E-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

E

EdgeMergingEnergyFunction<LabelType,NodeNameType> - Class in gov.sandia.cognition.graph.inference: Our implementation of belief propagation requires that there be at most one edge between any pair of nodes.
EdgeMergingEnergyFunction(NodeNameAwareEnergyFunction<LabelType, NodeNameType>) - Constructor for class gov.sandia.cognition.graph.inference.EdgeMergingEnergyFunction: Initializes this edge merging energy function
effectiveRank(double) - Method in class gov.sandia.cognition.math.matrix.decomposition.AbstractSingularValueDecomposition
effectiveRank(double) - Method in interface gov.sandia.cognition.math.matrix.decomposition.SingularValueDecomposition: Returns the effective rank of the underlying matrix by counting the number of singular values whose values are larger than effectiveZero
effectiveZero - Variable in class gov.sandia.cognition.text.term.relation.TermVectorSimilarityNetworkCreator: The value to treat as zero.
EigenDecomposition - Interface in gov.sandia.cognition.math.matrix.decomposition: Performs a right eigendecomposition for symmetric or asymmetric matrices
EigenDecompositionRightMTJ - Class in gov.sandia.cognition.math.matrix.mtj.decomposition: Computes the right (standard) eigendecomposition of the given matrix.
EigenvectorPowerIteration - Class in gov.sandia.cognition.math.matrix.decomposition: Implementation of the Eigenvector Power Iteration algorithm.
EigenvectorPowerIteration() - Constructor for class gov.sandia.cognition.math.matrix.decomposition.EigenvectorPowerIteration: Creates a new instance of EigenvectorPowerIteration.
ElementWiseDifferentiableVectorFunction - Class in gov.sandia.cognition.learning.function.vector: An ElementWiseVectorFunction that is also a DifferentiableVectorFunction
ElementWiseDifferentiableVectorFunction() - Constructor for class gov.sandia.cognition.learning.function.vector.ElementWiseDifferentiableVectorFunction: Creates a new ElementWiseDifferentiableVectorFunction with a linear scalar function as the default function (f(x_i) = x_i).
ElementWiseDifferentiableVectorFunction(DifferentiableUnivariateScalarFunction) - Constructor for class gov.sandia.cognition.learning.function.vector.ElementWiseDifferentiableVectorFunction: Creates a new instance of ElementWiseDifferentiableVectorFunction
ElementWiseVectorFunction - Class in gov.sandia.cognition.learning.function.vector: A VectorFunction that operates on each element of the Vector indepenently of all others.
ElementWiseVectorFunction() - Constructor for class gov.sandia.cognition.learning.function.vector.ElementWiseVectorFunction: Creates a new ElementWiseVectorFunction with a default linear scalar function.
ElementWiseVectorFunction(UnivariateScalarFunction) - Constructor for class gov.sandia.cognition.learning.function.vector.ElementWiseVectorFunction: Creates a new instance of ElementWiseVectorFunction
ElementWiseVectorFunction(ElementWiseVectorFunction) - Constructor for class gov.sandia.cognition.learning.function.vector.ElementWiseVectorFunction: Copy Constructor
emissionFunctions - Variable in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel: The PDFs that emit symbols from each state.
EMLearner(Random) - Constructor for class gov.sandia.cognition.statistics.distribution.MixtureOfGaussians.EMLearner: Creates a new instance of EMLearner
EMLearner(int, Random) - Constructor for class gov.sandia.cognition.statistics.distribution.MixtureOfGaussians.EMLearner: Creates a new instance of EMLearner
EMLearner(int, MultivariateGaussian.WeightedMaximumLikelihoodEstimator, Random) - Constructor for class gov.sandia.cognition.statistics.distribution.MixtureOfGaussians.EMLearner: Creates a new instance of EMLearner
EMLearner(Random) - Constructor for class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.EMLearner: Default constructor
EMLearner(int, DistributionWeightedEstimator<Double, ? extends SmoothUnivariateDistribution>, Random) - Constructor for class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.EMLearner: Creates a new instance of EMLearner
EMLearner(Random, Collection<? extends DistributionWeightedEstimator<Double, ? extends SmoothUnivariateDistribution>>) - Constructor for class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.EMLearner: Creates a new instance of EMLearner
encode(InputType, Vector) - Method in class gov.sandia.cognition.data.convert.vector.AbstractToVectorEncoder
encode(InputType, Vector) - Method in interface gov.sandia.cognition.data.convert.vector.DataToVectorEncoder: Encodes the given object into the given Vector.
encode(InputType, Vector, int) - Method in interface gov.sandia.cognition.data.convert.vector.DataToVectorEncoder: Encodes the given object into the given Vector, starting at the given index.
encode(InputType, Vector, int) - Method in class gov.sandia.cognition.data.convert.vector.NumberConverterToVectorAdapter: Encodes the given object into the vector at the given index by using the number converter that this object is adapting.
encode(Number, Vector, int) - Method in class gov.sandia.cognition.data.convert.vector.NumberToVectorEncoder: Encodes the given number into the given vector at the given index.
encode(InputType, Vector, int) - Method in class gov.sandia.cognition.data.convert.vector.UniqueBooleanVectorEncoder: Encodes the given object into the given vector at the given starting index by using a unique boolean encoding, where the given input value is compared to each of the encoder's values using equality.
encode(InputType, Vector, int) - Method in class gov.sandia.cognition.learning.function.distance.DivergencesEvaluator
EnergyFunction<LabelType> - Interface in gov.sandia.cognition.graph.inference: This interface defines the methods necessary for an object to be passed into an iterative message-passing data inferencing solver (such as belief propagation).
EnergyFunctionSolver<LabelType> - Interface in gov.sandia.cognition.graph.inference: Interface for any belief propagation implementation.
ensemble - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractBaggingLearner: The ensemble being created by the learner.
ensemble - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.AdaBoost: The ensemble learned by the algorithm.
Ensemble<MemberType> - Interface in gov.sandia.cognition.learning.algorithm.ensemble: The Ensemble interface defines the functionality of an "ensemble" that is typically created by combining together the result of multiple learning algorithms.
ensemble - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner: The current ensemble.
ensemble - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.MultiCategoryAdaBoost: The ensemble learned by the algorithm.
ensembleSize - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.OnlineBaggingCategorizerLearner: The size of the ensemble to create.
ensureCapacity(int) - Method in class gov.sandia.cognition.collection.DynamicArrayMap: Ensures that the capacity of the underlying array can hold the given minimum capacity.
entries - Variable in class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation: The entry information that the algorithm keeps.
entropy - Variable in class gov.sandia.cognition.text.term.vector.weighter.global.EntropyGlobalTermWeighter: A vector caching the global entropy weight of the document collection.
EntropyEvaluator - Class in gov.sandia.cognition.learning.function.vector: Takes a vector of inputs and computes the log base 2 entropy of the input.
EntropyEvaluator() - Constructor for class gov.sandia.cognition.learning.function.vector.EntropyEvaluator: Creates a new instance of EntropyEvaluator
EntropyGlobalTermWeighter - Class in gov.sandia.cognition.text.term.vector.weighter.global: Implements the entropy global term weighting scheme.
EntropyGlobalTermWeighter() - Constructor for class gov.sandia.cognition.text.term.vector.weighter.global.EntropyGlobalTermWeighter: Creates a new EntropyGlobalTermWeighter.
EntropyGlobalTermWeighter(VectorFactory<? extends Vector>) - Constructor for class gov.sandia.cognition.text.term.vector.weighter.global.EntropyGlobalTermWeighter: Creates a new EntropyGlobalTermWeighter.
Entry(InputOutputPair<? extends InputType, ? extends Boolean>) - Constructor for class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation.Entry: Creates a new Entry for the given example.
EntryIndexComparator<EntryType> - Interface in gov.sandia.cognition.math.matrix: The EntryIndexComparator interface defines the functionality of a comparator for index entries.
EntryIndexComparator.Compare - Enum in gov.sandia.cognition.math.matrix: Indicates which of two iterators has the lowest index
entrySet() - Method in class gov.sandia.cognition.collection.AbstractLogNumberMap
entrySet() - Method in class gov.sandia.cognition.collection.AbstractMutableDoubleMap
entrySet() - Method in class gov.sandia.cognition.collection.DynamicArrayMap
entrySet() - Method in interface gov.sandia.cognition.collection.ScalarMap: Gets the set of entries in this scalar map.
epsilon - Variable in class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalDeviation: Epsilon is the cached value 1 + phi^2.
equals(Collection<? extends T>, Collection<? extends T>) - Static method in class gov.sandia.cognition.collection.CollectionUtil: Check if two collections return exactly the same objects in the same order.
equals(Iterable<? extends T>, Iterable<? extends T>) - Static method in class gov.sandia.cognition.collection.CollectionUtil: Check if two iterables return exactly the same objects in the same order.
equals(Object) - Method in class gov.sandia.cognition.collection.DoubleArrayList
equals(Object) - Method in class gov.sandia.cognition.collection.IntArrayList
equals(Object) - Method in class gov.sandia.cognition.framework.AbstractSemanticIdentifier: Determines if this identifier is equal to another Object.
equals(SemanticIdentifier) - Method in class gov.sandia.cognition.framework.AbstractSemanticIdentifier: Determines if this identifier is equal to the given one by comparing the identifier number only.
equals(Object) - Method in class gov.sandia.cognition.framework.DefaultSemanticLabel: Determines if this label is equal to a given object.
equals(DefaultSemanticLabel) - Method in class gov.sandia.cognition.framework.DefaultSemanticLabel: Determines is this DefaultSemanticLabel is equal to the given one.
equals(Object) - Method in class gov.sandia.cognition.framework.learning.converter.AbstractCogxelPairConverter
equals(AbstractCogxelPairConverter<?, ?, ?>) - Method in class gov.sandia.cognition.framework.learning.converter.AbstractCogxelPairConverter: Returns true if the two converters have equal internal converters.
equals(Object) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelBooleanConverter
equals(CogxelBooleanConverter) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelBooleanConverter: This converter equals another converter of the same type if their labels are equal.
equals(Object) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelDoubleConverter
equals(CogxelDoubleConverter) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelDoubleConverter: Returns true if the two converters have the same label.
equals(Object) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelInputOutputPairConverter
equals(Object) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelTargetEstimatePairConverter
equals(Object) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelVectorConverter
equals(CogxelVectorConverter) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelVectorConverter: Returns true if the two converters have the same labels.
equals(Object) - Method in interface gov.sandia.cognition.framework.SemanticIdentifier: Determines if this identifier is equal to another Object.
equals(SemanticIdentifier) - Method in interface gov.sandia.cognition.framework.SemanticIdentifier: Determines if this identifier is equal to the given one by comparing the identifier number only.
equals(Object) - Method in interface gov.sandia.cognition.framework.SemanticLabel: Determines if this label is equal to a given object.
equals(Object) - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.ConjugateGradientMatrixSolver
equals(Object) - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.ConjugateGradientWithPreconditionerMatrixSolver
equals(Object) - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.IterativeMatrixSolver
equals(Object) - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.MatrixVectorMultiplier
equals(Object) - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.OverconstrainedConjugateGradientMatrixMinimizer
equals(Object) - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.OverconstrainedMatrixVectorMultiplier
equals(Object) - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.SteepestDescentMatrixSolver
equals(Object) - Method in class gov.sandia.cognition.learning.function.scalar.VectorEntryFunction
equals(Object) - Method in class gov.sandia.cognition.math.ComplexNumber
equals(ComplexNumber, double) - Method in class gov.sandia.cognition.math.ComplexNumber
equals(Object) - Method in class gov.sandia.cognition.math.geometry.KDTree.Neighborhood.Neighbor
equals(Object) - Method in class gov.sandia.cognition.math.LogNumber
equals(LogNumber, double) - Method in class gov.sandia.cognition.math.LogNumber
equals(Object) - Method in class gov.sandia.cognition.math.matrix.AbstractMatrix
equals(Matrix, double) - Method in class gov.sandia.cognition.math.matrix.AbstractMatrix
equals(Object) - Method in class gov.sandia.cognition.math.matrix.AbstractVector
equals(Vector, double) - Method in class gov.sandia.cognition.math.matrix.AbstractVector
equals(Object) - Method in class gov.sandia.cognition.math.matrix.AbstractVectorSpace
equals(VectorType, double) - Method in class gov.sandia.cognition.math.matrix.AbstractVectorSpace
equals(Object) - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
equals(InfiniteVector<KeyType>, double) - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
equals(AbstractMTJMatrix, double) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix: Determines if the matrices are effectively equal to each other
equals(Vector, double) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseVector
equals(Quaternion) - Method in interface gov.sandia.cognition.math.matrix.Quaternion: Determines if this quaternion is equal to the given quaternion.
