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

F

factorCount - Variable in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner

The number of factors to use.

FactorizationMachine - Class in gov.sandia.cognition.learning.algorithm.factor.machine

Implements a Factorization Machine.

FactorizationMachine() - Constructor for class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachine

Creates a new, empty FactorizationMachine.

FactorizationMachine(int, int) - Constructor for class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachine

Creates a new, empty FactorizationMachine of the given input dimensionality (d) and factor count (k).

FactorizationMachine(double, Vector, Matrix) - Constructor for class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachine

Creates a new FactorizationMachine with the given parameters.

FactorizationMachineAlternatingLeastSquares - Class in gov.sandia.cognition.learning.algorithm.factor.machine

Implements an Alternating Least Squares (ALS) algorithm for learning a Factorization Machine.

FactorizationMachineAlternatingLeastSquares() - Constructor for class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachineAlternatingLeastSquares

Creates a new FactorizationMachineAlternatingLeastSquares with default parameter values.

FactorizationMachineAlternatingLeastSquares(int, double, double, double, double, int, double, Random) - Constructor for class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachineAlternatingLeastSquares

Creates a new FactorizationMachineAlternatingLeastSquares.

FactorizationMachineStochasticGradient - Class in gov.sandia.cognition.learning.algorithm.factor.machine

Implements a Stochastic Gradient Descent (SGD) algorithm for learning a Factorization Machine.

FactorizationMachineStochasticGradient() - Constructor for class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachineStochasticGradient

Creates a new FactorizationMachineStochasticGradient with default parameters.

FactorizationMachineStochasticGradient(int, double, double, double, double, double, int, Random) - Constructor for class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachineStochasticGradient

Creates a new AbstractFactorizationMachineLearner.

factorRegularization - Variable in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner

The regularization term for the factor matrix.

factors - Variable in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachine

The k x d factor matrix (v) with k factors for each dimension.

Factory<CreatedType> - Interface in gov.sandia.cognition.factory

The Factory interface defines a very general interface for a factory object that can be used to create some other type of object.

factory - Variable in class gov.sandia.cognition.learning.performance.categorization.ConfusionMatrixPerformanceEvaluator

The factory used to create the confusion matrix of the evaluator.

Factory() - Constructor for class gov.sandia.cognition.learning.performance.categorization.DefaultConfusionMatrix.Factory

Creates a new Factory.

falseNegativesCount - Variable in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrix

Number of false negatives.

falsePositivesCount - Variable in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrix

Number of false positives.

falseValue - Variable in class gov.sandia.cognition.data.convert.number.DefaultBooleanToNumberConverter

The number to use to represent a false value.

FeatureHashing - Class in gov.sandia.cognition.learning.data.feature

Implements a function that applies vector feature hashing.

FeatureHashing() - Constructor for class gov.sandia.cognition.learning.data.feature.FeatureHashing

Creates a new FeatureHashing.

FeatureHashing(int) - Constructor for class gov.sandia.cognition.learning.data.feature.FeatureHashing

Creates a new FeatureHashing with the given output size.

FeatureHashing(int, HashFunction, VectorFactory<?>) - Constructor for class gov.sandia.cognition.learning.data.feature.FeatureHashing

Creates a new FeatureHashing with the given parameters.

featureStddev - Variable in class gov.sandia.cognition.learning.algorithm.delta.AbstractDeltaCategorizer

The stddev of each feature.

FeedforwardNeuralNetwork - Class in gov.sandia.cognition.learning.function.vector

A feedforward neural network that can have an arbitrary number of layers, and an arbitrary squashing (activation) function assigned to each layer.

FeedforwardNeuralNetwork(ArrayList<Integer>, ArrayList<? extends UnivariateScalarFunction>) - Constructor for class gov.sandia.cognition.learning.function.vector.FeedforwardNeuralNetwork

Creates a new instance of FeedforwardNeuralNetwork

FeedforwardNeuralNetwork(int, int, int, UnivariateScalarFunction) - Constructor for class gov.sandia.cognition.learning.function.vector.FeedforwardNeuralNetwork

Creates a new instance of FeedforwardNeuralNetwork

FeedforwardNeuralNetwork(ArrayList<? extends GeneralizedLinearModel>) - Constructor for class gov.sandia.cognition.learning.function.vector.FeedforwardNeuralNetwork

Creates a new instance of FeedforwardNeuralNetwork

Field<FieldType extends Field<FieldType>> - Interface in gov.sandia.cognition.math

Defines something similar to a mathematical field.

