to a vector by counting the occurrence of each term.

convertToVector(Iterable<? extends Termable>, VectorFactory<?>) - Method in class gov.sandia.cognition.text.term.vector.BagOfWordsTransform

Converts a given list of terms to a vector by counting the occurrence of each term.

convertToVector(Iterable<? extends Termable>, TermIndex, VectorFactory<?>) - Static method in class gov.sandia.cognition.text.term.vector.BagOfWordsTransform

Converts a given list of terms to a vector by counting the occurrence of each term.

ConvexReceiverOperatingCharacteristic - Class in gov.sandia.cognition.statistics.method

Computes the convex hull of the Receiver Operating Characteristic (ROC), which a mathematician might call a "concave down" function.

convolve(MultivariateGaussian) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian

Convolves this Gaussian with the other Gaussian.

convolve(UnivariateGaussian) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian

Convolves this Gaussian with the other Gaussian.

cooleyTukeyFFT(ArrayList<ComplexNumber>) - Static method in class gov.sandia.cognition.math.signals.FourierTransform

Computes the Cooley-Tukey Radix-2 Fast Fourier Transform (FFT).

copy(boolean[]) - Static method in class gov.sandia.cognition.collection.ArrayUtil

Creates a new copy of the given array.

copy(int[]) - Static method in class gov.sandia.cognition.collection.ArrayUtil

Creates a new copy of the given array.

copy(long[]) - Static method in class gov.sandia.cognition.collection.ArrayUtil

Creates a new copy of the given array.

copy(double[]) - Static method in class gov.sandia.cognition.collection.ArrayUtil

Creates a new copy of the given array.

copy(T[]) - Static method in class gov.sandia.cognition.collection.ArrayUtil

Creates a new copy of the given array.

copyArray(double[][]) - Method in class gov.sandia.cognition.math.matrix.MatrixFactory

Copies the values from the array into the Matrix

copyArray(double[]) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseVectorFactoryMTJ

Creates a DenseVector based on the given array values.

copyArray(double[]) - Method in class gov.sandia.cognition.math.matrix.VectorFactory

Copies the values from the array into the Vector

copyArray(int, int[], double[]) - Method in class gov.sandia.cognition.math.matrix.VectorFactory

Creates a new vector of the given dimensionality by setting the values at the given indices.

copyColumnVectors(Collection<? extends Vectorizable>) - Method in class gov.sandia.cognition.math.matrix.MatrixFactory

Creates a new matrix by copying the given set of column vectors.

copyColumnVectors(Vectorizable...) - Method in class gov.sandia.cognition.math.matrix.MatrixFactory

Creates a new matrix by copying the given set of column vectors.

copyMap(int, Map<Integer, ? extends Number>) - Method in class gov.sandia.cognition.math.matrix.VectorFactory

Creates a vector from the given map of indices to values.

copyMatrix(Matrix) - Method in class gov.sandia.cognition.math.matrix.custom.CustomDenseMatrixFactory

Creates a deep copy of m into a DenseMatrix and returns it.

copyMatrix(Matrix) - Method in class gov.sandia.cognition.math.matrix.custom.CustomDiagonalMatrixFactory

Creates a deep copy new Matrix given another, argument is unchanged

copyMatrix(Matrix) - Method in class gov.sandia.cognition.math.matrix.custom.CustomSparseMatrixFactory

Creates a deep copy new Matrix given another, argument is unchanged

copyMatrix(Matrix) - Method in class gov.sandia.cognition.math.matrix.MatrixFactory

Creates a deep copy new Matrix given another, argument is unchanged

copyMatrix(Matrix) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseMatrixFactoryMTJ

copyMatrix(Matrix) - Method in class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixFactoryMTJ

copyMatrix(Matrix) - Method in class gov.sandia.cognition.math.matrix.mtj.SparseMatrixFactoryMTJ

copyRowVectors(Collection<? extends Vectorizable>) - Method in class gov.sandia.cognition.math.matrix.MatrixFactory

Creates a new matrix by copying the given set of row vectors.

copyRowVectors(Vectorizable...) - Method in class gov.sandia.cognition.math.matrix.MatrixFactory

Creates a new matrix by copying the given set of row vectors.

copyValues(double...) - Method in class gov.sandia.cognition.math.matrix.VectorFactory

Copies the values from the given doubles into a Vector

copyValues(Collection<? extends Number>) - Method in class gov.sandia.cognition.math.matrix.VectorFactory

Copies the values from the given Collection.

copyVector(Vector) - Method in class gov.sandia.cognition.math.matrix.custom.CustomDenseVectorFactory

copyVector(Vector) - Method in class gov.sandia.cognition.math.matrix.custom.CustomSparseVectorFactory

copyVector(Vector) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseVectorFactoryMTJ

copyVector(Vector) - Method in class gov.sandia.cognition.math.matrix.mtj.SparseVectorFactoryMTJ

copyVector(Vector) - Method in class gov.sandia.cognition.math.matrix.VectorFactory

Creates a deep copy new Vector given another, argument is unchanged

cosine(VectorType) - Method in class gov.sandia.cognition.math.matrix.AbstractVectorSpace

cosine(InfiniteVector<KeyType>) - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector

cosine(VectorType) - Method in interface gov.sandia.cognition.math.matrix.VectorSpace

Computes the cosine between two Vectors

CosineDeltaCategorizer<CategoryType> - Class in gov.sandia.cognition.learning.algorithm.delta

The Cosine Delta algorithm implementation.

CosineDeltaCategorizer(CosineDeltaCategorizer.Learner<CategoryType>, ArrayList<Double>, ArrayList<Double>) - Constructor for class gov.sandia.cognition.learning.algorithm.delta.CosineDeltaCategorizer

Constructor that takes learner, featureStddev, and featureMeans.

CosineDeltaCategorizer.Learner<CategoryType> - Class in gov.sandia.cognition.learning.algorithm.delta

Learner for a CosineDeltaCategorizer.

CosineDistanceMetric - Class in gov.sandia.cognition.learning.function.distance

The CosineDistanceMetric class implements a semimetric between two vectors based on the cosine between the vectors.

CosineDistanceMetric() - Constructor for class gov.sandia.cognition.learning.function.distance.CosineDistanceMetric

Creates a new instance of CosineDistanceMetric.

CosineFunction - Class in gov.sandia.cognition.learning.function.scalar

A closed-form cosine function.

CosineFunction() - Constructor for class gov.sandia.cognition.learning.function.scalar.CosineFunction

Creates a new instance of CosineFunction

CosineFunction(double, double) - Constructor for class gov.sandia.cognition.learning.function.scalar.CosineFunction

Creates a new instance of CosineFunction

CosineFunction(double, double, double) - Constructor for class gov.sandia.cognition.learning.function.scalar.CosineFunction

Creates a new instance of CosineFunction

CosineSimilarityFunction - Class in gov.sandia.cognition.text.term.vector

A vector cosine similarity function.

CosineSimilarityFunction() - Constructor for class gov.sandia.cognition.text.term.vector.CosineSimilarityFunction

Creates a new CosineSimilarityFunction.

CostFunction<EvaluatedType,CostParametersType> - Interface in gov.sandia.cognition.learning.function.cost

The CostFunction interface defines the interface to evaluate some object to determine its cost.

costFunction - Variable in class gov.sandia.cognition.statistics.method.DistributionParameterEstimator.DistributionWrapper

Cost function to use in the minimization procedure

costParameters - Variable in class gov.sandia.cognition.learning.function.cost.AbstractCostFunction

The parameters of the cost function.

CostSpeedupEnergyFunction<LabelType,NodeNameType> - Class in gov.sandia.cognition.graph.inference

This class trades memory usage (to store all of the costs) for compute time.

