- bag - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractBaggingLearner
-
The current bag of data.
- BagBasedCategorizerEnsembleLearner<InputType,CategoryType> - Interface in gov.sandia.cognition.learning.algorithm.ensemble
-
Interface for a bag-based ensemble learner.
- BaggingCategorizerLearner<InputType,CategoryType> - Class in gov.sandia.cognition.learning.algorithm.ensemble
-
Learns an categorization ensemble by randomly sampling with replacement
(duplicates allowed) some percentage of the size of the data (defaults to
100%) on each iteration to train a new ensemble member.
- BaggingCategorizerLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.BaggingCategorizerLearner
-
Creates a new instance of BaggingCategorizerLearner.
- BaggingCategorizerLearner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, CategoryType>>, ? extends Evaluator<? super InputType, ? extends CategoryType>>) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.BaggingCategorizerLearner
-
Creates a new instance of BaggingCategorizerLearner.
- BaggingCategorizerLearner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, CategoryType>>, ? extends Evaluator<? super InputType, ? extends CategoryType>>, int, double, Random) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.BaggingCategorizerLearner
-
Creates a new instance of BaggingCategorizerLearner.
- BaggingCategorizerLearner.OutOfBagErrorStoppingCriteria<InputType,CategoryType> - Class in gov.sandia.cognition.learning.algorithm.ensemble
-
Implements a stopping criteria for bagging that uses the out-of-bag
error to determine when to stop learning the ensemble.
- BaggingRegressionLearner<InputType> - Class in gov.sandia.cognition.learning.algorithm.ensemble
-
Learns an ensemble for regression by randomly sampling with replacement
(duplicates allowed) some percentage of the size of the data (defaults to
100%) on each iteration to train a new ensemble member.
- BaggingRegressionLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.BaggingRegressionLearner
-
Creates a new, empty BaggingRegressionLearner.
- BaggingRegressionLearner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, Double>>, ? extends Evaluator<? super InputType, ? extends Number>>) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.BaggingRegressionLearner
-
Creates a new instance of BaggingRegressionLearner.
- BaggingRegressionLearner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, Double>>, ? extends Evaluator<? super InputType, ? extends Number>>, int, double, Random) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.BaggingRegressionLearner
-
Creates a new instance of BaggingRegressionLearner.
- BagOfWordsTransform - Class in gov.sandia.cognition.text.term.vector
-
Transforms a list of term occurrences into a vector of counts.
- BagOfWordsTransform() - Constructor for class gov.sandia.cognition.text.term.vector.BagOfWordsTransform
-
Creates a new BagOfWordsTransform.
- BagOfWordsTransform(TermIndex) - Constructor for class gov.sandia.cognition.text.term.vector.BagOfWordsTransform
-
Creates a new BagOfWordsTransform with the given term index.
- BagOfWordsTransform(TermIndex, VectorFactory<? extends Vector>) - Constructor for class gov.sandia.cognition.text.term.vector.BagOfWordsTransform
-
Creates a new BagOfWordsTransform with the given term index.
- Ballseptron - Class in gov.sandia.cognition.learning.algorithm.perceptron
-
An implementation of the Ballseptron algorithm.
- Ballseptron() - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.Ballseptron
-
Creates a new Ballseptron with default parameters.
- Ballseptron(double) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.Ballseptron
-
Creates a new Ballseptron with the given radius.
- baseHandler - Variable in class gov.sandia.cognition.io.serialization.GZIPSerializationHandler
-
The base handler that is being wrapped in a GZip.
- Basic() - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Forgetron.Basic
-
Creates a new Forgetron.Basic with a null kernel and default
budget.
- Basic(Kernel<? super InputType>, int) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Forgetron.Basic
-
Creates a new Forgetron.Basic with the given kernel and
budget.
- BatchAndIncrementalLearner<DataType,ResultType> - Interface in gov.sandia.cognition.learning.algorithm
-
Interface for an algorithm that is both a batch and incremental learner.
- BatchClusterer<DataType,ClusterType extends Cluster<DataType>> - Interface in gov.sandia.cognition.learning.algorithm.clustering
-
The BatchClusterer interface defines the functionality of a batch
clustering algorithm.