Field - Interface in gov.sandia.cognition.text.document

Defines the interface for a field in the document.

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

This class has methods that automatically compute confidence intervals for Double/double Fields in dataclasses.

FieldConfidenceInterval(Field, ConfidenceInterval) - Constructor for class gov.sandia.cognition.statistics.method.FieldConfidenceInterval

Creates a new instance of FieldConfidenceInterval

fieldMap - Variable in class gov.sandia.cognition.text.document.AbstractDocument

A mapping of field names to fields.

fieldName - Variable in class gov.sandia.cognition.text.convert.DocumentSingleFieldConverter

The name of the field to extract.

fieldNames - Variable in class gov.sandia.cognition.text.convert.DocumentFieldConcatenator

The list of fields to concatenate together from a document.

fieldSeparator - Variable in class gov.sandia.cognition.text.convert.DocumentFieldConcatenator

The field separator.

FileReference - Interface in gov.sandia.cognition.text.document

Represents a document reference that is to an actual file.

FileSerializationHandler<SerializedType> - Interface in gov.sandia.cognition.io.serialization

Defines the functionality of a serialization handler that can write an object to a file and read an object from a file.

FileUtil - Class in gov.sandia.cognition.io

The FileUtil class defines some useful utilities for dealing with files.

FileUtil() - Constructor for class gov.sandia.cognition.io.FileUtil

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

Fills the internal bag field by sampling the given number of samples.

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

filterTerm(TermOccurrence) - Method in class gov.sandia.cognition.text.term.filter.DictionaryFilter

filterTerm(TermOccurrence) - Method in class gov.sandia.cognition.text.term.filter.LowerCaseTermFilter

filterTerm(TermOccurrence) - Method in interface gov.sandia.cognition.text.term.filter.SingleTermFilter

Takes a single term occurrence and filters that occurrence into a new occurrence or returns null, indicating that the filter rejects that term.

filterTerm(TermOccurrence) - Method in class gov.sandia.cognition.text.term.filter.stem.PorterEnglishStemmingFilter

filterTerm(TermOccurrence) - Method in class gov.sandia.cognition.text.term.filter.StopListFilter

filterTerm(TermOccurrence) - Method in class gov.sandia.cognition.text.term.filter.StringEvaluatorSingleTermFilter

filterTerm(TermOccurrence) - Method in class gov.sandia.cognition.text.term.filter.SynonymFilter

filterTerm(TermOccurrence) - Method in class gov.sandia.cognition.text.term.filter.TermLengthFilter

filterTerms(Iterable<? extends TermOccurrence>) - Method in class gov.sandia.cognition.text.term.filter.AbstractSingleTermFilter

filterTerms(Iterable<? extends TermOccurrence>) - Method in class gov.sandia.cognition.text.term.filter.NGramFilter

filterTerms(Iterable<? extends TermOccurrence>) - Method in interface gov.sandia.cognition.text.term.filter.TermFilter

Filters the given list of terms into a new list of terms based on some internal criteria for what constitutes a term.

find(DataType) - Method in class gov.sandia.cognition.math.geometry.Quadtree

Locates the node in the tree that has the smallest bounding box that contains the item.

find(Vector2) - Method in class gov.sandia.cognition.math.geometry.Quadtree

Locates the node in the tree that has the smallest bounding box that contains the point.

find(double, double) - Method in class gov.sandia.cognition.math.geometry.Quadtree

Locates the node in the tree that has the smallest bounding box that contains the point.

findBest(Iterable<String>, String) - Method in class gov.sandia.cognition.text.spelling.SimpleStatisticalSpellingCorrector

Finds the best word from a given list of words by finding the one with the highest count in the dictionary.

findBestCategory(Vector) - Method in class gov.sandia.cognition.learning.function.categorization.WinnerTakeAllCategorizer

Finds the best category (and its output value) from the given vector of outputs from a vector evaluator.

findBin(ValueType) - Method in interface gov.sandia.cognition.statistics.method.Binner

Finds the bin for the provided value, or null if a corresponding bin for the value does not exist.

findBin(ValueType) - Method in class gov.sandia.cognition.statistics.method.TreeSetBinner

findChild(DataType) - Method in class gov.sandia.cognition.math.geometry.Quadtree.Node

Finds the child corresponding to the given point.

findChild(Vector2) - Method in class gov.sandia.cognition.math.geometry.Quadtree.Node

Finds the child corresponding to the given point.

findChild(double, double) - Method in class gov.sandia.cognition.math.geometry.Quadtree.Node

Finds the child corresponding to the given point.