CostSpeedupEnergyFunction(NodeNameAwareEnergyFunction<LabelType, NodeNameType>) - Constructor for class gov.sandia.cognition.graph.inference.CostSpeedupEnergyFunction

Initializes this with the wrapped function and empty values for the pairwise costs (which are only computed and stored as needed).

couldWrite(File) - Static method in class gov.sandia.cognition.io.FileUtil

Attempts to determine if the application might be able to write to the given file, which may or may not already exists.

count - Variable in class gov.sandia.cognition.statistics.AbstractSufficientStatistic

Number of data points used to create this SufficientStatistic

counterFactory - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner

Factory for counting votes.

countNonZeros() - Method in class gov.sandia.cognition.math.matrix.custom.DenseVector

Computes the number of non-zero entries in this

countNonZeros() - Method in class gov.sandia.cognition.math.matrix.custom.SparseVector

countNonZeros() - Method in class gov.sandia.cognition.math.matrix.mtj.DenseVector

countNonZeros() - Method in class gov.sandia.cognition.math.matrix.mtj.SparseVector

countNonZeros() - Method in interface gov.sandia.cognition.math.matrix.Vector

Counts the number of non-zero entries in the vector.

countOutputValues(Iterable<? extends InputOutputPair<?, ? extends OutputType>>) - Static method in class gov.sandia.cognition.learning.data.DatasetUtil

Creates a data histogram over the output values from the given data.

covariance - Variable in class gov.sandia.cognition.learning.function.categorization.DefaultConfidenceWeightedBinaryCategorizer

The covariance matrix.

covarianceDivisor - Variable in class gov.sandia.cognition.statistics.distribution.NormalInverseWishartDistribution

Term that divides the covariance sampled from the inverseWishart, must be greater than zero.

create() - Static method in class gov.sandia.cognition.data.convert.IdentityDataConverter

Convenience method to create a new IdentityDataConverter.

create(Evaluator<? super InputType, ? extends IntermediateType>, Evaluator<? super IntermediateType, ? extends OutputType>) - Static method in class gov.sandia.cognition.evaluator.CompositeEvaluatorPair

A convenience method for creating composite evaluators.

create(Evaluator<? super InputType, ? extends FirstIntermediateType>, Evaluator<? super FirstIntermediateType, ? extends SecondIntermediateType>, Evaluator<? super SecondIntermediateType, ? extends OutputType>) - Static method in class gov.sandia.cognition.evaluator.CompositeEvaluatorTriple

A convenience method for creating composite evaluators.

create(ForwardType, ReverseType) - Static method in class gov.sandia.cognition.evaluator.ForwardReverseEvaluatorPair

Convenience method for creating a new forward-reverse evaluator pair.

create() - Static method in class gov.sandia.cognition.evaluator.IdentityEvaluator

Convenience method for creating an identity evaluator.

create(Map<InputType, OutputType>) - Static method in class gov.sandia.cognition.evaluator.ValueMapper

Creates an evaluator who is backed by the given map.

create() - Method in class gov.sandia.cognition.factory.ConstructorBasedFactory

Creates a new object by calling the constructor inside the factory with the parameters it is configured to take.

create() - Method in class gov.sandia.cognition.factory.DefaultFactory

Creates a new object using the default constructor of the class that the factory contains.

create() - Method in interface gov.sandia.cognition.factory.Factory

Creates a new instance of an object.

create() - Method in class gov.sandia.cognition.factory.PrototypeFactory

Creates a new object by calling the clone method on the prototype in the factory.

create(ValueType) - Static method in class gov.sandia.cognition.learning.algorithm.baseline.ConstantLearner

Creates a new ConstantLearner.

create() - Static method in class gov.sandia.cognition.learning.algorithm.baseline.IdentityLearner

A convenience method for creating a IdentityLearner.

create(int, Random) - Static method in class gov.sandia.cognition.learning.algorithm.clustering.KMeansFactory

Creates a new parallelized k-means clustering algorithm for vector data with the given number of clusters (k) and random number generator.

create(int, Semimetric<? super Vector>, Random) - Static method in class gov.sandia.cognition.learning.algorithm.clustering.KMeansFactory

Creates a new parallelized k-means clustering algorithm for vector data with the given number of clusters (k), distance metric, and random number generator.

create() - Method in class gov.sandia.cognition.learning.algorithm.clustering.KMeansFactory

create(int) - Static method in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer

Create a partitional clusterer, using Euclidean distance and a vector mean centroid cluster creator.

create(BatchLearner<? super Collection<? extends InputType>, ? extends Evaluator<? super InputType, ? extends IntermediateType>>, BatchLearner<? super Collection<? extends IntermediateType>, ? extends Evaluator<? super IntermediateType, ? extends OutputType>>) - Static method in class gov.sandia.cognition.learning.algorithm.CompositeBatchLearnerPair

Creates a new CompositeBatchLearnerPair from the given learners.

create(IncrementalLearner<? super InputOutputPair<? extends InputType, CategoryType>, MemberType>, int, double, Random) - Static method in class gov.sandia.cognition.learning.algorithm.ensemble.OnlineBaggingCategorizerLearner

Convenience method for creating an OnlineBaggingCategorizerLearner.

create(BatchLearner<? super Collection<? extends InputType>, ? extends Evaluator<? super InputType, ? extends TransformedInputType>>, BatchLearner<? super Collection<? extends InputOutputPair<? extends TransformedInputType, TransformedOutputType>>, ? extends Evaluator<? super TransformedInputType, ? extends TransformedOutputType>>, BatchLearner<? super Collection<? extends OutputType>, ? extends ReversibleEvaluator<OutputType, TransformedOutputType, ?>>) - Static method in class gov.sandia.cognition.learning.algorithm.InputOutputTransformedBatchLearner

Creates a new InputOutputTransformedBatchLearner from the three learners.

create(Evaluator<? super InputType, ? extends TransformedInputType>, BatchLearner<? super Collection<? extends InputOutputPair<? extends TransformedInputType, TransformedOutputType>>, ? extends Evaluator<? super TransformedInputType, ? extends TransformedOutputType>>, ReversibleEvaluator<OutputType, TransformedOutputType, ?>) - Static method in class gov.sandia.cognition.learning.algorithm.InputOutputTransformedBatchLearner

Creates a new InputOutputTransformedBatchLearner from the predefined input and output transforms and the supervised learner.

create(BatchLearner<? super Collection<? extends InputType>, ? extends Evaluator<? super InputType, ? extends TransformedInputType>>, BatchLearner<? super Collection<? extends InputOutputPair<? extends TransformedInputType, TransformedOutputType>>, ? extends Evaluator<? super TransformedInputType, ? extends TransformedOutputType>>, ReversibleEvaluator<OutputType, TransformedOutputType, ?>) - Static method in class gov.sandia.cognition.learning.algorithm.InputOutputTransformedBatchLearner

Creates a new InputOutputTransformedBatchLearner from the unsupervised input transform learner, supervised learners, and output transform.

create(Evaluator<? super InputType, ? extends TransformedInputType>, BatchLearner<? super Collection<? extends InputOutputPair<? extends TransformedInputType, TransformedOutputType>>, ? extends Evaluator<? super TransformedInputType, ? extends TransformedOutputType>>, BatchLearner<? super Collection<? extends OutputType>, ? extends ReversibleEvaluator<OutputType, TransformedOutputType, ?>>) - Static method in class gov.sandia.cognition.learning.algorithm.InputOutputTransformedBatchLearner

Creates a new InputOutputTransformedBatchLearner from the input transform, supervised learner, and unsupervised output transform learner.

create() - Static method in class gov.sandia.cognition.learning.data.DefaultInputOutputPair

Convenience method to create a new DefaultInputOutputPair.

create(InputType, OutputType) - Static method in class gov.sandia.cognition.learning.data.DefaultInputOutputPair

Convenience method to create a new DefaultInputOutputPair.

create(Collection<DataType>, Collection<DataType>) - Static method in class gov.sandia.cognition.learning.data.DefaultPartitionedDataset

Convenience method to create a new DefaultPartitionedDataset from the two given collections.

create() - Static method in class gov.sandia.cognition.learning.data.DefaultTargetEstimatePair

Convenience method for creating a new DefaultTargetEstimatePair.

create(TargetType, EstimateType) - Static method in class gov.sandia.cognition.learning.data.DefaultTargetEstimatePair

Convenience method for creating a new DefaultTargetEstimatePair.

create(ValueType, DiscriminantType) - Static method in class gov.sandia.cognition.learning.data.DefaultValueDiscriminantPair

Convenience method for creating a new DefaultValueDiscriminantPair with the given value and discriminant.

create() - Static method in class gov.sandia.cognition.learning.data.DefaultWeightedInputOutputPair

Convenience method to create a new, empty DefaultWeightedInputOutputPair.

create(InputType, OutputType, double) - Static method in class gov.sandia.cognition.learning.data.DefaultWeightedInputOutputPair

Convenience method to create a new DefaultWeightedInputOutputPair.

create() - Static method in class gov.sandia.cognition.learning.data.DefaultWeightedTargetEstimatePair

Convenience method for creating a new DefaultWeightedTargetEstimatePair.

create(TargetType, EstimateType) - Static method in class gov.sandia.cognition.learning.data.DefaultWeightedTargetEstimatePair

Convenience method for creating a new DefaultWeightedTargetEstimatePair.

create(TargetType, EstimateType, double) - Static method in class gov.sandia.cognition.learning.data.DefaultWeightedTargetEstimatePair

Convenience method for creating a new DefaultWeightedTargetEstimatePair.

