A-Index (Cognitive Foundry)

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

A

absoluteValue() - Method in class gov.sandia.cognition.math.LogNumber: Returns a new LogNumber that represents the absolute value of this LogNumber.
absoluteValue() - Method in class gov.sandia.cognition.time.DefaultDuration
absoluteValue() - Method in interface gov.sandia.cognition.time.Duration: Returns the absolute value of this duration.
absoluteValueEquals() - Method in class gov.sandia.cognition.math.LogNumber: Transforms this value to be its absolute value.
AbstractAnytimeAlgorithm<ResultType> - Class in gov.sandia.cognition.algorithm: A partial implementation of the common functionality of an AnytimeAlgorithm.
AbstractAnytimeAlgorithm(int) - Constructor for class gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm: Creates a new instance of AbstractAnytimeAlgorithm.
AbstractAnytimeBatchLearner<DataType,ResultType> - Class in gov.sandia.cognition.learning.algorithm: The AbstractAnytimeBatchLearner abstract class implements a standard method for conforming to the BatchLearner and AnytimeLearner (IterativeAlgorithm and StoppableAlgorithm) interfaces.
AbstractAnytimeBatchLearner(int) - Constructor for class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner: Creates a new instance of AbstractAnytimeBatchLearner.
AbstractAnytimeFunctionMinimizer<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>> - Class in gov.sandia.cognition.learning.algorithm.minimization: A partial implementation of a minimization algorithm that is iterative, stoppable, and approximate.
AbstractAnytimeFunctionMinimizer(InputType, double, int) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.AbstractAnytimeFunctionMinimizer: Creates a new instance of AbstractStandardIterativeMinimizationAlgorithm
AbstractAnytimeLineMinimizer<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>> - Class in gov.sandia.cognition.learning.algorithm.minimization.line: Partial AnytimeAlgorithm implementation of a LineMinimizer.
AbstractAnytimeLineMinimizer(LineBracketInterpolator<? super EvaluatorType>) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.AbstractAnytimeLineMinimizer: Creates a new instance of AbstractAnytimeLineMinimizer
AbstractAnytimeLineMinimizer(LineBracketInterpolator<? super EvaluatorType>, LineBracket, Double, double, int) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.AbstractAnytimeLineMinimizer: Creates a new instance of AbstractAnytimeLineMinimizer
AbstractAnytimeSupervisedBatchLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>> - Class in gov.sandia.cognition.learning.algorithm: The AbstractAnytimeSupervisedBatchLearner abstract class extends the AbstractAnytimeBatchLearner to implement the SupervisedBatchLearner interface.
AbstractAnytimeSupervisedBatchLearner(int) - Constructor for class gov.sandia.cognition.learning.algorithm.AbstractAnytimeSupervisedBatchLearner: Creates a new instance of AbstractAnytimeSupervisedBatchLearner.
AbstractBaggingLearner<InputType,OutputType,MemberType,EnsembleType extends Evaluator<? super InputType,? extends OutputType>> - Class in gov.sandia.cognition.learning.algorithm.ensemble: Learns an ensemble by randomly sampling with replacement (duplicates allowed) some percentage of the size of the data (defaults to 100%) on each iteration to train a new ensemble member.
AbstractBaggingLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.AbstractBaggingLearner: Creates a new instance of AbstractBaggingLearner.
AbstractBaggingLearner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, OutputType>>, ? extends MemberType>) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.AbstractBaggingLearner: Creates a new instance of AbstractBaggingLearner.
AbstractBaggingLearner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, OutputType>>, ? extends MemberType>, int, double, Random) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.AbstractBaggingLearner: Creates a new instance of AbstractBaggingLearner.
AbstractBatchAndIncrementalLearner<DataType,ResultType> - Class in gov.sandia.cognition.learning.algorithm: An abstract class that has both batch learning ability as well as online learning ability by taking a Collection of input data.
AbstractBatchAndIncrementalLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.AbstractBatchAndIncrementalLearner: Creates a new instance of AbstractBatchAndIncrementalLearner.
AbstractBatchLearnerContainer<LearnerType extends BatchLearner<?,?>> - Class in gov.sandia.cognition.learning.algorithm: An abstract class for objects that contain a batch learning algorithm.
AbstractBatchLearnerContainer() - Constructor for class gov.sandia.cognition.learning.algorithm.AbstractBatchLearnerContainer: Creates a new AbstractBatchLearnerWrapper with a null learner.
AbstractBatchLearnerContainer(LearnerType) - Constructor for class gov.sandia.cognition.learning.algorithm.AbstractBatchLearnerContainer: Creates a new AbstractBatchLearnerWrapper with the given learner.
AbstractBaumWelchAlgorithm<ObservationType,DataType> - Class in gov.sandia.cognition.learning.algorithm.hmm: Partial implementation of the Baum-Welch algorithm.
AbstractBaumWelchAlgorithm(HiddenMarkovModel<ObservationType>, BatchLearner<Collection<? extends WeightedValue<? extends ObservationType>>, ? extends ComputableDistribution<ObservationType>>, boolean) - Constructor for class gov.sandia.cognition.learning.algorithm.hmm.AbstractBaumWelchAlgorithm: Creates a new instance of AbstractBaumWelchAlgorithm
AbstractBayesianParameter<ParameterType,ConditionalType extends ClosedFormDistribution<?>,PriorType extends Distribution<ParameterType>> - Class in gov.sandia.cognition.statistics.bayesian: Partial implementation of BayesianParameter
AbstractBayesianParameter() - Constructor for class gov.sandia.cognition.statistics.bayesian.AbstractBayesianParameter: Creates a new instance of AbstractBayesianParameter
AbstractBayesianParameter(ConditionalType, String, PriorType) - Constructor for class gov.sandia.cognition.statistics.bayesian.AbstractBayesianParameter: Creates a new instance of AbstractBayesianParameter
AbstractBinaryCategorizer<InputType> - Class in gov.sandia.cognition.learning.function.categorization: The AbstractBinaryCategorizer implements the commonality of the BinaryCategorizer, holding the collection of possible values.
AbstractBinaryCategorizer() - Constructor for class gov.sandia.cognition.learning.function.categorization.AbstractBinaryCategorizer: Creates a new AbstractBinaryCategorizer.
AbstractBinaryConfusionMatrix - Class in gov.sandia.cognition.learning.performance.categorization: An abstract implementation of the BinaryConfusionMatrix interface.
AbstractBinaryConfusionMatrix() - Constructor for class gov.sandia.cognition.learning.performance.categorization.AbstractBinaryConfusionMatrix: Creates a new AbstractBinaryConfusionMatrix.
AbstractBracketedRootFinder - Class in gov.sandia.cognition.learning.algorithm.root: Partial implementation of RootFinder that maintains a bracket on the root.
AbstractBracketedRootFinder() - Constructor for class gov.sandia.cognition.learning.algorithm.root.AbstractBracketedRootFinder: Creates a new instance of AbstractBracketedRootFinder
AbstractCategorizer<InputType,CategoryType> - Class in gov.sandia.cognition.learning.function.categorization: An abstract implementation of the Categorizer interface.
AbstractCategorizer() - Constructor for class gov.sandia.cognition.learning.function.categorization.AbstractCategorizer: Creates a new AbstractCategorizer with an empty category set.
AbstractCategorizer(Set<CategoryType>) - Constructor for class gov.sandia.cognition.learning.function.categorization.AbstractCategorizer: Creates a new AbstractCategorizer with the given category set.
AbstractCategorizerOutOfBagStoppingCriteria<InputType,CategoryType> - Class in gov.sandia.cognition.learning.algorithm.ensemble: Abstract class for implementing a out-of-bag stopping criteria for a bagging-based ensemble.
AbstractCategorizerOutOfBagStoppingCriteria() - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.AbstractCategorizerOutOfBagStoppingCriteria: Creates a new OutOfBagErrorStoppingCriteria.
AbstractCategorizerOutOfBagStoppingCriteria(int) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.AbstractCategorizerOutOfBagStoppingCriteria: Creates a new OutOfBagErrorStoppingCriteria with the given smoothing window size.
AbstractCharacterBasedTokenizer - Class in gov.sandia.cognition.text.token: An abstract implementation of a tokenizer that considers each character individually.
AbstractCharacterBasedTokenizer() - Constructor for class gov.sandia.cognition.text.token.AbstractCharacterBasedTokenizer: Creates a new LetterNumberTokenizer.
AbstractCloneableSerializable - Class in gov.sandia.cognition.util: The AbstractCloneableserializable abstract class implements a default version of the clone method that calls the Object clone method, but traps the exception that can be thrown.
AbstractCloneableSerializable() - Constructor for class gov.sandia.cognition.util.AbstractCloneableSerializable: Creates a new instance of AbstractCloneableSerializable.
AbstractClosedFormIntegerDistribution - Class in gov.sandia.cognition.statistics: An abstract class for closed-form integer distributions.
AbstractClosedFormIntegerDistribution() - Constructor for class gov.sandia.cognition.statistics.AbstractClosedFormIntegerDistribution: Creates a new AbstractClosedFormIntegerDistribution.
AbstractClosedFormSmoothUnivariateDistribution - Class in gov.sandia.cognition.statistics: Partial implementation of SmoothUnivariateDistribution
AbstractClosedFormSmoothUnivariateDistribution() - Constructor for class gov.sandia.cognition.statistics.AbstractClosedFormSmoothUnivariateDistribution
AbstractClosedFormUnivariateDistribution<NumberType extends java.lang.Number> - Class in gov.sandia.cognition.statistics: Partial implementation of a ClosedFormUnivariateDistribution.
AbstractClosedFormUnivariateDistribution() - Constructor for class gov.sandia.cognition.statistics.AbstractClosedFormUnivariateDistribution
AbstractClusterHierarchyNode<DataType,ClusterType extends Cluster<DataType>> - Class in gov.sandia.cognition.learning.algorithm.clustering.hierarchy: An abstract implementation of the ClusterHierarchyNode class.
AbstractClusterHierarchyNode() - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.hierarchy.AbstractClusterHierarchyNode: Creates a new AbstractClusterHierarchyNode.
AbstractClusterHierarchyNode(ClusterType) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.hierarchy.AbstractClusterHierarchyNode: Creates a new AbstractClusterHierarchyNode.
AbstractClusterToClusterDivergenceFunction<ClusterType extends Cluster<DataType>,DataType> - Class in gov.sandia.cognition.learning.algorithm.clustering.divergence: The AbstractClusterToClusterDivergenceFunction class is an abstract class that helps out implementations of ClusterToClusterDivergenceFunction implementations by holding a DivergenceFunction between elements of a cluster.
AbstractClusterToClusterDivergenceFunction(DivergenceFunction<? super DataType, ? super DataType>) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.divergence.AbstractClusterToClusterDivergenceFunction: Creates a new instance of AbstractClusterToClusterDivergenceFunction
AbstractCognitiveModel - Class in gov.sandia.cognition.framework: The AbstractCognitiveModel class is an abstract class that implements common functionality of classes that implement the CognitiveModel interface may wish to have.
AbstractCognitiveModel() - Constructor for class gov.sandia.cognition.framework.AbstractCognitiveModel: Creates a new instance of AbstractCognitiveModel.
AbstractCognitiveModelFactory - Class in gov.sandia.cognition.framework: The AbstractCognitiveModelFactory class defines common functionality among CognitiveModelFactory implementations.
AbstractCognitiveModelFactory() - Constructor for class gov.sandia.cognition.framework.AbstractCognitiveModelFactory: Creates a new instance of AbstractCognitiveModelFactory.
AbstractCognitiveModelFactory(Collection<CognitiveModuleFactory>) - Constructor for class gov.sandia.cognition.framework.AbstractCognitiveModelFactory: Creates a new instance of AbstractCognitiveModelFactory.
AbstractCognitiveModelLite - Class in gov.sandia.cognition.framework.lite: The AbstractCognitiveModelLite class is an abstract class that implements common functionality of classes that share general functionality with the CognitiveModelLite - i.e.
AbstractCognitiveModelLite() - Constructor for class gov.sandia.cognition.framework.lite.AbstractCognitiveModelLite: Creates a new instance of AbstractCognitiveModelLite.
AbstractCogxelConverter<DataType> - Class in gov.sandia.cognition.framework.learning.converter: Partial implementation of CogxelConverter
AbstractCogxelConverter() - Constructor for class gov.sandia.cognition.framework.learning.converter.AbstractCogxelConverter: Default constructor
AbstractCogxelConverter(SemanticIdentifierMap) - Constructor for class gov.sandia.cognition.framework.learning.converter.AbstractCogxelConverter: Creates a new instance of AbstractCogxelConverter
AbstractCogxelPairConverter<FirstType,SecondType,PairType extends Pair<FirstType,SecondType>> - Class in gov.sandia.cognition.framework.learning.converter: Partial implementation of CogxelConverters based on a Pair
AbstractCogxelPairConverter() - Constructor for class gov.sandia.cognition.framework.learning.converter.AbstractCogxelPairConverter: Creates a new instance of AbstractCogxelPairConverter
AbstractCogxelPairConverter(CogxelConverter<FirstType>, CogxelConverter<SecondType>) - Constructor for class gov.sandia.cognition.framework.learning.converter.AbstractCogxelPairConverter: Creates a new instance of AbstractCogxelPairConverter
AbstractCogxelPairConverter(CogxelConverter<FirstType>, CogxelConverter<SecondType>, SemanticIdentifierMap) - Constructor for class gov.sandia.cognition.framework.learning.converter.AbstractCogxelPairConverter: Creates a new instance of AbstractCogxelPairConverter
AbstractCombinationsIterator(int, int) - Constructor for class gov.sandia.cognition.math.Combinations.AbstractCombinationsIterator: Creates a new instance of AbstractCombinationsIterator
AbstractConcurrentCognitiveModule - Class in gov.sandia.cognition.framework.concurrent: The AbstractConcurrentCognitiveModule class is an abstract class that implements common functionality of classes that implement the ConcurrentCognitiveModule interface.
