- m - Variable in class gov.sandia.cognition.learning.algorithm.minimization.matrix.MatrixVectorMultiplier
-
The matrix to multiply with.
- makeProposal(Random) - Method in class gov.sandia.cognition.statistics.bayesian.ImportanceSampling.DefaultUpdater
-
- makeProposal(Random) - Method in interface gov.sandia.cognition.statistics.bayesian.ImportanceSampling.Updater
-
Samples from the parameter prior
- makeProposal(ParameterType) - Method in interface gov.sandia.cognition.statistics.bayesian.MetropolisHastingsAlgorithm.Updater
-
Makes a proposal update given the current parameter set
- makeProposal(Random) - Method in class gov.sandia.cognition.statistics.bayesian.RejectionSampling.DefaultUpdater
-
- makeProposal(Random) - Method in interface gov.sandia.cognition.statistics.bayesian.RejectionSampling.Updater
-
Samples from the parameter prior
- ManhattanDistanceMetric - Class in gov.sandia.cognition.learning.function.distance
-
The ManhattanDistanceMetric class implements a distance metric
between two vectors that is implemented as the sum of the absolute value of
the difference between the elements in the vectors.
- ManhattanDistanceMetric() - Constructor for class gov.sandia.cognition.learning.function.distance.ManhattanDistanceMetric
-
Creates a new instance of ManhattanDistanceMetric.
- MannWhitneyUConfidence - Class in gov.sandia.cognition.statistics.method
-
Performs a Mann-Whitney U-test on the given data (usually simply called a
"U-test", sometimes called a Wilcoxon-Mann-Whitney U-test, or
Wilcoxon rank-sum test).
- MannWhitneyUConfidence() - Constructor for class gov.sandia.cognition.statistics.method.MannWhitneyUConfidence
-
Creates a new instance of MannWhitneyUConfidence
- MannWhitneyUConfidence.Statistic - Class in gov.sandia.cognition.statistics.method
-
Statistics from the Mann-Whitney U-test
- map - Variable in class gov.sandia.cognition.collection.AbstractLogNumberMap.SimpleEntrySet
-
Backing map
- map - Variable in class gov.sandia.cognition.collection.AbstractMutableDoubleMap.SimpleEntrySet
-
Backing map
- map - Variable in class gov.sandia.cognition.collection.AbstractScalarMap
-
Map backing that performs the storage.
- MarkovChain - Class in gov.sandia.cognition.learning.algorithm.hmm
-
A Markov chain is a random process that has a finite number of states with
random transition probabilities between states at discrete time steps.
- MarkovChain() - Constructor for class gov.sandia.cognition.learning.algorithm.hmm.MarkovChain
-
Default constructor.
- MarkovChain(int) - Constructor for class gov.sandia.cognition.learning.algorithm.hmm.MarkovChain
-
Creates a new instance of ContinuousDensityHiddenMarkovModel
with uniform initial and transition probabilities.
- MarkovChain(Vector, Matrix) - Constructor for class gov.sandia.cognition.learning.algorithm.hmm.MarkovChain
-
Creates a new instance of ContinuousDensityHiddenMarkovModel
- MarkovChainMonteCarlo<ObservationType,ParameterType> - Interface in gov.sandia.cognition.statistics.bayesian
-
Defines the functionality of a Markov chain Monte Carlo algorithm.
- MarkovInequality - Class in gov.sandia.cognition.statistics.method
-
Implementation of the Markov Inequality hypothesis test.
- MarkovInequality() - Constructor for class gov.sandia.cognition.statistics.method.MarkovInequality
-
Creates a new instance of MarkovInequality
- MathUtil - Class in gov.sandia.cognition.math
-
The MathUtil class implements mathematical utility functions.
- MathUtil() - Constructor for class gov.sandia.cognition.math.MathUtil
-
- Matrix - Interface in gov.sandia.cognition.math.matrix
-
Defines the base functionality for all implementations of a Matrix
- MatrixBasedTermSimilarityNetwork - Class in gov.sandia.cognition.text.term.relation
-
A relation network between terms based on their similarity.
- MatrixBasedTermSimilarityNetwork(TermIndex, Matrix) - Constructor for class gov.sandia.cognition.text.term.relation.MatrixBasedTermSimilarityNetwork
-
Creates a new MatrixBasedTermSimilarityNetwork.
- MatrixEntry - Interface in gov.sandia.cognition.math.matrix
-
Interface that specifies the functionality for a matrix entry
- MatrixEntryIndexComparatorMTJ - Class in gov.sandia.cognition.math.matrix.mtj
-
An index comparator for MTJ matrices.
- MatrixEntryIndexComparatorMTJ() - Constructor for class gov.sandia.cognition.math.matrix.mtj.MatrixEntryIndexComparatorMTJ
-
Creates a new instance of MatrixEntryComparatorMTJ
- MatrixFactory<MatrixType extends Matrix> - Class in gov.sandia.cognition.math.matrix
-
Abstract factory for creating Matrix objects.
- MatrixFactory() - Constructor for class gov.sandia.cognition.math.matrix.MatrixFactory
-
Creates a new MatrixFactory.
- matrixFactory - Variable in class gov.sandia.cognition.text.term.relation.TermVectorSimilarityNetworkCreator
-
The matrix factory to create the matrix that backs the similarity
network.
- matrixFactory - Variable in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis
-
The matrix factory.
- MatrixFactoryContainer - Interface in gov.sandia.cognition.math.matrix
-
Interface for a container for a matrix factory.
- MatrixJacobian() - Constructor for class gov.sandia.cognition.math.matrix.NumericalDifferentiator.MatrixJacobian
-
Default constructor
- MatrixJacobian(Evaluator<? super Vector, Vector>) - Constructor for class gov.sandia.cognition.math.matrix.NumericalDifferentiator.MatrixJacobian
-
Creates a new instance of VectorJacobian
- MatrixJacobian(Evaluator<? super Vector, Vector>, double) - Constructor for class gov.sandia.cognition.math.matrix.NumericalDifferentiator.MatrixJacobian
-
Create a new instance of VectorJacobian
- MatrixReader - Class in gov.sandia.cognition.math.matrix
-
Reads a Matrix from the specified reader.
- MatrixReader(Reader) - Constructor for class gov.sandia.cognition.math.matrix.MatrixReader
-
Creates a new instance of MatrixReader
- MatrixUnionIterator - Class in gov.sandia.cognition.math.matrix
-
Iterator that stops at all nonzero entries for EITHER underlying matrix
- MatrixUnionIterator(Iterator<MatrixEntry>, Iterator<MatrixEntry>, TwoMatrixEntry, EntryIndexComparator<MatrixEntry>) - Constructor for class gov.sandia.cognition.math.matrix.MatrixUnionIterator
-
Creates a new instance of MatrixUnionIterator
- MatrixUnionIteratorMTJ - Class in gov.sandia.cognition.math.matrix.mtj
-
Implementation of MatrixUnionIterator for MTJ-based matrices.
- MatrixUnionIteratorMTJ(AbstractMTJMatrix, AbstractMTJMatrix) - Constructor for class gov.sandia.cognition.math.matrix.mtj.MatrixUnionIteratorMTJ
-
Creates a new instance of AbstractMTJMatrixIntersectionIterator,
iterating at rowIndex = 0, columnIndex = 0
- MatrixVectorMultiplier - Class in gov.sandia.cognition.learning.algorithm.minimization.matrix
-
The necessary multiplication wrapper class to match the FunctionMinimizer
interface.
- MatrixVectorMultiplier(Matrix) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.matrix.MatrixVectorMultiplier
-
Clones the input matrix to prevent any later edits to the input from
changing the results of iterative multiplications.
- MatrixVectorMultiplierDiagonalPreconditioner - Class in gov.sandia.cognition.learning.algorithm.minimization.matrix
-
Implements a diagonal preconditioner for a matrix-vector multiplier.
- MatrixVectorMultiplierDiagonalPreconditioner(Matrix) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.matrix.MatrixVectorMultiplierDiagonalPreconditioner
-
- MatrixVectorMultiplierWithPreconditioner - Class in gov.sandia.cognition.learning.algorithm.minimization.matrix
-
As various preconditioner operations could be created, this class defines the
interface that must be followed by them.
