- dampingFactor - Variable in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation
-
The damping factor (lambda).
- data - Variable in class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner
-
The data to learn from.
- data - Variable in class gov.sandia.cognition.learning.algorithm.clustering.ParallelizedKMeansClusterer.CreateClustersFromAssignments
-
Data set to use for the task
- data - Variable in class gov.sandia.cognition.learning.algorithm.hmm.ParallelHiddenMarkovModel.LogLikelihoodTask
-
Data to compute the log-likelihood of
- data - Variable in class gov.sandia.cognition.learning.algorithm.nearest.NearestNeighborExhaustive
-
The data that nearest-neighbor is performed over.
- data - Variable in class gov.sandia.cognition.learning.algorithm.pca.KernelPrincipalComponentsAnalysis.Function
-
The data that the KPCA was performed over.
- DataConverter<InputType,OutputType> - Interface in gov.sandia.cognition.data.convert
-
Defines an object used to convert data from one type to another.
- DataCountTreeSetBinnedMapHistogram<ValueType extends java.lang.Comparable<? super ValueType>> - Class in gov.sandia.cognition.statistics.distribution
-
The DataCountTreeSetBinnedMapHistogram class extends a
DefaultDataDistribution by mapping values to user defined bins
using a TreeSetBinner.
- DataCountTreeSetBinnedMapHistogram(TreeSetBinner<ValueType>) - Constructor for class gov.sandia.cognition.statistics.distribution.DataCountTreeSetBinnedMapHistogram
-
Creates a new DataCountTreeBinnedMapHistogram using the provided
TreeSetBinner.
- DataCountTreeSetBinnedMapHistogram(Collection<? extends ValueType>) - Constructor for class gov.sandia.cognition.statistics.distribution.DataCountTreeSetBinnedMapHistogram
-
Creates a new DataCountTreeBinnedMapHistogram using
the provided list of bin boundaries.
- DataDistribution<DataType> - Interface in gov.sandia.cognition.statistics
-
A distribution of data from which we can sample and perform Ring operations.
- DataDistribution.PMF<KeyType> - Interface in gov.sandia.cognition.statistics
-
Interface for the probability mass function (PMF) of a data distribution.
- dataFullEstimates - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner
-
The running estimate of the ensemble for each example.
- dataInBag - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractBaggingLearner
-
An indicator of whether or not the data is in the current bag.
- dataInBag - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner
-
A counter for each example indicating how many times it exists in the
current bag.
- dataIndices - Variable in class gov.sandia.cognition.learning.algorithm.clustering.MiniBatchKMeansClusterer
-
Indices of the data.
- dataList - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractBaggingLearner
-
The data stored for efficient random access.
- dataList - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner
-
The data represented as an array list.
- dataList - Variable in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachineAlternatingLeastSquares
-
The data in the form that it can be accessed in O(1) as a list.
- dataList - Variable in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachineStochasticGradient
-
The input data represented as a list for fast access.
- dataList - Variable in class gov.sandia.cognition.learning.algorithm.svm.PrimalEstimatedSubGradient
-
The data represented as a list.
- dataOutOfBagEstimates - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner
-
The running estimate of the ensemble for each example where an ensemble
member can only vote on elements that were not in the bag used to train
it.
- DataPartitioner<DataType> - Interface in gov.sandia.cognition.learning.data
-
The DataPartitioner interface defines the functionality of an object
that can create a PartitionedDataset from a collection of data.
- dataPerCategory - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.CategoryBalancedBaggingLearner
-
The mapping of categories to indices of examples belonging to the category.
- DataPoint(ScalarThresholdBinaryCategorizer, DefaultBinaryConfusionMatrix) - Constructor for class gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic.DataPoint
-
Creates a new instance of DataPoint
- dataSampleSize - Variable in class gov.sandia.cognition.learning.algorithm.svm.PrimalEstimatedSubGradient
-
The minimum of the sample size and the data size.
- DatasetUtil - Class in gov.sandia.cognition.learning.data
-
Static class containing utility methods for handling Collections of data
in the learning package.
- DatasetUtil() - Constructor for class gov.sandia.cognition.learning.data.DatasetUtil
-
- dataSize - Variable in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachineAlternatingLeastSquares
-
The size of the data.
- dataSize - Variable in class gov.sandia.cognition.learning.algorithm.svm.PrimalEstimatedSubGradient
-
The size of the data in the training set.
- DataToVectorEncoder<InputType> - Interface in gov.sandia.cognition.data.convert.vector
-
Defines a converter that can be used to encode data into a Vector.
- date - Variable in class gov.sandia.cognition.text.document.DefaultDateField
-
The date stored in the field.
- DAY - Static variable in class gov.sandia.cognition.time.DefaultDuration
-
A day in duration.
- DBSCANClusterer<DataType extends Vectorizable,ClusterType extends Cluster<DataType>> - Class in gov.sandia.cognition.learning.algorithm.clustering
-
The DBSCAN algorithm requires three parameters: a distance
metric, a value for neighborhood radius, and a value for the minimum number
of surrounding neighbors for a point to be considered non-noise.
- DBSCANClusterer(Semimetric<? super DataType>, ClusterCreator<ClusterType, DataType>) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.DBSCANClusterer
-
Creates a new instance of DBSCANClusterer.
- DBSCANClusterer(double, int, Semimetric<? super DataType>, ClusterCreator<ClusterType, DataType>) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.DBSCANClusterer
-
Creates a new instance of AffinityPropagation.
- decider - Variable in class gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeNode
-
The decider used to make a decision as to which child use.
- deciderLearner - Variable in class gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeLearner
-
The learning algorithm for the decision function.
- DeciderLearner<InputType,OutputType,CategoryType,DeciderType extends Categorizer<? super InputType,? extends CategoryType>> - Interface in gov.sandia.cognition.learning.algorithm.tree
-
The DeciderLearner interface defines the functionality of a learner
that can be used to learn a decision function inside a decision tree.
- DecisionTree<InputType,OutputType> - Class in gov.sandia.cognition.learning.algorithm.tree
-
The DecisionTree class implements a standard decision tree that is
made up of DecisionTreeNode objects.
- DecisionTree() - Constructor for class gov.sandia.cognition.learning.algorithm.tree.DecisionTree
-
Creates a new instance of DecisionTree.
- DecisionTree(DecisionTreeNode<InputType, OutputType>) - Constructor for class gov.sandia.cognition.learning.algorithm.tree.DecisionTree
-
Creates a new instance of DecisionTree.
- DecisionTreeNode<InputType,OutputType> - Interface in gov.sandia.cognition.learning.algorithm.tree
-
The DecisionTreeNode interface defines the functionality of a node
in a decision tree.
- decompress() - Method in class gov.sandia.cognition.math.matrix.custom.SparseMatrix
-
This method is provided so that the calling programmer can explicitly
declare when a matrix should be decompressed from the compressed Yale
format.
- decompress() - Method in class gov.sandia.cognition.math.matrix.custom.SparseVector
-
The compressed representation should allow for quicker mathematical
operations, but does not permit editing the values in the vector.
- decoupleVectorDataset(Collection<? extends Vector>) - Static method in class gov.sandia.cognition.learning.data.DatasetUtil
-
Takes a dataset of M-dimensional Vectors and turns it into M
datasets of Doubles
- decoupleVectorPairDataset(Collection<? extends InputOutputPair<? extends Vector, ? extends Vector>>) - Static method in class gov.sandia.cognition.learning.data.DatasetUtil
-
Takes a set of equal-dimension Vector-Vector InputOutputPairs and
turns them into a collection of Double-Double InputOutputPairs.
- decrement(KeyType) - Method in class gov.sandia.cognition.collection.AbstractScalarMap
-
- decrement(KeyType, double) - Method in class gov.sandia.cognition.collection.AbstractScalarMap
-
- decrement(KeyType) - Method in interface gov.sandia.cognition.collection.ScalarMap
-
Decrements the value associated with a given key by 1.0.
- decrement(KeyType, double) - Method in interface gov.sandia.cognition.collection.ScalarMap
-
Decrements the value associated with the given key by the given amount.
- decrement(int, int) - Method in class gov.sandia.cognition.math.matrix.AbstractMatrix
-
- decrement(int, int, double) - Method in class gov.sandia.cognition.math.matrix.AbstractMatrix
-
- decrement(int) - Method in class gov.sandia.cognition.math.matrix.AbstractVector
-
- decrement(int, double) - Method in class gov.sandia.cognition.math.matrix.AbstractVector
-
- decrement(int, int) - Method in interface gov.sandia.cognition.math.matrix.Matrix
-
Decrements the value at the given position by 1.
- decrement(int, int, double) - Method in interface gov.sandia.cognition.math.matrix.Matrix
-
Decrements the value at the given position by the given value.
- decrement(int) - Method in interface gov.sandia.cognition.math.matrix.Vector
-
Decrements the value of the given index by 1.
- decrement(int, double) - Method in interface gov.sandia.cognition.math.matrix.Vector
-
Decrements the value of the given index by the given value.
- decrement(ValueType, double) - Method in class gov.sandia.cognition.statistics.distribution.DataCountTreeSetBinnedMapHistogram
-
- decrementAll(Iterable<? extends KeyType>) - Method in class gov.sandia.cognition.collection.AbstractScalarMap
-
- decrementAll(ScalarMap<? extends KeyType>) - Method in class gov.sandia.cognition.collection.AbstractScalarMap
-
- decrementAll(Iterable<? extends KeyType>) - Method in interface gov.sandia.cognition.collection.ScalarMap
-
Decrements the values associated all of the given keys by 1.0.
- decrementAll(ScalarMap<? extends KeyType>) - Method in interface gov.sandia.cognition.collection.ScalarMap
-
Decrements all the keys in this map by the values in the other one.
- deepCopy(DirectedNodeEdgeGraph<NodeType>) - Static method in class gov.sandia.cognition.graph.GraphUtil
-
Makes a deep copy of the input graph, matching implementation of the
interface.
- deepCopy(T) - Static method in class gov.sandia.cognition.util.ObjectUtil
-
Performs a deep copy of a given object.
- DEFALUT_SCALE - Static variable in class gov.sandia.cognition.statistics.distribution.ParetoDistribution
-
Default scale, 1.0.
- DEFAULT_AGGRESSIVENESS - Static variable in class gov.sandia.cognition.learning.algorithm.perceptron.OnlinePassiveAggressivePerceptron.AbstractSoftMargin
-
The default aggressiveness is 0.001.
- DEFAULT_ALPHA - Static variable in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel
-
Default concentration parameter of the Dirichlet Process, 1.0.
- DEFAULT_ALPHA - Static variable in class gov.sandia.cognition.statistics.distribution.BetaDistribution
-
Default alpha, 2.0.
- DEFAULT_ALPHA - Static variable in class gov.sandia.cognition.statistics.distribution.ChineseRestaurantProcess
-
Default concentration parameter, 1.0.
- DEFAULT_ALPHA - Static variable in class gov.sandia.cognition.statistics.method.MultipleComparisonExperiment
-
Default alpha (p-value threshold), 0.05.
- DEFAULT_ALPHA - Static variable in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler
-
The default value of alpha is 5.0.
- DEFAULT_BETA - Static variable in class gov.sandia.cognition.statistics.distribution.BetaDistribution
-
Default beta, 2.0.
- DEFAULT_BETA - Static variable in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler
-
The default value of beta is 0.5.
- DEFAULT_BIAS - Static variable in class gov.sandia.cognition.learning.algorithm.ensemble.AdditiveEnsemble
-
The default bias is 0.0.
- DEFAULT_BIAS - Static variable in class gov.sandia.cognition.learning.algorithm.ensemble.WeightedAdditiveEnsemble
-
The default bias is 0.0.
- DEFAULT_BIAS - Static variable in class gov.sandia.cognition.learning.function.categorization.KernelBinaryCategorizer
-
The default value for the bias is 0.0.
- DEFAULT_BIAS - Static variable in class gov.sandia.cognition.learning.function.categorization.LinearBinaryCategorizer
-
The default bias is 0.0.
- DEFAULT_BIAS - Static variable in class gov.sandia.cognition.learning.function.scalar.KernelScalarFunction
-
The default value for the bias is 0.0.
- DEFAULT_BIAS - Static variable in class gov.sandia.cognition.learning.function.scalar.LinearVectorScalarFunction
-
The default bias is 0.0.
- DEFAULT_BIAS_ENABLED - Static variable in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner
-
The default for bias enabled is true.
- DEFAULT_BIAS_REGULARIZATION - Static variable in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner
-
The default bias regularization parameter is 0.0.
- DEFAULT_BLOCK_EXPERIMENT_COMPARISON - Static variable in class gov.sandia.cognition.statistics.method.MultipleComparisonExperiment
-
Default block-experiment comparison, FriedmanConfidence.
- DEFAULT_BUDGET - Static variable in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.AbstractOnlineBudgetedKernelBinaryCategorizerLearner
-
The default budget is 100.
- DEFAULT_BURN_IN_ITERATIONS - Static variable in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler
-
The default number of burn-in iterations is 2000.
- DEFAULT_CAPACITY - Static variable in class gov.sandia.cognition.learning.function.scalar.KolmogorovSmirnovEvaluator
-
Default capacity, 100.
- DEFAULT_CDF - Static variable in class gov.sandia.cognition.learning.function.scalar.KolmogorovSmirnovEvaluator
-
Default CDF, a 3-DOF Chi-Square.
- DEFAULT_CENTER_DATA - Static variable in class gov.sandia.cognition.learning.algorithm.pca.KernelPrincipalComponentsAnalysis
-
The default setting for centering data is true.
- DEFAULT_COMPONENT_COUNT - Static variable in class gov.sandia.cognition.learning.algorithm.pca.KernelPrincipalComponentsAnalysis
-
The default number of components to create is 10.
- DEFAULT_CONFIDENCE - Static variable in class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalDeviation
-
The default confidence is 0.85.
- DEFAULT_CONFIDENCE - Static variable in class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalVariance
-
The default confidence is 0.85.
- DEFAULT_CONSTANT - Static variable in class gov.sandia.cognition.learning.function.kernel.PolynomialKernel
-
The default constant is 1.0.
- DEFAULT_CONSTANT - Static variable in class gov.sandia.cognition.learning.function.kernel.SigmoidKernel
-
The default value for the constant is 0.0.
- DEFAULT_CONSTANT_VALUE - Static variable in class gov.sandia.cognition.learning.function.scalar.LocallyWeightedKernelScalarFunction
-
The default constant value is 0.0.
- DEFAULT_CONSTANT_WEIGHT - Static variable in class gov.sandia.cognition.learning.function.scalar.LocallyWeightedKernelScalarFunction
-
The default constant weight is 0.0.
- DEFAULT_COOLING_FACTOR - Static variable in class gov.sandia.cognition.learning.algorithm.annealing.SimulatedAnnealer
-
The default cooling factor for learning, 0.1.
- DEFAULT_COST_FUNCTION - Static variable in class gov.sandia.cognition.learning.algorithm.regression.AbstractMinimizerBasedParameterCostMinimizer
-
Default cost function, SumSquaredErrorCostFunction
- DEFAULT_COVARIANCE - Static variable in class gov.sandia.cognition.learning.algorithm.clustering.cluster.GaussianClusterCreator
-
Default covariance, 1.0E-4
- DEFAULT_COVARIANCE - Static variable in class gov.sandia.cognition.learning.algorithm.clustering.initializer.NeighborhoodGaussianClusterInitializer
-
Default covariance to put on the diagonal entries, 1.0.
- DEFAULT_COVARIANCE - Static variable in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.IncrementalEstimator
-
Default covariance, 1.0E-5.
- DEFAULT_COVARIANCE - Static variable in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.MaximumLikelihoodEstimator
-
Default covariance used in estimation, 1.0E-5.
- DEFAULT_COVARIANCE - Static variable in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.SufficientStatistic
-
Default covariance of the statistics, 1.0E-5.
- DEFAULT_COVARIANCE - Static variable in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.WeightedMaximumLikelihoodEstimator
-
Default covariance used in estimation, 1.0E-5.
- DEFAULT_COVARIANCE_DIVISOR - Static variable in class gov.sandia.cognition.statistics.distribution.NormalInverseWishartDistribution
-
Default covariance divisor, 1.0.
- DEFAULT_COVARIANCE_INVERSE - Static variable in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.IncrementalEstimatorCovarianceInverse
-
Default covariance Inverse, 99999.99999999999.
- DEFAULT_COVARIANCE_INVERSE - Static variable in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.SufficientStatisticCovarianceInverse
-
Default covariance of the statistics, 99999.99999999999.
- DEFAULT_COVARIANCE_SYMMETRY_TOLERANCE - Static variable in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian
-
Tolerance check for symmetry covariance tolerance, 1.0E-5.
- DEFAULT_CURVATURE_CONDITION - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerDerivativeBased
-
This is a fairly accurate line search, 0.1.
- DEFAULT_DAMPING - Static variable in class gov.sandia.cognition.learning.algorithm.regression.LevenbergMarquardtEstimation
-
Default initial value of the damping factor 1.0
- DEFAULT_DAMPING_DIVISOR - Static variable in class gov.sandia.cognition.learning.algorithm.regression.FletcherXuHybridEstimation
-
Divisor of the damping factor on unsuccessful iteration, dividing
damping factor on cost-reducing iteration 2.0
- DEFAULT_DAMPING_DIVISOR - Static variable in class gov.sandia.cognition.learning.algorithm.regression.LevenbergMarquardtEstimation
-
Divisor of the damping factor on unsuccessful iteration, dividing
damping factor on cost-reducing iteration 10.0
- DEFAULT_DAMPING_FACTOR - Static variable in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation
-
The default damping factor (lambda) is 0.5.
- DEFAULT_DEFAULT_COVARIANCE - Static variable in class gov.sandia.cognition.learning.data.feature.MultivariateDecorrelator.DiagonalCovarianceLearner
-
The default value for default covariance is 1.0E-5.
- DEFAULT_DEFAULT_COVARIANCE - Static variable in class gov.sandia.cognition.learning.data.feature.MultivariateDecorrelator.FullCovarianceLearner
-
The default value for default covariance is 1.0E-5.
- DEFAULT_DEFAULT_VARIANCE - Static variable in class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalDeviation
-
The default variance is 1.0.
- DEFAULT_DEFAULT_VARIANCE - Static variable in class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalVariance
-
The default variance is 1.0.
- DEFAULT_DEFAULT_VARIANCE - Static variable in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.IncrementalEstimator
-
The default value for the default variance is 1.0E-5.
- DEFAULT_DEGREE - Static variable in class gov.sandia.cognition.learning.function.kernel.PolynomialKernel
-
The default degree is 2.
- DEFAULT_DEGREES_OF_FREEDOM - Static variable in class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution
-
Default degrees of freedom, 2.0.
- DEFAULT_DEGREES_OF_FREEDOM - Static variable in class gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution
-
Default degrees of freedom, 3.0.
- DEFAULT_DEGREES_OF_FREEDOM - Static variable in class gov.sandia.cognition.statistics.distribution.StudentizedRangeDistribution
-
Default degrees of freedom, 1d/0d.
- DEFAULT_DEGREES_OF_FREEDOM - Static variable in class gov.sandia.cognition.statistics.distribution.StudentTDistribution
-
Default degrees of freedom, 3.0.
- DEFAULT_DELIMITER - Static variable in class gov.sandia.cognition.math.matrix.AbstractVector
-
The default delimiter for a vector.
- DEFAULT_DELTA_SIZE - Static variable in class gov.sandia.cognition.learning.algorithm.gradient.GradientDescendableApproximator
-
Default deltaSize, 1.0E-5
- DEFAULT_DEMOTE_TO_ZERO - Static variable in class gov.sandia.cognition.learning.algorithm.perceptron.Winnow
-
The default value of demoteToZero is false.
- DEFAULT_DENSE_INSTANCE - Static variable in class gov.sandia.cognition.math.matrix.MatrixFactory
-
The default dense implementation of a MatrixFactory.
- DEFAULT_DENSE_INSTANCE - Static variable in class gov.sandia.cognition.math.matrix.VectorFactory
-
The default VectorFactory instance.
- DEFAULT_DERIVATIVE_GAIN - Static variable in class gov.sandia.cognition.math.signals.PIDController
-
Default derivative-error gain, 0.25.
- DEFAULT_DIAGONAL_INSTANCE - Static variable in class gov.sandia.cognition.math.matrix.MatrixFactory
-
The default implementation of a factory that creates a DiagonalMatrix
- DEFAULT_DIMENSION - Static variable in class gov.sandia.cognition.statistics.bayesian.KalmanFilter
-
Default autonomous dimension, 1.
- DEFAULT_DIMENSIONALITY - Static variable in class gov.sandia.cognition.learning.algorithm.clustering.DirichletProcessClustering
-
Default dimensionality, 2.
- DEFAULT_DIMENSIONALITY - Static variable in class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanBayesianEstimator
-
Default dimensionality, 1.