AbstractConcurrentCognitiveModule() - Constructor for class gov.sandia.cognition.framework.concurrent.AbstractConcurrentCognitiveModule
AbstractConfidenceStatistic - Class in gov.sandia.cognition.statistics.method: Abstract implementation of ConfidenceStatistic.
AbstractConfidenceStatistic(double) - Constructor for class gov.sandia.cognition.statistics.method.AbstractConfidenceStatistic: Creates a new instance of AbstractConfidenceStatistic
AbstractConfidenceWeightedBinaryCategorizer - Class in gov.sandia.cognition.learning.function.categorization: Unit tests for class AbstractConfidenceWeightedBinaryCategorizer.
AbstractConfidenceWeightedBinaryCategorizer() - Constructor for class gov.sandia.cognition.learning.function.categorization.AbstractConfidenceWeightedBinaryCategorizer: Creates a new, uninitialized AbstractConfidenceWeightedBinaryCategorizer.
AbstractConfidenceWeightedBinaryCategorizer(Vector) - Constructor for class gov.sandia.cognition.learning.function.categorization.AbstractConfidenceWeightedBinaryCategorizer: Creates a new AbstractConfidenceWeightedBinaryCategorizer with the given mean vector.
AbstractConfusionMatrix<CategoryType> - Class in gov.sandia.cognition.learning.performance.categorization: An abstract implementation of the ConfusionMatrix interface.
AbstractConfusionMatrix() - Constructor for class gov.sandia.cognition.learning.performance.categorization.AbstractConfusionMatrix: Creates a new AbstractConfusionMatrix.
AbstractConjugatePriorBayesianEstimator<ObservationType,ParameterType,ConditionalType extends ClosedFormDistribution<ObservationType>,BeliefType extends ClosedFormDistribution<ParameterType>> - Class in gov.sandia.cognition.statistics.bayesian.conjugate: Partial implementation of ConjugatePriorBayesianEstimator that contains a initial belief (prior) distribution function.
AbstractConjugatePriorBayesianEstimator(BayesianParameter<ParameterType, ConditionalType, BeliefType>) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.AbstractConjugatePriorBayesianEstimator: Creates a new instance of AbstractConjugatePriorBayesianEstimator
AbstractCostFunction<EvaluatedType,CostParametersType> - Class in gov.sandia.cognition.learning.function.cost: Partial implementation of CostFunction.
AbstractCostFunction() - Constructor for class gov.sandia.cognition.learning.function.cost.AbstractCostFunction: Creates a new instance of AbstractCostFunction
AbstractCostFunction(CostParametersType) - Constructor for class gov.sandia.cognition.learning.function.cost.AbstractCostFunction: Creates a new instance of AbstractCostFunction
AbstractDataConverter<InputType,OutputType> - Class in gov.sandia.cognition.data.convert: Abstract implementation of DataConverter interface.
AbstractDataConverter() - Constructor for class gov.sandia.cognition.data.convert.AbstractDataConverter: Creates a new AbstractDataConverter.
AbstractDataDistribution<KeyType> - Class in gov.sandia.cognition.statistics: An abstract implementation of the DataDistribution interface.
AbstractDataDistribution(Map<KeyType, MutableDouble>) - Constructor for class gov.sandia.cognition.statistics.AbstractDataDistribution: Creates a new AbstractDataDistribution.
AbstractDecisionTreeLearner<InputType,OutputType> - Class in gov.sandia.cognition.learning.algorithm.tree: The AbstractDecisionTreeLearner implements common functionality for learning algorithms that learn a decision tree.
AbstractDecisionTreeLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeLearner: Creates a new instance of AbstractDecisionTreeLearner
AbstractDecisionTreeLearner(DeciderLearner<? super InputType, OutputType, ?, ?>) - Constructor for class gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeLearner: Creates a new instance of AbstractDecisionTreeLearner.
AbstractDecisionTreeNode<InputType,OutputType,InteriorType> - Class in gov.sandia.cognition.learning.algorithm.tree: The AbstractDecisionTreeNode class implements common functionality for a decision tree node.
AbstractDecisionTreeNode() - Constructor for class gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeNode: Creates a new instance of AbstractDecisionTreeNode
AbstractDecisionTreeNode(DecisionTreeNode<InputType, OutputType>, Categorizer<? super InputType, ? extends InteriorType>, Object) - Constructor for class gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeNode: Creates a new instance of CategorizationTreeNode.
AbstractDeltaCategorizer<CategoryType> - Class in gov.sandia.cognition.learning.algorithm.delta: The Burrows Delta algorithm is primarily used for authorship attribution, but can be used for other applications.
AbstractDeltaCategorizer(AbstractDeltaCategorizer.AbstractLearner<CategoryType>, ArrayList<Double>) - Constructor for class gov.sandia.cognition.learning.algorithm.delta.AbstractDeltaCategorizer: Constructor that takes a learner and featureStddev.
AbstractDeltaCategorizer.AbstractLearner<CategoryType> - Class in gov.sandia.cognition.learning.algorithm.delta: Abstract learner for delta algorithms.
AbstractDifferentiableUnivariateScalarFunction - Class in gov.sandia.cognition.math: Partial implementation of DifferentiableUnivariateScalarFunction that implements the differentiate(Double) method with a callback to the differentiate(double) method, so that a concrete class only to implement the differentiate(double) method
AbstractDifferentiableUnivariateScalarFunction() - Constructor for class gov.sandia.cognition.math.AbstractDifferentiableUnivariateScalarFunction: Creates a new instance of AbstractDifferentiableUnivariateScalarFunction
AbstractDiscriminantBinaryCategorizer<InputType> - Class in gov.sandia.cognition.learning.function.categorization: An abstract implementation of the DiscriminantBinaryCategorizer interface.
AbstractDiscriminantBinaryCategorizer() - Constructor for class gov.sandia.cognition.learning.function.categorization.AbstractDiscriminantBinaryCategorizer: Creates a new AbstractDiscriminantBinaryCategorizer.
AbstractDiscriminantCategorizer<InputType,CategoryType,DiscriminantType extends java.lang.Comparable<? super DiscriminantType>> - Class in gov.sandia.cognition.learning.function.categorization: An abstract implementation of the DiscriminantCategorizer interface.
AbstractDiscriminantCategorizer() - Constructor for class gov.sandia.cognition.learning.function.categorization.AbstractDiscriminantCategorizer: Creates a new AbstractDiscriminantCategorizer with an empty set of categories.
AbstractDiscriminantCategorizer(Set<CategoryType>) - Constructor for class gov.sandia.cognition.learning.function.categorization.AbstractDiscriminantCategorizer: Creates a new AbstractCategorizer with the given category set.
AbstractDistribution<DataType> - Class in gov.sandia.cognition.statistics: Partial implementation of Distribution.
AbstractDistribution() - Constructor for class gov.sandia.cognition.statistics.AbstractDistribution
AbstractDocument - Class in gov.sandia.cognition.text.document: An abstract implementation of the Document interface.
AbstractDocument() - Constructor for class gov.sandia.cognition.text.document.AbstractDocument: Creates a new AbstractDocument.
AbstractDocumentExtractor - Class in gov.sandia.cognition.text.document.extractor: An abstract implementation of the DocumentExtractor interface.
AbstractDocumentExtractor() - Constructor for class gov.sandia.cognition.text.document.extractor.AbstractDocumentExtractor: Creates a new AbstractDocumentExtractor.
AbstractEigenDecomposition - Class in gov.sandia.cognition.math.matrix.decomposition: Abstract partial implementation of the EigenDecomposition interface
AbstractEigenDecomposition(ComplexNumber[], Matrix, Matrix) - Constructor for class gov.sandia.cognition.math.matrix.decomposition.AbstractEigenDecomposition: Stores the given eigenvalues and eigenvectors internally, the eigenvalues and eigenvectors will not be sorted.
AbstractEigenDecomposition(ComplexNumber[], Matrix, Matrix, boolean) - Constructor for class gov.sandia.cognition.math.matrix.decomposition.AbstractEigenDecomposition: Creates a new eigendecomposition using the given eigenvalues and eigenvectors...
AbstractEntropyBasedGlobalTermWeighter - Class in gov.sandia.cognition.text.term.vector.weighter.global: An abstract implementation of a global term weighting scheme that keeps track of the sum of the entropy term (f_ij * log(f_ij)) over all documents.
AbstractEntropyBasedGlobalTermWeighter() - Constructor for class gov.sandia.cognition.text.term.vector.weighter.global.AbstractEntropyBasedGlobalTermWeighter: Creates a new AbstractEntropyBasedGlobalTermWeighter.
AbstractEntropyBasedGlobalTermWeighter(VectorFactory<? extends Vector>) - Constructor for class gov.sandia.cognition.text.term.vector.weighter.global.AbstractEntropyBasedGlobalTermWeighter: Creates a new AbstractEntropyBasedGlobalTermWeighter.
AbstractEnvelope() - Constructor for class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.AbstractEnvelope: Default constructor
AbstractEuclideanRing<RingType extends EuclideanRing<RingType>> - Class in gov.sandia.cognition.math: An abstract implementation of the EuclideanRing interface.
AbstractEuclideanRing() - Constructor for class gov.sandia.cognition.math.AbstractEuclideanRing: Creates a new AbstractEuclideanRing.
AbstractFactorizationMachineLearner - Class in gov.sandia.cognition.learning.algorithm.factor.machine: An abstract class for learning FactorizationMachines.
AbstractFactorizationMachineLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner: Creates a new AbstractFactorizationMachineLearner.
AbstractFactorizationMachineLearner(int, double, double, double, double, int, Random) - Constructor for class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner: Creates a new AbstractFactorizationMachineLearner.
AbstractField<FieldType extends Field<FieldType>> - Class in gov.sandia.cognition.math: An abstract implementation of the Field interface.
AbstractField() - Constructor for class gov.sandia.cognition.math.AbstractField: Creates a new AbstractField.
AbstractField - Class in gov.sandia.cognition.text.document: An abstract implementation of the Field interface.
AbstractField() - Constructor for class gov.sandia.cognition.text.document.AbstractField: Creates a new AbstractField.
AbstractField(String) - Constructor for class gov.sandia.cognition.text.document.AbstractField: Creates a new AbstractField
AbstractFileSerializationHandler<SerializedType> - Class in gov.sandia.cognition.io.serialization: An abstract implementation of FileSerializationHandler.
AbstractFileSerializationHandler() - Constructor for class gov.sandia.cognition.io.serialization.AbstractFileSerializationHandler: Creates a new AbstractFileSerializationHandler.
AbstractFrequencyBasedGlobalTermWeighter - Class in gov.sandia.cognition.text.term.vector.weighter.global: An abstract GlobalTermWeighter that keeps track of term frequencies in documents.
AbstractFrequencyBasedGlobalTermWeighter() - Constructor for class gov.sandia.cognition.text.term.vector.weighter.global.AbstractFrequencyBasedGlobalTermWeighter: Creates a new AbstractCountingBasedGlobalTermWeighter.
AbstractFrequencyBasedGlobalTermWeighter(VectorFactory<? extends Vector>) - Constructor for class gov.sandia.cognition.text.term.vector.weighter.global.AbstractFrequencyBasedGlobalTermWeighter: Creates a new AbstractCountingBasedGlobalTermWeighter.
AbstractGlobalTermWeighter - Class in gov.sandia.cognition.text.term.vector.weighter.global: An abstract implementation of the GlobalTermWeighter interface.
AbstractGlobalTermWeighter() - Constructor for class gov.sandia.cognition.text.term.vector.weighter.global.AbstractGlobalTermWeighter: Creates a new AbstractGlobalTermWeighter.
AbstractGlobalTermWeighter(VectorFactory<? extends Vector>) - Constructor for class gov.sandia.cognition.text.term.vector.weighter.global.AbstractGlobalTermWeighter: Creates a new AbstractGlobalTermWeighter.
AbstractHashFunction - Class in gov.sandia.cognition.hash: Partial implementation of HashFunction
AbstractHashFunction() - Constructor for class gov.sandia.cognition.hash.AbstractHashFunction: Creates a new instance of AbstractHashFunction
AbstractIncrementalEstimator<DataType,DistributionType extends Distribution<? extends DataType>,SufficientStatisticsType extends SufficientStatistic<DataType,DistributionType>> - Class in gov.sandia.cognition.statistics: Partial implementation of IncrementalEstimator.
AbstractIncrementalEstimator() - Constructor for class gov.sandia.cognition.statistics.AbstractIncrementalEstimator: Creates a new instance of AbstractIncrementalEstimator
AbstractInputOutputPair<InputType,OutputType> - Class in gov.sandia.cognition.learning.data: An abstract implementation of the InputOutputPair interface.
AbstractInputOutputPair() - Constructor for class gov.sandia.cognition.learning.data.AbstractInputOutputPair: Creates a new AbstractInputOutputPair.
AbstractIterativeAlgorithm - Class in gov.sandia.cognition.algorithm: The AbstractIterativeAlgorithm class implements a simple part of the IterativeAlgorithm interface that manages the listeners for the algorithm.
AbstractIterativeAlgorithm() - Constructor for class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm: Creates a new instance of AbstractIterativeAlgorithm.