- MatrixWriter - Class in gov.sandia.cognition.math.matrix
-
Writes a Matrix to a Java-based Writer (files, etc.)
- MatrixWriter(Writer) - Constructor for class gov.sandia.cognition.math.matrix.MatrixWriter
-
Creates a new instance of MatrixWriter
- max(LogNumber) - Method in class gov.sandia.cognition.math.LogNumber
-
A new LogNumber that is the maximum of this and another.
- max(UnsignedLogNumber) - Method in class gov.sandia.cognition.math.UnsignedLogNumber
-
A new LogNumber that is the maximum of this and another.
- MAX_STEP - Static variable in class gov.sandia.cognition.learning.algorithm.root.RootFinderSecantMethod
-
Maximum step size allowed, 1.0
- MAX_VALUE - Static variable in class gov.sandia.cognition.time.DefaultDuration
-
The maximum value of a duration.
- maxCriterionDecrease - Variable in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer
-
The maximum decrease in training criterion allowed.
- maxDepth - Variable in class gov.sandia.cognition.learning.algorithm.tree.CategorizationTreeLearner
-
The maximum depth for the tree.
- maxDepth - Variable in class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeLearner
-
The maximum depth for the tree.
- maxDistance - Variable in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer
-
The maximum distance between clusters allowed.
- maxEquals(LogNumber) - Method in class gov.sandia.cognition.math.LogNumber
-
Changes this value to be the maximum of this value or the given value.
- maxEquals(UnsignedLogNumber) - Method in class gov.sandia.cognition.math.UnsignedLogNumber
-
Changes this value to be the maximum of this value or the given value.
- MaximumAPosterioriCategorizer<ObservationType,CategoryType> - Class in gov.sandia.cognition.learning.function.categorization
-
Categorizer that returns the category with the highest posterior likelihood
for a given observation.
- MaximumAPosterioriCategorizer() - Constructor for class gov.sandia.cognition.learning.function.categorization.MaximumAPosterioriCategorizer
-
Creates a new instance of MaximumAPosterioriCategorizer
- MaximumAPosterioriCategorizer.Learner<ObservationType,CategoryType> - Class in gov.sandia.cognition.learning.function.categorization
-
Learner for the MAP categorizer
- maximumLength - Variable in class gov.sandia.cognition.text.term.filter.TermLengthFilter
-
The maximum allowed length.
- MaximumLikelihoodDistributionEstimator<DataType> - Class in gov.sandia.cognition.statistics.method
-
Estimates the most-likely distribution, and corresponding parameters, of
that generated the given data from a pre-determined collection of
candidate parameteric distributions.
- MaximumLikelihoodDistributionEstimator() - Constructor for class gov.sandia.cognition.statistics.method.MaximumLikelihoodDistributionEstimator
-
Creates a new instance of MaximumLikelihoodDistributionEstimator
- MaximumLikelihoodDistributionEstimator(Collection<? extends ClosedFormComputableDistribution<DataType>>) - Constructor for class gov.sandia.cognition.statistics.method.MaximumLikelihoodDistributionEstimator
-
Creates a new instance of MaximumLikelihoodDistributionEstimator
- MaximumLikelihoodDistributionEstimator.DistributionEstimationTask<DataType> - Class in gov.sandia.cognition.statistics.method
-
Estimates the optimal parameters of a single distribution
- MaximumLikelihoodEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.BinomialDistribution.MaximumLikelihoodEstimator
-
Default constructor
- MaximumLikelihoodEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.ExponentialDistribution.MaximumLikelihoodEstimator
-
Default estimator.
- MaximumLikelihoodEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.GeometricDistribution.MaximumLikelihoodEstimator
-
Default constructor
- MaximumLikelihoodEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.LaplaceDistribution.MaximumLikelihoodEstimator
-
Default constructor
- MaximumLikelihoodEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.LogNormalDistribution.MaximumLikelihoodEstimator
-
Default constructor
- MaximumLikelihoodEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.MaximumLikelihoodEstimator
-
Default constructor;
- MaximumLikelihoodEstimator(double) - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.MaximumLikelihoodEstimator
-
Creates a new instance of MaximumLikelihoodEstimator
- MaximumLikelihoodEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.NegativeBinomialDistribution.MaximumLikelihoodEstimator
-
Default constructor
- MaximumLikelihoodEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.PoissonDistribution.MaximumLikelihoodEstimator
-
Default constructor
- MaximumLikelihoodEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.StudentTDistribution.MaximumLikelihoodEstimator
-
Default constructor
- MaximumLikelihoodEstimator(double) - Constructor for class gov.sandia.cognition.statistics.distribution.StudentTDistribution.MaximumLikelihoodEstimator
-
Creates a new instance of MaximumLikelihoodEstimator
- MaximumLikelihoodEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.UniformDistribution.MaximumLikelihoodEstimator
-
Default constructor
- MaximumLikelihoodEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution.MaximumLikelihoodEstimator
-
- MaximumLikelihoodEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.MaximumLikelihoodEstimator
-
Default constructor
- MaximumLikelihoodEstimator(double) - Constructor for class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.MaximumLikelihoodEstimator
-
Creates a new instance of MaximumLikelihoodEstimator
- maxIterations - Variable in class gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm
-
Maximum number of iterations before stopping
- maxIterations - Variable in class gov.sandia.cognition.learning.algorithm.minimization.matrix.IterativeMatrixSolver
-
Execution will stop after this number of iterations even if it has not
converged.
- maxIterations - Variable in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis.Result
-
The maximum number of iterations for the E-M evaluation.
- maxWeight - Variable in class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation
-
The maximum weight for a support vector.
- mcmcUpdate() - Method in class gov.sandia.cognition.statistics.bayesian.AbstractMarkovChainMonteCarlo
-
Performs a valid MCMC update step.
- mcmcUpdate() - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel
-
- mcmcUpdate() - Method in class gov.sandia.cognition.statistics.bayesian.MetropolisHastingsAlgorithm
-
- MD5Hash - Class in gov.sandia.cognition.hash
-
Implementation of the MD5 128-bit (16-byte) cryptographic hash function.
- MD5Hash() - Constructor for class gov.sandia.cognition.hash.MD5Hash
-
Default constructor
- mean - Variable in class gov.sandia.cognition.learning.data.feature.StandardDistributionNormalizer
-
The mean of normalization.
- MEAN - Static variable in class gov.sandia.cognition.statistics.distribution.KolmogorovDistribution
-
Value of the mean, found empirically, as I can't seem to find the
answer in any reference I can get my hands on, 0.868481392844716.
- mean - Variable in class gov.sandia.cognition.statistics.distribution.LaplaceDistribution
-
Mean of the distribution
- mean - Variable in class gov.sandia.cognition.statistics.distribution.LogisticDistribution
-
Mean (median and mode) of the distribution.
- mean - Variable in class gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution
-
Mean, or noncentrality parameter, of the distribution
- mean - Variable in class gov.sandia.cognition.statistics.distribution.StudentTDistribution
-
Mean, or noncentrality parameter, of the distribution
- mean - Variable in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian
-
First central moment (expectation) of the distribution
- mean - Variable in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.SufficientStatistic
-
The mean of the Gaussian.
- MEAN_GETTER - Static variable in class gov.sandia.cognition.statistics.DefaultDistributionParameter
-
Getter for the mean, "getMean".
- MEAN_NAME - Static variable in class gov.sandia.cognition.statistics.DefaultDistributionParameter
-
Name of the mean property, "mean".
- MEAN_SETTER - Static variable in class gov.sandia.cognition.statistics.DefaultDistributionParameter
-
Setter for the mean, "setMean".
- MeanAbsoluteErrorEvaluator<InputType> - Class in gov.sandia.cognition.learning.performance
-
The MeanAbsoluteError class implements a method for computing the
performance of a supervised learner for a scalar function by the mean
absolute value between the target and estimated outputs.
- MeanAbsoluteErrorEvaluator() - Constructor for class gov.sandia.cognition.learning.performance.MeanAbsoluteErrorEvaluator
-
Creates a new instance of MeanAbsoluteError
- MeanL1CostFunction - Class in gov.sandia.cognition.learning.function.cost
-
Cost function that evaluates the mean 1-norm error (absolute value of
difference) weighted by a sample "weight" that is embedded in each sample.