- BatchCostMinimizationLearner<CostParametersType,ResultType> - Interface in gov.sandia.cognition.learning.algorithm
-
The BatchCostMinimizationLearner interface defines the functionality
of a cost-minimization learning algorithm should follow.
- BatchGaussianLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.bayes.VectorNaiveBayesCategorizer.BatchGaussianLearner
-
Creates a new BatchGaussianLearner.
- BatchHierarchicalClusterer<DataType,ClusterType extends Cluster<DataType>> - Interface in gov.sandia.cognition.learning.algorithm.clustering.hierarchy
-
The BatchHierarchicalClusterer interface defines the functionality
of a batch hierarchical clustering algorithm.
- BatchLearner<DataType,ResultType> - Interface in gov.sandia.cognition.learning.algorithm
-
The BatchLearner interface defines the general functionality
of an object that is the implementation of a data-driven, batch machine
learning algorithm.
- BatchLearnerContainer<LearnerType extends BatchLearner<?,?>> - Interface in gov.sandia.cognition.learning.algorithm
-
An interface for an object that contains a batch learner.
- BatchMultiPerceptron<CategoryType> - Class in gov.sandia.cognition.learning.algorithm.perceptron
-
Implements a multi-class version of the standard batch Perceptron learning
algorithm.
- BatchMultiPerceptron() - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.BatchMultiPerceptron
-
Creates a new BatchMultiPerceptron with default parameters.
- BatchMultiPerceptron(int) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.BatchMultiPerceptron
-
Creates a new BatchMultiPerceptron with the given maximum number
of iterations and a default margin of 0.0.
- BatchMultiPerceptron(int, double) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.BatchMultiPerceptron
-
Creates a new BatchMultiPerceptron with the given maximum
number of iterations and margin to enforce.
- BatchMultiPerceptron(int, double, VectorFactory<?>) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.BatchMultiPerceptron
-
Creates a new BatchMultiPerceptron with the given parameters.
- BatchTemporalDataSource<DataType extends Temporal> - Interface in gov.sandia.cognition.data.temporal
-
Defines the interface for an offline temporal data source, which can
be resampled.
- BaumWelchAlgorithm<ObservationType> - Class in gov.sandia.cognition.learning.algorithm.hmm
-
Implements the Baum-Welch algorithm, also known as the "forward-backward
algorithm", the expectation-maximization algorithm, etc for
Hidden Markov Models (HMMs).
- BaumWelchAlgorithm() - Constructor for class gov.sandia.cognition.learning.algorithm.hmm.BaumWelchAlgorithm
-
Creates a new instance of BaumWelchAlgorithm
- BaumWelchAlgorithm(HiddenMarkovModel<ObservationType>, BatchLearner<Collection<? extends WeightedValue<? extends ObservationType>>, ? extends ComputableDistribution<ObservationType>>, boolean) - Constructor for class gov.sandia.cognition.learning.algorithm.hmm.BaumWelchAlgorithm
-
Creates a new instance of BaumWelchAlgorithm
- BayesianCredibleInterval - Class in gov.sandia.cognition.statistics.bayesian
-
A Bayesian credible interval defines a bound that a scalar parameter is
within the given interval.
- BayesianCredibleInterval(double, double, double, double) - Constructor for class gov.sandia.cognition.statistics.bayesian.BayesianCredibleInterval
-
Creates a new instance of ConfidenceInterval
- BayesianEstimator<ObservationType,ParameterType,PosteriorType extends Distribution<? extends ParameterType>> - Interface in gov.sandia.cognition.statistics.bayesian
-
A type of estimation procedure based on Bayes's rule, which allows us
to estimate the uncertainty of parameters given a set of observations
that we are given.
- BayesianEstimatorPredictor<ObservationType,ParameterType,PosteriorType extends Distribution<? extends ParameterType>> - Interface in gov.sandia.cognition.statistics.bayesian
-
A BayesianEstimator that can also compute the predictive distribution of
new data given the posterior.
- BayesianLinearRegression - Class in gov.sandia.cognition.statistics.bayesian
-
Computes a Bayesian linear estimator for a given feature function
and a set of observed data.