equals(Quaternion, double) - Method in interface gov.sandia.cognition.math.matrix.Quaternion: Determines if this quaternion is equal to the given quaternion, using a given threshold for determining if two numbers are equal.
equals(Object) - Method in class gov.sandia.cognition.math.MutableDouble
equals(MutableDouble) - Method in class gov.sandia.cognition.math.MutableDouble: Determines if this MutableDouble is equal to another MutableDouble.
equals(double) - Method in class gov.sandia.cognition.math.MutableDouble: Determines if this MutableDouble's value is equal to a given double.
equals(MutableDouble, double) - Method in class gov.sandia.cognition.math.MutableDouble
equals(Object) - Method in class gov.sandia.cognition.math.MutableInteger
equals(MutableInteger) - Method in class gov.sandia.cognition.math.MutableInteger: Determines if this MutableInteger is equal to another MutableInteger.
equals(int) - Method in class gov.sandia.cognition.math.MutableInteger: Determines if this MutableInteger's value is equal to a given integer.
equals(MutableInteger, double) - Method in class gov.sandia.cognition.math.MutableInteger
equals(Object) - Method in class gov.sandia.cognition.math.MutableLong
equals(MutableLong) - Method in class gov.sandia.cognition.math.MutableLong: Determines if this MutableLong is equal to another MutableLong.
equals(long) - Method in class gov.sandia.cognition.math.MutableLong: Determines if this MutableLong's value is equal to a given long.
equals(MutableLong, double) - Method in class gov.sandia.cognition.math.MutableLong
equals(Object) - Method in interface gov.sandia.cognition.math.Ring: Determines if two RingType objects are equal
equals(RingType, double) - Method in interface gov.sandia.cognition.math.Ring: Determines if two RingType objects are effectively equal
equals(Object) - Method in class gov.sandia.cognition.math.UnsignedLogNumber
equals(UnsignedLogNumber, double) - Method in class gov.sandia.cognition.math.UnsignedLogNumber
equals(Object) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian
equals(Object) - Method in class gov.sandia.cognition.statistics.TransferEntropy.TransferEntropyDistributionObject
equals(TransferEntropy.TransferEntropyPartialSumObject) - Method in class gov.sandia.cognition.statistics.TransferEntropy.TransferEntropyPartialSumObject: Checks if this partial sum is equal to the other one.
equals(Object) - Method in class gov.sandia.cognition.statistics.UnivariateRandomVariable
equals(RandomVariable<Number>, double) - Method in class gov.sandia.cognition.statistics.UnivariateRandomVariable
equals(Object) - Method in class gov.sandia.cognition.text.DefaultTextual
equals(Object) - Method in class gov.sandia.cognition.text.term.AbstractTerm
equals(Term) - Method in class gov.sandia.cognition.text.term.AbstractTerm: Determines if this term is equal to another term.
equals(Object) - Method in class gov.sandia.cognition.text.term.DefaultIndexedTerm
equals(DefaultIndexedTerm) - Method in class gov.sandia.cognition.text.term.DefaultIndexedTerm: Determines if this object is equal to the given object.
equals(Object) - Method in class gov.sandia.cognition.text.term.DefaultTermOccurrence
equals(DefaultTermOccurrence) - Method in class gov.sandia.cognition.text.term.DefaultTermOccurrence: Determines if this DefaultTermOccurrence equals a given DefaultTermOccurrence.
equals(Object) - Method in class gov.sandia.cognition.text.term.relation.IndexedTermSimilarityRelation
equals(IndexedTermSimilarityRelation) - Method in class gov.sandia.cognition.text.term.relation.IndexedTermSimilarityRelation: Determines if this object is equal to the given object.
equals(Object) - Method in class gov.sandia.cognition.time.DefaultDuration
equals(Duration) - Method in class gov.sandia.cognition.time.DefaultDuration
equals(Duration) - Method in interface gov.sandia.cognition.time.Duration: Determines if this duration is equivalent to the given duration.
equals(Object) - Method in class gov.sandia.cognition.util.DefaultKeyValuePair
equals(Object) - Method in class gov.sandia.cognition.util.DefaultPair
equals(Pair<FirstType, SecondType>) - Method in class gov.sandia.cognition.util.DefaultPair
equalsSafe(Object, Object) - Static method in class gov.sandia.cognition.util.ObjectUtil: Determines if two objects are equals in a way that is safe for dealing with null.
errorCount - Variable in class gov.sandia.cognition.learning.algorithm.perceptron.BatchMultiPerceptron: The number of errors on the most recent iteration.
errorCount - Variable in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Forgetron.Result: The number of errors that the categorizer has made in the learning step.
errorCount - Variable in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineShiftingPerceptron.LinearResult: The number of errors made by the categorizer so far.
ErrorFunction() - Constructor for class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.ErrorFunction: Default constructor.
EstimableDistribution<ObservationType,DistributionType extends EstimableDistribution<ObservationType,? extends DistributionType>> - Interface in gov.sandia.cognition.statistics: A Distribution that has an estimator associated with it, typically a closed-form estimator.
EstimableWeightedDistribution<ObservationType,DistributionType extends EstimableWeightedDistribution<ObservationType,? extends DistributionType>> - Interface in gov.sandia.cognition.statistics: A Distribution that has an estimator associated with it, typically a closed-form estimator, that can estimate the distribution from weighted data.
estimate - Variable in class gov.sandia.cognition.learning.data.DefaultTargetEstimatePair: Estimate (prediction) of the target value.
estimateContinuousDistribution(Collection<Double>) - Static method in class gov.sandia.cognition.statistics.method.MaximumLikelihoodDistributionEstimator: Estimates a continuous distribution.
estimateDiscreteDistribution(Collection<? extends Number>) - Static method in class gov.sandia.cognition.statistics.method.MaximumLikelihoodDistributionEstimator: Estimates a discrete distribution.
estimateEigenvalue(Matrix, Vector) - Static method in class gov.sandia.cognition.math.matrix.decomposition.EigenvectorPowerIteration: Finds the eigenvalue associated with the given Matrix and eigenvector.
estimateEigenvector(Vector, Matrix, double, int) - Static method in class gov.sandia.cognition.math.matrix.decomposition.EigenvectorPowerIteration: Estimates the eigenvector corresponding to the largest magnitude eigenvalue.
estimateEigenvectors(Matrix, int) - Static method in class gov.sandia.cognition.math.matrix.decomposition.EigenvectorPowerIteration: Estimates the top eigenvectors of the given matrix using the power iteration algorithm.
estimateEigenvectors(Matrix, int, double, int) - Static method in class gov.sandia.cognition.math.matrix.decomposition.EigenvectorPowerIteration: Estimates the top eigenvectors of the given matrix using the power iteration algorithm.
estimateScalarFactor(UnivariateProbabilityDensityFunction) - Method in class gov.sandia.cognition.statistics.bayesian.RejectionSampling.ScalarEstimator: Estimates the minimum scalar needed for the sampler distribution to envelope the conjunctive distribution
estimator - Variable in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.MultivariateMeanUpdater: Bayesian estimator for the parameters
Estimator() - Constructor for class gov.sandia.cognition.statistics.distribution.DefaultDataDistribution.Estimator: Default constructor
Estimator() - Constructor for class gov.sandia.cognition.statistics.distribution.ScalarDataDistribution.Estimator: Default constructor
eta - Variable in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Projectron: The eta parameter, which ends up controlling the number of supports created.
etaSampler - Variable in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel: Creates a new value of "eta" which, in turn, helps sample a new alpha.
euclideanDistance(VectorType) - Method in class gov.sandia.cognition.math.matrix.AbstractVectorSpace
euclideanDistance(InfiniteVector<KeyType>) - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
euclideanDistance(VectorType) - Method in interface gov.sandia.cognition.math.matrix.VectorSpace: Euclidean distance between this and other, which is the 2-norm between the difference of the Vectors
EuclideanDistanceCostFunction - Class in gov.sandia.cognition.learning.function.cost: The EuclideanDistanceCostFunction class implements a CostFunction that calculates the Euclidean distance the given Vectorizable and the goal vector.
EuclideanDistanceCostFunction() - Constructor for class gov.sandia.cognition.learning.function.cost.EuclideanDistanceCostFunction: Creates a new EuclideanDistanceCostFunction with no initial goal.
EuclideanDistanceCostFunction(Vector) - Constructor for class gov.sandia.cognition.learning.function.cost.EuclideanDistanceCostFunction: Creates a new instance of EuclideanDistanceCostFunction.
EuclideanDistanceMetric - Class in gov.sandia.cognition.learning.function.distance: The EuclideanDistanceMetric implements a distance metric that computes the Euclidean distance between two points.
EuclideanDistanceMetric() - Constructor for class gov.sandia.cognition.learning.function.distance.EuclideanDistanceMetric: Creates a new instance of EuclideanDistanceMetric.
euclideanDistanceSquared(Vector) - Method in class gov.sandia.cognition.math.matrix.AbstractVector
euclideanDistanceSquared(DenseVector) - Method in class gov.sandia.cognition.math.matrix.custom.DenseVector
euclideanDistanceSquared(SparseVector) - Method in class gov.sandia.cognition.math.matrix.custom.DenseVector
euclideanDistanceSquared(DenseVector) - Method in class gov.sandia.cognition.math.matrix.custom.SparseVector
euclideanDistanceSquared(SparseVector) - Method in class gov.sandia.cognition.math.matrix.custom.SparseVector
euclideanDistanceSquared(InfiniteVector<KeyType>) - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
euclideanDistanceSquared(Vector) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseVector
euclideanDistanceSquared(Vector) - Method in class gov.sandia.cognition.math.matrix.mtj.SparseVector
euclideanDistanceSquared(VectorType) - Method in interface gov.sandia.cognition.math.matrix.VectorSpace: Squared Euclidean distance between this and other, which is the 2-norm between the difference of the Vectors
EuclideanDistanceSquaredMetric - Class in gov.sandia.cognition.learning.function.distance: The EuclideanDistanceSquaredMetric implements a distance metric that computes the squared Euclidean distance between two points.
EuclideanDistanceSquaredMetric() - Constructor for class gov.sandia.cognition.learning.function.distance.EuclideanDistanceSquaredMetric: Creates a new instance of EuclideanDistanceSquaredMetric.
EuclideanRing<RingType extends EuclideanRing<RingType>> - Interface in gov.sandia.cognition.math: Defines something similar to a Euclidean ring from abstract algebra.
Eva32Hash - Class in gov.sandia.cognition.hash: This implements the 32-bit "evahash" due to Robert Jenkins.
Eva32Hash() - Constructor for class gov.sandia.cognition.hash.Eva32Hash: Creates a new instance of Eva32Hash
Eva64Hash - Class in gov.sandia.cognition.hash: This implements the 64-bit "evahash" due to Robert Jenkins.