- DEFAULT_DIMENSIONALITY - Static variable in class gov.sandia.cognition.statistics.distribution.InverseWishartDistribution
-
Default inverse scale dimensionality, 2.
- DEFAULT_DIMENSIONALITY - Static variable in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian
-
Default dimensionality of the Gaussian, 2
- DEFAULT_DIMENSIONALITY - Static variable in class gov.sandia.cognition.statistics.distribution.MultivariateGaussianInverseGammaDistribution
-
Default dimensionality, 2.
- DEFAULT_DIMENSIONALITY - Static variable in class gov.sandia.cognition.statistics.distribution.MultivariatePolyaDistribution
-
Default dimensionality, 2.
- DEFAULT_DIMENSIONALITY - Static variable in class gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution
-
Default dimensionality, 2.
- DEFAULT_DIMENSIONALITY - Static variable in class gov.sandia.cognition.statistics.distribution.NormalInverseWishartDistribution
-
Default dimensionality of the precision matrix, 2.
- DEFAULT_EFFECTIVE_ZERO - Static variable in class gov.sandia.cognition.learning.algorithm.svm.SequentialMinimalOptimization
-
The default effective value for zero is 1.0E-10.
- DEFAULT_EFFECTIVE_ZERO - Static variable in class gov.sandia.cognition.text.term.relation.TermVectorSimilarityNetworkCreator
-
The default effective zero value is 0.0.
- DEFAULT_ENSEMBLE_SIZE - Static variable in class gov.sandia.cognition.learning.algorithm.ensemble.OnlineBaggingCategorizerLearner
-
The default ensemble size is 100.
- DEFAULT_EPS - Static variable in class gov.sandia.cognition.graph.inference.SumProductInferencingAlgorithm
-
The default stopping epsilon that will be used
- DEFAULT_EPS - Static variable in class gov.sandia.cognition.learning.algorithm.clustering.DBSCANClusterer
-
The default eps is 0.5.
- DEFAULT_ERROR_TOLERANCE - Static variable in class gov.sandia.cognition.learning.algorithm.svm.SequentialMinimalOptimization
-
The default error tolerance is 0.001, which is what was recommended in
the original Sequential Minimal Optimization paper.
- DEFAULT_ETA - Static variable in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Projectron
-
The default value of eta is 0.01.
- DEFAULT_FACTOR_COUNT - Static variable in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner
-
The default number of factors is 10.
- DEFAULT_FACTOR_REGULARIZATION - Static variable in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner
-
The default factor regularization parameter is 0.01.
- DEFAULT_FALSE_VALUE - Static variable in class gov.sandia.cognition.data.convert.number.DefaultBooleanToNumberConverter
-
The default value for false is -1.0.
- DEFAULT_FIELD_SEPARATOR - Static variable in class gov.sandia.cognition.text.convert.DocumentFieldConcatenator
-
The default field separator is a newline.
- DEFAULT_FORMAT - Static variable in class gov.sandia.cognition.algorithm.event.IterationMeasurablePerformanceReporter
-
The default format for reporting performance is "Iteration %d. %s: %s".
- DEFAULT_FORMAT - Static variable in class gov.sandia.cognition.algorithm.event.IterationStartReporter
-
The default format is "Iteration %d".
- DEFAULT_FORMAT - Static variable in class gov.sandia.cognition.learning.performance.AnytimeBatchLearnerValidationPerformanceReporter
-
The default format for reporting performance is "Iteration %d. Train: %s. Validation: %s".
- DEFAULT_FUNCTION_MINIMIZER - Static variable in class gov.sandia.cognition.learning.algorithm.regression.ParameterDerivativeFreeCostMinimizer
-
Default function minimizer, FunctionMinimizerDirectionSetPowell
- DEFAULT_FUNCTION_MINIMIZER - Static variable in class gov.sandia.cognition.learning.algorithm.regression.ParameterDifferentiableCostMinimizer
-
Default function minimizer,
FunctionMinimizerBFGS with LineMinimizerBacktracking
- DEFAULT_GEOMETRIC_DECREASE - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerBacktracking
-
Default amount to decrease the step amount each iteration, 0.5.
- DEFAULT_HIGH_VALUE - Static variable in class gov.sandia.cognition.learning.function.scalar.ThresholdFunction
-
Default high value, 1.0
- DEFAULT_INDEX - Static variable in class gov.sandia.cognition.learning.algorithm.clustering.cluster.DefaultCluster
-
The default index is -1.
- DEFAULT_INDEX - Static variable in class gov.sandia.cognition.learning.function.categorization.VectorElementThresholdCategorizer
-
The default index is -1.
- DEFAULT_INDEX - Static variable in class gov.sandia.cognition.learning.function.scalar.VectorEntryFunction
-
The default index is 0.
- DEFAULT_INDEX - Static variable in class gov.sandia.cognition.text.term.DefaultIndexedTerm
-
The default index is -1.
- DEFAULT_INITIAL_CAPACITY - Static variable in class gov.sandia.cognition.collection.DynamicArrayMap
-
The default initial capacity is 10.
- DEFAULT_INITIAL_CAPACITY - Static variable in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
-
The default capacity is 16.
- DEFAULT_INITIAL_CAPACITY - Static variable in class gov.sandia.cognition.statistics.distribution.DefaultDataDistribution
-
Default initial capacity, 10.
- DEFAULT_INITIAL_GUESS - Static variable in class gov.sandia.cognition.learning.algorithm.root.RootBracketExpander
-
Default initial guess, 0.0
- DEFAULT_INITIALIZATION_RANGE - Static variable in class gov.sandia.cognition.learning.function.vector.ThreeLayerFeedforwardNeuralNetwork
-
Default initialization range, 0.001.
- DEFAULT_INTEGRAL_GAIN - Static variable in class gov.sandia.cognition.math.signals.PIDController
-
Default integral-error gain, 0.0.
- DEFAULT_INTERPOLATOR - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerDerivativeBased
-
Default interpolator to use to create a new candidate point to evaluate
- DEFAULT_INTERPOLATOR - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerDerivativeFree
-
Default interpolation algorithm, LineBracketInterpolatorBrent.
- DEFAULT_ITERATION - Static variable in class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm
-
The default iteration number is zero.
- DEFAULT_ITERATIONS_PER_SAMPLE - Static variable in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler
-
The default number of iterations per sample is 100.
- DEFAULT_K - Static variable in interface gov.sandia.cognition.learning.algorithm.nearest.KNearestNeighbor
-
The default value for k is 1.
- DEFAULT_KAPPA - Static variable in class gov.sandia.cognition.learning.function.kernel.SigmoidKernel
-
The default value for kappa is 1.0.
- DEFAULT_KERNEL_CACHE_SIZE - Static variable in class gov.sandia.cognition.learning.algorithm.svm.SequentialMinimalOptimization
-
The default size of the kernel cache.
- DEFAULT_KNOWN_VARIANCE - Static variable in class gov.sandia.cognition.statistics.bayesian.conjugate.UnivariateGaussianMeanBayesianEstimator
-
Default known variance of the estimated distribution, 1.0.
- DEFAULT_LAMBDA - Static variable in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineShiftingPerceptron
-
The default value of lambda is 0.001.
- DEFAULT_LEAF_COUNT_THRESHOLD - Static variable in class gov.sandia.cognition.learning.algorithm.tree.CategorizationTreeLearner
-
The default threshold for making a leaf node based on count.
- DEFAULT_LEAF_COUNT_THRESHOLD - Static variable in class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeLearner
-
The default threshold for making a leaf node based on count.
- DEFAULT_LEAKAGE - Static variable in class gov.sandia.cognition.learning.function.scalar.LeakyRectifiedLinearFunction
-
The default leakage is 0.01.
- DEFAULT_LEARNING_RATE - Static variable in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachineStochasticGradient
-
The default learning rate is 0.001.
- DEFAULT_LEARNING_RATE - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerGradientDescent
-
Default learning rate
- DEFAULT_LENGTH - Static variable in class gov.sandia.cognition.text.AbstractOccurrenceInText
-
The default length is 0.
- DEFAULT_LINE_MINIMIZER - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerConjugateGradient
-
Default line minimization algorithm, LineMinimizerDerivativeBased
- DEFAULT_LINE_MINIMIZER - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerDirectionSetPowell
-
Default line minimization algorithm, LineMinimizerDerivativeFree
- DEFAULT_LINE_MINIMIZER - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerQuasiNewton
-
Default line minimization algorithm, LineMinimizerDerivativeBased
- DEFAULT_LINE_MINIMIZER - Static variable in class gov.sandia.cognition.learning.algorithm.regression.FletcherXuHybridEstimation
-
Default line minimization algorithm, LineMinimizerDerivativeFree
- DEFAULT_LINE_MINIMIZER - Static variable in class gov.sandia.cognition.learning.algorithm.regression.GaussNewtonAlgorithm
-
Default line minimizer, LineMinimizerDerivativeBased.
- DEFAULT_LOCATION - Static variable in class gov.sandia.cognition.statistics.distribution.CauchyDistribution
-
Default location, 0.0.
- DEFAULT_LOCATION - Static variable in class gov.sandia.cognition.statistics.distribution.NormalInverseGammaDistribution
-
Default location, 0.0.
- DEFAULT_LOG_NORMAL_MEAN - Static variable in class gov.sandia.cognition.statistics.distribution.LogNormalDistribution
-
Default log normal mean, 0.0.
- DEFAULT_LOG_NORMAL_VARIANCE - Static variable in class gov.sandia.cognition.statistics.distribution.LogNormalDistribution
-
Default log normal variance, 1.0.
- DEFAULT_LOW_VALUE - Static variable in class gov.sandia.cognition.learning.function.scalar.ThresholdFunction
-
Default low value, -1.0
- DEFAULT_MARGIN_NEGATIVE - Static variable in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelPerceptron
-
The default negative margin, 0.0.
- DEFAULT_MARGIN_NEGATIVE - Static variable in class gov.sandia.cognition.learning.algorithm.perceptron.Perceptron
-
The default negative margin, 0.0.
- DEFAULT_MARGIN_POSITIVE - Static variable in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelPerceptron
-
The default positive margin, 0.0.
- DEFAULT_MARGIN_POSITIVE - Static variable in class gov.sandia.cognition.learning.algorithm.perceptron.Perceptron
-
The default positive margin, 0.0.
- DEFAULT_MAX - Static variable in class gov.sandia.cognition.statistics.distribution.UniformDistribution
-
Default max, 1.0.
- DEFAULT_MAX_BUFFER_SIZE - Static variable in class gov.sandia.cognition.learning.data.feature.LinearRegressionCoefficientExtractor
-
Default maximum buffer size, 20.
- DEFAULT_MAX_CRITERION_DECREASE - Static variable in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer
-
The default maximum decrease in the training criterion is 1.0.
- DEFAULT_MAX_DEPTH - Static variable in class gov.sandia.cognition.learning.algorithm.tree.CategorizationTreeLearner
-
The default maximum depth to grow the tree to.
- DEFAULT_MAX_DEPTH - Static variable in class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeLearner
-
The default maximum depth to grow the tree to.
- DEFAULT_MAX_DISTANCE - Static variable in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer
-
The default maximum distance is 1.7976931348623157E308.
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.graph.inference.SumProductInferencingAlgorithm
-
The default maximum number of iterations that will be run
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.annealing.SimulatedAnnealer
-
The default number of maximum iterations, 1000.
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation
-
The default maximum number of iterations is 100.
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer
-
The default maximum number of iterations 2147483647
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.clustering.DBSCANClusterer
-
The default maximum number of iterations 2147483647
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.clustering.KMeansClusterer
-
The default maximum number of iterations is 1000.
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.clustering.MiniBatchKMeansClusterer
-
The default maximum number of iterations is 100000.
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer
-
The default maximum number of iterations is 2147483647.
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractBaggingLearner
-
The default maximum number of iterations is 100.
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.ensemble.AdaBoost
-
The default maximum number of iterations is 100.
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner
-
The default maximum number of iterations is 100.
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.ensemble.MultiCategoryAdaBoost
-
The default maximum number of iterations is 100.
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner
-
The default maximum number of iterations is 100.
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.genetic.GeneticAlgorithm
-
The default maximum number of iterations, 1000.
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.hmm.AbstractBaumWelchAlgorithm
-
Default maximum number of iterations, 100.
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerConjugateGradient
-
Default maximum number of iterations before stopping, 1000
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerDirectionSetPowell
-
Default maximum number of iterations before stopping, 1000
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerGradientDescent
-
Default max iterations
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerNelderMead
-
Default max iterations, 4000
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerQuasiNewton
-
Default maximum number of iterations before stopping, 1000
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.line.AbstractAnytimeLineMinimizer
-
Default number of iterations to run the algorithm, 100
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.perceptron.BatchMultiPerceptron
-
The default maximum number of iterations, 100.
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelAdatron
-
The default maximum number of iterations, 100.
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelPerceptron
-
The default maximum number of iterations, 100.
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.perceptron.Perceptron
-
The default maximum number of iterations, 100.
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.regression.AbstractLogisticRegression
-
Default number of iterations before stopping, 100
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.regression.AbstractParameterCostMinimizer
-
Default maximum number of iterations before stopping 1000
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.regression.KernelBasedIterativeRegression
-
The default maximum number of iterations, 100.
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.regression.KernelWeightedRobustRegression
-
Default maximum number of iterations before stopping
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.regression.LogisticRegression
-
Default number of iterations before stopping, 100
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.root.AbstractRootFinder
-
Default maximum number of iterations, 1000.
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.root.RootBracketExpander
-
Default max iterations, 100
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.svm.PrimalEstimatedSubGradient
-
The default maximum number of iterations is 10000.
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.svm.SequentialMinimalOptimization
-
The default maximum number of iterations is 1000.
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation
-
The default maximum number of iterations, 1000.
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.statistics.distribution.MixtureOfGaussians.EMLearner
-
Default max iterations, 100.
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.EMLearner
-
Default max iterations, 100.
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler
-
The default maximum number is iterations is 10000.
- DEFAULT_MAX_ITERATIONS - Static variable in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis
-
The default maximum number of iterations is 250.
- DEFAULT_MAX_ITERATIONS_WITHOUT_IMPROVEMENT - Static variable in class gov.sandia.cognition.learning.algorithm.regression.LevenbergMarquardtEstimation
-
Default maximum number of iterations without improvement before stopping 4
- DEFAULT_MAX_MAGNITUDE - Static variable in class gov.sandia.cognition.learning.function.scalar.AtanFunction
-
The default max magnitude, which is PI / 2.
- DEFAULT_MAX_MIN_DISTANCE - Static variable in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer
-
Deprecated.
- DEFAULT_MAX_NUM_POINTS - Static variable in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling
-
Default number of points, 50.
- DEFAULT_MAX_PENALTY - Static variable in class gov.sandia.cognition.learning.algorithm.svm.SequentialMinimalOptimization
-
The default maximum penalty is infinite, which means that it is
hard-assignment.
- DEFAULT_MAX_SUPPORT - Static variable in class gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution
-
The default maximum support is 0.
- DEFAULT_MAX_WEIGHT - Static variable in class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation
-
The default maximum weight is 100.0.
- DEFAULT_MAXIMUM_ITERATIONS - Static variable in class gov.sandia.cognition.math.matrix.decomposition.EigenvectorPowerIteration
-
Default maximum iterations for power iteration, 100.
- DEFAULT_MAXIMUM_LENGTH - Static variable in class gov.sandia.cognition.text.term.filter.TermLengthFilter
-
The default maximum length is 28.
- DEFAULT_MEAN - Static variable in class gov.sandia.cognition.learning.data.feature.StandardDistributionNormalizer
-
The default mean is 0.0.
- DEFAULT_MEAN - Static variable in class gov.sandia.cognition.statistics.distribution.LaplaceDistribution
-
Default mean, 0.0.
- DEFAULT_MEAN - Static variable in class gov.sandia.cognition.statistics.distribution.LogisticDistribution
-
Default mean, 0.0.
- DEFAULT_MEAN - Static variable in class gov.sandia.cognition.statistics.distribution.StudentTDistribution
-
Default mean, 0.0.
- DEFAULT_MEAN - Static variable in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian
-
Default mean, 0.0.
- DEFAULT_MEASUREMENT_VARIANCE - Static variable in class gov.sandia.cognition.statistics.bayesian.GaussianProcessRegression
-
Default assumed variance of the measurements, 1.0.
- DEFAULT_MIN - Static variable in class gov.sandia.cognition.statistics.distribution.UniformDistribution
-
Default min, 0.0.
- DEFAULT_MIN_CHANGE - Static variable in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachineAlternatingLeastSquares
-
The default minimum change is 1.0E-5.
- DEFAULT_MIN_CHANGE - Static variable in class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation
-
The default minimum change is 1.0E-4.
- DEFAULT_MIN_CLUSTER_SIZE - Static variable in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer
-
The default minimum number of elements per cluster is 1.
- DEFAULT_MIN_FUNCTION_VALUE - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerDerivativeBased
-
Default minimum function value, 0.0.
- DEFAULT_MIN_MARGIN - Static variable in class gov.sandia.cognition.learning.algorithm.perceptron.BatchMultiPerceptron
-
The default minimum margin is 0.0.
- DEFAULT_MIN_MARGIN - Static variable in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineBinaryMarginInfusedRelaxedAlgorithm
-
The default minimum margin is 0.0.
- DEFAULT_MIN_MARGIN - Static variable in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineMultiPerceptron
-
The default minimum margin is 0.0.
- DEFAULT_MIN_MARGIN - Static variable in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineMultiPerceptron.ProportionalUpdate
-
The default minimum margin is 0.001.
- DEFAULT_MIN_NUM_CLUSTERS - Static variable in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer
-
The default minimum number of clusters is 1.
- DEFAULT_MIN_SAMPLES - Static variable in class gov.sandia.cognition.learning.algorithm.clustering.DBSCANClusterer
-
The default minimum samples is 5.
- DEFAULT_MIN_SENSITIVITY - Static variable in class gov.sandia.cognition.learning.algorithm.regression.KernelBasedIterativeRegression
-
The default minimum sensitivity, 10.0.
- DEFAULT_MIN_SPLIT_SIZE - Static variable in class gov.sandia.cognition.learning.algorithm.tree.AbstractVectorThresholdMaximumGainLearner
-
The default value for the minimum split size is 1.
- DEFAULT_MIN_SPLIT_SIZE - Static variable in class gov.sandia.cognition.learning.algorithm.tree.VectorThresholdVarianceLearner
-
The default value for the minimum split size is 1.
- DEFAULT_MIN_SUPPORT - Static variable in class gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution
-
The default minimum support is 0.
- DEFAULT_MINIMUM_CHANGE - Static variable in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis
-
The default minimum change is 1.0E-10.
- DEFAULT_MINIMUM_LENGTH - Static variable in class gov.sandia.cognition.text.term.filter.TermLengthFilter
-
The default minimum length is 3.
- DEFAULT_MOMENTUM - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerGradientDescent
-
Default momentum
- DEFAULT_N - Static variable in class gov.sandia.cognition.statistics.bayesian.conjugate.BinomialBayesianEstimator
-
Default n, 1.
- DEFAULT_N - Static variable in class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution
-
Default n, 1.
- DEFAULT_N - Static variable in class gov.sandia.cognition.statistics.distribution.BinomialDistribution
-
Default N, 1.
- DEFAULT_NAME - Static variable in class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModule
-
Default name given to modules of this type
- DEFAULT_NAME - Static variable in class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleFactoryLearner
-
Default name for this module
- DEFAULT_NAME - Static variable in class gov.sandia.cognition.text.document.DefaultTextField
-
The default name for the field is the empty string.
- DEFAULT_NULL_VALUE - Static variable in class gov.sandia.cognition.data.convert.number.DefaultBooleanToNumberConverter
-
The default value for null is 0.0.
- DEFAULT_NUM_CLASSES - Static variable in class gov.sandia.cognition.statistics.bayesian.conjugate.MultinomialBayesianEstimator
-
Default number of classes/labels, 2.
- DEFAULT_NUM_CLASSES - Static variable in class gov.sandia.cognition.statistics.distribution.CategoricalDistribution
-
Default number of classes (labels or parameters), 2.
- DEFAULT_NUM_CLASSES - Static variable in class gov.sandia.cognition.statistics.distribution.MultinomialDistribution
-
Default number of classes (labels or parameters), 2.
- DEFAULT_NUM_CLUSTERS - Static variable in class gov.sandia.cognition.learning.algorithm.clustering.KMeansFactory
-
The default number of clusters is 10.
- DEFAULT_NUM_CUSTOMERS - Static variable in class gov.sandia.cognition.statistics.distribution.ChineseRestaurantProcess
-
Default number of customers, 2.
- DEFAULT_NUM_FOLDS - Static variable in class gov.sandia.cognition.learning.experiment.CrossFoldCreator
-
The default number of folds is 10.
- DEFAULT_NUM_FOLDS - Static variable in class gov.sandia.cognition.learning.experiment.RandomFoldCreator
-
The default number of folds is 10.
