R-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

R

r - Variable in class gov.sandia.cognition.learning.algorithm.confidence.AdaptiveRegularizationOfWeights: The r parameter that controls regularization weight.
R - Variable in class gov.sandia.cognition.math.matrix.custom.DenseMatrix.QR: The upper-triangular matrix
r - Variable in class gov.sandia.cognition.statistics.distribution.NegativeBinomialDistribution: Number of trials before the experiment is stopped, must be greater than zero.
RadialBasisKernel - Class in gov.sandia.cognition.learning.function.kernel: The RadialBasisKernel implements the standard radial basis kernel, which is:
exp( -||x - y||^2 / (2 * sigma^2) )
where sigma is the parameter that controls the bandwidth of the kernel.
RadialBasisKernel() - Constructor for class gov.sandia.cognition.learning.function.kernel.RadialBasisKernel: Creates a new instance of RadialBasisKernel with a default value for sigma of 1.0.
RadialBasisKernel(double) - Constructor for class gov.sandia.cognition.learning.function.kernel.RadialBasisKernel: Creates a new instance of RadialBasisKernel with the given value for sigma.
RadialBasisKernel(RadialBasisKernel) - Constructor for class gov.sandia.cognition.learning.function.kernel.RadialBasisKernel: Creates a new copy of a RadialBasisKernel.
radius - Variable in class gov.sandia.cognition.learning.algorithm.perceptron.Ballseptron: The radius enforced by the algorithm.
random - Variable in class gov.sandia.cognition.learning.algorithm.clustering.initializer.AbstractMinDistanceFixedClusterInitializer: The random number generator to use.
random - Variable in class gov.sandia.cognition.learning.algorithm.clustering.MiniBatchKMeansClusterer: The random number generator to use for initialization and subset selection.
random - Variable in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer: The source of randomness used during initial partitioning.
random - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractBaggingLearner: The random number generator to use.
random - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner: The random number generator to use.
random - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.OnlineBaggingCategorizerLearner: The random number generator to use.
random - Variable in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner: The random number generator to use.
random - Variable in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.OnlineKernelRandomizedBudgetPerceptron: The random number generator.
random - Variable in class gov.sandia.cognition.learning.algorithm.svm.PrimalEstimatedSubGradient: The random number generator to use.
random - Variable in class gov.sandia.cognition.statistics.bayesian.AbstractMarkovChainMonteCarlo: Random number generator.
random - Variable in class gov.sandia.cognition.statistics.bayesian.AbstractParticleFilter: Random number generator.
random - Variable in class gov.sandia.cognition.statistics.distribution.MixtureOfGaussians.EMLearner: Random number generator.
random - Variable in class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.EMLearner: Random number generator.
random - Variable in class gov.sandia.cognition.statistics.distribution.StudentizedRangeDistribution: Random number generator for Monte Carlo simulations
random - Variable in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler: The random number generator to use.
random - Variable in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis: The random number generator to use.
random - Variable in class gov.sandia.cognition.util.AbstractRandomized: The random number generator for this object to use.
RandomByTwoFoldCreator<DataType> - Class in gov.sandia.cognition.learning.experiment: A validation fold creator that takes a given collection of data and randomly splits it in half a given number of times, returning two folds for each split, using one half as training and the other half as testing.
RandomByTwoFoldCreator() - Constructor for class gov.sandia.cognition.learning.experiment.RandomByTwoFoldCreator: Creates a new RandomByTwoFoldCreator with a default number of splits.
RandomByTwoFoldCreator(int) - Constructor for class gov.sandia.cognition.learning.experiment.RandomByTwoFoldCreator: Creates a new RandomByTwoFoldCreator with a given number of splits.
RandomByTwoFoldCreator(int, Random) - Constructor for class gov.sandia.cognition.learning.experiment.RandomByTwoFoldCreator: Creates a new RandomByTwoFoldCreator with a given number of splits.
RandomClusterInitializer<ClusterType extends Cluster<DataType>,DataType> - Class in gov.sandia.cognition.learning.algorithm.clustering.initializer: Creates initial clusters by selecting random data points as singleton clusters.
RandomClusterInitializer(ClusterCreator<ClusterType, DataType>, Random) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.initializer.RandomClusterInitializer: Creates a new random cluster creator.
RandomDataPartitioner<DataType> - Class in gov.sandia.cognition.learning.data: The RandomDataPartitioner class implements a randomized data partitioner that takes a collection of data and randomly splits it into training and testing sets based on a fixed percentage of training data.
RandomDataPartitioner() - Constructor for class gov.sandia.cognition.learning.data.RandomDataPartitioner: Creates a new instance of RandomDataPartitioner.
RandomDataPartitioner(double, Random) - Constructor for class gov.sandia.cognition.learning.data.RandomDataPartitioner: Creates a new instance of RandomDataPartitioner.
RandomFoldCreator<DataType> - Class in gov.sandia.cognition.learning.experiment: The RandomFoldCreator class makes use of a randomized data partitioner to create a set number of folds for a set of data by passing the data to the data partitioner multiple times.
RandomFoldCreator() - Constructor for class gov.sandia.cognition.learning.experiment.RandomFoldCreator: Creates a new instance of RandomFoldCreator.
RandomFoldCreator(int, RandomizedDataPartitioner<DataType>) - Constructor for class gov.sandia.cognition.learning.experiment.RandomFoldCreator: Creates a new instance of RandomFoldCreator.
RandomForestFactory - Class in gov.sandia.cognition.learning.algorithm.tree: A factory class for creating Random Forest learners.
RandomForestFactory() - Constructor for class gov.sandia.cognition.learning.algorithm.tree.RandomForestFactory
Randomized - Interface in gov.sandia.cognition.util: The Randomized interface defines the functionality of an object whose computations are based in part on an underlying random number generator (a Random object).
RandomizedDataPartitioner<DataType> - Interface in gov.sandia.cognition.learning.data: The RandomizedDataPartitioner extends a DataPartitioner to indicate that is it is randomized, which means that its partitions are based (at least in part) on an underlying random number generator.
randomizeOrder() - Method in class gov.sandia.cognition.collection.IntArrayList: Takes the elements of this and alters their order to a random order (using a variant of Fisher-Yates shuffle called Durstenfeld shuffle).
randomizeOrder(Random) - Method in class gov.sandia.cognition.collection.IntArrayList: Takes the elements of this and alters their order to a random order (using a variant of Fisher-Yates shuffle called Durstenfeld shuffle).
