- U - Variable in class gov.sandia.cognition.math.matrix.custom.DenseMatrix.LU
-
The upper triangular matrix resulting from the factorization.
- U - Variable in class gov.sandia.cognition.math.matrix.custom.DenseMatrix.SVD
-
The left basis matrix.
- uncompensatedAlpha - Variable in class gov.sandia.cognition.statistics.method.AbstractMultipleHypothesisComparison.Statistic
-
Uncompensated alpha (p-value threshold) for the multiple comparison
test
- UniformDistribution - Class in gov.sandia.cognition.statistics.distribution
-
Contains the (very simple) definition of a continuous Uniform distribution,
parameterized between the minimum and maximum bounds.
- UniformDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.UniformDistribution
-
Creates a new instance of UniformDistribution
- UniformDistribution(double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.UniformDistribution
-
Creates a new instance of UniformDistribution
- UniformDistribution(UniformDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.UniformDistribution
-
Copy constructor
- UniformDistribution.CDF - Class in gov.sandia.cognition.statistics.distribution
-
Cumulative Distribution Function of a uniform
- UniformDistribution.MaximumLikelihoodEstimator - Class in gov.sandia.cognition.statistics.distribution
-
Maximum Likelihood Estimator of a uniform distribution.
- UniformDistribution.PDF - Class in gov.sandia.cognition.statistics.distribution
-
Probability density function of a Uniform Distribution
- UniformDistributionBayesianEstimator - Class in gov.sandia.cognition.statistics.bayesian.conjugate
-
A Bayesian estimator for a conditional Uniform(0,theta) distribution using
its conjugate prior Pareto distribution.
- UniformDistributionBayesianEstimator() - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.UniformDistributionBayesianEstimator
-
Creates a new instance of UniformDistributionBayesianEstimator
- UniformDistributionBayesianEstimator(ParetoDistribution) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.UniformDistributionBayesianEstimator
-
Creates a new instance of UniformDistributionBayesianEstimator
- UniformDistributionBayesianEstimator(UniformDistribution, ParetoDistribution) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.UniformDistributionBayesianEstimator
-
Creates a new instance of PoissonBayesianEstimator
- UniformDistributionBayesianEstimator(BayesianParameter<Double, UniformDistribution, ParetoDistribution>) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.UniformDistributionBayesianEstimator
-
Creates a new instance
- UniformDistributionBayesianEstimator.Parameter - Class in gov.sandia.cognition.statistics.bayesian.conjugate
-
Parameter of this conjugate prior relationship.
- UniformIntegerDistribution - Class in gov.sandia.cognition.statistics.distribution
-
Contains the (very simple) definition of a continuous Uniform distribution,
parameterized between the minimum and maximum bounds.
- UniformIntegerDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution
-
- UniformIntegerDistribution(int, int) - Constructor for class gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution
-
- UniformIntegerDistribution(UniformIntegerDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution
-
- UniformIntegerDistribution.CDF - Class in gov.sandia.cognition.statistics.distribution
-
Implements the cumulative distribution function for the discrete
uniform distribution.
- UniformIntegerDistribution.MaximumLikelihoodEstimator - Class in gov.sandia.cognition.statistics.distribution
-
Implements a maximum likelihood estimator for the discrete uniform
distribution.
- UniformIntegerDistribution.PMF - Class in gov.sandia.cognition.statistics.distribution
-
Probability mass function of a discrete uniform distribution.
- UniformUpdate() - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.OnlineMultiPerceptron.UniformUpdate
-
Creates a new OnlineMultiPerceptron.UniformUpdate.
- UniformUpdate(double) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.OnlineMultiPerceptron.UniformUpdate
-
Creates a new OnlineMultiPerceptron.UniformUpdate with the
given minimum margin.
- UniformUpdate(double, VectorFactory<?>) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.OnlineMultiPerceptron.UniformUpdate
-
Creates a new OnlineMultiPerceptron.UniformUpdate with the
given minimum margin and backing vector factory.
- UniqueBooleanVectorEncoder<InputType> - Class in gov.sandia.cognition.data.convert.vector
-
An encoder for arbitrary objects that encodes an equality comparison between
a given input and a set of unique values.
- UniqueBooleanVectorEncoder(List<InputType>, DataToVectorEncoder<Boolean>) - Constructor for class gov.sandia.cognition.data.convert.vector.UniqueBooleanVectorEncoder
-
Creates a new UniqueBooleanVectorEncoder.
