- N - Variable in class gov.sandia.cognition.math.Combinations.AbstractCombinationsIterator
-
Universe set size
- n - Variable in class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution
-
Number of observations, similar to the Binomial N,
must be greater than zero
- NAME - Static variable in class gov.sandia.cognition.statistics.bayesian.conjugate.BernoulliBayesianEstimator.Parameter
-
Name of the parameter, "p".
- NAME - Static variable in class gov.sandia.cognition.statistics.bayesian.conjugate.BinomialBayesianEstimator.Parameter
-
Name of the parameter, "p".
- NAME - Static variable in class gov.sandia.cognition.statistics.bayesian.conjugate.ExponentialBayesianEstimator.Parameter
-
Default name of the parameter, "rate".
- NAME - Static variable in class gov.sandia.cognition.statistics.bayesian.conjugate.GammaInverseScaleBayesianEstimator.Parameter
-
Default name, "inverse-scale".
- NAME - Static variable in class gov.sandia.cognition.statistics.bayesian.conjugate.MultinomialBayesianEstimator.Parameter
-
Name of the parameter, "parameters".
- NAME - Static variable in class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanBayesianEstimator.Parameter
-
Name of the parameter, "mean".
- NAME - Static variable in class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanCovarianceBayesianEstimator.Parameter
-
Name of the parameter, "meanAndCovariance".
- NAME - Static variable in class gov.sandia.cognition.statistics.bayesian.conjugate.PoissonBayesianEstimator.Parameter
-
Name of the parameter, "rate".
- NAME - Static variable in class gov.sandia.cognition.statistics.bayesian.conjugate.UniformDistributionBayesianEstimator.Parameter
-
Name of the parameter, "maxSupport".
- NAME - Static variable in class gov.sandia.cognition.statistics.bayesian.conjugate.UnivariateGaussianMeanBayesianEstimator.Parameter
-
Name of the parameter, "mean".
- NAME - Static variable in class gov.sandia.cognition.statistics.bayesian.conjugate.UnivariateGaussianMeanVarianceBayesianEstimator.Parameter
-
Name of the parameter, "meanAndVariance".
- name - Variable in class gov.sandia.cognition.util.AbstractNamed
-
The name of the object.
- Named - Interface in gov.sandia.cognition.util
-
The Named interface defines an Object that has a useful name
attached to it, which is common for many types of Objects.
- NamedValue<ValueType> - Interface in gov.sandia.cognition.util
-
The NamedValue class describes a name-value pair.
- nativeBlasAvailable() - Static method in class gov.sandia.cognition.math.matrix.custom.NativeBlasHandler
-
Returns true if a native version of BLAS was found.
- NativeBlasHandler - Class in gov.sandia.cognition.math.matrix.custom
-
This class provides a uniform interface between working with a native-coded
BLAS package or the jBLAS package.
- NativeBlasHandler() - Constructor for class gov.sandia.cognition.math.matrix.custom.NativeBlasHandler
-
Initializes the BLAS instances, searching for the native BLAS
implementation.
- NativeMatrixTests - Class in gov.sandia.cognition.math.matrix.mtj
-
Tests to see if native versions of LAPACK and BLAS are loaded.
- NativeMatrixTests() - Constructor for class gov.sandia.cognition.math.matrix.mtj.NativeMatrixTests
-
- NearestNeighbor<InputType,OutputType> - Interface in gov.sandia.cognition.learning.algorithm.nearest
-
The NearestNeighborExhaustive class implements a simple evaluator
that looks up a given input object in a collection of input-output pair
examples and returns the output associated with the most similar input.
- NearestNeighborExhaustive<InputType,OutputType> - Class in gov.sandia.cognition.learning.algorithm.nearest
-
The NearestNeighborExhaustive class implements a simple evaluator
that looks up a given input object in a collection of input-output pair
examples and returns the output associated with the most similar input.
- NearestNeighborExhaustive() - Constructor for class gov.sandia.cognition.learning.algorithm.nearest.NearestNeighborExhaustive
-
Creates a new instance of NearestNeighborExhaustive.
- NearestNeighborExhaustive(DivergenceFunction<? super InputType, ? super InputType>) - Constructor for class gov.sandia.cognition.learning.algorithm.nearest.NearestNeighborExhaustive
-
Creates a new instance of NearestNeighborExhaustive.