Eva64Hash() - Constructor for class gov.sandia.cognition.hash.Eva64Hash: Creates a new instance of Eva64Hash
evaluate(DataType) - Method in class gov.sandia.cognition.data.convert.IdentityDataConverter: Returns the given input.
evaluate(Boolean) - Method in class gov.sandia.cognition.data.convert.number.DefaultBooleanToNumberConverter: Converts an input boolean to a number.
evaluate(Number) - Method in class gov.sandia.cognition.data.convert.number.DefaultBooleanToNumberConverter.Reverse: Converts the given number to a boolean.
evaluate(String) - Method in class gov.sandia.cognition.data.convert.number.StringToDoubleConverter: Converts the given String to a Double.
evaluate(String) - Method in class gov.sandia.cognition.data.convert.number.StringToIntegerConverter: Converts the given String to a Integer.
evaluate(Object) - Method in class gov.sandia.cognition.data.convert.ObjectToStringConverter: Converts the given Object to an String by calling the toString method.
evaluate(InputType) - Method in class gov.sandia.cognition.data.convert.vector.AbstractToVectorEncoder: Converts the given object to a Vector.
evaluate(InputType, StateType) - Method in class gov.sandia.cognition.evaluator.AbstractStatefulEvaluator
evaluate(InputType) - Method in class gov.sandia.cognition.evaluator.CompositeEvaluatorList
evaluate(InputType) - Method in class gov.sandia.cognition.evaluator.CompositeEvaluatorPair
evaluate(InputType) - Method in class gov.sandia.cognition.evaluator.CompositeEvaluatorTriple
evaluate(InputType) - Method in interface gov.sandia.cognition.evaluator.Evaluator: Evaluates the function on the given input and returns the output.
evaluate(InputType) - Method in class gov.sandia.cognition.evaluator.ForwardReverseEvaluatorPair
evaluate(DataType) - Method in class gov.sandia.cognition.evaluator.IdentityEvaluator: Returns the given input.
evaluate(InputType) - Method in interface gov.sandia.cognition.evaluator.StatefulEvaluator: Evaluates the object using the given input and current state objects, returning the output.
evaluate(InputType, StateType) - Method in interface gov.sandia.cognition.evaluator.StatefulEvaluator: Evaluates the object using the given input and the given state object, returning the output.
evaluate(DataType) - Method in class gov.sandia.cognition.evaluator.ValueClamper
evaluate(InputType) - Method in class gov.sandia.cognition.evaluator.ValueMapper
evaluate() - Method in interface gov.sandia.cognition.framework.concurrent.ConcurrentCognitiveModule: Perform evaluation of sampled and held input state information that was captured by a call to readState, and hold the resultant output changes to model and module state pending a call to writeState NOTE: input and output state is held temporarily for the sole purpose of supporting concurrency of module evaluation; state is NEVER retained locally across module update cycles
evaluate() - Method in class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModule: Perform evaluation of sampled and held input state information that was captured by a call to readState, and hold the resultant output changes to model and module state pending a call to writeState NOTE: input and output state is held temporarily for the sole purpose of supporting concurrency of module evaluation; state is NEVER retained locally across module update cycles
evaluate() - Method in class gov.sandia.cognition.framework.lite.ArrayBasedPerceptionModule: Perform evaluation of sampled and held input state information that was captured by a call to readState, and hold the resultant output changes to model and module state pending a call to writeState NOTE: input and output state is held temporarily for the sole purpose of supporting concurrency of module evaluation; state is NEVER retained locally across module update cycles
evaluate(byte[]) - Method in class gov.sandia.cognition.hash.AbstractHashFunction
evaluate(byte[], byte[]) - Method in class gov.sandia.cognition.hash.AbstractHashFunction
evaluate(byte[], byte[]) - Method in interface gov.sandia.cognition.hash.HashFunction: Evaluates the input byte array with the given seed.
evaluate(Collection<InputType>) - Method in class gov.sandia.cognition.learning.algorithm.bayes.DiscreteNaiveBayesCategorizer
evaluate(Vectorizable) - Method in class gov.sandia.cognition.learning.algorithm.bayes.VectorNaiveBayesCategorizer
evaluate(CentroidCluster<DataType>, DataType) - Method in class gov.sandia.cognition.learning.algorithm.clustering.divergence.CentroidClusterDivergenceFunction: Evaluates the divergence between the cluster centroid and the given object.
evaluate(CentroidCluster<DataType>, CentroidCluster<DataType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.divergence.ClusterCentroidDivergenceFunction: This method computes the complete link distance between the two given Clusters.
evaluate(ClusterType, ClusterType) - Method in class gov.sandia.cognition.learning.algorithm.clustering.divergence.ClusterCompleteLinkDivergenceFunction: This method computes the complete link distance between the two given Clusters.
evaluate(ClusterType, ClusterType) - Method in class gov.sandia.cognition.learning.algorithm.clustering.divergence.ClusterMeanLinkDivergenceFunction: The method computes the complete link distance between the two given Clusters.
evaluate(ClusterType, ClusterType) - Method in class gov.sandia.cognition.learning.algorithm.clustering.divergence.ClusterSingleLinkDivergenceFunction: This method computes the complete link distance between the two given Clusters.
evaluate(GaussianCluster, Vector) - Method in class gov.sandia.cognition.learning.algorithm.clustering.divergence.GaussianClusterDivergenceFunction: Evaluates the divergence between the Gaussian cluster and the given vector, which is the negative of likelihood that the cluster was generated.
evaluate(ClusterType) - Method in interface gov.sandia.cognition.learning.algorithm.clustering.divergence.WithinClusterDivergence: Evaluates the divergence within a cluster.
evaluate(ClusterType) - Method in class gov.sandia.cognition.learning.algorithm.clustering.divergence.WithinClusterDivergenceWrapper
evaluate(NormalizedCentroidCluster<V>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.divergence.WithinNormalizedCentroidClusterCosineDivergence: Evaluate the this function on the provided cluster.
evaluate(Vector) - Method in class gov.sandia.cognition.learning.algorithm.delta.BurrowsDeltaCategorizer: Evaluates an unknown input, but does not return the discriminant value.
evaluate(Vector) - Method in class gov.sandia.cognition.learning.algorithm.delta.CosineDeltaCategorizer: Evaluates an unknown input, but does not return the discriminant value.
evaluate(InputType) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AdditiveEnsemble
evaluate(InputType) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AveragingEnsemble
evaluate(InputType) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.VotingCategorizerEnsemble
evaluate(InputType) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.WeightedAdditiveEnsemble
evaluate(InputType) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.WeightedAveragingEnsemble
evaluate(InputType) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.WeightedBinaryEnsemble: Evaluates the ensemble.
evaluate(InputType) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.WeightedVotingCategorizerEnsemble: Evaluates the ensemble.
evaluate(Vector) - Method in class gov.sandia.cognition.learning.algorithm.gradient.GradientDescendableApproximator
evaluate(double) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.DirectionalVectorToScalarFunction: Evaluates the vector function along the direction using the scale factor "input" and vectorOffset
evaluate(double) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerDerivativeBased.InternalFunction
evaluate(InputOutputSlopeTriplet) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.WolfeConditions: Evaluates if the trial point meets the Wolfe conditions
evaluate(Vector) - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.MatrixVectorMultiplier: Returns m times input.
evaluate(Vector) - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.OverconstrainedMatrixVectorMultiplier: Returns m times input.
evaluate(InputType) - Method in class gov.sandia.cognition.learning.algorithm.nearest.AbstractKNearestNeighbor
evaluate(InputType) - Method in class gov.sandia.cognition.learning.algorithm.nearest.NearestNeighborExhaustive: Evaluates the object to do nearest-neighbor lookup for the given input.
evaluate(InputType) - Method in class gov.sandia.cognition.learning.algorithm.nearest.NearestNeighborKDTree
evaluate(DataType) - Method in class gov.sandia.cognition.learning.algorithm.pca.KernelPrincipalComponentsAnalysis.Function
evaluate(Vector) - Method in class gov.sandia.cognition.learning.algorithm.pca.PrincipalComponentsAnalysisFunction: Computes the reduced-dimension representation of the input by subtracting the mean and mapping it through the dimension-reduction matrix multiplication
evaluate(InputType) - Method in class gov.sandia.cognition.learning.algorithm.regression.LocallyWeightedFunction: This function re-weights the dataset according to the Kernel value between the input and each input in the dataset.
evaluate(Vector) - Method in class gov.sandia.cognition.learning.algorithm.regression.ParameterDerivativeFreeCostMinimizer.ParameterCostEvaluatorDerivativeFree
evaluate(Vector) - Method in class gov.sandia.cognition.learning.algorithm.regression.ParameterDifferentiableCostMinimizer.ParameterCostEvaluatorDerivativeBased
evaluate(double) - Method in class gov.sandia.cognition.learning.algorithm.root.SolverFunction
evaluate(Vector) - Method in class gov.sandia.cognition.learning.algorithm.semisupervised.valence.MultipartiteValenceMatrix: Overrides the default implementation so that L_tilde can be raised to a power and the diagonal weights can be added implicitly (which is much faster and memory efficient than the explicit representation).
evaluate(InputType) - Method in class gov.sandia.cognition.learning.algorithm.tree.DecisionTree: Evaluates the decision tree against the given input.
evaluate(DataType) - Method in class gov.sandia.cognition.learning.data.feature.DelayFunction: Returns the input from delaySamples previous evalutes
evaluate(Vector) - Method in class gov.sandia.cognition.learning.data.feature.FeatureHashing
evaluate(Vector) - Method in class gov.sandia.cognition.learning.data.feature.LinearRegressionCoefficientExtractor
evaluate(Vectorizable) - Method in class gov.sandia.cognition.learning.data.feature.MultivariateDecorrelator: Normalizes the given double value by subtracting the mean and dividing by the standard deviation (the square root of the variance).
evaluate(double) - Method in class gov.sandia.cognition.learning.data.feature.StandardDistributionNormalizer: Normalizes the given double value by subtracting the mean and dividing by the standard deviation (the square root of the variance).
evaluate(Pair<BatchLearner<? super Collection<? extends FoldDataType>, ? extends LearnedType>, BatchLearner<? super Collection<? extends FoldDataType>, ? extends LearnedType>>, Collection<? extends InputDataType>) - Method in class gov.sandia.cognition.learning.experiment.LearnerComparisonExperiment: Evaluates the two batch learners using the given data on the same set of validation folds and returns the resulting information including the confidence statistic that the two are different along with the summary of their performance.
evaluate(BatchLearner<? super Collection<? extends FoldDataType>, ? extends LearnedType>, BatchLearner<? super Collection<? extends FoldDataType>, ? extends LearnedType>, Collection<? extends InputDataType>) - Method in class gov.sandia.cognition.learning.experiment.LearnerComparisonExperiment: Evaluates the two batch learners using the given data on the same set of validation folds and returns the resulting information including the confidence statistic that the two are different along with the summary of their performance.
evaluate(BatchLearner<? super Collection<? extends FoldDataType>, ? extends LearnedType>, Collection<? extends InputDataType>) - Method in class gov.sandia.cognition.learning.experiment.LearnerValidationExperiment: Deprecated.