- UnitTermWeightNormalizer - Class in gov.sandia.cognition.text.term.vector.weighter.normalize
-
Normalizes term weights to be a unit vector.
- UnitTermWeightNormalizer() - Constructor for class gov.sandia.cognition.text.term.vector.weighter.normalize.UnitTermWeightNormalizer
-
Creates a new UnitTermWeightNormalizer.
- unitVector() - Method in class gov.sandia.cognition.math.matrix.AbstractVectorSpace
-
- unitVector() - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
-
- unitVector() - Method in interface gov.sandia.cognition.math.matrix.VectorSpace
-
Returns the unit vector of this vector.
- unitVectorEquals() - Method in class gov.sandia.cognition.math.matrix.AbstractVectorSpace
-
- unitVectorEquals() - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
-
- unitVectorEquals() - Method in interface gov.sandia.cognition.math.matrix.VectorSpace
-
Modifies this vector to be a the unit vector.
- UnivariateDistribution<NumberType extends java.lang.Number> - Interface in gov.sandia.cognition.statistics
-
A Distribution that takes Doubles as inputs and can compute its variance.
- UnivariateGaussian - Class in gov.sandia.cognition.statistics.distribution
-
This class contains internal classes that implement useful functions based
on the Gaussian distribution.
- UnivariateGaussian() - Constructor for class gov.sandia.cognition.statistics.distribution.UnivariateGaussian
-
Creates a new instance of UnivariateGaussian
with zero mean and unit variance
- UnivariateGaussian(double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.UnivariateGaussian
-
Creates a new instance of UnivariateGaussian
- UnivariateGaussian(UnivariateGaussian) - Constructor for class gov.sandia.cognition.statistics.distribution.UnivariateGaussian
-
Copy constructor
- UnivariateGaussian.CDF - Class in gov.sandia.cognition.statistics.distribution
-
CDF of the underlying Gaussian.
- UnivariateGaussian.CDF.Inverse - Class in gov.sandia.cognition.statistics.distribution
-
Inverts the CumulativeDistribution function.
- UnivariateGaussian.ErrorFunction - Class in gov.sandia.cognition.statistics.distribution
-
Gaussian Error Function, useful for computing the cumulative distribution
function for a Gaussian.
- UnivariateGaussian.ErrorFunction.Inverse - Class in gov.sandia.cognition.statistics.distribution
-
Inverse of the ErrorFunction
- UnivariateGaussian.IncrementalEstimator - Class in gov.sandia.cognition.statistics.distribution
-
Implements an incremental estimator for the sufficient statistics for
a UnivariateGaussian.
- UnivariateGaussian.MaximumLikelihoodEstimator - Class in gov.sandia.cognition.statistics.distribution
-
Creates a UnivariateGaussian from data
- UnivariateGaussian.PDF - Class in gov.sandia.cognition.statistics.distribution
-
PDF of the underlying Gaussian.
- UnivariateGaussian.SufficientStatistic - Class in gov.sandia.cognition.statistics.distribution
-
Captures the sufficient statistics of a UnivariateGaussian, which are
the values to estimate the mean and variance.
- UnivariateGaussian.WeightedMaximumLikelihoodEstimator - Class in gov.sandia.cognition.statistics.distribution
-
Creates a UnivariateGaussian from weighted data
- UnivariateGaussianMeanBayesianEstimator - Class in gov.sandia.cognition.statistics.bayesian.conjugate
-
Bayesian estimator for the mean of a UnivariateGaussian using its conjugate
prior, which is also a UnivariateGaussian.
- UnivariateGaussianMeanBayesianEstimator() - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.UnivariateGaussianMeanBayesianEstimator
-
Creates a new instance of UnivariateGaussianMeanBayesianEstimator
- UnivariateGaussianMeanBayesianEstimator(double) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.UnivariateGaussianMeanBayesianEstimator
-
Creates a new instance of UnivariateGaussianMeanBayesianEstimator
- UnivariateGaussianMeanBayesianEstimator(double, UnivariateGaussian) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.UnivariateGaussianMeanBayesianEstimator
-
Creates a new instance of UnivariateGaussianMeanBayesianEstimator
- UnivariateGaussianMeanBayesianEstimator(UnivariateGaussian, UnivariateGaussian) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.UnivariateGaussianMeanBayesianEstimator
-
Creates a new instance
- UnivariateGaussianMeanBayesianEstimator(BayesianParameter<Double, UnivariateGaussian, UnivariateGaussian>) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.UnivariateGaussianMeanBayesianEstimator
-
Creates a new instance
- UnivariateGaussianMeanBayesianEstimator.Parameter - Class in gov.sandia.cognition.statistics.bayesian.conjugate
-
Parameter of this conjugate prior relationship.