- NearestNeighborExhaustive(DivergenceFunction<? super InputType, ? super InputType>, Collection<? extends InputOutputPair<? extends InputType, OutputType>>) - Constructor for class gov.sandia.cognition.learning.algorithm.nearest.NearestNeighborExhaustive
-
Creates a new instance of NearestNeighborExhaustive.
- NearestNeighborExhaustive.Learner<InputType,OutputType> - Class in gov.sandia.cognition.learning.algorithm.nearest
-
The NearestNeighborExhaustive.Learner class implements a batch learner for
the NearestNeighborExhaustive class.
- NearestNeighborKDTree<InputType extends Vectorizable,OutputType> - Class in gov.sandia.cognition.learning.algorithm.nearest
-
A KDTree-based implementation of the nearest neighbor algorithm.
- NearestNeighborKDTree() - Constructor for class gov.sandia.cognition.learning.algorithm.nearest.NearestNeighborKDTree
-
Creates a new instance of NearestNeighborKDTree.
- NearestNeighborKDTree(KDTree<InputType, OutputType, InputOutputPair<? extends InputType, OutputType>>, DivergenceFunction<? super InputType, ? super InputType>) - Constructor for class gov.sandia.cognition.learning.algorithm.nearest.NearestNeighborKDTree
-
Creates a new instance of NearestNeighborKDTree
- NearestNeighborKDTree.Learner<InputType extends Vectorizable,OutputType> - Class in gov.sandia.cognition.learning.algorithm.nearest
-
This is a BatchLearner interface for creating a new NearestNeighbor
from a given dataset, simply a pass-through to the constructor of
NearestNeighbor
- negative() - Method in class gov.sandia.cognition.math.AbstractRing
-
- negative - Variable in class gov.sandia.cognition.math.LogNumber
-
The sign of the value, sign(value).
- negative() - Method in class gov.sandia.cognition.math.LogNumber
-
- negative() - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
-
- negative() - Method in class gov.sandia.cognition.math.MutableDouble
-
- negative() - Method in class gov.sandia.cognition.math.MutableInteger
-
- negative() - Method in class gov.sandia.cognition.math.MutableLong
-
- negative() - Method in interface gov.sandia.cognition.math.Ring
-
Returns the element-wise negation of this, such that
this.plus( this.negative() ) has only zero elements.
- negative() - Method in class gov.sandia.cognition.math.UnsignedLogNumber
-
- negative() - Method in class gov.sandia.cognition.time.DefaultDuration
-
- negative() - Method in interface gov.sandia.cognition.time.Duration
-
Returns the negative of this duration.
- NegativeBinomialDistribution - Class in gov.sandia.cognition.statistics.distribution
-
Negative binomial distribution, also known as the Polya distribution,
gives the number of successes of a series of Bernoulli trials before
recording a given number of failures.
- NegativeBinomialDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.NegativeBinomialDistribution
-
Creates a new instance of NegativeBinomialDistribution
- NegativeBinomialDistribution(double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.NegativeBinomialDistribution
-
Creates a new instance of NegativeBinomialDistribution
- NegativeBinomialDistribution(NegativeBinomialDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.NegativeBinomialDistribution
-
Copy constructor
- NegativeBinomialDistribution.CDF - Class in gov.sandia.cognition.statistics.distribution
-
CDF of the NegativeBinomialDistribution
- NegativeBinomialDistribution.MaximumLikelihoodEstimator - Class in gov.sandia.cognition.statistics.distribution
-
Maximum likelihood estimator of the distribution
- NegativeBinomialDistribution.PMF - Class in gov.sandia.cognition.statistics.distribution
-
PMF of the NegativeBinomialDistribution.