- DEFAULT_NUM_INITIAL_CLUSTERS - Static variable in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel
-
Default number of initial clusters
- DEFAULT_NUM_REQUESTED_CLUSTERS - Static variable in class gov.sandia.cognition.learning.algorithm.clustering.KMeansClusterer
-
The default number of requested clusters is 10.
- DEFAULT_NUM_REQUESTED_CLUSTERS - Static variable in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer
-
The default number of requested clusters is 2147483647.
- DEFAULT_NUM_SAMPLES - Static variable in class gov.sandia.cognition.statistics.bayesian.AbstractMarkovChainMonteCarlo
-
Default number of sample/iterations, 1000.
- DEFAULT_NUM_SAMPLES - Static variable in class gov.sandia.cognition.statistics.bayesian.ImportanceSampling
-
Default maximum number of samples, 1000.
- DEFAULT_NUM_SAMPLES - Static variable in class gov.sandia.cognition.statistics.bayesian.RejectionSampling
-
Default number of samples, 1000.
- DEFAULT_NUM_SAMPLES - Static variable in class gov.sandia.cognition.statistics.distribution.StudentizedRangeDistribution
-
Number of samples to draw for Monte Carlo estimates, 1000.
- DEFAULT_NUM_SAMPLES - Static variable in class gov.sandia.cognition.statistics.UnivariateRandomVariable
-
Default number of samples to draw from a distribution to perform the
empirical algebra approximation, 10000.
- DEFAULT_NUM_SPLITS - Static variable in class gov.sandia.cognition.learning.experiment.RandomByTwoFoldCreator
-
The default number of splits is 5.
- DEFAULT_NUM_STATES - Static variable in class gov.sandia.cognition.learning.algorithm.hmm.MarkovChain
-
Default number of states, 3.
- DEFAULT_NUM_THREADS - Static variable in class gov.sandia.cognition.graph.inference.SumProductInferencingAlgorithm
-
The default number of threads that will be used
- DEFAULT_NUM_TRIALS - Static variable in class gov.sandia.cognition.learning.experiment.LearnerRepeatExperiment
-
The default number of trials is 10.
- DEFAULT_NUM_TRIALS - Static variable in class gov.sandia.cognition.statistics.bayesian.conjugate.MultinomialBayesianEstimator
-
Default number of trials, 2.
- DEFAULT_NUM_TRIALS - Static variable in class gov.sandia.cognition.statistics.distribution.MultinomialDistribution
-
Default number of trials, 1.
- DEFAULT_NUM_TRIALS - Static variable in class gov.sandia.cognition.statistics.distribution.MultivariatePolyaDistribution
-
Default number of trials, 1.
- DEFAULT_NUMERICAL_DERIVATIVE - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerBacktracking
-
Default flag to use numerical differentiation, true.
- DEFAULT_OFFSET - Static variable in class gov.sandia.cognition.learning.function.scalar.LinearFunction
-
The default offset is 0.0.
- DEFAULT_OUTLIER_PERCENT - Static variable in class gov.sandia.cognition.learning.data.feature.StandardDistributionNormalizer.Learner
-
The default percentage of outliers is 0.0.
- DEFAULT_OUTPUT_DIMENSIONALITY - Static variable in class gov.sandia.cognition.learning.data.feature.FeatureHashing
-
The default output dimensionality is 100.
- DEFAULT_OUTPUT_VARIANCE - Static variable in class gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression
-
Default output variance, 1.0.
- DEFAULT_OVERRELAXATION - Static variable in class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation
-
The default overrelaxation is 1.3.
- DEFAULT_P - Static variable in class gov.sandia.cognition.statistics.distribution.BernoulliDistribution
-
Default Bernoulli parameter, 0.5
- DEFAULT_P - Static variable in class gov.sandia.cognition.statistics.distribution.BinomialDistribution
-
Default p, 0.5.
- DEFAULT_P - Static variable in class gov.sandia.cognition.statistics.distribution.GeometricDistribution
-
Default p, 0.5.
- DEFAULT_P - Static variable in class gov.sandia.cognition.statistics.distribution.NegativeBinomialDistribution
-
Default p, 0.5.
- DEFAULT_PAIRWISE_TEST - Static variable in class gov.sandia.cognition.statistics.method.AbstractPairwiseMultipleHypothesisComparison
-
Default pair-wise confidence test: Student's Paired t-test.
- DEFAULT_PERCENT_TO_SAMPLE - Static variable in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractBaggingLearner
-
The default percent to sample is 1.0 (which represents 100%).
- DEFAULT_PERCENT_TO_SAMPLE - Static variable in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner
-
The default percent to sample 0.1.
- DEFAULT_PERCENT_TO_SAMPLE - Static variable in class gov.sandia.cognition.learning.algorithm.ensemble.OnlineBaggingCategorizerLearner
-
The default percent to sample is 1.0 (which represents 100%).
- DEFAULT_PERCENT_TO_SAMPLE - Static variable in class gov.sandia.cognition.learning.algorithm.tree.RandomSubVectorThresholdLearner
-
The default percent to sample is 0.1.
- DEFAULT_POINT - Static variable in class gov.sandia.cognition.statistics.distribution.DeterministicDistribution
-
Default point, 0.0
- DEFAULT_POST_HOC_TEST - Static variable in class gov.sandia.cognition.statistics.method.MultipleComparisonExperiment
-
Default post-host multiple hypothesis comparison test, NemenyiConfidence.
- DEFAULT_POWER - Static variable in class gov.sandia.cognition.learning.function.distance.MinkowskiDistanceMetric
-
The default power is 2.0.
- DEFAULT_PRECISION - Static variable in class gov.sandia.cognition.statistics.distribution.NormalInverseGammaDistribution
-
Default precision, 1.0.
- DEFAULT_PRECISION - Static variable in class gov.sandia.cognition.statistics.distribution.StudentTDistribution
-
Default precision, 1.0.
- DEFAULT_PRECISION - Static variable in class gov.sandia.cognition.text.evaluation.DefaultPrecisionRecallPair
-
The default precision is 0.0.
- DEFAULT_PREDICTION_HORIZION - Static variable in class gov.sandia.cognition.learning.algorithm.SequencePredictionLearner
-
The default prediction horizon is 1.
- DEFAULT_PREDICTION_HORIZON - Static variable in class gov.sandia.cognition.learning.algorithm.TimeSeriesPredictionLearner
-
Default prediction horizon, 1.
- DEFAULT_PRIME - Static variable in class gov.sandia.cognition.hash.FNV1a32Hash
-
Default FNV-1 prime, 16777619.
- DEFAULT_PRIME - Static variable in class gov.sandia.cognition.hash.FNV1a64Hash
-
Default FNV-1 prime, 1099511628211L.
- DEFAULT_PROBABILITY - Static variable in class gov.sandia.cognition.learning.algorithm.genetic.reproducer.VectorizableCrossoverFunction
-
Default probability of cross over, 0.5.
- DEFAULT_PROPORTION_INCORRECT_IN_SAMPLE - Static variable in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner
-
By default use 50% incorrect (and 50%) correct in the percent to sample.
- DEFAULT_PROPORTIONAL_GAIN - Static variable in class gov.sandia.cognition.math.signals.PIDController
-
Default proportional-error gain, 0.5.
- DEFAULT_PSEUDO_INVERSE_TOLERANCE - Static variable in class gov.sandia.cognition.learning.algorithm.regression.LinearBasisRegression
-
Tolerance for the pseudo inverse in the learn method, 1.0E-10.
- DEFAULT_PSEUDO_INVERSE_TOLERANCE - Static variable in class gov.sandia.cognition.learning.algorithm.regression.LinearRegression
-
Tolerance for the pseudo inverse in the learn method, 1.0E-10.
- DEFAULT_PSEUDO_INVERSE_TOLERANCE - Static variable in class gov.sandia.cognition.learning.algorithm.regression.MultivariateLinearRegression
-
Tolerance for the pseudo inverse in the learn method, 1.0E-10.
- DEFAULT_R - Static variable in class gov.sandia.cognition.learning.algorithm.confidence.AdaptiveRegularizationOfWeights
-
The default value of r is 0.001.
- DEFAULT_R - Static variable in class gov.sandia.cognition.statistics.distribution.NegativeBinomialDistribution
-
Default r, 1.0.
- DEFAULT_RADIUS - Static variable in class gov.sandia.cognition.learning.algorithm.perceptron.Ballseptron
-
The default radius is 0.1.
- DEFAULT_RANDOM_SEED - Static variable in class gov.sandia.cognition.learning.function.vector.ThreeLayerFeedforwardNeuralNetwork
-
Default random seed, 1.
- DEFAULT_RANGE - Static variable in class gov.sandia.cognition.learning.algorithm.clustering.initializer.NeighborhoodGaussianClusterInitializer
-
Default range of the "neighborhood", 1.0.
- DEFAULT_RATE - Static variable in class gov.sandia.cognition.statistics.distribution.ExponentialDistribution
-
Default rate, 1.0.
- DEFAULT_RATE - Static variable in class gov.sandia.cognition.statistics.distribution.PoissonDistribution
-
Default rate parameter, 1.0.
- DEFAULT_RECALL - Static variable in class gov.sandia.cognition.text.evaluation.DefaultPrecisionRecallPair
-
The default recall is 0.0.
- DEFAULT_REDUCTION_TEST - Static variable in class gov.sandia.cognition.learning.algorithm.regression.FletcherXuHybridEstimation
-
Reduction test for Equation 6.1.16 in Fletcher PMOO, 0.2 given on page 117.
- DEFAULT_REESTIMATE_ALPHA - Static variable in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel
-
The default value for re-estimating alpha is true.
- DEFAULT_REESTIMATE_INITIAL_PROBABILITY - Static variable in class gov.sandia.cognition.learning.algorithm.hmm.AbstractBaumWelchAlgorithm
-
Default flag to re-estimate initial probabilities, true.
- DEFAULT_REGULARIZATION - Static variable in class gov.sandia.cognition.learning.algorithm.regression.AbstractLogisticRegression
-
Default regularization, 0.0.
- DEFAULT_REGULARIZATION - Static variable in class gov.sandia.cognition.learning.algorithm.regression.LinearRegression
-
Default regularization, 0.0.
- DEFAULT_REGULARIZATION - Static variable in class gov.sandia.cognition.learning.algorithm.regression.LogisticRegression
-
Default regularization, 0.0.
- DEFAULT_REGULARIZATION - Static variable in class gov.sandia.cognition.learning.algorithm.regression.MultivariateLinearRegression
-
Default regularization, 0.0.
- DEFAULT_REGULARIZATION_WEIGHT - Static variable in class gov.sandia.cognition.learning.algorithm.svm.PrimalEstimatedSubGradient
-
The default regularization weight is 1.0E-4.
- DEFAULT_REQUESTED_RANK - Static variable in class gov.sandia.cognition.text.topic.LatentSemanticAnalysis
-
The default requested rank is 10.
- DEFAULT_REQUESTED_RANK - Static variable in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis
-
The default requested rank is 10.
- DEFAULT_ROOT_BRACKETER - Static variable in class gov.sandia.cognition.learning.algorithm.root.AbstractBracketedRootFinder
-
Default root-bracketing algorithm, RootBracketExpander.
- DEFAULT_ROOT_FINDER - Static variable in class gov.sandia.cognition.statistics.method.InverseTransformSampling
-
Default root finding method for the algorithm, RootFinderRiddersMethod.
- DEFAULT_SAMPLE_SIZE - Static variable in class gov.sandia.cognition.learning.algorithm.svm.PrimalEstimatedSubGradient
-
The default sample size is 100.
- DEFAULT_SAMPLES - Static variable in class gov.sandia.cognition.learning.algorithm.clustering.DirichletProcessClustering
-
Default number of samples, 1000.
- DEFAULT_SCALE - Static variable in class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution
-
Default scale, 1.0.
- DEFAULT_SCALE - Static variable in class gov.sandia.cognition.statistics.distribution.CauchyDistribution
-
Default scale, 1.0.
- DEFAULT_SCALE - Static variable in class gov.sandia.cognition.statistics.distribution.GammaDistribution
-
Default scale, 1.0.
- DEFAULT_SCALE - Static variable in class gov.sandia.cognition.statistics.distribution.InverseGammaDistribution
-
Default scale, 1.0.
- DEFAULT_SCALE - Static variable in class gov.sandia.cognition.statistics.distribution.LaplaceDistribution
-
Default scale, 1.0.
- DEFAULT_SCALE - Static variable in class gov.sandia.cognition.statistics.distribution.LogisticDistribution
-
Default scale, 1.0.
- DEFAULT_SCALE - Static variable in class gov.sandia.cognition.statistics.distribution.NormalInverseGammaDistribution
-
Default scale, 1.0.
- DEFAULT_SCALE - Static variable in class gov.sandia.cognition.statistics.distribution.WeibullDistribution
-
Default scale, 1.0
- DEFAULT_SCALE_FACTOR - Static variable in class gov.sandia.cognition.learning.function.vector.LinearVectorFunction
-
Scale factor for default constructor, 1.0.
- DEFAULT_SEED - Static variable in class gov.sandia.cognition.hash.Eva32Hash
-
Default seed: ( 0, 0, 0, 0 ).
- DEFAULT_SEED - Static variable in class gov.sandia.cognition.hash.Eva64Hash
-
Default seed: ( 0, 0, 0, 0, 0, 0, 0, 0 ).
- DEFAULT_SEED - Static variable in class gov.sandia.cognition.hash.FNV1a32Hash
-
Byte representation of DEFAULT_SEED_INT
- DEFAULT_SEED - Static variable in class gov.sandia.cognition.hash.FNV1a64Hash
-
Byte representation of DEFAULT_SEED_INT
- DEFAULT_SEED - Static variable in class gov.sandia.cognition.hash.MD5Hash
-
Default seed
- DEFAULT_SEED - Static variable in class gov.sandia.cognition.hash.Murmur32Hash
-
Default seed: ( 0, 0, 0, 0).
- DEFAULT_SEED - Static variable in class gov.sandia.cognition.hash.Prime32Hash
-
Default seed, the first seed from the SHA-2 256-bit hash.
- DEFAULT_SEED - Static variable in class gov.sandia.cognition.hash.Prime64Hash
-
Default seed, the first seed from the SHA-2 512-bit hash.
- DEFAULT_SEED - Static variable in class gov.sandia.cognition.hash.SHA1Hash
-
Default seed
- DEFAULT_SEED - Static variable in class gov.sandia.cognition.hash.SHA256Hash
-
Default seed
- DEFAULT_SEED - Static variable in class gov.sandia.cognition.hash.SHA512Hash
-
Default seed
- DEFAULT_SEED_INT - Static variable in class gov.sandia.cognition.hash.FNV1a32Hash
-
Default FNV-1 seed, -2128831035 == (signed) 2166136261
- DEFAULT_SEED_LONG - Static variable in class gov.sandia.cognition.hash.FNV1a64Hash
-
Default FNV-1 seed, -3750763034362895579L == (signed) 14695981039346656037
- DEFAULT_SEED_SCALE - Static variable in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner
-
The default seed initialization scale is 0.01.
- DEFAULT_SELF_DIVERGENCE - Static variable in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation
-
The default self similarity is 0.0.
- DEFAULT_SHAPE - Static variable in class gov.sandia.cognition.statistics.bayesian.conjugate.GammaInverseScaleBayesianEstimator
-
Default shape, 1.0.
- DEFAULT_SHAPE - Static variable in class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution
-
Default shape, 1.0.
- DEFAULT_SHAPE - Static variable in class gov.sandia.cognition.statistics.distribution.GammaDistribution
-
Default shape, 1.0.
- DEFAULT_SHAPE - Static variable in class gov.sandia.cognition.statistics.distribution.InverseGammaDistribution
-
Default shape, 3.0.
- DEFAULT_SHAPE - Static variable in class gov.sandia.cognition.statistics.distribution.NormalInverseGammaDistribution
-
Default shape, 3.0.
- DEFAULT_SHAPE - Static variable in class gov.sandia.cognition.statistics.distribution.ParetoDistribution
-
Default shape, 2.0.
- DEFAULT_SHAPE - Static variable in class gov.sandia.cognition.statistics.distribution.WeibullDistribution
-
Default shape, 1.0.
- DEFAULT_SHAPE - Static variable in class gov.sandia.cognition.statistics.distribution.YuleSimonDistribution
-
Default shape, 3.0.
- DEFAULT_SHIFT - Static variable in class gov.sandia.cognition.statistics.distribution.ParetoDistribution
-
Default shift, 0.0.
- DEFAULT_SIGMA - Static variable in class gov.sandia.cognition.learning.function.kernel.RadialBasisKernel
-
The default value for sigma is 1.0.
- DEFAULT_SIZE - Static variable in class gov.sandia.cognition.learning.data.feature.RandomSubspace
-
The default size is 10.
- DEFAULT_SIZE - Static variable in class gov.sandia.cognition.text.term.filter.NGramFilter
-
The default is a bigram.
- DEFAULT_SLOPE - Static variable in class gov.sandia.cognition.learning.function.scalar.LinearFunction
-
The default slope is 1.0.
- DEFAULT_SLOPE_CONDITION - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerDerivativeBased
-
This is a fairly accurate line search, 0.01.
- DEFAULT_SMOOTHING_WINDOW_SIZE - Static variable in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractCategorizerOutOfBagStoppingCriteria
-
The default smoothing window size is 25.
- DEFAULT_SPARSE_INSTANCE - Static variable in class gov.sandia.cognition.math.matrix.MatrixFactory
-
The default sparse implementation of a MatrixFactory.
- DEFAULT_SPARSE_INSTANCE - Static variable in class gov.sandia.cognition.math.matrix.VectorFactory
-
The default SparseVectorFactory instance.
- DEFAULT_SPLIT_THRESHOLD - Static variable in class gov.sandia.cognition.math.geometry.Quadtree
-
This is the default minimum number of items allowed in a leaf node,
10.
- DEFAULT_SQUASHING_FUNCTION - Static variable in class gov.sandia.cognition.learning.function.vector.ThreeLayerFeedforwardNeuralNetwork
-
Default squashing function, AtanFunction.
- DEFAULT_START - Static variable in class gov.sandia.cognition.text.AbstractOccurrenceInText
-
The default start is 0.
- DEFAULT_STARTING_TEMPERATURE - Static variable in class gov.sandia.cognition.learning.algorithm.annealing.SimulatedAnnealer
-
The default starting temperature for the algorithm, 1.0.
- DEFAULT_STOPPING_THRESHOLD - Static variable in class gov.sandia.cognition.math.matrix.decomposition.EigenvectorPowerIteration
-
Default stopping threshold for power iteration, 1.0E-5.
- DEFAULT_SUFFICIENT_DECREASE - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerBacktracking
-
Default sufficient decrease value, 0.5
- DEFAULT_TEXT_FILE_EXTENSIONS - Static variable in class gov.sandia.cognition.text.document.extractor.TextDocumentExtractor
-
The default set of file extensions for text files.
- DEFAULT_THRESHOLD - Static variable in class gov.sandia.cognition.learning.function.categorization.AbstractThresholdBinaryCategorizer
-
Default threshold, 0.0.
- DEFAULT_THRESHOLD - Static variable in class gov.sandia.cognition.learning.function.scalar.ThresholdFunction
-
Default threshold, 0.0
- DEFAULT_TOLERANCE - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerConjugateGradient
-
Default tolerance, 1.0E-5
- DEFAULT_TOLERANCE - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerDirectionSetPowell
-
Default tolerance, 1.0E-5
- DEFAULT_TOLERANCE - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerGradientDescent
-
Default tolerance
- DEFAULT_TOLERANCE - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerNelderMead
-
Default tolerance, 0.001
- DEFAULT_TOLERANCE - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerQuasiNewton
-
Default tolerance, 1.0E-5
- DEFAULT_TOLERANCE - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.line.AbstractAnytimeLineMinimizer
-
Default tolerance of the algorithm 1.0E-5
- DEFAULT_TOLERANCE - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.line.interpolator.AbstractLineBracketInterpolator
-
Default collinearity or identity tolerance, 1.0E-6
- DEFAULT_TOLERANCE - Static variable in class gov.sandia.cognition.learning.algorithm.regression.AbstractLogisticRegression
-
Default tolerance change in weights before stopping, 1.0E-10
- DEFAULT_TOLERANCE - Static variable in class gov.sandia.cognition.learning.algorithm.regression.AbstractParameterCostMinimizer
-
Default convergence criterion 1.0E-7
- DEFAULT_TOLERANCE - Static variable in class gov.sandia.cognition.learning.algorithm.regression.KernelWeightedRobustRegression
-
Default tolerance stopping criterion
- DEFAULT_TOLERANCE - Static variable in class gov.sandia.cognition.learning.algorithm.regression.LogisticRegression
-
Default tolerance change in weights before stopping, 1.0E-10
- DEFAULT_TOLERANCE - Static variable in class gov.sandia.cognition.learning.algorithm.root.AbstractRootFinder
-
Default tolerance of the algorithm, 1.0E-5.