- BayesianLinearRegression(int) - Constructor for class gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression
-
Creates a new instance of BayesianLinearRegression
- BayesianLinearRegression(double, MultivariateGaussian) - Constructor for class gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression
-
Creates a new instance of BayesianLinearRegression
- BayesianLinearRegression.IncrementalEstimator - Class in gov.sandia.cognition.statistics.bayesian
-
Incremental estimator for BayesianLinearRegression
- BayesianLinearRegression.IncrementalEstimator.SufficientStatistic - Class in gov.sandia.cognition.statistics.bayesian
-
SufficientStatistic for incremental Bayesian linear regression
- BayesianLinearRegression.PredictiveDistribution - Class in gov.sandia.cognition.statistics.bayesian
-
Creates the predictive distribution for the likelihood of a given point.
- BayesianParameter<ParameterType,ConditionalType extends Distribution<?>,PriorType extends Distribution<ParameterType>> - Interface in gov.sandia.cognition.statistics.bayesian
-
A parameter from a Distribution that has an assumed Distribution of
values.
- BayesianRegression<OutputType,PosteriorType extends Distribution<? extends Vector>> - Interface in gov.sandia.cognition.statistics.bayesian
-
A type of regression algorithm maps a Vector space, and the
weights of this Vector space are represented as a posterior distribution
given the observed InputOutputPairs.
- BayesianRobustLinearRegression - Class in gov.sandia.cognition.statistics.bayesian
-
Computes a Bayesian linear estimator for a given feature function given
a set of InputOutputPair observed values.
- BayesianRobustLinearRegression(int) - Constructor for class gov.sandia.cognition.statistics.bayesian.BayesianRobustLinearRegression
-
Creates a new instance of BayesianLinearRegression
- BayesianRobustLinearRegression(InverseGammaDistribution, MultivariateGaussian) - Constructor for class gov.sandia.cognition.statistics.bayesian.BayesianRobustLinearRegression
-
Creates a new instance of BayesianRobustLinearRegression
- BayesianRobustLinearRegression.IncrementalEstimator - Class in gov.sandia.cognition.statistics.bayesian
-
Incremental estimator for BayesianRobustLinearRegression
- BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic - Class in gov.sandia.cognition.statistics.bayesian
-
SufficientStatistic for incremental Bayesian linear regression
- BayesianRobustLinearRegression.PredictiveDistribution - Class in gov.sandia.cognition.statistics.bayesian
-
Predictive distribution of future data given the posterior of
the weights given the data.
- BayesianUtil - Class in gov.sandia.cognition.statistics.bayesian
-
Contains generally useful utilities for Bayesian statistics.
- BayesianUtil() - Constructor for class gov.sandia.cognition.statistics.bayesian.BayesianUtil
-
- BernoulliBayesianEstimator - Class in gov.sandia.cognition.statistics.bayesian.conjugate
-
A Bayesian estimator for the parameter of a BernoulliDistribution using
the conjugate prior BetaDistribution.
- BernoulliBayesianEstimator() - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.BernoulliBayesianEstimator
-
Creates a new instance of BernoulliBayesianEstimator
- BernoulliBayesianEstimator(BetaDistribution) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.BernoulliBayesianEstimator
-
Creates a new instance of BernoulliBayesianEstimator
- BernoulliBayesianEstimator(BernoulliDistribution, BetaDistribution) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.BernoulliBayesianEstimator
-
Creates a new instance of BernoulliBayesianEstimator
- BernoulliBayesianEstimator(BayesianParameter<Double, BernoulliDistribution, BetaDistribution>) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.BernoulliBayesianEstimator
-
Creates a new instance of BernoulliBayesianEstimator
- BernoulliBayesianEstimator.Parameter - Class in gov.sandia.cognition.statistics.bayesian.conjugate
-
Parameter of this conjugate prior relationship.
- BernoulliConfidence - Class in gov.sandia.cognition.statistics.method
-
Computes the Bernoulli confidence interval.
- BernoulliConfidence() - Constructor for class gov.sandia.cognition.statistics.method.BernoulliConfidence
-
Creates a new instance of BernoulliConfidence
- BernoulliDistribution - Class in gov.sandia.cognition.statistics.distribution
-
A Bernoulli distribution, which takes a value of "1" with probability "p"
and value of "0" with probability "1-p".
- BernoulliDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.BernoulliDistribution
-
Creates a new instance of BernoulliDistribution
- BernoulliDistribution(double) - Constructor for class gov.sandia.cognition.statistics.distribution.BernoulliDistribution
-
Creates a new instance of BernoulliDistribution
- BernoulliDistribution(BernoulliDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.BernoulliDistribution
-
Copy Constructor
- BernoulliDistribution.CDF - Class in gov.sandia.cognition.statistics.distribution
-
CDF of a Bernoulli distribution.
- BernoulliDistribution.PMF - Class in gov.sandia.cognition.statistics.distribution
-
PMF of the Bernoulli distribution.
- beta - Variable in class gov.sandia.cognition.learning.algorithm.hmm.ParallelHiddenMarkovModel.StateObservationLikelihoodTask
-
Beta at time n.
- beta - Variable in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler
-
The beta parameter controlling the Dirichlet distribution for the
topic-term probabilities.
- BetaBinomialDistribution - Class in gov.sandia.cognition.statistics.distribution
-
A Binomial distribution where the binomial parameter, p, is set according
to a Beta distribution instead of a single value.
- BetaBinomialDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution
-
Creates a new instance of BetaBinomialDistribution
- BetaBinomialDistribution(int, double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution
-
Creates a new instance of BetaBinomialDistribution
- BetaBinomialDistribution(BetaBinomialDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution
-
Copy constructor
- BetaBinomialDistribution.CDF - Class in gov.sandia.cognition.statistics.distribution
-
CDF of BetaBinomialDistribution
- BetaBinomialDistribution.MomentMatchingEstimator - Class in gov.sandia.cognition.statistics.distribution
-
Estimates the parameters of a Beta-binomial distribution using the matching
of moments, not maximum likelihood.
- BetaBinomialDistribution.PMF - Class in gov.sandia.cognition.statistics.distribution
-
PMF of the BetaBinomialDistribution
- BetaDistribution - Class in gov.sandia.cognition.statistics.distribution
-
Computes the Beta-family of probability distributions.
- BetaDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.BetaDistribution
-
Default constructor.
- BetaDistribution(double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.BetaDistribution
-
Creates a new instance of BetaDistribution
- BetaDistribution(BetaDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.BetaDistribution
-
Copy Constructor
- BetaDistribution.CDF - Class in gov.sandia.cognition.statistics.distribution
-
CDF of the Beta-family distribution
- BetaDistribution.MomentMatchingEstimator - Class in gov.sandia.cognition.statistics.distribution
-
Estimates the parameters of a Beta distribution using the matching
of moments, not maximum likelihood.
- BetaDistribution.PDF - Class in gov.sandia.cognition.statistics.distribution
-
Beta distribution probability density function
- BetaDistribution.WeightedMomentMatchingEstimator - Class in gov.sandia.cognition.statistics.distribution
-
Estimates the parameters of a Beta distribution using the matching
of moments, not maximum likelihood.
- BFGSupdateRule(Matrix, Vector, Vector, double) - Static method in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerBFGS
-
BFGS Quasi-Newton update rule
- bias - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.AdditiveEnsemble
-
The initial offset value that the ensemble outputs are added to.
- bias - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.WeightedAdditiveEnsemble
-
The initial offset value that the ensemble outputs are added to.
- bias - Variable in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachine
-
The bias term (b).
- bias - Variable in class gov.sandia.cognition.learning.function.categorization.KernelBinaryCategorizer
-
The bias term.
- bias - Variable in class gov.sandia.cognition.learning.function.categorization.LinearBinaryCategorizer
-
The bias term.
- bias - Variable in class gov.sandia.cognition.learning.function.scalar.KernelScalarFunction
-
The bias term.
- bias - Variable in class gov.sandia.cognition.learning.function.scalar.LinearDiscriminantWithBias
-
Bias term that gets added to the output of the dot product.
- bias - Variable in class gov.sandia.cognition.learning.function.vector.MultivariateDiscriminantWithBias
-
Bias term that gets added the output of the matrix multiplication.
- biasEnabled - Variable in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner
-
True if the bias term is enabled.