- NegativeBinomialDistribution.WeightedMaximumLikelihoodEstimator - Class in gov.sandia.cognition.statistics.distribution
-
Weighted maximum likelihood estimator of the distribution
- negativeEquals() - Method in class gov.sandia.cognition.math.AbstractRing
-
- negativeEquals() - Method in class gov.sandia.cognition.math.LogNumber
-
- negativeEquals() - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
-
- negativeEquals() - Method in class gov.sandia.cognition.math.MutableDouble
-
- negativeEquals() - Method in class gov.sandia.cognition.math.MutableInteger
-
- negativeEquals() - Method in class gov.sandia.cognition.math.MutableLong
-
- negativeEquals() - Method in interface gov.sandia.cognition.math.Ring
-
Inline element-wise negation of this
- negativeEquals() - Method in class gov.sandia.cognition.math.UnsignedLogNumber
-
- NegativeLogLikelihood<DataType> - Class in gov.sandia.cognition.learning.function.cost
-
CostFunction for computing the maximum likelihood
(because we are minimizing the negative of the log likelihood)
- NegativeLogLikelihood() - Constructor for class gov.sandia.cognition.learning.function.cost.NegativeLogLikelihood
-
Default constructor
- NegativeLogLikelihood(Collection<? extends DataType>) - Constructor for class gov.sandia.cognition.learning.function.cost.NegativeLogLikelihood
-
Creates a new instance of NegativeLogLikelihood
- NegativeLogLikelihoodTask(Collection<? extends DataType>) - Constructor for class gov.sandia.cognition.learning.function.cost.ParallelNegativeLogLikelihood.NegativeLogLikelihoodTask
-
Creates a new instance of NegativeLogLikelihoodTask
- Neighbor(OutputType, double) - Constructor for class gov.sandia.cognition.learning.algorithm.nearest.KNearestNeighborExhaustive.Neighbor
-
Creates a new neighbor.
- Neighbor(PairType, double) - Constructor for class gov.sandia.cognition.math.geometry.KDTree.Neighborhood.Neighbor
-
Creates a new neighbor.
- Neighborhood(int) - Constructor for class gov.sandia.cognition.math.geometry.KDTree.Neighborhood
-
Creates a new Neighborhood.
- NeighborhoodGaussianClusterInitializer - Class in gov.sandia.cognition.learning.algorithm.clustering.initializer
-
Creates GaussianClusters near existing, but not on top of, data points.
- NeighborhoodGaussianClusterInitializer() - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.initializer.NeighborhoodGaussianClusterInitializer
-
Default constructor.
- NeighborhoodGaussianClusterInitializer(Random) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.initializer.NeighborhoodGaussianClusterInitializer
-
Creates a new instance of NeighborhoodGaussianClusterInitializer
- NeighborhoodGaussianClusterInitializer(double, double, Random) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.initializer.NeighborhoodGaussianClusterInitializer
-
Creates a new instance of NeighborhoodGaussianClusterInitializer
- NeighborhoodIterator() - Constructor for class gov.sandia.cognition.math.geometry.KDTree.Neighborhood.NeighborhoodIterator
-
Default constructor.
- neighborIds(int) - Method in class gov.sandia.cognition.graph.GraphMetrics
-
Returns the ids of all neighbors for the input node id.
- neighborIds(NodeNameType) - Method in class gov.sandia.cognition.graph.GraphMetrics
-
Returns the ids of all neighbors for the input node name.
- neighbors(int) - Method in class gov.sandia.cognition.graph.GraphMetrics
-
Returns the names of all neighbors for the input node id.
- neighbors(NodeNameType) - Method in class gov.sandia.cognition.graph.GraphMetrics
-
Returns the names of all neighbors for the input node name.
- NemenyiConfidence - Class in gov.sandia.cognition.statistics.method
-
The Nemenyi test is the rank-based analogue of the Tukey multiple-comparison
test.
- NemenyiConfidence() - Constructor for class gov.sandia.cognition.statistics.method.NemenyiConfidence
-
Creates a new instance of NemenyiConfidence
- NemenyiConfidence.Statistic - Class in gov.sandia.cognition.statistics.method
-
Statistic from Nemenyi's multiple comparison test
- next() - Method in class gov.sandia.cognition.collection.AbstractLogNumberMap.SimpleIterator
-
- next() - Method in class gov.sandia.cognition.collection.AbstractMutableDoubleMap.SimpleIterator
-
- next() - Method in class gov.sandia.cognition.collection.FiniteCapacityBuffer.InternalIterator
-
- next() - Method in class gov.sandia.cognition.collection.MultiIterator
- next() - Method in class gov.sandia.cognition.math.Combinations.IndexIterator
-
- next() - Method in class gov.sandia.cognition.math.Combinations.SubsetIterator
-
- next() - Method in class gov.sandia.cognition.math.geometry.KDTree.InOrderKDTreeIterator
-
- next() - Method in class gov.sandia.cognition.math.geometry.KDTree.Neighborhood.NeighborhoodIterator
-
- next() - Method in class gov.sandia.cognition.math.matrix.custom.VectorIterator
-
- next() - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector.SimpleIterator
-
- next() - Method in class gov.sandia.cognition.math.matrix.MatrixUnionIterator
- next() - Method in class gov.sandia.cognition.math.matrix.VectorUnionIterator
-
- next() - Method in class gov.sandia.cognition.statistics.distribution.MultinomialDistribution.Domain.MultinomialIterator
-
- nextDouble() - Method in class gov.sandia.cognition.util.DoubleReuseRandom
-
Returns the next double from the array
- nextIndex() - Method in class gov.sandia.cognition.collection.FiniteCapacityBuffer.InternalIterator
-
- nextNonEmptyLine(BufferedReader) - Static method in class gov.sandia.cognition.io.CSVUtility
-
Returns the next non-empty line from the given BufferedReader as an
array of the CSV entries.