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

Queries into the map to find a SemanticLabel

findIdentifier(SemanticLabel) - Method in interface gov.sandia.cognition.framework.SemanticIdentifierMap

Queries into the map to find a SemanticLabel

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

Finds the vector index for the given SemanticIdentifier.

findInputIndexForIdentifier(SemanticIdentifier) - Method in class gov.sandia.cognition.framework.lite.AbstractSemanticMemoryLite

Finds the input vector index for a given identifier.

findInputIndexForIdentifier(SemanticIdentifier) - Method in class gov.sandia.cognition.framework.lite.MutableSemanticMemoryLite

Finds the input vector index for a given identifier.

findInputIndexForIdentifier(SemanticIdentifier) - Method in class gov.sandia.cognition.framework.lite.SharedSemanticMemoryLite

Finds the input vector index for a given identifier.

findItems(Rectangle2D) - Method in class gov.sandia.cognition.math.geometry.Quadtree

Finds all of the items in the quadtree that are contained in the given rectangle.

findItems(Rectangle2D, LinkedList<DataType>) - Method in class gov.sandia.cognition.math.geometry.Quadtree.Node

Finds all of the items that fall within the region defined by this node (and its children) and adds it to the given list.

findKthLargest(int, ArrayList<? extends ComparableType>, Comparator<? super ComparableType>) - Static method in class gov.sandia.cognition.collection.CollectionUtil

Returns the set of indices of the data array such that data[return[0..k-1]] ≤ data[return[k]] ≤ data[return[k+1...N-1]].

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

Finds the line segment that contains the input

findMinimum(LineBracket, double, double, EvaluatorType) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.interpolator.AbstractLineBracketInterpolatorPolynomial

findMinimum(LineBracket, double, double, EvaluatorType) - Method in interface gov.sandia.cognition.learning.algorithm.minimization.line.interpolator.LineBracketInterpolator

Finds the minimum of the bracket using the interpolation/extrapolation routine, where the minimum must lie between the minx and maxx values on the x-axis.

findMinimum(LineBracket, double, double, Evaluator<Double, Double>) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.interpolator.LineBracketInterpolatorBrent

findMinimum(LineBracket, double, double, Evaluator<Double, Double>) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.interpolator.LineBracketInterpolatorGoldenSection

findMostLikelyState(int, Vector) - Method in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel

Finds the most-likely next state given the previous "delta" in the Viterbi algorithm.

findNearest(VectorType, int, Metric<? super VectorType>) - Method in class gov.sandia.cognition.math.geometry.KDTree

Finds the "num" nearest neighbors to the given "key" stored in the KDTree.

findNearest(VectorType, int, KDTree.Neighborhood<VectorType, DataType, PairType>, Metric<? super VectorType>) - Method in class gov.sandia.cognition.math.geometry.KDTree

Finds the "num" nearest neighbors to the given "key" stored in the KDTree.

findNearestWithinRadius(VectorType, double, Metric<? super VectorType>) - Method in class gov.sandia.cognition.math.geometry.KDTree

Finds the neighbors within a given distance to the given "key" stored in the KDTree.

findNearestWithinRadius(VectorType, double, KDTree.Neighborhood<VectorType, DataType, PairType>, Metric<? super VectorType>) - Method in class gov.sandia.cognition.math.geometry.KDTree

Finds the neighbors within a given distance to the given "key" stored in the KDTree.

findNodes(Rectangle2D) - Method in class gov.sandia.cognition.math.geometry.Quadtree

Finds the list of nodes that overlap with the given rectangle, chosing the highest-level nodes in the tree that are contained in the rectangle.