Use evaluatePerformance instead. Performs the experiment.
evaluate(InputType) - Method in class gov.sandia.cognition.learning.function.categorization.AbstractDiscriminantBinaryCategorizer
evaluate(InputType) - Method in class gov.sandia.cognition.learning.function.categorization.AbstractDiscriminantCategorizer
evaluate(InputType) - Method in class gov.sandia.cognition.learning.function.categorization.CompositeCategorizer
evaluate(InputType) - Method in class gov.sandia.cognition.learning.function.categorization.EvaluatorToCategorizerAdapter
evaluate(Vectorizable) - Method in class gov.sandia.cognition.learning.function.categorization.LinearMultiCategorizer
evaluate(ObservationType) - Method in class gov.sandia.cognition.learning.function.categorization.MaximumAPosterioriCategorizer
evaluate(Object) - Method in class gov.sandia.cognition.learning.function.ConstantEvaluator: Evaluating this object just returns the constant output value.
evaluate(Evaluator<? super Vector, ? extends Vector>) - Method in class gov.sandia.cognition.learning.function.cost.AbstractParallelizableCostFunction
evaluate(Evaluator<? super InputType, ? extends TargetType>) - Method in class gov.sandia.cognition.learning.function.cost.AbstractSupervisedCostFunction
evaluate(Collection<? extends ClusterType>) - Method in class gov.sandia.cognition.learning.function.cost.ClusterDistortionMeasure
evaluate(ClusterType) - Method in class gov.sandia.cognition.learning.function.cost.ClusterDistortionMeasure: Evaluates the distortion for a single cluster
evaluate(EvaluatedType) - Method in interface gov.sandia.cognition.learning.function.cost.CostFunction: Computes the cost of the given target.
evaluate(Vectorizable) - Method in class gov.sandia.cognition.learning.function.cost.EuclideanDistanceCostFunction: Evaluates the Euclidean distance between the provided target and the goal.
evaluate(UnivariateDistribution<DataType>) - Method in class gov.sandia.cognition.learning.function.cost.KolmogorovSmirnovDivergence
evaluate(ComputableDistribution<DataType>) - Method in class gov.sandia.cognition.learning.function.cost.NegativeLogLikelihood
evaluate(ProbabilityFunction<DataType>, Collection<? extends DataType>) - Static method in class gov.sandia.cognition.learning.function.cost.NegativeLogLikelihood: Evaluates the negative log-likelihood of the given collection of data according to the given probability function.
evaluate(Collection<? extends ClusterType>) - Method in class gov.sandia.cognition.learning.function.cost.ParallelClusterDistortionMeasure
evaluate(Evaluator<? super Vector, ? extends Vector>) - Method in class gov.sandia.cognition.learning.function.cost.ParallelizedCostFunctionContainer
evaluate(ComputableDistribution<DataType>) - Method in class gov.sandia.cognition.learning.function.cost.ParallelNegativeLogLikelihood
evaluate(Vectorizable, Vectorizable) - Method in class gov.sandia.cognition.learning.function.distance.ChebyshevDistanceMetric
evaluate(Vectorizable, Vectorizable) - Method in class gov.sandia.cognition.learning.function.distance.CosineDistanceMetric: Evaluates the cosine distance between the two given vectors.
evaluate(Vectorizable, Vectorizable) - Method in class gov.sandia.cognition.learning.function.distance.EuclideanDistanceMetric: Evaluates the Euclidean distance between the two given vectors.
evaluate(Vectorizable, Vectorizable) - Method in class gov.sandia.cognition.learning.function.distance.EuclideanDistanceSquaredMetric: The evaluates the squared Euclidean distance between the two given vectors.
evaluate(Object, Object) - Method in class gov.sandia.cognition.learning.function.distance.IdentityDistanceMetric
evaluate(Vectorizable, Vectorizable) - Method in class gov.sandia.cognition.learning.function.distance.ManhattanDistanceMetric: Evaluates the Manhattan distance between the two given vectors.
evaluate(Vectorizable, Vectorizable) - Method in class gov.sandia.cognition.learning.function.distance.MinkowskiDistanceMetric
evaluate(Vectorizable, Vectorizable) - Method in class gov.sandia.cognition.learning.function.distance.WeightedEuclideanDistanceMetric: Evaluates the weighted Euclidean distance between two vectors.
evaluate(InputType, InputType) - Method in class gov.sandia.cognition.learning.function.kernel.ExponentialKernel: The exponential kernel takes the exponential of applying another kernel to the two given inputs.
evaluate(InputType, InputType) - Method in interface gov.sandia.cognition.learning.function.kernel.Kernel: The role of a kernel is to evaluate some function that is equivalent to an inner product in some vector space.
evaluate(InputType, InputType) - Method in class gov.sandia.cognition.learning.function.kernel.KernelDistanceMetric: Computes the distance between the two given objects using the Kernel it was given.
evaluate(Vectorizable, Vectorizable) - Method in class gov.sandia.cognition.learning.function.kernel.LinearKernel: Evaluates the linear kernel by taking the inner product of the two vectors.
evaluate(InputType, InputType) - Method in class gov.sandia.cognition.learning.function.kernel.NormalizedKernel: Evaluates the normalized kernel by passing the evaluation off to the internal kernel and then normalizing the results.
evaluate(Vectorizable, Vectorizable) - Method in class gov.sandia.cognition.learning.function.kernel.PolynomialKernel: This kernel just evaluates the polynomial kernel between the two given vectors, which is: (x dot y + c)^d.
evaluate(InputType, InputType) - Method in class gov.sandia.cognition.learning.function.kernel.ProductKernel: The addition kernel applies multiple kernels to the given inputs and returns their product.
evaluate(Vectorizable, Vectorizable) - Method in class gov.sandia.cognition.learning.function.kernel.RadialBasisKernel: Evaluates the following kernel between the two given vectors: exp( -||x - y||^2 / (2 * sigma^2))
evaluate(InputType, InputType) - Method in class gov.sandia.cognition.learning.function.kernel.ScalarFunctionKernel: Evaluates the kernel on the given inputs by first applying the scalar function to each input and then taking the product of the two returned scalar values.
evaluate(Vectorizable, Vectorizable) - Method in class gov.sandia.cognition.learning.function.kernel.SigmoidKernel: Evaluates the sigmoid kernel between the two given vectors, which is: tanh(kappa * (x dot y) + c)
evaluate(InputType, InputType) - Method in class gov.sandia.cognition.learning.function.kernel.SumKernel: The addition kernel applies multiple kernels to the given inputs and returns their sum.
evaluate(Vectorizable, Vectorizable) - Method in class gov.sandia.cognition.learning.function.kernel.VectorFunctionKernel: Evaluates the kernel on the given inputs by first applying the vector function to each input vector and then evaluating the kernel on the results of the vector function.
evaluate(InputType, InputType) - Method in class gov.sandia.cognition.learning.function.kernel.WeightedKernel: The weighted kernel just passes the kernel evaluation to its own internal kernel and then multiplies it by the weight.
evaluate(Object, Object) - Method in class gov.sandia.cognition.learning.function.kernel.ZeroKernel: Returns zero regardless of the input values.
evaluate(InputType) - Method in class gov.sandia.cognition.learning.function.LinearCombinationFunction: Evaluates the LinearCombinationFunction about the given input.
evaluate(double) - Method in class gov.sandia.cognition.learning.function.scalar.AtanFunction
evaluate(double) - Method in class gov.sandia.cognition.learning.function.scalar.CosineFunction
evaluate(double) - Method in class gov.sandia.cognition.learning.function.scalar.HardSigmoidFunction
evaluate(double) - Method in class gov.sandia.cognition.learning.function.scalar.HardTanHFunction
evaluate(Double) - Method in class gov.sandia.cognition.learning.function.scalar.IdentityScalarFunction
evaluate(double) - Method in class gov.sandia.cognition.learning.function.scalar.IdentityScalarFunction
evaluate(InputType) - Method in class gov.sandia.cognition.learning.function.scalar.KernelScalarFunction: Evaluates the given input vector as a double by: sum w_i * k(input, x_i)
evaluate(Double) - Method in class gov.sandia.cognition.learning.function.scalar.KolmogorovSmirnovEvaluator: takes in the double value and adds it to finite capacity buffer then computes the KS null hypothesis probability on the samples in the buffer against the particular CDF specified.
evaluate(double) - Method in class gov.sandia.cognition.learning.function.scalar.LeakyRectifiedLinearFunction
evaluate(InputType) - Method in class gov.sandia.cognition.learning.function.scalar.LinearCombinationScalarFunction
evaluate(double) - Method in class gov.sandia.cognition.learning.function.scalar.LinearFunction
evaluate(InputType) - Method in class gov.sandia.cognition.learning.function.scalar.LocallyWeightedKernelScalarFunction: Categorizes the given input vector as a double by: (sum w_i * k(input, x_i)) / (sum k(input, x_i))
evaluate(double) - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Cubic
evaluate(double, double, double, double, double) - Static method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Cubic: Evaluates a quadratic polynomial of the form y(x) = q0 + q1*x + q2*x^2 + q3*x^3 for a given value of "x"
evaluate(double) - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction: Evaluates the polynomial for the given input, returning Math.pow( input, exponent )
evaluate(double) - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Linear
evaluate(double) - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Quadratic
evaluate(double, double, double, double) - Static method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Quadratic: Evaluates a quadratic polynomial of the form y(x) = q0 + q1*x + q2*x^2 for a given value of "x"
evaluate(double) - Method in class gov.sandia.cognition.learning.function.scalar.RectifiedLinearFunction
evaluate(double) - Method in class gov.sandia.cognition.learning.function.scalar.SigmoidFunction: Evaluates the squashing function on the given input value.
evaluate(double) - Method in class gov.sandia.cognition.learning.function.scalar.SoftPlusFunction
evaluate(double) - Method in class gov.sandia.cognition.learning.function.scalar.TanHFunction: Evaluates the squashing function on the given input value.
evaluate(double) - Method in class gov.sandia.cognition.learning.function.scalar.ThresholdFunction: Maps the input onto the set {LOW_VALUE, HIGH_VALUE}
evaluate(Vector) - Method in class gov.sandia.cognition.learning.function.vector.ElementWiseVectorFunction: Applies the scalar function to each element of the input Vector independently of all others, returning a Vector of equal dimension as the input
evaluate(Vector, UnivariateScalarFunction) - Static method in class gov.sandia.cognition.learning.function.vector.ElementWiseVectorFunction: Applies the scalar function to each element of the input Vector independently of all others, returning a Vector of equal dimension as the input
evaluate(Vector) - Method in class gov.sandia.cognition.learning.function.vector.EntropyEvaluator
evaluate(Vector) - Method in class gov.sandia.cognition.learning.function.vector.FeedforwardNeuralNetwork
evaluate(Vector) - Method in class gov.sandia.cognition.learning.function.vector.GaussianContextRecognizer
evaluate(Vector) - Method in class gov.sandia.cognition.learning.function.vector.GeneralizedLinearModel
evaluate(Vector) - Method in class gov.sandia.cognition.learning.function.vector.LinearCombinationVectorFunction
evaluate(Vector) - Method in class gov.sandia.cognition.learning.function.vector.LinearVectorFunction
evaluate(Vector) - Method in class gov.sandia.cognition.learning.function.vector.MultivariateDiscriminant
evaluate(Vector) - Method in class gov.sandia.cognition.learning.function.vector.MultivariateDiscriminantWithBias
evaluate(InputType) - Method in class gov.sandia.cognition.learning.function.vector.ScalarBasisSet
evaluate(Vectorizable) - Method in class gov.sandia.cognition.learning.function.vector.SubVectorEvaluator
evaluate(Vector) - Method in class gov.sandia.cognition.learning.function.vector.ThreeLayerFeedforwardNeuralNetwork
evaluate(Vectorizable) - Method in class gov.sandia.cognition.learning.function.vector.VectorizableVectorConverter: Evaluates the given input by converting it to a vector by calling the proper method on the given Vectorizable.
evaluate(Vectorizable) - Method in class gov.sandia.cognition.learning.function.vector.VectorizableVectorConverterWithBias: Evaluates the given vectorizable input by converting it to a vector and then creating a new vector of one extra dimensionality and adding a single element with bias 1.0 to the end.
evaluate(FirstType, SecondType) - Method in interface gov.sandia.cognition.math.DivergenceFunction: Evaluates the divergence between the two given objects.
evaluate(InputType) - Method in class gov.sandia.cognition.math.matrix.NumericalDifferentiator
evaluate(InputType) - Method in interface gov.sandia.cognition.math.ScalarFunction: Returns the result of calling evaluateAsDouble.
evaluate(Double) - Method in class gov.sandia.cognition.math.signals.AutoRegressiveMovingAverageFilter
evaluate(Collection<Double>) - Method in class gov.sandia.cognition.math.signals.FourierTransform: Computes the Fast Fourier Transform of the given input data using the Cooley-Tukey Radix-2 FFT, with brute-force DFT computation on odd subsequence computation.