- UnivariateGaussianMeanVarianceBayesianEstimator - Class in gov.sandia.cognition.statistics.bayesian.conjugate
-
Computes the mean and variance of a univariate Gaussian using the
conjugate prior NormalInverseGammaDistribution
- UnivariateGaussianMeanVarianceBayesianEstimator() - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.UnivariateGaussianMeanVarianceBayesianEstimator
-
Creates a new instance of UnivariateGaussianMeanVarianceBayesianEstimator
- UnivariateGaussianMeanVarianceBayesianEstimator(NormalInverseGammaDistribution) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.UnivariateGaussianMeanVarianceBayesianEstimator
-
Creates a new instance of UnivariateGaussianMeanVarianceBayesianEstimator
- UnivariateGaussianMeanVarianceBayesianEstimator(UnivariateGaussian, NormalInverseGammaDistribution) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.UnivariateGaussianMeanVarianceBayesianEstimator
-
Creates a new instance of UnivariateGaussianMeanVarianceBayesianEstimator
- UnivariateGaussianMeanVarianceBayesianEstimator(BayesianParameter<Vector, UnivariateGaussian, NormalInverseGammaDistribution>) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.UnivariateGaussianMeanVarianceBayesianEstimator
-
Creates a new instance of UnivariateGaussianMeanVarianceBayesianEstimator
- UnivariateGaussianMeanVarianceBayesianEstimator.Parameter - Class in gov.sandia.cognition.statistics.bayesian.conjugate
-
Parameter for this conjugate prior estimator.
- UnivariateLinearRegression - Class in gov.sandia.cognition.learning.algorithm.regression
-
An implementation of simple univariate linear regression.
- UnivariateLinearRegression() - Constructor for class gov.sandia.cognition.learning.algorithm.regression.UnivariateLinearRegression
-
Creates a new UnivariateLinearRegression.
- UnivariateMonteCarloIntegrator - Class in gov.sandia.cognition.statistics.montecarlo
-
A Monte Carlo integrator for univariate (scalar) outputs.
- UnivariateMonteCarloIntegrator() - Constructor for class gov.sandia.cognition.statistics.montecarlo.UnivariateMonteCarloIntegrator
-
Creates a new instance of UnivariateMonteCarloIntegrator
- UnivariateProbabilityDensityFunction - Interface in gov.sandia.cognition.statistics
-
A PDF that takes doubles as input.
- UnivariateRandomVariable - Class in gov.sandia.cognition.statistics
-
This is an implementation of a RandomVariable for scalar distributions.
- UnivariateRandomVariable(UnivariateDistribution<? extends Number>, Random) - Constructor for class gov.sandia.cognition.statistics.UnivariateRandomVariable
-
Creates a new instance of UnivariateRandomVariable
- UnivariateRandomVariable(UnivariateDistribution<? extends Number>, Random, int) - Constructor for class gov.sandia.cognition.statistics.UnivariateRandomVariable
-
Creates a new instance of UnivariateRandomVariable
- UnivariateRegression<InputType,EvaluatorType extends Evaluator<? super InputType,? extends java.lang.Double>> - Interface in gov.sandia.cognition.learning.algorithm.regression
-
A type of Regression algorithm that has a single dependent (output) variable
that we are trying to predict.
- UnivariateScalarFunction - Interface in gov.sandia.cognition.math
-
Simple interface that describes a function that maps the reals to the reals,
has a Double to Double and double to double.
- UnivariateStatisticsUtil - Class in gov.sandia.cognition.math
-
Some static methods for computing generally useful univariate statistics.
- UnivariateStatisticsUtil() - Constructor for class gov.sandia.cognition.math.UnivariateStatisticsUtil
-
- UnivariateSummaryStatistics - Class in gov.sandia.cognition.math
-
A Bayesian-style synopsis of a Collection of scalar data.
- UnivariateSummaryStatistics(double, double, double[], double, double, double, int, double, double, double, double) - Constructor for class gov.sandia.cognition.math.UnivariateSummaryStatistics
-
Creates a new set of scalar summary statistics.
- UnsignedLogNumber - Class in gov.sandia.cognition.math
-
Represents an unsigned number in log space, storing log(value) and operating
directly on it.