AbstractIterativeAlgorithm(int) - Constructor for class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm: Creates a new instance of AbstractIterativeAlgorithm.
AbstractIterativeAlgorithmListener - Class in gov.sandia.cognition.algorithm.event: An abstract implementation of the IterativeAlgorithmListener interface that provides default implementations of the event methods that do nothing.
AbstractIterativeAlgorithmListener() - Constructor for class gov.sandia.cognition.algorithm.event.AbstractIterativeAlgorithmListener: Creates a new AbstractIterativeAlgorithmListener.
AbstractKalmanFilter - Class in gov.sandia.cognition.statistics.bayesian: Contains fields useful to both Kalman filters and extended Kalman filters.
AbstractKalmanFilter(Vector, Matrix, Matrix) - Constructor for class gov.sandia.cognition.statistics.bayesian.AbstractKalmanFilter: Creates a new instance of AbstractKalmanFilter
AbstractKernelizableBinaryCategorizerOnlineLearner - Class in gov.sandia.cognition.learning.algorithm.perceptron: An abstract implementation of the KernelizableBinaryCategorizerOnlineLearner interface.
AbstractKernelizableBinaryCategorizerOnlineLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.AbstractKernelizableBinaryCategorizerOnlineLearner: Creates a new AbstractKernelizableBinaryCategorizerOnlineLearner.
AbstractKernelizableBinaryCategorizerOnlineLearner(VectorFactory<?>) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.AbstractKernelizableBinaryCategorizerOnlineLearner: Creates a new AbstractKernelizableBinaryCategorizerOnlineLearner with the given vector factory.
AbstractKNearestNeighbor<InputType,OutputType> - Class in gov.sandia.cognition.learning.algorithm.nearest: Partial implementation of KNearestNeighbor.
AbstractKNearestNeighbor(int, DivergenceFunction<? super InputType, ? super InputType>, Summarizer<? super OutputType, ? extends OutputType>) - Constructor for class gov.sandia.cognition.learning.algorithm.nearest.AbstractKNearestNeighbor: Creates a new instance of KNearestNeighbor
AbstractLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.delta.AbstractDeltaCategorizer.AbstractLearner: Default constructor.
AbstractLearningExperiment - Class in gov.sandia.cognition.learning.experiment: The AbstractLearningExperiment class implements the general functionality of the LearningExperiment interface, which is mainly the handling of listeners and firing of events.
AbstractLearningExperiment() - Constructor for class gov.sandia.cognition.learning.experiment.AbstractLearningExperiment: Creates a new instance of AbstractLearningExperiment.
AbstractLinearCombinationOnlineLearner - Class in gov.sandia.cognition.learning.algorithm.perceptron: An abstract class for online learning of linear binary categorizers that take the form of a weighted sum of inputs.
AbstractLinearCombinationOnlineLearner(boolean) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.AbstractLinearCombinationOnlineLearner: Creates a new AbstractLinearCombinationOnlineLearner with default parameters.
AbstractLinearCombinationOnlineLearner(boolean, VectorFactory<?>) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.AbstractLinearCombinationOnlineLearner: Creates a new AbstractLinearCombinationOnlineLearner with the given parameters.
AbstractLineBracketInterpolator<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>> - Class in gov.sandia.cognition.learning.algorithm.minimization.line.interpolator: Partial implementation of LinearBracketInterpolator
AbstractLineBracketInterpolator() - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.interpolator.AbstractLineBracketInterpolator: Default constructor
AbstractLineBracketInterpolator(double) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.interpolator.AbstractLineBracketInterpolator: Creates a new instance of AbstractLineBracketInterpolator
AbstractLineBracketInterpolatorPolynomial<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>> - Class in gov.sandia.cognition.learning.algorithm.minimization.line.interpolator: Partial implementation of a LineBracketInterpolator based on a closed-form polynomial function.
AbstractLineBracketInterpolatorPolynomial(double) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.interpolator.AbstractLineBracketInterpolatorPolynomial: Creates a new instance of AbstractLineBracketInterpolatorPolynomial
AbstractLocalTermWeighter - Class in gov.sandia.cognition.text.term.vector.weighter.local: Abstract implementation of the LocalTermWeighter interface.
AbstractLocalTermWeighter() - Constructor for class gov.sandia.cognition.text.term.vector.weighter.local.AbstractLocalTermWeighter: Creates a new AbstractLocalTermWeighter.
AbstractLocalTermWeighter(VectorFactory<? extends Vector>) - Constructor for class gov.sandia.cognition.text.term.vector.weighter.local.AbstractLocalTermWeighter: Creates a new AbstractLocalTermWeighter.
AbstractLogisticRegression<InputType,OutputType,FunctionType extends Evaluator<? super InputType,OutputType>> - Class in gov.sandia.cognition.learning.algorithm.regression: Abstract partial implementation for logistic regression classes.
AbstractLogisticRegression(double, double, int) - Constructor for class gov.sandia.cognition.learning.algorithm.regression.AbstractLogisticRegression: Creates a new instance of AbstractLogisticRegression
AbstractLogNumberMap<KeyType> - Class in gov.sandia.cognition.collection: A partial implementation of a ScalarMap with a LogNumber value
AbstractLogNumberMap(Map<KeyType, LogNumber>) - Constructor for class gov.sandia.cognition.collection.AbstractLogNumberMap: Creates a new instance of AbstractMutableDoubleMap
AbstractLogNumberMap.SimpleEntry<KeyType> - Class in gov.sandia.cognition.collection: Entry for the AbstractScalarMap
AbstractLogNumberMap.SimpleEntrySet<KeyType> - Class in gov.sandia.cognition.collection: Simple Entry Set for DefaultInfiniteVector
AbstractLogNumberMap.SimpleIterator<KeyType> - Class in gov.sandia.cognition.collection: Simple iterator for DefaultInfiniteVector
AbstractMarkovChainMonteCarlo<ObservationType,ParameterType> - Class in gov.sandia.cognition.statistics.bayesian: Partial abstract implementation of MarkovChainMonteCarlo.
AbstractMarkovChainMonteCarlo() - Constructor for class gov.sandia.cognition.statistics.bayesian.AbstractMarkovChainMonteCarlo: Creates a new instance of AbstractMarkovChainMonteCarlo
AbstractMatrix - Class in gov.sandia.cognition.math.matrix: Abstract implementation of some low-hanging functions in the Matrix interface.
AbstractMatrix() - Constructor for class gov.sandia.cognition.math.matrix.AbstractMatrix: Creates a new instance of AbstractMatrix.
AbstractMinDistanceFixedClusterInitializer<ClusterType extends Cluster<DataType>,DataType> - Class in gov.sandia.cognition.learning.algorithm.clustering.initializer: Implements an abstract FixedClusterInitializer that works by using the minimum distance from a point to the cluster.
AbstractMinDistanceFixedClusterInitializer() - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.initializer.AbstractMinDistanceFixedClusterInitializer: Creates a new, empty instance of AbstractMinDistanceFixedClusterInitializer.
AbstractMinDistanceFixedClusterInitializer(DivergenceFunction<? super DataType, ? super DataType>, ClusterCreator<ClusterType, DataType>, Random) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.initializer.AbstractMinDistanceFixedClusterInitializer: Creates a new instance of AbstractMinDistanceFixedClusterInitializer.
AbstractMinimizerBasedParameterCostMinimizer<ResultType extends VectorizableVectorFunction,EvaluatorType extends Evaluator<? super Vector,? extends java.lang.Double>> - Class in gov.sandia.cognition.learning.algorithm.regression: Partial implementation of ParameterCostMinimizer, based on the algorithms from the minimization package.
AbstractMinimizerBasedParameterCostMinimizer(FunctionMinimizer<Vector, Double, ? super EvaluatorType>) - Constructor for class gov.sandia.cognition.learning.algorithm.regression.AbstractMinimizerBasedParameterCostMinimizer: Creates a new instance of AbstractMinimizerBasedParameterCostMinimizer
AbstractMinimizerBasedParameterCostMinimizer(FunctionMinimizer<Vector, Double, ? super EvaluatorType>, SupervisedCostFunction<Vector, Vector>) - Constructor for class gov.sandia.cognition.learning.algorithm.regression.AbstractMinimizerBasedParameterCostMinimizer: Creates a new instance of AbstractMinimizerBasedParameterCostMinimizer
AbstractMTJMatrix - Class in gov.sandia.cognition.math.matrix.mtj: Relies on internal MTJ matrix to do some of the heavy lifting, without assuming that the underlying matrix is Dense or Sparse
AbstractMTJMatrix(Matrix) - Constructor for class gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix: Creates a new instance of AbstractMTJMatrix
AbstractMTJVector - Class in gov.sandia.cognition.math.matrix.mtj: Implementation of the Vector interface that relies on MTJ Vectors, but does not specify sparse or dense storage.
AbstractMTJVector(Vector) - Constructor for class gov.sandia.cognition.math.matrix.mtj.AbstractMTJVector: Creates a new instance of AbstractMTJVector
AbstractMultipleHypothesisComparison<TreatmentData,StatisticType extends MultipleHypothesisComparison.Statistic> - Class in gov.sandia.cognition.statistics.method: Partial implementation of MultipleHypothesisComparison
AbstractMultipleHypothesisComparison() - Constructor for class gov.sandia.cognition.statistics.method.AbstractMultipleHypothesisComparison: Default constructor
AbstractMultipleHypothesisComparison.Statistic - Class in gov.sandia.cognition.statistics.method: Partial implementation of MultipleHypothesisComparison.Statistic
AbstractMultiTextualConverter<InputType,OutputType extends Textual> - Class in gov.sandia.cognition.text.convert: An abstract implementation of the MultiTextualConverter interface.
AbstractMultiTextualConverter() - Constructor for class gov.sandia.cognition.text.convert.AbstractMultiTextualConverter: Creates a new AbstractMultiTextualConverter.
AbstractMutableDoubleMap<KeyType> - Class in gov.sandia.cognition.collection: A partial implementation of a ScalarMap with a MutableDouble value
AbstractMutableDoubleMap(Map<KeyType, MutableDouble>) - Constructor for class gov.sandia.cognition.collection.AbstractMutableDoubleMap: Creates a new instance of AbstractMutableDoubleMap
AbstractMutableDoubleMap.Entry<KeyType> - Interface in gov.sandia.cognition.collection: Interface for entries.
AbstractMutableDoubleMap.SimpleEntry<KeyType> - Class in gov.sandia.cognition.collection: Entry for the AbstractScalarMap
AbstractMutableDoubleMap.SimpleEntrySet<KeyType> - Class in gov.sandia.cognition.collection: Simple Entry Set for DefaultInfiniteVector
AbstractMutableDoubleMap.SimpleIterator<KeyType> - Class in gov.sandia.cognition.collection: Simple iterator for DefaultInfiniteVector
AbstractNamed - Class in gov.sandia.cognition.util: The AbstractNamed class implements the Named interface in a standard way by having a name field inside the object.
AbstractNamed() - Constructor for class gov.sandia.cognition.util.AbstractNamed: Creates a new instance of AbstractNamed.
AbstractNamed(String) - Constructor for class gov.sandia.cognition.util.AbstractNamed: Creates a new instance of AbstractNamed with the given name.
AbstractNearestNeighbor<InputType,OutputType> - Class in gov.sandia.cognition.learning.algorithm.nearest: Partial implementation of KNearestNeighbor.
AbstractNearestNeighbor() - Constructor for class gov.sandia.cognition.learning.algorithm.nearest.AbstractNearestNeighbor: Creates a new instance of AbstractNearestNeighbor
AbstractNearestNeighbor(DivergenceFunction<? super InputType, ? super InputType>) - Constructor for class gov.sandia.cognition.learning.algorithm.nearest.AbstractNearestNeighbor: Creates a new instance of AbstractNearestNeighbor
AbstractOccurrenceInText<DataType> - Class in gov.sandia.cognition.text: An abstract implementation of the OccurrenceInText interface.
AbstractOccurrenceInText() - Constructor for class gov.sandia.cognition.text.AbstractOccurrenceInText: Creates a new AbstractOccurrenceInText.
AbstractOccurrenceInText(int, int) - Constructor for class gov.sandia.cognition.text.AbstractOccurrenceInText: Creates a new AbstractOccurrenceInText.
AbstractOnlineBudgetedKernelBinaryCategorizerLearner<InputType> - Class in gov.sandia.cognition.learning.algorithm.perceptron.kernel: An abstract implementation of the BudgetedKernelBinaryCategorizerLearner for online learners.
AbstractOnlineBudgetedKernelBinaryCategorizerLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.kernel.AbstractOnlineBudgetedKernelBinaryCategorizerLearner: Creates a new AbstractOnlineBudgetedKernelBinaryCategorizerLearner with a null kernel and default budget.
AbstractOnlineBudgetedKernelBinaryCategorizerLearner(Kernel<? super InputType>, int) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.kernel.AbstractOnlineBudgetedKernelBinaryCategorizerLearner: Creates a new AbstractOnlineBudgetedKernelBinaryCategorizerLearner with the given parameters.
AbstractOnlineKernelBinaryCategorizerLearner<InputType> - Class in gov.sandia.cognition.learning.algorithm.perceptron.kernel: An abstract class for an online kernel binary categorizer learner.
AbstractOnlineKernelBinaryCategorizerLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.kernel.AbstractOnlineKernelBinaryCategorizerLearner: Creates a new AbstractOnlineKernelBinaryCategorizerLearner with a null kernel.
AbstractOnlineKernelBinaryCategorizerLearner(Kernel<? super InputType>) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.kernel.AbstractOnlineKernelBinaryCategorizerLearner: Creates a new AbstractOnlineKernelBinaryCategorizerLearner with the given kernel.