- biasRegularization - Variable in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner
-
The regularization term for the bias.
- BIG_Z - Static variable in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian
-
A big value to input into the Gaussian CDF that will get 1.0
probability, 100.0.
- binarizeOnTrueCategory(ConfusionMatrix<CategoryType>, CategoryType) - Static method in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrix
-
Takes a general confusion matrix and creates a binary form of it using
true category.
- binarizeOnTrueSet(ConfusionMatrix<CategoryType>, Set<? super CategoryType>) - Static method in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrix
-
Takes a general confusion matrix and creates a binary form of it using
the given set of true categories.
- BINARY_CATEGORIES - Static variable in class gov.sandia.cognition.learning.function.categorization.AbstractBinaryCategorizer
-
The possible categories for a binary categorizer.
- BINARY_SERIALIZED_EXTENSION - Static variable in class gov.sandia.cognition.framework.io.ModelFileHandler
-
The extension for binary serialized models.
- BinaryBaggingLearner<InputType> - Class in gov.sandia.cognition.learning.algorithm.ensemble
-
The BinaryBaggingLearner implements the Bagging learning algorithm.
- BinaryBaggingLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.BinaryBaggingLearner
-
Creates a new instance of BinaryBaggingLearner.
- BinaryBaggingLearner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, Boolean>>, ? extends Evaluator<? super InputType, ? extends Boolean>>) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.BinaryBaggingLearner
-
Creates a new instance of BinaryBaggingLearner.
- BinaryBaggingLearner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, Boolean>>, ? extends Evaluator<? super InputType, ? extends Boolean>>, int) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.BinaryBaggingLearner
-
Creates a new instance of BinaryBaggingLearner.
- BinaryBaggingLearner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, Boolean>>, ? extends Evaluator<? super InputType, ? extends Boolean>>, int, Random) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.BinaryBaggingLearner
-
Creates a new instance of BinaryBaggingLearner.
- BinaryBaggingLearner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, Boolean>>, ? extends Evaluator<? super InputType, ? extends Boolean>>, int, double, Random) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.BinaryBaggingLearner
-
Creates a new instance of BinaryBaggingLearner.
- BinaryCategorizer<InputType> - Interface in gov.sandia.cognition.learning.function.categorization
-
The BinaryCategorizer extends the Categorizer
interface by enforcing that the output categories are boolean values, which
means that the categorizer is performing binary categorization.
- BinaryCategorizerSelector<InputType> - Class in gov.sandia.cognition.learning.algorithm.ensemble
-
The BinaryCategorizerSelector class implements a "weak learner"
meant for use in boosting algorithms that selects the best
BinaryCategorizer from a pre-set list by picking the one with the
best weighted error.
- BinaryCategorizerSelector() - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.BinaryCategorizerSelector
-
Creates a new instance of BinaryCategorizerSelector.
- BinaryCategorizerSelector(Collection<BinaryCategorizer<? super InputType>>) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.BinaryCategorizerSelector
-
Creates a new instance of BinaryCategorizerSelector.
- BinaryClusterHierarchyNode<DataType,ClusterType extends Cluster<DataType>> - Class in gov.sandia.cognition.learning.algorithm.clustering.hierarchy
-
Implements a binary cluster hierarchy node.
- BinaryClusterHierarchyNode() - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.hierarchy.BinaryClusterHierarchyNode
-
Creates a new BinaryClusterHierarchyNode.
- BinaryClusterHierarchyNode(ClusterType) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.hierarchy.BinaryClusterHierarchyNode
-
Creates a new BinaryClusterHierarchyNode.
- BinaryClusterHierarchyNode(ClusterType, ClusterHierarchyNode<DataType, ClusterType>, ClusterHierarchyNode<DataType, ClusterType>) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.hierarchy.BinaryClusterHierarchyNode
-
Creates a new BinaryClusterHierarchyNode.
- BinaryConfusionMatrix - Interface in gov.sandia.cognition.learning.performance.categorization
-
An interface for a binary confusion matrix.
- BinaryLocalTermWeighter - Class in gov.sandia.cognition.text.term.vector.weighter.local
-
Makes the given term weights binary, by creating a vector that contains a
1.0 for all non-zero entries in the given vector and a 0.0 for the all the
zeros.