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

Finds the terminal node for the given input.

findTerminalNode(InputType, DecisionTreeNode<InputType, OutputType>) - Method in class gov.sandia.cognition.learning.algorithm.tree.DecisionTree

Finds the terminal node for the given input, starting from the given node.

findUniqueOutputs(Iterable<? extends InputOutputPair<?, ? extends OutputType>>) - Static method in class gov.sandia.cognition.learning.data.DatasetUtil

Creates a set containing the unique output values from the given data.

FiniteCapacityBuffer<DataType> - Class in gov.sandia.cognition.collection

A finite capacity buffer backed by a fixed array.

FiniteCapacityBuffer() - Constructor for class gov.sandia.cognition.collection.FiniteCapacityBuffer

Default constructor with capacity of one.

FiniteCapacityBuffer(int) - Constructor for class gov.sandia.cognition.collection.FiniteCapacityBuffer

Creates a new instance of FiniteCapacityBuffer.

FiniteCapacityBuffer(FiniteCapacityBuffer<DataType>) - Constructor for class gov.sandia.cognition.collection.FiniteCapacityBuffer

Copy constructor.

FiniteCapacityBuffer.InternalIterator - Class in gov.sandia.cognition.collection

Iterator for FiniteCapacityBuffer

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

Fires off a algorithm ended event for the algorithm, which notifies all the listeners that the algorithm has ended.

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

Fires off a algorithm started event for this algorithm, which notifies all of the listeners that the algorithm has started.

fireExperimentEnded() - Method in class gov.sandia.cognition.learning.experiment.AbstractLearningExperiment

Fires the experimentEnded event for all listeners.

fireExperimentStarted() - Method in class gov.sandia.cognition.learning.experiment.AbstractLearningExperiment

Fires the experimentStarted event for all listeners.

fireModelStateChangedEvent() - Method in class gov.sandia.cognition.framework.AbstractCognitiveModel

Triggers the CognitiveModelStateChangeEvent on all the registered CognitiveModelListners.

fireModelStateChangedEvent(CognitiveModelStateChangeEvent) - Method in class gov.sandia.cognition.framework.AbstractCognitiveModel

Triggers the CognitiveModelStateChangeEvent on all the registered CognitiveModelListners.

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

Fires off a SemanticIdentifierMapEvent of type SemanticIdentifierAdded for the given identifier.

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

Fires off a algorithm ended event for the algorithm, which notifies all the listeners that a step has ended.

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

Fires off a algorithm started event for the algorithm, which notifies all the listeners that a step has started.

fireTrialEnded() - Method in class gov.sandia.cognition.learning.experiment.AbstractLearningExperiment

Fires the trialEnded event for all listeners.

fireTrialStarted() - Method in class gov.sandia.cognition.learning.experiment.AbstractLearningExperiment

Fires the trialStarted event for all listeners.

first - Variable in class gov.sandia.cognition.util.DefaultPair

The first object.

first - Variable in class gov.sandia.cognition.util.DefaultTriple

The first object.

firstChild - Variable in class gov.sandia.cognition.learning.algorithm.clustering.hierarchy.BinaryClusterHierarchyNode

The first child node.

firstIndicesForRows - Variable in class gov.sandia.cognition.math.matrix.custom.SparseMatrix

Part of the compressed Yale format.

firstLearner - Variable in class gov.sandia.cognition.learning.algorithm.CompositeBatchLearnerPair

The first learner that is trained on the input data.

FisherLinearDiscriminantBinaryCategorizer - Class in gov.sandia.cognition.learning.function.categorization

A Fisher Linear Discriminant classifier, which creates an optimal linear separating plane between two Gaussian classes of different covariances.

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

Default constructor

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

Creates a new of FisherLinearDiscriminantBinaryCategorizer.

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

Creates a new of FisherLinearDiscriminantBinaryCategorizer.

FisherLinearDiscriminantBinaryCategorizer.ClosedFormSolver - Class in gov.sandia.cognition.learning.function.categorization

This class implements a closed form solver for the Fisher linear discriminant binary categorizer.

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

This is an implementation of the Fisher Sign Test, which is a robust nonparameteric test to determine if two groups have a different mean.