AbstractOnlineLinearBinaryCategorizerLearner - Class in gov.sandia.cognition.learning.algorithm.perceptron: An abstract class for online (incremental) learning algorithms that produce an LinearBinaryCategorizer.
AbstractOnlineLinearBinaryCategorizerLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.AbstractOnlineLinearBinaryCategorizerLearner: Creates a new AbstractOnlineLinearBinaryCategorizerLearner with the default vector factory.
AbstractOnlineLinearBinaryCategorizerLearner(VectorFactory<?>) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.AbstractOnlineLinearBinaryCategorizerLearner: Creates a new AbstractOnlineLinearBinaryCategorizerLearner with the given vector factory.
AbstractPairwiseMultipleHypothesisComparison<StatisticType extends AbstractPairwiseMultipleHypothesisComparison.Statistic> - Class in gov.sandia.cognition.statistics.method: A multiple-hypothesis comparison algorithm based on making multiple pair-wise null-hypothesis comparisons.
AbstractPairwiseMultipleHypothesisComparison(NullHypothesisEvaluator<Collection<? extends Number>>) - Constructor for class gov.sandia.cognition.statistics.method.AbstractPairwiseMultipleHypothesisComparison: Creates a new instance of BonferroniCorrection
AbstractPairwiseMultipleHypothesisComparison.Statistic - Class in gov.sandia.cognition.statistics.method: Result from a pairwise multiple-comparison statistic.
AbstractParallelAlgorithm - Class in gov.sandia.cognition.algorithm: Partial implementation of ParallelAlgorithm.
AbstractParallelAlgorithm() - Constructor for class gov.sandia.cognition.algorithm.AbstractParallelAlgorithm: Creates a new instance of AbstractParallelAlgorithm
AbstractParallelAlgorithm(ThreadPoolExecutor) - Constructor for class gov.sandia.cognition.algorithm.AbstractParallelAlgorithm: Creates a new instance of AbstractParallelAlgorithm
AbstractParallelizableCostFunction - Class in gov.sandia.cognition.learning.function.cost: Partial implementation of the ParallelizableCostFunction
AbstractParallelizableCostFunction(Collection<? extends InputOutputPair<? extends Vector, Vector>>) - Constructor for class gov.sandia.cognition.learning.function.cost.AbstractParallelizableCostFunction: Creates a new instance of AbstractParallelizableCostFunction
AbstractParameterCostMinimizer<ResultType extends VectorizableVectorFunction,CostFunctionType extends SupervisedCostFunction<Vector,Vector>> - Class in gov.sandia.cognition.learning.algorithm.regression: Partial implementation of ParameterCostMinimizer.
AbstractParameterCostMinimizer(CostFunctionType, int, double) - Constructor for class gov.sandia.cognition.learning.algorithm.regression.AbstractParameterCostMinimizer: Creates a new instance of AbstractParameterCostMinimizer
AbstractParticleFilter<ObservationType,ParameterType> - Class in gov.sandia.cognition.statistics.bayesian: Partial abstract implementation of ParticleFilter.
AbstractParticleFilter() - Constructor for class gov.sandia.cognition.statistics.bayesian.AbstractParticleFilter: Default constructor.
AbstractPrincipalComponentsAnalysis - Class in gov.sandia.cognition.learning.algorithm.pca: Abstract implementation of PCA.
AbstractPrincipalComponentsAnalysis(int, PrincipalComponentsAnalysisFunction) - Constructor for class gov.sandia.cognition.learning.algorithm.pca.AbstractPrincipalComponentsAnalysis: Creates a new instance of AbstractPrincipalComponentsAnalysis
AbstractRandomized - Class in gov.sandia.cognition.util: The AbstractRandomized abstract class implements the Randomized interface by containing the random object in a protected field.
AbstractRandomized(Random) - Constructor for class gov.sandia.cognition.util.AbstractRandomized: Creates a new instance of AbstractRandomized with the given Random object.
AbstractRandomVariable<DataType> - Class in gov.sandia.cognition.statistics: Partial implementation of RandomVariable.
AbstractRandomVariable() - Constructor for class gov.sandia.cognition.statistics.AbstractRandomVariable
AbstractRegressor<InputType> - Class in gov.sandia.cognition.learning.function.regression: An abstract implementation of the Regressor interface.
AbstractRegressor() - Constructor for class gov.sandia.cognition.learning.function.regression.AbstractRegressor: Creates a new AbstractRegressor.
AbstractRelation<SourceType,TargetType> - Class in gov.sandia.cognition.text.relation: An abstract implementation of a relation between two objects.
AbstractRelation() - Constructor for class gov.sandia.cognition.text.relation.AbstractRelation: Creates a new AbstractRelation with null source and target.
AbstractRelation(SourceType, TargetType) - Constructor for class gov.sandia.cognition.text.relation.AbstractRelation: Creates a new AbstractRelation with the given source and target.
AbstractReverseCachedDataConverter<InputType,OutputType,ReverseConverterType extends DataConverter<? super OutputType,? extends InputType>> - Class in gov.sandia.cognition.data.convert: Abstract implementation of ReversibleDataConverter that caches the reverse converter.
AbstractReverseCachedDataConverter() - Constructor for class gov.sandia.cognition.data.convert.AbstractReverseCachedDataConverter: Creates a new AbstractReverseCachedDataConverter.
AbstractReversibleDataConverter<InputType,OutputType> - Class in gov.sandia.cognition.data.convert: Abstract implementation of sthe ReversibleDataConverter interface.
AbstractReversibleDataConverter() - Constructor for class gov.sandia.cognition.data.convert.AbstractReversibleDataConverter: Creates a new AbstractReversibleDataConverter.
AbstractRing<RingType extends Ring<RingType>> - Class in gov.sandia.cognition.math: Implements the non-inline versions of the various Ring functions.
AbstractRing() - Constructor for class gov.sandia.cognition.math.AbstractRing
AbstractRootFinder - Class in gov.sandia.cognition.learning.algorithm.root: Partial implementation of RootFinder.
AbstractRootFinder() - Constructor for class gov.sandia.cognition.learning.algorithm.root.AbstractRootFinder: Creates a new instance of AbstractRootFinder
AbstractScalarFunction<InputType> - Class in gov.sandia.cognition.math: An abstract implementation of the ScalarFunction interface.
AbstractScalarFunction() - Constructor for class gov.sandia.cognition.math.AbstractScalarFunction: Creates a new AbstractScalarFunction.
AbstractScalarMap<KeyType,NumberType extends java.lang.Number> - Class in gov.sandia.cognition.collection: Partial implementation of ScalarMap
AbstractScalarMap(Map<KeyType, NumberType>) - Constructor for class gov.sandia.cognition.collection.AbstractScalarMap: Creates a new instance of AbstractScalarMap
AbstractSelector<GenomeType> - Class in gov.sandia.cognition.learning.algorithm.genetic.selector: The AbstractSelector class provides some common functionality for implementations of Selectors.
AbstractSelector() - Constructor for class gov.sandia.cognition.learning.algorithm.genetic.selector.AbstractSelector: Creates a new instance of AbstractSelector.
AbstractSemanticIdentifier - Class in gov.sandia.cognition.framework: The AbstractSemanticIdentifier class implements the basic methods that are needed for a SemanticIdentifier to provide a good speed improvement.
AbstractSemanticIdentifier() - Constructor for class gov.sandia.cognition.framework.AbstractSemanticIdentifier: Creates a new instance of AbstractSemanticIdentifier
AbstractSemanticMemoryLite - Class in gov.sandia.cognition.framework.lite: The AbstractSemanticMemoryLite implements the common functionality among SemanticMemoryLite modules.
AbstractSemanticMemoryLite(SemanticIdentifierMap, PatternRecognizerLite) - Constructor for class gov.sandia.cognition.framework.lite.AbstractSemanticMemoryLite: Creates a new instance of AbstractSemanticMemoryLite.
AbstractSingleDocumentExtractor - Class in gov.sandia.cognition.text.document.extractor: An abstract implementation of the SingleDocumentExtractor interface.
AbstractSingleDocumentExtractor() - Constructor for class gov.sandia.cognition.text.document.extractor.AbstractSingleDocumentExtractor: Creates a new AbstractSingleDocumentExtractor.
AbstractSingleTermFilter - Class in gov.sandia.cognition.text.term.filter: An abstract implementation of the SingleTermFilter interface.
AbstractSingleTermFilter() - Constructor for class gov.sandia.cognition.text.term.filter.AbstractSingleTermFilter: Creates a new AbstractSingleTermFilter.
AbstractSingleTextualConverter<InputType,OutputType extends Textual> - Class in gov.sandia.cognition.text.convert: An abstract implementation of the SingleTextualConverter interface.
AbstractSingleTextualConverter() - Constructor for class gov.sandia.cognition.text.convert.AbstractSingleTextualConverter: Creates a new AbstractSingleTextualConverter.
AbstractSingularValueDecomposition - Class in gov.sandia.cognition.math.matrix.decomposition: Abstract container class that stores the matrices for a Singular Value Decomposition (SVD) and related operations but does not actually perform a singular value decomposition
AbstractSingularValueDecomposition() - Constructor for class gov.sandia.cognition.math.matrix.decomposition.AbstractSingularValueDecomposition: Default constructor that nulls out all matrices
AbstractSingularValueDecomposition(Matrix, Matrix, Matrix) - Constructor for class gov.sandia.cognition.math.matrix.decomposition.AbstractSingularValueDecomposition: Creates a new instance of AbstractSingularValueDecomposition where U*S*Vtranspose = original_matrix
AbstractSoftMargin() - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.OnlinePassiveAggressivePerceptron.AbstractSoftMargin: Creates a new AbstractSoftMargin with default parameters.
AbstractSoftMargin(double) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.OnlinePassiveAggressivePerceptron.AbstractSoftMargin: Creates a new AbstractSoftMargin with the given aggressiveness.
AbstractSoftMargin(double, VectorFactory<?>) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.OnlinePassiveAggressivePerceptron.AbstractSoftMargin: Creates a new AbstractSoftMargin with the given parameters.
AbstractSparseMatrix - Class in gov.sandia.cognition.math.matrix.mtj: Implements some generic operations that any sparse-matrix representation must do.
AbstractSparseMatrix(Matrix) - Constructor for class gov.sandia.cognition.math.matrix.mtj.AbstractSparseMatrix: Creates a new instance of AbstractSparseMatrix using the given MTJ matrix
AbstractStatefulEvaluator<InputType,OutputType,StateType extends CloneableSerializable> - Class in gov.sandia.cognition.evaluator: The AbstractStatefulEvalutor class is an abstract implementation of the StatefulEvalutor interface.
AbstractStatefulEvaluator() - Constructor for class gov.sandia.cognition.evaluator.AbstractStatefulEvaluator: Creates a new instance of AbstractStatefulEvaluator.
AbstractStatefulEvaluator(StateType) - Constructor for class gov.sandia.cognition.evaluator.AbstractStatefulEvaluator: Creates a new instance of AbstractStatefulEvaluator.
AbstractStreamSerializationHandler<SerializedType> - Class in gov.sandia.cognition.io.serialization: An abstract implementation of StreamSerializationHandler.
AbstractStreamSerializationHandler() - Constructor for class gov.sandia.cognition.io.serialization.AbstractStreamSerializationHandler: Creates a new AbstractStreamSerializationHandler.
AbstractSufficientStatistic<DataType,DistributionType> - Class in gov.sandia.cognition.statistics: Partial implementation of SufficientStatistic
AbstractSufficientStatistic() - Constructor for class gov.sandia.cognition.statistics.AbstractSufficientStatistic: Creates a new instance of AbstractSufficientStatistic
AbstractSupervisedBatchAndIncrementalLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>> - Class in gov.sandia.cognition.learning.algorithm: An abstract implementation of the batch and incremental learning for an incremental supervised learner.
AbstractSupervisedBatchAndIncrementalLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.AbstractSupervisedBatchAndIncrementalLearner: Creates a new AbstractSupervisedBatchAndIncrementalLearner.
AbstractSupervisedCostFunction<InputType,TargetType> - Class in gov.sandia.cognition.learning.function.cost: Partial implementation of SupervisedCostFunction
AbstractSupervisedCostFunction() - Constructor for class gov.sandia.cognition.learning.function.cost.AbstractSupervisedCostFunction: Creates a new instance of AbstractSupervisedCostFunction
AbstractSupervisedCostFunction(Collection<? extends InputOutputPair<? extends InputType, TargetType>>) - Constructor for class gov.sandia.cognition.learning.function.cost.AbstractSupervisedCostFunction: Creates a new instance of AbstractSupervisedCostFunction
AbstractSupervisedPerformanceEvaluator<InputType,TargetType,EstimateType,ResultType> - Class in gov.sandia.cognition.learning.performance: The AbstractSupervisedPerformanceEvaluator class contains an abstract implementation of the SupervisedPerformanceEvaluator class.
AbstractSupervisedPerformanceEvaluator() - Constructor for class gov.sandia.cognition.learning.performance.AbstractSupervisedPerformanceEvaluator: Creates a new AbstractSupervisedPerformanceEvaluator.
AbstractTargetEstimatePair<TargetType,EstimateType> - Class in gov.sandia.cognition.learning.data: An abstract implementation of the TargetEstimatePair.
AbstractTargetEstimatePair() - Constructor for class gov.sandia.cognition.learning.data.AbstractTargetEstimatePair: Creates a new AbstractTargetEstimatePair.