- BinaryLocalTermWeighter() - Constructor for class gov.sandia.cognition.text.term.vector.weighter.local.BinaryLocalTermWeighter
-
Creates a new BinaryLocalTermWeighter.
- BinaryLocalTermWeighter(VectorFactory<? extends Vector>) - Constructor for class gov.sandia.cognition.text.term.vector.weighter.local.BinaryLocalTermWeighter
-
Creates a new BinaryLocalTermWeighter.
- BinaryVersusCategorizer<InputType,CategoryType> - Class in gov.sandia.cognition.learning.function.categorization
-
An adapter that allows binary categorizers to be adapted for multi-category
problems by applying a binary categorizer to each pair of categories.
- BinaryVersusCategorizer() - Constructor for class gov.sandia.cognition.learning.function.categorization.BinaryVersusCategorizer
-
Creates a new BinaryVersusCategorizer.
- BinaryVersusCategorizer(Set<CategoryType>) - Constructor for class gov.sandia.cognition.learning.function.categorization.BinaryVersusCategorizer
-
Creates a new BinaryVersusCategorizer with the given
categories and an empty set of evaluators.
- BinaryVersusCategorizer(Set<CategoryType>, Map<Pair<CategoryType, CategoryType>, Evaluator<? super InputType, Boolean>>) - Constructor for class gov.sandia.cognition.learning.function.categorization.BinaryVersusCategorizer
-
Creates a new BinaryVersusCategorizer.
- BinaryVersusCategorizer.Learner<InputType,CategoryType> - Class in gov.sandia.cognition.learning.function.categorization
-
A learner for the BinaryVersusCategorizer.
- Binner<ValueType,BinnedType> - Interface in gov.sandia.cognition.statistics.method
-
Defines the functionality for a class that assigns values to some sort of
bin.
- BinomialBayesianEstimator - Class in gov.sandia.cognition.statistics.bayesian.conjugate
-
A Bayesian estimator for the parameter of a Bernoulli parameter, p,
of a BinomialDistribution using the conjugate prior BetaDistribution.
- BinomialBayesianEstimator() - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.BinomialBayesianEstimator
-
Creates a new instance of BinomialBayesianEstimator
- BinomialBayesianEstimator(int) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.BinomialBayesianEstimator
-
Creates a new instance of BinomialBayesianEstimator
- BinomialBayesianEstimator(int, BetaDistribution) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.BinomialBayesianEstimator
-
Creates a new instance of BinomialBayesianEstimator
- BinomialBayesianEstimator(BinomialDistribution, BetaDistribution) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.BinomialBayesianEstimator
-
Creates a new instance of BinomialBayesianEstimator
- BinomialBayesianEstimator(BayesianParameter<Double, BinomialDistribution, BetaDistribution>) - Constructor for class gov.sandia.cognition.statistics.bayesian.conjugate.BinomialBayesianEstimator
-
Creates a new instance of BinomialBayesianEstimator
- BinomialBayesianEstimator.Parameter - Class in gov.sandia.cognition.statistics.bayesian.conjugate
-
Parameter of this relationship
- binomialCoefficient(int, int) - Static method in class gov.sandia.cognition.math.MathUtil
-
Returns the binomial coefficient for "N choose k".
- BinomialDistribution - Class in gov.sandia.cognition.statistics.distribution
-
Binomial distribution, which is a collection of Bernoulli trials
- BinomialDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.BinomialDistribution
-
Default constructor.
- BinomialDistribution(int, double) - Constructor for class gov.sandia.cognition.statistics.distribution.BinomialDistribution
-
Creates a new instance of BinomialDistribution
- BinomialDistribution(BinomialDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.BinomialDistribution
-
Copy constructor
- BinomialDistribution.CDF - Class in gov.sandia.cognition.statistics.distribution
-
CDF of the Binomial distribution, which is the probability of getting
up to "x" successes in "N" trials with a Bernoulli probability of "p"
- BinomialDistribution.MaximumLikelihoodEstimator - Class in gov.sandia.cognition.statistics.distribution
-
Maximum likelihood estimator of the distribution
- BinomialDistribution.PMF - Class in gov.sandia.cognition.statistics.distribution
-
The Probability Mass Function of a binomial distribution.