- UnsignedLogNumber() - Constructor for class gov.sandia.cognition.math.UnsignedLogNumber
-
Creates the LogNumber representing zero.
- UnsignedLogNumber(double) - Constructor for class gov.sandia.cognition.math.UnsignedLogNumber
-
Creates a new LogNumber from the given value in log-space.
- UnsignedLogNumber(UnsignedLogNumber) - Constructor for class gov.sandia.cognition.math.UnsignedLogNumber
-
Copies a given LogNumber.
- update(CognitiveModelInput) - Method in interface gov.sandia.cognition.framework.CognitiveModel
-
Updates the model by updating all the modules using the given input.
- update(CognitiveModelState, CognitiveModuleState) - Method in interface gov.sandia.cognition.framework.CognitiveModule
-
This method is the main method for a CognitiveModule.
- update(CognitiveModelState, CognitiveModuleState) - Method in class gov.sandia.cognition.framework.concurrent.AbstractConcurrentCognitiveModule
-
This method provides backwards compatibility with the basic,
non-concurrent CognitiveModule interface.
- update(CognitiveModelInput) - Method in class gov.sandia.cognition.framework.concurrent.MultithreadedCognitiveModel
-
Updates the state of the model from the new input.
- update(CognitiveModelState, CognitiveModuleState) - Method in class gov.sandia.cognition.framework.lite.AbstractSemanticMemoryLite
-
Updates the state of the cognitive model by modifying the given
CognitiveModelState object.
- update(CognitiveModelInput) - Method in class gov.sandia.cognition.framework.lite.CognitiveModelLite
-
Updates the state of the model from the new input.
- update(CognitiveModelState, CognitiveModuleState) - Method in class gov.sandia.cognition.framework.lite.VectorBasedPerceptionModule
-
This method is the main method for a CognitiveModule.
- update(byte[], int, int) - Method in class gov.sandia.cognition.hash.MD5Hash
-
Interior MD5 update step
- update(ResultType, Iterable<? extends DataType>) - Method in class gov.sandia.cognition.learning.algorithm.AbstractBatchAndIncrementalLearner
-
- update(ResultType, InputOutputPair<? extends InputType, OutputType>) - Method in class gov.sandia.cognition.learning.algorithm.AbstractSupervisedBatchAndIncrementalLearner
-
- update(Collection<InputType>, CategoryType) - Method in class gov.sandia.cognition.learning.algorithm.bayes.DiscreteNaiveBayesCategorizer
-
Updates the probability tables from observing the sample inputs and
category.
- update(VectorNaiveBayesCategorizer<CategoryType, DistributionType>, InputOutputPair<? extends Vectorizable, CategoryType>) - Method in class gov.sandia.cognition.learning.algorithm.bayes.VectorNaiveBayesCategorizer.OnlineLearner
-
- update(DefaultConfidenceWeightedBinaryCategorizer, Vectorizable, Boolean) - Method in class gov.sandia.cognition.learning.algorithm.confidence.AdaptiveRegularizationOfWeights
-
- update(DefaultConfidenceWeightedBinaryCategorizer, Vector, boolean) - Method in class gov.sandia.cognition.learning.algorithm.confidence.AdaptiveRegularizationOfWeights
-
Perform an update for the target using the given input and associated
label.
- update(DiagonalConfidenceWeightedBinaryCategorizer, Vectorizable, Boolean) - Method in class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalDeviation
-
- update(DiagonalConfidenceWeightedBinaryCategorizer, Vector, boolean) - Method in class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalDeviation
-
Updates the target using the given input and associated label.
- update(DiagonalConfidenceWeightedBinaryCategorizer, Vector, boolean) - Method in class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalDeviationProject
-
- update(DiagonalConfidenceWeightedBinaryCategorizer, Vectorizable, Boolean) - Method in class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalVariance
-
- update(DiagonalConfidenceWeightedBinaryCategorizer, Vector, boolean) - Method in class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalVariance
-
Updates the target using the given input and associated label.
- update(DiagonalConfidenceWeightedBinaryCategorizer, Vector, boolean) - Method in class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalVarianceProject
-
- update(VotingCategorizerEnsemble<InputType, CategoryType, MemberType>, InputType, CategoryType) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.OnlineBaggingCategorizerLearner
-
- update(VotingCategorizerEnsemble<InputType, CategoryType, MemberType>, InputOutputPair<? extends InputType, CategoryType>) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.OnlineBaggingCategorizerLearner
-
- update(InputOutputPair<? extends Vector, Double>) - Method in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachineStochasticGradient
-
Performs a single update of step of the stochastic gradient descent
by updating according to the given example.