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

Default Constructor

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

Contains the parameters from the Sign Test null-hypothesis evaluation

fit(InputOutputSlopeTriplet, InputOutputSlopeTriplet) - Static method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Cubic

Fits a cubic to two InputOutputSlopeTriplets using a closed-form solution

fit(InputOutputPair<Double, Double>, InputOutputPair<Double, Double>) - Static method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Linear

Fits a linear (straight-line) curve to the given data points

fit(InputOutputSlopeTriplet) - Static method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Linear

Fits a linear (stright-line) curve to the given data point

fit(InputOutputPair<Double, Double>, InputOutputPair<Double, Double>, InputOutputPair<Double, Double>) - Static method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Quadratic

Fits a quadratic to three points

fit(InputOutputSlopeTriplet, InputOutputPair<Double, Double>) - Static method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Quadratic

Fits a quadratic to two points, one of which has slope information.

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

Fits a single MultivariateGaussian to the given MixtureOfGaussians

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

The FixedClusterInitializer interface defines the functionality of a class that can initialize a given number of clusters from a set of elements.

FletcherXuHybridEstimation - Class in gov.sandia.cognition.learning.algorithm.regression

The Fletcher-Xu hybrid estimation for solving the nonlinear least-squares parameters.

FletcherXuHybridEstimation() - Constructor for class gov.sandia.cognition.learning.algorithm.regression.FletcherXuHybridEstimation

Creates a new instance of FletcherXuHybridEstimation

FletcherXuHybridEstimation(LineMinimizer<?>) - Constructor for class gov.sandia.cognition.learning.algorithm.regression.FletcherXuHybridEstimation

Creates a new instance of FletcherXuHybridEstimation

FletcherXuHybridEstimation(LineMinimizer<?>, double) - Constructor for class gov.sandia.cognition.learning.algorithm.regression.FletcherXuHybridEstimation

Creates a new instance of FletcherXuHybridEstimation

FletcherXuHybridEstimation(LineMinimizer<?>, double, double) - Constructor for class gov.sandia.cognition.learning.algorithm.regression.FletcherXuHybridEstimation

Creates a new instance of FletcherXuHybridEstimation

FletcherXuHybridEstimation(LineMinimizer<?>, double, double, int, double) - Constructor for class gov.sandia.cognition.learning.algorithm.regression.FletcherXuHybridEstimation

Creates a new instance of FletcherXuHybridEstimation

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

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

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

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

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

fn - Variable in class gov.sandia.cognition.graph.inference.SumProductInferencingAlgorithm

The input energy function to learn against

FNV1a32Hash - Class in gov.sandia.cognition.hash

Implementation of the 32-bit (4-byte) Fowler-Noll-Vo (FNV-1a) hash function.

FNV1a32Hash() - Constructor for class gov.sandia.cognition.hash.FNV1a32Hash

Creates a new instance of FNV1a32Hash

FNV1a64Hash - Class in gov.sandia.cognition.hash

Implementation of the 32-bit (4-byte) Fowler-Noll-Vo (FNV-1a) hash function.

FNV1a64Hash() - Constructor for class gov.sandia.cognition.hash.FNV1a64Hash

Creates a new instance of FNV1a32Hash

foldCreator - Variable in class gov.sandia.cognition.learning.experiment.AbstractValidationFoldExperiment

The method to use to create the validation folds.

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

forEachElement(Vector.IndexValueConsumer) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseVector

forEachElement(Vector.IndexValueConsumer) - Method in class gov.sandia.cognition.math.matrix.mtj.SparseVector

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

Applies the given function to each element in this vector.

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

forEachEntry(InfiniteVector.KeyValueConsumer<? super KeyType>) - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector

forEachEntry(InfiniteVector.KeyValueConsumer<? super KeyType>) - Method in interface gov.sandia.cognition.math.matrix.InfiniteVector

Applies the given function to each active entry in this vector.

forEachEntry(Vector.IndexValueConsumer) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseVector

forEachEntry(Vector.IndexValueConsumer) - Method in class gov.sandia.cognition.math.matrix.mtj.SparseVector

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

Applies the given function to each active entry in this vector.

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

forEachNonZero(InfiniteVector.KeyValueConsumer<? super KeyType>) - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector

forEachNonZero(InfiniteVector.KeyValueConsumer<? super KeyType>) - Method in interface gov.sandia.cognition.math.matrix.InfiniteVector

Applies the given function to each non-zero entry in this vector.

forEachNonZero(Vector.IndexValueConsumer) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseVector

forEachNonZero(Vector.IndexValueConsumer) - Method in class gov.sandia.cognition.math.matrix.mtj.SparseVector

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

Applies the given function to each non-zero entry in this vector.