RandomSubspace - Class in gov.sandia.cognition.learning.data.feature: Selects a random subspace from the given vector, which is a random set of indices.
RandomSubspace() - Constructor for class gov.sandia.cognition.learning.data.feature.RandomSubspace: Creates a new RandomSubspace with the default size.
RandomSubspace(int) - Constructor for class gov.sandia.cognition.learning.data.feature.RandomSubspace: Creates a new RandomSubspace with the given size.
RandomSubspace(int, Random) - Constructor for class gov.sandia.cognition.learning.data.feature.RandomSubspace: Creates a new RandomSubspace with the given parameters.
RandomSubspace(int, Random, VectorFactory<?>) - Constructor for class gov.sandia.cognition.learning.data.feature.RandomSubspace: Creates a new RandomSubspace with the given parameters.
RandomSubVectorThresholdLearner<OutputType> - Class in gov.sandia.cognition.learning.algorithm.tree: Learns a decision function by taking a randomly sampling a subspace from a given set of input vectors and then learning a threshold function by passing the subspace vectors to a sublearner.
RandomSubVectorThresholdLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.tree.RandomSubVectorThresholdLearner: Creates a new RandomSubVectorThresholdLearner.
RandomSubVectorThresholdLearner(DeciderLearner<Vectorizable, OutputType, Boolean, VectorElementThresholdCategorizer>, double, Random) - Constructor for class gov.sandia.cognition.learning.algorithm.tree.RandomSubVectorThresholdLearner: Creates a new RandomSubVectorThresholdLearner.
RandomSubVectorThresholdLearner(DeciderLearner<Vectorizable, OutputType, Boolean, VectorElementThresholdCategorizer>, double, Random, VectorFactory<? extends Vector>) - Constructor for class gov.sandia.cognition.learning.algorithm.tree.RandomSubVectorThresholdLearner: Creates a new RandomSubVectorThresholdLearner.
RandomSubVectorThresholdLearner(DeciderLearner<Vectorizable, OutputType, Boolean, VectorElementThresholdCategorizer>, double, int[], Random, VectorFactory<? extends Vector>) - Constructor for class gov.sandia.cognition.learning.algorithm.tree.RandomSubVectorThresholdLearner: Creates a new RandomSubVectorThresholdLearner.
RandomVariable<DataType> - Interface in gov.sandia.cognition.statistics: Describes the functionality of a random variable.
RandomWalker(boolean, Random) - Constructor for class gov.sandia.cognition.graph.GraphWalker.RandomWalker: Creates a random walker with directedness specified
range(int) - Static method in class gov.sandia.cognition.collection.IntArrayList: Creates a new instance pre-loaded with values [0 ..
range(int, int) - Static method in class gov.sandia.cognition.collection.IntArrayList: Creates a new instance pre-loaded with values [min ..
RangeExcludedArrayList<E> - Class in gov.sandia.cognition.collection: The RangeExcludedArrayList class implements a light-weight list on top of an ArrayList where a certain range of values is excluded from the list.
RangeExcludedArrayList(ArrayList<E>, int, int) - Constructor for class gov.sandia.cognition.collection.RangeExcludedArrayList: Creates a new instance of RangeExcludedArrayList.
rank() - Method in class gov.sandia.cognition.math.matrix.AbstractMatrix
rank(double) - Method in class gov.sandia.cognition.math.matrix.custom.DenseMatrix
rank(double) - Method in class gov.sandia.cognition.math.matrix.custom.DiagonalMatrix
rank(double) - Method in class gov.sandia.cognition.math.matrix.custom.SparseMatrix: Computes the effective rank of this, which is the number of linearly independent rows and columns in this.
rank() - Method in class gov.sandia.cognition.math.matrix.decomposition.AbstractSingularValueDecomposition
rank() - Method in interface gov.sandia.cognition.math.matrix.decomposition.SingularValueDecomposition: Returns the rank of the underlying matrix by calling this.effectiveRank with an effectiveZero = 0.0
rank() - Method in interface gov.sandia.cognition.math.matrix.Matrix: Computes the rank of this, which is the number of linearly independent rows and columns in this.
rank(double) - Method in interface gov.sandia.cognition.math.matrix.Matrix: Computes the effective rank of this, which is the number of linearly independent rows and columns in this.
rank(double) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix
rank(double) - Method in class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixMTJ
ranks(Collection<? extends Number>) - Static method in class gov.sandia.cognition.statistics.method.WilcoxonSignedRankConfidence: Returns the ranks of the values in ascending order
rate - Variable in class gov.sandia.cognition.statistics.distribution.ExponentialDistribution: Rate, or inverse scale, of the distribution, must be greater than zero.
rate - Variable in class gov.sandia.cognition.statistics.distribution.PoissonDistribution: Expected number of occurrences during the integer interval, must be greater than zero.
rawErrorRates - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractCategorizerOutOfBagStoppingCriteria: The raw out-of-bag error rate, per iteration.
read() - Method in interface gov.sandia.cognition.data.temporal.TemporalDataReadChannel: Reads the next value from the data reader.
read(InputStream) - Static method in class gov.sandia.cognition.io.XStreamSerializationHandler: Reads an Object from the given InputStream.
read(Reader) - Static method in class gov.sandia.cognition.io.XStreamSerializationHandler: Reads an Object from the given Reader.
read() - Method in class gov.sandia.cognition.math.matrix.MatrixReader: Reads the next Matrix found in the specified Reader
read() - Method in class gov.sandia.cognition.math.matrix.VectorReader: Reads a Vector off of the next line in the file
readChannel() - Method in interface gov.sandia.cognition.data.temporal.BatchTemporalDataSource: Gets the read channel for the data source.
readChannel() - Method in interface gov.sandia.cognition.data.temporal.TemporalDataSource: Gets the data channel for the data source.