- DEFAULT_TOLERANCE - Static variable in class gov.sandia.cognition.statistics.distribution.MixtureOfGaussians.EMLearner
-
Default tolerance, 1.0E-5.
- DEFAULT_TOLERANCE - Static variable in class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.EMLearner
-
Default tolerance, 1.0E-5.
- DEFAULT_TOLERANCE - Static variable in class gov.sandia.cognition.statistics.method.InverseTransformSampling
-
Tolerance for Newton's method, 1.0E-10.
- DEFAULT_TOLERANCE - Static variable in class gov.sandia.cognition.statistics.method.StudentTConfidence
-
Default tolerance for the standard deviation, 1.0E-10.
- DEFAULT_TOPIC_COUNT - Static variable in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler
-
The default topic count is 10.
- DEFAULT_TRAINING_PERCENT - Static variable in class gov.sandia.cognition.learning.data.RandomDataPartitioner
-
The default percentage of training data is 50%.
- DEFAULT_TREATMENT_COUNT - Static variable in class gov.sandia.cognition.statistics.distribution.StudentizedRangeDistribution
-
Default treatment count, 2.
- DEFAULT_TRUE_VALUE - Static variable in class gov.sandia.cognition.data.convert.number.DefaultBooleanToNumberConverter
-
The default value for true is 1.0.
- DEFAULT_UNCOMPENSATED_ALPHA - Static variable in interface gov.sandia.cognition.statistics.method.MultipleHypothesisComparison
-
Default uncompensatedAlpha, 0.05.
- DEFAULT_UPDATE_BIAS - Static variable in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineBinaryMarginInfusedRelaxedAlgorithm
-
MIRA does not use a bias by default.
- DEFAULT_UPDATE_BIAS - Static variable in class gov.sandia.cognition.learning.algorithm.perceptron.OnlinePassiveAggressivePerceptron
-
By default the Passive-Aggressive Perceptron does not use a bias.
- DEFAULT_UPDATE_BIAS - Static variable in class gov.sandia.cognition.learning.algorithm.perceptron.OnlinePerceptron
-
By default the bias is updated.
- DEFAULT_UPDATE_BIAS - Static variable in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineShiftingPerceptron
-
Algorithm does not update the bias by default.
- DEFAULT_V1 - Static variable in class gov.sandia.cognition.statistics.distribution.SnedecorFDistribution
-
Default value of v1, 3.0.
- DEFAULT_V2 - Static variable in class gov.sandia.cognition.statistics.distribution.SnedecorFDistribution
-
Default value of v2, 5.0.
- DEFAULT_VALUE - Static variable in class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeNode
-
The default value for the node is 0.0.
- DEFAULT_VARIANCE - Static variable in class gov.sandia.cognition.learning.data.feature.StandardDistributionNormalizer
-
The default variance is 1.0.
- DEFAULT_VARIANCE - Static variable in class gov.sandia.cognition.statistics.distribution.StudentTDistribution.MaximumLikelihoodEstimator
-
Typical value of a defaultVariance, 1.0E-5
- DEFAULT_VARIANCE - Static variable in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian
-
Default variance, 1.0.
- DEFAULT_VARIANCE - Static variable in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.MaximumLikelihoodEstimator
-
Typical value of a defaultVariance, 1.0E-5
- DEFAULT_VARIANCE - Static variable in class gov.sandia.cognition.statistics.montecarlo.MultivariateMonteCarloIntegrator
-
Default variance to add to the Gaussian, 0.0.
- DEFAULT_VARIANCE - Static variable in class gov.sandia.cognition.statistics.montecarlo.UnivariateMonteCarloIntegrator
-
Default variance to add to the Gaussian, 0.0.
- DEFAULT_VOTE_OUT_OF_BAG_ONLY - Static variable in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner
-
The default value to vote out-of-bag.
- DEFAULT_WEIGHT - Static variable in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractWeightedEnsemble
-
The default weight when adding a member is 1.0.
- DEFAULT_WEIGHT - Static variable in class gov.sandia.cognition.learning.algorithm.ensemble.WeightedBinaryEnsemble
-
The default weight when adding a member is 1.0.
- DEFAULT_WEIGHT - Static variable in class gov.sandia.cognition.learning.algorithm.ensemble.WeightedVotingCategorizerEnsemble
-
The default weight when adding a member is 1.0.
- DEFAULT_WEIGHT - Static variable in class gov.sandia.cognition.learning.data.DefaultWeightedInputOutputPair
-
The default weight is 1.0.
- DEFAULT_WEIGHT - Static variable in class gov.sandia.cognition.learning.data.DefaultWeightedTargetEstimatePair
-
The default weight is 1.0.
- DEFAULT_WEIGHT - Static variable in class gov.sandia.cognition.learning.data.DefaultWeightedValueDiscriminant
-
The default weight is 0.0.
- DEFAULT_WEIGHT - Static variable in class gov.sandia.cognition.learning.function.kernel.WeightedKernel
-
The default weight is 1.0.
- DEFAULT_WEIGHT - Static variable in class gov.sandia.cognition.util.AbstractWeighted
-
Default weight, 1.0
- DEFAULT_WEIGHT - Static variable in class gov.sandia.cognition.util.DefaultWeightedPair
-
The default weight for the pair is 0.0.
- DEFAULT_WEIGHT_REGULARIZATION - Static variable in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner
-
The default weight regularization parameter is 0.001.
- DEFAULT_WEIGHT_UPDATE - Static variable in class gov.sandia.cognition.learning.algorithm.perceptron.Winnow
-
The default value of the weight update is 2.0.
- DEFAULT_WEIGHT_VARIANCE - Static variable in class gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression
-
Default weight variance, 1.0.
- DEFAULT_WEIGHT_VARIANCE - Static variable in class gov.sandia.cognition.statistics.bayesian.BayesianRobustLinearRegression
-
Default weight variance, 1.0.
- DEFAULT_WEIGHTS_ENABLED - Static variable in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner
-
The default for weights enabled is true.
- DefaultBayesianParameter<ParameterType,ConditionalType extends ClosedFormDistribution<?>,PriorType extends Distribution<ParameterType>> - Class in gov.sandia.cognition.statistics.bayesian
-
Default implementation of BayesianParameter using reflection.
- DefaultBayesianParameter(ConditionalType, String) - Constructor for class gov.sandia.cognition.statistics.bayesian.DefaultBayesianParameter
-
Creates a new instance of DefaultBayesianParameter
- DefaultBayesianParameter(ConditionalType, String, PriorType) - Constructor for class gov.sandia.cognition.statistics.bayesian.DefaultBayesianParameter
-
Creates a new instance of DefaultBayesianParameter
- DefaultBinaryConfusionMatrix - Class in gov.sandia.cognition.learning.performance.categorization
-
A default implementation of the BinaryConfusionMatrix.
- DefaultBinaryConfusionMatrix() - Constructor for class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrix
-
Creates a new, empty DefaultBinaryConfusionMatrix.
- DefaultBinaryConfusionMatrix.ActualPredictedPairSummarizer - Class in gov.sandia.cognition.learning.performance.categorization
-
A confusion matrix summarizer that summarizes actual-predicted pairs.
- DefaultBinaryConfusionMatrix.CombineSummarizer - Class in gov.sandia.cognition.learning.performance.categorization
-
A confusion matrix summarizer that adds together confusion matrices.
- DefaultBinaryConfusionMatrix.PerformanceEvaluator<InputType> - Class in gov.sandia.cognition.learning.performance.categorization
-
An implementation of the SupervisedPerformanceEvaluator interface
for creating a DefaultBinaryConfusionMatrix.
- DefaultBinaryConfusionMatrixConfidenceInterval - Class in gov.sandia.cognition.learning.performance.categorization
-
Puts Student-t ConfidenceIntervals on each entry of the ConfusionMatrix
- DefaultBinaryConfusionMatrixConfidenceInterval(double, ConfidenceInterval, ConfidenceInterval, ConfidenceInterval, ConfidenceInterval) - Constructor for class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrixConfidenceInterval
-
Creates a new instance of ConfusionMatrixConfidenceInterval
- DefaultBinaryConfusionMatrixConfidenceInterval.Summary - Class in gov.sandia.cognition.learning.performance.categorization
-
An implementation of the Summarizer interface for creating a
ConfusionMatrixInterval
- DefaultBooleanToNumberConverter - Class in gov.sandia.cognition.data.convert.number
-
Converts a Boolean to a Number by using predefined values
for true, false, and (optionally) null.
- DefaultBooleanToNumberConverter() - Constructor for class gov.sandia.cognition.data.convert.number.DefaultBooleanToNumberConverter
-
Creates a new DefaultBooleanToNumberConverter with default values.
- DefaultBooleanToNumberConverter(Number, Number, Number) - Constructor for class gov.sandia.cognition.data.convert.number.DefaultBooleanToNumberConverter
-
Creates a new DefaultBooleanToNumberConverter.
- DefaultBooleanToNumberConverter.Reverse - Class in gov.sandia.cognition.data.convert.number
-
The reverse converter for the DefaultBooleanToNumberConverter.
- DefaultCluster<ClusterType> - Class in gov.sandia.cognition.learning.algorithm.clustering.cluster
-
The DefaultCluster class implements a default cluster which contains a
list of members in an ArrayList along with an index that identifies the
cluster.
- DefaultCluster() - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.cluster.DefaultCluster
-
Creates a new instance of CentroidCluster.
- DefaultCluster(int) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.cluster.DefaultCluster
-
Creates a new instance of CentroidCluster.
- DefaultCluster(Collection<? extends ClusterType>) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.cluster.DefaultCluster
-
Creates a new instance of CentroidCluster.
- DefaultCluster(int, Collection<? extends ClusterType>) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.cluster.DefaultCluster
-
Creates a new instance of CentroidCluster.
- DefaultClusterCreator<DataType> - Class in gov.sandia.cognition.learning.algorithm.clustering.cluster
-
The DefaultClusterCreator class implements a default
ClusterCreator that just creates a DefaultCluster from the
given list of members.
- DefaultClusterCreator() - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.cluster.DefaultClusterCreator
-
Creates a new DefaultClusterCreator.
- DefaultClusterHierarchyNode<DataType,ClusterType extends Cluster<DataType>> - Class in gov.sandia.cognition.learning.algorithm.clustering.hierarchy
-
A default implementation of the cluster hierarchy node.
- DefaultClusterHierarchyNode() - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.hierarchy.DefaultClusterHierarchyNode
-
Creates a new DefaultClusterHierarchyNode.
- DefaultClusterHierarchyNode(ClusterType) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.hierarchy.DefaultClusterHierarchyNode
-
Creates a new DefaultClusterHierarchyNode.
- DefaultClusterHierarchyNode(ClusterType, List<ClusterHierarchyNode<DataType, ClusterType>>) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.hierarchy.DefaultClusterHierarchyNode
-
Creates a new DefaultClusterHierarchyNode.
- DefaultClusterHierarchyNode(ClusterType, ClusterHierarchyNode<DataType, ClusterType>, ClusterHierarchyNode<DataType, ClusterType>) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.hierarchy.DefaultClusterHierarchyNode
-
Creates a new DefaultClusterHierarchyNode.
- DefaultCogxel - Class in gov.sandia.cognition.framework
-
The DefaultCogxel provides a default implementation of the
Cogxel interface that just stores the necessary peices of
information: the SemanticIdentifier and its activation.
- DefaultCogxel(SemanticIdentifier) - Constructor for class gov.sandia.cognition.framework.DefaultCogxel
-
Creates a new instance of Cogxel.
- DefaultCogxel(SemanticIdentifier, double) - Constructor for class gov.sandia.cognition.framework.DefaultCogxel
-
Creates a new instance of Cogxel.
- DefaultCogxel(DefaultCogxel) - Constructor for class gov.sandia.cognition.framework.DefaultCogxel
-
Creates a copy of a given Cogxel.
- DefaultCogxelFactory - Class in gov.sandia.cognition.framework
-
This class implements a CogxelFactory that returns the default type of
DefaultCogxel.
- DefaultCogxelFactory() - Constructor for class gov.sandia.cognition.framework.DefaultCogxelFactory
-
Creates a new instance of DefaultCogxelFactory.
- DefaultComparator<T extends java.lang.Comparable<? super T>> - Class in gov.sandia.cognition.collection
-
A default comparator that just calls compare on the comparable generic
it uses.
- DefaultComparator() - Constructor for class gov.sandia.cognition.collection.DefaultComparator
-
Creates a new DefaultComparator.
- DefaultConfidenceWeightedBinaryCategorizer - Class in gov.sandia.cognition.learning.function.categorization
-
A default implementation of the ConfidenceWeightedBinaryCategorizer
that stores a full mean and covariance matrix.
- DefaultConfidenceWeightedBinaryCategorizer() - Constructor for class gov.sandia.cognition.learning.function.categorization.DefaultConfidenceWeightedBinaryCategorizer
-
Creates a new, uninitialized DefaultConfidenceWeightedBinaryCategorizer.
- DefaultConfidenceWeightedBinaryCategorizer(Vector, Matrix) - Constructor for class gov.sandia.cognition.learning.function.categorization.DefaultConfidenceWeightedBinaryCategorizer
-
Creates a new DefaultConfidenceWeightedBinaryCategorizer with
the given mean and covariance.
- DefaultConfusionMatrix<CategoryType> - Class in gov.sandia.cognition.learning.performance.categorization
-
A default implementation of the ConfusionMatrix interface.
- DefaultConfusionMatrix() - Constructor for class gov.sandia.cognition.learning.performance.categorization.DefaultConfusionMatrix
-
Creates a new, empty DefaultConfusionMatrix.
- DefaultConfusionMatrix(ConfusionMatrix<? extends CategoryType>) - Constructor for class gov.sandia.cognition.learning.performance.categorization.DefaultConfusionMatrix
-
Creates a copy of a given confusion matrix.
- DefaultConfusionMatrix.ActualPredictedPairSummarizer<CategoryType> - Class in gov.sandia.cognition.learning.performance.categorization
-
A confusion matrix summarizer that summarizes actual-predicted pairs.
- DefaultConfusionMatrix.CombineSummarizer<CategoryType> - Class in gov.sandia.cognition.learning.performance.categorization
-
A confusion matrix summarizer that adds together confusion matrices.
- DefaultConfusionMatrix.Factory<CategoryType> - Class in gov.sandia.cognition.learning.performance.categorization
-
A factory for default confusion matrices.
- defaultCovariance - Variable in class gov.sandia.cognition.learning.data.feature.MultivariateDecorrelator.DiagonalCovarianceLearner
-
The default covariance.
- defaultCovariance - Variable in class gov.sandia.cognition.learning.data.feature.MultivariateDecorrelator.FullCovarianceLearner
-
The default covariance.
- defaultCovariance - Variable in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.SufficientStatistic
-
Default covariance of the distribution
- defaultCovarianceInverse - Variable in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.SufficientStatisticCovarianceInverse
-
Default covariance inverse of the distribution
- defaultDaemonThreadFactory() - Static method in class gov.sandia.cognition.algorithm.ParallelUtil
-
Creates a version of the default thread factory from
Executors.defaultThreadFactory() that creates daemon threads.
- DefaultDataDistribution<KeyType> - Class in gov.sandia.cognition.statistics.distribution
-
A default implementation of ScalarDataDistribution that uses a
backing map.
- DefaultDataDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.DefaultDataDistribution
-
Default constructor
- DefaultDataDistribution(int) - Constructor for class gov.sandia.cognition.statistics.distribution.DefaultDataDistribution
-
Creates a new instance of DefaultDataDistribution
- DefaultDataDistribution(DataDistribution<? extends KeyType>) - Constructor for class gov.sandia.cognition.statistics.distribution.DefaultDataDistribution
-
Creates a new instance of DefaultDataDistribution
- DefaultDataDistribution(Iterable<? extends KeyType>) - Constructor for class gov.sandia.cognition.statistics.distribution.DefaultDataDistribution
-
Creates a new instance of ScalarDataDistribution
- DefaultDataDistribution(Map<KeyType, MutableDouble>, double) - Constructor for class gov.sandia.cognition.statistics.distribution.DefaultDataDistribution
-
Creates a new instance of
- DefaultDataDistribution.DefaultFactory<DataType> - Class in gov.sandia.cognition.statistics.distribution
-
A factory for DefaultDataDistribution objects using some given
initial capacity for them.
- DefaultDataDistribution.Estimator<KeyType> - Class in gov.sandia.cognition.statistics.distribution
-
Estimator for a DefaultDataDistribution
- DefaultDataDistribution.PMF<KeyType> - Class in gov.sandia.cognition.statistics.distribution
-
PMF of the DefaultDataDistribution
- DefaultDataDistribution.WeightedEstimator<KeyType> - Class in gov.sandia.cognition.statistics.distribution
-
A weighted estimator for a DefaultDataDistribution
- DefaultDateField - Class in gov.sandia.cognition.text.document
-
A field for storing a date.
- DefaultDateField() - Constructor for class gov.sandia.cognition.text.document.DefaultDateField
-
Creates a new, empty DefaultDateField.
- DefaultDateField(String, Date) - Constructor for class gov.sandia.cognition.text.document.DefaultDateField
-
Creates a new DefaultDateField.
- DefaultDistributionParameter<ParameterType,ConditionalType extends ClosedFormDistribution<?>> - Class in gov.sandia.cognition.statistics
-
Default implementation of DistributionParameter using introspection.
- DefaultDistributionParameter(ConditionalType, String) - Constructor for class gov.sandia.cognition.statistics.DefaultDistributionParameter
-
Creates a new instance of DefaultDistributionParameter
- DefaultDistributionParameter(ConditionalType, PropertyDescriptor) - Constructor for class gov.sandia.cognition.statistics.DefaultDistributionParameter
-
Creates a new instance of DefaultDistributionParameter
- DefaultDivergenceFunctionContainer<FirstType,SecondType> - Class in gov.sandia.cognition.learning.function.distance
-
The DefaultDivergenceFunctionContainer class implements an object
that holds a divergence function.
- DefaultDivergenceFunctionContainer() - Constructor for class gov.sandia.cognition.learning.function.distance.DefaultDivergenceFunctionContainer
-
Creates a new instance of DefaultDivergenceFunctionContainer.
- DefaultDivergenceFunctionContainer(DivergenceFunction<? super FirstType, ? super SecondType>) - Constructor for class gov.sandia.cognition.learning.function.distance.DefaultDivergenceFunctionContainer
-
Creates a new instance of DefaultDivergenceFunctionContainer.
- DefaultDivergenceFunctionContainer(DefaultDivergenceFunctionContainer<? super FirstType, ? super SecondType>) - Constructor for class gov.sandia.cognition.learning.function.distance.DefaultDivergenceFunctionContainer
-
Creates a new instance of DefaultDivergenceFunctionContainer.
- DefaultDocument - Class in gov.sandia.cognition.text.document
-
A default implementation of the Document interface.
- DefaultDocument() - Constructor for class gov.sandia.cognition.text.document.DefaultDocument
-
Creates a new DefaultDocument.
- DefaultDuration - Class in gov.sandia.cognition.time
-
A default implementation of the Duration interface.
- DefaultDuration(double) - Constructor for class gov.sandia.cognition.time.DefaultDuration
-
Creates a new DefaultDuration representing the given number of
milliseconds.
- DefaultFactory<CreatedType> - Class in gov.sandia.cognition.factory
-
The DefaultFactory class is a default implementation of the
Factory interface that takes a class as its parameter and uses the
default constructor of the class, called through newInstance(), to create
new objects of that class.
- DefaultFactory(Class<? extends CreatedType>) - Constructor for class gov.sandia.cognition.factory.DefaultFactory
-
Creates a new DefaultFactory for the given class.
- DefaultFactory() - Constructor for class gov.sandia.cognition.statistics.distribution.DefaultDataDistribution.DefaultFactory
-
Creates a new DefaultFactory with a default
initial domain capacity.
- DefaultFactory(int) - Constructor for class gov.sandia.cognition.statistics.distribution.DefaultDataDistribution.DefaultFactory
-
Creates a new DefaultFactory with a given
initial domain capacity.
- DefaultIdentifiedValue<IdentifierType,ValueType> - Class in gov.sandia.cognition.util
-
A default implementation of the IdentifiedValue interface that
stores a value along with its identifier.
- DefaultIdentifiedValue() - Constructor for class gov.sandia.cognition.util.DefaultIdentifiedValue
-
Creates a new DefaultIdentifiedValue with null identifier and
value.
- DefaultIdentifiedValue(IdentifierType, ValueType) - Constructor for class gov.sandia.cognition.util.DefaultIdentifiedValue
-
Creates a new DefaultIdentifiedValue with the given identifier
and value.
- DefaultIncrementalClusterCreator<DataType> - Class in gov.sandia.cognition.learning.algorithm.clustering.cluster
-
A default implementation of the IncrementalClusterCreator interface
that just creates a cluster as having a collection of members.