- update(ResultType, DataType) - Method in interface gov.sandia.cognition.learning.algorithm.IncrementalLearner
-
The update method updates an object of ResultType using
the given new data of type DataType, using some form of
"learning" algorithm.
- update(ResultType, Iterable<? extends DataType>) - Method in interface gov.sandia.cognition.learning.algorithm.IncrementalLearner
-
The update method updates an object of ResultType using
the given new Iterable containing some number of type DataType,
using some form of "learning" algorithm.
- update(DefaultKernelBinaryCategorizer<InputType>, Iterable<? extends InputOutputPair<? extends InputType, Boolean>>) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.AbstractKernelizableBinaryCategorizerOnlineLearner
-
- update(DefaultKernelBinaryCategorizer<InputType>, InputOutputPair<? extends InputType, Boolean>) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.AbstractKernelizableBinaryCategorizerOnlineLearner
-
- update(DefaultKernelBinaryCategorizer<InputType>, InputType, Boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.AbstractKernelizableBinaryCategorizerOnlineLearner
-
- update(LinearBinaryCategorizer, Vector, boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.AbstractLinearCombinationOnlineLearner
-
- update(DefaultKernelBinaryCategorizer<InputType>, InputType, boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.AbstractLinearCombinationOnlineLearner
-
- update(LinearBinaryCategorizer, Vectorizable, Boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.AbstractOnlineLinearBinaryCategorizerLearner
-
- update(LinearBinaryCategorizer, Vector, boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.AbstractOnlineLinearBinaryCategorizerLearner
-
The update method updates an object of ResultType using
the given a new supervised input-output pair, using some form of
"learning" algorithm.
- update(LinearBinaryCategorizer, Vector, boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.AggressiveRelaxedOnlineMaximumMarginAlgorithm
-
- update(DefaultKernelBinaryCategorizer<InputType>, InputType, boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.AggressiveRelaxedOnlineMaximumMarginAlgorithm
-
- update(LinearBinaryCategorizer, Vector, boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.Ballseptron
-
- update(DefaultKernelBinaryCategorizer<InputType>, InputType, boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.Ballseptron
-
- update(DefaultKernelBinaryCategorizer<InputType>, InputType, Boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.AbstractOnlineKernelBinaryCategorizerLearner
-
- update(DefaultKernelBinaryCategorizer<InputType>, InputType, boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.AbstractOnlineKernelBinaryCategorizerLearner
-
Updates the target categorizer based on the given input and its
associated output.
- update(DefaultKernelBinaryCategorizer<InputType>, InputType, boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Forgetron.Basic
-
- update(DefaultKernelBinaryCategorizer<InputType>, InputType, boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Forgetron
-
- update(DefaultKernelBinaryCategorizer<InputType>, InputOutputPair<? extends InputType, Boolean>) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelBinaryCategorizerOnlineLearnerAdapter
-
- update(DefaultKernelBinaryCategorizer<InputType>, InputType, Boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelBinaryCategorizerOnlineLearnerAdapter
-
- update(DefaultKernelBinaryCategorizer<InputType>, InputType, boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelBinaryCategorizerOnlineLearnerAdapter
-
- update(DefaultKernelBinaryCategorizer<InputType>, InputType, boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.OnlineKernelPerceptron
-
- update(DefaultKernelBinaryCategorizer<InputType>, InputType, boolean, boolean) - Static method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.OnlineKernelPerceptron
-
Performs a Perceptron update step on the given target.
- update(DefaultKernelBinaryCategorizer<InputType>, InputType, boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.OnlineKernelRandomizedBudgetPerceptron
-
- update(DefaultKernelBinaryCategorizer<InputType>, InputType, boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Projectron
-
- update(DefaultKernelBinaryCategorizer<InputType>, InputType, boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.RemoveOldestKernelPerceptron
-
- update(DefaultKernelBinaryCategorizer<InputType>, InputType, boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Stoptron
-
- update(DefaultKernelBinaryCategorizer<InputType>, Iterable<? extends InputOutputPair<? extends InputType, Boolean>>) - Method in interface gov.sandia.cognition.learning.algorithm.perceptron.KernelizableBinaryCategorizerOnlineLearner
-
Performs a kernel-based incremental update step on the given object
using the given supervised data.