readCollection(boolean) - Method in class gov.sandia.cognition.math.matrix.VectorReader: Reads a collection of Vectors from the Reader
ReaderTokenizer - Class in gov.sandia.cognition.io: Reads each line from a Reader, and returns each line as a List of Strings
ReaderTokenizer(Reader) - Constructor for class gov.sandia.cognition.io.ReaderTokenizer: Creates a new instance of ReaderTokenizer
readFile(String) - Static method in class gov.sandia.cognition.io.CSVUtility: Reads a CSV file into a list of arrays of string values.
readFile(String, char) - Static method in class gov.sandia.cognition.io.CSVUtility: Reads a CSV file into a list of arrays of string values.
readFromFile(File) - Static method in class gov.sandia.cognition.io.ObjectSerializationHandler: Reads a Java serialized Object from the given File and returns it.
readFromFile(String) - Method in class gov.sandia.cognition.io.serialization.AbstractFileSerializationHandler
readFromFile(File) - Method in class gov.sandia.cognition.io.serialization.AbstractStreamSerializationHandler
readFromFile(String) - Method in interface gov.sandia.cognition.io.serialization.FileSerializationHandler: Reads an object from the given file.
readFromFile(File) - Method in interface gov.sandia.cognition.io.serialization.FileSerializationHandler: Reads an object from the given file.
readFromFile(String) - Static method in class gov.sandia.cognition.io.XStreamSerializationHandler: Reads an Object from the given file name.
readFromFile(File) - Static method in class gov.sandia.cognition.io.XStreamSerializationHandler: Reads an Object from the given file name.
readFromStream(BufferedInputStream) - Static method in class gov.sandia.cognition.io.ObjectSerializationHandler: Reads a Java serialized Object from the given stream and returns it.
readMetaData(URLConnection) - Method in class gov.sandia.cognition.text.document.DefaultDocument: Reads the file name and title from the given URL.
readModel(String) - Static method in class gov.sandia.cognition.framework.io.ModelFileHandler: Attempts to read a CognitiveModel from the given file name.
readModel(File) - Static method in class gov.sandia.cognition.framework.io.ModelFileHandler: Attempts to read a CognitiveModel from the given file.
readModelBinarySerialized(File) - Static method in class gov.sandia.cognition.framework.io.ModelFileHandler: Attempts to read a CognitiveModel from the given file in binary serialized format.
readModelCSV(File) - Static method in class gov.sandia.cognition.framework.io.ModelFileHandler: Attempts to read a CognitiveModel from the given file in CSV format.
readModelFactory(String) - Static method in class gov.sandia.cognition.framework.io.ModelFileHandler: Attempts to read a CognitiveModelFactory from the given file name.
readModelFactory(File) - Static method in class gov.sandia.cognition.framework.io.ModelFileHandler: Attempts to read a CognitiveModelFactory from the given file.
readModelFactoryBinarySerialized(File) - Static method in class gov.sandia.cognition.framework.io.ModelFileHandler: Attempts to read a CognitiveModelFactory from the given file in binary serialized format.
readModelFactoryCSV(File) - Static method in class gov.sandia.cognition.framework.io.ModelFileHandler: Attempts to read a CognitiveModelFactory from the given file in CSV format.
readModelFactoryXMLSerialized(File) - Static method in class gov.sandia.cognition.framework.io.ModelFileHandler: Attempts to read a CognitiveModelFactory from the given file in XML serialized format.
readModelFromFile(String) - Static method in class gov.sandia.cognition.framework.io.SerializedModelHandler: Reads a serialized CognitiveModel from the given file.
readModelFromFile(File) - Static method in class gov.sandia.cognition.framework.io.SerializedModelHandler: Reads a serialized CognitiveModel from the given file.
readModelXMLSerialized(File) - Static method in class gov.sandia.cognition.framework.io.ModelFileHandler: Attempts to read a CognitiveModel from the given file in XML serialized format.
readNextLine() - Method in class gov.sandia.cognition.io.ReaderTokenizer: Reads the next line of the Reader, returning each token on the line as an entry in an ArrayList
readObject(InputStream) - Method in class gov.sandia.cognition.io.serialization.AbstractTextSerializationHandler
readObject(InputStream) - Method in class gov.sandia.cognition.io.serialization.GZIPSerializationHandler
readObject(InputStream) - Method in class gov.sandia.cognition.io.serialization.JavaDefaultBinarySerializationHandler
readObject(InputStream) - Method in interface gov.sandia.cognition.io.serialization.StreamSerializationHandler: Reads an object from the given stream.
readObject(Reader) - Method in interface gov.sandia.cognition.io.serialization.TextSerializationHandler: Reads an object from the given reader.
readObject(Reader) - Method in class gov.sandia.cognition.io.serialization.XStreamSerializationHandler
readObjectFromFile(File) - Static method in class gov.sandia.cognition.framework.io.SerializedModelHandler: Reads a serialized Java Object from the given File.
readParameters() - Method in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler: Reads the current set of parameters.
readResolve() - Method in class gov.sandia.cognition.algorithm.AnytimeAlgorithmWrapper: This method is detected by the Java Serialization code and is called on deserialization.
readState(CognitiveModelState, CognitiveModuleState) - Method in interface gov.sandia.cognition.framework.concurrent.ConcurrentCognitiveModule: Read in and temporarily hold input state information required for performing module evaluation.
readState(CognitiveModelState, CognitiveModuleState) - Method in class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModule: Read in and temporarily hold input state information required for performing module evaluation.
readState(CognitiveModelState, CognitiveModuleState) - Method in class gov.sandia.cognition.framework.learning.StatefulEvaluatorBasedCognitiveModule: Read in and temporarily hold input state information required for performing module evaluation.
readState(CognitiveModelState, CognitiveModuleState) - Method in class gov.sandia.cognition.framework.lite.ArrayBasedPerceptionModule: Read in and temporarily hold input state information required for performing module evaluation.
readText() - Method in class gov.sandia.cognition.text.AbstractTextual
readText() - Method in class gov.sandia.cognition.text.document.AbstractField
readText() - Method in interface gov.sandia.cognition.text.document.Field: Gets a new reader for the content of the field.
readText() - Method in interface gov.sandia.cognition.text.Textual: Returns a new text reader for the text in this object.
rebalance() - Method in class gov.sandia.cognition.learning.algorithm.nearest.KNearestNeighborKDTree: Rebalances the internal KDTree to make the search more efficient.
reblanace() - Method in class gov.sandia.cognition.math.geometry.KDTree: Rebalances the KDTree.
rebuild() - Method in class gov.sandia.cognition.math.geometry.Quadtree: Rebuilds the entire quadtree.