- DefaultIncrementalClusterCreator() - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.cluster.DefaultIncrementalClusterCreator
-
Creates a new DefaultIncrementalClusterCreator.
- DefaultIndexedTerm - Class in gov.sandia.cognition.text.term
-
Default implementation of the IndexedTerm interface.
- DefaultIndexedTerm() - Constructor for class gov.sandia.cognition.text.term.DefaultIndexedTerm
-
Creates a new DefaultIndexedTerm with default values.
- DefaultIndexedTerm(int, Term) - Constructor for class gov.sandia.cognition.text.term.DefaultIndexedTerm
-
Creates a new DefaultIndexedTerm with the given index and term.
- DefaultIndexer<ValueType> - Class in gov.sandia.cognition.collection
-
A default implementation of the
Indexer interface that simply maps
objects to a range from 0 to n-1 in the order they are given.
- DefaultIndexer() - Constructor for class gov.sandia.cognition.collection.DefaultIndexer
-
- DefaultIndexer(int) - Constructor for class gov.sandia.cognition.collection.DefaultIndexer
-
- DefaultIndexer(Collection<? extends ValueType>) - Constructor for class gov.sandia.cognition.collection.DefaultIndexer
-
Creates a new
DefaultIndexer and adds he given collection of
values to it.
- DefaultInfiniteVector<KeyType> - Class in gov.sandia.cognition.math.matrix
-
An implementation of an InfiniteVector backed by a
LinkedHashMap.
- DefaultInfiniteVector() - Constructor for class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
-
Creates a new, empty instance of DefaultInfiniteVector.
- DefaultInfiniteVector(int) - Constructor for class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
-
Creates a new, empty instance of DefaultInfiniteVector with the
given initial capacity.
- DefaultInfiniteVector(LinkedHashMap<KeyType, MutableDouble>) - Constructor for class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
-
Creates a new AbstractMapInfiniteVector with the given backing
map.
- DefaultInfiniteVector.SimpleIterator - Class in gov.sandia.cognition.math.matrix
-
Simple iterator for DefaultInfiniteVector
- DefaultInputOutputPair<InputType,OutputType> - Class in gov.sandia.cognition.learning.data
-
A default implementation of the InputOutputPair interface.
- DefaultInputOutputPair() - Constructor for class gov.sandia.cognition.learning.data.DefaultInputOutputPair
-
Creates a new DefaultInputOutputPair with both the input and
output as null.
- DefaultInputOutputPair(InputType, OutputType) - Constructor for class gov.sandia.cognition.learning.data.DefaultInputOutputPair
-
Creates a new DefaultInputOutputPair with the given input and
output.
- DefaultInputOutputPair(Pair<? extends InputType, ? extends OutputType>) - Constructor for class gov.sandia.cognition.learning.data.DefaultInputOutputPair
-
Creates a new DefaultInputOutputPair using the first element of
the given pair as the input and the second element of the given pair as
the output.
- DefaultKernelBinaryCategorizer<InputType> - Class in gov.sandia.cognition.learning.function.categorization
-
A default implementation of the KernelBinaryCategorizer that uses
the standard way of representing the examples (supports) using a
DefaultWeightedValue.
- DefaultKernelBinaryCategorizer() - Constructor for class gov.sandia.cognition.learning.function.categorization.DefaultKernelBinaryCategorizer
-
Creates a new DefaultKernelBinaryCategorizer with a null kernel,
no examples, and a zero bias.
- DefaultKernelBinaryCategorizer(Kernel<? super InputType>) - Constructor for class gov.sandia.cognition.learning.function.categorization.DefaultKernelBinaryCategorizer
-
Creates a new DefaultKernelBinaryCategorizer with the given
kernel, no examples, and a zero bias.
- DefaultKernelBinaryCategorizer(Kernel<? super InputType>, Collection<DefaultWeightedValue<InputType>>, double) - Constructor for class gov.sandia.cognition.learning.function.categorization.DefaultKernelBinaryCategorizer
-
Creates a new DefaultKernelBinaryCategorizer with the given
parameters.
- DefaultKernelContainer<InputType> - Class in gov.sandia.cognition.learning.function.kernel
-
The DefaultKernelContainer class implements an object that
contains a kernel inside.
- DefaultKernelContainer() - Constructor for class gov.sandia.cognition.learning.function.kernel.DefaultKernelContainer
-
Creates a new instance of KernelContainer.
- DefaultKernelContainer(Kernel<? super InputType>) - Constructor for class gov.sandia.cognition.learning.function.kernel.DefaultKernelContainer
-
Creates a new instance of KernelContainer with the given kernel.
- DefaultKernelContainer(DefaultKernelContainer<? super InputType>) - Constructor for class gov.sandia.cognition.learning.function.kernel.DefaultKernelContainer
-
Creates a new copy of a KernelContainer and the kernel inside.
- DefaultKernelsContainer<InputType> - Class in gov.sandia.cognition.learning.function.kernel
-
The DefaultKernelsContainer class implements a container of kernels.
- DefaultKernelsContainer() - Constructor for class gov.sandia.cognition.learning.function.kernel.DefaultKernelsContainer
-
Creates a new instance of DefaultKernelsContainer.
- DefaultKernelsContainer(Collection<? extends Kernel<? super InputType>>) - Constructor for class gov.sandia.cognition.learning.function.kernel.DefaultKernelsContainer
-
Creates a new instance of DefaultKernelsContainer.
- DefaultKernelsContainer(DefaultKernelsContainer<InputType>) - Constructor for class gov.sandia.cognition.learning.function.kernel.DefaultKernelsContainer
-
Creates a new copy of the DefaultKernelsConainer.
- DefaultKeyValuePair<KeyType,ValueType> - Class in gov.sandia.cognition.util
-
A default implementation of the KeyValuePair interface.
- DefaultKeyValuePair() - Constructor for class gov.sandia.cognition.util.DefaultKeyValuePair
-
Creates a new, empty DefaultKeyValuePair.
- DefaultKeyValuePair(KeyType, ValueType) - Constructor for class gov.sandia.cognition.util.DefaultKeyValuePair
-
Creates a new DefaultKeyValuePair from the given key and value.
- DefaultKeyValuePair(KeyValuePair<? extends KeyType, ? extends ValueType>) - Constructor for class gov.sandia.cognition.util.DefaultKeyValuePair
-
Creates a new DefaultKeyValuePair as a shallow copy of the
given key-value pair.
- DefaultMultiCollection<EntryType> - Class in gov.sandia.cognition.collection
-
The DefaultMultiCollection class implements a Collection that just
contains a set of internal collections inside.
- DefaultMultiCollection(Collection<EntryType>, Collection<EntryType>) - Constructor for class gov.sandia.cognition.collection.DefaultMultiCollection
-
Creates a new instance of DefaultMultiCollection.
- DefaultMultiCollection(Collection<? extends Collection<EntryType>>) - Constructor for class gov.sandia.cognition.collection.DefaultMultiCollection
-
Creates a new instance of DefaultMultiCollection.
- DefaultNamedValue<ValueType> - Class in gov.sandia.cognition.util
-
The DefaultNamedValue class implements a container of a name-value
pair.
- DefaultNamedValue() - Constructor for class gov.sandia.cognition.util.DefaultNamedValue
-
Creates a new instance of DefaultNamedValue.
- DefaultNamedValue(String, ValueType) - Constructor for class gov.sandia.cognition.util.DefaultNamedValue
-
Creates a new instance of DefaultNamedValue from the given name and value.
- DefaultNamedValue(DefaultNamedValue<? extends ValueType>) - Constructor for class gov.sandia.cognition.util.DefaultNamedValue
-
Creates a shallow copy of the given DefaultNamedValue.
- DefaultPair<FirstType,SecondType> - Class in gov.sandia.cognition.util
-
The DefaultPair class implements a simple structure for a pair
of two objects, potentially of different types.
- DefaultPair() - Constructor for class gov.sandia.cognition.util.DefaultPair
-
Creates a new instance of DefaultPair with no members.
- DefaultPair(FirstType, SecondType) - Constructor for class gov.sandia.cognition.util.DefaultPair
-
Creates a new instance of DefaultPair.
- DefaultPair(Pair<? extends FirstType, ? extends SecondType>) - Constructor for class gov.sandia.cognition.util.DefaultPair
-
Copy constructor.
- DefaultPartitionedDataset<DataType> - Class in gov.sandia.cognition.learning.data
-
The PartitionedDataset class provides a simple container for the training
and testing datasets to be held together.
- DefaultPartitionedDataset(Collection<DataType>, Collection<DataType>) - Constructor for class gov.sandia.cognition.learning.data.DefaultPartitionedDataset
-
Creates a new instance of PartitionedDataset.
- DefaultPrecisionRecallPair - Class in gov.sandia.cognition.text.evaluation
-
A default implementation of the PrecisionRecallPair interface.
- DefaultPrecisionRecallPair() - Constructor for class gov.sandia.cognition.text.evaluation.DefaultPrecisionRecallPair
-
Creates a new DefaultPrecisionRecallPair.
- DefaultPrecisionRecallPair(double, double) - Constructor for class gov.sandia.cognition.text.evaluation.DefaultPrecisionRecallPair
-
Creates a new DefaultPrecisionRecallPair.
- DefaultSemanticIdentifier - Class in gov.sandia.cognition.framework
-
The DefaultSemanticIdentifier class implements a default
version of the SemanticIdentifier interface that stores the
SemanticLabel the identifier is for and the unique identifier
integer.
- DefaultSemanticIdentifier(SemanticLabel, int) - Constructor for class gov.sandia.cognition.framework.DefaultSemanticIdentifier
-
Creates a new instance of SemanticIdentifier.
- DefaultSemanticIdentifierMap - Class in gov.sandia.cognition.framework
-
The DefaultSemanticIdentifierMap is an implementation of
SemanticIdentifierMap that is backed by a HashMap (a hashtable).
- DefaultSemanticIdentifierMap() - Constructor for class gov.sandia.cognition.framework.DefaultSemanticIdentifierMap
-
Creates a new instance of DefaultSemanticIdentifierMap.
- DefaultSemanticLabel - Class in gov.sandia.cognition.framework
-
This class implements a semantic label using a string.
- DefaultSemanticLabel(String) - Constructor for class gov.sandia.cognition.framework.DefaultSemanticLabel
-
Creates a new DefaultSemanticLabel.
- DefaultSemanticNetwork - Class in gov.sandia.cognition.framework
-
This class contains a default implementation of a SemanticNetwork.
- DefaultSemanticNetwork() - Constructor for class gov.sandia.cognition.framework.DefaultSemanticNetwork
-
Creates a new instance of DefaultSemanticNetwork
- DefaultStopList - Class in gov.sandia.cognition.text.term.filter
-
A default, case-insensitive stop-list.
- DefaultStopList() - Constructor for class gov.sandia.cognition.text.term.filter.DefaultStopList
-
Creates a new, empty DefaultStopList.
- DefaultStopList(Iterable<String>) - Constructor for class gov.sandia.cognition.text.term.filter.DefaultStopList
-
Creates a new DefaultStopList with the given set of words.
- DefaultTargetEstimatePair<TargetType,EstimateType> - Class in gov.sandia.cognition.learning.data
-
A default implementation of the TargetEstimatePair.
- DefaultTargetEstimatePair() - Constructor for class gov.sandia.cognition.learning.data.DefaultTargetEstimatePair
-
Creates a new instance of TargetEstimatePair, with null target
and estimate values.
- DefaultTargetEstimatePair(TargetType, EstimateType) - Constructor for class gov.sandia.cognition.learning.data.DefaultTargetEstimatePair
-
Creates a new instance of TargetEstimatePair with the given
target and estimate values.
- DefaultTargetEstimatePair(Pair<? extends TargetType, ? extends EstimateType>) - Constructor for class gov.sandia.cognition.learning.data.DefaultTargetEstimatePair
-
Creates a shallow copy of another target-estimate pair.
- DefaultTemporalValue<ValueType> - Class in gov.sandia.cognition.util
-
The DefaultTemporalValue class is a default implementation of the
TemporalValue interface.
- DefaultTemporalValue() - Constructor for class gov.sandia.cognition.util.DefaultTemporalValue
-
Creates a new, empty DefaultTemporalValue.
- DefaultTemporalValue(Date, ValueType) - Constructor for class gov.sandia.cognition.util.DefaultTemporalValue
-
Creates a new DefaultTemporalValue.
- DefaultTerm - Class in gov.sandia.cognition.text.term
-
A default implementation of the Term interface.
- DefaultTerm() - Constructor for class gov.sandia.cognition.text.term.DefaultTerm
-
Creates a new DefaultTerm that contains the empty string.
- DefaultTerm(String) - Constructor for class gov.sandia.cognition.text.term.DefaultTerm
-
Creates a new DefaultTerm with the given text.
- DefaultTermCounts - Class in gov.sandia.cognition.text.term
-
A default implementation of the TermCounts interface.
- DefaultTermCounts() - Constructor for class gov.sandia.cognition.text.term.DefaultTermCounts
-
Creates a new, empty DefaultTermCounts.
- DefaultTermIndex - Class in gov.sandia.cognition.text.term
-
A default implementation of the TermIndex interface.
- DefaultTermIndex() - Constructor for class gov.sandia.cognition.text.term.DefaultTermIndex
-
Creates a new, empty DefaultTermIndex.
- DefaultTermNGram - Class in gov.sandia.cognition.text.term
-
A default implementation of the TermNGram interface.
- DefaultTermNGram() - Constructor for class gov.sandia.cognition.text.term.DefaultTermNGram
-
Creates a new DefaultTermNGram.
- DefaultTermNGram(Term...) - Constructor for class gov.sandia.cognition.text.term.DefaultTermNGram
-
Creates a new DefaultTermNGram.
- DefaultTermOccurrence - Class in gov.sandia.cognition.text.term
-
A default implementation of the TermOccurrence interface.
- DefaultTermOccurrence() - Constructor for class gov.sandia.cognition.text.term.DefaultTermOccurrence
-
Creates a new DefaultTermOccurrence with default values.
- DefaultTermOccurrence(Term, int, int) - Constructor for class gov.sandia.cognition.text.term.DefaultTermOccurrence
-
Creates a new DefaultTermOccurrence.
- DefaultTextField - Class in gov.sandia.cognition.text.document
-
A default implementation of the Field interface.
- DefaultTextField() - Constructor for class gov.sandia.cognition.text.document.DefaultTextField
-
Creates a new DefaultTextField with default name and text, both
of which are the empty string.
- DefaultTextField(String, String) - Constructor for class gov.sandia.cognition.text.document.DefaultTextField
-
Creates a new DefaultTextField with the given name and text.
- DefaultTextual - Class in gov.sandia.cognition.text
-
A default implementation of the Textual interface that just stores
a string value.
- DefaultTextual() - Constructor for class gov.sandia.cognition.text.DefaultTextual
-
Creates a new DefaultTextual containing the empty string.
- DefaultTextual(String) - Constructor for class gov.sandia.cognition.text.DefaultTextual
-
Creates a new DefaultTextual containing the given text.
- DefaultTextual(Textual) - Constructor for class gov.sandia.cognition.text.DefaultTextual
-
Creates a new DefaultTextual that takes the string text value
from the given Textual object.
- DefaultToken - Class in gov.sandia.cognition.text.token
-
A default implementation of the Token interface.
- DefaultToken() - Constructor for class gov.sandia.cognition.text.token.DefaultToken
-
Creates a new Token.
- DefaultToken(String, int) - Constructor for class gov.sandia.cognition.text.token.DefaultToken
-
Creates a new Token with the given text.
- DefaultToken(String, int, int) - Constructor for class gov.sandia.cognition.text.token.DefaultToken
-
Creates a new Token with the given text, start, and length.
- DefaultTriple<FirstType,SecondType,ThirdType> - Class in gov.sandia.cognition.util
-
The DefaultTriple class implements a simple structure for a triple
of three objects, potentially of different types.
- DefaultTriple() - Constructor for class gov.sandia.cognition.util.DefaultTriple
-
Creates a new instance of DefaultTriple with initial null values.
- DefaultTriple(FirstType, SecondType, ThirdType) - Constructor for class gov.sandia.cognition.util.DefaultTriple
-
Creates a new instance of DefaultTriple.
- DefaultTwoVectorEntry - Class in gov.sandia.cognition.math.matrix
-
Stores an entry for two vectors.
- DefaultTwoVectorEntry(Vector, Vector) - Constructor for class gov.sandia.cognition.math.matrix.DefaultTwoVectorEntry
-
Creates a new instance of DefaultTwoVectorEntry.
- DefaultTwoVectorEntry(Vector, Vector, int) - Constructor for class gov.sandia.cognition.math.matrix.DefaultTwoVectorEntry
-
Creates a new instance of DefaultTwoVectorEntry.
- DefaultUpdater() - Constructor for class gov.sandia.cognition.statistics.bayesian.ImportanceSampling.DefaultUpdater
-
Default constructor.
- DefaultUpdater(BayesianParameter<ParameterType, ? extends ProbabilityFunction<ObservationType>, ? extends ProbabilityFunction<ParameterType>>) - Constructor for class gov.sandia.cognition.statistics.bayesian.ImportanceSampling.DefaultUpdater
-
Creates a new instance of DefaultUpdater
- DefaultUpdater() - Constructor for class gov.sandia.cognition.statistics.bayesian.RejectionSampling.DefaultUpdater
-
Default constructor.
- DefaultUpdater(BayesianParameter<ParameterType, ? extends ProbabilityFunction<ObservationType>, ? extends ProbabilityFunction<ParameterType>>) - Constructor for class gov.sandia.cognition.statistics.bayesian.RejectionSampling.DefaultUpdater
-
Creates a new instance of DefaultUpdater
- DefaultUpdater(BayesianParameter<ParameterType, ? extends ProbabilityFunction<ObservationType>, ? extends ProbabilityFunction<ParameterType>>, ProbabilityFunction<ParameterType>) - Constructor for class gov.sandia.cognition.statistics.bayesian.RejectionSampling.DefaultUpdater
-
Creates a new instance of DefaultUpdater
- DefaultUpdater(BayesianParameter<ParameterType, ? extends ProbabilityFunction<ObservationType>, ? extends ProbabilityFunction<ParameterType>>, Double, ProbabilityFunction<ParameterType>) - Constructor for class gov.sandia.cognition.statistics.bayesian.RejectionSampling.DefaultUpdater
-
Creates a new instance of DefaultUpdater
- DefaultValueDiscriminantPair<ValueType,DiscriminantType extends java.lang.Comparable<? super DiscriminantType>> - Class in gov.sandia.cognition.learning.data
-
A default implementation of the ValueDiscriminantPair interface.
- DefaultValueDiscriminantPair() - Constructor for class gov.sandia.cognition.learning.data.DefaultValueDiscriminantPair
-
Creates a new DefaultValueDiscriminantPair with null value and
discriminant.
- DefaultValueDiscriminantPair(ValueType, DiscriminantType) - Constructor for class gov.sandia.cognition.learning.data.DefaultValueDiscriminantPair
-
Creates a new DefaultValueDiscriminantPair with the given value
and discriminant.
- defaultVariance - Variable in class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalDeviation
-
The default variance, which the diagonal of the covariance matrix is
initialized to.
- defaultVariance - Variable in class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalVariance
-
The default variance, which the diagonal of the covariance matrix is
initialized to.
- defaultVariance - Variable in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.IncrementalEstimator
-
Amount to add to the variance to keep it from being 0.0.
- DefaultVectorFactoryContainer - Class in gov.sandia.cognition.math.matrix
-
A default implementation of the VectorFactoryContainer interface.
- DefaultVectorFactoryContainer() - Constructor for class gov.sandia.cognition.math.matrix.DefaultVectorFactoryContainer
-
Creates a new DefaultVectorFactoryContainer.
- DefaultVectorFactoryContainer(VectorFactory<? extends Vector>) - Constructor for class gov.sandia.cognition.math.matrix.DefaultVectorFactoryContainer
-
Creates a new DefaultVectorFactoryContainer with the given factory.
- DefaultWeightedInputOutputPair<InputType,OutputType> - Class in gov.sandia.cognition.learning.data
-
A default implementation of the WeightedInputOutputPair interface.
- DefaultWeightedInputOutputPair() - Constructor for class gov.sandia.cognition.learning.data.DefaultWeightedInputOutputPair
-
Creates a new DefaultWeightedInputOutputPair with null as the
input and output and a default weight of 1.0.
- DefaultWeightedInputOutputPair(InputType, OutputType, double) - Constructor for class gov.sandia.cognition.learning.data.DefaultWeightedInputOutputPair
-
Creates a new DefaultWeightedInputOutputPair with the given
input, output, and weight.
- DefaultWeightedInputOutputPair(Pair<? extends InputType, ? extends OutputType>, double) - Constructor for class gov.sandia.cognition.learning.data.DefaultWeightedInputOutputPair
-
Creates a new DefaultWeightedInputOutputPair with the given
input and output from the given pair plus a weight.