- update(DefaultKernelBinaryCategorizer<InputType>, InputOutputPair<? extends InputType, Boolean>) - Method in interface gov.sandia.cognition.learning.algorithm.perceptron.KernelizableBinaryCategorizerOnlineLearner
-
Performs a kernel-based incremental update step on the given object
using the given supervised data.
- update(DefaultKernelBinaryCategorizer<InputType>, InputType, Boolean) - Method in interface gov.sandia.cognition.learning.algorithm.perceptron.KernelizableBinaryCategorizerOnlineLearner
-
Performs a kernel-based incremental update step on the given object
using the given supervised data.
- update(DefaultKernelBinaryCategorizer<InputType>, InputType, boolean) - Method in interface gov.sandia.cognition.learning.algorithm.perceptron.KernelizableBinaryCategorizerOnlineLearner
-
Performs a kernel-based incremental update step on the given object
using the given supervised data.
- update(LinearBinaryCategorizer, Iterable<? extends InputOutputPair<? extends Vectorizable, Boolean>>, VectorFactory<?>) - Method in interface gov.sandia.cognition.learning.algorithm.perceptron.LinearizableBinaryCategorizerOnlineLearner
-
Performs a linear incremental update step on the given object using the
given supervised data.
- update(LinearBinaryCategorizer, InputOutputPair<? extends Vectorizable, Boolean>, VectorFactory<?>) - Method in interface gov.sandia.cognition.learning.algorithm.perceptron.LinearizableBinaryCategorizerOnlineLearner
-
Performs a linear incremental update step on the given object using the
given supervised data.
- update(LinearBinaryCategorizer, Vectorizable, Boolean, VectorFactory<?>) - Method in interface gov.sandia.cognition.learning.algorithm.perceptron.LinearizableBinaryCategorizerOnlineLearner
-
Performs a linear incremental update step on the given object using the
given supervised data.
- update(LinearBinaryCategorizer, Vectorizable, boolean, VectorFactory<?>) - Method in interface gov.sandia.cognition.learning.algorithm.perceptron.LinearizableBinaryCategorizerOnlineLearner
-
Performs a linear incremental update step on the given object using the
given supervised data.
- update(LinearMultiCategorizer<CategoryType>, InputOutputPair<? extends Vectorizable, CategoryType>) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineMultiPerceptron.ProportionalUpdate
-
- update(LinearMultiCategorizer<CategoryType>, InputOutputPair<? extends Vectorizable, CategoryType>) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineMultiPerceptron.UniformUpdate
-
- update(LinearMultiCategorizer<CategoryType>, InputOutputPair<? extends Vectorizable, CategoryType>) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineMultiPerceptron
-
- update(WeightedBinaryEnsemble<Vectorizable, LinearBinaryCategorizer>, Vectorizable, Boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineVotedPerceptron
-
- update(WeightedBinaryEnsemble<Vectorizable, LinearBinaryCategorizer>, Vector, boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineVotedPerceptron
-
The update method updates an object of ResultType using
the given a new supervised input-output pair, using some form of
"learning" algorithm.
- update(LinearBinaryCategorizer, Vector, boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.RelaxedOnlineMaximumMarginAlgorithm
-
- update(DefaultKernelBinaryCategorizer<InputType>, InputType, boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.RelaxedOnlineMaximumMarginAlgorithm
-
- update(LinearBinaryCategorizer, Vector, boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.Winnow
-
- update(ResultType, InputType, OutputType) - Method in interface gov.sandia.cognition.learning.algorithm.SupervisedIncrementalLearner
-
The update method updates an object of ResultType using
the given a new supervised input-output pair, using some form of
"learning" algorithm.
- update - Variable in class gov.sandia.cognition.learning.algorithm.svm.PrimalEstimatedSubGradient
-
A vector used to compute the update for the weight vector.
- update(SuccessiveOverrelaxation<InputType>.Entry) - Method in class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation
-
Performs an update step on the given entry using the successive
overrelaxation procedure.