- nextNonEmptyLine(BufferedReader, char) - Static method in class gov.sandia.cognition.io.CSVUtility
-
Returns the next non-empty line from the given BufferedReader as an
array of the CSV entries.
- nextPrime(long) - Static method in class gov.sandia.cognition.hash.HashFunctionUtil
-
Returns the next-greater prime, sort of like "ceil" but for primes.
- NGramFilter - Class in gov.sandia.cognition.text.term.filter
-
A term filter that creates an n-gram of terms.
- NGramFilter() - Constructor for class gov.sandia.cognition.text.term.filter.NGramFilter
-
Creates a new NGramFilter with the default size.
- NGramFilter(int) - Constructor for class gov.sandia.cognition.text.term.filter.NGramFilter
-
Creates a new NGramFilter with the given size.
- Node(Quadtree<DataType>.Node, Rectangle2D.Double) - Constructor for class gov.sandia.cognition.math.geometry.Quadtree.Node
-
Creates a new Node with the given parent and region bounds.
- NodeNameAwareEnergyFunction<LabelType,NodeNameType> - Interface in gov.sandia.cognition.graph.inference
-
Helpful interface that adds per-node labeling and getting results from energy
functions using the graph's node label type.
- NodePartitioning<NodeNameType> - Interface in gov.sandia.cognition.graph.community
-
Interface for all graph partitioning classes
- nodes - Variable in class gov.sandia.cognition.graph.inference.SumProductInferencingAlgorithm
-
This internally stores the nodes with their values for the learning
- nodeValue - Variable in class gov.sandia.cognition.math.geometry.KDTree.InOrderKDTreeIterator
-
Value of the node
- norm(double) - Method in class gov.sandia.cognition.math.matrix.AbstractVectorSpace
-
- norm(double) - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
-
- norm(double) - Method in interface gov.sandia.cognition.math.matrix.VectorSpace
-
Returns the p-norm of the Vector with the given power.
- norm1() - Method in class gov.sandia.cognition.math.matrix.AbstractVectorSpace
-
- norm1() - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
-
- norm1() - Method in interface gov.sandia.cognition.math.matrix.VectorSpace
-
1-norm of the vector (sum of absolute values in the vector)
- norm2(ValueType, Kernel<? super ValueType>) - Static method in class gov.sandia.cognition.learning.function.kernel.KernelUtil
-
Computes the 2-norm of the given value according to the given kernel.
- norm2(DefaultKernelBinaryCategorizer<InputType>) - Static method in class gov.sandia.cognition.learning.function.kernel.KernelUtil
-
Computes the 2-norm of the weight vector implied by the given kernel
binary categorizer.
- norm2() - Method in class gov.sandia.cognition.math.matrix.AbstractVectorSpace
-
- norm2() - Method in class gov.sandia.cognition.math.matrix.decomposition.AbstractSingularValueDecomposition
-
- norm2() - Method in interface gov.sandia.cognition.math.matrix.decomposition.SingularValueDecomposition
-
Returns the associated 2-norm (spectral norm) of the underlying matrix,
which is simply the largest singular value
- norm2() - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
-
- norm2() - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJVector
-
- norm2() - Method in interface gov.sandia.cognition.math.matrix.VectorSpace
-
2-norm of the vector (aka Euclidean distance of the vector)
- norm2Squared(ValueType, Kernel<? super ValueType>) - Static method in class gov.sandia.cognition.learning.function.kernel.KernelUtil
-
Computes the squared 2-norm of the given value according to the given
kernel.