- DefaultWeightedInputOutputPair(WeightedInputOutputPair<? extends InputType, ? extends OutputType>) - Constructor for class gov.sandia.cognition.learning.data.DefaultWeightedInputOutputPair
-
Creates a new DefaultWeightedInputOutputPair that is a shallow
copy of the given WeightedInputOuptutPair.
- DefaultWeightedPair<FirstType,SecondType> - Class in gov.sandia.cognition.util
-
The DefaultWeightedPair class extends the DefaultPair class
to add a weight to the pair.
- DefaultWeightedPair() - Constructor for class gov.sandia.cognition.util.DefaultWeightedPair
-
Creates a new instance of DefaultWeightedPair.
- DefaultWeightedPair(FirstType, SecondType, double) - Constructor for class gov.sandia.cognition.util.DefaultWeightedPair
-
Creates a new instance of DefaultWeightedPair.
- DefaultWeightedTargetEstimatePair<TargetType,EstimateType> - Class in gov.sandia.cognition.learning.data
-
Extends TargetEstimatePair with an additional weight field.
- DefaultWeightedTargetEstimatePair() - Constructor for class gov.sandia.cognition.learning.data.DefaultWeightedTargetEstimatePair
-
Creates a new WeightedTargetEstimatePair with nulls for the
target and estimate and 1.0 for the weight.
- DefaultWeightedTargetEstimatePair(TargetType, EstimateType, double) - Constructor for class gov.sandia.cognition.learning.data.DefaultWeightedTargetEstimatePair
-
Creates a new WeightedTargetEstimatePair with the given target,
estimate, and weight.
- DefaultWeightedTargetEstimatePair(DefaultWeightedTargetEstimatePair<? extends TargetType, ? extends EstimateType>) - Constructor for class gov.sandia.cognition.learning.data.DefaultWeightedTargetEstimatePair
-
Creates a new WeightedTargetEstimatePair as a shallow copy of
the given other object.
- DefaultWeightedValue<ValueType> - Class in gov.sandia.cognition.util
-
The WeightedValue class implements a simple generic container
that holds a value and a weight assigned to the value.
- DefaultWeightedValue() - Constructor for class gov.sandia.cognition.util.DefaultWeightedValue
-
Creates a new instance of WeightedValue.
- DefaultWeightedValue(ValueType) - Constructor for class gov.sandia.cognition.util.DefaultWeightedValue
-
Creates a new instance of
WeightedValue with the given value
and a default weight of
1.0.
- DefaultWeightedValue(ValueType, double) - Constructor for class gov.sandia.cognition.util.DefaultWeightedValue
-
Creates a new instance of WeightedValue.
- DefaultWeightedValue(WeightedValue<? extends ValueType>) - Constructor for class gov.sandia.cognition.util.DefaultWeightedValue
-
Creates a new shallow copy of a WeightedValue.
- DefaultWeightedValue.WeightComparator - Class in gov.sandia.cognition.util
-
A comparator for weighted values based on the weight.
- DefaultWeightedValueDiscriminant<ValueType> - Class in gov.sandia.cognition.learning.data
-
An implementation of ValueDiscriminantPair that stores a double
as the discriminant.
- DefaultWeightedValueDiscriminant() - Constructor for class gov.sandia.cognition.learning.data.DefaultWeightedValueDiscriminant
-
Creates a DefaultWeightedValueDiscriminant with a null value
and a weight of 0.0.
- DefaultWeightedValueDiscriminant(ValueType, double) - Constructor for class gov.sandia.cognition.learning.data.DefaultWeightedValueDiscriminant
-
Creates a DefaultWeightedValueDiscriminant with the given
value and weight.
- DefaultWeightedValueDiscriminant(WeightedValue<? extends ValueType>) - Constructor for class gov.sandia.cognition.learning.data.DefaultWeightedValueDiscriminant
-
Creates a new DefaultWeightedValueDiscriminant whose weight
and value are taken from the given weighted value.
- degree(int) - Method in class gov.sandia.cognition.graph.GraphMetrics
-
Return the degree for the input nodeId.
- degree(NodeNameType) - Method in class gov.sandia.cognition.graph.GraphMetrics
-
Return the degree for the input node.
- degree - Variable in class gov.sandia.cognition.learning.function.kernel.PolynomialKernel
-
The degree of the polynomial.
- degreeAssortativity() - Method in class gov.sandia.cognition.graph.GraphMetrics
-
Returns the whole-graph degree assortativity score.
- degreesOfFreedom - Variable in class gov.sandia.cognition.statistics.distribution.InverseWishartDistribution
-
Degrees of freedom, must be greater than the inverse scale
dimensionality.
- degreesOfFreedom - Variable in class gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution
-
Degrees of freedom in the distribution, usually the number of
datapoints - 1, DOFs must be greater than zero.
- degreesOfFreedom - Variable in class gov.sandia.cognition.statistics.distribution.StudentizedRangeDistribution
-
Number of subjects in each treatment minus one.
- degreesOfFreedom - Variable in class gov.sandia.cognition.statistics.distribution.StudentTDistribution
-
Degrees of freedom in the distribution, usually the number of
datapoints - 1, DOFs must be greater than zero.
- DelayFunction<DataType> - Class in gov.sandia.cognition.learning.data.feature
-
Delays the input and returns the input from the parameterized number of
samples previous.
- DelayFunction() - Constructor for class gov.sandia.cognition.learning.data.feature.DelayFunction
-
Default constructor.
- DelayFunction(int) - Constructor for class gov.sandia.cognition.learning.data.feature.DelayFunction
-
Creates a new instance of DelayFunction
- DelayFunction(DelayFunction<DataType>) - Constructor for class gov.sandia.cognition.learning.data.feature.DelayFunction
-
Copy Constructor
- demoteToZero - Variable in class gov.sandia.cognition.learning.algorithm.perceptron.Winnow
-
An option to demote to zero.
- DenseMatrix - Class in gov.sandia.cognition.math.matrix.custom
-
A dense matrix implementation.
- DenseMatrix(int, int) - Constructor for class gov.sandia.cognition.math.matrix.custom.DenseMatrix
-
Creates a zero matrix of the specified dimensions
- DenseMatrix(int, int, double) - Constructor for class gov.sandia.cognition.math.matrix.custom.DenseMatrix
-
Creates a matrix of default val in all cells of the specified dimensions
- DenseMatrix(DenseMatrix) - Constructor for class gov.sandia.cognition.math.matrix.custom.DenseMatrix
-
Copy constructor that creates a deep copy of the input matrix.
- DenseMatrix(Matrix) - Constructor for class gov.sandia.cognition.math.matrix.custom.DenseMatrix
-
Copy constructor that copies any input Matrix -- creating a deep copy of
the matrix.
- DenseMatrix(double[][]) - Constructor for class gov.sandia.cognition.math.matrix.custom.DenseMatrix
-
Constructor for creating a dense matrix from a 2-d double array.
- DenseMatrix(List<List<Double>>) - Constructor for class gov.sandia.cognition.math.matrix.custom.DenseMatrix
-
Constructor for creating a dense matrix from a 2-d double List.
- DenseMatrix() - Constructor for class gov.sandia.cognition.math.matrix.custom.DenseMatrix
-
This should never be called by anything or anyone other than Java's
serialization code.
- DenseMatrix - Class in gov.sandia.cognition.math.matrix.mtj
-
Matrix that represents all its entries using a fixed-size storage scheme,
based on MTJ's DenseMatrix storage class.
- DenseMatrix(int, int) - Constructor for class gov.sandia.cognition.math.matrix.mtj.DenseMatrix
-
Creates a new instance of DenseMatrix
- DenseMatrix(DenseMatrix) - Constructor for class gov.sandia.cognition.math.matrix.mtj.DenseMatrix
-
Creates a new instance of DenseMatrix
- DenseMatrix(Matrix) - Constructor for class gov.sandia.cognition.math.matrix.mtj.DenseMatrix
-
Creates a new instance of DenseMatrix
- DenseMatrix(DenseMatrix) - Constructor for class gov.sandia.cognition.math.matrix.mtj.DenseMatrix
-
Creates a new instance of DenseMatrix
- DenseMatrix(MatrixReader) - Constructor for class gov.sandia.cognition.math.matrix.mtj.DenseMatrix
-
Creates a new instance of DenseMatrix
- DenseMatrix.LU - Class in gov.sandia.cognition.math.matrix.custom
-
Simple container class for LU decompositions.
- DenseMatrix.QR - Class in gov.sandia.cognition.math.matrix.custom
-
Container class that stores the Q and R matrices formed by the QR
decomposition of this.
- DenseMatrix.SVD - Class in gov.sandia.cognition.math.matrix.custom
-
Simple container class for Singular Value Decomposition (SVD) results.
- DenseMatrixFactoryMTJ - Class in gov.sandia.cognition.math.matrix.mtj
-
MatrixFactory for creating MTJ's DenseMatrix-based Matrix
- DenseMatrixFactoryMTJ() - Constructor for class gov.sandia.cognition.math.matrix.mtj.DenseMatrixFactoryMTJ
-
Creates a new instance of DenseMatrixFactoryMTJ
- DenseMemoryGraph<NodeNameType> - Class in gov.sandia.cognition.graph
-
Most basic-est of graph types.
- DenseMemoryGraph() - Constructor for class gov.sandia.cognition.graph.DenseMemoryGraph
-
Initializes an empty graph.
- DenseMemoryGraph(int, int) - Constructor for class gov.sandia.cognition.graph.DenseMemoryGraph
-
Initializes an empty graph, but sets aside O(m + n) memory.
- DenseVector - Class in gov.sandia.cognition.math.matrix.custom
-
Our dense vector implementation.
- DenseVector() - Constructor for class gov.sandia.cognition.math.matrix.custom.DenseVector
-
This should never be called by anything or anyone other than Java's
serialization code.
- DenseVector(int) - Constructor for class gov.sandia.cognition.math.matrix.custom.DenseVector
-
Creates a dense vector of length n.
- DenseVector(int, double) - Constructor for class gov.sandia.cognition.math.matrix.custom.DenseVector
-
Initializes the vector to length n with all values initialized to
defaultVal.
- DenseVector(DenseVector) - Constructor for class gov.sandia.cognition.math.matrix.custom.DenseVector
-
Copy constructor copies the input dense vector into this
- DenseVector(double[]) - Constructor for class gov.sandia.cognition.math.matrix.custom.DenseVector
-
Helper constructor that copies the data from the array into this
- DenseVector(List<Double>) - Constructor for class gov.sandia.cognition.math.matrix.custom.DenseVector
-
Helper constructor that copies the data from the list into this
- DenseVector - Class in gov.sandia.cognition.math.matrix.mtj
-
A generally useful vector representation that allocates a fixed-size
underlying vector, based on MTJ's DenseVector
- DenseVector(int) - Constructor for class gov.sandia.cognition.math.matrix.mtj.DenseVector
-
Creates a new instance of DenseVector
- DenseVector(DenseVector) - Constructor for class gov.sandia.cognition.math.matrix.mtj.DenseVector
-
Creates a new instance of DenseVector
- DenseVector(Vector) - Constructor for class gov.sandia.cognition.math.matrix.mtj.DenseVector
-
Copy constructor for DenseVector
- DenseVector(AbstractMTJVector) - Constructor for class gov.sandia.cognition.math.matrix.mtj.DenseVector
-
Copy constructor
- DenseVector(double...) - Constructor for class gov.sandia.cognition.math.matrix.mtj.DenseVector
-
Creates a new instance of DenseVector
- DenseVector(DenseVector) - Constructor for class gov.sandia.cognition.math.matrix.mtj.DenseVector
-
Creates a new instance of DenseVector
- DenseVector(VectorReader) - Constructor for class gov.sandia.cognition.math.matrix.mtj.DenseVector
-
Creates a new instance of DenseVector
- DenseVectorFactoryMTJ - Class in gov.sandia.cognition.math.matrix.mtj
-
VectorFactory for MTJ's DenseVector-based Vector
- DenseVectorFactoryMTJ() - Constructor for class gov.sandia.cognition.math.matrix.mtj.DenseVectorFactoryMTJ
-
Creates a new instance of DenseVectorFactoryMTJ
- depth - Variable in class gov.sandia.cognition.math.geometry.Quadtree.Node
-
The depth of this node in the tree.
- deserialize(String) - Static method in class gov.sandia.cognition.graph.DenseMemoryGraph
-
Helper method for deserializing from a file
- deserialize(String) - Static method in class gov.sandia.cognition.graph.WeightedDenseMemoryGraph
-
Helper method for deserializing from a file
- destinationVectorPrior - Variable in class gov.sandia.cognition.statistics.TransferEntropy.TransferEntropyDistributionObject
-
The prior for the destination vector.
- DeterministicDistribution - Class in gov.sandia.cognition.statistics.distribution
-
A deterministic distribution that returns samples at a single point.
- DeterministicDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.DeterministicDistribution
-
Creates a new instance of DeterministicDistribution
- DeterministicDistribution(double) - Constructor for class gov.sandia.cognition.statistics.distribution.DeterministicDistribution
-
Creates a new instance of DeterministicDistribution
- DeterministicDistribution(DeterministicDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.DeterministicDistribution
-
Copy Constructor
- DeterministicDistribution.CDF - Class in gov.sandia.cognition.statistics.distribution
-
CDF of the deterministic distribution.
- DeterministicDistribution.PMF - Class in gov.sandia.cognition.statistics.distribution
-
PMF of the deterministic distribution.
- deviance(ComputableDistribution<ObservationType>, Iterable<? extends ObservationType>) - Static method in class gov.sandia.cognition.statistics.bayesian.BayesianUtil
-
Computes the deviance of the model, which is
-2log(p(observations|parameter)).
- DiagonalConfidenceWeightedBinaryCategorizer - Class in gov.sandia.cognition.learning.function.categorization
-
A confidence-weighted linear predictor with a diagonal covariance,
which is stored as a vector.
- DiagonalConfidenceWeightedBinaryCategorizer() - Constructor for class gov.sandia.cognition.learning.function.categorization.DiagonalConfidenceWeightedBinaryCategorizer
-
Creates a new DiagonalConfidenceWeightedBinaryCategorizer.
- DiagonalCovarianceLearner() - Constructor for class gov.sandia.cognition.learning.data.feature.MultivariateDecorrelator.DiagonalCovarianceLearner
-
Creates a new MultivariateDecorrelator.DiagonalCovarianceLearner
with the default value for default covariance.
- DiagonalCovarianceLearner(double) - Constructor for class gov.sandia.cognition.learning.data.feature.MultivariateDecorrelator.DiagonalCovarianceLearner
-
Creates a new MultivariateDecorrelator.DiagonalCovarianceLearner with
the given value for default covariance.
- DiagonalMatrix - Class in gov.sandia.cognition.math.matrix.custom
-
Diagonal matrices are a special case, but a rather common one with very quick
and simple solutions to multiplications, inverses, etc.
- DiagonalMatrix(int) - Constructor for class gov.sandia.cognition.math.matrix.custom.DiagonalMatrix
-
Creates a square (n x n) diagonal matrix initialized to zero.
- DiagonalMatrix(DiagonalMatrix) - Constructor for class gov.sandia.cognition.math.matrix.custom.DiagonalMatrix
-
Copy constructor.
- DiagonalMatrix(Matrix) - Constructor for class gov.sandia.cognition.math.matrix.custom.DiagonalMatrix
-
Creates a diagonal matrix as a copy of the given matrix.
- DiagonalMatrix() - Constructor for class gov.sandia.cognition.math.matrix.custom.DiagonalMatrix
-
This should never be called by anything or anyone other than Java's
serialization code.
- DiagonalMatrix - Interface in gov.sandia.cognition.math.matrix
-
Interface describing a diagonal matrix.
- DiagonalMatrixFactoryMTJ - Class in gov.sandia.cognition.math.matrix.mtj
-
An MatrixFactory that produces DiagonalMatrixMTJ matrices.
- DiagonalMatrixFactoryMTJ() - Constructor for class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixFactoryMTJ
-
Creates a new instance of DiagonalMatrixFactoryMTJ
- DiagonalMatrixMTJ - Class in gov.sandia.cognition.math.matrix.mtj
-
A diagonal matrix that wraps MTJ's BandMatrix class.
- DiagonalMatrixMTJ(int) - Constructor for class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixMTJ
-
Creates a new instance of DiagonalMatrixMTJ
- DiagonalMatrixMTJ(DiagonalMatrixMTJ) - Constructor for class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixMTJ
-
Copy Constructor
- DiagonalMatrixMTJ(double[]) - Constructor for class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixMTJ
-
Creates a new instance of DiagonalMatrixMTJ
- diagonalValues(double[]) - Method in class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixFactoryMTJ
-
Creates a diagonal matrix with the array of values on its diagonal
- DictionaryFilter - Class in gov.sandia.cognition.text.term.filter
-
A term filter that only allows terms in its dictionary.
- DictionaryFilter() - Constructor for class gov.sandia.cognition.text.term.filter.DictionaryFilter
-
Creates a new DictionaryFilter with an empty set of allowed
terms.
- DictionaryFilter(Set<Term>) - Constructor for class gov.sandia.cognition.text.term.filter.DictionaryFilter
-
Creates a new DictionaryFilter with a given set of allowed
terms.
- DifferentiableCostFunction - Interface in gov.sandia.cognition.learning.function.cost
-
The DifferentiableCostFunction is a cost function that can
be differentiated.
- DifferentiableEvaluator<InputType,OutputType,DerivativeType> - Interface in gov.sandia.cognition.math
-
Interface that indicates that the Evaluator can be differentiated about the
given input.
- DifferentiableFeedforwardNeuralNetwork - Class in gov.sandia.cognition.learning.function.vector
-
A feedforward neural network that can have an arbitrary number of layers,
and an arbitrary differentiable squashing (activation) function assigned to
each layer.
- DifferentiableFeedforwardNeuralNetwork(ArrayList<Integer>, ArrayList<DifferentiableUnivariateScalarFunction>, Random) - Constructor for class gov.sandia.cognition.learning.function.vector.DifferentiableFeedforwardNeuralNetwork
-
Creates a new instance of DifferentiableFeedforwardNeuralNetwork
- DifferentiableFeedforwardNeuralNetwork(int, int, int, DifferentiableVectorFunction, Random) - Constructor for class gov.sandia.cognition.learning.function.vector.DifferentiableFeedforwardNeuralNetwork
-
Creates a new instance of FeedforwardNeuralNetwork
- DifferentiableFeedforwardNeuralNetwork(int, int, int, DifferentiableUnivariateScalarFunction, Random) - Constructor for class gov.sandia.cognition.learning.function.vector.DifferentiableFeedforwardNeuralNetwork
-
Creates a new instance of FeedforwardNeuralNetwork
- DifferentiableFeedforwardNeuralNetwork(DifferentiableGeneralizedLinearModel...) - Constructor for class gov.sandia.cognition.learning.function.vector.DifferentiableFeedforwardNeuralNetwork
-
Creates a new instance of FeedforwardNeuralNetwork
- DifferentiableGeneralizedLinearModel - Class in gov.sandia.cognition.learning.function.vector
-
A GradientDescenable version of a GeneralizedLinearModel, in
other words, a GeneralizedLinearModel where the squashing
function is differentiable
- DifferentiableGeneralizedLinearModel() - Constructor for class gov.sandia.cognition.learning.function.vector.DifferentiableGeneralizedLinearModel
-
Default Constructor.