AbstractTemporal - Class in gov.sandia.cognition.util: Partial implementation of Temporal
AbstractTemporal() - Constructor for class gov.sandia.cognition.util.AbstractTemporal: Creates a new instance of AbstractTemporal
AbstractTemporal(Date) - Constructor for class gov.sandia.cognition.util.AbstractTemporal: Creates a new instance of AbstractTemporal
AbstractTerm - Class in gov.sandia.cognition.text.term: Creates a new AbstractTerm.
AbstractTerm() - Constructor for class gov.sandia.cognition.text.term.AbstractTerm: Creates a new AbstractTerm.
AbstractTermIndex - Class in gov.sandia.cognition.text.term: An abstract implementation of the TermIndex class that handles a lot of the convenience method implementations.
AbstractTermIndex() - Constructor for class gov.sandia.cognition.text.term.AbstractTermIndex: Creates a new AbstractTermIndex.
AbstractTermWeightNormalizer - Class in gov.sandia.cognition.text.term.vector.weighter.normalize: An abstract implementation of the TermWeightNormalizer interface.
AbstractTermWeightNormalizer() - Constructor for class gov.sandia.cognition.text.term.vector.weighter.normalize.AbstractTermWeightNormalizer: Creates a new AbstractTermWeightNormalizer.
AbstractTextSerializationHandler<SerializedType> - Class in gov.sandia.cognition.io.serialization: An abstract implementation of the TextSerializationHandler interface.
AbstractTextSerializationHandler() - Constructor for class gov.sandia.cognition.io.serialization.AbstractTextSerializationHandler: Creates a new AbstractTextSerializationHandler.
AbstractTextual - Class in gov.sandia.cognition.text: A default implementation of the Textual interface.
AbstractTextual() - Constructor for class gov.sandia.cognition.text.AbstractTextual: Creates a new AbstractTextual.
AbstractTextualConverter<InputType,OutputType extends Textual> - Class in gov.sandia.cognition.text.convert: An abstract implementation of the TextualConverter interface.
AbstractTextualConverter() - Constructor for class gov.sandia.cognition.text.convert.AbstractTextualConverter: Creates a new AbstractTextualConverter.
AbstractThresholdBinaryCategorizer<InputType> - Class in gov.sandia.cognition.learning.function.categorization: Categorizer that first maps the input space onto a real value, then uses a threshold to map the result onto lowValue (for strictly less than the threshold) or highValue (for greater than or equal to the threshold).
AbstractThresholdBinaryCategorizer(double) - Constructor for class gov.sandia.cognition.learning.function.categorization.AbstractThresholdBinaryCategorizer: Creates a new AbstractThresholdBinaryCategorizer
AbstractTokenizer - Class in gov.sandia.cognition.text.token: Abstract implementation of the Tokenizer interface.
AbstractTokenizer() - Constructor for class gov.sandia.cognition.text.token.AbstractTokenizer: Creates a new AbstractTokenizer.
AbstractToVectorEncoder<InputType> - Class in gov.sandia.cognition.data.convert.vector: An abstract implementation of the DataToVectorEncoder interface.
AbstractToVectorEncoder() - Constructor for class gov.sandia.cognition.data.convert.vector.AbstractToVectorEncoder: Creates a new AbstractToVectorEncoder.
AbstractToVectorEncoder(VectorFactory<?>) - Constructor for class gov.sandia.cognition.data.convert.vector.AbstractToVectorEncoder: Creates a new AbstractToVectorEncoder with the given vector factory.
AbstractUnivariateScalarFunction - Class in gov.sandia.cognition.math: Abstract implementation of ScalarFunction where the evaluate(Double) method calls back into the evaluate(double) method.
AbstractUnivariateScalarFunction() - Constructor for class gov.sandia.cognition.math.AbstractUnivariateScalarFunction: Creates a new AbstractUnivariateScalarFunction.
AbstractUnweightedEnsemble<MemberType> - Class in gov.sandia.cognition.learning.algorithm.ensemble: An abstract implementation of the Ensemble interface for unweighted ensembles.
AbstractUnweightedEnsemble() - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.AbstractUnweightedEnsemble: Creates a new AbstractUnweightedEnsemble.
AbstractUnweightedEnsemble(List<MemberType>) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.AbstractUnweightedEnsemble: Creates a new AbstractUnweightedEnsemble with the given list of members.
AbstractValidationFoldExperiment<InputDataType,FoldDataType> - Class in gov.sandia.cognition.learning.experiment: The AbstractValidationFoldExperiment class implements a common way of structuring an experiment around a ValidationFoldCreator object where the fold creator is used to create each of the individual trials of the experiment.
AbstractValidationFoldExperiment() - Constructor for class gov.sandia.cognition.learning.experiment.AbstractValidationFoldExperiment: Creates a new instance of AbstractValidationFoldExperiment.
AbstractValidationFoldExperiment(ValidationFoldCreator<InputDataType, FoldDataType>) - Constructor for class gov.sandia.cognition.learning.experiment.AbstractValidationFoldExperiment: Creates a new instance of AbstractValidationFoldExperiment.
AbstractValueDiscriminantPair<ValueType,DiscriminantType extends java.lang.Comparable<? super DiscriminantType>> - Class in gov.sandia.cognition.learning.data: An abstract implementation of the ValueDiscriminantPair interface.
AbstractValueDiscriminantPair() - Constructor for class gov.sandia.cognition.learning.data.AbstractValueDiscriminantPair: Creates a new AbstractValueDiscriminantPair.
AbstractVector - Class in gov.sandia.cognition.math.matrix: Abstract implementation of some of the Vector interface, in a storage-free manner
AbstractVector() - Constructor for class gov.sandia.cognition.math.matrix.AbstractVector
AbstractVectorSpace<VectorType extends VectorSpace<VectorType,? extends EntryType>,EntryType extends VectorSpace.Entry> - Class in gov.sandia.cognition.math.matrix: Partial implementation of VectorSpace
AbstractVectorSpace() - Constructor for class gov.sandia.cognition.math.matrix.AbstractVectorSpace: Creates a new instance of AbstractVectorSpace
AbstractVectorSpaceModel - Class in gov.sandia.cognition.text.term.vector: An abstract implementation of the VectorSpaceModel class.
AbstractVectorSpaceModel() - Constructor for class gov.sandia.cognition.text.term.vector.AbstractVectorSpaceModel: Creates a new AbstractVectorSpaceModel.
AbstractVectorThresholdMaximumGainLearner<OutputType> - Class in gov.sandia.cognition.learning.algorithm.tree: An abstract class for decider learners that produce a threshold function on a vector element based on maximizing some gain value.
AbstractVectorThresholdMaximumGainLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.tree.AbstractVectorThresholdMaximumGainLearner: Creates a new AbstractVectorThresholdMaximumGainLearner.
AbstractVectorThresholdMaximumGainLearner(int, int[]) - Constructor for class gov.sandia.cognition.learning.algorithm.tree.AbstractVectorThresholdMaximumGainLearner: Creates a new AbstractVectorThresholdMaximumGainLearner.
AbstractWeighted - Class in gov.sandia.cognition.util: Container class for a Weighted object
AbstractWeighted() - Constructor for class gov.sandia.cognition.util.AbstractWeighted: Creates a new instance of AbstractWeighted
AbstractWeighted(double) - Constructor for class gov.sandia.cognition.util.AbstractWeighted: Creates a new instance of AbstractWeighted
AbstractWeightedEnsemble<MemberType> - Class in gov.sandia.cognition.learning.algorithm.ensemble: An abstract implementation of the Ensemble interface for ensembles that have a weight associated with each member.
AbstractWeightedEnsemble() - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.AbstractWeightedEnsemble: Creates a new, empty of AbstractWeightedEnsemble.
AbstractWeightedEnsemble(List<WeightedValue<MemberType>>) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.AbstractWeightedEnsemble: Creates a new instance of AbstractWeightedEnsemble.
acceptNullHypothesis(int, int) - Method in class gov.sandia.cognition.statistics.method.AbstractPairwiseMultipleHypothesisComparison.Statistic
acceptNullHypothesis(int, int) - Method in class gov.sandia.cognition.statistics.method.MultipleComparisonExperiment.Statistic
acceptNullHypothesis(int, int) - Method in interface gov.sandia.cognition.statistics.method.MultipleHypothesisComparison.Statistic: Determines if the (i,j) null hypothesis should be accepted (true) or rejected (false) .
acceptNullHypothesis(int, int) - Method in class gov.sandia.cognition.statistics.method.NemenyiConfidence.Statistic
acceptNullHypothesis(int, int) - Method in class gov.sandia.cognition.statistics.method.TukeyKramerConfidence.Statistic
ACCESSED_DATE_FIELD_NAME - Static variable in class gov.sandia.cognition.text.document.AbstractDocument: The name of the last accessed date field is "accessedDate".
accumulate(RingType) - Method in class gov.sandia.cognition.math.RingAccumulator: Adds the given object to the accumulation.
accumulate(Iterable<? extends RingType>) - Method in class gov.sandia.cognition.math.RingAccumulator: Adds all of the given set of objects onto the accumulation.
Activatable - Interface in gov.sandia.cognition.framework: The Activatable interface provides a general definition for objects that can be isActivated.
ActivatableCogxel - Interface in gov.sandia.cognition.framework: ActivatableCogxel is an interface which defines a cogxel which is activatable.
ActualPredictedPairSummarizer() - Constructor for class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrix.ActualPredictedPairSummarizer: Creates a new CombineSummarizer.
ActualPredictedPairSummarizer() - Constructor for class gov.sandia.cognition.learning.performance.categorization.DefaultConfusionMatrix.ActualPredictedPairSummarizer: Creates a new CombineSummarizer.
AdaBoost<InputType> - Class in gov.sandia.cognition.learning.algorithm.ensemble: The AdaBoost class implements the Adaptive Boosting (AdaBoost) algorithm formulated by Yoav Freund and Robert Shapire.
AdaBoost() - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.AdaBoost: Creates a new instance of AdaBoost with no base learner.
AdaBoost(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, Boolean>>, ? extends Evaluator<? super InputType, ? extends Boolean>>) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.AdaBoost: Creates a new instance of AdaBoost.
AdaBoost(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, Boolean>>, ? extends Evaluator<? super InputType, ? extends Boolean>>, int) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.AdaBoost: Creates a new instance of AdaBoost.
adapt(ObjectType, DataType) - Method in interface gov.sandia.cognition.learning.parameter.ParameterAdapter: Adapt the parameter(s) of a given object based on the given data.
AdaptiveRegularizationOfWeights - Class in gov.sandia.cognition.learning.algorithm.confidence: An implementation of the Adaptive Regularization of Weights (AROW) algorithm for online learning of a linear binary categorizer.
AdaptiveRegularizationOfWeights() - Constructor for class gov.sandia.cognition.learning.algorithm.confidence.AdaptiveRegularizationOfWeights: Creates a new AdaptiveRegularizationOfWeights with default parameters.
AdaptiveRegularizationOfWeights(double) - Constructor for class gov.sandia.cognition.learning.algorithm.confidence.AdaptiveRegularizationOfWeights: Creates a new AdaptiveRegularizationOfWeights with the given parameters
AdaptiveRejectionSampling - Class in gov.sandia.cognition.statistics.bayesian: Samples form a univariate distribution using the method of adaptive rejection sampling, which is a very efficient method that iteratively improves the rejection and acceptance envelopes in response to additional points.
AdaptiveRejectionSampling() - Constructor for class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling: Creates a new instance of AdaptiveRejectionSampling
AdaptiveRejectionSampling.AbstractEnvelope - Class in gov.sandia.cognition.statistics.bayesian: Describes an enveloping function comprised of a sorted sequence of lines
AdaptiveRejectionSampling.LineSegment - Class in gov.sandia.cognition.statistics.bayesian: A line that has a minimum and maximum support (x-axis) value.
AdaptiveRejectionSampling.LogEvaluator<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>> - Class in gov.sandia.cognition.statistics.bayesian: Wraps an Evaluator and takes the natural logarithm of the evaluate method
AdaptiveRejectionSampling.LowerEnvelope - Class in gov.sandia.cognition.statistics.bayesian: Define the lower envelope for Adaptive Rejection Sampling
AdaptiveRejectionSampling.PDFLogEvaluator - Class in gov.sandia.cognition.statistics.bayesian: Wraps a PDF so that it returns the logEvaluate method.
AdaptiveRejectionSampling.Point - Class in gov.sandia.cognition.statistics.bayesian: An InputOutputPair that has a natural ordering according to their input (x-axis) values.