create(ValueType, double) - Static method in class gov.sandia.cognition.learning.data.DefaultWeightedValueDiscriminant

Convenience method for creating a new DefaultWeightedValueDiscriminant with the given value and weight.

create(WeightedValue<? extends ValueType>) - Static method in class gov.sandia.cognition.learning.data.DefaultWeightedValueDiscriminant

Convenience method for creating a new DefaultWeightedValueDiscriminant with a shallow copy of the given the given value and weight.

create(Collection<? extends DataType>, int) - Static method in class gov.sandia.cognition.learning.data.SequentialDataMultiPartitioner

Creates a partition of the given data into "numPartition" roughly equal sets, preserving their pre-existing sequential ordering, with the nonzero remainder elements going into the final partition.

create(OutputType) - Static method in class gov.sandia.cognition.learning.function.ConstantEvaluator

Creates a new ConstantEvaluator for the given value.

create(DivergenceFunction<? super ValueType, ? super InputType>, Collection<ValueType>) - Static method in class gov.sandia.cognition.learning.function.distance.DivergencesEvaluator

Convenience method for creation a DivergeceEvaluator.

create(BatchLearner<DataType, ? extends Collection<ValueType>>, DivergenceFunction<? super ValueType, ? super InputType>) - Static method in class gov.sandia.cognition.learning.function.distance.DivergencesEvaluator.Learner

Convenience method for creating a DivergencesEvaluator.Learner.

create(LearnerType) - Static method in class gov.sandia.cognition.learning.parameter.ParameterAdaptableBatchLearnerWrapper

A convenience method for creating the wrapper for a learner.

create(Iterable<? extends TargetEstimatePair<? extends Boolean, ? extends Boolean>>) - Static method in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrix

Creates a new DefaultBinaryConfusionMatrix from the given target-estimate pairs.

create(Iterable<? extends TargetEstimatePair<? extends Boolean, ? extends Boolean>>, boolean) - Static method in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrix

Creates a new DefaultBinaryConfusionMatrix from the given target-estimate pairs.

create() - Method in class gov.sandia.cognition.learning.performance.categorization.DefaultConfusionMatrix.Factory

create(DenseMatrix) - Static method in class gov.sandia.cognition.math.matrix.mtj.decomposition.CholeskyDecompositionMTJ

Creates a Cholesky decomposition of the symmetric positive definite matrix A.

create(DenseMatrix) - Static method in class gov.sandia.cognition.math.matrix.mtj.decomposition.EigenDecompositionRightMTJ

Creates a new instance of EigenDecompositionRightMTJ.

create(Matrix) - Static method in class gov.sandia.cognition.math.matrix.mtj.decomposition.SingularValueDecompositionMTJ

Creates a new instance of SingularValueDecompositionMTJ

create(Collection<? extends Number>) - Static method in class gov.sandia.cognition.math.UnivariateSummaryStatistics

Creates a new instance of UnivariateSummaryStatistics from a Collection of scalar values.

create() - Method in class gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression.IncrementalEstimator.SufficientStatistic

create(MultivariateGaussian) - Method in class gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression.IncrementalEstimator.SufficientStatistic

create() - Method in class gov.sandia.cognition.statistics.bayesian.BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic

create(MultivariateGaussianInverseGammaDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic

create(ConditionalType, String, PriorType) - Static method in class gov.sandia.cognition.statistics.bayesian.DefaultBayesianParameter

Creates a new instance of DefaultBayesianParameter

create() - Method in class gov.sandia.cognition.statistics.distribution.DefaultDataDistribution.DefaultFactory

create() - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.SufficientStatistic

create(MultivariateGaussian) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.SufficientStatistic

create() - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.SufficientStatisticCovarianceInverse

create(MultivariateGaussian) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.SufficientStatisticCovarianceInverse

create() - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.SufficientStatistic

create(UnivariateGaussian) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.SufficientStatistic

create(Collection<? extends InputOutputPair<Double, Boolean>>) - Static method in class gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic

Creates an ROC curve based on the scored data with target information

create(DistributionType) - Method in interface gov.sandia.cognition.statistics.SufficientStatistic

Modifies the given distribution with the parameters indicated by the sufficient statistics

create(Collection<? extends Vectorizable>, TermIndex) - Method in class gov.sandia.cognition.text.term.relation.TermVectorSimilarityNetworkCreator

Creates a new similarity network between the terms in the given documents.

create() - Static method in class gov.sandia.cognition.util.DefaultIdentifiedValue

Convenience method to create a new, empty DefaultIdentifiedValue.

create(IdentifierType, ValueType) - Static method in class gov.sandia.cognition.util.DefaultIdentifiedValue

Creates a new DefaultIdentifiedValue with the given identifier and value.

create() - Static method in class gov.sandia.cognition.util.DefaultKeyValuePair

Convenience method to create a new, empty DefaultKeyValuePair.

create(KeyType, ValueType) - Static method in class gov.sandia.cognition.util.DefaultKeyValuePair

Convenience method to create a new DefaultKeyValuePair from the given key and value.

create(String, T) - Static method in class gov.sandia.cognition.util.DefaultNamedValue

Convenience method for creating an new DefaultNamedValue.

create() - Static method in class gov.sandia.cognition.util.DefaultPair

Creates a new, empty DefaultPair with both values being null.

create(FirstType, SecondType) - Static method in class gov.sandia.cognition.util.DefaultPair

Creates a new DefaultPair from the given values.

create(ValueType, double) - Static method in class gov.sandia.cognition.util.DefaultWeightedValue

Convenience method to create a new WeightedValue.

createArrayList(DataType, DataType) - Static method in class gov.sandia.cognition.collection.CollectionUtil

Creates a new ArrayList from the given pair of values.

createAssignmentTasks() - Method in class gov.sandia.cognition.learning.algorithm.clustering.ParallelizedKMeansClusterer

Creates the assignment tasks given the number of threads requested

createBag(ArrayList<Integer>, ArrayList<Integer>) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.CategoryBalancedIVotingLearner

createBag(ArrayList<Integer>, ArrayList<Integer>) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner

Create the next sample (bag) of examples to learn the next ensemble member from.

createBalanced(Collection<? extends PairType>) - Static method in class gov.sandia.cognition.math.geometry.KDTree

Creates a balanced KDTree based on the given collection of Pairs.

createBodyConverter() - Static method in class gov.sandia.cognition.text.convert.CommonDocumentTextualConverterFactory

Creates a document text converter that extracts the body field.

createCategorizationLearner(int, double, double, int, int, Random) - Static method in class gov.sandia.cognition.learning.algorithm.tree.RandomForestFactory

Creates a random forest learner for categorization outputs.

createCluster(Collection<? extends DataType>) - Method in interface gov.sandia.cognition.learning.algorithm.clustering.cluster.ClusterCreator

Create a new cluster from the given members of that cluster.

createCluster(Collection<? extends DataType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.cluster.DefaultClusterCreator

Creates a new DefaultCluster from the given list of members.

createCluster() - Method in class gov.sandia.cognition.learning.algorithm.clustering.cluster.DefaultIncrementalClusterCreator

createCluster(Collection<? extends Vector>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.cluster.GaussianClusterCreator

Creates a GaussianCluster from a given set of vectors by fitting a Gaussian to those vectors.

createCluster() - Method in interface gov.sandia.cognition.learning.algorithm.clustering.cluster.IncrementalClusterCreator

Creates a new, empty cluster.

createCluster(Collection<? extends DataType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.cluster.MedoidClusterCreator

Creates a CentroidCluster at the member that minimizes the sum of divergence between all members

createCluster() - Method in class gov.sandia.cognition.learning.algorithm.clustering.cluster.NormalizedCentroidClusterCreator

createCluster(Collection<? extends Vectorizable>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.cluster.NormalizedCentroidClusterCreator

createCluster() - Method in class gov.sandia.cognition.learning.algorithm.clustering.cluster.VectorMeanCentroidClusterCreator

createCluster(Collection<? extends Vector>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.cluster.VectorMeanCentroidClusterCreator

createCluster(Collection<? extends Vector>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.cluster.VectorMeanMiniBatchCentroidClusterCreator

createCluster(Collection<ObservationType>, DirichletProcessMixtureModel.Updater<ObservationType>) - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel

Creates a cluster from the given cluster assignment

createClusterPosterior(Iterable<? extends Vector>, Random) - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.MultivariateMeanCovarianceUpdater

createClusterPosterior(Iterable<? extends Vector>, Random) - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.MultivariateMeanUpdater

createClusterPosterior(Iterable<? extends ObservationType>, Random) - Method in interface gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.Updater