- MeanL1CostFunction() - Constructor for class gov.sandia.cognition.learning.function.cost.MeanL1CostFunction
-
Default constructor
- MeanL1CostFunction(Collection<? extends InputOutputPair<? extends Vector, Vector>>) - Constructor for class gov.sandia.cognition.learning.function.cost.MeanL1CostFunction
-
Creates a new instance of MeanL1CostFunction
- MeanLearner - Class in gov.sandia.cognition.learning.algorithm.baseline
-
The MeanLearner class implements a baseline learner that computes
the mean of a given set of values.
- MeanLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.baseline.MeanLearner
-
Creates a new MeanLearner.
- MeanSquaredErrorCostFunction - Class in gov.sandia.cognition.learning.function.cost
-
The MeanSquaredErrorCostFunction implements a cost function for functions
that take as input a vector and return a vector.
- MeanSquaredErrorCostFunction() - Constructor for class gov.sandia.cognition.learning.function.cost.MeanSquaredErrorCostFunction
-
Creates a new instance of MeanSquaredErrorCostFunction with no initial
dataset.
- MeanSquaredErrorCostFunction(Collection<? extends InputOutputPair<? extends Vector, Vector>>) - Constructor for class gov.sandia.cognition.learning.function.cost.MeanSquaredErrorCostFunction
-
Creates a new instance of MeanSquaredErrorCostFunction
- MeanSquaredErrorEvaluator<InputType> - Class in gov.sandia.cognition.learning.performance
-
The MeanSquaredError class implements the method for computing the
performance of a supervised learner for a scalar function by the mean squared
between the target and estimated outputs.
- MeanSquaredErrorEvaluator() - Constructor for class gov.sandia.cognition.learning.performance.MeanSquaredErrorEvaluator
-
Creates a new instance of MeanSquaredError.
- meanSquaredResiduals - Variable in class gov.sandia.cognition.statistics.method.TukeyKramerConfidence.Statistic
-
Mean-squared difference over all subjects
- MeanZeroOneErrorEvaluator<InputType,DataType> - Class in gov.sandia.cognition.learning.performance
-
The MeanZeroOneErrorEvaluator class implements a method for
computing the performance of a supervised learner by the mean number of
incorrect values between the target and estimated outputs.
- MeanZeroOneErrorEvaluator() - Constructor for class gov.sandia.cognition.learning.performance.MeanZeroOneErrorEvaluator
-
Creates a new instance of MeanZeroOneErrorEvaluator.
- MeasurablePerformanceAlgorithm - Interface in gov.sandia.cognition.algorithm
-
An interface for an algorithm that has a measurable quantity of performance
that can be retrieved.
- measure(MultivariateGaussian, Vector) - Method in class gov.sandia.cognition.statistics.bayesian.AbstractKalmanFilter
-
Integrates a measurement into the system, refining the current
belief of the state of the system
- measure(MultivariateGaussian, Vector) - Method in class gov.sandia.cognition.statistics.bayesian.ExtendedKalmanFilter
-
- measure(MultivariateGaussian, Vector) - Method in class gov.sandia.cognition.statistics.bayesian.KalmanFilter
-
- measurementCovariance - Variable in class gov.sandia.cognition.statistics.bayesian.AbstractKalmanFilter
-
Covariance associated with the measurements.
- MedoidClusterCreator<DataType> - Class in gov.sandia.cognition.learning.algorithm.clustering.cluster
-
The MedoidClusterCreator class creates a
CentroidCluster at the sample that minimizes the sum
of the divergence to the objects assigned to the cluster.
- MedoidClusterCreator() - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.cluster.MedoidClusterCreator
-
Creates a new instance of MedoidClusterCreator
- MedoidClusterCreator(DivergenceFunction<? super DataType, ? super DataType>) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.cluster.MedoidClusterCreator
-
Creates a new instance of MedoidClusterCreator
- members - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractUnweightedEnsemble
-
The members of the ensemble.
- members - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractWeightedEnsemble
-
The members of the ensemble.
- members - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.VotingCategorizerEnsemble
-
The members of the ensemble.
- members - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.WeightedBinaryEnsemble
-
The members of the ensemble.
- members - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.WeightedVotingCategorizerEnsemble
-
The members of the ensemble.
- mergeClusters(int, int, double) - Method in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer
-
Merges two clusters together, creating a new BinaryTreeCluster
and updating the internal state.
- mergeCollections(Collection<InputType>, Collection<OutputType>) - Static method in class gov.sandia.cognition.learning.data.DefaultInputOutputPair
-
Takes two collections of data of equal size and creates a single
ArrayList of InputOutputPairs out of them.
- mergeCollections(Collection<? extends TargetType>, Collection<? extends EstimateType>) - Static method in class gov.sandia.cognition.learning.data.DefaultTargetEstimatePair
-
Merges together two Collections into a single target-estimate pair
Collection.
- mergeCollections(Collection<InputType>, Collection<OutputType>, Collection<? extends Number>) - Static method in class gov.sandia.cognition.learning.data.DefaultWeightedInputOutputPair
-
Takes two Collections of data and creates a single
ArrayList out of them.
- mergeCollections(Collection<FirstType>, Collection<SecondType>) - Static method in class gov.sandia.cognition.util.DefaultPair
-
Takes two collections of data of the same size and creates a new single
ArrayList<DefaultPair> out of their elements.
- mergeCollections(Collection<FirstType>, Collection<SecondType>, Collection<ThirdType>) - Static method in class gov.sandia.cognition.util.DefaultTriple
-
Takes three collections of data of the same size and creates a new single
ArrayList<DefaultTriple> out of their elements.
- Metric<EvaluatedType> - Interface in gov.sandia.cognition.math
-
A metric is a non-negative function that satisfies the following properties
g(x, y) + g(y, z) >= g(x, z)
g(x, y) == g(y, x)
g(x, x) == 0.
- MetropolisHastingsAlgorithm<ObservationType,ParameterType> - Class in gov.sandia.cognition.statistics.bayesian
-
An implementation of the Metropolis-Hastings MCMC algorithm, which is the
most general formulation of MCMC but can be slow.
- MetropolisHastingsAlgorithm() - Constructor for class gov.sandia.cognition.statistics.bayesian.MetropolisHastingsAlgorithm
-
Creates a new instance of MetropolisHastingsAlgorithm.
- MetropolisHastingsAlgorithm.Updater<ObservationType,ParameterType> - Interface in gov.sandia.cognition.statistics.bayesian
-
Creates proposals for the MCMC steps.
- MILLISECOND - Static variable in class gov.sandia.cognition.time.DefaultDuration
-
A millisecond in duration.
- MILLISECONDS_PER_DAY - Static variable in class gov.sandia.cognition.time.DefaultDuration
-
There are 86400000 milliseoncds per day.
- MILLISECONDS_PER_HOUR - Static variable in class gov.sandia.cognition.time.DefaultDuration
-
There are 3600000 milliseconds per hour.
- MILLISECONDS_PER_MINUTE - Static variable in class gov.sandia.cognition.time.DefaultDuration
-
There are 60000 milliseconds per minute.
- MILLISECONDS_PER_SECOND - Static variable in class gov.sandia.cognition.time.DefaultDuration
-
There are 1000 milliseconds per second.
- min(LogNumber) - Method in class gov.sandia.cognition.math.LogNumber
-
A new LogNumber that is the minimum of this and another.
- min(UnsignedLogNumber) - Method in class gov.sandia.cognition.math.UnsignedLogNumber
-
A new LogNumber that is the minimum of this and another.
- MIN_VALUE - Static variable in class gov.sandia.cognition.time.DefaultDuration
-
The minimum value of a duration.
- minChange - Variable in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachineAlternatingLeastSquares
-
The minimum change allowed in an iteration.
- minChange - Variable in class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation
-
The minimum change to allow for the algorithm to keep going.
- minClusters - Variable in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer
-
The array of indexes that maps the cluster index to the closest cluster.
- minClusterSize - Variable in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer
-
The minimum number of elements per cluster allowed.