ReceiverOperatingCharacteristic - Class in gov.sandia.cognition.statistics.method: Class that describes a Receiver Operating Characteristic (usually called an "ROC Curve").
ReceiverOperatingCharacteristic.DataPoint - Class in gov.sandia.cognition.statistics.method: Contains information about a datapoint on an ROC curve
ReceiverOperatingCharacteristic.DataPoint.Sorter - Class in gov.sandia.cognition.statistics.method: Sorts DataPoints in ascending order according to their falsePositiveRate (x-axis)
ReceiverOperatingCharacteristic.Statistic - Class in gov.sandia.cognition.statistics.method: Contains useful statistics derived from a ROC curve
recognize(CognitiveModuleState, Vector) - Method in interface gov.sandia.cognition.framework.lite.PatternRecognizerLite: Computes the recognition.
recognize(CognitiveModuleState, Vector) - Method in class gov.sandia.cognition.framework.lite.SimplePatternRecognizer: Computes the recognition.
RectifiedLinearFunction - Class in gov.sandia.cognition.learning.function.scalar: A rectified linear unit, which is the maximum of its input or 0.
RectifiedLinearFunction() - Constructor for class gov.sandia.cognition.learning.function.scalar.RectifiedLinearFunction: Creates a new RectifiedLinearFunction.
RecursiveBayesianEstimator<ObservationType,ParameterType,BeliefType extends Distribution<ParameterType>> - Interface in gov.sandia.cognition.statistics.bayesian: A recursive Bayesian estimator is an estimation method that uses the previous belief of the system parameter and a single observation to refine the estimate of the system parameter.
reestimateAlpha - Variable in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel: Flag to automatically re-estimate the alpha parameter
reestimateInitialProbabilities - Variable in class gov.sandia.cognition.learning.algorithm.hmm.AbstractBaumWelchAlgorithm: Flag to re-estimate the initial probability Vector.
reference - Variable in class gov.sandia.cognition.text.document.AbstractDocument: A reference to where the document came from.
Regression<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>> - Interface in gov.sandia.cognition.learning.algorithm.regression: A supervised learning algorithm that attempts to interpolate/extrapolate inputs given a training set of input/output pairs.
Regression(double...) - Constructor for class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Regression: Creates a new instance of Regression
regressionLearner - Variable in class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeLearner: The learning algorithm for the regression function.
RegressionTree<InputType> - Class in gov.sandia.cognition.learning.algorithm.tree: The RegressionTree class extends the DecisionTree class to implement a decision tree that does regression.
RegressionTree() - Constructor for class gov.sandia.cognition.learning.algorithm.tree.RegressionTree: Creates a new instance of RegressionTree.
RegressionTree(DecisionTreeNode<InputType, Double>) - Constructor for class gov.sandia.cognition.learning.algorithm.tree.RegressionTree: Creates a new instance of RegressionTree.
RegressionTreeLearner<InputType> - Class in gov.sandia.cognition.learning.algorithm.tree: The RegressionTreeLearner class implements a learning algorithm for a regression tree that makes use of a decider learner and a regression learner.
RegressionTreeLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeLearner: Creates a new instance of RegressionTreeLearner
RegressionTreeLearner(DeciderLearner<? super InputType, Double, ?, ?>) - Constructor for class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeLearner: Creates a new instance of CategorizationTreeLearner with a mean node learner
RegressionTreeLearner(DeciderLearner<? super InputType, Double, ?, ?>, BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, Double>>, ? extends Evaluator<? super InputType, Double>>) - Constructor for class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeLearner: Creates a new instance of CategorizationTreeLearner.
RegressionTreeLearner(DeciderLearner<? super InputType, Double, ?, ?>, BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, Double>>, ? extends Evaluator<? super InputType, Double>>, int, int) - Constructor for class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeLearner: Creates a new instance of CategorizationTreeLearner.
RegressionTreeNode<InputType,InteriorType> - Class in gov.sandia.cognition.learning.algorithm.tree: The RegressionTreeNode implements a DecisionTreeNode for a tree that does regression.
RegressionTreeNode() - Constructor for class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeNode: Creates a new instance of RegressionTreeNode.
RegressionTreeNode(DecisionTreeNode<InputType, Double>, double) - Constructor for class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeNode: Creates a new instance of RegressionTreeNode.
RegressionTreeNode(DecisionTreeNode<InputType, Double>, Categorizer<? super InputType, ? extends InteriorType>, double) - Constructor for class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeNode: Creates a new instance of RegressionTreeNode.
RegressionTreeNode(DecisionTreeNode<InputType, Double>, Evaluator<? super InputType, Double>, double) - Constructor for class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeNode: Creates a new instance of RegressionTreeNode.
RegressionTreeNode(DecisionTreeNode<InputType, Double>, Evaluator<? super InputType, Double>, double, Object) - Constructor for class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeNode: Creates a new instance of RegressionTreeNode.
RegressionTreeNode(DecisionTreeNode<InputType, Double>, Categorizer<? super InputType, ? extends InteriorType>, Evaluator<? super InputType, Double>, double, Object) - Constructor for class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeNode: Creates a new instance of RegressionTreeNode.
Regressor<InputType> - Interface in gov.sandia.cognition.learning.function.regression: Defines the functionality of a regression function, which is the model created by regression algorithms.
regularization - Variable in class gov.sandia.cognition.learning.algorithm.regression.AbstractLogisticRegression: L2 ridge regularization term, must be nonnegative, a value of zero is equivalent to unregularized regression.
regularizationWeight - Variable in class gov.sandia.cognition.learning.algorithm.svm.PrimalEstimatedSubGradient: The weight assigned to the regularization term in the algorithm, which is often represented as lambda.
regularizedIncompleteBetaFunction(double, double, double) - Static method in class gov.sandia.cognition.math.MathUtil: Computes the regularized incomplete Beta function.
reinitializeWeights() - Method in class gov.sandia.cognition.learning.function.vector.ThreeLayerFeedforwardNeuralNetwork: Reinitializes the neural network parameters based on its current setup.