Forgetron<InputType> - Class in gov.sandia.cognition.learning.algorithm.perceptron.kernel

An implementation of the "self-tuned" Forgetron algorithm, which is an online budgeted kernel binary categorizer learner.

Forgetron() - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Forgetron

Creates a new Forgetron with a null kernel and the default budget.

Forgetron(Kernel<? super InputType>, int) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Forgetron

Creates a new Forgetron with the given kernel and budget.

Forgetron.Basic<InputType> - Class in gov.sandia.cognition.learning.algorithm.perceptron.kernel

An implementation of the "basic" Forgetron algorithm, which is an online budgeted kernel binary categorizer learner.

Forgetron.Greedy<InputType> - Class in gov.sandia.cognition.learning.algorithm.perceptron.kernel

An implementation of the "greedy" Forgetron algorithm, which is an online budgeted kernel binary categorizer learner.

Forgetron.Result<InputType> - Class in gov.sandia.cognition.learning.algorithm.perceptron.kernel

The result object learned by the Forgetron, which extends the DefaultKernelBinaryCategorizer with some additional state information needed in the update step.

format - Variable in class gov.sandia.cognition.algorithm.event.IterationMeasurablePerformanceReporter

The format for the performance report, passed to String.format.

format - Variable in class gov.sandia.cognition.algorithm.event.IterationStartReporter

The format for the performance report, passed to String.format.

format - Variable in class gov.sandia.cognition.learning.performance.AnytimeBatchLearnerValidationPerformanceReporter

The format for the performance report, passed to String.format.

FORMAT_NAME - Static variable in class gov.sandia.cognition.framework.io.CSVDefaultCognitiveModelLiteHandler

The name of the format that the file parses.

FORMAT_VERSION - Static variable in class gov.sandia.cognition.framework.io.CSVDefaultCognitiveModelLiteHandler

The current version that is parsed.

forward - Variable in class gov.sandia.cognition.evaluator.ForwardReverseEvaluatorPair

The forward evaluator from input type to output type.

FORWARD_DIFFERENCE - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.line.DirectionalVectorToScalarFunction

Value used in the forward-difference derivative approximation

ForwardReverseEvaluatorPair<InputType,OutputType,ForwardType extends Evaluator<? super InputType,? extends OutputType>,ReverseType extends Evaluator<? super OutputType,? extends InputType>> - Class in gov.sandia.cognition.evaluator

Represents a both a (normal) forward evaluator and its reverse as a pair.

ForwardReverseEvaluatorPair() - Constructor for class gov.sandia.cognition.evaluator.ForwardReverseEvaluatorPair

Creates a new, empty ForwardReverseEvaluatorPair.

ForwardReverseEvaluatorPair(ForwardType, ReverseType) - Constructor for class gov.sandia.cognition.evaluator.ForwardReverseEvaluatorPair

Creates a new ForwardReverseEvaluatorPair.

FourierTransform - Class in gov.sandia.cognition.math.signals

Computes the Fast Fourier Transform, or brute-force discrete Fourier transform, of a discrete input sequence.

FourierTransform() - Constructor for class gov.sandia.cognition.math.signals.FourierTransform

Creates a new instance of FourierTransform

FourierTransform.Inverse - Class in gov.sandia.cognition.math.signals

Evaluator that inverts a Fourier transform.

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

The Friedman test determines if the rankings associated with various treatments are equal.

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

Creates a new instance of FriedmanConfidence

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

Confidence statistic associated with the Friedman test using the tighter F-statistic.

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

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

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

Converts from a CogxelState object to an object of type DataType.

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

Converts from a CogxelState object to an object of type DataType.

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

Converts from a CogxelState object to an object of type DataType.

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

Converts from a CogxelState object to an object of type DataType.

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

Converts from a CogxelState object to an object of type DataType.