- DifferentiableGeneralizedLinearModel(int, int, DifferentiableUnivariateScalarFunction) - Constructor for class gov.sandia.cognition.learning.function.vector.DifferentiableGeneralizedLinearModel
-
Creates a new instance of GeneralizedLinearModel
- DifferentiableGeneralizedLinearModel(MultivariateDiscriminant, DifferentiableVectorFunction) - Constructor for class gov.sandia.cognition.learning.function.vector.DifferentiableGeneralizedLinearModel
-
Creates a new instance of DifferentiableGeneralizedLinearModel
- DifferentiableGeneralizedLinearModel(MultivariateDiscriminant, DifferentiableUnivariateScalarFunction) - Constructor for class gov.sandia.cognition.learning.function.vector.DifferentiableGeneralizedLinearModel
-
Creates a new instance of DifferentiableGeneralizedLinearModel
- DifferentiableGeneralizedLinearModel(DifferentiableGeneralizedLinearModel) - Constructor for class gov.sandia.cognition.learning.function.vector.DifferentiableGeneralizedLinearModel
-
Creates a new instance of DifferentiableGeneralizedLinearModel
- DifferentiableUnivariateScalarFunction - Interface in gov.sandia.cognition.math
-
A differentiable univariate scalar function
- DifferentiableVectorFunction - Interface in gov.sandia.cognition.math.matrix
-
A VectorFunction that can is also differentiable
- differentiate(double) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.DirectionalVectorToDifferentiableScalarFunction
-
- differentiate(double) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.DirectionalVectorToScalarFunction
-
- differentiate(double) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerDerivativeBased.InternalFunction
-
- differentiate(Vector) - Method in class gov.sandia.cognition.learning.algorithm.regression.ParameterDerivativeFreeCostMinimizer.ParameterCostEvaluatorDerivativeFree
-
- differentiate(Vector) - Method in class gov.sandia.cognition.learning.algorithm.regression.ParameterDifferentiableCostMinimizer.ParameterCostEvaluatorDerivativeBased
-
- differentiate(double) - Method in class gov.sandia.cognition.learning.function.scalar.AtanFunction
-
- differentiate(double) - Method in class gov.sandia.cognition.learning.function.scalar.CosineFunction
-
- differentiate(double) - Method in class gov.sandia.cognition.learning.function.scalar.HardSigmoidFunction
-
- differentiate(double) - Method in class gov.sandia.cognition.learning.function.scalar.HardTanHFunction
-
- differentiate(double) - Method in class gov.sandia.cognition.learning.function.scalar.IdentityScalarFunction
-
- differentiate(double) - Method in class gov.sandia.cognition.learning.function.scalar.LeakyRectifiedLinearFunction
-
- differentiate(double) - Method in class gov.sandia.cognition.learning.function.scalar.LinearFunction
-
- differentiate(double) - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Cubic
-
- differentiate(double) - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction
-
- differentiate(double) - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Linear
-
- differentiate(double) - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Quadratic
-
- differentiate(double) - Method in class gov.sandia.cognition.learning.function.scalar.RectifiedLinearFunction
-
- differentiate(double) - Method in class gov.sandia.cognition.learning.function.scalar.SigmoidFunction
-
- differentiate(double) - Method in class gov.sandia.cognition.learning.function.scalar.SoftPlusFunction
-
- differentiate(double) - Method in class gov.sandia.cognition.learning.function.scalar.TanHFunction
-
- differentiate(Vector) - Method in class gov.sandia.cognition.learning.function.vector.DifferentiableGeneralizedLinearModel
-
- differentiate(Vector) - Method in class gov.sandia.cognition.learning.function.vector.ElementWiseDifferentiableVectorFunction
-
- differentiate(Vector) - Method in class gov.sandia.cognition.learning.function.vector.LinearVectorFunction
-
- differentiate(Vector) - Method in class gov.sandia.cognition.learning.function.vector.MultivariateDiscriminant
-
- differentiate(InputType) - Method in interface gov.sandia.cognition.math.DifferentiableEvaluator
-
Differentiates the output with respect to the input
- differentiate(double) - Method in interface gov.sandia.cognition.math.DifferentiableUnivariateScalarFunction
-
Differentiates the output of the function about the given input
- differentiate(Double) - Method in interface gov.sandia.cognition.math.DifferentiableUnivariateScalarFunction
-
- differentiate(Vector) - Method in interface gov.sandia.cognition.math.matrix.DifferentiableVectorFunction
-
Differentiate the VectorFunction at input and return
the Jacobian
- differentiate(double, Evaluator<? super Double, Double>) - Static method in class gov.sandia.cognition.math.matrix.NumericalDifferentiator.DoubleJacobian
-
Static access to the numerical differentiation procedure.
- differentiate(double, Evaluator<? super Double, Double>, double) - Static method in class gov.sandia.cognition.math.matrix.NumericalDifferentiator.DoubleJacobian
-
Static access to the numerical differentiation procedure.
- differentiate(Double) - Method in class gov.sandia.cognition.math.matrix.NumericalDifferentiator.DoubleJacobian
-
- differentiate(Vector, Evaluator<? super Vector, Vector>) - Static method in class gov.sandia.cognition.math.matrix.NumericalDifferentiator.MatrixJacobian
-
Static access to the numerical differentiation procedure.
- differentiate(Vectorizable, Evaluator<? super Vector, Vector>, double) - Static method in class gov.sandia.cognition.math.matrix.NumericalDifferentiator.MatrixJacobian
-
Static access to the numerical differentiation procedure.
- differentiate(Vector) - Method in class gov.sandia.cognition.math.matrix.NumericalDifferentiator.MatrixJacobian
-
- differentiate(Vectorizable, Evaluator<? super Vector, Double>) - Static method in class gov.sandia.cognition.math.matrix.NumericalDifferentiator.VectorJacobian
-
Static access to the numerical differentiation procedure.
- differentiate(Vectorizable, Evaluator<? super Vector, Double>, double) - Static method in class gov.sandia.cognition.math.matrix.NumericalDifferentiator.VectorJacobian
-
Static access to the numerical differentiation procedure.
- differentiate(Vector) - Method in class gov.sandia.cognition.math.matrix.NumericalDifferentiator.VectorJacobian
-
- differentiate(Double) - Method in class gov.sandia.cognition.statistics.distribution.BetaDistribution.CDF
-
- differentiate(Double) - Method in class gov.sandia.cognition.statistics.distribution.CauchyDistribution.CDF
-
- differentiate(Double) - Method in class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution.CDF
-
- differentiate(Double) - Method in class gov.sandia.cognition.statistics.distribution.ExponentialDistribution.CDF
-
- differentiate(Double) - Method in class gov.sandia.cognition.statistics.distribution.GammaDistribution.CDF
-
- differentiate(Double) - Method in class gov.sandia.cognition.statistics.distribution.InverseGammaDistribution.CDF
-
- differentiate(Double) - Method in class gov.sandia.cognition.statistics.distribution.LaplaceDistribution.CDF
-
- differentiate(Double) - Method in class gov.sandia.cognition.statistics.distribution.LogisticDistribution.CDF
-
- differentiate(Double) - Method in class gov.sandia.cognition.statistics.distribution.LogNormalDistribution.CDF
-
- differentiate(Double) - Method in class gov.sandia.cognition.statistics.distribution.ParetoDistribution.CDF
-
- differentiate(Double) - Method in class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.CDF
-
- differentiate(Double) - Method in class gov.sandia.cognition.statistics.distribution.StudentTDistribution.CDF
-
- differentiate(Double) - Method in class gov.sandia.cognition.statistics.distribution.UniformDistribution.CDF
-
- differentiate(Double) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.CDF
-
- differentiate(Double) - Method in class gov.sandia.cognition.statistics.distribution.WeibullDistribution.CDF
-
- differentiate(Vector) - Method in class gov.sandia.cognition.statistics.method.DistributionParameterEstimator.DistributionWrapper
-
- dimensionality - Variable in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner
-
The dimensionality of the input to the factorization machine.
- dimensionality - Variable in class gov.sandia.cognition.learning.algorithm.svm.PrimalEstimatedSubGradient
-
The dimensionality of the dataset.
- DimensionalityMismatchException - Exception in gov.sandia.cognition.math.matrix
-
Gets thrown when the dimensions don't agree for a matrix/vector operation
- DimensionalityMismatchException() - Constructor for exception gov.sandia.cognition.math.matrix.DimensionalityMismatchException
-
Creates a new instance of DimensionalityMismatchException
without detail message.
- DimensionalityMismatchException(String) - Constructor for exception gov.sandia.cognition.math.matrix.DimensionalityMismatchException
-
Constructs an instance of DimensionalityMismatchException
with the specified detail message.
- DimensionalityMismatchException(int, int) - Constructor for exception gov.sandia.cognition.math.matrix.DimensionalityMismatchException
-
Constructs and instance of DimensionalityMismatchException
with the two mismatching dimensions.
- DimensionFilterableLearner - Interface in gov.sandia.cognition.learning.algorithm
-
Interface for a learner that can be filtered by which dimensions it
includes in learning.
- dimensionsToConsider - Variable in class gov.sandia.cognition.learning.algorithm.tree.AbstractVectorThresholdMaximumGainLearner
-
The array of dimensions for the learner to consider.
- dimensionsToConsider - Variable in class gov.sandia.cognition.learning.algorithm.tree.RandomSubVectorThresholdLearner
-
The dimensions to sample from in the learner.
- dimensionsToConsider - Variable in class gov.sandia.cognition.learning.algorithm.tree.VectorThresholdVarianceLearner
-
The array of 0-based dimensions to consider in the input.
- DirectedNodeEdgeGraph<NodeNameType> - Interface in gov.sandia.cognition.graph
-
This interface defines the minimal set of methods necessary to create and
walk a directed graph.
- DirectedWeightedNodeEdgeGraph<NodeNameType> - Interface in gov.sandia.cognition.graph
-
Adds the necessary methods for a graph with weighted edges.
- DirectionalVectorToDifferentiableScalarFunction - Class in gov.sandia.cognition.learning.algorithm.minimization.line
-
Creates a truly differentiable scalar function from a differentiable Vector
function, instead of using a forward-differences approximation to the
derivative like DirectionalVectorToScalarFunction does.
- DirectionalVectorToDifferentiableScalarFunction(DifferentiableEvaluator<? super Vector, ? extends Double, Vector>, Vector, Vector) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.DirectionalVectorToDifferentiableScalarFunction
-
Creates a new instance of DirectionalVectorToDifferentiableScalarFunction
- DirectionalVectorToScalarFunction - Class in gov.sandia.cognition.learning.algorithm.minimization.line
-
Maps a vector function onto a scalar one by using a
directional vector and vector offset, and the parameter to the
function is a scalar value along the direction from the start-point
offset.
- DirectionalVectorToScalarFunction(Evaluator<? super Vector, ? extends Double>, Vector, Vector) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.DirectionalVectorToScalarFunction
-
Creates a new function that restricts the vectorFunction to a
particular vector direction
- DirectionalVectorToScalarFunction(DirectionalVectorToScalarFunction) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.DirectionalVectorToScalarFunction
-
Copy constructor
- DirectSampler<DataType> - Class in gov.sandia.cognition.statistics.montecarlo
-
Sampler that generates samples directly from a target distribution.
- DirectSampler() - Constructor for class gov.sandia.cognition.statistics.montecarlo.DirectSampler
-
Creates a new instance of DirectSampler
- DirichletDistribution - Class in gov.sandia.cognition.statistics.distribution
-
The Dirichlet distribution is the multivariate generalization of the beta
distribution.
- DirichletDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.DirichletDistribution
-
Creates a new instance of DirichletDistribution
- DirichletDistribution(int) - Constructor for class gov.sandia.cognition.statistics.distribution.DirichletDistribution
-
Creates a new instance of DirichletDistribution
- DirichletDistribution(Vector) - Constructor for class gov.sandia.cognition.statistics.distribution.DirichletDistribution
-
Creates a new instance of DirichletDistribution
- DirichletDistribution(DirichletDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.DirichletDistribution
-
Copy Constructor.
- DirichletDistribution.PDF - Class in gov.sandia.cognition.statistics.distribution
-
PDF of the Dirichlet distribution.
- DirichletProcessClustering - Class in gov.sandia.cognition.learning.algorithm.clustering
-
Clustering algorithm that wraps Dirichlet Process Mixture Model.
- DirichletProcessClustering() - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.DirichletProcessClustering
-
Creates a new instance of DirichletProcessClustering
- DirichletProcessClustering(int) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.DirichletProcessClustering
-
Creates a new instance of DirichletProcessClustering
- DirichletProcessClustering(DirichletProcessMixtureModel<Vector>) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.DirichletProcessClustering
-
Creates a new instance of DirichletProcessClustering
- DirichletProcessMixtureModel<ObservationType> - Class in gov.sandia.cognition.statistics.bayesian
-
An implementation of Dirichlet Process clustering, which estimates the
number of clusters and the centroids of the clusters from a set of
data.
- DirichletProcessMixtureModel() - Constructor for class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel
-
Creates a new instance of DirichletProcessMixtureModel
- DirichletProcessMixtureModel.DPMMCluster<ObservationType> - Class in gov.sandia.cognition.statistics.bayesian
-
Cluster for a step in the DPMM
- DirichletProcessMixtureModel.DPMMLogConditional - Class in gov.sandia.cognition.statistics.bayesian
-
Container for the log conditional likelihood
- DirichletProcessMixtureModel.MultivariateMeanCovarianceUpdater - Class in gov.sandia.cognition.statistics.bayesian
-
Updater that creates specified clusters with distinct means and covariances
- DirichletProcessMixtureModel.MultivariateMeanUpdater - Class in gov.sandia.cognition.statistics.bayesian
-
Updater that creates specified clusters with identical covariances
- DirichletProcessMixtureModel.Sample<ObservationType> - Class in gov.sandia.cognition.statistics.bayesian
-
A sample from the Dirichlet Process Mixture Model.
- DirichletProcessMixtureModel.Updater<ObservationType> - Interface in gov.sandia.cognition.statistics.bayesian
-
Updater for the DPMM
- DiscreteDistribution<DataType> - Interface in gov.sandia.cognition.statistics
-
A Distribution with a countable domain (input) set.
- discreteFourierTransform(ArrayList<Double>) - Static method in class gov.sandia.cognition.math.signals.FourierTransform
-
Computes the brute-force discrete Fourier transform of the input data.
- discreteFourierTransformComplex(ArrayList<ComplexNumber>) - Static method in class gov.sandia.cognition.math.signals.FourierTransform
-
Computes the brute-force discrete Fourier transform of the input data.
- DiscreteNaiveBayesCategorizer<InputType,CategoryType> - Class in gov.sandia.cognition.learning.algorithm.bayes
-
Implementation of a Naive Bayes Classifier for Discrete Data.
- DiscreteNaiveBayesCategorizer() - Constructor for class gov.sandia.cognition.learning.algorithm.bayes.DiscreteNaiveBayesCategorizer
-
Creates a new instance of DiscreteNaiveBayesCategorizer
- DiscreteNaiveBayesCategorizer(int) - Constructor for class gov.sandia.cognition.learning.algorithm.bayes.DiscreteNaiveBayesCategorizer
-
Creates a new instance of DiscreteNaiveBayesCategorizer.
- DiscreteNaiveBayesCategorizer(int, DefaultDataDistribution<CategoryType>, Map<CategoryType, List<DefaultDataDistribution<InputType>>>) - Constructor for class gov.sandia.cognition.learning.algorithm.bayes.DiscreteNaiveBayesCategorizer
-
Creates a new instance of DiscreteNaiveBayesCategorizer.
- DiscreteNaiveBayesCategorizer.Learner<InputType,CategoryType> - Class in gov.sandia.cognition.learning.algorithm.bayes
-
Learner for a DiscreteNaiveBayesCategorizer.
- DiscreteSamplingUtil - Class in gov.sandia.cognition.statistics
-
A utility class for sampling.
- DiscreteSamplingUtil() - Constructor for class gov.sandia.cognition.statistics.DiscreteSamplingUtil
-
- DiscreteTimeFilter<StateType extends CloneableSerializable> - Interface in gov.sandia.cognition.math.signals
-
A discrete-time filter.
- discriminant - Variable in class gov.sandia.cognition.learning.data.DefaultValueDiscriminantPair
-
The discriminant.
- DiscriminantBinaryCategorizer<InputType> - Interface in gov.sandia.cognition.learning.function.categorization
-
Interface for a linear discriminant categorizer in the binary categorization
domain.
- DiscriminantCategorizer<InputType,CategoryType,DiscriminantType extends java.lang.Comparable<? super DiscriminantType>> - Interface in gov.sandia.cognition.learning.function.categorization
-
Interface for a Categorizer that can produce a value to discriminate
between how well different instances fit a given category.
- DistanceSamplingClusterInitializer<ClusterType extends Cluster<DataType>,DataType> - Class in gov.sandia.cognition.learning.algorithm.clustering.initializer
-
Implements FixedClusterInitializer that initializes clusters by
first selecting a random point for the first cluster and then randomly
sampling each successive cluster based on the squared minimum distance from
the point to the existing selected clusters.
- DistanceSamplingClusterInitializer() - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.initializer.DistanceSamplingClusterInitializer
-
Creates a new, empty instance of MinDistanceSamplingClusterInitializer.
- DistanceSamplingClusterInitializer(DivergenceFunction<? super DataType, ? super DataType>, ClusterCreator<ClusterType, DataType>, Random) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.initializer.DistanceSamplingClusterInitializer
-
Creates a new instance of MinDistanceSamplingClusterInitializer.
- Distribution<DataType> - Interface in gov.sandia.cognition.statistics
-
Describes a very high-level distribution of data.
- distribution - Variable in class gov.sandia.cognition.statistics.method.DistributionParameterEstimator.DistributionWrapper
-
Distribution to estimate the parameters of
- DistributionEstimationTask(ClosedFormComputableDistribution<DataType>, Collection<? extends DataType>) - Constructor for class gov.sandia.cognition.statistics.method.MaximumLikelihoodDistributionEstimator.DistributionEstimationTask
-
Creates a new instance of DistributionEstimationTask
- distributionEstimator - Variable in class gov.sandia.cognition.learning.algorithm.bayes.VectorNaiveBayesCategorizer.Learner
-
The distributionLearner for the distribution of each dimension of each category.
- DistributionEstimator<ObservationType,DistributionType extends Distribution<? extends ObservationType>> - Interface in gov.sandia.cognition.statistics
-
A BatchLearner that estimates a Distribution.
- DistributionEstimatorTask(Collection<? extends ObservationType>, BatchLearner<Collection<? extends WeightedValue<? extends ObservationType>>, ? extends ComputableDistribution<ObservationType>>, int) - Constructor for class gov.sandia.cognition.learning.algorithm.hmm.ParallelBaumWelchAlgorithm.DistributionEstimatorTask
-
Creates an instance of DistributionEstimatorTask
- distributionEstimatorTasks - Variable in class gov.sandia.cognition.learning.algorithm.hmm.ParallelBaumWelchAlgorithm
-
Tasks for re-estimating the PDFs.
- distributionFunction - Variable in class gov.sandia.cognition.learning.algorithm.hmm.ParallelHiddenMarkovModel.ObservationLikelihoodTask
-
The PDF.
- distributionLearner - Variable in class gov.sandia.cognition.learning.algorithm.bayes.VectorNaiveBayesCategorizer.OnlineLearner
-
The incremental learner for the distribution used to represent each
dimension.
- distributionLearner - Variable in class gov.sandia.cognition.learning.algorithm.hmm.AbstractBaumWelchAlgorithm
-
Learner for the Distribution Functions of the HMM.
- distributionLearner - Variable in class gov.sandia.cognition.learning.algorithm.hmm.ParallelBaumWelchAlgorithm.DistributionEstimatorTask
-
My copy of the PDF estimator.
- DistributionParameter<ParameterType,ConditionalType extends Distribution<?>> - Interface in gov.sandia.cognition.statistics
-
Allows access to a parameter within a closed-form distribution, given by
the high-level String value.
- DistributionParameterEstimator<DataType,DistributionType extends ClosedFormDistribution<? extends DataType>> - Class in gov.sandia.cognition.statistics.method
-
A method of estimating the parameters of a distribution using an arbitrary
CostFunction and FunctionMinimizer algorithm.
- DistributionParameterEstimator(DistributionType, CostFunction<? super DistributionType, Collection<? extends DataType>>) - Constructor for class gov.sandia.cognition.statistics.method.DistributionParameterEstimator
-
Creates a new instance of DistributionParameterEstimator
- DistributionParameterEstimator(DistributionType, CostFunction<? super DistributionType, Collection<? extends DataType>>, FunctionMinimizer<Vector, Double, ? super DistributionParameterEstimator<DataType, DistributionType>.DistributionWrapper>) - Constructor for class gov.sandia.cognition.statistics.method.DistributionParameterEstimator
-
Creates a new instance of DistributionParameterEstimator
- DistributionParameterEstimator.DistributionWrapper - Class in gov.sandia.cognition.statistics.method
-
Maps the parameters of a Distribution and a CostFunction into a
Vector/Double Evaluator.
- DistributionParameterUtil - Class in gov.sandia.cognition.statistics
-
Functions to assist in creating DistributionParameters.
- DistributionParameterUtil() - Constructor for class gov.sandia.cognition.statistics.DistributionParameterUtil
-
- distributions - Variable in class gov.sandia.cognition.statistics.distribution.LinearMixtureModel
-
Underlying distributions from which we sample
- DistributionWeightedEstimator<ObservationType,DistributionType extends Distribution<? extends ObservationType>> - Interface in gov.sandia.cognition.statistics
-
A BatchLearner that estimates a Distribution from a Collection of
weighted data.
- DistributionWithMean<DataType> - Interface in gov.sandia.cognition.statistics
-
A Distribution that has a well-defined mean, or first central moment.
- DistributionWrapper(DistributionType, CostFunction<? super DistributionType, ? super Collection<? extends DataType>>) - Constructor for class gov.sandia.cognition.statistics.method.DistributionParameterEstimator.DistributionWrapper
-
Creates a new instance of DistributionWrapper
- divergence - Variable in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation
-
The divergence function to use.
- divergenceFunction - Variable in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer
-
The divergence function used to find the distance between two clusters.
- divergenceFunction - Variable in class gov.sandia.cognition.learning.algorithm.clustering.divergence.WithinClusterDivergenceWrapper
-
The divergence function.