- norm2Squared(DefaultKernelBinaryCategorizer<InputType>) - Static method in class gov.sandia.cognition.learning.function.kernel.KernelUtil
-
Computes the squared 2-norm of the weight vector implied by the given
kernel binary categorizer.
- norm2Squared() - Method in class gov.sandia.cognition.math.matrix.AbstractVectorSpace
-
- norm2Squared() - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
-
- norm2Squared() - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJVector
-
- norm2Squared() - Method in interface gov.sandia.cognition.math.matrix.VectorSpace
-
Squared 2-norm of the vector (aka squared Euclidean distance of the
vector)
- NormalInverseGammaDistribution - Class in gov.sandia.cognition.statistics.distribution
-
The normal inverse-gamma distribution is the product of a univariate
Gaussian distribution with an inverse-gamma distribution.
- NormalInverseGammaDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.NormalInverseGammaDistribution
-
Creates a new instance of NormalInverseGammaDistribution
- NormalInverseGammaDistribution(double, double, double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.NormalInverseGammaDistribution
-
Creates a new instance of NormalInverseGammaDistribution
- NormalInverseGammaDistribution(NormalInverseGammaDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.NormalInverseGammaDistribution
-
Copy constructor
- NormalInverseGammaDistribution.PDF - Class in gov.sandia.cognition.statistics.distribution
-
PDF of the NormalInverseGammaDistribution
- NormalInverseWishartDistribution - Class in gov.sandia.cognition.statistics.distribution
-
The normal inverse Wishart distribution
- NormalInverseWishartDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.NormalInverseWishartDistribution
-
Default constructor
- NormalInverseWishartDistribution(int) - Constructor for class gov.sandia.cognition.statistics.distribution.NormalInverseWishartDistribution
-
Creates a new instance of NormalInverseWishartDistribution
- NormalInverseWishartDistribution(int, double) - Constructor for class gov.sandia.cognition.statistics.distribution.NormalInverseWishartDistribution
-
Creates a new instance of NormalInverseWishartDistribution
- NormalInverseWishartDistribution(MultivariateGaussian, InverseWishartDistribution, double) - Constructor for class gov.sandia.cognition.statistics.distribution.NormalInverseWishartDistribution
-
Creates a new instance of NormalInverseWishartDistribution
- NormalInverseWishartDistribution(NormalInverseWishartDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.NormalInverseWishartDistribution
-
Copy constructor
- NormalInverseWishartDistribution.PDF - Class in gov.sandia.cognition.statistics.distribution
-
PDF of the normal inverse-Wishart distribution.
- normalize() - Method in class gov.sandia.cognition.learning.algorithm.hmm.MarkovChain
-
Normalizes this Markov chain.
- normalize() - Method in interface gov.sandia.cognition.math.matrix.Quaternion
-
Returns the normalized version of the quaternion.
- normalizedCentroid - Variable in class gov.sandia.cognition.learning.algorithm.clustering.cluster.NormalizedCentroidCluster
-
The normalized center of the cluster.
- NormalizedCentroidCluster<ClusterType> - Class in gov.sandia.cognition.learning.algorithm.clustering.cluster
-
Add the ability to store the centroid of the normalized vectors belonging to
a centroid cluster.
- NormalizedCentroidCluster() - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.cluster.NormalizedCentroidCluster
-
Creates a new instance of NormalizedCentroidCluster.
- NormalizedCentroidCluster(ClusterType, ClusterType) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.cluster.NormalizedCentroidCluster
-
Creates a new instance of NormalizedCentroidCluster.
- NormalizedCentroidCluster(int, ClusterType, ClusterType) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.cluster.NormalizedCentroidCluster
-
Creates a new instance of NormalizedCentroidCluster.
- NormalizedCentroidCluster(ClusterType, ClusterType, Collection<? extends ClusterType>) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.cluster.NormalizedCentroidCluster
-
Creates a new instance of NormalizedCentroidCluster.
- NormalizedCentroidCluster(int, ClusterType, ClusterType, Collection<? extends ClusterType>) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.cluster.NormalizedCentroidCluster
-
Creates a new instance of NormalizedCentroidCluster.