Updates the cluster from the values assigned to it

createClustersFromAssignments() - Method in class gov.sandia.cognition.learning.algorithm.clustering.KMeansClusterer

Creates the set of clusters using the current cluster assignments.

createClustersFromAssignments() - Method in class gov.sandia.cognition.learning.algorithm.clustering.ParallelizedKMeansClusterer

CreateClustersFromAssignments() - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.ParallelizedKMeansClusterer.CreateClustersFromAssignments

Creates a new instance of CreateClustersFromAssignments

createCogxel(SemanticIdentifier) - Method in interface gov.sandia.cognition.framework.CogxelFactory

Creates a new Cogxel for the given CogxelFactory from the given SemanticIdentifier.

createCogxel(SemanticIdentifier) - Method in class gov.sandia.cognition.framework.DefaultCogxelFactory

Creates a new Cogxel for the given CogxelFactory from the given SemanticIdentifier.

createCogxel(SemanticIdentifier) - Method in class gov.sandia.cognition.framework.lite.BooleanActivatableCogxelFactory

Creates a new Cogxel for the given CogxelFactory from the given SemanticIdentifier.

createConditionalDistribution(Vectorizable, Vector) - Method in class gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression

Creates the distribution from which the outputs are generated, given the weights and the input to consider.

createConditionalDistribution(Vectorizable, Vector) - Method in interface gov.sandia.cognition.statistics.bayesian.BayesianRegression

Creates the distribution from which the outputs are generated, given the weights and the input to consider.

createConditionalDistribution(Vectorizable, Vector) - Method in class gov.sandia.cognition.statistics.bayesian.BayesianRobustLinearRegression

Creates the distribution from which the outputs are generated, given the weights and the input to consider.

createConditionalDistribution(ParameterType) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.AbstractConjugatePriorBayesianEstimator

createConditionalDistribution(ParameterType) - Method in interface gov.sandia.cognition.statistics.bayesian.conjugate.ConjugatePriorBayesianEstimator

Creates an instance of the class conditional distribution, parameterized by the given parameter value.

createConditionalDistribution(Vector) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.MultinomialBayesianEstimator

createConditionalDistribution(Vector) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanBayesianEstimator

createdClass - Variable in class gov.sandia.cognition.factory.DefaultFactory

The class whose default constructor is used to create new objects.

createDefaultAlphabet() - Static method in class gov.sandia.cognition.text.spelling.SimpleStatisticalSpellingCorrector

Creates the default alphabet, which are the lower-case English letters.

createDefaultState() - Method in interface gov.sandia.cognition.evaluator.StatefulEvaluator

Creates a new default state object.

createDefaultState() - Method in class gov.sandia.cognition.learning.data.feature.DelayFunction

createDefaultState() - Method in class gov.sandia.cognition.learning.data.feature.LinearRegressionCoefficientExtractor

createDefaultState() - Method in class gov.sandia.cognition.learning.function.scalar.KolmogorovSmirnovEvaluator

createDefaultState() - Method in class gov.sandia.cognition.math.signals.AutoRegressiveMovingAverageFilter

createDefaultState() - Method in class gov.sandia.cognition.math.signals.LinearDynamicalSystem

createDefaultState() - Method in class gov.sandia.cognition.math.signals.MovingAverageFilter

createDefaultState() - Method in class gov.sandia.cognition.math.signals.PIDController

createDiagonal(Vectorizable) - Method in class gov.sandia.cognition.math.matrix.MatrixFactory

Creates a new square matrix whose number of rows and columns match the dimensionality of the given vector.

createDistributionEstimatorTasks() - Method in class gov.sandia.cognition.learning.algorithm.hmm.ParallelBaumWelchAlgorithm

Creates the DistributionEstimatorTask

createEmptyInputVector() - Method in interface gov.sandia.cognition.framework.lite.PatternRecognizerLite

Creates an empty vector to use for input.

createEmptyInputVector() - Method in class gov.sandia.cognition.framework.lite.SimplePatternRecognizer

Creates an empty vector to use for input.

createEmptyVector() - Method in class gov.sandia.cognition.framework.learning.converter.CogxelVectorConverter

Creates an empty Vector for the converter, of the proper dimensionality.

createEvaluationTasks(Collection<GenomeType>) - Method in class gov.sandia.cognition.learning.algorithm.genetic.ParallelizedGeneticAlgorithm

Creates the evaluation tasks to execute in parallel.

createFactory(T) - Static method in class gov.sandia.cognition.factory.PrototypeFactory

A convenience method for creating prototype factories.

createFolds(Collection<? extends DataType>) - Method in class gov.sandia.cognition.learning.experiment.CrossFoldCreator

Creates the requested number of cross-validation folds from the given data.

createFolds(Collection<? extends DataType>, int, Random) - Static method in class gov.sandia.cognition.learning.experiment.CrossFoldCreator

Creates the requested number of cross-validation folds from the given data.

createFolds(Collection<? extends DataType>) - Method in class gov.sandia.cognition.learning.experiment.LeaveOneOutFoldCreator

Creates a list of folds that is the same size as the given data.

createFolds(Collection<? extends DataType>) - Method in class gov.sandia.cognition.learning.experiment.RandomByTwoFoldCreator

createFolds(Collection<? extends DataType>) - Method in class gov.sandia.cognition.learning.experiment.RandomFoldCreator

Creates the folds from the given data by passing the data into the set data partitioner multiple times.

createFolds(Collection<? extends InputDataType>) - Method in interface gov.sandia.cognition.learning.experiment.ValidationFoldCreator

Creates a list of partitioned (training and testing) datasets from the given single dataset.

createFromActualPredictedPairs(Collection<? extends Pair<? extends Boolean, ? extends Boolean>>) - Static method in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrix

Creates a new DefaultConfusionMatrix from the given actual-predicted pairs.

createFromActualPredictedPairs(Collection<? extends Pair<? extends CategoryType, ? extends CategoryType>>) - Static method in class gov.sandia.cognition.learning.performance.categorization.DefaultConfusionMatrix

Creates a new DefaultConfusionMatrix from the given actual-predicted pairs.

createFromLogValue(double) - Static method in class gov.sandia.cognition.math.LogNumber

Creates a new LogNumber from the given value that is already in log-space.

createFromLogValue(boolean, double) - Static method in class gov.sandia.cognition.math.LogNumber

Creates a new LogNumber from the given value that is already in log-space.

createFromLogValue(double) - Static method in class gov.sandia.cognition.math.UnsignedLogNumber

Creates a new LogNumber from the given value that is already in log-space.

createFromTargetEstimatePairs(Collection<? extends Pair<Boolean, ? extends Number>>) - Static method in class gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic

Creates an ROC curve based on the scored data with target information.

createFromValue(double) - Static method in class gov.sandia.cognition.math.LogNumber

Creates a new LogNumber from the given value.

createFromValue(double) - Static method in class gov.sandia.cognition.math.UnsignedLogNumber

Creates a new LogNumber from the given value.

createGaussianRandom(int, int, Random) - Method in class gov.sandia.cognition.math.matrix.MatrixFactory

Creates a new Matrix filled with values sampled from a Gaussian distribution with mean 0 and variance 1.

createGaussianRandom(int, int, Random) - Method in class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixFactoryMTJ

createGaussianRandom(int, Random) - Method in class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixFactoryMTJ

Creates a new square Matrix of the given size with random values for the entries, Gaussian distributed with mean 0 and variance 1.

createGaussianRandom(int, Random) - Method in class gov.sandia.cognition.math.matrix.VectorFactory

Creates a vector with random values sampled from a Gaussian distribution with mean 0 and variance 1.

createHashMapWithSize(int) - Static method in class gov.sandia.cognition.collection.CollectionUtil

Creates a new HashMap with the given expected size.

createHashSetWithSize(int) - Static method in class gov.sandia.cognition.collection.CollectionUtil

Creates a new HashSet with the given expected size.

createIdentity(int) - Method in class gov.sandia.cognition.math.matrix.MatrixFactory

Creates a Matrix with ones (1) on the diagonal, and zeros (0) elsewhere

createIdentity(int, int) - Method in class gov.sandia.cognition.math.matrix.MatrixFactory

Creates a Matrix with ones (1) on the diagonal, and zeros (0) elsewhere

createInitialEnsemble() - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractBaggingLearner

Create the initial, empty ensemble for the algorithm to use.

