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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
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
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
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
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
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