AdaptiveRejectionSampling.UpperEnvelope - Class in gov.sandia.cognition.statistics.bayesian: Constructs the upper envelope for sampling.
add(ValueType) - Method in class gov.sandia.cognition.collection.DefaultIndexer
add(double) - Method in class gov.sandia.cognition.collection.DoubleArrayList: Adds element to the end of the vector (allocating more space as needed
add(DataType) - Method in class gov.sandia.cognition.collection.FiniteCapacityBuffer: Appends the element to the end of the buffer, removing excess elements if necessary
add(DataType) - Method in class gov.sandia.cognition.collection.FiniteCapacityBuffer.InternalIterator
add(ValueType) - Method in interface gov.sandia.cognition.collection.Indexer: Adds a value to the indexer and returns the index assigned to the value.
add(int) - Method in class gov.sandia.cognition.collection.IntArrayList: Adds element to the end of the vector (allocating more space as needed
add(MemberType) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractUnweightedEnsemble: Adds a given member to the ensemble.
add(MemberType) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractWeightedEnsemble: Adds the given regression function with a default weight of 1.0.
add(MemberType, double) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractWeightedEnsemble: Adds the given regression function with a given weight.
add(MemberType) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.VotingCategorizerEnsemble: Adds a given member to the ensemble.
add(MemberType, double) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.WeightedAveragingEnsemble
add(MemberType) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.WeightedBinaryEnsemble: Adds the given categorizer with a default weight of 1.0.
add(MemberType, double) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.WeightedBinaryEnsemble: Adds the given categorizer with a given weight.
add(MemberType) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.WeightedVotingCategorizerEnsemble: Adds the given categorizer with a default weight of 1.0.
add(MemberType, double) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.WeightedVotingCategorizerEnsemble: Adds the given categorizer with a given weight.
add(InputOutputPair<? extends InputType, OutputType>) - Method in class gov.sandia.cognition.learning.algorithm.nearest.AbstractNearestNeighbor
add(InputOutputPair<? extends InputType, OutputType>) - Method in interface gov.sandia.cognition.learning.algorithm.nearest.NearestNeighbor: Adds the Pair to the data.
add(InputType, double) - Method in class gov.sandia.cognition.learning.function.categorization.DefaultKernelBinaryCategorizer: Adds a new example of the given value with the given weight.
add(CategoryType, CategoryType) - Method in class gov.sandia.cognition.learning.performance.categorization.AbstractConfusionMatrix
add(CategoryType, CategoryType) - Method in interface gov.sandia.cognition.learning.performance.categorization.ConfusionMatrix: Adds a count of one to the matrix entry for the (actual, predicted) pair.
add(CategoryType, CategoryType, double) - Method in interface gov.sandia.cognition.learning.performance.categorization.ConfusionMatrix: Adds a given value to the matrix entry for the (actual, predicted) pair.
add(Boolean, Boolean, double) - Method in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrix
add(CategoryType, CategoryType, double) - Method in class gov.sandia.cognition.learning.performance.categorization.DefaultConfusionMatrix
add(PairType) - Method in class gov.sandia.cognition.math.geometry.KDTree
add(PairType, double) - Method in class gov.sandia.cognition.math.geometry.KDTree.Neighborhood: Adds the neighbor to the priority queue.
add(DataType) - Method in class gov.sandia.cognition.math.geometry.Quadtree: Adds an item to the tree.
add(double, double) - Static method in class gov.sandia.cognition.math.LogMath: Adds two log-domain values.
add(String) - Method in class gov.sandia.cognition.text.spelling.SimpleStatisticalSpellingCorrector: Adds a word to the dictionary of counts for the spelling corrector.
add(String, int) - Method in class gov.sandia.cognition.text.spelling.SimpleStatisticalSpellingCorrector: Adds a given number of counts for a word to the dictionary of counts for the spelling corrector.
add(Termable) - Method in class gov.sandia.cognition.text.term.AbstractTermIndex
add(Termable) - Method in class gov.sandia.cognition.text.term.DefaultTermCounts: Increments the count for a given term.
add(Term, int) - Method in class gov.sandia.cognition.text.term.DefaultTermCounts: Adds the given amount for the given term.
add(Term) - Method in class gov.sandia.cognition.text.term.DefaultTermIndex
add(String) - Method in class gov.sandia.cognition.text.term.filter.DefaultStopList: Adds a word to the stop list.
add(Termable) - Method in interface gov.sandia.cognition.text.term.TermIndex: Adds the given term to the index.
add(Term) - Method in interface gov.sandia.cognition.text.term.TermIndex: Adds the given term to the index.
add(Vectorizable) - Method in class gov.sandia.cognition.text.term.vector.AbstractVectorSpaceModel
add(Vectorizable) - Method in interface gov.sandia.cognition.text.term.vector.VectorSpaceModel: Adds a document to the model.
add(Vector) - Method in interface gov.sandia.cognition.text.term.vector.VectorSpaceModel: Adds a document to the model.
add(Vector) - Method in class gov.sandia.cognition.text.term.vector.weighter.global.AbstractEntropyBasedGlobalTermWeighter
add(Vector) - Method in class gov.sandia.cognition.text.term.vector.weighter.global.AbstractFrequencyBasedGlobalTermWeighter
add(Vector) - Method in class gov.sandia.cognition.text.term.vector.weighter.global.DominanceGlobalTermWeighter
add(Vector) - Method in class gov.sandia.cognition.text.term.vector.weighter.global.EntropyGlobalTermWeighter
add(Vector) - Method in class gov.sandia.cognition.text.term.vector.weighter.global.InverseDocumentFrequencyGlobalTermWeighter
addAll(Iterable<? extends ValueType>) - Method in class gov.sandia.cognition.collection.DefaultIndexer
addAll(Iterable<? extends ValueType>) - Method in interface gov.sandia.cognition.collection.Indexer: Adds all of the given values to the index.
addAll(ConfusionMatrix<OtherType>) - Method in class gov.sandia.cognition.learning.performance.categorization.AbstractConfusionMatrix
addAll(ConfusionMatrix<OtherType>) - Method in interface gov.sandia.cognition.learning.performance.categorization.ConfusionMatrix: Adds all of the values in the given confusion matrix to this confusion matrix.
addAll(Collection<? extends DataType>) - Method in class gov.sandia.cognition.math.geometry.Quadtree: Adds all the items to the
addAll(Iterable<? extends Termable>) - Method in class gov.sandia.cognition.text.term.AbstractTermIndex
addAll(Iterable<? extends Termable>) - Method in class gov.sandia.cognition.text.term.DefaultTermCounts: Adds all of the given terms to the counters; one for each term occurrence.
addAll(Iterable<String>) - Method in class gov.sandia.cognition.text.term.filter.DefaultStopList: Adds all of the given words to the stop list.
addAll(Iterable<? extends Termable>) - Method in interface gov.sandia.cognition.text.term.TermIndex: Adds all of the given terms to the index, if they are not already part of it.
addAll(Iterable<? extends Vectorizable>) - Method in class gov.sandia.cognition.text.term.vector.AbstractVectorSpaceModel
addAll(Iterable<? extends Vectorizable>) - Method in interface gov.sandia.cognition.text.term.vector.VectorSpaceModel: Adds all of the given documents to the model.
addCategory(CategoryType, double, ProbabilityFunction<ObservationType>) - Method in class gov.sandia.cognition.learning.function.categorization.MaximumAPosterioriCategorizer: Adds the given category with the given mass (which is divided by the masses of all categories to determine the prior probability weight) and the distribution function
addChild(InteriorType, DecisionTreeNode<InputType, OutputType>) - Method in class gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeNode: Adds a child for a given interior type.
addClusterMember(DefaultCluster<DataType>, DataType) - Method in class gov.sandia.cognition.learning.algorithm.clustering.cluster.DefaultIncrementalClusterCreator
addClusterMember(ClusterType, DataType) - Method in interface gov.sandia.cognition.learning.algorithm.clustering.cluster.IncrementalClusterCreator: Adds a member to the given cluster.
addClusterMember(NormalizedCentroidCluster<Vectorizable>, Vectorizable) - Method in class gov.sandia.cognition.learning.algorithm.clustering.cluster.NormalizedCentroidClusterCreator
addClusterMember(CentroidCluster<Vector>, Vector) - Method in class gov.sandia.cognition.learning.algorithm.clustering.cluster.VectorMeanCentroidClusterCreator
addCognitiveModelListener(CognitiveModelListener) - Method in class gov.sandia.cognition.framework.AbstractCognitiveModel: Adds a CognitiveModelListener to this model.
addCognitiveModelListener(CognitiveModelListener) - Method in interface gov.sandia.cognition.framework.CognitiveModel: Adds a CognitiveModelListener to this model.
addCogxel(Cogxel) - Method in interface gov.sandia.cognition.framework.CogxelState: Adds a Cogxel to the state, overriding the existing Cogxel, if it exists.
addCogxel(Cogxel) - Method in class gov.sandia.cognition.framework.lite.CogxelStateLite: Adds a Cogxel to the state, overriding the existing Cogxel, if it exists.
addDocumentTermOccurrences(DocIdType, Set<TermType>) - Method in class gov.sandia.cognition.text.algorithm.ValenceSpreader: Adds the input document with all of the input terms in the data.
addDocumentTermWeights(DocIdType, Map<TermType, Double>) - Method in class gov.sandia.cognition.text.algorithm.ValenceSpreader: Adds the input document with all of the input terms with their input scores (should be greater than 0) to the data.
addEdge(NodeNameType, NodeNameType) - Method in class gov.sandia.cognition.graph.DenseMemoryGraph
addEdge(NodeNameType, NodeNameType) - Method in interface gov.sandia.cognition.graph.DirectedNodeEdgeGraph: Adds a directed edge from left to right.
addEdge(NodeNameType, NodeNameType) - Method in interface gov.sandia.cognition.graph.DirectedWeightedNodeEdgeGraph: Adds a directed edge from left to right.
addEdge(NodeNameType, NodeNameType, double) - Method in interface gov.sandia.cognition.graph.DirectedWeightedNodeEdgeGraph: Adds a directed edge from left to right with the input weight.
addEdge(NodeNameType, NodeNameType) - Method in class gov.sandia.cognition.graph.WeightedDenseMemoryGraph
addEdge(NodeNameType, NodeNameType, double) - Method in class gov.sandia.cognition.graph.WeightedDenseMemoryGraph
addEnsembleMember(MemberType) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractBaggingLearner: Adds a new member to the ensemble.
addEnsembleMember(Evaluator<? super InputType, ? extends CategoryType>) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.BaggingCategorizerLearner
addEnsembleMember(Evaluator<? super InputType, ? extends Number>) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.BaggingRegressionLearner
addEnsembleMember(Evaluator<? super InputType, ? extends Boolean>) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.BinaryBaggingLearner
addField(Field) - Method in class gov.sandia.cognition.text.document.AbstractDocument: Adds a field to the document.
addField(Field) - Method in class gov.sandia.cognition.text.document.DefaultDocument
addFirst(DataType) - Method in class gov.sandia.cognition.collection.FiniteCapacityBuffer: Prepend the element to the beginning of the buffer, removing excess elements if necessary
addIterativeAlgorithmListener(IterativeAlgorithmListener) - Method in class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm
addIterativeAlgorithmListener(IterativeAlgorithmListener) - Method in interface gov.sandia.cognition.algorithm.IterativeAlgorithm: Adds a listener for the iterations of the algorithm.
addIterativeAlgorithmListener(IterativeAlgorithmListener) - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.IterativeMatrixSolver
AdditiveEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends java.lang.Number>> - Class in gov.sandia.cognition.learning.algorithm.ensemble: An ensemble of regression functions that determine the result by adding their outputs together.
AdditiveEnsemble() - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.AdditiveEnsemble: Creates a new, empty AdditiveEnsemble.
AdditiveEnsemble(List<MemberType>) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.AdditiveEnsemble: Creates a new AdditiveEnsemble with the given members and a bias of 0.
AdditiveEnsemble(List<MemberType>, double) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.AdditiveEnsemble: Creates a new AdditiveEnsemble with the given members.
addLabel(SemanticLabel) - Method in class gov.sandia.cognition.framework.DefaultSemanticIdentifierMap: Adds a SemanticLabel to the map, or returns an existing SemanticIdentifier if already in the map
addLabel(SemanticLabel) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelVectorConverter: Adds the given SemanticLabel to the list of labels used by the converter.
addLabel(SemanticLabel) - Method in interface gov.sandia.cognition.framework.SemanticIdentifierMap: Adds a SemanticLabel to the map, or returns an existing SemanticIdentifier if already in the map
addLabels(Collection<SemanticLabel>) - Method in class gov.sandia.cognition.framework.DefaultSemanticIdentifierMap: Adds a list of SemanticLabels to the map, returning the list of the corresponding SemanticIdentifiers for the given SemanticLabels.
addLabels(Iterable<SemanticLabel>) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelVectorConverter: Adds all of the given labels to the converter.
addLabels(SemanticLabel[]) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelVectorConverter: Adds all of the given labels to the converter.
addLabels(Collection<SemanticLabel>) - Method in interface gov.sandia.cognition.framework.SemanticIdentifierMap: Adds a list of SemanticLabels to the map, returning the list of the corresponding SemanticIdentifiers for the given SemanticLabels.
addLabelToIdentifierCache(SemanticLabel) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelVectorConverter: Adds the given label to the cache of SemanticIdentifiers.
addLast(DataType) - Method in class gov.sandia.cognition.collection.FiniteCapacityBuffer: Appends the element to the end of the buffer, removing excess elements if necessary
addLink(String, String, double) - Method in class gov.sandia.cognition.framework.io.CSVDefaultCognitiveModelLiteHandler: Called when the "link" directive is seen.
addListener(ProcessLauncherListener) - Method in class gov.sandia.cognition.io.ProcessLauncher: Adds the listener to the event queue
addListener(LearningExperimentListener) - Method in class gov.sandia.cognition.learning.experiment.AbstractLearningExperiment
addListener(LearningExperimentListener) - Method in interface gov.sandia.cognition.learning.experiment.LearningExperiment: Adds the given listener to this object.
addModuleFactory(CognitiveModuleFactory) - Method in class gov.sandia.cognition.framework.AbstractCognitiveModelFactory: Adds a CognitiveModuleFactory to be used by this factory when creating a new CognitiveModel.
addNode(SemanticLabel) - Method in class gov.sandia.cognition.framework.DefaultSemanticNetwork: Adds a node to the semantic network.
addNode(String) - Method in class gov.sandia.cognition.framework.io.CSVDefaultCognitiveModelLiteHandler: Called when the "node" directive is seen.