createInitialEnsemble() - Method in class gov.sandia.cognition.learning.algorithm.ensemble.BaggingCategorizerLearner

createInitialEnsemble() - Method in class gov.sandia.cognition.learning.algorithm.ensemble.BaggingRegressionLearner

createInitialEnsemble() - Method in class gov.sandia.cognition.learning.algorithm.ensemble.BinaryBaggingLearner

createInitialLearnedObject() - Method in class gov.sandia.cognition.learning.algorithm.bayes.VectorNaiveBayesCategorizer.OnlineLearner

createInitialLearnedObject() - Method in class gov.sandia.cognition.learning.algorithm.confidence.AdaptiveRegularizationOfWeights

createInitialLearnedObject() - Method in class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalDeviation

createInitialLearnedObject() - Method in class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalVariance

createInitialLearnedObject() - Method in class gov.sandia.cognition.learning.algorithm.ensemble.OnlineBaggingCategorizerLearner

createInitialLearnedObject() - Method in interface gov.sandia.cognition.learning.algorithm.IncrementalLearner

Creates a new initial learned object, before any data is given.

createInitialLearnedObject(Kernel<? super InputType>) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.AbstractKernelizableBinaryCategorizerOnlineLearner

createInitialLearnedObject(Kernel<? super InputType>) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.AbstractLinearCombinationOnlineLearner

createInitialLearnedObject() - Method in class gov.sandia.cognition.learning.algorithm.perceptron.AbstractOnlineLinearBinaryCategorizerLearner

createInitialLearnedObject() - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.AbstractOnlineKernelBinaryCategorizerLearner

createInitialLearnedObject() - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Forgetron.Basic

createInitialLearnedObject() - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Forgetron

createInitialLearnedObject() - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelBinaryCategorizerOnlineLearnerAdapter

createInitialLearnedObject() - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.RemoveOldestKernelPerceptron

createInitialLearnedObject(Kernel<? super InputType>) - Method in interface gov.sandia.cognition.learning.algorithm.perceptron.KernelizableBinaryCategorizerOnlineLearner

Creates the initial learned object with a given kernel.

createInitialLearnedObject() - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineMultiPerceptron

createInitialLearnedObject() - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineShiftingPerceptron

createInitialLearnedObject() - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineVotedPerceptron

createInitialLearnedObject() - Method in class gov.sandia.cognition.learning.algorithm.perceptron.Winnow

createInitialLearnedObject() - Method in class gov.sandia.cognition.statistics.bayesian.AbstractMarkovChainMonteCarlo

Creates the initial parameters from which to start the Markov chain.

createInitialLearnedObject() - Method in class gov.sandia.cognition.statistics.bayesian.AbstractParticleFilter

createInitialLearnedObject() - Method in class gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression.IncrementalEstimator

createInitialLearnedObject() - Method in class gov.sandia.cognition.statistics.bayesian.BayesianRobustLinearRegression.IncrementalEstimator

createInitialLearnedObject() - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.AbstractConjugatePriorBayesianEstimator

createInitialLearnedObject() - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel

createInitialLearnedObject() - Method in class gov.sandia.cognition.statistics.bayesian.ExtendedKalmanFilter

createInitialLearnedObject() - Method in class gov.sandia.cognition.statistics.bayesian.KalmanFilter

createInitialLearnedObject() - Method in class gov.sandia.cognition.statistics.bayesian.MetropolisHastingsAlgorithm

createInitialLearnedObject() - Method in class gov.sandia.cognition.statistics.distribution.DefaultDataDistribution.Estimator

createInitialLearnedObject() - Method in class gov.sandia.cognition.statistics.distribution.DefaultDataDistribution.WeightedEstimator

createInitialLearnedObject() - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.IncrementalEstimator

createInitialLearnedObject() - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.IncrementalEstimatorCovarianceInverse

createInitialLearnedObject() - Method in class gov.sandia.cognition.statistics.distribution.ScalarDataDistribution.Estimator

createInitialLearnedObject() - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.IncrementalEstimator

createInitialLearnedObject() - Method in class gov.sandia.cognition.text.spelling.SimpleStatisticalSpellingCorrector.Learner

createInitialLinearLearnedObject(VectorFactory<?>) - Method in interface gov.sandia.cognition.learning.algorithm.perceptron.LinearizableBinaryCategorizerOnlineLearner

Creates the initial learned object.

createInitialParameter() - Method in interface gov.sandia.cognition.statistics.bayesian.MetropolisHastingsAlgorithm.Updater

Creates the initial parameterization

createInitialParticles(int) - Method in interface gov.sandia.cognition.statistics.bayesian.ParticleFilter.Updater

Creates the initial particles.

createInputTransformed(Evaluator<? super InputType, ? extends IntermediateType>, BatchLearner<? super Collection<? extends IntermediateType>, ? extends Evaluator<? super IntermediateType, ? extends OutputType>>) - Static method in class gov.sandia.cognition.learning.algorithm.CompositeBatchLearnerPair

Creates a new CompositeBatchLearnerPair from the given input transform and learner.

createInputTransformed(BatchLearner<? super Collection<? extends InputType>, ? extends Evaluator<? super InputType, ? extends TransformedInputType>>, BatchLearner<? super Collection<? extends InputOutputPair<? extends TransformedInputType, OutputType>>, ? extends Evaluator<? super TransformedInputType, ? extends OutputType>>) - Static method in class gov.sandia.cognition.learning.algorithm.InputOutputTransformedBatchLearner

Creates a new InputOutputTransformedBatchLearner from the input and supervised learners, performing no transformation on the output type.

createInputTransformed(Evaluator<? super InputType, ? extends TransformedInputType>, BatchLearner<? super Collection<? extends InputOutputPair<? extends TransformedInputType, OutputType>>, ? extends Evaluator<? super TransformedInputType, ? extends OutputType>>) - Static method in class gov.sandia.cognition.learning.algorithm.InputOutputTransformedBatchLearner

Creates a new InputOutputTransformedBatchLearner from the predefined input transform and the supervised learner.

createInternalFunction() - Method in class gov.sandia.cognition.learning.algorithm.regression.AbstractMinimizerBasedParameterCostMinimizer

Creates the internal function that maps the parameter set of result as the input to the function, so that the minimization algorithms can perturb this input in their minimization schemes.

createInternalFunction() - Method in class gov.sandia.cognition.learning.algorithm.regression.ParameterDerivativeFreeCostMinimizer

createInternalFunction() - Method in class gov.sandia.cognition.learning.algorithm.regression.ParameterDifferentiableCostMinimizer

createKernelLearner(Kernel<? super InputType>) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.AbstractKernelizableBinaryCategorizerOnlineLearner

createKernelLearner(Kernel<? super InputType>) - Method in interface gov.sandia.cognition.learning.algorithm.perceptron.KernelizableBinaryCategorizerOnlineLearner

Creates a new kernel-based learner using the standard learning interfaces based on this learner and its parameters.

createLinearLearner(VectorFactory<?>) - Method in interface gov.sandia.cognition.learning.algorithm.perceptron.LinearizableBinaryCategorizerOnlineLearner

Creates a new linear learner using the standard learning interfaces based on this learner and its parameters.

createLinkedHashMapWithSize(int) - Static method in class gov.sandia.cognition.collection.CollectionUtil

Creates a new LinkedHashMap with the given expected size.

createLinkedHashSetWithSize(int) - Static method in class gov.sandia.cognition.collection.CollectionUtil

Creates a new LinkedHashSet with the given expected size.

createLogDominanceWeighter() - Static method in class gov.sandia.cognition.text.term.vector.weighter.CommonTermWeighterFactory

Creates a log-dominance weighting scheme.

createLogEntropyWeighter() - Static method in class gov.sandia.cognition.text.term.vector.weighter.CommonTermWeighterFactory

Creates a log-entropy weighting scheme.

createMatrix(int, int) - Method in class gov.sandia.cognition.math.matrix.custom.CustomDenseMatrixFactory

Creates a new all-zero DenseMatrix of the specified dimensions.

createMatrix(int, int) - Method in class gov.sandia.cognition.math.matrix.custom.CustomDiagonalMatrixFactory

Creates an empty Matrix of the specified dimensions, all elements must be all zeros!

createMatrix(int, int) - Method in class gov.sandia.cognition.math.matrix.custom.CustomSparseMatrixFactory

Creates an empty Matrix of the specified dimensions, all elements must be all zeros!

createMatrix(int, int) - Method in class gov.sandia.cognition.math.matrix.MatrixFactory

Creates an empty Matrix of the specified dimensions, all elements must be all zeros!

createMatrix(int, int, double) - Method in class gov.sandia.cognition.math.matrix.MatrixFactory

Creates a matrix with the given initial value.

createMatrix(int, int) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseMatrixFactoryMTJ

createMatrix(int, int) - Method in class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixFactoryMTJ

createMatrix(int) - Method in class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixFactoryMTJ

Creates a square diagonal matrix of dimensionality "dim" with zeros along the diagonal

createMatrix(int, int, double) - Method in class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixFactoryMTJ

Creates a matrix with the given initial value on all of the elements of the diagonal.

createMatrix(int, int) - Method in class gov.sandia.cognition.math.matrix.mtj.SparseMatrixFactoryMTJ

createModel() - Method in interface gov.sandia.cognition.framework.CognitiveModelFactory

Creates a CognitiveModel from the factory.

createModel() - Method in class gov.sandia.cognition.framework.concurrent.MultithreadedCognitiveModelFactory

Creates a MultithreadedCognitiveModel using the CognitiveModuleFactories that are part of the model factory.

createModel() - Method in class gov.sandia.cognition.framework.lite.CognitiveModelLiteFactory

Creates a CognitiveModelLite using the CognitiveModuleFactories that are part of the model factory.

createModule(CognitiveModel) - Method in interface gov.sandia.cognition.framework.CognitiveModuleFactory

Creates a new CognitiveModule for the given CognitiveModel.

createModule(CognitiveModel) - Method in class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleFactory

Creates a new CognitiveModule for the given CognitiveModel.

createModule(CognitiveModel) - Method in class gov.sandia.cognition.framework.lite.ArrayBasedPerceptionModuleFactory

Creates a new CognitiveModule for the given CognitiveModel.

createModule(CognitiveModel) - Method in class gov.sandia.cognition.framework.lite.MutableSemanticMemoryLiteFactory

Creates a new MutableSemanticMemoryLite module for the given model.

createModule(CognitiveModel) - Method in class gov.sandia.cognition.framework.lite.SharedSemanticMemoryLiteFactory

Creates a new SharedSemanticMemoryLite module for the given model.

createModule(CognitiveModel) - Method in class gov.sandia.cognition.framework.lite.VectorBasedPerceptionModuleFactory

Creates a new CognitiveModule for the given CognitiveModel.

createNamedValuesList(Collection<T>) - Static method in class gov.sandia.cognition.util.DefaultNamedValue

Creates a list of named values from a collection of named objects.

createOutputTransformed(BatchLearner<? super Collection<? extends InputType>, ? extends Evaluator<? super InputType, ? extends IntermediateType>>, Evaluator<? super IntermediateType, ? extends OutputType>) - Static method in class gov.sandia.cognition.learning.algorithm.CompositeBatchLearnerPair

Creates a new CompositeBatchLearnerPair from the given learner and output transform..

createOutputTransformed(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, TransformedOutputType>>, ? extends Evaluator<? super InputType, ? extends TransformedOutputType>>, BatchLearner<? super Collection<? extends OutputType>, ? extends ReversibleEvaluator<OutputType, TransformedOutputType, ?>>) - Static method in class gov.sandia.cognition.learning.algorithm.InputOutputTransformedBatchLearner

Creates a new InputOutputTransformedBatchLearner from the supervised and output learners, performing no transformation on the input type.

createOutputTransformed(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, TransformedOutputType>>, ? extends Evaluator<? super InputType, ? extends TransformedOutputType>>, ReversibleEvaluator<OutputType, TransformedOutputType, ?>) - Static method in class gov.sandia.cognition.learning.algorithm.InputOutputTransformedBatchLearner

Creates a new InputOutputTransformedBatchLearner from the predefined output transforms and the supervised learner.

createPair(FirstType, SecondType) - Method in class gov.sandia.cognition.framework.learning.converter.AbstractCogxelPairConverter

Creates a Pair from the needed data

createPair(InputType, OutputType) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelInputOutputPairConverter

createPair(TargetType, EstimateType) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelTargetEstimatePairConverter

createParameter(BernoulliDistribution, BetaDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.BernoulliBayesianEstimator

createParameter(BinomialDistribution, BetaDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.BinomialBayesianEstimator

createParameter(ConditionalType, BeliefType) - Method in interface gov.sandia.cognition.statistics.bayesian.conjugate.ConjugatePriorBayesianEstimator

Creates a parameter linking the conditional and prior distributions

createParameter(ExponentialDistribution, GammaDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.ExponentialBayesianEstimator

createParameter(GammaDistribution, GammaDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.GammaInverseScaleBayesianEstimator

createParameter(MultinomialDistribution, DirichletDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.MultinomialBayesianEstimator

createParameter(MultivariateGaussian, MultivariateGaussian) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanBayesianEstimator

createParameter(MultivariateGaussian, NormalInverseWishartDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanCovarianceBayesianEstimator

createParameter(PoissonDistribution, GammaDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.PoissonBayesianEstimator

createParameter(UniformDistribution, ParetoDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.UniformDistributionBayesianEstimator

createParameter(UnivariateGaussian, UnivariateGaussian) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.UnivariateGaussianMeanBayesianEstimator

createParameter(UnivariateGaussian, NormalInverseGammaDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.UnivariateGaussianMeanVarianceBayesianEstimator

createPartialPermutation(int, int, Random) - Static method in class gov.sandia.cognition.math.Permutation

Creates a random partial permutation of size k of the numbers 0 (inclusive) through n (exclusive).

createPartition(Collection<? extends DataType>) - Method in interface gov.sandia.cognition.learning.data.DataPartitioner

Partitions the given collection of data into a training set and a testing set.

createPartition(Collection<? extends DataType>) - Method in class gov.sandia.cognition.learning.data.RandomDataPartitioner

Randomly partitions the given data into a training and testing set.

createPartition(Collection<? extends DataType>, double, Random) - Static method in class gov.sandia.cognition.learning.data.RandomDataPartitioner

Randomly partitions the given data into a training and testing set.

createPartitions() - Method in class gov.sandia.cognition.learning.function.cost.ParallelizedCostFunctionContainer

Splits the data across the numComponents cost functions

createPermutation(int, Random) - Static method in class gov.sandia.cognition.math.Permutation

Creates a random permutation of the numbers 0 (inclusive) through n (exclusive).

createPolynomials(double...) - Static method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction

Creates an array of PolynomialFunctions from the array of their exponents

createPredictionDataset(Collection<? extends DataType>, int) - Static method in class gov.sandia.cognition.learning.algorithm.SequencePredictionLearner

Takes a collection and creates a multi-collection of sequences of input-output pairs that are from the given sequence with the given prediction horizon.

createPredictionDataset(MultiCollection<? extends DataType>, int) - Static method in class gov.sandia.cognition.learning.algorithm.SequencePredictionLearner

Takes a multi-collection and creates a multi-collection of sequences of input-output pairs that are from the given sequence with the given prediction horizon.

createPredictionDataset(int, Collection<? extends InputOutputPair<? extends InputType, OutputType>>) - Static method in class gov.sandia.cognition.learning.algorithm.TimeSeriesPredictionLearner

Creates a dataset that can be used to predict the future by "predictionHorizon" samples.

createPredictiveDistribution(PosteriorType) - Method in interface gov.sandia.cognition.statistics.bayesian.BayesianEstimatorPredictor

Creates the predictive distribution of new data given the posterior.

createPredictiveDistribution(MultivariateGaussian) - Method in class gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression

Creates the predictive distribution of outputs given the weight posterior

createPredictiveDistribution(PosteriorType) - Method in interface gov.sandia.cognition.statistics.bayesian.BayesianRegression

Creates the predictive distribution of outputs given the weight posterior

createPredictiveDistribution(MultivariateGaussianInverseGammaDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.BayesianRobustLinearRegression

createPredictiveDistribution(BetaDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.BinomialBayesianEstimator

createPredictiveDistribution(GammaDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.ExponentialBayesianEstimator

createPredictiveDistribution(DirichletDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.MultinomialBayesianEstimator

createPredictiveDistribution(MultivariateGaussian) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanBayesianEstimator

createPredictiveDistribution(NormalInverseWishartDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanCovarianceBayesianEstimator

createPredictiveDistribution(GammaDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.PoissonBayesianEstimator

createPredictiveDistribution(UnivariateGaussian) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.UnivariateGaussianMeanBayesianEstimator

Creates the predictive distribution from the given posterior.

createPredictiveDistribution(NormalInverseGammaDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.UnivariateGaussianMeanVarianceBayesianEstimator

createPredictiveDistribution(MultivariateGaussian, ArrayList<InputType>) - Method in class gov.sandia.cognition.statistics.bayesian.GaussianProcessRegression

Creates the predictive distribution for future points.

createPriorPredictive(Iterable<? extends Vector>) - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.MultivariateMeanCovarianceUpdater

createPriorPredictive(Iterable<? extends Vector>) - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.MultivariateMeanUpdater

createPriorPredictive(Iterable<? extends ObservationType>) - Method in interface gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.Updater