evaluate(Collection<ComplexNumber>) - Method in class gov.sandia.cognition.math.signals.FourierTransform.Inverse
evaluate(Vector) - Method in class gov.sandia.cognition.math.signals.LinearDynamicalSystem
evaluate(Double) - Method in class gov.sandia.cognition.math.signals.MovingAverageFilter
evaluate(Double) - Method in class gov.sandia.cognition.math.signals.PIDController
evaluate(double) - Method in interface gov.sandia.cognition.math.UnivariateScalarFunction: Produces a double output for the given double input
evaluate(Double) - Method in interface gov.sandia.cognition.math.UnivariateScalarFunction
evaluate(double) - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.AbstractEnvelope
evaluate(double) - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.LogEvaluator
evaluate(double) - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.PDFLogEvaluator
evaluate(Vectorizable) - Method in class gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression.PredictiveDistribution
evaluate(Vectorizable) - Method in class gov.sandia.cognition.statistics.bayesian.BayesianRobustLinearRegression.PredictiveDistribution
evaluate(Vector) - Method in class gov.sandia.cognition.statistics.bayesian.ExtendedKalmanFilter.ModelJacobianEvaluator
evaluate(InputType) - Method in class gov.sandia.cognition.statistics.bayesian.GaussianProcessRegression.PredictiveDistribution
evaluate(Double) - Method in class gov.sandia.cognition.statistics.bayesian.RejectionSampling.ScalarEstimator.MinimizerFunction
evaluate(Number) - Method in class gov.sandia.cognition.statistics.distribution.BernoulliDistribution.CDF
evaluate(Number) - Method in class gov.sandia.cognition.statistics.distribution.BernoulliDistribution.PMF
evaluate(Number) - Method in class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution.CDF
evaluate(Number) - Method in class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution.PMF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.BetaDistribution.CDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.BetaDistribution.CDF
evaluate(double, double, double) - Static method in class gov.sandia.cognition.statistics.distribution.BetaDistribution.CDF: Evaluate the Beta-distribution CDF for Beta(x;alpha,beta)
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.BetaDistribution.PDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.BetaDistribution.PDF
evaluate(double, double, double) - Static method in class gov.sandia.cognition.statistics.distribution.BetaDistribution.PDF: Evaluate the Beta-distribution PDF for beta(x;alpha,beta)
evaluate(Number) - Method in class gov.sandia.cognition.statistics.distribution.BinomialDistribution.CDF
evaluate(int, int, double) - Static method in class gov.sandia.cognition.statistics.distribution.BinomialDistribution.CDF: Evaluates the CDF for integer values of x, N, and double p
evaluate(Number) - Method in class gov.sandia.cognition.statistics.distribution.BinomialDistribution.PMF: Returns the binomial PMF for the parameters N, k, p, which is the probability of exactly k successes in N experiments with expected per-trial success probability (Bernoulli) p
evaluate(int, int, double) - Static method in class gov.sandia.cognition.statistics.distribution.BinomialDistribution.PMF: Returns the binomial CDF for the parameters N, k, p, which is the probability of exactly k successes in N experiments with expected per-trial success probability (Bernoulli) p
evaluate(Vector) - Method in class gov.sandia.cognition.statistics.distribution.CategoricalDistribution.PMF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.CauchyDistribution.CDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.CauchyDistribution.CDF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.CauchyDistribution.PDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.CauchyDistribution.PDF
evaluate(Vector) - Method in class gov.sandia.cognition.statistics.distribution.ChineseRestaurantProcess.PMF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution.CDF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution.CDF
evaluate(double, double) - Static method in class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution.CDF: Computes the values of the Chi-Square CDF for the given input and degrees of freedom
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution.PDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution.PDF
evaluate(double, double) - Static method in class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution.PDF: Evaluates the chi-square PDF for the given input and DOFs
evaluate(KeyType) - Method in class gov.sandia.cognition.statistics.distribution.DefaultDataDistribution.PMF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.DeterministicDistribution.CDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.DeterministicDistribution.CDF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.DeterministicDistribution.PMF
evaluate(Vector) - Method in class gov.sandia.cognition.statistics.distribution.DirichletDistribution.PDF: Evaluates the Dirichlet PDF about the given input.
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.ExponentialDistribution.CDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.ExponentialDistribution.CDF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.ExponentialDistribution.PDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.ExponentialDistribution.PDF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.GammaDistribution.CDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.GammaDistribution.CDF
evaluate(double, double, double) - Static method in class gov.sandia.cognition.statistics.distribution.GammaDistribution.CDF: Evaluates the CDF of the Gamma distribution about x, given the shape and scale parameters
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.GammaDistribution.PDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.GammaDistribution.PDF
evaluate(double, double, double) - Static method in class gov.sandia.cognition.statistics.distribution.GammaDistribution.PDF: Evaluates the Gamma PDF about the input "x", using the given shape and scale
evaluate(Number) - Method in class gov.sandia.cognition.statistics.distribution.GeometricDistribution.CDF
evaluate(Number) - Method in class gov.sandia.cognition.statistics.distribution.GeometricDistribution.PMF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.InverseGammaDistribution.CDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.InverseGammaDistribution.CDF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.InverseGammaDistribution.PDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.InverseGammaDistribution.PDF
evaluate(Matrix) - Method in class gov.sandia.cognition.statistics.distribution.InverseWishartDistribution.PDF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.KolmogorovDistribution.CDF
evaluate(double) - Static method in class gov.sandia.cognition.statistics.distribution.KolmogorovDistribution.CDF: This is the probks() function from Numerical Recipes in C, pp.
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.LaplaceDistribution.CDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.LaplaceDistribution.CDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.LaplaceDistribution.PDF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.LaplaceDistribution.PDF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.LogisticDistribution.CDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.LogisticDistribution.CDF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.LogisticDistribution.PDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.LogisticDistribution.PDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.LogNormalDistribution.CDF
evaluate(double, double, double) - Static method in class gov.sandia.cognition.statistics.distribution.LogNormalDistribution.CDF: Evaluates the Log-Normal CDF for the given input and parameters
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.LogNormalDistribution.CDF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.LogNormalDistribution.PDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.LogNormalDistribution.PDF
evaluate(double, double, double) - Static method in class gov.sandia.cognition.statistics.distribution.LogNormalDistribution.PDF: Evaluates the Log-Normal PDF for the given input and parameters logNormalMean, logNormalVariance
evaluate(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MultinomialDistribution.PMF
evaluate(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.PDF
evaluate(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateMixtureDensityModel.PDF
evaluate(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MultivariatePolyaDistribution.PMF
evaluate(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution.PDF
evaluate(Number) - Method in class gov.sandia.cognition.statistics.distribution.NegativeBinomialDistribution.CDF
evaluate(Number) - Method in class gov.sandia.cognition.statistics.distribution.NegativeBinomialDistribution.PMF
evaluate(Vector) - Method in class gov.sandia.cognition.statistics.distribution.NormalInverseGammaDistribution.PDF
evaluate(Matrix) - Method in class gov.sandia.cognition.statistics.distribution.NormalInverseWishartDistribution.PDF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.ParetoDistribution.CDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.ParetoDistribution.CDF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.ParetoDistribution.PDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.ParetoDistribution.PDF
evaluate(Number) - Method in class gov.sandia.cognition.statistics.distribution.PoissonDistribution.CDF
evaluate(Number) - Method in class gov.sandia.cognition.statistics.distribution.PoissonDistribution.PMF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.ScalarDataDistribution.CDF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.ScalarDataDistribution.PMF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.CDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.CDF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.PDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.PDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.SnedecorFDistribution.CDF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.SnedecorFDistribution.CDF
evaluate(double, double, double) - Static method in class gov.sandia.cognition.statistics.distribution.SnedecorFDistribution.CDF: Evaluates the F-distribution CDF(input,v1,v2)
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.StudentizedRangeDistribution.CDF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.StudentTDistribution.CDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.StudentTDistribution.CDF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.StudentTDistribution.PDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.StudentTDistribution.PDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.UniformDistribution.CDF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.UniformDistribution.CDF
evaluate(double, double, double) - Static method in class gov.sandia.cognition.statistics.distribution.UniformDistribution.CDF: Evaluates the Uniform(minSupport,maxSupport) CDF for the given input
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.UniformDistribution.PDF
evaluate(double, double, double) - Static method in class gov.sandia.cognition.statistics.distribution.UniformDistribution.PDF: Evaluates the Uniform(minSupport,maxSupport) PDF for the given input
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.UniformDistribution.PDF
evaluate(Number) - Method in class gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution.CDF
evaluate(int, int, int) - Static method in class gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution.CDF: Evaluates the cumulative density function of the discrete uniform distribution.
evaluate(Number) - Method in class gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution.PMF
evaluate(int, int, int) - Static method in class gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution.PMF: Evaluates the probability mass function of the discrete uniform distribution.
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.CDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.CDF
evaluate(double, double, double) - Static method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.CDF: Computes the cumulative distribution of a Normalized Gaussian distribution using the errorFunction method.
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.CDF.Inverse
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.CDF.Inverse: Evaluates the Inverse UnivariateGaussian CDF for the given probability.
evaluate(double, double, double) - Static method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.CDF.Inverse: Evaluates the Inverse UnivariateGaussian CDF for the given probability.
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.ErrorFunction: Computes the Gaussian Error Function using Horner's Method.
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.ErrorFunction.Inverse: Inverse of the error function.
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.PDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.PDF
evaluate(double, double, double) - Static method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.PDF: Computes the value of the Probability Density Function at the input
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.WeibullDistribution.CDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.WeibullDistribution.CDF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.distribution.WeibullDistribution.PDF
evaluate(double) - Method in class gov.sandia.cognition.statistics.distribution.WeibullDistribution.PDF
evaluate(Number) - Method in class gov.sandia.cognition.statistics.distribution.YuleSimonDistribution.CDF
evaluate(Number) - Method in class gov.sandia.cognition.statistics.distribution.YuleSimonDistribution.PMF
evaluate(Double) - Method in class gov.sandia.cognition.statistics.method.ConvexReceiverOperatingCharacteristic: Computes the convex hull values using a trapezoid interpolation.
evaluate(Vector) - Method in class gov.sandia.cognition.statistics.method.DistributionParameterEstimator.DistributionWrapper
evaluate(Double) - Method in class gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic: Evaluates the "pessimistic" value of the truePositiveRate for a given falsePositiveRate.
evaluate(Document) - Method in class gov.sandia.cognition.text.convert.DocumentFieldConcatenator
evaluate(Document) - Method in class gov.sandia.cognition.text.convert.DocumentSingleFieldConverter
evaluate(Object) - Method in class gov.sandia.cognition.text.convert.ObjectToStringTextualConverter
evaluate(InputType) - Method in class gov.sandia.cognition.text.convert.SingleToMultiTextualConverterAdapter
evaluate(FromType, ToType) - Method in interface gov.sandia.cognition.text.relation.SimilarityFunction: Evaluates the similarity between the two given objects.
evaluate(String) - Method in class gov.sandia.cognition.text.spelling.SimpleStatisticalSpellingCorrector
evaluate(Iterable<? extends Termable>) - Method in class gov.sandia.cognition.text.term.vector.BagOfWordsTransform
evaluate(Vectorizable, Vectorizable) - Method in class gov.sandia.cognition.text.term.vector.CosineSimilarityFunction
evaluate(Vector) - Method in class gov.sandia.cognition.text.term.vector.weighter.CompositeLocalGlobalTermWeighter
evaluate(Vectorizable) - Method in class gov.sandia.cognition.text.topic.LatentSemanticAnalysis.Transform
evaluate(Vectorizable) - Method in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis.Result
evaluateAmalgamate(Collection<Object>) - Method in interface gov.sandia.cognition.learning.function.cost.ParallelizableCostFunction: Amalgamates the linear components of the cost function into a single Double.
evaluateAmalgamate(Collection<Object>) - Method in class gov.sandia.cognition.learning.function.cost.SumSquaredErrorCostFunction
evaluateAsBernoulli(Vectorizable) - Method in class gov.sandia.cognition.learning.function.categorization.AbstractConfidenceWeightedBinaryCategorizer
evaluateAsBernoulli(Vectorizable) - Method in interface gov.sandia.cognition.learning.function.categorization.ConfidenceWeightedBinaryCategorizer: Returns a Bernoulli distribution over the output of the distribution of weight vectors times the input, with the confidence that the categorizer was trained using.
evaluateAsDouble(InputType) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AdditiveEnsemble
evaluateAsDouble(InputType) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AveragingEnsemble
evaluateAsDouble(InputType) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.WeightedAdditiveEnsemble
evaluateAsDouble(InputType) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.WeightedAveragingEnsemble
evaluateAsDouble(InputType) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.WeightedBinaryEnsemble: Evaluates the ensemble on the given input and returns the result as a double.
evaluateAsDouble(Vector) - Method in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachine
evaluateAsDouble(InputType) - Method in class gov.sandia.cognition.learning.algorithm.tree.RegressionTree
evaluateAsDouble(InputType) - Method in class gov.sandia.cognition.learning.function.categorization.AbstractThresholdBinaryCategorizer
evaluateAsDouble(InputType) - Method in interface gov.sandia.cognition.learning.function.categorization.DiscriminantBinaryCategorizer: Categorizes the given input vector as a double where values greater than zero are in the true category and less than zero are in the false category.
evaluateAsDouble(InputType) - Method in class gov.sandia.cognition.learning.function.categorization.KernelBinaryCategorizer: Categorizes the given input vector as a double by: sum w_i * k(input, x_i)
evaluateAsDouble(Vectorizable) - Method in class gov.sandia.cognition.learning.function.categorization.LinearBinaryCategorizer: Categorizes the given input vector as a double by: weights * input + bias
evaluateAsDouble(Vector) - Method in class gov.sandia.cognition.learning.function.categorization.LinearBinaryCategorizer: A convenience method for evaluating a Vector object as a double, thus avoiding the convertToVector call from Vectorizable.
evaluateAsDouble(Vectorizable, CategoryType) - Method in class gov.sandia.cognition.learning.function.categorization.LinearMultiCategorizer: Evaluates how much the given input matches the prototype for the given category.
evaluateAsDouble(Vector, CategoryType) - Method in class gov.sandia.cognition.learning.function.categorization.LinearMultiCategorizer: Evaluates how much the given input matches the prototype for the given category.