- NormalizedCentroidClusterCreator - Class in gov.sandia.cognition.learning.algorithm.clustering.cluster
-
A cluster creator for
NormalizedCentroidClusters which are clusters
that have a normalized centroid in addition to the usual centroid.
- NormalizedCentroidClusterCreator() - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.cluster.NormalizedCentroidClusterCreator
-
Creates a new instance of VectorizableCentroidClusterCreator()
- NormalizedKernel<InputType> - Class in gov.sandia.cognition.learning.function.kernel
-
The NormalizedKernel class implements an Kernel
that returns a normalized value between 0.0 and 1.0 by normalizing the
results of a given kernel.
- NormalizedKernel() - Constructor for class gov.sandia.cognition.learning.function.kernel.NormalizedKernel
-
Creates a new instance of NormalizedKernel.
- NormalizedKernel(Kernel<? super InputType>) - Constructor for class gov.sandia.cognition.learning.function.kernel.NormalizedKernel
-
Creates a new instance of NormalizedKernel using the given kernel.
- NormalizedKernel(NormalizedKernel<? super InputType>) - Constructor for class gov.sandia.cognition.learning.function.kernel.NormalizedKernel
-
Creates a new copy of a NormalizedKernel.
- NormalizedLogLocalTermWeighter - Class in gov.sandia.cognition.text.term.vector.weighter.local
-
Implements a normalized version of the log local weighter.
- NormalizedLogLocalTermWeighter() - Constructor for class gov.sandia.cognition.text.term.vector.weighter.local.NormalizedLogLocalTermWeighter
-
Creates a new NormalizedLogLocalTermWeighter.
- NormalizedLogLocalTermWeighter(VectorFactory<? extends Vector>) - Constructor for class gov.sandia.cognition.text.term.vector.weighter.local.NormalizedLogLocalTermWeighter
-
Creates a new NormalizedLogLocalTermWeighter
- normalizeEquals() - Method in interface gov.sandia.cognition.math.matrix.Quaternion
-
Normalizes the quaternion in-place.
- normalizer - Variable in class gov.sandia.cognition.text.term.vector.weighter.CompositeLocalGlobalTermWeighter
-
The weight normalizer.
- normalizeTransitionMatrix(Matrix, int) - Static method in class gov.sandia.cognition.learning.algorithm.hmm.MarkovChain
-
Normalizes a column of the transition-probability matrix
- normalizeTransitionMatrix(Matrix) - Method in class gov.sandia.cognition.learning.algorithm.hmm.MarkovChain
-
Normalizes the transition-probability matrix
- normalizeTransitionMatrix(Matrix) - Method in class gov.sandia.cognition.learning.algorithm.hmm.ParallelHiddenMarkovModel
-
- NormalizeTransitionTask() - Constructor for class gov.sandia.cognition.learning.algorithm.hmm.ParallelHiddenMarkovModel.NormalizeTransitionTask
-
Default constructor.
- normalizeTransitionTasks - Variable in class gov.sandia.cognition.learning.algorithm.hmm.ParallelHiddenMarkovModel
-
NormalizeTransitionTasks.
- normalizeWeights(Vector, Vector, Vector) - Method in interface gov.sandia.cognition.text.term.vector.weighter.normalize.TermWeightNormalizer
-
Normalizes the given weight vector.
- normalizeWeights(Vector, Vector, Vector) - Method in class gov.sandia.cognition.text.term.vector.weighter.normalize.UnitTermWeightNormalizer
-
- normFrobenius() - Method in class gov.sandia.cognition.math.matrix.custom.DenseMatrix
-
- normFrobenius() - Method in class gov.sandia.cognition.math.matrix.custom.DiagonalMatrix
-
- normFrobenius() - Method in class gov.sandia.cognition.math.matrix.custom.SparseMatrix
-
Compute the Frobenius norm of this, which is just a fancy
way of saying that I will square each element, add those up, and square
root the result.
- normFrobenius() - Method in interface gov.sandia.cognition.math.matrix.Matrix
-
Compute the Frobenius norm of this, which is just a fancy
way of saying that I will square each element, add those up, and square
root the result.