Creates the prior predictive distribution from the data.

createRandom(int, BatchLearner<Collection<? extends WeightedValue<? extends ObservationType>>, ? extends ComputableDistribution<ObservationType>>, Collection<? extends ObservationType>, Random) - Static method in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel

Creates a Hidden Markov Model with the same PMF/PDF for each state, but sampling the columns of the transition matrix and the initial probability distributions from a diffuse Dirichlet.

createRandom(int, ComputableDistribution<ObservationType>, Random) - Static method in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel

Creates a Hidden Markov Model with the same PMF/PDF for each state, but sampling the columns of the transition matrix and the initial probability distributions from a diffuse Dirichlet.

createRandom(Collection<? extends ProbabilityFunction<ObservationType>>, Random) - Static method in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel

Creates a Hidden Markov Model with the given probability function for each state, but sampling the columns of the transition matrix and the initial probability distributions from a diffuse Dirichlet.

createRandom(Random) - Static method in class gov.sandia.cognition.math.matrix.mtj.Vector3

Creates a new random Vector3 from the given Random object.

createRegressionLearner(int, double, double, int, int, Random) - Static method in class gov.sandia.cognition.learning.algorithm.tree.RandomForestFactory

Creates a random forest learner for categorization outputs.

createReordering(Collection<? extends DataType>, Random) - Static method in class gov.sandia.cognition.math.Permutation

Takes a collection and returns an array list that contains a permutation of the elements of that collection.

createReverse() - Method in class gov.sandia.cognition.data.convert.AbstractReverseCachedDataConverter

Creates a new reverse converter.

createReverse() - Method in class gov.sandia.cognition.data.convert.number.DefaultBooleanToNumberConverter

createReverse() - Method in class gov.sandia.cognition.data.convert.number.StringToDoubleConverter

createReverse() - Method in class gov.sandia.cognition.data.convert.number.StringToIntegerConverter

createSequentialPartitions(Iterable<? extends DataType>, int) - Static method in class gov.sandia.cognition.collection.CollectionUtil

Creates a partition of the given data into "numPartition" roughly equal sets, preserving their pre-existing sequential ordering, with the nonzero remainder elements going into the final partition.

createSequentialPartitions(List<? extends DataType>, int) - Static method in class gov.sandia.cognition.collection.CollectionUtil

Creates a partition of the given data into "numPartition" roughly equal sets, preserving their pre-existing sequential ordering, with the nonzero remainder elements going into the final partition.

createSpherical(int) - Static method in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer

Create a spherical partitional clusterer, using Cosine distance and a vector mean centroid cluster creator.

createTerm(Token) - Method in interface gov.sandia.cognition.text.term.TermFactory

Creates a new term from the given token.

createTerm(String) - Method in interface gov.sandia.cognition.text.term.TermFactory

Creates a new term from the given string.

createTFIDFWeighter() - Static method in class gov.sandia.cognition.text.term.vector.weighter.CommonTermWeighterFactory

Creates a term-frequency inverse-document-frequency (TF-IDF) weighting scheme but without any normalization.

createTFIDFWeighterWithUnitNormalization() - Static method in class gov.sandia.cognition.text.term.vector.weighter.CommonTermWeighterFactory

Creates a term-frequency inverse-document-frequency (TF-IDF) weighting scheme with unit vector normalization (2-norm).

createTFWeighter() - Static method in class gov.sandia.cognition.text.term.vector.weighter.CommonTermWeighterFactory

Creates a term-frequency (TF) weighting scheme.

createThreadPool() - Static method in class gov.sandia.cognition.algorithm.ParallelUtil

Creates a thread pool with the "optimal" number of threads.

createThreadPool(int) - Static method in class gov.sandia.cognition.algorithm.ParallelUtil

Creates a thread pool with the given number of threads.

createThreadPool() - Method in class gov.sandia.cognition.learning.function.cost.ParallelizedCostFunctionContainer

Creates the thread pool using the Foundry's global thread pool.

createTitleAuthorBodyConverter() - Static method in class gov.sandia.cognition.text.convert.CommonDocumentTextualConverterFactory

Creates a document text converter that puts the title, author, and body fields together.

createTitleBodyConverter() - Static method in class gov.sandia.cognition.text.convert.CommonDocumentTextualConverterFactory

Creates a document text converter that puts the title and body fields together.

createUniformInitialProbability(int) - Static method in class gov.sandia.cognition.learning.algorithm.hmm.MarkovChain

Creates a uniform initial-probability Vector

createUniformRandom(int, int, Random) - Method in class gov.sandia.cognition.math.matrix.MatrixFactory

Creates a new Matrix filled with values sampled uniformly between 0 and 1.

createUniformRandom(int, int, double, double, Random) - Method in class gov.sandia.cognition.math.matrix.MatrixFactory

Creates a new Matrix of the given size with random values for the entries, uniformly distributed between the given minimum and maximum values.

createUniformRandom(int, int, Random) - Method in class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixFactoryMTJ

createUniformRandom(int, int, double, double, Random) - Method in class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixFactoryMTJ

createUniformRandom(int, double, double, Random) - Method in class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixFactoryMTJ

Creates a new square Matrix of the given size with random values for the entries, uniformly distributed between the given minimum and maximum values.

createUniformRandom(int, Random) - Method in class gov.sandia.cognition.math.matrix.VectorFactory

Creates a vector with random values for the entries uniformly sampled between 0 and 1.

createUniformRandom(int, double, double, Random) - Method in class gov.sandia.cognition.math.matrix.VectorFactory

Creates a Vector with random values for the entries, uniformly distributed between "min" and "max"

createUniformTransitionProbability(int) - Static method in class gov.sandia.cognition.learning.algorithm.hmm.MarkovChain

Creates a uniform transition-probability Matrix

createUnweighted(Collection<? extends TargetEstimatePair<? extends CategoryType, ? extends CategoryType>>) - Static method in class gov.sandia.cognition.learning.performance.categorization.DefaultConfusionMatrix

Creates a new DefaultConfusionMatrix from the given actual-predicted pairs.

createVector(int) - Method in class gov.sandia.cognition.math.matrix.custom.CustomDenseVectorFactory

createVector(int) - Method in class gov.sandia.cognition.math.matrix.custom.CustomSparseVectorFactory

createVector(int) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseVectorFactoryMTJ

createVector(int, double) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseVectorFactoryMTJ

createVector(int) - Method in class gov.sandia.cognition.math.matrix.mtj.SparseVectorFactoryMTJ

createVector(int) - Method in class gov.sandia.cognition.math.matrix.VectorFactory

Creates an empty Vector of the specified dimension, all elements must be all zeros!

createVector(int, double) - Method in class gov.sandia.cognition.math.matrix.VectorFactory

Creates a Vector with the given initial value for all elements.

createVector1D(double) - Method in class gov.sandia.cognition.math.matrix.custom.CustomDenseVectorFactory

createVector1D(double) - Method in class gov.sandia.cognition.math.matrix.custom.CustomSparseVectorFactory

Creates a one-dimensional vector with the given x coordinate: (x).

createVector1D(double) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseVectorFactoryMTJ

createVector1D(double) - Method in class gov.sandia.cognition.math.matrix.mtj.SparseVectorFactoryMTJ

createVector1D() - Method in class gov.sandia.cognition.math.matrix.VectorFactory

Creates a one-dimensional zero vector: (0.0).

createVector1D(double) - Method in class gov.sandia.cognition.math.matrix.VectorFactory

Creates a one-dimensional vector with the given x coordinate: (x).

createVector2D(double, double) - Method in class gov.sandia.cognition.math.matrix.custom.CustomDenseVectorFactory

createVector2D(double, double) - Method in class gov.sandia.cognition.math.matrix.custom.CustomSparseVectorFactory

Creates a two-dimensional vector with the given x and y coordinates: (x, y).

createVector2D(double, double) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseVectorFactoryMTJ

createVector2D(double, double) - Method in class gov.sandia.cognition.math.matrix.mtj.SparseVectorFactoryMTJ

createVector2D() - Method in class gov.sandia.cognition.math.matrix.VectorFactory

Creates a two-dimensional zero vector: (0.0, 0.0).

createVector2D(double, double) - Method in class gov.sandia.cognition.math.matrix.VectorFactory

Creates a two-dimensional vector with the given x and y coordinates: (x, y).

createVector3D(double, double, double) - Method in class gov.sandia.cognition.math.matrix.custom.CustomDenseVectorFactory

createVector3D(double, double, double) - Method in class gov.sandia.cognition.math.matrix.custom.CustomSparseVectorFactory

Creates a three-dimensional vector with the given x, y, and z coordinates: (x, y, z).