addNode(SemanticLabel) - Method in interface gov.sandia.cognition.framework.lite.MutablePatternRecognizerLite: Adds a node to the pattern recognizer.
addNode(SemanticLabel) - Method in class gov.sandia.cognition.framework.lite.MutableSemanticMemoryLite: Adds a node to the semantic memory.
addNode(SemanticLabel) - Method in class gov.sandia.cognition.framework.lite.SimplePatternRecognizer: Adds a node to the pattern recognizer.
addNode(NodeNameType) - Method in class gov.sandia.cognition.graph.DenseMemoryGraph
addNode(NodeNameType) - Method in interface gov.sandia.cognition.graph.DirectedNodeEdgeGraph: Adds the input node to the graph.
addParameterAdapter(ParameterAdapter<? super ObjectType, ? super DataType>) - Method in interface gov.sandia.cognition.learning.parameter.ParameterAdaptable: Adds the given parameter adapter to this object.
addParameterAdapter(ParameterAdapter<? super LearnerType, ? super DataType>) - Method in class gov.sandia.cognition.learning.parameter.ParameterAdaptableBatchLearnerWrapper
addPoint(double, double) - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling: Adds a point to the set, which will adject the upper and lower envelopes
addRelationship(int, int, int, int, double) - Method in class gov.sandia.cognition.learning.algorithm.semisupervised.valence.MultipartiteValenceMatrix: Adds a relationship between the two indexed elements.
addSemanticIdentifierMapListener(SemanticIdentifierMapListener) - Method in class gov.sandia.cognition.framework.DefaultSemanticIdentifierMap: Adds a listener to this semantic identifier map.
addSemanticIdentifierMapListener(SemanticIdentifierMapListener) - Method in interface gov.sandia.cognition.framework.SemanticIdentifierMap: Adds a listener to this semantic identifier map.
addWeightedDocument(DocIdType, double) - Method in class gov.sandia.cognition.text.algorithm.ValenceSpreader: Adds the input documentId with its associated score.
addWeightedDocument(DocIdType, double, double) - Method in class gov.sandia.cognition.text.algorithm.ValenceSpreader: Adds the input documentId with its associated score/trust.
addWeightedTerm(TermType, double) - Method in class gov.sandia.cognition.text.algorithm.ValenceSpreader: Adds the input term with its associated score.
addWeightedTerm(TermType, double, double) - Method in class gov.sandia.cognition.text.algorithm.ValenceSpreader: Adds the input term with its associated score and trust level.
adjust(double, int) - Static method in class gov.sandia.cognition.statistics.method.BonferroniCorrection: Computes the Bonferroni multiple-comparison correction
adjust(double, int) - Static method in class gov.sandia.cognition.statistics.method.SidakCorrection: Computes the Sidak multiple-comparison correction
adjustedAlpha - Variable in class gov.sandia.cognition.statistics.method.AdjustedPValueStatistic: Adjusted alpha term to account for multiple comparisons
adjustedAlphas - Variable in class gov.sandia.cognition.statistics.method.HolmCorrection.Statistic: Matrix of adjusted alphas (p-value thresholds) for each comparison
adjustedAlphas - Variable in class gov.sandia.cognition.statistics.method.ShafferStaticCorrection.Statistic: Matrix of adjusted alphas (p-value thresholds) for each comparison
AdjustedPValueStatistic - Class in gov.sandia.cognition.statistics.method: A multiple-comparison statistic derived from a single adjusted p-value.
AdjustedPValueStatistic(Collection<? extends Collection<? extends Number>>, double, double, NullHypothesisEvaluator<Collection<? extends Number>>) - Constructor for class gov.sandia.cognition.statistics.method.AdjustedPValueStatistic: Creates a new instance of StudentizedMultipleComparisonStatistic
advanceInternalIterators() - Method in class gov.sandia.cognition.math.matrix.MatrixUnionIterator: Internal routine for advancing the internal iterators
advanceInternalIterators() - Method in class gov.sandia.cognition.math.matrix.VectorUnionIterator: Internal method for advancing the internal Iterators
AffinityPropagation<DataType> - Class in gov.sandia.cognition.learning.algorithm.clustering: The AffinityPropagation algorithm requires three parameters: a divergence function, a value to use for self-divergence, and a damping factor (called lambda in the paper; 0.5 is the default).
AffinityPropagation() - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation: Creates a new instance of AffinityPropagation.
AffinityPropagation(DivergenceFunction<? super DataType, ? super DataType>, double) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation: Creates a new instance of AffinityPropagation.
AffinityPropagation(DivergenceFunction<? super DataType, ? super DataType>, double, double) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation: Creates a new instance of AffinityPropagation.
AffinityPropagation(DivergenceFunction<? super DataType, ? super DataType>, double, double, int) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation: Creates a new instance of AffinityPropagation.
AgglomerativeClusterer<DataType,ClusterType extends Cluster<DataType>> - Class in gov.sandia.cognition.learning.algorithm.clustering: The AgglomerativeClusterer implements an agglomerative clustering algorithm, which is a type of hierarchical clustering algorithm.
AgglomerativeClusterer() - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer: Creates a new instance of AgglomerativeClusterer.
AgglomerativeClusterer(ClusterToClusterDivergenceFunction<? super ClusterType, ? super DataType>, ClusterCreator<ClusterType, DataType>) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer: Initializes the clustering to use the given metric between clusters, and the given cluster creator.
AgglomerativeClusterer(ClusterToClusterDivergenceFunction<? super ClusterType, ? super DataType>, ClusterCreator<ClusterType, DataType>, int) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer: Initializes the clustering to use the given metric between clusters, the given cluster creator, and the minimum number of clusters to allow.
AgglomerativeClusterer(ClusterToClusterDivergenceFunction<? super ClusterType, ? super DataType>, ClusterCreator<ClusterType, DataType>, double) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer: Initializes the clustering to use the given metric between clusters, the given cluster merger, and the maximum distance between clusters to allow when merging.
AgglomerativeClusterer(ClusterToClusterDivergenceFunction<? super ClusterType, ? super DataType>, ClusterCreator<ClusterType, DataType>, int, double) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer: Initializes the clustering to use the given metric between clusters, the given cluster merger, the minimum number of clusters to allow, and the maximum minimum distance between clusters to allow.
AgglomerativeClusterer.HierarchyNode<DataType,ClusterType extends Cluster<DataType>> - Class in gov.sandia.cognition.learning.algorithm.clustering: Holds the hierarchy information for the agglomerative clusterer.
aggressiveness - Variable in class gov.sandia.cognition.learning.algorithm.perceptron.OnlinePassiveAggressivePerceptron.AbstractSoftMargin: The aggressiveness parameter (C), which is the trade-off between aggressive updating to meet an incorrect example and keeping history around.
AggressiveRelaxedOnlineMaximumMarginAlgorithm - Class in gov.sandia.cognition.learning.algorithm.perceptron: An implementation of the Aggressive Relaxed Online Maximum Margin Algorithm (AROMMA).
AggressiveRelaxedOnlineMaximumMarginAlgorithm() - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.AggressiveRelaxedOnlineMaximumMarginAlgorithm: Creates a new AggressiveRelaxedOnlineMaximumMarginAlgorithm.
AggressiveRelaxedOnlineMaximumMarginAlgorithm(VectorFactory<?>) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.AggressiveRelaxedOnlineMaximumMarginAlgorithm: Creates a new AggressiveRelaxedOnlineMaximumMarginAlgorithm with the given vector factory.
algorithmEnded(IterativeAlgorithm) - Method in class gov.sandia.cognition.algorithm.AnytimeAlgorithmWrapper
algorithmEnded(IterativeAlgorithm) - Method in class gov.sandia.cognition.algorithm.event.AbstractIterativeAlgorithmListener
algorithmEnded(IterativeAlgorithm) - Method in interface gov.sandia.cognition.algorithm.IterativeAlgorithmListener: This method is called when the algorithm has ended, after the last step of the algorithm.
algorithmEnded(IterativeAlgorithm) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractCategorizerOutOfBagStoppingCriteria
algorithmEnded(IterativeAlgorithm) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.BaggingCategorizerLearner.OutOfBagErrorStoppingCriteria
algorithmStarted(IterativeAlgorithm) - Method in class gov.sandia.cognition.algorithm.AnytimeAlgorithmWrapper
algorithmStarted(IterativeAlgorithm) - Method in class gov.sandia.cognition.algorithm.event.AbstractIterativeAlgorithmListener
algorithmStarted(IterativeAlgorithm) - Method in interface gov.sandia.cognition.algorithm.IterativeAlgorithmListener: This method is called when a algorithm has started, before the first step of the algorithm.
algorithmStarted(IterativeAlgorithm) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractCategorizerOutOfBagStoppingCriteria
algorithmStarted(IterativeAlgorithm) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.BaggingCategorizerLearner.OutOfBagErrorStoppingCriteria
algorithmStarted(IterativeAlgorithm) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner.OutOfBagErrorStoppingCriteria
allowedTerms - Variable in class gov.sandia.cognition.text.term.filter.DictionaryFilter: The set of terms allowed by the filter.
alnorm(double, boolean) - Static method in class gov.sandia.cognition.statistics.distribution.StudentizedRangeDistribution.APStat: Algorithm AS66 Applied Statistics (1973) vol22 no.3.
alpha - Variable in class gov.sandia.cognition.learning.algorithm.hmm.ParallelHiddenMarkovModel.StateObservationLikelihoodTask: Alpha at time n.
alpha - Variable in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.Sample: Scaling parameter which defines the strength of the base distribution, must be greater than zero.
alpha - Variable in class gov.sandia.cognition.statistics.distribution.ChineseRestaurantProcess: CRP concentration parameter, must be greater than zero.
alpha - Variable in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler: The alpha parameter controlling the Dirichlet distribution for the document-topic probabilities.
alphabet - Variable in class gov.sandia.cognition.text.spelling.SimpleStatisticalSpellingCorrector: The alphabet of lower case characters.
alphabet - Variable in class gov.sandia.cognition.text.spelling.SimpleStatisticalSpellingCorrector.Learner: The alphabet of lower case characters.
alphaInverseSampler - Variable in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel: Samples a new alpha-inverse.
AnalysisOfVarianceOneWay - Class in gov.sandia.cognition.statistics.method: Analysis of Variance single-factor null-hypothesis testing procedure, usually called "1-way ANOVA".
AnalysisOfVarianceOneWay() - Constructor for class gov.sandia.cognition.statistics.method.AnalysisOfVarianceOneWay: Creates a new instance of AnalysisOfVarianceOneWay
AnalysisOfVarianceOneWay.Statistic - Class in gov.sandia.cognition.statistics.method: Returns the confidence statistic for an ANOVA test
angle(VectorType) - Method in class gov.sandia.cognition.math.matrix.AbstractVectorSpace
angle(InfiniteVector<KeyType>) - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
angle(VectorType) - Method in interface gov.sandia.cognition.math.matrix.VectorSpace: Computes the angle between two Vectors.
AnytimeAlgorithm<ResultType> - Interface in gov.sandia.cognition.algorithm: The AnytimeAlgorithm interface defines the functionality of an iterative algorithm that is stoppable and can return intermediate results.
AnytimeAlgorithmWrapper<ResultType,InternalAlgorithm extends AnytimeAlgorithm<?>> - Class in gov.sandia.cognition.algorithm: Wraps an AnytimeAlgorithm.
AnytimeAlgorithmWrapper() - Constructor for class gov.sandia.cognition.algorithm.AnytimeAlgorithmWrapper: Creates a new instance of AnytimeAlgorithmWrapper
AnytimeAlgorithmWrapper(InternalAlgorithm) - Constructor for class gov.sandia.cognition.algorithm.AnytimeAlgorithmWrapper: Creates a new instance of AnytimeAlgorithmWrapper
AnytimeBatchLearner<DataType,ResultType> - Interface in gov.sandia.cognition.learning.algorithm: A batch learner that is also and Anytime algorithm.
AnytimeBatchLearnerValidationPerformanceReporter<DataType,ObjectType> - Class in gov.sandia.cognition.learning.performance: A performance reporter for a validation set.
AnytimeBatchLearnerValidationPerformanceReporter(PerformanceEvaluator<? super ObjectType, ? super DataType, ?>, DataType) - Constructor for class gov.sandia.cognition.learning.performance.AnytimeBatchLearnerValidationPerformanceReporter: Creates a new AnytimeBatchLearnerValidationPerformanceReporter that reports to the given print stream using the default format.
AnytimeBatchLearnerValidationPerformanceReporter(PerformanceEvaluator<? super ObjectType, ? super DataType, ?>, DataType, PrintStream) - Constructor for class gov.sandia.cognition.learning.performance.AnytimeBatchLearnerValidationPerformanceReporter: Creates a new AnytimeBatchLearnerValidationPerformanceReporter that reports to the given print stream using the default format.
AnytimeBatchLearnerValidationPerformanceReporter(PerformanceEvaluator<? super ObjectType, ? super DataType, ?>, DataType, String) - Constructor for class gov.sandia.cognition.learning.performance.AnytimeBatchLearnerValidationPerformanceReporter: Creates a new AnytimeBatchLearnerValidationPerformanceReporter that reports to System.out and the given format.