RejectionSampling<ObservationType,ParameterType> - Class in gov.sandia.cognition.statistics.bayesian: Rejection sampling is a method of inferring hidden parameters by using an easy-to-sample-from distribution (times a scale factor) that envelopes another distribution that is difficult to sample from.
RejectionSampling() - Constructor for class gov.sandia.cognition.statistics.bayesian.RejectionSampling: Creates a new instance of RejectionSampling
RejectionSampling.DefaultUpdater<ObservationType,ParameterType> - Class in gov.sandia.cognition.statistics.bayesian: Default ImportanceSampling Updater that uses a BayesianParameter to compute the quantities of interest.
RejectionSampling.ScalarEstimator<ObservationType> - Class in gov.sandia.cognition.statistics.bayesian: Routine for estimating the minimum scalar needed to envelop the conjunctive distribution.
RejectionSampling.ScalarEstimator.MinimizerFunction - Class in gov.sandia.cognition.statistics.bayesian: Minimization function that measures the difference between the logarithm of the sampler function minus the logarithm of the conjunctive distribution.
RejectionSampling.Updater<ObservationType,ParameterType> - Interface in gov.sandia.cognition.statistics.bayesian: Updater for ImportanceSampling
RelationNetwork<ObjectType,RelationType> - Interface in gov.sandia.cognition.text.relation: An interface for a network of relations between objects.
relationsFrom(ObjectType) - Method in interface gov.sandia.cognition.text.relation.RelationNetwork: Gets all of the relations where the given object is the source.
relationsFrom(IndexedTerm) - Method in class gov.sandia.cognition.text.term.relation.MatrixBasedTermSimilarityNetwork
relationsOf(ObjectType) - Method in interface gov.sandia.cognition.text.relation.RelationNetwork: Gets all of the relations that involve the given object.
relationsOf(IndexedTerm) - Method in class gov.sandia.cognition.text.term.relation.MatrixBasedTermSimilarityNetwork
relationsTo(ObjectType) - Method in interface gov.sandia.cognition.text.relation.RelationNetwork: Gets all of the relations where the given object is the target.
relationsTo(IndexedTerm) - Method in class gov.sandia.cognition.text.term.relation.MatrixBasedTermSimilarityNetwork
RelaxedOnlineMaximumMarginAlgorithm - Class in gov.sandia.cognition.learning.algorithm.perceptron: An implementation of the Relaxed Online Maximum Margin Algorithm (ROMMA).
RelaxedOnlineMaximumMarginAlgorithm() - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.RelaxedOnlineMaximumMarginAlgorithm: Creates a new RelaxedOnlineMaximumMarginAlgorithm.
RelaxedOnlineMaximumMarginAlgorithm(VectorFactory<?>) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.RelaxedOnlineMaximumMarginAlgorithm: Creates a new RelaxedOnlineMaximumMarginAlgorithm with the given vector factory.
remove() - Method in class gov.sandia.cognition.collection.AbstractLogNumberMap.SimpleIterator
remove() - Method in class gov.sandia.cognition.collection.AbstractMutableDoubleMap.SimpleIterator
remove(Object) - Method in class gov.sandia.cognition.collection.DynamicArrayMap: Runs in O(1).
remove(int) - Method in class gov.sandia.cognition.collection.DynamicArrayMap: Removes the value for the given key from the mapping.
remove() - Method in class gov.sandia.cognition.collection.FiniteCapacityBuffer.InternalIterator
remove(Object) - Method in class gov.sandia.cognition.collection.FiniteCapacityBuffer
remove(int) - Method in class gov.sandia.cognition.collection.FiniteCapacityBuffer
remove() - Method in class gov.sandia.cognition.collection.MultiIterator
remove(int) - Method in class gov.sandia.cognition.learning.function.categorization.DefaultKernelBinaryCategorizer: Removes the i-th example.
remove() - Method in class gov.sandia.cognition.math.Combinations.AbstractCombinationsIterator
remove() - Method in class gov.sandia.cognition.math.geometry.KDTree.InOrderKDTreeIterator
remove() - Method in class gov.sandia.cognition.math.geometry.KDTree.Neighborhood.NeighborhoodIterator
remove() - Method in class gov.sandia.cognition.math.matrix.custom.VectorIterator: Throws UnsupportedOperationException because you can't remove elements from a DenseVector.
remove() - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector.SimpleIterator
remove() - Method in class gov.sandia.cognition.math.matrix.MatrixUnionIterator
remove() - Method in class gov.sandia.cognition.math.matrix.mtj.MatrixUnionIteratorMTJ
remove() - Method in class gov.sandia.cognition.math.matrix.VectorUnionIterator
remove() - Method in class gov.sandia.cognition.statistics.distribution.MultinomialDistribution.Domain.MultinomialIterator
remove(double) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.SufficientStatistic: Removes a value to the sufficient statistics for the Gaussian.
remove(Vectorizable) - Method in class gov.sandia.cognition.text.term.vector.AbstractVectorSpaceModel
remove(Vectorizable) - Method in interface gov.sandia.cognition.text.term.vector.VectorSpaceModel: Removes the document from the model.
remove(Vector) - Method in interface gov.sandia.cognition.text.term.vector.VectorSpaceModel: Removes the document from the model.
remove(Vector) - Method in class gov.sandia.cognition.text.term.vector.weighter.global.AbstractEntropyBasedGlobalTermWeighter
remove(Vector) - Method in class gov.sandia.cognition.text.term.vector.weighter.global.AbstractFrequencyBasedGlobalTermWeighter
remove(Vector) - Method in class gov.sandia.cognition.text.term.vector.weighter.global.DominanceGlobalTermWeighter
remove(Vector) - Method in class gov.sandia.cognition.text.term.vector.weighter.global.EntropyGlobalTermWeighter
remove(Vector) - Method in class gov.sandia.cognition.text.term.vector.weighter.global.InverseDocumentFrequencyGlobalTermWeighter
removeAll(Iterable<? extends Vectorizable>) - Method in class gov.sandia.cognition.text.term.vector.AbstractVectorSpaceModel
removeAll(Iterable<? extends Vectorizable>) - Method in interface gov.sandia.cognition.text.term.vector.VectorSpaceModel: Removes all of the given documents from the model.