- divergenceFunction - Variable in class gov.sandia.cognition.learning.algorithm.clustering.KMeansClusterer
-
The divergence function between cluster being used.
- divergenceFunction - Variable in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer
-
- divergenceFunction - Variable in class gov.sandia.cognition.learning.function.distance.DefaultDivergenceFunctionContainer
-
The internal divergence function for the object to use.
- divergenceFunction - Variable in class gov.sandia.cognition.learning.function.distance.DivergencesEvaluator
-
The divergence function to apply between the data and the input.
- divergenceFunction - Variable in class gov.sandia.cognition.learning.function.distance.DivergencesEvaluator.Learner
-
The divergence function to apply between the data and the input.
- DivergenceFunction<FirstType,SecondType> - Interface in gov.sandia.cognition.math
-
The DivergenceFunction class defines the functionality of something that
computes the divergence between two objects.
- DivergenceFunctionContainer<FirstType,SecondType> - Interface in gov.sandia.cognition.learning.function.distance
-
Interface for a class that holds a divergence function.
- DivergencesEvaluator<InputType,ValueType> - Class in gov.sandia.cognition.learning.function.distance
-
Evaluates the divergence (distance) between an input and a list of values,
storing the resulting divergence values in a vector.
- DivergencesEvaluator() - Constructor for class gov.sandia.cognition.learning.function.distance.DivergencesEvaluator
-
Creates a new DivergencesEvaluator with a null divergence
function and an empty set of values.
- DivergencesEvaluator(DivergenceFunction<? super ValueType, ? super InputType>, Collection<ValueType>) - Constructor for class gov.sandia.cognition.learning.function.distance.DivergencesEvaluator
-
Creates a new DivergencesEvaluator with the given divergence
and values.
- DivergencesEvaluator(DivergenceFunction<? super ValueType, ? super InputType>, Collection<ValueType>, VectorFactory<?>) - Constructor for class gov.sandia.cognition.learning.function.distance.DivergencesEvaluator
-
Creates a new DivergencesEvaluator with the given divergence
and values.
- DivergencesEvaluator.Learner<DataType,InputType,ValueType> - Class in gov.sandia.cognition.learning.function.distance
-
A learner adapter for the DivergencesEvaluator.
- divide(RingType) - Method in class gov.sandia.cognition.math.AbstractEuclideanRing
-
- divide(ComplexNumber) - Method in class gov.sandia.cognition.math.ComplexNumber
-
Arithmetic division of this by other using polar coordinates:
magnitude = this.magnitude / other.magnitude
phase = this.phase - other.phase
answer.realPart = magnitude * cos( phase )
answer.imaginaryPart = magnitude * sin( phase )
- divide(RingType) - Method in interface gov.sandia.cognition.math.EuclideanRing
-
Divides this value by the other value, returning the
result of the division as a new value.
- divide(double, double) - Static method in class gov.sandia.cognition.math.LogMath
-
Divides two log-domain values.
- divide(LogNumber) - Method in class gov.sandia.cognition.math.LogNumber
-
Divides this value by another value and returns the result.
- divide(MutableDouble) - Method in class gov.sandia.cognition.math.MutableDouble
-
- divide(MutableInteger) - Method in class gov.sandia.cognition.math.MutableInteger
-
- divide(MutableLong) - Method in class gov.sandia.cognition.math.MutableLong
-
- divide(UnsignedLogNumber) - Method in class gov.sandia.cognition.math.UnsignedLogNumber
-
Divides this value by another value and returns the result.
- divide(Duration) - Method in class gov.sandia.cognition.time.DefaultDuration
-
- divide(double) - Method in class gov.sandia.cognition.time.DefaultDuration
-
- divide(Duration) - Method in interface gov.sandia.cognition.time.Duration
-
Divides this duration by the given duration and returns the ratio.
- divide(double) - Method in interface gov.sandia.cognition.time.Duration
-
Divides this duration by the given scalar value and returns the result.
- divideByNorm1(Vector) - Static method in class gov.sandia.cognition.math.matrix.VectorUtil
-
Returns a new vector whose elements are the elements of the original
vector, divided by the 1-norm of the vector (the sum of the absolute
values of the elements).
- divideByNorm1Equals(Vector) - Static method in class gov.sandia.cognition.math.matrix.VectorUtil
-
Divides all of the given elements of the vector by the 1-norm (the sum
of the absolute values of the elements).
- divideByNorm2(Vector) - Static method in class gov.sandia.cognition.math.matrix.VectorUtil
-
Returns a new vector whose elements are the elements of the original
vector, divided by the 2-norm of the vector (the square root of the sum
of the squared values of the elements).
- divideByNorm2Equals(Vector) - Static method in class gov.sandia.cognition.math.matrix.VectorUtil
-
Divides all of the given elements of the vector by the 2-norm (the square
root of the sum of the squared values of the elements).
- dividedBy(ComplexNumber) - Method in class gov.sandia.cognition.math.ComplexNumber
-
- dividedByEquals(ComplexNumber) - Method in class gov.sandia.cognition.math.ComplexNumber
-
- divideEquals(ComplexNumber) - Method in class gov.sandia.cognition.math.ComplexNumber
-
Inline arithmetic division of this by other:
this.magnitude /= other.magnitude
this.phase -= other.phase
- divideEquals(RingType) - Method in interface gov.sandia.cognition.math.EuclideanRing
-
Inline divises this value by the other value, storing
the result inside this.
- divideEquals(LogNumber) - Method in class gov.sandia.cognition.math.LogNumber
-
Divides this value by another value and stores the result in
this value.
- divideEquals(MutableDouble) - Method in class gov.sandia.cognition.math.MutableDouble
-
- divideEquals(MutableInteger) - Method in class gov.sandia.cognition.math.MutableInteger
-
- divideEquals(MutableLong) - Method in class gov.sandia.cognition.math.MutableLong
-
- divideEquals(UnsignedLogNumber) - Method in class gov.sandia.cognition.math.UnsignedLogNumber
-
Divides this value by another value and stores the result in
this value.
- Document - Interface in gov.sandia.cognition.text.document
-
Defines the interface for a document.
- Documentation - Annotation Type in gov.sandia.cognition.annotation
-
The Documentation annotation marks that something is just
documentation.
- documentCount - Variable in class gov.sandia.cognition.text.term.vector.weighter.global.AbstractFrequencyBasedGlobalTermWeighter
-
The number of documents the weight is computed over.
- documentCount - Variable in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler
-
The number of documents in the dataset.
- documentCount - Variable in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis
-
The number of documents.
- DocumentExtractionException - Exception in gov.sandia.cognition.text.document.extractor
-
An exception that occurs during document extraction.
- DocumentExtractionException() - Constructor for exception gov.sandia.cognition.text.document.extractor.DocumentExtractionException
-
Creates a new instance of DocumentExtractionException
without detail message.
- DocumentExtractionException(String) - Constructor for exception gov.sandia.cognition.text.document.extractor.DocumentExtractionException
-
Constructs an instance of DocumentExtractionException
with the specified detail message.
- DocumentExtractionException(Throwable) - Constructor for exception gov.sandia.cognition.text.document.extractor.DocumentExtractionException
-
Constructs an instance of DocumentExtractionException
with the specified cause.
- DocumentExtractionException(String, Throwable) - Constructor for exception gov.sandia.cognition.text.document.extractor.DocumentExtractionException
-
Constructs an instance of DocumentExtractionException
with the specified detail message and cause.
- DocumentExtractor - Interface in gov.sandia.cognition.text.document.extractor
-
Interface for extracting documents from files.
- DocumentFieldConcatenator - Class in gov.sandia.cognition.text.convert
-
A document-text converter that concatenates multiple text fields from a
document together for further processing.
- DocumentFieldConcatenator() - Constructor for class gov.sandia.cognition.text.convert.DocumentFieldConcatenator
-
Creates a new DocumentFieldConcatenator with an empty list of
fields and a newline separator.
- DocumentFieldConcatenator(List<String>, String) - Constructor for class gov.sandia.cognition.text.convert.DocumentFieldConcatenator
-
Creates a new DocumentFieldConcatenator with the given field
names and field separator.
- DocumentReference - Interface in gov.sandia.cognition.text.document
-
Interface for a reference to a document.
- DocumentSampleTask(Vector, int, int) - Constructor for class gov.sandia.cognition.text.topic.ParallelLatentDirichletAllocationVectorGibbsSampler.DocumentSampleTask
-
Creates a new instance of DocumentSampleTask
- documentsByTerms - Variable in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis
-
The document-by-term matrix.
- DocumentSingleFieldConverter - Class in gov.sandia.cognition.text.convert
-
Extracts a single field from a document.
- DocumentSingleFieldConverter() - Constructor for class gov.sandia.cognition.text.convert.DocumentSingleFieldConverter
-
Creates a new DocumentSingleFieldConverter with the body field
as the field to extract.
- DocumentSingleFieldConverter(String) - Constructor for class gov.sandia.cognition.text.convert.DocumentSingleFieldConverter
-
Creates a new DocumentSingleFieldConverter with the given field
to extract.
- documentTermCounts - Variable in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler
-
For each unique term (unique per document), the number of times that term
occurs in the document.
- documentTermPairsCounts - Variable in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler
-
the number of unique terms in each document.
- documentTerms - Variable in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler
-
For each unique term (unique per document) which term id it maps to.
- documentTopicCount - Variable in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler
-
For each document, the number of terms assigned to each topic.
- documentTopicProbabilities - Variable in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler.Result
-
The document-topic probabilities, which are often called the theta
model parameters.
- documentTopicSum - Variable in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler
-
The number of term occurrences in each document.
- documentWeights - Variable in class gov.sandia.cognition.text.algorithm.ValenceSpreader.Result
-
The weights assigned to all of the input documents.
- Domain(int, int) - Constructor for class gov.sandia.cognition.statistics.distribution.MultinomialDistribution.Domain
-
Creates a new instance of Domain
- dominance - Variable in class gov.sandia.cognition.text.term.vector.weighter.global.DominanceGlobalTermWeighter
-
A vector caching the global dominance weight of the document collection.
- DominanceGlobalTermWeighter - Class in gov.sandia.cognition.text.term.vector.weighter.global
-
Implements the dominance term gloal weighting scheme.
- DominanceGlobalTermWeighter() - Constructor for class gov.sandia.cognition.text.term.vector.weighter.global.DominanceGlobalTermWeighter
-
Creates a new DominanceGlobalTermWeighter.
- DominanceGlobalTermWeighter(VectorFactory<? extends Vector>) - Constructor for class gov.sandia.cognition.text.term.vector.weighter.global.DominanceGlobalTermWeighter
-
Creates a new DominanceGlobalTermWeighter.
- dot(VectorType) - Method in class gov.sandia.cognition.math.matrix.AbstractVectorSpace
-
- dot(InfiniteVector<KeyType>) - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
-
- dot(VectorType) - Method in interface gov.sandia.cognition.math.matrix.VectorSpace
-
The inner product of this vector with the given vector.
- dotDivide(Matrix) - Method in class gov.sandia.cognition.math.matrix.AbstractMatrix
-
- dotDivide(Vector) - Method in class gov.sandia.cognition.math.matrix.AbstractVector
-
- dotDivide(Matrix) - Method in interface gov.sandia.cognition.math.matrix.Matrix
-
Element-wise division of this by other.
- dotDivide(Vector) - Method in interface gov.sandia.cognition.math.matrix.Vector
-
Element-wise division of this by other.
- dotDivideEquals(Matrix) - Method in class gov.sandia.cognition.math.matrix.AbstractMatrix
-
- dotDivideEquals(Vector) - Method in class gov.sandia.cognition.math.matrix.AbstractVector
-
- dotDivideEquals(Matrix) - Method in interface gov.sandia.cognition.math.matrix.Matrix
-
Inline element-wise division of this by other.
- dotDivideEquals(Vector) - Method in interface gov.sandia.cognition.math.matrix.Vector
-
Inline element-wise division of this by other.
- dotProduct(Vector) - Method in class gov.sandia.cognition.math.matrix.AbstractVector
-
- dotProduct(DenseVector) - Method in class gov.sandia.cognition.math.matrix.custom.DenseVector
-
- dotProduct(SparseVector) - Method in class gov.sandia.cognition.math.matrix.custom.DenseVector
-
- dotProduct(SparseVector) - Method in class gov.sandia.cognition.math.matrix.custom.SparseVector
-
- dotProduct(DenseVector) - Method in class gov.sandia.cognition.math.matrix.custom.SparseVector
-
- dotProduct(InfiniteVector<KeyType>) - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
-
- dotProduct(Vector) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJVector
-
- dotProduct(AbstractMTJVector) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJVector
-
Inner Vector product between two Vectors
- dotProduct(VectorType) - Method in interface gov.sandia.cognition.math.matrix.VectorSpace
-
The inner product of this vector with the given vector.
- dotTimes(RingType) - Method in class gov.sandia.cognition.math.AbstractRing
-
- dotTimes(LogNumber) - Method in class gov.sandia.cognition.math.LogNumber
-
- dotTimes(Vector) - Method in class gov.sandia.cognition.math.matrix.custom.DenseVector
-
- dotTimes(InfiniteVector<KeyType>) - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
-
- dotTimes(Matrix) - Method in interface gov.sandia.cognition.math.matrix.DiagonalMatrix
-
- dotTimes(Matrix) - Method in class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixMTJ
-
- dotTimes(MutableDouble) - Method in class gov.sandia.cognition.math.MutableDouble
-
- dotTimes(MutableInteger) - Method in class gov.sandia.cognition.math.MutableInteger
-
- dotTimes(MutableLong) - Method in class gov.sandia.cognition.math.MutableLong
-
- dotTimes(RingType) - Method in interface gov.sandia.cognition.math.Ring
-
Element-wise multiplication of this and other
- dotTimes(UnsignedLogNumber) - Method in class gov.sandia.cognition.math.UnsignedLogNumber
-
- dotTimesEquals(ComplexNumber) - Method in class gov.sandia.cognition.math.ComplexNumber
-
- dotTimesEquals(LogNumber) - Method in class gov.sandia.cognition.math.LogNumber
-
- dotTimesEquals(Matrix) - Method in class gov.sandia.cognition.math.matrix.AbstractMatrix
-
- dotTimesEquals(Vector) - Method in class gov.sandia.cognition.math.matrix.AbstractVector
-
- dotTimesEquals(SparseMatrix) - Method in class gov.sandia.cognition.math.matrix.custom.DenseMatrix
-
Type-specific version of dotTimesEquals for combining whatever type this
is with the input sparse matrix.
- dotTimesEquals(DenseMatrix) - Method in class gov.sandia.cognition.math.matrix.custom.DenseMatrix
-
- dotTimesEquals(DiagonalMatrix) - Method in class gov.sandia.cognition.math.matrix.custom.DenseMatrix
-
Type-specific version of dotTimesEquals for combining whatever type this
is with the input diagonal matrix.
- dotTimesEquals(DenseVector) - Method in class gov.sandia.cognition.math.matrix.custom.DenseVector
-
- dotTimesEquals(SparseVector) - Method in class gov.sandia.cognition.math.matrix.custom.DenseVector
-
- dotTimesEquals(SparseMatrix) - Method in class gov.sandia.cognition.math.matrix.custom.DiagonalMatrix
-
- dotTimesEquals(DenseMatrix) - Method in class gov.sandia.cognition.math.matrix.custom.DiagonalMatrix
-
- dotTimesEquals(DiagonalMatrix) - Method in class gov.sandia.cognition.math.matrix.custom.DiagonalMatrix
-
- dotTimesEquals(SparseMatrix) - Method in class gov.sandia.cognition.math.matrix.custom.SparseMatrix
-
Type-specific version of dotTimesEquals for combining whatever type this
is with the input sparse matrix.
- dotTimesEquals(DenseMatrix) - Method in class gov.sandia.cognition.math.matrix.custom.SparseMatrix
-
Type-specific version of dotTimesEquals for combining whatever type this
is with the input dense matrix.
- dotTimesEquals(DiagonalMatrix) - Method in class gov.sandia.cognition.math.matrix.custom.SparseMatrix
-
Type-specific version of dotTimesEquals for combining whatever type this
is with the input diagonal matrix.
- dotTimesEquals(DenseVector) - Method in class gov.sandia.cognition.math.matrix.custom.SparseVector
-
- dotTimesEquals(SparseVector) - Method in class gov.sandia.cognition.math.matrix.custom.SparseVector
-
- dotTimesEquals(InfiniteVector<KeyType>) - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
-
- dotTimesEquals(Matrix) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix
-
- dotTimesEquals(AbstractMTJMatrix) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix
-
Inline element-wise multiplication of the elements in this
and matrix, modifies the elements of this
- dotTimesEquals(Vector) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJVector
-
- dotTimesEquals(AbstractMTJMatrix) - Method in class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixMTJ
-
- dotTimesEquals(MutableDouble) - Method in class gov.sandia.cognition.math.MutableDouble
-
- dotTimesEquals(MutableInteger) - Method in class gov.sandia.cognition.math.MutableInteger
-
- dotTimesEquals(MutableLong) - Method in class gov.sandia.cognition.math.MutableLong
-
- dotTimesEquals(RingType) - Method in interface gov.sandia.cognition.math.Ring
-
Inline element-wise multiplication of this and
other
- dotTimesEquals(UnsignedLogNumber) - Method in class gov.sandia.cognition.math.UnsignedLogNumber
-
- dotTimesEquals(RandomVariable<Number>) - Method in class gov.sandia.cognition.statistics.UnivariateRandomVariable
-
- DoubleArrayList - Class in gov.sandia.cognition.collection
-
A memory-dense, base-type double vector that permits adding new elements,
altering elements, etc.
- DoubleArrayList() - Constructor for class gov.sandia.cognition.collection.DoubleArrayList
-
Initializes an empty vector with a default allocation.
- DoubleArrayList(int) - Constructor for class gov.sandia.cognition.collection.DoubleArrayList
-
Initializes an empty vector (allocating startSize locations for
additions).
- DoubleArrayList(DoubleArrayList) - Constructor for class gov.sandia.cognition.collection.DoubleArrayList
-
Copy constructor
- DoubleJacobian() - Constructor for class gov.sandia.cognition.math.matrix.NumericalDifferentiator.DoubleJacobian
-
Default constructor
- DoubleJacobian(Evaluator<? super Double, Double>) - Constructor for class gov.sandia.cognition.math.matrix.NumericalDifferentiator.DoubleJacobian
-
Creates a new instance of VectorJacobian
- DoubleJacobian(Evaluator<? super Double, Double>, double) - Constructor for class gov.sandia.cognition.math.matrix.NumericalDifferentiator.DoubleJacobian
-
Create a new instance of VectorJacobian
- DoubleReuseRandom - Class in gov.sandia.cognition.util
-
An extension of java.util.Random that builds a list of random doubles
and then serves up the random values from the array, returning to the beginning
when the end is reached.
- DoubleReuseRandom(int) - Constructor for class gov.sandia.cognition.util.DoubleReuseRandom
-
Creates DoubleReuseRandom with the given parameters
- DoubleReuseRandom(int, long) - Constructor for class gov.sandia.cognition.util.DoubleReuseRandom
-
Creates DoubleReuseRandom with the given parameters
- doubleValue() - Method in class gov.sandia.cognition.math.LogNumber
-
- doubleValue() - Method in class gov.sandia.cognition.math.MutableDouble
-
- doubleValue() - Method in class gov.sandia.cognition.math.MutableInteger
-
- doubleValue() - Method in class gov.sandia.cognition.math.MutableLong
-
- doubleValue() - Method in class gov.sandia.cognition.math.UnsignedLogNumber
-
- DPMMAssignments(ArrayList<Integer>, DirichletProcessMixtureModel.DPMMLogConditional) - Constructor for class gov.sandia.cognition.statistics.bayesian.ParallelDirichletProcessMixtureModel.DPMMAssignments
-
Constructor
- DPMMCluster(Collection<? extends ObservationType>, ProbabilityFunction<? super ObservationType>) - Constructor for class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.DPMMCluster
-
Creates a new instance of DPMMCluster
- DPMMLogConditional() - Constructor for class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.DPMMLogConditional
-
Default constructor
- Duration - Interface in gov.sandia.cognition.time
-
Represents a duration of time.
- DynamicArrayMap<ValueType> - Class in gov.sandia.cognition.collection
-
A DynamicArrayList is a class that implements a map from an
integer to an Object type on top of an expanding array.
- DynamicArrayMap() - Constructor for class gov.sandia.cognition.collection.DynamicArrayMap
-
Creates a new instance of DynamicArrayMap.
- DynamicArrayMap(int) - Constructor for class gov.sandia.cognition.collection.DynamicArrayMap
-
Creates a new instance of DynamicArrayMap with the given initial
capacity.
- DynamicArrayMap(DynamicArrayMap<? extends ValueType>) - Constructor for class gov.sandia.cognition.collection.DynamicArrayMap
-
Creates a new instance of DynamicArrayMap using the given mapping to copy
into this map.