- normFrobenius() - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix
-
- normFrobenius() - Method in class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixMTJ
-
- normFrobeniusSquared() - Method in class gov.sandia.cognition.math.matrix.custom.DenseMatrix
-
- normFrobeniusSquared() - Method in class gov.sandia.cognition.math.matrix.custom.DiagonalMatrix
-
- normFrobeniusSquared() - Method in class gov.sandia.cognition.math.matrix.custom.SparseMatrix
-
Compute the squared Frobenius norm of this, which is just a
fancy way of saying that I will square each element and add those up.
- normFrobeniusSquared() - Method in interface gov.sandia.cognition.math.matrix.Matrix
-
Compute the squared Frobenius norm of this, which is just a
fancy way of saying that I will square each element and add those up.
- normFrobeniusSquared() - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix
-
- normInfinity() - Method in class gov.sandia.cognition.math.matrix.AbstractVectorSpace
-
- normInfinity() - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
-
- normInfinity() - Method in interface gov.sandia.cognition.math.matrix.VectorSpace
-
Returns the infinity norm of the Vector, which is the maximum
absolute value of an element in the Vector.
- NullHypothesisEvaluator<DataType> - Interface in gov.sandia.cognition.statistics.method
-
Evaluates the probability that the null-hypothesis is correct.
- nullHypothesisProbabilities - Variable in class gov.sandia.cognition.statistics.method.AbstractMultipleHypothesisComparison.Statistic
-
Null Hypothesis probability associated with the (i,j)
treatment comparison
- nullHypothesisProbability - Variable in class gov.sandia.cognition.statistics.method.AbstractConfidenceStatistic
-
Probability of the null hypothesis, often called "p-value"
- nullValue - Variable in class gov.sandia.cognition.data.convert.number.DefaultBooleanToNumberConverter
-
The number to use to represent a null value.
- num - Variable in class gov.sandia.cognition.math.geometry.KDTree
-
Number of elements in this subtree.
- NumberAverager - Class in gov.sandia.cognition.math
-
Returns an average (arithmetic mean) of a collection of Numbers
- NumberAverager() - Constructor for class gov.sandia.cognition.math.NumberAverager
-
Creates a new instance of NumberAverager
- NumberComparator - Class in gov.sandia.cognition.collection
-
Compares two Numbers (base class of Double, Integer, etc.) for sorting.
- NumberComparator() - Constructor for class gov.sandia.cognition.collection.NumberComparator
-
Creates a new instance of NumberComparator
- NumberConverterToVectorAdapter<InputType> - Class in gov.sandia.cognition.data.convert.vector
-
Adapts a DataConverter that outputs a number to be a
VectorEncoder.
- NumberConverterToVectorAdapter() - Constructor for class gov.sandia.cognition.data.convert.vector.NumberConverterToVectorAdapter
-
Creates a new NumberConverterToVectorAdapter.
- NumberConverterToVectorAdapter(DataConverter<? super InputType, ? extends Number>) - Constructor for class gov.sandia.cognition.data.convert.vector.NumberConverterToVectorAdapter
-
Creates a new NumberConverterToVectorAdapter for the given
converter.
- numberOfAppearances - Variable in class gov.sandia.cognition.statistics.TransferEntropy.TransferEntropyPartialSumObject
-
The number of appearances.
- NumberToVectorEncoder - Class in gov.sandia.cognition.data.convert.vector
-
An encoder that encodes a number as an element of a Vector.
- NumberToVectorEncoder() - Constructor for class gov.sandia.cognition.data.convert.vector.NumberToVectorEncoder
-
Creates a new NumberToVectorEncoder.
- numCorrectToSample - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner
-
The number of correct examples to sample on each iteration.
- numCustomers - Variable in class gov.sandia.cognition.statistics.distribution.ChineseRestaurantProcess
-
Total number of customers that we will arrange around tables,
must be greater than zero.
- numEdges() - Method in class gov.sandia.cognition.graph.GraphMetrics
-
Returns the number of edges in the graph.
- numEdges() - Method in class gov.sandia.cognition.graph.inference.CostSpeedupEnergyFunction
-
- numEdges() - Method in class gov.sandia.cognition.graph.inference.EdgeMergingEnergyFunction
-
- numEdges() - Method in interface gov.sandia.cognition.graph.inference.EnergyFunction
-
Returns the number of paths that messages should be passed down.