removeCluster(int) - Method in class gov.sandia.cognition.learning.algorithm.clustering.KMeansClustererWithRemoval: Removes the cluster at the specified index, and does the internal bookkeeping as well
removeClusterMember(DefaultCluster<DataType>, DataType) - Method in class gov.sandia.cognition.learning.algorithm.clustering.cluster.DefaultIncrementalClusterCreator
removeClusterMember(ClusterType, DataType) - Method in interface gov.sandia.cognition.learning.algorithm.clustering.cluster.IncrementalClusterCreator: Removes a member from the given cluster.
removeClusterMember(NormalizedCentroidCluster<Vectorizable>, Vectorizable) - Method in class gov.sandia.cognition.learning.algorithm.clustering.cluster.NormalizedCentroidClusterCreator
removeClusterMember(CentroidCluster<Vector>, Vector) - Method in class gov.sandia.cognition.learning.algorithm.clustering.cluster.VectorMeanCentroidClusterCreator
removeCognitiveModelListener(CognitiveModelListener) - Method in class gov.sandia.cognition.framework.AbstractCognitiveModel: Removes a CognitiveModelListener from this model.
removeCognitiveModelListener(CognitiveModelListener) - Method in interface gov.sandia.cognition.framework.CognitiveModel: Removes a CognitiveModelListener from this model.
removeCogxel(SemanticIdentifier) - Method in interface gov.sandia.cognition.framework.CogxelState: Removes a Cogxel from the state, if it exists.
removeCogxel(Cogxel) - Method in interface gov.sandia.cognition.framework.CogxelState: Removes a Cogxel from the CogxelState, if it exists.
removeCogxel(SemanticIdentifier) - Method in class gov.sandia.cognition.framework.lite.CogxelStateLite: Removes a Cogxel from the state, if it exists.
removeCogxel(Cogxel) - Method in class gov.sandia.cognition.framework.lite.CogxelStateLite: Removes a Cogxel from the CogxelState, if it exists.
removeElement(Iterable<DataType>, int) - Static method in class gov.sandia.cognition.collection.CollectionUtil: Removes and returns the indexed value into the Iterable.
removeExtension(String) - Static method in class gov.sandia.cognition.io.FileUtil: Takes a file name and returns the name of the file without the extension on it.
removeField(String) - Method in class gov.sandia.cognition.text.document.AbstractDocument: Removes a field of the given name from the document.
removeField(String) - Method in class gov.sandia.cognition.text.document.DefaultDocument
removeIterativeAlgorithmListener(IterativeAlgorithmListener) - Method in class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm
removeIterativeAlgorithmListener(IterativeAlgorithmListener) - Method in interface gov.sandia.cognition.algorithm.IterativeAlgorithm: Removes a listener for the iterations of the algorithm.
removeIterativeAlgorithmListener(IterativeAlgorithmListener) - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.IterativeMatrixSolver
removeListener(ProcessLauncherListener) - Method in class gov.sandia.cognition.io.ProcessLauncher: Removes the given object from the event queue
removeListener(LearningExperimentListener) - Method in class gov.sandia.cognition.learning.experiment.AbstractLearningExperiment
removeListener(LearningExperimentListener) - Method in interface gov.sandia.cognition.learning.experiment.LearningExperiment: Removes the given listener from this object.
removeNode(SemanticLabel) - Method in class gov.sandia.cognition.framework.DefaultSemanticNetwork: Removes a node from the semantic network including all of the incoming and outgoing links of the node.
removeNode(SemanticLabel) - Method in interface gov.sandia.cognition.framework.lite.MutablePatternRecognizerLite: Removes a node and all links associated with that node from the pattern recognizer.
removeNode(SemanticLabel) - Method in class gov.sandia.cognition.framework.lite.MutableSemanticMemoryLite: Removes a node and all links associated with that node from the semantic memory.
removeNode(SemanticLabel) - Method in class gov.sandia.cognition.framework.lite.SimplePatternRecognizer: Removes a node and all links associated with that node from the pattern recognizer.
RemoveOldestKernelPerceptron<InputType> - Class in gov.sandia.cognition.learning.algorithm.perceptron.kernel: A budget kernel Perceptron that always removes the oldest item.
RemoveOldestKernelPerceptron() - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.kernel.RemoveOldestKernelPerceptron: Creates a new RemoveOldestKernelPerceptron with a null kernel and default budget.
RemoveOldestKernelPerceptron(Kernel<? super InputType>, int) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.kernel.RemoveOldestKernelPerceptron: Creates a new RemoveOldestKernelPerceptron with the given parameters.
removeParameterAdapter(ParameterAdapter<? super ObjectType, ? super DataType>) - Method in interface gov.sandia.cognition.learning.parameter.ParameterAdaptable: Removes the given parameter adapter from this object.
removeParameterAdapter(ParameterAdapter<? super LearnerType, ? super DataType>) - Method in class gov.sandia.cognition.learning.parameter.ParameterAdaptableBatchLearnerWrapper
removeSemanticIdentifierMapListener(SemanticIdentifierMapListener) - Method in class gov.sandia.cognition.framework.DefaultSemanticIdentifierMap: Removes a listener from this semantic identifier map.
removeSemanticIdentifierMapListener(SemanticIdentifierMapListener) - Method in interface gov.sandia.cognition.framework.SemanticIdentifierMap: Removes a listener from this semantic identifier map.
removeUnusedClusters() - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.Sample: Removes the unused clusters from the Sample.
reorder(ArrayList<DataType>, Random) - Static method in class gov.sandia.cognition.math.Permutation: Reorders the elements in a given array list to be a permuted ordering of elements in the collection.
reproduce(Collection<EvaluatedGenome<GenomeType>>) - Method in class gov.sandia.cognition.learning.algorithm.genetic.reproducer.CrossoverReproducer: Produces a new population of genomes from the supplied population using crossover.
reproduce(Collection<EvaluatedGenome<GenomeType>>) - Method in class gov.sandia.cognition.learning.algorithm.genetic.reproducer.MultiReproducer: Applies the supplied reproducers to the population of genomes.
reproduce(Collection<EvaluatedGenome<GenomeType>>) - Method in class gov.sandia.cognition.learning.algorithm.genetic.reproducer.MutationReproducer: Produces a new mutated population based on the supplied population.
reproduce(Collection<EvaluatedGenome<GenomeType>>) - Method in interface gov.sandia.cognition.learning.algorithm.genetic.reproducer.Reproducer: Applies a reproduction algorithm to the given collection of genomes and their associated score from the cost function.
reproduce(Collection<EvaluatedGenome<GenomeType>>) - Method in class gov.sandia.cognition.learning.algorithm.genetic.selector.AbstractSelector: Applies the selection algorithm to the given collection of genomes and their associated score from the cost function.