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.learning.function.scalar.IdentityScalarFunction
evaluateAsDouble(InputType) - Method in class gov.sandia.cognition.learning.function.scalar.KernelScalarFunction: Evaluates the given input vector as a double by: sum w_i * k(input, x_i)
evaluateAsDouble(InputType) - Method in class gov.sandia.cognition.learning.function.scalar.LinearCombinationScalarFunction
evaluateAsDouble(Vectorizable) - Method in class gov.sandia.cognition.learning.function.scalar.LinearDiscriminant
evaluateAsDouble(Vectorizable) - Method in class gov.sandia.cognition.learning.function.scalar.LinearDiscriminantWithBias
evaluateAsDouble(Vectorizable) - Method in class gov.sandia.cognition.learning.function.scalar.LinearVectorScalarFunction: Evaluate the given input vector as a double by: weights * input + bias
evaluateAsDouble(Vector) - Method in class gov.sandia.cognition.learning.function.scalar.LinearVectorScalarFunction: A convenience method for evaluating a Vector object as a double, thus avoiding the convertToVector call from Vectorizable.
evaluateAsDouble(Vectorizable) - Method in class gov.sandia.cognition.learning.function.scalar.VectorEntryFunction: Returns the vector value at the specified index.
evaluateAsDouble(InputType) - Method in class gov.sandia.cognition.learning.function.scalar.VectorFunctionLinearDiscriminant
evaluateAsDouble(InputType) - Method in class gov.sandia.cognition.learning.function.scalar.VectorFunctionToScalarFunction
evaluateAsDouble(InputType) - Method in interface gov.sandia.cognition.math.ScalarFunction: Evaluates the scalar function as a double.
evaluateAsDouble(Double) - Method in interface gov.sandia.cognition.math.UnivariateScalarFunction
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.BetaDistribution.CDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.BetaDistribution.PDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.CauchyDistribution.CDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.CauchyDistribution.PDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution.CDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution.PDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.DeterministicDistribution.CDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.ExponentialDistribution.CDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.ExponentialDistribution.PDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.GammaDistribution.CDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.GammaDistribution.PDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.InverseGammaDistribution.CDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.InverseGammaDistribution.PDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.LaplaceDistribution.CDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.LaplaceDistribution.PDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.LogisticDistribution.CDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.LogisticDistribution.PDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.LogNormalDistribution.CDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.LogNormalDistribution.PDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.ParetoDistribution.CDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.ParetoDistribution.PDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.CDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.PDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.SnedecorFDistribution.CDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.StudentTDistribution.CDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.StudentTDistribution.PDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.UniformDistribution.CDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.UniformDistribution.PDF
evaluateAsDouble(int) - Method in class gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution.CDF: Evaluates the cumulative distribution function for the input.
evaluateAsDouble(int) - Method in class gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution.PMF: Evaluates the input value for the PMF to compute its mass as a double.
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.CDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.CDF.Inverse
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.PDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.WeibullDistribution.CDF
evaluateAsDouble(Double) - Method in class gov.sandia.cognition.statistics.distribution.WeibullDistribution.PDF
evaluateAsGaussian(Vectorizable) - Method in interface gov.sandia.cognition.learning.function.categorization.ConfidenceWeightedBinaryCategorizer: Returns the univariate Gaussian distribution over the output of the distribution of weight vectors times the input, with the confidence that the categorizer was trained using.
evaluateAsGaussian(Vectorizable) - Method in class gov.sandia.cognition.learning.function.categorization.DefaultConfidenceWeightedBinaryCategorizer
evaluateAsGaussian(Vectorizable) - Method in class gov.sandia.cognition.learning.function.categorization.DiagonalConfidenceWeightedBinaryCategorizer
evaluateAsVotes(InputType) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.VotingCategorizerEnsemble: Evaluates the ensemble as votes.
evaluateAsVotes(InputType) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.WeightedVotingCategorizerEnsemble: Evaluates the ensemble on the given input and returns the distribution of votes over the output categories.
evaluateAtEachLayer(Vector) - Method in class gov.sandia.cognition.learning.function.vector.FeedforwardNeuralNetwork: Returns the activations that occured at each layer
EvaluatedGenome<GenomeType> - Class in gov.sandia.cognition.learning.algorithm.genetic: The EvaluatedGenome class wraps together a Genome and its cost score.
EvaluatedGenome(double, GenomeType) - Constructor for class gov.sandia.cognition.learning.algorithm.genetic.EvaluatedGenome: Creates a new instance of EvaluatedGenome.
evaluateGaussianHypothesis(Collection<Double>) - Static method in class gov.sandia.cognition.statistics.method.KolmogorovSmirnovConfidence: Evaluates the Hypothesis that the given data were generated according to a UnivariateGaussian distribution.
EvaluateGenome(ArrayList<GenomeType>) - Constructor for class gov.sandia.cognition.learning.algorithm.genetic.ParallelizedGeneticAlgorithm.EvaluateGenome: Creates a new instance of EvaluateGenome
evaluateGoldsteinCondition(InputOutputPair<Double, Double>) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.WolfeConditions: Evaluates the Goldstein (Armijo) conditions, which is purely a sufficient decrease condition.
evaluateGoldsteinCondition(InputOutputSlopeTriplet, InputOutputPair<Double, Double>, double) - Static method in class gov.sandia.cognition.learning.algorithm.minimization.line.WolfeConditions: Evaluates the Goldstein (Armijo) conditions, which is purely a sufficient decrease condition.
evaluateHiddenLayerActivation(Vector) - Method in class gov.sandia.cognition.learning.function.vector.ThreeLayerFeedforwardNeuralNetwork: Computes the raw (unsquashed) activation at the hidden layer for the given input.
evaluateInto(byte[], byte[]) - Method in class gov.sandia.cognition.hash.AbstractHashFunction
evaluateInto(byte[], byte[], byte[]) - Method in class gov.sandia.cognition.hash.Eva32Hash
evaluateInto(byte[], byte[], byte[]) - Method in class gov.sandia.cognition.hash.Eva64Hash
evaluateInto(byte[], byte[], byte[]) - Method in class gov.sandia.cognition.hash.FNV1a32Hash
evaluateInto(byte[], byte[], byte[]) - Method in class gov.sandia.cognition.hash.FNV1a64Hash
evaluateInto(byte[], byte[]) - Method in interface gov.sandia.cognition.hash.HashFunction: Evaluates the input into the given output
evaluateInto(byte[], byte[], byte[]) - Method in interface gov.sandia.cognition.hash.HashFunction: Evaluates the input into the given output
evaluateInto(byte[], byte[], byte[]) - Method in class gov.sandia.cognition.hash.MD5Hash
evaluateInto(byte[], byte[], byte[]) - Method in class gov.sandia.cognition.hash.Murmur32Hash
evaluateInto(byte[], byte[], byte[]) - Method in class gov.sandia.cognition.hash.Prime32Hash
evaluateInto(byte[], byte[], byte[]) - Method in class gov.sandia.cognition.hash.Prime64Hash
evaluateInto(byte[], byte[], byte[]) - Method in class gov.sandia.cognition.hash.SHA1Hash
evaluateInto(byte[], byte[], byte[]) - Method in class gov.sandia.cognition.hash.SHA256Hash
evaluateInto(byte[], byte[], byte[]) - Method in class gov.sandia.cognition.hash.SHA512Hash
evaluateNode(InputType, DecisionTreeNode<InputType, OutputType>) - Method in class gov.sandia.cognition.learning.algorithm.tree.DecisionTree: Evaluates an input against the given node of a decision tree, using recursion to come up with the answer.
evaluateNullHypotheses(Collection<? extends TreatmentData>) - Method in class gov.sandia.cognition.statistics.method.AbstractMultipleHypothesisComparison
evaluateNullHypotheses(Collection<? extends TreatmentData>, double) - Method in class gov.sandia.cognition.statistics.method.AbstractMultipleHypothesisComparison
evaluateNullHypotheses(Collection<? extends Collection<? extends Number>>, double) - Method in class gov.sandia.cognition.statistics.method.BonferroniCorrection
evaluateNullHypotheses(Collection<? extends Collection<? extends Number>>, double) - Method in class gov.sandia.cognition.statistics.method.HolmCorrection
evaluateNullHypotheses(Collection<? extends Collection<? extends Number>>) - Method in class gov.sandia.cognition.statistics.method.MultipleComparisonExperiment
evaluateNullHypotheses(Collection<? extends Collection<? extends Number>>, double) - Method in class gov.sandia.cognition.statistics.method.MultipleComparisonExperiment
evaluateNullHypotheses(Collection<? extends TreatmentData>) - Method in interface gov.sandia.cognition.statistics.method.MultipleHypothesisComparison: Evaluates the null hypotheses associated with the given collection of data.
evaluateNullHypotheses(Collection<? extends TreatmentData>, double) - Method in interface gov.sandia.cognition.statistics.method.MultipleHypothesisComparison: Evaluates the null hypotheses associated with the given collection of data.
evaluateNullHypotheses(Collection<? extends Collection<? extends Number>>, double) - Method in class gov.sandia.cognition.statistics.method.NemenyiConfidence
evaluateNullHypotheses(Collection<? extends Collection<? extends Number>>, double) - Method in class gov.sandia.cognition.statistics.method.ShafferStaticCorrection
evaluateNullHypotheses(Collection<? extends Collection<? extends Number>>, double) - Method in class gov.sandia.cognition.statistics.method.SidakCorrection
evaluateNullHypotheses(Collection<? extends Collection<? extends Number>>, double) - Method in class gov.sandia.cognition.statistics.method.TukeyKramerConfidence
evaluateNullHypothesis(Collection<? extends Collection<? extends Number>>) - Method in class gov.sandia.cognition.statistics.method.AnalysisOfVarianceOneWay
evaluateNullHypothesis(Collection<? extends Number>, Collection<? extends Number>) - Method in class gov.sandia.cognition.statistics.method.AnalysisOfVarianceOneWay: Evaluates the ANOVA statistics for the two given treatments, each treatment can have a different number of samples
evaluateNullHypothesis(Collection<? extends Collection<? extends DataType>>) - Method in interface gov.sandia.cognition.statistics.method.BlockExperimentComparison: Evaluates the null hypothesis for the given block-design treatments
evaluateNullHypothesis(Collection<? extends DomainType>, ProbabilityMassFunction<DomainType>) - Static method in class gov.sandia.cognition.statistics.method.ChiSquareConfidence: Computes the chi-square test between a collection of data and a Probability Mass Function that may have create the observed data.