createVector3D(double, double, double) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseVectorFactoryMTJ

createVector3D(double, double, double) - Method in class gov.sandia.cognition.math.matrix.mtj.SparseVectorFactoryMTJ

createVector3D() - Method in class gov.sandia.cognition.math.matrix.VectorFactory

Creates a three-dimensional zero vector: (0.0, 0.0, 0.0).

createVector3D(double, double, double) - Method in class gov.sandia.cognition.math.matrix.VectorFactory

Creates a three-dimensional vector with the given x, y, and z coordinates: (x, y, z).

createVectorCapacity(int, int) - Method in class gov.sandia.cognition.math.matrix.custom.CustomDenseVectorFactory

createVectorCapacity(int, int) - Method in class gov.sandia.cognition.math.matrix.custom.CustomSparseVectorFactory

createVectorCapacity(int, int) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseVectorFactoryMTJ

createVectorCapacity(int, int) - Method in class gov.sandia.cognition.math.matrix.mtj.SparseVectorFactoryMTJ

createVectorCapacity(int, int) - Method in class gov.sandia.cognition.math.matrix.VectorFactory

Creates a new, empty vector with the given dimensionality and expected number of nonzero elements.

createWeightDistribution() - Method in class gov.sandia.cognition.learning.function.categorization.AbstractConfidenceWeightedBinaryCategorizer

createWeightDistribution() - Method in interface gov.sandia.cognition.learning.function.categorization.ConfidenceWeightedBinaryCategorizer

Creates a multivariate Gaussian distribution that represents the distribution of weight vectors that the algorithm has learned.

createWrapper(DenseMatrix) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseMatrixFactoryMTJ

Creates a new wrapper for a dense MTJ matrix.

createWrapper(DenseVector) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseVectorFactoryMTJ

Creates a new wrapper for a dense MTJ vector.

createWrapper(FlexCompColMatrix) - Method in class gov.sandia.cognition.math.matrix.mtj.SparseMatrixFactoryMTJ

Creates a new wrapper for a sparse column MTJ matrix.

createWrapper(FlexCompRowMatrix) - Method in class gov.sandia.cognition.math.matrix.mtj.SparseMatrixFactoryMTJ

Creates a new wrapper for a sparse row MTJ matrix.

createWrapper(SparseVector) - Method in class gov.sandia.cognition.math.matrix.mtj.SparseVectorFactoryMTJ

Creates a new wrapper for a sparse MTJ vector.

creator - Variable in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer

The merger used to merge two clusters into one element.

creator - Variable in class gov.sandia.cognition.learning.algorithm.clustering.initializer.AbstractMinDistanceFixedClusterInitializer

The ClusterCreator to create the initial clusters from.

creator - Variable in class gov.sandia.cognition.learning.algorithm.clustering.initializer.RandomClusterInitializer

The creator for new clusters.

creator - Variable in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer

The merger used to merge two clusters into one cluster.

CrossFoldCreator<DataType> - Class in gov.sandia.cognition.learning.experiment

The CrossFoldCreator implements a validation fold creator that creates folds for a typical k-fold cross-validation experiment.

CrossFoldCreator() - Constructor for class gov.sandia.cognition.learning.experiment.CrossFoldCreator

Creates a new instance of CrossFoldCreator with a default number of folds (10) and a default Random number generator.

CrossFoldCreator(int) - Constructor for class gov.sandia.cognition.learning.experiment.CrossFoldCreator

Creates a new CrossFoldCreator.

CrossFoldCreator(int, Random) - Constructor for class gov.sandia.cognition.learning.experiment.CrossFoldCreator

Creates a new CrossFoldCreator.

crossover(GenomeType, GenomeType) - Method in interface gov.sandia.cognition.learning.algorithm.genetic.reproducer.CrossoverFunction

Crosses over the provided genomes to produce a new genome.

crossover(Vectorizable, Vectorizable) - Method in class gov.sandia.cognition.learning.algorithm.genetic.reproducer.VectorizableCrossoverFunction

Crosses over each element of the parent vectors.

CrossoverFunction<GenomeType> - Interface in gov.sandia.cognition.learning.algorithm.genetic.reproducer

The CrossoverFunction interface implements standard functionality for implementing crossover for genetic algorithms.

CrossoverReproducer<GenomeType> - Class in gov.sandia.cognition.learning.algorithm.genetic.reproducer

The CrossoverReproducer takes a population of genomes, and applies the supplied CrossoverFunction to produce a new population.

CrossoverReproducer(Selector<GenomeType>, CrossoverFunction<GenomeType>) - Constructor for class gov.sandia.cognition.learning.algorithm.genetic.reproducer.CrossoverReproducer

Creates a new instance of CrossoverReproducer

CSV_EXTENSION - Static variable in class gov.sandia.cognition.framework.io.ModelFileHandler

The extension for comma-separated-value models.

CSVDefaultCognitiveModelLiteHandler - Class in gov.sandia.cognition.framework.io

The CSVDefaultCognitiveModelLiteHandler class implements a format handler for CSV files that specify a default setup for a CognitiveModelLite.

CSVDefaultCognitiveModelLiteHandler() - Constructor for class gov.sandia.cognition.framework.io.CSVDefaultCognitiveModelLiteHandler

Creates a new instance of CommaSeparatedValueHandler.

CSVParseException - Exception in gov.sandia.cognition.io

The CSVParseException class implements an exception that is thrown while parsing a CSV file.

CSVParseException() - Constructor for exception gov.sandia.cognition.io.CSVParseException

Creates a new instance of CSVParseException without detail message.

CSVParseException(String) - Constructor for exception gov.sandia.cognition.io.CSVParseException

Constructs an instance of CSVParseException with the specified detail message.

CSVParseException(Throwable) - Constructor for exception gov.sandia.cognition.io.CSVParseException

Constructs an instance of CSVParseException with the given cause of the exception.

CSVParseException(String, Throwable) - Constructor for exception gov.sandia.cognition.io.CSVParseException

Constructs an instance of CSVParseException with the given message and cause of the exception.

CSVUtility - Class in gov.sandia.cognition.io

The CSVUtility class implements some utility functions for dealing with comma-separated value (CSV) file types.

CSVUtility() - Constructor for class gov.sandia.cognition.io.CSVUtility

Cubic(double, double, double, double) - Constructor for class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Cubic

Creates a new instance of Quadratic

Cubic(PolynomialFunction.Cubic) - Constructor for class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Cubic

Copy constructor

CumulativeDistributionFunction<NumberType extends java.lang.Number> - Interface in gov.sandia.cognition.statistics

Functionality of a cumulative distribution function.

currentBag - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner

The current bag used to train the current ensemble member.

currentCorrectIndices - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner

The indices of examples that the ensemble currently gets correct.

currentEnsembleCorrect - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner

A boolean for each example indicating if the ensemble currently gets the example correct or incorrect.

currentIncorrectIndices - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner

The indices of examples that the ensemble currently gets incorrect.

currentInput - Variable in class gov.sandia.cognition.statistics.bayesian.AbstractKalmanFilter

Current input to the model.

currentMember - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner

The currently learned member of the ensemble.

currentMemberEstimates - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner

The estimates of the current member for each example.

currentParameter - Variable in class gov.sandia.cognition.statistics.bayesian.AbstractMarkovChainMonteCarlo

The current parameters in the random walk.

CustomDenseMatrixFactory - Class in gov.sandia.cognition.math.matrix.custom

Factory that creates DenseMatrix instances.

CustomDenseMatrixFactory() - Constructor for class gov.sandia.cognition.math.matrix.custom.CustomDenseMatrixFactory

CustomDenseVectorFactory - Class in gov.sandia.cognition.math.matrix.custom

Dense vector factory.

CustomDenseVectorFactory() - Constructor for class gov.sandia.cognition.math.matrix.custom.CustomDenseVectorFactory

CustomDiagonalMatrixFactory - Class in gov.sandia.cognition.math.matrix.custom

Factory for diagonal matrices.

CustomDiagonalMatrixFactory() - Constructor for class gov.sandia.cognition.math.matrix.custom.CustomDiagonalMatrixFactory

CustomSparseMatrixFactory - Class in gov.sandia.cognition.math.matrix.custom

Factory for Sparse Matrices.

CustomSparseMatrixFactory() - Constructor for class gov.sandia.cognition.math.matrix.custom.CustomSparseMatrixFactory

CustomSparseVectorFactory - Class in gov.sandia.cognition.math.matrix.custom

Generates Sparse Vectors with all settings initialized properly

CustomSparseVectorFactory() - Constructor for class gov.sandia.cognition.math.matrix.custom.CustomSparseVectorFactory

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