Reproducer<GenomeType> - Interface in gov.sandia.cognition.learning.algorithm.genetic.reproducer: The Reproducer interface defines the functionality of a reproduction algorithm in a genetic algorithm.
requestedRank - Variable in class gov.sandia.cognition.text.topic.LatentSemanticAnalysis: The rank requested for the result LSA.
requestedRank - Variable in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis: The requested rank to reduce the dimensionality to.
resample(double) - Method in interface gov.sandia.cognition.data.temporal.BatchTemporalDataSource: Resamples the data at the given sample period using zero-order hold.
resetCognitiveState() - Method in interface gov.sandia.cognition.framework.CognitiveModel: Resets the current cognitive state.
resetCognitiveState() - Method in class gov.sandia.cognition.framework.lite.AbstractCognitiveModelLite: Resets the current state of the model.
resetLines() - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.AbstractEnvelope: Resets the line segments
resetState() - Method in class gov.sandia.cognition.evaluator.AbstractStatefulEvaluator
resetState() - Method in interface gov.sandia.cognition.evaluator.StatefulEvaluator: Resets the state of the evaluator to a default state.
resize(int) - Method in class gov.sandia.cognition.collection.DoubleArrayList: Replace the internal storage with a new buffer.
resize(int) - Method in class gov.sandia.cognition.collection.IntArrayList: Replace the internal storage with a new buffer.
responsibilities - Variable in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation: The array of example-example responsibilities.
result - Variable in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner: The current factorization machine output learned by the algorithm.
result - Variable in class gov.sandia.cognition.learning.algorithm.hmm.AbstractBaumWelchAlgorithm: Result of the Baum-Welch Algorithm
result - Variable in class gov.sandia.cognition.learning.algorithm.minimization.AbstractAnytimeFunctionMinimizer: Resulting minimum input-output pair
result - Variable in class gov.sandia.cognition.learning.algorithm.perceptron.BatchMultiPerceptron: The linear categorizer created by the algorithm.
Result() - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Forgetron.Result: Creates a new Result with a null kernel.
Result(Kernel<? super InputType>) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Forgetron.Result: Creates a new Result with the given kernel.
result - Variable in class gov.sandia.cognition.learning.algorithm.regression.AbstractLogisticRegression: Return value from the algorithm
result - Variable in class gov.sandia.cognition.learning.algorithm.root.RootFinderNewtonsMethod: Resulting estimated location of the root.
result - Variable in class gov.sandia.cognition.learning.algorithm.svm.PrimalEstimatedSubGradient: The categorizer learned as a result of the algorithm.
result - Variable in class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation: The result categorizer.
Result(ConfidenceStatistic, DefaultPair<SummaryType, SummaryType>) - Constructor for class gov.sandia.cognition.learning.experiment.LearnerComparisonExperiment.Result: Creates a new instance of Result.
Result() - Constructor for class gov.sandia.cognition.text.algorithm.ValenceSpreader.Result
result - Variable in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler: The result probabilities.
Result(int, int, int, int) - Constructor for class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler.Result: Creates a new Result.
result - Variable in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis: The result being produced by the algorithm.
Result(int, ProbabilisticLatentSemanticAnalysis.LatentData[]) - Constructor for class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis.Result: Creates a new probabilistic latent semantic analysis transform.
reverse(boolean[]) - Static method in class gov.sandia.cognition.collection.ArrayUtil: Reverses the ordering of elements in an array.
reverse(int[]) - Static method in class gov.sandia.cognition.collection.ArrayUtil: Reverses the ordering of elements in an array.
reverse(long[]) - Static method in class gov.sandia.cognition.collection.ArrayUtil: Reverses the ordering of elements in an array.
reverse(double[]) - Static method in class gov.sandia.cognition.collection.ArrayUtil: Reverses the ordering of elements in an array.
reverse(Object[]) - Static method in class gov.sandia.cognition.collection.ArrayUtil: Reverses the ordering of elements in an array.
reverse - Variable in class gov.sandia.cognition.data.convert.AbstractReverseCachedDataConverter: A cached value of the reverse converter.
reverse() - Method in class gov.sandia.cognition.data.convert.AbstractReverseCachedDataConverter: Gets the data converter that performs the reverse conversion.
reverse() - Method in class gov.sandia.cognition.data.convert.IdentityDataConverter: The reverse converter is this converter, since it is an identity converter.
Reverse() - Constructor for class gov.sandia.cognition.data.convert.number.DefaultBooleanToNumberConverter.Reverse: Creates a new reverse converter for the DefaultBooleanToNumberConverter.
reverse() - Method in class gov.sandia.cognition.data.convert.number.DefaultBooleanToNumberConverter.Reverse: Reverses the converter, which is the original converter.
reverse() - Method in interface gov.sandia.cognition.data.convert.ReversibleDataConverter: Gets the data converter that performs the reverse conversion.
reverse - Variable in class gov.sandia.cognition.evaluator.ForwardReverseEvaluatorPair: The reverse evaluator from output type to input type.
reverse() - Method in class gov.sandia.cognition.evaluator.ForwardReverseEvaluatorPair
reverse() - Method in class gov.sandia.cognition.evaluator.IdentityEvaluator
reverse() - Method in interface gov.sandia.cognition.evaluator.ReversibleEvaluator: Gets the data converter that performs the reverse conversion.
ReversibleDataConverter<InputType,OutputType> - Interface in gov.sandia.cognition.data.convert: Represents a DataConverter whose conversion can be reversed.