- update(SufficientStatisticsType, DataType) - Method in class gov.sandia.cognition.statistics.AbstractIncrementalEstimator
-
- update(Iterable<? extends DataType>) - Method in class gov.sandia.cognition.statistics.AbstractSufficientStatistic
-
- update(MultivariateGaussian, Vector) - Method in class gov.sandia.cognition.statistics.bayesian.AbstractKalmanFilter
-
- update(InputOutputPair<? extends Vectorizable, Double>) - Method in class gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression.IncrementalEstimator.SufficientStatistic
-
- update(BayesianLinearRegression.IncrementalEstimator.SufficientStatistic, InputOutputPair<? extends Vectorizable, Double>) - Method in class gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression.IncrementalEstimator
-
- update(BayesianLinearRegression.IncrementalEstimator.SufficientStatistic, Iterable<? extends InputOutputPair<? extends Vectorizable, Double>>) - Method in class gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression.IncrementalEstimator
-
- update(InputOutputPair<? extends Vectorizable, Double>) - Method in class gov.sandia.cognition.statistics.bayesian.BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic
-
- update(BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic, InputOutputPair<? extends Vectorizable, Double>) - Method in class gov.sandia.cognition.statistics.bayesian.BayesianRobustLinearRegression.IncrementalEstimator
-
- update(BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic, Iterable<? extends InputOutputPair<? extends Vectorizable, Double>>) - Method in class gov.sandia.cognition.statistics.bayesian.BayesianRobustLinearRegression.IncrementalEstimator
-
- update(BetaDistribution, Number) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.BernoulliBayesianEstimator
-
- update(BetaDistribution, Number) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.BinomialBayesianEstimator
-
- update(GammaDistribution, Double) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.ExponentialBayesianEstimator
-
- update(GammaDistribution, Double) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.GammaInverseScaleBayesianEstimator
-
- update(DirichletDistribution, Vector) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.MultinomialBayesianEstimator
-
- update(MultivariateGaussian, Iterable<? extends Vector>) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanBayesianEstimator
-
- update(MultivariateGaussian, Vector) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanBayesianEstimator
-
- update(NormalInverseWishartDistribution, Vector) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanCovarianceBayesianEstimator
-
- update(NormalInverseWishartDistribution, Iterable<? extends Vector>) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanCovarianceBayesianEstimator
-
- update(GammaDistribution, Number) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.PoissonBayesianEstimator
-
- update(ParetoDistribution, Double) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.UniformDistributionBayesianEstimator
-
- update(UnivariateGaussian, Double) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.UnivariateGaussianMeanBayesianEstimator
-
- update(NormalInverseGammaDistribution, Double) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.UnivariateGaussianMeanVarianceBayesianEstimator
-
- update(NormalInverseGammaDistribution, Iterable<? extends Double>) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.UnivariateGaussianMeanVarianceBayesianEstimator
-
- update(ParameterType) - Method in interface gov.sandia.cognition.statistics.bayesian.ParticleFilter.Updater
-
Makes a proposal update given the current parameter set
- update(DataDistribution<ParameterType>, ObservationType) - Method in class gov.sandia.cognition.statistics.bayesian.SamplingImportanceResamplingParticleFilter
-
- update(DefaultDataDistribution.PMF<KeyType>, KeyType) - Method in class gov.sandia.cognition.statistics.distribution.DefaultDataDistribution.Estimator
-
- update(DefaultDataDistribution.PMF<KeyType>, WeightedValue<? extends KeyType>) - Method in class gov.sandia.cognition.statistics.distribution.DefaultDataDistribution.WeightedEstimator
-
- update(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.SufficientStatistic
-
- update(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.SufficientStatisticCovarianceInverse
-
- update(ScalarDataDistribution, Double) - Method in class gov.sandia.cognition.statistics.distribution.ScalarDataDistribution.Estimator
-
- update(Double) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.SufficientStatistic
-
- update(double) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.SufficientStatistic
-
Adds a value to the sufficient statistics for the Gaussian.
- update(DataType) - Method in interface gov.sandia.cognition.statistics.SufficientStatistic
-
Updates the sufficient statistics from the given value
- update(Iterable<? extends DataType>) - Method in interface gov.sandia.cognition.statistics.SufficientStatistic
-
Updates the sufficient statistics from the given set of values
- update(SimpleStatisticalSpellingCorrector, String) - Method in class gov.sandia.cognition.text.spelling.SimpleStatisticalSpellingCorrector.Learner
-
- updateAlpha(double, int) - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel
-
Runs the Gibbs sampler for the concentration parameter, alpha, given
the data.
- updateAssignments() - Method in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation
-
Updates the assignments of all the examples to their exemplars (clusters)
using the current availability and responsibility values.
- updateAvailabilities() - Method in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation
-
Updates the availabilities matrix based on the current responsibility
values.