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

Converts from a CogxelState object to an object of type DataType.

fromDays(double) - Static method in class gov.sandia.cognition.time.DefaultDuration

Creates a new DefaultDuration from the given number of days.

fromHexString(String) - Static method in class gov.sandia.cognition.hash.HashFunctionUtil

Converts the hex-string back into an array of bytes

fromHours(double) - Static method in class gov.sandia.cognition.time.DefaultDuration

Creates a new DefaultDuration from the given number of hours.

fromInfiniteVector(InfiniteVector<? extends KeyType>) - Method in class gov.sandia.cognition.statistics.AbstractDataDistribution

fromInfiniteVector(InfiniteVector<? extends DataType>) - Method in interface gov.sandia.cognition.statistics.DataDistribution

Replaces the entries in this data distribution with the entries in the given infinite vector.

fromLog(double) - Static method in class gov.sandia.cognition.math.LogMath

Converts a number from log-domain representation (x = exp(logX)).

fromMilliseconds(double) - Static method in class gov.sandia.cognition.time.DefaultDuration

Creates a new DefaultDuration from the given number of milliseconds.

fromMinutes(double) - Static method in class gov.sandia.cognition.time.DefaultDuration

Creates a new DefaultDuration from the given number of minutes.

fromSeconds(double) - Static method in class gov.sandia.cognition.time.DefaultDuration

Creates a new DefaultDuration from the given number of seconds.

FullCovarianceLearner() - Constructor for class gov.sandia.cognition.learning.data.feature.MultivariateDecorrelator.FullCovarianceLearner

Creates a new MultivariateDecorrelator.FullCovarianceLearner with the default value for default covariance.

FullCovarianceLearner(double) - Constructor for class gov.sandia.cognition.learning.data.feature.MultivariateDecorrelator.FullCovarianceLearner

Creates a new MultivariateDecorrelator.FullCovarianceLearner with the given value for default covariance.

Function() - Constructor for class gov.sandia.cognition.learning.algorithm.pca.KernelPrincipalComponentsAnalysis.Function

Creates a new, empty Kernel Principal Components Analysis function.

Function(Kernel<? super DataType>, List<? extends DataType>, Matrix, boolean, Matrix) - Constructor for class gov.sandia.cognition.learning.algorithm.pca.KernelPrincipalComponentsAnalysis.Function

Creates a new Kernel Principal Components Analysis function.

Function() - Constructor for class gov.sandia.cognition.learning.algorithm.regression.LogisticRegression.Function

Creates a new LogisticRegression.Function.

Function(int) - Constructor for class gov.sandia.cognition.learning.algorithm.regression.LogisticRegression.Function

Creates a new instance of Function

function - Variable in class gov.sandia.cognition.learning.function.kernel.ScalarFunctionKernel

The scalar function for the kernel to use.

function - Variable in class gov.sandia.cognition.learning.function.kernel.VectorFunctionKernel

The vector function to use.

function - Variable in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.LogEvaluator

Evaluator to wrap and compute the natural logarithm of.

FUNCTION_EVALUATIONS - Static variable in class gov.sandia.cognition.statistics.method.InverseTransformSampling

Number of function evaluations needed to invert the distribution.

FunctionMinimizer<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>> - Interface in gov.sandia.cognition.learning.algorithm.minimization

Interface for unconstrained minimization of nonlinear functions.

FunctionMinimizerBFGS - Class in gov.sandia.cognition.learning.algorithm.minimization

Implementation of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) Quasi-Newton nonlinear minimization algorithm.

FunctionMinimizerBFGS() - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerBFGS

Creates a new instance of FunctionMinimizerBFGS

FunctionMinimizerBFGS(LineMinimizer<?>) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerBFGS

Creates a new instance of FunctionMinimizerBFGS

FunctionMinimizerBFGS(LineMinimizer<?>, Vector, double, int) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerBFGS

Creates a new instance of FunctionMinimizerBFGS

FunctionMinimizerConjugateGradient - Class in gov.sandia.cognition.learning.algorithm.minimization

Conjugate gradient method is a class of algorithms for finding the unconstrained local minimum of a nonlinear function.

FunctionMinimizerConjugateGradient(LineMinimizer<?>, Vector, double, int) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerConjugateGradient

Creates a new instance of FunctionMinimizerConjugateGradient

FunctionMinimizerDFP - Class in gov.sandia.cognition.learning.algorithm.minimization

Implementation of the Davidon-Fletcher-Powell (DFP) formula for a Quasi-Newton minimization update.