- minDistances - Variable in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer
-
An array list mapping the cached minimum distance from the cluster with
the given index to any other clusters.
- minEquals(LogNumber) - Method in class gov.sandia.cognition.math.LogNumber
-
Changes this value to be the minimum of this value or the given value.
- minEquals(UnsignedLogNumber) - Method in class gov.sandia.cognition.math.UnsignedLogNumber
-
Changes this value to be the minimum of this value or the given value.
- MiniBatchCentroidCluster - Class in gov.sandia.cognition.learning.algorithm.clustering.cluster
-
- MiniBatchCentroidCluster(Collection<? extends Vector>) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.cluster.MiniBatchCentroidCluster
-
- MiniBatchKMeansClusterer<DataType extends Vector> - Class in gov.sandia.cognition.learning.algorithm.clustering
-
Approximates k-means clustering by working on random subsets of the
data.
- MiniBatchKMeansClusterer(int) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.MiniBatchKMeansClusterer
-
Create a clusterer with the default parameters.
- MiniBatchKMeansClusterer(int, int, FixedClusterInitializer<MiniBatchCentroidCluster, Vector>, Semimetric<? super Vector>, ClusterCreator<MiniBatchCentroidCluster, Vector>, Random) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.MiniBatchKMeansClusterer
-
- MiniBatchKMeansClusterer.Builder<DataType extends Vector> - Class in gov.sandia.cognition.learning.algorithm.clustering
-
- MinimizationStoppingCriterion - Class in gov.sandia.cognition.learning.algorithm.minimization
-
Implementation of almost zero-gradient convergence test for function
minimizers.
- MinimizationStoppingCriterion() - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.MinimizationStoppingCriterion
-
- minimizeAlongDirection(DirectionalVectorToScalarFunction, Double, Vector) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.AbstractAnytimeLineMinimizer
-
- minimizeAlongDirection(DirectionalVectorToScalarFunction, Double, Vector) - Method in interface gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizer
-
Minimizes a Vector function along the direction given by the
DirectionalVectorToScalarFunction.
- MinimizerBasedRootFinder - Class in gov.sandia.cognition.learning.algorithm.root
-
A root finder that uses minimization techniques to find the roots
(zero-crossings).
- MinimizerBasedRootFinder() - Constructor for class gov.sandia.cognition.learning.algorithm.root.MinimizerBasedRootFinder
-
Creates a new instance of MinimizerBasedRootFinder
- MinimizerBasedRootFinder(LineMinimizer<Evaluator<Double, Double>>) - Constructor for class gov.sandia.cognition.learning.algorithm.root.MinimizerBasedRootFinder
-
Creates a new instance of MinimizerBasedRootFinder
- MinimizerFunction(ProbabilityFunction<Double>) - Constructor for class gov.sandia.cognition.statistics.bayesian.RejectionSampling.ScalarEstimator.MinimizerFunction
-
Creates a new instance of MinimizerFunction
- minimumChange - Variable in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis
-
The minimum change required in log-likelihood to continue iterating.
- minimumChange - Variable in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis.Result
-
The minimum change in log-likelihood for the E-M evaluation.
- minimumLength - Variable in class gov.sandia.cognition.text.term.filter.TermLengthFilter
-
The minimum allowed length.
- MinkowskiDistanceMetric - Class in gov.sandia.cognition.learning.function.distance
-
An implementation of the Minkowski distance metric.
- MinkowskiDistanceMetric() - Constructor for class gov.sandia.cognition.learning.function.distance.MinkowskiDistanceMetric
-
Creates a new MinkowskiDistanceMetric with the default power of
2.0.
- MinkowskiDistanceMetric(double) - Constructor for class gov.sandia.cognition.learning.function.distance.MinkowskiDistanceMetric
-
Creates a new MinkowskiDistanceMetric with the given power.
- minMargin - Variable in class gov.sandia.cognition.learning.algorithm.perceptron.BatchMultiPerceptron
-
The minimum margin to enforce.
- minMargin - Variable in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineBinaryMarginInfusedRelaxedAlgorithm
-
The minimum margin to enforce.
- minMargin - Variable in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineMultiPerceptron
-
The minimum margin to enforce.
- minNumClusters - Variable in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer
-
The minimum number of clusters allowed.
- minSplitSize - Variable in class gov.sandia.cognition.learning.algorithm.tree.AbstractVectorThresholdMaximumGainLearner
-
The threshold for allowing a split to be made, determined by how many
instances fall in each left or right sides of the split.
- minSplitSize - Variable in class gov.sandia.cognition.learning.algorithm.tree.VectorThresholdVarianceLearner
-
The threshold for allowing a split to be made, determined by how many
instances fall in each left or right sides of the split.
- minus(RingType) - Method in class gov.sandia.cognition.math.AbstractRing
-
- minus(LogNumber) - Method in class gov.sandia.cognition.math.LogNumber
-
- minus(Vector) - Method in class gov.sandia.cognition.math.matrix.custom.SparseVector
-
- minus(InfiniteVector<KeyType>) - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
-
- minus(MutableDouble) - Method in class gov.sandia.cognition.math.MutableDouble
-
- minus(MutableInteger) - Method in class gov.sandia.cognition.math.MutableInteger
-
- minus(MutableLong) - Method in class gov.sandia.cognition.math.MutableLong
-
- minus(RingType) - Method in interface gov.sandia.cognition.math.Ring
-
Arithmetic subtraction of other from this
- minus(UnsignedLogNumber) - Method in class gov.sandia.cognition.math.UnsignedLogNumber
-
- minus(Duration) - Method in class gov.sandia.cognition.time.DefaultDuration
-
- minus(Duration) - Method in interface gov.sandia.cognition.time.Duration
-
Subtracts the given duration from this duration and returns the
difference.
- minusEquals(ComplexNumber) - Method in class gov.sandia.cognition.math.ComplexNumber
-
- minusEquals(LogNumber) - Method in class gov.sandia.cognition.math.LogNumber
-
- minusEquals(Matrix) - Method in class gov.sandia.cognition.math.matrix.AbstractMatrix
-
- minusEquals(Vector) - Method in class gov.sandia.cognition.math.matrix.AbstractVector
-
- minusEquals(SparseMatrix) - Method in class gov.sandia.cognition.math.matrix.custom.DenseMatrix
-
- minusEquals(DenseMatrix) - Method in class gov.sandia.cognition.math.matrix.custom.DenseMatrix
-
- minusEquals(DiagonalMatrix) - Method in class gov.sandia.cognition.math.matrix.custom.DenseMatrix
-
- minusEquals(DenseVector) - Method in class gov.sandia.cognition.math.matrix.custom.DenseVector
-
- minusEquals(SparseVector) - Method in class gov.sandia.cognition.math.matrix.custom.DenseVector
-
- minusEquals(SparseMatrix) - Method in class gov.sandia.cognition.math.matrix.custom.DiagonalMatrix
-
Type-specific version of minusEquals for combining whatever type this is
with the input sparse matrix.
- minusEquals(DenseMatrix) - Method in class gov.sandia.cognition.math.matrix.custom.DiagonalMatrix
-
Type-specific version of minusEquals for combining whatever type this is
with the input dense matrix.
- minusEquals(DiagonalMatrix) - Method in class gov.sandia.cognition.math.matrix.custom.DiagonalMatrix
-
- minusEquals(SparseMatrix) - Method in class gov.sandia.cognition.math.matrix.custom.SparseMatrix
-
Type-specific version of minusEquals for combining whatever type this is
with the input sparse matrix.
- minusEquals(DenseMatrix) - Method in class gov.sandia.cognition.math.matrix.custom.SparseMatrix
-
Type-specific version of minusEquals for combining whatever type this is
with the input dense matrix.
- minusEquals(DiagonalMatrix) - Method in class gov.sandia.cognition.math.matrix.custom.SparseMatrix
-
Type-specific version of minusEquals for combining whatever type this is
with the input diagonal matrix.
- minusEquals(DenseVector) - Method in class gov.sandia.cognition.math.matrix.custom.SparseVector
-
Type-specific version of minusEquals for combining whatever type this is
with the input dense vector.