- updateBias - Variable in class gov.sandia.cognition.learning.algorithm.perceptron.AbstractLinearCombinationOnlineLearner
-
An option controlling whether or not the bias is updated or not.
- updateCluster(Vector) - Method in class gov.sandia.cognition.learning.algorithm.clustering.cluster.MiniBatchCentroidCluster
-
Updates the cluster for the given point.
- updateCluster(Collection<? extends Vector>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.cluster.MiniBatchCentroidCluster
-
Updates the clusters for all the given points.
- updateClusters(ArrayList<Collection<ObservationType>>) - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel
-
Update each cluster according to the data assigned to it
- updateClusters(ArrayList<Collection<ObservationType>>) - Method in class gov.sandia.cognition.statistics.bayesian.ParallelDirichletProcessMixtureModel
-
- updateConditionalDistribution(Random) - Method in class gov.sandia.cognition.statistics.bayesian.AbstractBayesianParameter
-
- updateConditionalDistribution(Random) - Method in interface gov.sandia.cognition.statistics.bayesian.BayesianParameter
-
Updates the conditional distribution by sampling from the prior
distribution and assigning through the DistributionParameter.
- updateConditionalDistribution(Random) - Method in class gov.sandia.cognition.statistics.bayesian.DefaultBayesianParameter
-
- updateHessianInverse(Matrix, Vector, Vector) - Method in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerBFGS
-
- updateHessianInverse(Matrix, Vector, Vector) - Method in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerDFP
-
- updateHessianInverse(Matrix, Vector, Vector) - Method in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerQuasiNewton
-
The step that makes BFGS/DFP/SR1 different from each other.
- updateInitialProbabilities(ArrayList<Vector>) - Method in class gov.sandia.cognition.learning.algorithm.hmm.BaumWelchAlgorithm
-
Updates the initial probabilities from sequenceGammas
- updateMinDistance(int) - Method in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer
-
Updates the cached minimum distance for this cluster by
comparing it to all the other clusters.
- updateOutOfBagEstimates() - Method in class gov.sandia.cognition.learning.algorithm.ensemble.BaggingCategorizerLearner.OutOfBagErrorStoppingCriteria
-
Updates the out-of-bag estimates that this ensemble keeps.
- updateProbabilityFunctions(ArrayList<Vector>) - Method in class gov.sandia.cognition.learning.algorithm.hmm.BaumWelchAlgorithm
-
Updates the probability function from the concatenated gammas from
all sequences
- updateProbabilityFunctions(ArrayList<Vector>) - Method in class gov.sandia.cognition.learning.algorithm.hmm.ParallelBaumWelchAlgorithm
-
- updater - Variable in class gov.sandia.cognition.statistics.bayesian.AbstractParticleFilter
-
Updates the particle given an existing particle.
- updater - Variable in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel
-
Creates the clusters and predictive prior distributions
- updater - Variable in class gov.sandia.cognition.statistics.bayesian.ImportanceSampling
-
Updater for the ImportanceSampling algorithm.
- updater - Variable in class gov.sandia.cognition.statistics.bayesian.MetropolisHastingsAlgorithm
-
The object that makes proposal samples from the current location.
- updater - Variable in class gov.sandia.cognition.statistics.bayesian.RejectionSampling
-
Updater for the ImportanceSampling algorithm.
- updateResponsibilities() - Method in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation
-
Updates the responsibilities matrix using the similarity values and the
current availability values.
- updateSequenceLogLikelihoods(HiddenMarkovModel<ObservationType>) - Method in class gov.sandia.cognition.learning.algorithm.hmm.BaumWelchAlgorithm
-
Updates the internal sequence likelihoods for the given HMM
- updateTransitionMatrix(ArrayList<Matrix>) - Method in class gov.sandia.cognition.learning.algorithm.hmm.BaumWelchAlgorithm
-
Computes an updated transition matrix from the scaled estimates
- upperBounds - Variable in class gov.sandia.cognition.learning.algorithm.clustering.OptimizedKMeansClusterer
-
The upper bounds on the distance to the current assigned cluster.
- UpperEnvelope() - Constructor for class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.UpperEnvelope
-
Default constructor
- upperLeft - Variable in class gov.sandia.cognition.math.geometry.Quadtree.Node
-
The child for the upper-left quadrant of this node.
- upperRight - Variable in class gov.sandia.cognition.math.geometry.Quadtree.Node
-
The child for the upper-right quadrant of this node.
- useCachedClusters - Variable in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer
-
Whether or not the current learning is using cached cluster results.