FunctionMinimizerDFP() - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerDFP

Creates a new instance of FunctionMinimizerBFGS

FunctionMinimizerDFP(LineMinimizer<?>) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerDFP

Creates a new instance of FunctionMinimizerBFGS

FunctionMinimizerDFP(LineMinimizer<?>, Vector, double, int) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerDFP

Creates a new instance of FunctionMinimizerBFGS

FunctionMinimizerDirectionSetPowell - Class in gov.sandia.cognition.learning.algorithm.minimization

Implementation of the derivative-free unconstrained nonlinear direction-set minimization algorithm called "Powell's Method" by Numerical Recipes.

FunctionMinimizerDirectionSetPowell() - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerDirectionSetPowell

Default constructor

FunctionMinimizerDirectionSetPowell(LineMinimizer<?>) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerDirectionSetPowell

Creates a new instance of FunctionMinimizerDirectionSetPowell

FunctionMinimizerDirectionSetPowell(LineMinimizer<?>, Vector, double, int) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerDirectionSetPowell

Creates a new instance of FunctionMinimizerDirectionSetPowell

FunctionMinimizerFletcherReeves - Class in gov.sandia.cognition.learning.algorithm.minimization

This is an implementation of the Fletcher-Reeves conjugate gradient minimization procedure.

FunctionMinimizerFletcherReeves() - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerFletcherReeves

Creates a new instance of FunctionMinimizerPolakRibiere

FunctionMinimizerFletcherReeves(LineMinimizer<?>) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerFletcherReeves

Creates a new instance of FunctionMinimizerPolakRibiere

FunctionMinimizerFletcherReeves(LineMinimizer<?>, Vector, double, int) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerFletcherReeves

Creates a new instance of FunctionMinimizerConjugateGradient

FunctionMinimizerGradientDescent - Class in gov.sandia.cognition.learning.algorithm.minimization

This is an implementation of the classic Gradient Descent algorithm, also known as Steepest Descent, Backpropagation (for neural nets), or Hill Climbing.

FunctionMinimizerGradientDescent() - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerGradientDescent

Creates a new instance of FunctionMinimizerGradientDescent

FunctionMinimizerGradientDescent(double, double) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerGradientDescent

Creates a new instance of FunctionMinimizerGradientDescent

FunctionMinimizerGradientDescent(double, double, Vector, double, int) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerGradientDescent

Creates a new instance of FunctionMinimizerGradientDescent

FunctionMinimizerLiuStorey - Class in gov.sandia.cognition.learning.algorithm.minimization

This is an implementation of the Liu-Storey conjugate gradient minimization procedure.

FunctionMinimizerLiuStorey() - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerLiuStorey

Creates a new instance of FunctionMinimizerLiuStorey

FunctionMinimizerLiuStorey(LineMinimizer<?>) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerLiuStorey

Creates a new instance of FunctionMinimizerLiuStorey

FunctionMinimizerLiuStorey(LineMinimizer<?>, Vector, double, int) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerLiuStorey

Creates a new instance of FunctionMinimizerLiuStorey

FunctionMinimizerNelderMead - Class in gov.sandia.cognition.learning.algorithm.minimization

Implementation of the Downhill Simplex minimization algorithm, also known as the Nelder-Mead method.

FunctionMinimizerNelderMead() - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerNelderMead

Creates a new instance of FunctionMinimizerNelderMead

FunctionMinimizerPolakRibiere - Class in gov.sandia.cognition.learning.algorithm.minimization

This is an implementation of the Polack-Ribiere conjugate gradient minimization procedure.

FunctionMinimizerPolakRibiere() - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerPolakRibiere

Creates a new instance of FunctionMinimizerPolakRibiere

FunctionMinimizerPolakRibiere(LineMinimizer<?>) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerPolakRibiere

Creates a new instance of FunctionMinimizerPolakRibiere

FunctionMinimizerPolakRibiere(LineMinimizer<?>, Vector, double, int) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerPolakRibiere

Creates a new instance of FunctionMinimizerConjugateGradient

FunctionMinimizerQuasiNewton - Class in gov.sandia.cognition.learning.algorithm.minimization

This is an abstract implementation of the Quasi-Newton minimization method, sometimes called "Variable-Metric methods." This family of minimization algorithms uses first-order gradient information to find a locally minimum to a scalar function.

FunctionMinimizerQuasiNewton(LineMinimizer<?>, Vector, double, int) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerQuasiNewton

Creates a new instance of FunctionMinimizerBFGS

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