- minusEquals(SparseVector) - Method in class gov.sandia.cognition.math.matrix.custom.SparseVector
-
- minusEquals(InfiniteVector<KeyType>) - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
-
- minusEquals(Matrix) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix
-
- minusEquals(AbstractMTJMatrix) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix
-
Subtracts the elements of matrix from the elements of
this, modifies the elements of this
- minusEquals(Vector) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJVector
-
- minusEquals(AbstractMTJVector) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJVector
-
Inline subtraction of the elements of other from the elements of this
- minusEquals(MutableDouble) - Method in class gov.sandia.cognition.math.MutableDouble
-
- minusEquals(MutableInteger) - Method in class gov.sandia.cognition.math.MutableInteger
-
- minusEquals(MutableLong) - Method in class gov.sandia.cognition.math.MutableLong
-
- minusEquals(RingType) - Method in interface gov.sandia.cognition.math.Ring
-
Inline arithmetic subtraction of other from
this
- minusEquals(UnsignedLogNumber) - Method in class gov.sandia.cognition.math.UnsignedLogNumber
-
- minusEquals(RandomVariable<Number>) - Method in class gov.sandia.cognition.statistics.UnivariateRandomVariable
-
- MINUTE - Static variable in class gov.sandia.cognition.time.DefaultDuration
-
A minute in duration.
- MINUTES_PER_HOUR - Static variable in class gov.sandia.cognition.time.DefaultDuration
-
There are 60 minutes per hour.
- MixtureOfGaussians - Class in gov.sandia.cognition.statistics.distribution
-
Creates a probability density function (pdf) comprising of a collection of
MultivariateGaussian and corresponding prior probability distribution that
a particular MultivariateGaussian generates observations.
- MixtureOfGaussians() - Constructor for class gov.sandia.cognition.statistics.distribution.MixtureOfGaussians
-
- MixtureOfGaussians.EMLearner - Class in gov.sandia.cognition.statistics.distribution
-
An Expectation-Maximization based "soft" assignment learner.
- MixtureOfGaussians.Learner - Class in gov.sandia.cognition.statistics.distribution
-
A hard-assignment learner for a MixtureOfGaussians
- MixtureOfGaussians.PDF - Class in gov.sandia.cognition.statistics.distribution
-
PDF of the MixtureOfGaussians
- model - Variable in class gov.sandia.cognition.statistics.bayesian.KalmanFilter
-
Motion model of the underlying system.
- modelCovariance - Variable in class gov.sandia.cognition.statistics.bayesian.AbstractKalmanFilter
-
Covariance associated with the system's model.
- ModelFileHandler - Class in gov.sandia.cognition.framework.io
-
The ModelFileHandler class is an entry point for reading files
that contain a CognitiveModel or
CognitiveModelFactory objects.
- ModelFileHandler() - Constructor for class gov.sandia.cognition.framework.io.ModelFileHandler
-
Creates a new instance of ModelIOHandler
- ModelingApproximation - Annotation Type in gov.sandia.cognition.annotation
-
The ModelingApproximation annotation indicates that an approximation
is being used in modeling a component.
- ModelJacobianEvaluator() - Constructor for class gov.sandia.cognition.statistics.bayesian.ExtendedKalmanFilter.ModelJacobianEvaluator
-
Creates a new instance of ModelJacobianEvaluator
- modelStateChanged(CognitiveModelStateChangeEvent) - Method in interface gov.sandia.cognition.framework.CognitiveModelListener
-
This method is called on the listener when the model's state has
changed.
- modularity() - Method in class gov.sandia.cognition.graph.community.Louvain
-
Returns the modularity of the most recent partitioning of the graph
- MODULE_NAME - Static variable in class gov.sandia.cognition.framework.lite.ArrayBasedPerceptionModule
-
The name of the module.
- MODULE_NAME - Static variable in class gov.sandia.cognition.framework.lite.VectorBasedPerceptionModule
-
The name of the module.
- MomentMatchingEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution.MomentMatchingEstimator
-
Default constructor
- MomentMatchingEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.BetaDistribution.MomentMatchingEstimator
-
Default constructor
- MomentMatchingEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.GammaDistribution.MomentMatchingEstimator
-
Default constructor
- MonteCarloIntegrator<OutputType> - Interface in gov.sandia.cognition.statistics.montecarlo
-
Monte Carlo integration is a way of compute the integral of a function using
samples from another.
- MonteCarloSampler<DataType,SampleType,FunctionType extends Evaluator<? super DataType,java.lang.Double>> - Interface in gov.sandia.cognition.statistics.montecarlo
-
A sampling technique based on the Monte Carlo method.
- MostFrequentLearner<OutputType> - Class in gov.sandia.cognition.learning.algorithm.baseline
-
The MostFrequentLearner class implements a baseline learner that
computes the most frequent output value.
- MostFrequentLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.baseline.MostFrequentLearner
-
Creates a new MostFrequentLearner.
- MostFrequentSummarizer<DataType> - Class in gov.sandia.cognition.learning.function.summarizer
-
Summarizes a set of values by returning the most frequent value.
- MostFrequentSummarizer() - Constructor for class gov.sandia.cognition.learning.function.summarizer.MostFrequentSummarizer
-
Creates a new MostFrequentSummarizer.
- motionModel - Variable in class gov.sandia.cognition.statistics.bayesian.ExtendedKalmanFilter
-
Model that determines how inputs and the previous state are updated.
- MovingAverageFilter - Class in gov.sandia.cognition.math.signals
-
A type of filter using a moving-average calculation.
- MovingAverageFilter(int) - Constructor for class gov.sandia.cognition.math.signals.MovingAverageFilter
-
Creates a new instance of MovingAverageFilter
- MovingAverageFilter(double...) - Constructor for class gov.sandia.cognition.math.signals.MovingAverageFilter
-
Creates a new instance of MovingAverageFilter
- MovingAverageFilter(Vector) - Constructor for class gov.sandia.cognition.math.signals.MovingAverageFilter
-
Creates a new instance of MovingAverageFilter
- MultiCategoryAdaBoost<InputType,CategoryType> - Class in gov.sandia.cognition.learning.algorithm.ensemble
-
An implementation of a multi-class version of the Adaptive Boosting
(AdaBoost) algorithm, known as AdaBoost.M1.
- MultiCategoryAdaBoost() - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.MultiCategoryAdaBoost
-
Creates a new MultiCategoryAdaBoost with default parameters and a null
weak learner.
- MultiCategoryAdaBoost(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, CategoryType>>, ? extends Evaluator<? super InputType, ? extends CategoryType>>, int) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.MultiCategoryAdaBoost
-
Creates a new MultiCategoryAdaBoost with the given parameters.
- MultiCollection<T> - Interface in gov.sandia.cognition.collection
-
An interface for a collection that is made up of a group of subcollections.
- multicollection - Variable in class gov.sandia.cognition.learning.algorithm.hmm.BaumWelchAlgorithm
-
The multi-collection of sequences
- MultiIterator<EntryType> - Class in gov.sandia.cognition.collection
-
The MultiIterator class implements an iterator that iterates over a
bunch of internal iterators, exhausting one before moving to the next.
- MultiIterator(Collection<? extends Iterable<EntryType>>) - Constructor for class gov.sandia.cognition.collection.MultiIterator
-
Creates a new instance of MultiIterator.
- MultinomialBayesianEstimator - Class in gov.sandia.cognition.statistics.bayesian.conjugate
-
A Bayesian estimator for the parameters of a MultinomialDistribution using
its conjugate prior distribution, the DirichletDistribution.
- MultinomialBayesianEstimator() - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.MultinomialBayesianEstimator
-
Creates a new instance of MultinomialBayesianEstimator
- MultinomialBayesianEstimator(int) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.MultinomialBayesianEstimator
-
Creates a new instance of MultinomialBayesianEstimator
- MultinomialBayesianEstimator(int, int) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.MultinomialBayesianEstimator
-
Creates a new instance of MultinomialBayesianEstimator
- MultinomialBayesianEstimator(DirichletDistribution, int) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.MultinomialBayesianEstimator
-
Creates a new instance of MultinomialBayesianEstimator
- MultinomialBayesianEstimator(MultinomialDistribution, DirichletDistribution) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.MultinomialBayesianEstimator
-
Creates a new instance of PoissonBayesianEstimator
- MultinomialBayesianEstimator(BayesianParameter<Vector, MultinomialDistribution, DirichletDistribution>) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.MultinomialBayesianEstimator
-
Creates a new instance
- MultinomialBayesianEstimator.Parameter - Class in gov.sandia.cognition.statistics.bayesian.conjugate
-
Parameter of this conjugate prior relationship.