evaluateNullHypothesis(Collection<? extends Number>, Collection<? extends Number>) - Method in class gov.sandia.cognition.statistics.method.ChiSquareConfidence
evaluateNullHypothesis(Collection<? extends Number>, Collection<? extends Number>) - Method in class gov.sandia.cognition.statistics.method.FisherSignConfidence
evaluateNullHypothesis(Collection<? extends Number>, Collection<? extends Number>) - Method in class gov.sandia.cognition.statistics.method.FriedmanConfidence
evaluateNullHypothesis(Collection<? extends Collection<? extends Number>>) - Method in class gov.sandia.cognition.statistics.method.FriedmanConfidence
evaluateNullHypothesis(Collection<? extends Number>, Collection<? extends Number>) - Method in class gov.sandia.cognition.statistics.method.GaussianConfidence
evaluateNullHypothesis(Collection<? extends Double>, double) - Static method in class gov.sandia.cognition.statistics.method.GaussianConfidence: Computes the probability that the input was drawn from the estimated UnivariateGaussian distribution.
evaluateNullHypothesis(Collection<? extends Number>, Collection<? extends Number>) - Method in class gov.sandia.cognition.statistics.method.KolmogorovSmirnovConfidence: This is the standard K-S test for two distributions of data.
evaluateNullHypothesis(Collection<? extends DomainType>, CumulativeDistributionFunction<DomainType>) - Static method in class gov.sandia.cognition.statistics.method.KolmogorovSmirnovConfidence: This is the standard K-S test for determining if the given data were generated by the given CDF.
evaluateNullHypothesis(Collection<? extends InputOutputPair<? extends Number, Boolean>>) - Method in class gov.sandia.cognition.statistics.method.MannWhitneyUConfidence: Performs a U-test on the score-class pairs.
evaluateNullHypothesis(Collection<? extends Number>, Collection<? extends Number>) - Method in class gov.sandia.cognition.statistics.method.MannWhitneyUConfidence
evaluateNullHypothesis(Collection<? extends Collection<? extends Number>>) - Method in class gov.sandia.cognition.statistics.method.MultipleComparisonExperiment
evaluateNullHypothesis(Collection<? extends Number>, Collection<? extends Number>) - Method in class gov.sandia.cognition.statistics.method.MultipleComparisonExperiment
evaluateNullHypothesis(DataType, DataType) - Method in interface gov.sandia.cognition.statistics.method.NullHypothesisEvaluator: Computes the probability that two data were generated by the same distribution.
evaluateNullHypothesis(Collection<? extends Number>, Collection<? extends Number>) - Method in class gov.sandia.cognition.statistics.method.StudentTConfidence: Computes a paired Student-t test for the given data.
evaluateNullHypothesis(Collection<? extends Number>, Collection<? extends Number>) - Method in class gov.sandia.cognition.statistics.method.WilcoxonSignedRankConfidence
evaluateOutputFromSquashedHiddenLayerActivation(Vector) - Method in class gov.sandia.cognition.learning.function.vector.ThreeLayerFeedforwardNeuralNetwork: Evaluates the output from the squashed hidden-layer activation.
evaluatePartial(Evaluator<? super Vector, ? extends Vector>) - Method in interface gov.sandia.cognition.learning.function.cost.ParallelizableCostFunction: Computes the partial (linear) component of the cost function.
evaluatePartial(Evaluator<? super Vector, ? extends Vector>) - Method in class gov.sandia.cognition.learning.function.cost.SumSquaredErrorCostFunction
evaluatePerformance(BatchLearner<? super Collection<? extends InputDataType>, ? extends LearnedType>, PartitionedDataset<? extends InputDataType>) - Method in class gov.sandia.cognition.learning.experiment.LearnerRepeatExperiment: Performs the experiment.
evaluatePerformance(BatchLearner<? super Collection<? extends FoldDataType>, ? extends LearnedType>, Collection<? extends InputDataType>) - Method in class gov.sandia.cognition.learning.experiment.LearnerValidationExperiment
evaluatePerformance(IncrementalLearner<? super DataType, LearnedType>, Collection<? extends DataType>) - Method in class gov.sandia.cognition.learning.experiment.OnlineLearnerValidationExperiment: Performs the experiment.
evaluatePerformance(Collection<? extends TargetEstimatePair<? extends TargetType, ? extends TargetType>>) - Method in class gov.sandia.cognition.learning.function.cost.AbstractSupervisedCostFunction
evaluatePerformance(Collection<? extends TargetEstimatePair<? extends Vector, ? extends Vector>>) - Method in class gov.sandia.cognition.learning.function.cost.MeanL1CostFunction
evaluatePerformance(Collection<? extends TargetEstimatePair<? extends Vector, ? extends Vector>>) - Method in class gov.sandia.cognition.learning.function.cost.MeanSquaredErrorCostFunction
evaluatePerformance(Collection<? extends TargetEstimatePair<? extends Vector, ? extends Vector>>) - Method in class gov.sandia.cognition.learning.function.cost.ParallelizedCostFunctionContainer
evaluatePerformance(Collection<? extends TargetEstimatePair<? extends Vector, ? extends Vector>>) - Method in class gov.sandia.cognition.learning.function.cost.SumSquaredErrorCostFunction
evaluatePerformance(Evaluator<? super InputType, ? extends EstimateType>, Collection<? extends InputOutputPair<? extends InputType, TargetType>>) - Method in class gov.sandia.cognition.learning.performance.AbstractSupervisedPerformanceEvaluator: Evaluates the performance accuracy of the given estimates against the given targets.
evaluatePerformance(Collection<? extends TargetEstimatePair<? extends TargetType, ? extends EstimateType>>) - Method in class gov.sandia.cognition.learning.performance.AbstractSupervisedPerformanceEvaluator
evaluatePerformance(Collection<? extends TargetEstimatePair<? extends CategoryType, ? extends CategoryType>>) - Method in class gov.sandia.cognition.learning.performance.categorization.ConfusionMatrixPerformanceEvaluator
evaluatePerformance(Collection<? extends TargetEstimatePair<? extends Boolean, ? extends Boolean>>) - Method in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrix.PerformanceEvaluator
evaluatePerformance(Collection<? extends TargetEstimatePair<? extends Double, ? extends Double>>) - Method in class gov.sandia.cognition.learning.performance.MeanAbsoluteErrorEvaluator: Evaluates the performance accuracy of the given estimates against the given targets.
evaluatePerformance(Collection<? extends TargetEstimatePair<? extends Double, ? extends Double>>) - Method in class gov.sandia.cognition.learning.performance.MeanSquaredErrorEvaluator: Evaluates the performance accuracy of the given estimates against the given targets.
evaluatePerformance(Collection<? extends TargetEstimatePair<? extends DataType, ? extends DataType>>) - Method in class gov.sandia.cognition.learning.performance.MeanZeroOneErrorEvaluator: Evaluates the performance accuracy of the given estimates against the given targets.
evaluatePerformance(ObjectType, DataType) - Method in interface gov.sandia.cognition.learning.performance.PerformanceEvaluator: Evaluates the performance of an object with regards to given data.
evaluatePerformance(Collection<? extends TargetEstimatePair<? extends Double, ? extends Double>>) - Method in class gov.sandia.cognition.learning.performance.RootMeanSquaredErrorEvaluator: Evaluates the performance accuracy of the given estimates against the given targets.
evaluatePerformance(Collection<? extends TargetEstimatePair<? extends TargetType, ? extends EstimateType>>) - Method in interface gov.sandia.cognition.learning.performance.SupervisedPerformanceEvaluator: Evaluates the performance accuracy of the given estimates against the given targets.
evaluatePopulation(Collection<GenomeType>) - Method in class gov.sandia.cognition.learning.algorithm.genetic.GeneticAlgorithm: Converts a population of genomes into evaluated genomes.
evaluatePopulation(Collection<GenomeType>) - Method in class gov.sandia.cognition.learning.algorithm.genetic.ParallelizedGeneticAlgorithm: Converts a population of genomes into evaluated genomes.
evaluateReverse(OutputType) - Method in class gov.sandia.cognition.evaluator.ForwardReverseEvaluatorPair: Evaluates the reverse evaluator on a given object of output type.
evaluateSquashedHiddenLayerActivation(Vector) - Method in class gov.sandia.cognition.learning.function.vector.ThreeLayerFeedforwardNeuralNetwork: Evaluates the squashed hidden-layer activation from its raw activation value.
evaluateStrictCurvatureCondition(double) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.WolfeConditions: Evaluates the strict curvature condition.
evaluateStrictCurvatureCondition(double, double, double) - Static method in class gov.sandia.cognition.learning.algorithm.minimization.line.WolfeConditions: Evaluates the strict curvature condition.
evaluateWithDiscriminant(Collection<InputType>) - Method in class gov.sandia.cognition.learning.algorithm.bayes.DiscreteNaiveBayesCategorizer
evaluateWithDiscriminant(Vectorizable) - Method in class gov.sandia.cognition.learning.algorithm.bayes.VectorNaiveBayesCategorizer
evaluateWithDiscriminant(Vector) - Method in class gov.sandia.cognition.learning.algorithm.delta.AbstractDeltaCategorizer: This abstract method should implement evaluation aspect of this general algorithm.
evaluateWithDiscriminant(Vector) - Method in class gov.sandia.cognition.learning.algorithm.delta.BurrowsDeltaCategorizer: This method implements the evaluation aspect of BurrowsDelta.
evaluateWithDiscriminant(Vector) - Method in class gov.sandia.cognition.learning.algorithm.delta.CosineDeltaCategorizer: This method implements the evaluation aspect of CosineDelta.
evaluateWithDiscriminant(InputType) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.VotingCategorizerEnsemble
evaluateWithDiscriminant(InputType) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.WeightedVotingCategorizerEnsemble: Evaluates the ensemble on the given input and returns the category that has the most weighted votes as a pair containing the category and the percent of the weighted votes that it obtained.
evaluateWithDiscriminant(InputType) - Method in class gov.sandia.cognition.learning.function.categorization.AbstractDiscriminantBinaryCategorizer
evaluateWithDiscriminant(InputType) - Method in class gov.sandia.cognition.learning.function.categorization.BinaryVersusCategorizer
evaluateWithDiscriminant(InputType) - Method in interface gov.sandia.cognition.learning.function.categorization.DiscriminantCategorizer: Evaluate the categorizer on the given input to produce the expected category plus a discriminant for later producing an ordering of how well items fit into that category.
evaluateWithDiscriminant(Vectorizable) - Method in class gov.sandia.cognition.learning.function.categorization.LinearMultiCategorizer
evaluateWithDiscriminant(ObservationType) - Method in class gov.sandia.cognition.learning.function.categorization.MaximumAPosterioriCategorizer
evaluateWithDiscriminant(InputType) - Method in class gov.sandia.cognition.learning.function.categorization.WinnerTakeAllCategorizer: Evaluates the categorizer and returns the category along with a weight.
evaluateWithoutThreshold(InputType) - Method in class gov.sandia.cognition.learning.function.categorization.AbstractThresholdBinaryCategorizer: Computes the discriminant.
evaluateWithoutThreshold(InputType) - Method in class gov.sandia.cognition.learning.function.categorization.ScalarFunctionToBinaryCategorizerAdapter
evaluateWithoutThreshold(Double) - Method in class gov.sandia.cognition.learning.function.categorization.ScalarThresholdBinaryCategorizer
evaluateWithoutThreshold(Vectorizable) - Method in class gov.sandia.cognition.learning.function.categorization.VectorElementThresholdCategorizer
Evaluator<InputType,OutputType> - Interface in gov.sandia.cognition.evaluator: The Evaluator interface is a general interface to a function that can take an input and produce an output.
evaluator - Variable in class gov.sandia.cognition.learning.function.categorization.ScalarFunctionToBinaryCategorizerAdapter: The scalar evaluator.
evaluator - Variable in class gov.sandia.cognition.learning.function.categorization.WinnerTakeAllCategorizer: The evaluator that outputs a vector to return.
evaluator - Variable in class gov.sandia.cognition.text.term.filter.StringEvaluatorSingleTermFilter: The evaluator to adapt.
EvaluatorBasedCognitiveModule<InputType,OutputType> - Class in gov.sandia.cognition.framework.learning: The EvaluatorBasedCognitiveModule implements a CognitiveModule that wraps an Evaluator object.