AnytimeBatchLearnerValidationPerformanceReporter(PerformanceEvaluator<? super ObjectType, ? super DataType, ?>, DataType, PrintStream, String) - Constructor for class gov.sandia.cognition.learning.performance.AnytimeBatchLearnerValidationPerformanceReporter: Creates a new AnytimeBatchLearnerValidationPerformanceReporter that reports to the given print stream and format.
appendBias(Collection<? extends Vector>) - Static method in class gov.sandia.cognition.learning.data.DatasetUtil: Appends a bias (constant 1.0) to the end of each Vector in the dataset, the original dataset is unmodified.
appendBias(Collection<? extends Vector>, double) - Static method in class gov.sandia.cognition.learning.data.DatasetUtil: Appends "biasValue" to the end of each Vector in the dataset, the original dataset is unmodified.
applyUpdate(DefaultKernelBinaryCategorizer<InputType>, InputType, double, double, double, double, Vector) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Projectron: Apply the update for the Projectron.
applyUpdate(DefaultKernelBinaryCategorizer<InputType>, InputType, double, double, double, double, Vector) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Projectron.LinearSoftMargin
APStat() - Constructor for class gov.sandia.cognition.statistics.distribution.StudentizedRangeDistribution.APStat
areAllOutputsEqual(Collection<? extends InputOutputPair<? extends InputType, OutputType>>) - Method in class gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeLearner: Determines if all of the output values in the collection are equal.
areLocalItemsSame() - Method in class gov.sandia.cognition.math.geometry.Quadtree.Node: Returns true if this is a leaf and all the local items are the same.
ArgumentChecker - Class in gov.sandia.cognition.util: A utility class for checking arguments to a function.
ArgumentChecker() - Constructor for class gov.sandia.cognition.util.ArgumentChecker
ArrayBasedCognitiveModelInput - Class in gov.sandia.cognition.framework.lite: The ArrayBasedCognitiveModelInput class implements a CognitiveModelInput that is used by the ArrayBasedPerceptionModule.
ArrayBasedCognitiveModelInput(SemanticIdentifier[], double[]) - Constructor for class gov.sandia.cognition.framework.lite.ArrayBasedCognitiveModelInput: Creates a new instance of ArrayBasedCognitiveModelInput using the two given arrays underneath by copying them.
ArrayBasedCognitiveModelInput(SemanticIdentifier[], double[], boolean) - Constructor for class gov.sandia.cognition.framework.lite.ArrayBasedCognitiveModelInput: Creates a new instance of ArrayBasedCognitiveModelInput using the two given arrays underneath.
ArrayBasedPerceptionModule - Class in gov.sandia.cognition.framework.lite: The ArrayBasedPerceptionModule class implements a simple CognitiveModule that sets the state of Cogxels based on a given input of SemanticIdentifiers and their associated activation values.
ArrayBasedPerceptionModule(SemanticIdentifierMap, CogxelFactory) - Constructor for class gov.sandia.cognition.framework.lite.ArrayBasedPerceptionModule: Creates a new instance of ArrayBasedPerceptionModule.
ArrayBasedPerceptionModuleFactory - Class in gov.sandia.cognition.framework.lite: The ArrayBasedPerceptionModuleFactory class implements a CognitiveModuleFactory for ArrayBasedPerceptionModules.
ArrayBasedPerceptionModuleFactory() - Constructor for class gov.sandia.cognition.framework.lite.ArrayBasedPerceptionModuleFactory: Creates a new instance of ArrayBasedPerceptionModuleFactory.
ArrayBasedPerceptionModuleFactory(CogxelFactory) - Constructor for class gov.sandia.cognition.framework.lite.ArrayBasedPerceptionModuleFactory: Creates a new instance of ArrayBasedPerceptionModuleFactory.
ArrayIndexSorter - Class in gov.sandia.cognition.util: Returns the indices of the array sorted in ascending or descending order
ArrayIndexSorter() - Constructor for class gov.sandia.cognition.util.ArrayIndexSorter
ArrayUtil - Class in gov.sandia.cognition.collection: Utility class for handling arrays.
ArrayUtil() - Constructor for class gov.sandia.cognition.collection.ArrayUtil
asArrayList(Iterable<DataType>) - Static method in class gov.sandia.cognition.collection.CollectionUtil: Returns the Collection as an ArrayList.
asDivergence() - Method in interface gov.sandia.cognition.text.relation.SimilarityFunction: Converts the similarity function into a divergence function.
asDivergence() - Method in class gov.sandia.cognition.text.term.vector.CosineSimilarityFunction
asMap() - Method in class gov.sandia.cognition.collection.AbstractMutableDoubleMap
asMap() - Method in class gov.sandia.cognition.collection.AbstractScalarMap
asMap() - Method in class gov.sandia.cognition.collection.DefaultIndexer
asMap() - Method in interface gov.sandia.cognition.collection.Indexer: Gets mapping of values to indices.
asMap() - Method in interface gov.sandia.cognition.collection.ScalarMap: Gets a java.util.Map that contains the same data as in this scalar map.
asMultiCollection(Collection<EntryType>) - Static method in class gov.sandia.cognition.learning.data.DatasetUtil: Takes a collection and returns a multi-collection version of that collection.
assertArgumentIsNode(SemanticLabel) - Method in class gov.sandia.cognition.framework.lite.MutableSemanticMemoryLite: Asserts that the given argument is a node, throwing an IllegalArgumentException if it is not a node.
assertDimensionalitiesAllEqual(Iterable<? extends Vectorizable>) - Static method in class gov.sandia.cognition.learning.data.DatasetUtil: Asserts that all of the dimensionalities of the vectors in the given set of data are the same.
assertDimensionalitiesAllEqual(Iterable<? extends Vectorizable>, int) - Static method in class gov.sandia.cognition.math.matrix.VectorUtil: Asserts that all of the dimensionalities of the vectors in the given set of data equal the given dimensionality.
assertDimensionalityEquals(int) - Method in class gov.sandia.cognition.math.matrix.AbstractVector
assertDimensionalityEquals(int) - Method in interface gov.sandia.cognition.math.matrix.Vector: Asserts that the dimensionality of this vector equals the given dimensionality.
assertEqualDimensionality(Vector, Vector) - Static method in class gov.sandia.cognition.math.matrix.AbstractVector: Throws a DimensionalityMismatchException if first.getDimensionality() != second.getDimensionality(), otherwise this function has no effect
assertInputDimensionalitiesAllEqual(Iterable<? extends InputOutputPair<? extends Vectorizable, ?>>) - Static method in class gov.sandia.cognition.learning.data.DatasetUtil: Asserts that all of the dimensionalities of the input vectors in the given set of input-output pairs are the same.
assertInputDimensionalitiesAllEqual(Iterable<? extends InputOutputPair<? extends Vectorizable, ?>>, int) - Static method in class gov.sandia.cognition.learning.data.DatasetUtil: Asserts that all of the dimensionalities of the input vectors in the given set of input-output pairs equal the given dimensionality.
assertIsInRangeExclusive(String, double, double, double) - Static method in class gov.sandia.cognition.util.ArgumentChecker: Asserts that the given argument is in the given range, exclusive.
assertIsInRangeInclusive(String, double, double, double) - Static method in class gov.sandia.cognition.util.ArgumentChecker: Asserts that the given argument is in the given range, inclusive.
assertIsNonNegative(String, int) - Static method in class gov.sandia.cognition.util.ArgumentChecker: Asserts that the given argument is non-negative (>=0.0).
assertIsNonNegative(String, long) - Static method in class gov.sandia.cognition.util.ArgumentChecker: Asserts that the given argument is non-negative (>=0.0).
assertIsNonNegative(String, double) - Static method in class gov.sandia.cognition.util.ArgumentChecker: Asserts that the given argument is non-negative (>=0.0).
assertIsNotNull(String, Object) - Static method in class gov.sandia.cognition.util.ArgumentChecker: Asserts that the given value is not null.
assertIsPositive(String, int) - Static method in class gov.sandia.cognition.util.ArgumentChecker: Asserts the given value is positive (> 0).
assertIsPositive(String, long) - Static method in class gov.sandia.cognition.util.ArgumentChecker: Asserts the given value is positive (> 0).
assertIsPositive(String, double) - Static method in class gov.sandia.cognition.util.ArgumentChecker: Asserts the given value is positive (> 0.0).
assertIsProbability(double) - Static method in class gov.sandia.cognition.math.ProbabilityUtil: Checks to make sure that probability is between 0.0 and 1.0.
assertMultiplicationDimensions(Matrix) - Method in class gov.sandia.cognition.math.matrix.AbstractMatrix
assertMultiplicationDimensions(Matrix) - Method in interface gov.sandia.cognition.math.matrix.Matrix: Checks to see if the dimensions are appropriate for: this.times(postMultiplicationMatrix).
assertSameDimensionality(Vector) - Method in class gov.sandia.cognition.math.matrix.AbstractVector
assertSameDimensionality(Vector) - Method in interface gov.sandia.cognition.math.matrix.Vector: Asserts that this vector has the same dimensionality as the given vector.
assertSameDimensions(Matrix) - Method in class gov.sandia.cognition.math.matrix.AbstractMatrix
assertSameDimensions(Matrix) - Method in interface gov.sandia.cognition.math.matrix.Matrix: Throws a DimensionalityMismatchException if dimensions between this and otherMatrix aren't the same
assignCluster(int, int) - Method in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation: Assigns example "i" to the new cluster index.
assignDataFromIndices() - Method in class gov.sandia.cognition.learning.algorithm.clustering.KMeansClusterer: Puts the data into a list of lists for each cluster to then estimate
AssignDataToCluster(Collection<DataType>) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.ParallelizedKMeansClusterer.AssignDataToCluster: Creates a new instance of AssignDataToCluster
assignDataToClusters(Collection<? extends DataType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.KMeansClusterer: Creates the cluster assignments given the current locations of clusters
assignDataToClusters(Collection<? extends Vector>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.MiniBatchKMeansClusterer
assignDataToClusters(Collection<? extends DataType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.ParallelizedKMeansClusterer
assignments - Variable in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation: The assignments of each example to an exemplar (cluster).
assignments - Variable in class gov.sandia.cognition.learning.algorithm.clustering.KMeansClusterer: The current assignments of elements to clusters.
assignments - Variable in class gov.sandia.cognition.statistics.bayesian.ParallelDirichletProcessMixtureModel.DPMMAssignments: List of assignment indices
assignmentTasks - Variable in class gov.sandia.cognition.statistics.bayesian.ParallelDirichletProcessMixtureModel: Tasks that assign observations to clusters
assignObservationsToClusters(int, DirichletProcessMixtureModel.DPMMLogConditional) - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel: Assigns observations to each of the K clusters, plus the as-yet-uncreated new cluster
assignObservationsToClusters(int, DirichletProcessMixtureModel.DPMMLogConditional) - Method in class gov.sandia.cognition.statistics.bayesian.ParallelDirichletProcessMixtureModel
assignObservationToCluster(ObservationType, double[], DirichletProcessMixtureModel.DPMMLogConditional) - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel: Probabilistically assigns an observation to a cluster
assignParameterMethods(Distribution<?>, String) - Method in class gov.sandia.cognition.statistics.DefaultDistributionParameter: Assigns the getter and setter from the given conditionalDistribution and parameter name.
asTerm() - Method in class gov.sandia.cognition.text.term.AbstractTerm
asTerm() - Method in class gov.sandia.cognition.text.term.DefaultIndexedTerm
asTerm() - Method in class gov.sandia.cognition.text.term.DefaultTermOccurrence
asTerm() - Method in interface gov.sandia.cognition.text.term.Termable: Get the term for the object.
asTerm() - Method in class gov.sandia.cognition.text.token.DefaultToken
asTerm() - Method in interface gov.sandia.cognition.text.token.Token: Tokens always are treated as DefaultTerm objects that contain the text of the Token.
asVectorCollection(Collection<? extends Vectorizable>) - Static method in class gov.sandia.cognition.learning.data.DatasetUtil: Takes a collection of Vectorizable objects and returns a collection of Vector objects of the same size.
AtanFunction - Class in gov.sandia.cognition.learning.function.scalar: Returns the element-wise arctangent of the input vector, compressed between -maxMagnitude and maxMagnitude (instead of just -PI/2 and PI/2)
AtanFunction() - Constructor for class gov.sandia.cognition.learning.function.scalar.AtanFunction: Creates a new instance of AtanFunction with the standard unit magnitude of -PI / 2 to PI / 2.
AtanFunction(double) - Constructor for class gov.sandia.cognition.learning.function.scalar.AtanFunction: Creates a new instance of AtanFunction.
AUTHOR_FIELD_NAME - Static variable in class gov.sandia.cognition.text.document.AbstractDocument: The name of the author field is "author".
AutoRegressiveMovingAverageFilter - Class in gov.sandia.cognition.math.signals: A type of filter using a moving-average calculation.
AutoRegressiveMovingAverageFilter(int, int) - Constructor for class gov.sandia.cognition.math.signals.AutoRegressiveMovingAverageFilter: Creates a new instance of AutoRegressiveMovingAverageFilter
AutoRegressiveMovingAverageFilter(double[], double[]) - Constructor for class gov.sandia.cognition.math.signals.AutoRegressiveMovingAverageFilter: Creates a new instance of AutoRegressiveMovingAverageFilter
AutoRegressiveMovingAverageFilter(Vector, Vector) - Constructor for class gov.sandia.cognition.math.signals.AutoRegressiveMovingAverageFilter: Creates a new instance of AutoRegressiveMovingAverageFilter
availabilities - Variable in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation: The array of example-example availabilities.
AveragingEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends java.lang.Number>> - Class in gov.sandia.cognition.learning.algorithm.ensemble: An ensemble for regression functions that averages together the output value of each ensemble member to get the final output.
AveragingEnsemble() - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.AveragingEnsemble: Creates a new, empty AdditiveEnsemble.
AveragingEnsemble(List<MemberType>) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.AveragingEnsemble: Creates a new AdditiveEnsemble with the given

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