- numEdges() - Method in class gov.sandia.cognition.graph.inference.GraphWrappingEnergyFunction
-
- numEdgeTriangles(int) - Method in class gov.sandia.cognition.graph.GraphMetrics
-
Returns the number of triangles the input edge participates in.
- NumericalDifferentiator<InputType,OutputType,DerivativeType> - Class in gov.sandia.cognition.math.matrix
-
Automatically differentiates a function by the method of forward differences.
- NumericalDifferentiator(Evaluator<? super InputType, OutputType>, double) - Constructor for class gov.sandia.cognition.math.matrix.NumericalDifferentiator
-
Creates a new instance of NumericalDifferentiator
- NumericalDifferentiator.DoubleJacobian - Class in gov.sandia.cognition.math.matrix
-
Numerical differentiator based on a Vector Jacobian.
- NumericalDifferentiator.MatrixJacobian - Class in gov.sandia.cognition.math.matrix
-
Numerical differentiator based on a Matrix Jacobian.
- NumericalDifferentiator.VectorJacobian - Class in gov.sandia.cognition.math.matrix
-
Numerical differentiator based on a Vector Jacobian.
- NumericMap<KeyType> - Interface in gov.sandia.cognition.collection
-
An interface for a mapping of keys to numeric values.
- numFolds - Variable in class gov.sandia.cognition.learning.experiment.CrossFoldCreator
-
The number of folds to create.
- numFolds - Variable in class gov.sandia.cognition.learning.experiment.RandomFoldCreator
-
The number of folds to create.
- numIncorrectToSample - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner
-
The number of incorrect examples to sample on each iteration.
- numLevels() - Method in class gov.sandia.cognition.graph.community.Louvain.LouvainHierarchy
-
The number of levels in the hierarchy.
- numModules - Variable in class gov.sandia.cognition.framework.lite.AbstractCognitiveModelLite
-
The number of modules.
- numNeighbors(int) - Method in class gov.sandia.cognition.graph.GraphMetrics
-
Returns the number of neighbors for the input node id.
- numNeighbors(NodeNameType) - Method in class gov.sandia.cognition.graph.GraphMetrics
-
Returns the number of neighbors for the input node name.
- numNodes() - Method in class gov.sandia.cognition.graph.GraphMetrics
-
Returns the number of nodes in the graph.
- numNodes() - Method in class gov.sandia.cognition.graph.inference.CostSpeedupEnergyFunction
-
- numNodes() - Method in class gov.sandia.cognition.graph.inference.EdgeMergingEnergyFunction
-
- numNodes() - Method in interface gov.sandia.cognition.graph.inference.EnergyFunction
-
Returns the number of nodes in the energy function.
- numNodes() - Method in class gov.sandia.cognition.graph.inference.GraphWrappingEnergyFunction
-
- numNodeTriangles(int) - Method in class gov.sandia.cognition.graph.GraphMetrics
-
Returns the number of triangles the node participates in.
- numNodeTriangles(NodeNameType) - Method in class gov.sandia.cognition.graph.GraphMetrics
-
Returns the number of triangles the node participates in.
- numParticles - Variable in class gov.sandia.cognition.statistics.bayesian.AbstractParticleFilter
-
Number of particles in the filter.
- numRequestedClusters - Variable in class gov.sandia.cognition.learning.algorithm.clustering.KMeansClusterer
-
The number of clusters requested.
- numRequestedClusters - Variable in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer
-
The number of clusters requested.
- numSplits - Variable in class gov.sandia.cognition.learning.experiment.RandomByTwoFoldCreator
-
The number of splits.
- numSuccessors(int) - Method in class gov.sandia.cognition.graph.GraphMetrics
-
Returns the number of direct successors for the input node.
- numSuccessors(NodeNameType) - Method in class gov.sandia.cognition.graph.GraphMetrics
-
Returns the number of direct successors for the input node.
- numTrials - Variable in class gov.sandia.cognition.learning.experiment.AbstractValidationFoldExperiment
-
The number of trials in the experiment, which is the number of folds
in the experiment.
- numTrials - Variable in class gov.sandia.cognition.learning.experiment.LearnerRepeatExperiment
-
The number of trials to repeat the learning.
- numTrials - Variable in class gov.sandia.cognition.learning.experiment.OnlineLearnerValidationExperiment
-
The number of trials in the experiment, which is the number of folds
in the experiment.