- blasAvailable() - Static method in class gov.sandia.cognition.math.matrix.custom.NativeBlasHandler
-
Returns true if BLAS is available in any form.
- BLOCK_SIZE - Static variable in class gov.sandia.cognition.hash.SHA256Hash
-
Length of the hash block is 512 bits (64 bytes), 64.
- BLOCK_SIZE - Static variable in class gov.sandia.cognition.hash.SHA512Hash
-
Length of the hash block is 1024 bits (128 bytes), 128.
- BlockExperimentComparison<DataType> - Interface in gov.sandia.cognition.statistics.method
-
Implements a null-hypothesis multiple-comparison test from a block-design
experiment.
- blockExperimentResult - Variable in class gov.sandia.cognition.statistics.method.MultipleComparisonExperiment.Statistic
-
Result from the block-experiment null-hypothesis test
- BODY_FIELD_NAME - Static variable in class gov.sandia.cognition.text.document.AbstractDocument
-
The name of the body field is "body".
- BonferroniCorrection - Class in gov.sandia.cognition.statistics.method
-
The Bonferroni correction takes a pair-wise null-hypothesis test and
generalizes it to multiple comparisons by adjusting the requisite p-value
to find significance as alpha / NumComparisons.
- BonferroniCorrection() - Constructor for class gov.sandia.cognition.statistics.method.BonferroniCorrection
-
Default constructor
- BonferroniCorrection(NullHypothesisEvaluator<Collection<? extends Number>>) - Constructor for class gov.sandia.cognition.statistics.method.BonferroniCorrection
-
Creates a new instance of BonferroniCorrection
- BooleanActivatableCogxel - Class in gov.sandia.cognition.framework.lite
-
BooleanActivatableCogxel extends the DefaultCogxel class to add an "activated" flag.
- BooleanActivatableCogxel(BooleanActivatableCogxel) - Constructor for class gov.sandia.cognition.framework.lite.BooleanActivatableCogxel
-
Creates copy of the given cogxel.
- BooleanActivatableCogxel(SemanticIdentifier) - Constructor for class gov.sandia.cognition.framework.lite.BooleanActivatableCogxel
-
Creates a new instance of BooleanActivatableCogxel.
- BooleanActivatableCogxel(SemanticIdentifier, double) - Constructor for class gov.sandia.cognition.framework.lite.BooleanActivatableCogxel
-
Creates a new instance of BooleanActivatableCogxel.
- BooleanActivatableCogxel(SemanticIdentifier, boolean) - Constructor for class gov.sandia.cognition.framework.lite.BooleanActivatableCogxel
-
Creates a new instance of BooleanActivatableCogxel.
- BooleanActivatableCogxel(SemanticIdentifier, double, boolean) - Constructor for class gov.sandia.cognition.framework.lite.BooleanActivatableCogxel
-
Creates a new instance of BooleanActivatableCogxel.
- BooleanActivatableCogxelFactory - Class in gov.sandia.cognition.framework.lite
-
This class implements a CogxelFactory, which creates ActivatableCogxels.
- BooleanActivatableCogxelFactory() - Constructor for class gov.sandia.cognition.framework.lite.BooleanActivatableCogxelFactory
-
Creates a new instance of BooleanActivatableCogxelFactory.
- booleanConverter - Variable in class gov.sandia.cognition.data.convert.vector.UniqueBooleanVectorEncoder
-
The boolean encoder for the equality comparison between each of the
possible values and a given input.
- bounds - Variable in class gov.sandia.cognition.math.geometry.Quadtree.Node
-
The two-dimensional bounds for this node.
- boundsContain(DataType) - Method in class gov.sandia.cognition.math.geometry.Quadtree
-
Determines if the given point is within the bounds of the quadtree.
- boundsContain(Vector2) - Method in class gov.sandia.cognition.math.geometry.Quadtree
-
Determines if the given point is within the bounds of the quadtree.
- boundsContain(double, double) - Method in class gov.sandia.cognition.math.geometry.Quadtree
-
Determines if the given point is within the bounds of the quadtree.