- MultinomialDistribution - Class in gov.sandia.cognition.statistics.distribution
-
A multinomial distribution is the multivariate/multiclass generalization
of the Binomial distribution.
- MultinomialDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.MultinomialDistribution
-
Creates a new instance of MultinomialDistribution
- MultinomialDistribution(int, int) - Constructor for class gov.sandia.cognition.statistics.distribution.MultinomialDistribution
-
Creates a new instance of MultinomialDistribution
- MultinomialDistribution(Vector, int) - Constructor for class gov.sandia.cognition.statistics.distribution.MultinomialDistribution
-
Creates a new instance of MultinomialDistribution
- MultinomialDistribution(MultinomialDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.MultinomialDistribution
-
Copy constructor
- MultinomialDistribution.Domain - Class in gov.sandia.cognition.statistics.distribution
-
Allows the iteration through the set of subsets.
- MultinomialDistribution.Domain.MultinomialIterator - Class in gov.sandia.cognition.statistics.distribution
-
An Iterator over a Domain
- MultinomialDistribution.PMF - Class in gov.sandia.cognition.statistics.distribution
-
Probability Mass Function of the Multinomial Distribution.
- MultinomialIterator(int, int) - Constructor for class gov.sandia.cognition.statistics.distribution.MultinomialDistribution.Domain.MultinomialIterator
-
Creates a new instance of Domain
- MultipartiteValenceMatrix - Class in gov.sandia.cognition.learning.algorithm.semisupervised.valence
-
This class implements a semi-supervised learning algorithm for spreading
"valence" across a multi-partite graph.
- MultipartiteValenceMatrix() - Constructor for class gov.sandia.cognition.learning.algorithm.semisupervised.valence.MultipartiteValenceMatrix
-
NEVER call this from real code.
- MultipartiteValenceMatrix(List<Integer>, int) - Constructor for class gov.sandia.cognition.learning.algorithm.semisupervised.valence.MultipartiteValenceMatrix
-
Initializees this with the expected size of each partition, and the power
to raise the L_tilde matrix to.
- MultipartiteValenceMatrix(List<Integer>, int, int) - Constructor for class gov.sandia.cognition.learning.algorithm.semisupervised.valence.MultipartiteValenceMatrix
-
Initializes this with the expected size of each partition, and the power
to raise the L_tilde matrix to.
- MultipleComparisonExperiment - Class in gov.sandia.cognition.statistics.method
-
A multiple comparisons experiment that does a block comparison and then a
post-hoc test.
- MultipleComparisonExperiment() - Constructor for class gov.sandia.cognition.statistics.method.MultipleComparisonExperiment
-
Creates a new instance of MultipleComparisonExperiment
- MultipleComparisonExperiment(BlockExperimentComparison<Number>, MultipleHypothesisComparison<Collection<? extends Number>>, double) - Constructor for class gov.sandia.cognition.statistics.method.MultipleComparisonExperiment
-
Creates a new instance of MultipleComparisonExperiment
- MultipleComparisonExperiment.Statistic - Class in gov.sandia.cognition.statistics.method
-
Result of running the MultipleHypothesisComparison hypothesis test
- multipleComparisonResult - Variable in class gov.sandia.cognition.statistics.method.MultipleComparisonExperiment.Statistic
-
Result from the multiple hypothesis comparison null-hypothesis test,
which will exist if the block-experiment null hypothesis is rejected
- MultipleHypothesisComparison<TreatmentData> - Interface in gov.sandia.cognition.statistics.method
-
Describes the functionality of an algorithm for accepting or rejecting
multiple null hypothesis at the same time.
- MultipleHypothesisComparison.Statistic - Interface in gov.sandia.cognition.statistics.method
-
Statistic associated with the multiple hypothesis comparison
- multiply(double, double) - Static method in class gov.sandia.cognition.math.LogMath
-
Multiplies two log-domain values.
- MultiReproducer<GenomeType> - Class in gov.sandia.cognition.learning.algorithm.genetic.reproducer
-
The MultiReproducer class implements a Reproducer that takes multiple
Reproducers and applies them to a population.
- MultiReproducer(Collection<Reproducer<GenomeType>>) - Constructor for class gov.sandia.cognition.learning.algorithm.genetic.reproducer.MultiReproducer
-
Creates a new instance of MultiReproducer.
- MultiTextualConverter<InputType,OutputType extends Textual> - Interface in gov.sandia.cognition.text.convert
-
Interface for an TextConverter that converts an input into possibly
multiple output textual objects.
- MultithreadedCognitiveModel - Class in gov.sandia.cognition.framework.concurrent
-
This class provides a multithreaded implementation of the CognitiveModel
interface.
- MultithreadedCognitiveModel(int, CognitiveModuleFactory...) - Constructor for class gov.sandia.cognition.framework.concurrent.MultithreadedCognitiveModel
-
Creates a new instance of MultithreadedCognitiveModel.
- MultithreadedCognitiveModel(int, Iterable<? extends CognitiveModuleFactory>) - Constructor for class gov.sandia.cognition.framework.concurrent.MultithreadedCognitiveModel
-
Creates a new instance of MultithreadedCognitiveModel.
- MultithreadedCognitiveModel.ModuleEvaluator - Class in gov.sandia.cognition.framework.concurrent
-
Implements a Callable class for executing the evaluation of a
CognitiveModule on a thread
- MultithreadedCognitiveModelFactory - Class in gov.sandia.cognition.framework.concurrent
-
This class defines a CognitiveModelFactory for creating
MultithreadedCognitiveModel objects.
- MultithreadedCognitiveModelFactory(int) - Constructor for class gov.sandia.cognition.framework.concurrent.MultithreadedCognitiveModelFactory
-
Creates a new instance of MultithreadedCognitiveModelFactory.
- MultivariateCumulativeDistributionFunction - Class in gov.sandia.cognition.statistics.montecarlo
-
Utility class for multivariate cumulative distribution functions (CDF).
- MultivariateCumulativeDistributionFunction() - Constructor for class gov.sandia.cognition.statistics.montecarlo.MultivariateCumulativeDistributionFunction
-
- MultivariateDecorrelator - Class in gov.sandia.cognition.learning.data.feature
-
Decorrelates a data using a mean and full or diagonal covariance matrix.
- MultivariateDecorrelator() - Constructor for class gov.sandia.cognition.learning.data.feature.MultivariateDecorrelator
-
Creates a new instance of MultivariateDecorrelator with no underlying
Gaussian.
- MultivariateDecorrelator(Vector, Matrix) - Constructor for class gov.sandia.cognition.learning.data.feature.MultivariateDecorrelator
-
Creates a new instance of MultivariateDecorrelator with the given
mean and variance.
- MultivariateDecorrelator(MultivariateGaussian) - Constructor for class gov.sandia.cognition.learning.data.feature.MultivariateDecorrelator
-
Creates a new instance of MultivariateDecorrelator with the given
multivariate Gaussian.
- MultivariateDecorrelator(MultivariateDecorrelator) - Constructor for class gov.sandia.cognition.learning.data.feature.MultivariateDecorrelator
-
Copy constructor.
- MultivariateDecorrelator.DiagonalCovarianceLearner - Class in gov.sandia.cognition.learning.data.feature
-
The DiagonalCovarianceLearner class implements a BatchLearner
object for a MultivariateDecorrelator.
- MultivariateDecorrelator.FullCovarianceLearner - Class in gov.sandia.cognition.learning.data.feature
-
The FullCovarianceLearner class implements a BatchLearner
object for a MultivariateDecorrelator.
- MultivariateDiscriminant - Class in gov.sandia.cognition.learning.function.vector
-
Allows learning algorithms (vectorizing, differentiating) on a matrix*vector
multiply.