ReversibleEvaluator<InputType,OutputType,ReverseType extends Evaluator<? super OutputType,? extends InputType>> - Interface in gov.sandia.cognition.evaluator: Represents a Evaluator whose evaluation can be reversed.
rhs - Variable in class gov.sandia.cognition.learning.algorithm.minimization.matrix.IterativeMatrixSolver: The right-hand-side vector (b).
rightChild - Variable in class gov.sandia.cognition.math.geometry.KDTree: Right child of this subtree.
rightIterator - Variable in class gov.sandia.cognition.math.geometry.KDTree.InOrderKDTreeIterator: Iterator for the right subtree.
Ring<RingType extends Ring<RingType>> - Interface in gov.sandia.cognition.math: Defines something similar to a mathematical ring.
RingAccumulator<RingType extends Ring<RingType>> - Class in gov.sandia.cognition.math: The RingAccumulator class implements a simple object that is used to accumulate objects that implement the Ring interface.
RingAccumulator() - Constructor for class gov.sandia.cognition.math.RingAccumulator: Creates a new instance of RingAccumulator
RingAccumulator(Iterable<? extends RingType>) - Constructor for class gov.sandia.cognition.math.RingAccumulator: Creates a new instance of RingAccumulator, adding all of the given items to start with.
RingAverager<RingType extends Ring<RingType>> - Class in gov.sandia.cognition.math: A type of Averager for Rings (Matrices, Vectors, ComplexNumbers).
RingAverager() - Constructor for class gov.sandia.cognition.math.RingAverager: Creates a new instance of RingAverager
root - Variable in class gov.sandia.cognition.math.geometry.Quadtree: The root node of the tree.
RootBracketer - Interface in gov.sandia.cognition.learning.algorithm.root: Defines the functionality of a algorithm that finds a bracket of a root from an initial guess.
RootBracketExpander - Class in gov.sandia.cognition.learning.algorithm.root: The root-bracketing expansion algorithm.
RootBracketExpander() - Constructor for class gov.sandia.cognition.learning.algorithm.root.RootBracketExpander: Creates a new instance of RootBracketExpander
RootFinder - Interface in gov.sandia.cognition.learning.algorithm.root: Defines the functionality of a root-finding algorithm.
RootFinderBisectionMethod - Class in gov.sandia.cognition.learning.algorithm.root: Bisection algorithm for root finding.
RootFinderBisectionMethod() - Constructor for class gov.sandia.cognition.learning.algorithm.root.RootFinderBisectionMethod: Creates a new instance of RootFinderBisectionMethod
RootFinderFalsePositionMethod - Class in gov.sandia.cognition.learning.algorithm.root: The false-position algorithm for root finding.
RootFinderFalsePositionMethod() - Constructor for class gov.sandia.cognition.learning.algorithm.root.RootFinderFalsePositionMethod: Creates a new instance of RootFinderFalsePositionMethod
RootFinderNewtonsMethod - Class in gov.sandia.cognition.learning.algorithm.root: Newton's method, sometimes called Newton-Raphson method, uses first-order derivative information to iteratively locate a root.
RootFinderNewtonsMethod() - Constructor for class gov.sandia.cognition.learning.algorithm.root.RootFinderNewtonsMethod: Creates a new instance of RootFinderNewtonsMethod
RootFinderRiddersMethod - Class in gov.sandia.cognition.learning.algorithm.root: The root-finding algorithm due to Ridders.
RootFinderRiddersMethod() - Constructor for class gov.sandia.cognition.learning.algorithm.root.RootFinderRiddersMethod: Creates a new instance of RootFinderRiddersMethod
RootFinderSecantMethod - Class in gov.sandia.cognition.learning.algorithm.root: The secant algorithm for root finding.
RootFinderSecantMethod() - Constructor for class gov.sandia.cognition.learning.algorithm.root.RootFinderSecantMethod: Creates a new instance of RootFinderSecantMethod
RootMeanSquaredErrorEvaluator<InputType> - Class in gov.sandia.cognition.learning.performance: The RootMeanSquaredErrorEvaluator class implements a method for computing the performance of a supervised learner for a scalar function by the root mean squared error (RMSE or RSE) between the target and estimated outputs.
RootMeanSquaredErrorEvaluator() - Constructor for class gov.sandia.cognition.learning.performance.RootMeanSquaredErrorEvaluator: Creates a new RootMeanSquaredErrorEvaluator.
rootNode - Variable in class gov.sandia.cognition.learning.algorithm.tree.DecisionTree: The root node of the decision tree.
roots() - Method in interface gov.sandia.cognition.learning.function.scalar.PolynomialFunction.ClosedForm: Finds the real-valued roots (zero crossings) of the polynomial
roots() - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Cubic
roots() - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Linear
roots() - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Quadratic: Finds the roots (zero-crossings) of the quadratic, which has at most two, but possibly one or zero
roots(double, double, double) - Static method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Quadratic: Finds the roots of the quadratic equation using the quadratic formula.
rotate(Vector) - Method in interface gov.sandia.cognition.math.matrix.Quaternion: Rotates the given vector by this quaternion by performing a multiplication with this quaternion's rotation matrix: M * v.
run() - Method in class gov.sandia.cognition.io.ProcessLauncher
runExperiment(Collection<PartitionedDataset<FoldDataType>>) - Method in class gov.sandia.cognition.learning.experiment.AbstractValidationFoldExperiment: Runs the underlying validation fold experiment using the given data.
runExperiment(PartitionedDataset<? extends InputDataType>) - Method in class gov.sandia.cognition.learning.experiment.LearnerRepeatExperiment: Runs the experiment.
runExperiment(Collection<PartitionedDataset<FoldDataType>>) - Method in class gov.sandia.cognition.learning.experiment.ParallelLearnerValidationExperiment
runTrial(PartitionedDataset<FoldDataType>) - Method in class gov.sandia.cognition.learning.experiment.AbstractValidationFoldExperiment: Runs a single trial of the experiment on one fold of the data.
runTrial(PartitionedDataset<FoldDataType>) - Method in class gov.sandia.cognition.learning.experiment.LearnerComparisonExperiment: Runs a single trial of the experiment on one fold of the data.
runTrial(PartitionedDataset<? extends InputDataType>) - Method in class gov.sandia.cognition.learning.experiment.LearnerRepeatExperiment: Runs one trial in the experiment.
runTrial(PartitionedDataset<FoldDataType>) - Method in class gov.sandia.cognition.learning.experiment.LearnerValidationExperiment

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