- boundsContain(Vector2) - Method in class gov.sandia.cognition.math.geometry.Quadtree.Node
-
Returns true if the given point is within the bounds of this node.
- boundsContain(Point2D) - Method in class gov.sandia.cognition.math.geometry.Quadtree.Node
-
Returns true if the given point is within the bounds of this node.
- boundsContain(double, double) - Method in class gov.sandia.cognition.math.geometry.Quadtree.Node
-
Returns true if the given point is within the bounds of this node.
- boundsContain(Rectangle2D) - Method in class gov.sandia.cognition.math.geometry.Quadtree.Node
-
Returns true if the given rectangle is completely within the bounds
of this node.
- boundsOverlap(Rectangle2D) - Method in class gov.sandia.cognition.math.geometry.Quadtree.Node
-
Returns true if the given rectangle intersects the bounds for this
node.
- bracketingStep() - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.AbstractAnytimeLineMinimizer
-
- bracketingStep() - Method in interface gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizer
-
Continues the bracketing phase of the algorithm, which attempts to
place a bracket around a known minimum.
- bracketingStep() - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerBacktracking
-
- bracketingStep() - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerDerivativeBased
-
- bracketingStep() - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerDerivativeFree
-
Here's the general idea of derivative-free minimum bracketing:
Given an initial point, a={x,f(x)}, we're looking to find a triplet
of points {a,b,c} such that bx is between ax and cx.
- budget - Variable in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.AbstractOnlineBudgetedKernelBinaryCategorizerLearner
-
The budget of the number of examples to keep.
- BudgetedKernelBinaryCategorizerLearner - Interface in gov.sandia.cognition.learning.algorithm.perceptron.kernel
-
Interface for a budgeted kernel binary categorizer learner.
- build() - Method in class gov.sandia.cognition.learning.algorithm.clustering.MiniBatchKMeansClusterer.Builder
-
Builds the clusterer.
- Builder(int) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.MiniBatchKMeansClusterer.Builder
-
Create a mini-batch k-means clusterer builder and set it to
the given number of clusters.
- Builder(int, Semimetric<? super Vector>) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.MiniBatchKMeansClusterer.Builder
-
Create a mini-batch k-means clusterer builder and set it to
the given number of clusters.
- buildIdentifierCache() - Method in class gov.sandia.cognition.framework.learning.converter.AbstractCogxelConverter
-
Rebuilds the cache of SemanticIdentifier objects.
- buildIdentifierCache() - Method in class gov.sandia.cognition.framework.learning.converter.CogxelBooleanConverter
-
- buildIdentifierCache() - Method in class gov.sandia.cognition.framework.learning.converter.CogxelDoubleConverter
-
Rebuilds the cache of SemanticIdentifier objects.
- buildIdentifierCache() - Method in class gov.sandia.cognition.framework.learning.converter.CogxelVectorConverter
-
Rebuilds the cache of SemanticIdentifier objects.
- buildInputMapping() - Method in class gov.sandia.cognition.framework.lite.MutableSemanticMemoryLite
-
Builds the mapping of identifiers to input indices.
- buildNodeToIDMap() - Method in class gov.sandia.cognition.framework.lite.SimplePatternRecognizer
-
Builds the map of nodes to identifiers from the list of nodes.
- buildOutputMapping() - Method in class gov.sandia.cognition.framework.lite.AbstractSemanticMemoryLite
-
Builds the mapping of output identifiers to their labels.
- burnInIterations - Variable in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler
-
The number of burn-in iterations for the Markov Chain Monte Carlo
algorithm to run before sampling begins.
- BurrowsDeltaCategorizer<CategoryType> - Class in gov.sandia.cognition.learning.algorithm.delta
-
The regular Burrows' Delta algorithm implementation.
- BurrowsDeltaCategorizer(BurrowsDeltaCategorizer.Learner<CategoryType>, ArrayList<Double>) - Constructor for class gov.sandia.cognition.learning.algorithm.delta.BurrowsDeltaCategorizer
-
Constructor that takes a learner and featureStddev.
- BurrowsDeltaCategorizer.Learner<CategoryType> - Class in gov.sandia.cognition.learning.algorithm.delta
-
Learner for a BurrowsDeltaCategorizer.