- MultivariateDiscriminant() - Constructor for class gov.sandia.cognition.learning.function.vector.MultivariateDiscriminant
-
Default constructor.
- MultivariateDiscriminant(int, int) - Constructor for class gov.sandia.cognition.learning.function.vector.MultivariateDiscriminant
-
Creates a new MultivariateDiscriminant
- MultivariateDiscriminant(Matrix) - Constructor for class gov.sandia.cognition.learning.function.vector.MultivariateDiscriminant
-
Creates a new instance of MatrixVectorMultiplyFunction.
- MultivariateDiscriminant(MultivariateDiscriminant) - Constructor for class gov.sandia.cognition.learning.function.vector.MultivariateDiscriminant
-
Copy constructor
- MultivariateDiscriminantWithBias - Class in gov.sandia.cognition.learning.function.vector
-
A multivariate discriminant (matrix multiply) plus a constant vector
that gets added to the output of the discriminant.
- MultivariateDiscriminantWithBias() - Constructor for class gov.sandia.cognition.learning.function.vector.MultivariateDiscriminantWithBias
-
Default constructor.
- MultivariateDiscriminantWithBias(int, int) - Constructor for class gov.sandia.cognition.learning.function.vector.MultivariateDiscriminantWithBias
-
Creates a new MultivariateDiscriminantWithBias
- MultivariateDiscriminantWithBias(Matrix) - Constructor for class gov.sandia.cognition.learning.function.vector.MultivariateDiscriminantWithBias
-
Creates a new instance of MultivariateDiscriminantWithBias.
- MultivariateDiscriminantWithBias(Matrix, Vector) - Constructor for class gov.sandia.cognition.learning.function.vector.MultivariateDiscriminantWithBias
-
Creates a new instance of MultivariateDiscriminantWithBias.
- MultivariateGammaFunction() - Constructor for class gov.sandia.cognition.statistics.distribution.InverseWishartDistribution.MultivariateGammaFunction
-
- MultivariateGaussian - Class in gov.sandia.cognition.statistics.distribution
-
The MultivariateGaussian class implements a multidimensional Gaussian
distribution that contains a mean vector and a covariance matrix.
- MultivariateGaussian() - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariateGaussian
-
Default constructor.
- MultivariateGaussian(int) - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariateGaussian
-
Creates a new instance of MultivariateGaussian.
- MultivariateGaussian(Vector, Matrix) - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariateGaussian
-
Creates a new instance of MultivariateGaussian.
- MultivariateGaussian(MultivariateGaussian) - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariateGaussian
-
Creates a new instance of MultivariateGaussian.
- MultivariateGaussian.IncrementalEstimator - Class in gov.sandia.cognition.statistics.distribution
-
The estimator that creates a MultivariateGaussian from a stream of
values.
- MultivariateGaussian.IncrementalEstimatorCovarianceInverse - Class in gov.sandia.cognition.statistics.distribution
-
The estimator that creates a MultivariateGaussian from a stream of values
by estimating the mean and covariance inverse (as opposed to the
covariance directly), without ever performing a matrix inversion.
- MultivariateGaussian.MaximumLikelihoodEstimator - Class in gov.sandia.cognition.statistics.distribution
-
Computes the Maximum Likelihood Estimate of the MultivariateGaussian
given a set of Vectors
- MultivariateGaussian.PDF - Class in gov.sandia.cognition.statistics.distribution
-
PDF of a multivariate Gaussian
- MultivariateGaussian.SufficientStatistic - Class in gov.sandia.cognition.statistics.distribution
-
Implements the sufficient statistics of the MultivariateGaussian.
- MultivariateGaussian.SufficientStatisticCovarianceInverse - Class in gov.sandia.cognition.statistics.distribution
-
Implements the sufficient statistics of the MultivariateGaussian while
estimating the inverse of the covariance matrix.
- MultivariateGaussian.WeightedMaximumLikelihoodEstimator - Class in gov.sandia.cognition.statistics.distribution
-
Computes the Weighted Maximum Likelihood Estimate of the
MultivariateGaussian given a weighted set of Vectors
- MultivariateGaussianInverseGammaDistribution - Class in gov.sandia.cognition.statistics.distribution
-
A distribution where the mean is selected by a multivariate Gaussian and
a variance parameter (either for a univariate Gaussian or isotropic Gaussian)
is determined by an Inverse-Gamma distribution.
- MultivariateGaussianInverseGammaDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariateGaussianInverseGammaDistribution
-
Default constructor
- MultivariateGaussianInverseGammaDistribution(int) - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariateGaussianInverseGammaDistribution
-
Creates a new instance of MultivariateGaussianInverseGammaDistribution
- MultivariateGaussianInverseGammaDistribution(MultivariateGaussian, InverseGammaDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariateGaussianInverseGammaDistribution
-
Creates a new instance of MultivariateGaussianInverseGammaDistribution
- MultivariateGaussianMeanBayesianEstimator - Class in gov.sandia.cognition.statistics.bayesian.conjugate
-
Bayesian estimator for the mean of a MultivariateGaussian using its conjugate
prior, which is also a MultivariateGaussian.
- MultivariateGaussianMeanBayesianEstimator() - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanBayesianEstimator
-
Creates a new instance of MultivariateGaussianMeanBayesianEstimator
- MultivariateGaussianMeanBayesianEstimator(int) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanBayesianEstimator
-
Creates a new instance of MultivariateGaussianMeanBayesianEstimator
- MultivariateGaussianMeanBayesianEstimator(Matrix) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanBayesianEstimator
-
Creates a new instance of MultivariateGaussianMeanBayesianEstimator
- MultivariateGaussianMeanBayesianEstimator(Matrix, MultivariateGaussian) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanBayesianEstimator
-
Creates a new instance of MultivariateGaussianMeanBayesianEstimator
- MultivariateGaussianMeanBayesianEstimator(MultivariateGaussian, MultivariateGaussian) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanBayesianEstimator
-
Creates a new instance of PoissonBayesianEstimator
- MultivariateGaussianMeanBayesianEstimator(BayesianParameter<Vector, MultivariateGaussian, MultivariateGaussian>) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanBayesianEstimator
-
Creates a new instance
- MultivariateGaussianMeanBayesianEstimator.Parameter - Class in gov.sandia.cognition.statistics.bayesian.conjugate
-
Parameter of this conjugate prior relationship.
- MultivariateGaussianMeanCovarianceBayesianEstimator - Class in gov.sandia.cognition.statistics.bayesian.conjugate
-
Performs robust estimation of both the mean and covariance of a
MultivariateGaussian conditional distribution using the conjugate prior
Normal-Inverse-Wishart distribution.
- MultivariateGaussianMeanCovarianceBayesianEstimator() - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanCovarianceBayesianEstimator
-
Creates a new instance of MultivariateGaussianMeanCovarianceBayesianEstimator
- MultivariateGaussianMeanCovarianceBayesianEstimator(int) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanCovarianceBayesianEstimator
-
Creates a new instance of MultivariateGaussianMeanCovarianceBayesianEstimator
- MultivariateGaussianMeanCovarianceBayesianEstimator(NormalInverseWishartDistribution) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanCovarianceBayesianEstimator
-
Creates a new instance of MultivariateGaussianMeanCovarianceBayesianEstimator
- MultivariateGaussianMeanCovarianceBayesianEstimator(MultivariateGaussian, NormalInverseWishartDistribution) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanCovarianceBayesianEstimator
-
Creates a new instance
- MultivariateGaussianMeanCovarianceBayesianEstimator(BayesianParameter<Matrix, MultivariateGaussian, NormalInverseWishartDistribution>) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanCovarianceBayesianEstimator
-
Creates a new instance
- MultivariateGaussianMeanCovarianceBayesianEstimator.Parameter - Class in gov.sandia.cognition.statistics.bayesian.conjugate
-
Parameter for this conjugate prior estimator.
- MultivariateLinearRegression - Class in gov.sandia.cognition.learning.algorithm.regression
-
Performs multivariate regression with an explicit bias term, with optional
L2 regularization.