EvaluatorBasedCognitiveModule(CognitiveModel, EvaluatorBasedCognitiveModuleSettings<InputType, OutputType>, String) - Constructor for class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModule: Creates a new instance of EvaluatorBasedCognitiveModule.
EvaluatorBasedCognitiveModuleFactory<InputType,OutputType> - Class in gov.sandia.cognition.framework.learning: The EvaluatorBasedCognitiveModuleFactory class implements a factory for the EvaluatorBasedCognitiveModule.
EvaluatorBasedCognitiveModuleFactory() - Constructor for class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleFactory: Creates a new instance of EvaluatorBasedCognitiveModuleFactory.
EvaluatorBasedCognitiveModuleFactory(EvaluatorBasedCognitiveModuleSettings<InputType, OutputType>, String) - Constructor for class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleFactory: Creates a new instance of EvaluatorBasedCognitiveModuleFactory.
EvaluatorBasedCognitiveModuleFactory(EvaluatorBasedCognitiveModuleFactory<InputType, OutputType>) - Constructor for class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleFactory: Creates a new copy of a EvaluatorBasedCognitiveModuleFactory.
EvaluatorBasedCognitiveModuleFactoryLearner<InputType,OutputType,LearningDataType> - Class in gov.sandia.cognition.framework.learning: The EvaluatorBasedCognitiveModuleFactoryLearner class implements a CognitiveModuleFactoryLearner for the EvaluatorBasedCognitiveModuleFactory.
EvaluatorBasedCognitiveModuleFactoryLearner() - Constructor for class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleFactoryLearner: Creates a new instance of CognitiveModuleFactoryEvaluatorLearner.
EvaluatorBasedCognitiveModuleFactoryLearner(BatchLearner<? super Collection<LearningDataType>, ? extends Evaluator<InputType, OutputType>>, String) - Constructor for class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleFactoryLearner: Creates a new instance of CognitiveModuleFactoryEvaluatorLearner.
EvaluatorBasedCognitiveModuleFactoryLearner(BatchLearner<? super Collection<LearningDataType>, ? extends Evaluator<? super InputType, ? extends OutputType>>, String, CogxelConverter<InputType>, CogxelConverter<OutputType>, CogxelConverter<LearningDataType>) - Constructor for class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleFactoryLearner: Creates a new instance of CognitiveModuleFactoryEvaluatorLearner.
EvaluatorBasedCognitiveModuleFactoryLearner(EvaluatorBasedCognitiveModuleFactoryLearner<InputType, OutputType, LearningDataType>) - Constructor for class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleFactoryLearner: Creates a new copy of a of CognitiveModuleFactoryEvaluatorLearner.
EvaluatorBasedCognitiveModuleSettings<InputType,OutputType> - Class in gov.sandia.cognition.framework.learning: The EvaluatorBasedCognitiveModuleSettings class implements the settings for the EvaluatorBasedCognitiveModule.
EvaluatorBasedCognitiveModuleSettings() - Constructor for class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleSettings: Creates a new instance of EvaluatorBasedCognitiveModuleSettings.
EvaluatorBasedCognitiveModuleSettings(Evaluator<? super InputType, ? extends OutputType>, CogxelConverter<InputType>, CogxelConverter<OutputType>) - Constructor for class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleSettings: Creates a new instance of EvaluatorBasedCognitiveModuleSettings.
EvaluatorBasedCognitiveModuleSettings(EvaluatorBasedCognitiveModuleSettings<InputType, OutputType>) - Constructor for class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleSettings: Creates a new instance of EvaluatorBasedCognitiveModuleSettings that is a copy of the given EvaluatorBasedCognitiveModuleSettings.
EvaluatorToCategorizerAdapter<InputType,CategoryType> - Class in gov.sandia.cognition.learning.function.categorization: The EvaluatorToCategorizerAdapter class implements an adapter from a general Evaluator to be a Categorizer.
EvaluatorToCategorizerAdapter() - Constructor for class gov.sandia.cognition.learning.function.categorization.EvaluatorToCategorizerAdapter: Creates a new EvaluatorToCategorizerAdapter.
EvaluatorToCategorizerAdapter(Evaluator<? super InputType, ? extends CategoryType>, Set<CategoryType>) - Constructor for class gov.sandia.cognition.learning.function.categorization.EvaluatorToCategorizerAdapter: Creates a new EvaluatorToCategorizerAdapter.
EvaluatorToCategorizerAdapter.Learner<InputType,CategoryType> - Class in gov.sandia.cognition.learning.function.categorization: The EvaluatorToCategorizerAdapter.Learner class implements a simple supervised learner for a EvaluatorToCategorizerAdapter.
examineExample(int) - Method in class gov.sandia.cognition.learning.algorithm.svm.SequentialMinimalOptimization: Examines one example in order to try and take a step of SMO using it.
example - Variable in class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation.Entry: The example the data pertains to.
exampleCount - Variable in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation: The number of examples.
examples - Variable in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation: The examples.
examples - Variable in class gov.sandia.cognition.learning.function.categorization.KernelBinaryCategorizer: The list of weighted examples that are used for categorization.
examples - Variable in class gov.sandia.cognition.learning.function.scalar.KernelScalarFunction: The list of weighted examples that are used for categorization.
executeInParallel(Collection<? extends Callable<ResultType>>) - Static method in class gov.sandia.cognition.algorithm.ParallelUtil: Executes the given Callable tasks in parallel using a default thread pool
executeInParallel(Collection<? extends Callable<ResultType>>, ThreadPoolExecutor) - Static method in class gov.sandia.cognition.algorithm.ParallelUtil: Executes the given Callable tasks in parallel using a given thread pool
executeInParallel(Collection<? extends Callable<ResultType>>, ParallelAlgorithm) - Static method in class gov.sandia.cognition.algorithm.ParallelUtil: Executes the given Callable tasks in parallel using a given thread pool
executeInSequence(Collection<? extends Callable<ResultType>>) - Static method in class gov.sandia.cognition.algorithm.ParallelUtil: Executes the given Callable tasks sequentially in series.
expectedDeviance(BayesianParameter<ParameterType, ? extends ComputableDistribution<ObservationType>, ?>, Iterable<? extends ObservationType>, Random, int) - Static method in class gov.sandia.cognition.statistics.bayesian.BayesianUtil: Computes the expected deviance of the model by sampling parameters from the posterior and then computing the deviance using the conditional distribution.
experimentEnded(LearningExperiment) - Method in interface gov.sandia.cognition.learning.experiment.LearningExperimentListener: Fired when the experiment has ended.
experimentStarted(LearningExperiment) - Method in interface gov.sandia.cognition.learning.experiment.LearningExperimentListener: Fired when the experiment has started.
expMinus1Plus(double) - Static method in class gov.sandia.cognition.math.MathUtil: Computes exp(x - 1).
ExponentialBayesianEstimator - Class in gov.sandia.cognition.statistics.bayesian.conjugate: Conjugate prior Bayesian estimator of the "rate" parameter of an Exponential distribution using the conjugate prior Gamma distribution.
ExponentialBayesianEstimator() - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.ExponentialBayesianEstimator: Default constructor.
ExponentialBayesianEstimator(GammaDistribution) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.ExponentialBayesianEstimator: Creates a new instance of ExponentialBayesianEstimator
ExponentialBayesianEstimator(ExponentialDistribution, GammaDistribution) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.ExponentialBayesianEstimator: Creates a new instance of ExponentialBayesianEstimator
ExponentialBayesianEstimator(BayesianParameter<Double, ExponentialDistribution, GammaDistribution>) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.ExponentialBayesianEstimator: Creates a new instance of ExponentialBayesianEstimator
ExponentialBayesianEstimator.Parameter - Class in gov.sandia.cognition.statistics.bayesian.conjugate: Bayesian parameter describing this conjugate relationship.
ExponentialDistribution - Class in gov.sandia.cognition.statistics.distribution: An Exponential distribution describes the time between events in a poisson process, resulting in a memoryless distribution.
ExponentialDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.ExponentialDistribution: Creates a new instance of ExponentialDistribution
ExponentialDistribution(double) - Constructor for class gov.sandia.cognition.statistics.distribution.ExponentialDistribution: Creates a new instance of ExponentialDistribution
ExponentialDistribution(ExponentialDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.ExponentialDistribution: Creates a new instance of ExponentialDistribution
ExponentialDistribution.CDF - Class in gov.sandia.cognition.statistics.distribution: CDF of the ExponentialDistribution.
ExponentialDistribution.MaximumLikelihoodEstimator - Class in gov.sandia.cognition.statistics.distribution: Creates a ExponentialDistribution from data
ExponentialDistribution.PDF - Class in gov.sandia.cognition.statistics.distribution: PDF of the ExponentialDistribution.
ExponentialDistribution.WeightedMaximumLikelihoodEstimator - Class in gov.sandia.cognition.statistics.distribution: Creates a ExponentialDistribution from weighted data
ExponentialKernel<InputType> - Class in gov.sandia.cognition.learning.function.kernel: The ExponentialKernel class implements a kernel that applies the exponential function to the result of another kernel.
ExponentialKernel() - Constructor for class gov.sandia.cognition.learning.function.kernel.ExponentialKernel: Creates a new instance of ExponentialKernel.
ExponentialKernel(Kernel<? super InputType>) - Constructor for class gov.sandia.cognition.learning.function.kernel.ExponentialKernel: Creates a new instance of ExponentialKernel.
ExtendedKalmanFilter - Class in gov.sandia.cognition.statistics.bayesian: Implements the Extended Kalman Filter (EKF), which is an extension of the Kalman filter that allows nonlinear motion and observation models.
ExtendedKalmanFilter() - Constructor for class gov.sandia.cognition.statistics.bayesian.ExtendedKalmanFilter: Creates a new instance of ExtendedKalmanFilter
ExtendedKalmanFilter(StatefulEvaluator<Vector, Vector, Vector>, Evaluator<Vector, Vector>, Vector, Matrix, Matrix) - Constructor for class gov.sandia.cognition.statistics.bayesian.ExtendedKalmanFilter: Creates a new instance of ExtendedKalmanFilter
ExtendedKalmanFilter.ModelJacobianEvaluator - Class in gov.sandia.cognition.statistics.bayesian: Holds the input constant while perturbing the state to estimate the Jacobian (A) matrix
extractAll(File) - Method in class gov.sandia.cognition.text.document.extractor.AbstractDocumentExtractor
extractAll(URI) - Method in class gov.sandia.cognition.text.document.extractor.AbstractDocumentExtractor
extractAll(File) - Method in class gov.sandia.cognition.text.document.extractor.AbstractSingleDocumentExtractor
extractAll(URI) - Method in class gov.sandia.cognition.text.document.extractor.AbstractSingleDocumentExtractor
extractAll(URLConnection) - Method in class gov.sandia.cognition.text.document.extractor.AbstractSingleDocumentExtractor
extractAll(File) - Method in interface gov.sandia.cognition.text.document.extractor.DocumentExtractor: Attempts to extract all of the documents from the given file.
extractAll(URI) - Method in interface gov.sandia.cognition.text.document.extractor.DocumentExtractor: Attempts to extract all of the documents from the given file.
extractAll(URLConnection) - Method in interface gov.sandia.cognition.text.document.extractor.DocumentExtractor: Attempts to extract all of the documents from the given file.
extractDocument(File) - Method in class gov.sandia.cognition.text.document.extractor.AbstractSingleDocumentExtractor
extractDocument(URI) - Method in class gov.sandia.cognition.text.document.extractor.AbstractSingleDocumentExtractor
extractDocument(File) - Method in interface gov.sandia.cognition.text.document.extractor.SingleDocumentExtractor: Attempts to extract a document from the given file.
extractDocument(URI) - Method in interface gov.sandia.cognition.text.document.extractor.SingleDocumentExtractor: Attempts to extract a document from the given file.
extractDocument(URLConnection) - Method in interface gov.sandia.cognition.text.document.extractor.SingleDocumentExtractor: Attempts to extract a document from the given file.
extractDocument(URLConnection) - Method in class gov.sandia.cognition.text.document.extractor.TextDocumentExtractor

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