- MultivariateLinearRegression() - Constructor for class gov.sandia.cognition.learning.algorithm.regression.MultivariateLinearRegression
-
Creates a new instance of MultivariateLinearRegression
- MultivariateMeanCovarianceUpdater() - Constructor for class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.MultivariateMeanCovarianceUpdater
-
Default constructor
- MultivariateMeanCovarianceUpdater(int) - Constructor for class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.MultivariateMeanCovarianceUpdater
-
Creates a new instance of MultivariateMeanCovarianceUpdater
- MultivariateMeanCovarianceUpdater(MultivariateGaussianMeanCovarianceBayesianEstimator) - Constructor for class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.MultivariateMeanCovarianceUpdater
-
Creates a new instance of MultivariateMeanCovarianceUpdater
- MultivariateMeanUpdater() - Constructor for class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.MultivariateMeanUpdater
-
Default constructor
- MultivariateMeanUpdater(int) - Constructor for class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.MultivariateMeanUpdater
-
Creates a new instance of MeanCovarianceUpdater
- MultivariateMeanUpdater(MultivariateGaussianMeanBayesianEstimator) - Constructor for class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.MultivariateMeanUpdater
-
Creates a new instance of MeanUpdater
- MultivariateMixtureDensityModel<DistributionType extends ClosedFormComputableDistribution<Vector>> - Class in gov.sandia.cognition.statistics.distribution
-
A LinearMixtureModel of multivariate distributions with associated PDFs.
- MultivariateMixtureDensityModel(Collection<? extends DistributionType>) - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariateMixtureDensityModel
-
Creates a new instance of MultivariateMixtureDensityModel
- MultivariateMixtureDensityModel(Collection<? extends DistributionType>, double[]) - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariateMixtureDensityModel
-
Creates a new instance of MultivariateMixtureDensityModel
- MultivariateMixtureDensityModel(MultivariateMixtureDensityModel<? extends DistributionType>) - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariateMixtureDensityModel
-
Copy Constructor
- MultivariateMixtureDensityModel.PDF<DistributionType extends ClosedFormComputableDistribution<Vector>> - Class in gov.sandia.cognition.statistics.distribution
-
PDF of the MultivariateMixtureDensityModel
- MultivariateMonteCarloIntegrator - Class in gov.sandia.cognition.statistics.montecarlo
-
A Monte Carlo integrator for multivariate (vector) outputs.
- MultivariateMonteCarloIntegrator() - Constructor for class gov.sandia.cognition.statistics.montecarlo.MultivariateMonteCarloIntegrator
-
Creates a new instance of MultivariateMonteCarloIntegrator
- MultivariatePolyaDistribution - Class in gov.sandia.cognition.statistics.distribution
-
A multivariate Polya Distribution, also known as a Dirichlet-Multinomial
model, is a compound distribution where the parameters of a multinomial
are drawn from a Dirichlet distribution with fixed parameters and a constant
number of trials and then the observations are generated by this
multinomial.
- MultivariatePolyaDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariatePolyaDistribution
-
Creates a new instance of DirichletDistribution
- MultivariatePolyaDistribution(int, int) - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariatePolyaDistribution
-
Creates a new instance of MultivariatePolyaDistribution
- MultivariatePolyaDistribution(Vector, int) - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariatePolyaDistribution
-
Creates a new instance of MultivariatePolyaDistribution
- MultivariatePolyaDistribution(MultivariatePolyaDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariatePolyaDistribution
-
Copy Constructor.
- MultivariatePolyaDistribution.PMF - Class in gov.sandia.cognition.statistics.distribution
-
PMF of the MultivariatePolyaDistribution
- MultivariateRegression<InputType,EvaluatorType extends Evaluator<? super InputType,? extends Vectorizable>> - Interface in gov.sandia.cognition.learning.algorithm.regression
-
A regression algorithm that maps one or more independent (input) variables
onto multiple output variables.
- MultivariateStatisticsUtil - Class in gov.sandia.cognition.math
-
Some static methods for computing generally useful multivariate statistics.
- MultivariateStatisticsUtil() - Constructor for class gov.sandia.cognition.math.MultivariateStatisticsUtil
-
- MultivariateStudentTDistribution - Class in gov.sandia.cognition.statistics.distribution
-
Multivariate generalization of the noncentral Student's t-distribution.
- MultivariateStudentTDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution
-
Creates a new instance of MultivariateStudentTDistribution
- MultivariateStudentTDistribution(int) - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution
-
Creates a distribution with the given dimensionality.
- MultivariateStudentTDistribution(double, Vector, Matrix) - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution
-
Creates a distribution with the given dimensionality.
- MultivariateStudentTDistribution(MultivariateStudentTDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution
-
Copy constructor
- MultivariateStudentTDistribution.PDF - Class in gov.sandia.cognition.statistics.distribution
-
PDF of the MultivariateStudentTDistribution
- Murmur32Hash - Class in gov.sandia.cognition.hash
-
Implementation of the MurmurHash2 32-bit (4-byte) non-cryptographic hash
function.
- Murmur32Hash() - Constructor for class gov.sandia.cognition.hash.Murmur32Hash
-
Default constructor
- MutableDouble - Class in gov.sandia.cognition.math
-
A mutable object containing a double.
- MutableDouble() - Constructor for class gov.sandia.cognition.math.MutableDouble
-
Creates an MutableDouble with an initial value of zero.
- MutableDouble(double) - Constructor for class gov.sandia.cognition.math.MutableDouble
-
Creates an MutableDouble with the given value.
- MutableDouble(MutableDouble) - Constructor for class gov.sandia.cognition.math.MutableDouble
-
Creates a copy of a MutableDouble.
- MutableInteger - Class in gov.sandia.cognition.math
-
A mutable object containing an integer.
- MutableInteger() - Constructor for class gov.sandia.cognition.math.MutableInteger
-
Creates an MutableInteger with an initial value of zero.
- MutableInteger(int) - Constructor for class gov.sandia.cognition.math.MutableInteger
-
Creates an MutableInteger with the given value.
- MutableInteger(MutableInteger) - Constructor for class gov.sandia.cognition.math.MutableInteger
-
Creates a copy of a MutableInteger.
- MutableLong - Class in gov.sandia.cognition.math
-
A mutable object containing a long.
- MutableLong() - Constructor for class gov.sandia.cognition.math.MutableLong
-
Creates an MutableLong with an initial value of zero.
- MutableLong(long) - Constructor for class gov.sandia.cognition.math.MutableLong
-
Creates an MutableLong with the given value.
- MutableLong(MutableLong) - Constructor for class gov.sandia.cognition.math.MutableLong
-
Creates a copy of a MutableLong.
- MutablePatternRecognizerLite - Interface in gov.sandia.cognition.framework.lite
-
The MutablePatternRecognizerLite interface extends the PatternRecognizerLite
interface to add methods for changing the recognizer dynamically.
- MutableSemanticMemoryLite - Class in gov.sandia.cognition.framework.lite
-
The MutableSemanticMemoryLite implements a SemanticMemory that can be
dynamically changed.
- MutableSemanticMemoryLite(SemanticIdentifierMap, MutablePatternRecognizerLite) - Constructor for class gov.sandia.cognition.framework.lite.MutableSemanticMemoryLite
-
Creates a new instance of MutableSemanticMemoryLite.
- MutableSemanticMemoryLiteFactory - Class in gov.sandia.cognition.framework.lite
-
The MutableSemanticMemoryLiteFactory implements a CognitiveModuleFactory
for MutableSemanticMemoryLite modules.
- MutableSemanticMemoryLiteFactory(MutablePatternRecognizerLite) - Constructor for class gov.sandia.cognition.framework.lite.MutableSemanticMemoryLiteFactory
-
Creates a new instance of MutableSemanticMemoryLiteFactory.
- MutationReproducer<GenomeType> - Class in gov.sandia.cognition.learning.algorithm.genetic.reproducer
-
The MutationReproducer class implements a Reproducer that applies a
Perturber to the supplied population to produce a new population.
- MutationReproducer(Perturber<GenomeType>, Selector<GenomeType>) - Constructor for class gov.sandia.cognition.learning.algorithm.genetic.reproducer.MutationReproducer
-
Creates a new instance of MutationReproducer