Uses of Interface
gov.sandia.cognition.algorithm.IterativeAlgorithm

Packages that use IterativeAlgorithm

Package	Description
gov.sandia.cognition.algorithm	Provides general interfaces and implementations for algorithms.
gov.sandia.cognition.algorithm.event	Provides useful components for handling algorithm events.
gov.sandia.cognition.learning.algorithm	Provides general interfaces for learning algorithms.
gov.sandia.cognition.learning.algorithm.annealing	Provides the Simulated Annealing algorithm.
gov.sandia.cognition.learning.algorithm.clustering	Provides clustering algorithms.
gov.sandia.cognition.learning.algorithm.ensemble	Provides ensemble methods.
gov.sandia.cognition.learning.algorithm.factor.machine	Provides factorization machine algorithms.
gov.sandia.cognition.learning.algorithm.genetic	Provides a genetic algorithm implementation.
gov.sandia.cognition.learning.algorithm.hmm	Provides hidden Markov model (HMM) algorithms.
gov.sandia.cognition.learning.algorithm.minimization	Provides minimization algorithms.
gov.sandia.cognition.learning.algorithm.minimization.line	Provides line (scalar) minimization algorithms.
gov.sandia.cognition.learning.algorithm.minimization.matrix	Provides matrix solving algorithms.
gov.sandia.cognition.learning.algorithm.pca	Provides implementations of Principle Components Analysis (PCA).
gov.sandia.cognition.learning.algorithm.perceptron	Provides the Perceptron algorithm and some of its variations.
gov.sandia.cognition.learning.algorithm.perceptron.kernel
gov.sandia.cognition.learning.algorithm.regression	Provides regression algorithms, such as Linear Regression.
gov.sandia.cognition.learning.algorithm.root	Provides algorithms for finding the roots, or zero crossings, of scalar functions.
gov.sandia.cognition.learning.algorithm.svm	Provides implementations of Support Vector Machine (SVM) learning algorithms.
gov.sandia.cognition.learning.algorithm.tree	Provides decision tree learning algorithms.
gov.sandia.cognition.learning.function.vector	Provides functions that output vectors.
gov.sandia.cognition.learning.performance	Provides performance measures.
gov.sandia.cognition.math	Provides classes for mathematical computation.
gov.sandia.cognition.statistics.bayesian	Provides algorithms for computing Bayesian estimates of parameters.
gov.sandia.cognition.statistics.distribution	Provides statistical distributions.
gov.sandia.cognition.statistics.method	Provides algorithms for evaluating statistical data and conducting statistical inference, particularly frequentist methods.
gov.sandia.cognition.text.topic	Provides topic modeling algorithms.

Uses of IterativeAlgorithm in gov.sandia.cognition.algorithm

Subinterfaces of IterativeAlgorithm in gov.sandia.cognition.algorithm

Modifier and Type	Interface and Description
interface	AnytimeAlgorithm<ResultType> The AnytimeAlgorithm interface defines the functionality of an iterative algorithm that is stoppable and can return intermediate results.

Classes in gov.sandia.cognition.algorithm that implement IterativeAlgorithm

Modifier and Type	Class and Description
class	AbstractAnytimeAlgorithm<ResultType> A partial implementation of the common functionality of an AnytimeAlgorithm.
class	AbstractIterativeAlgorithm The AbstractIterativeAlgorithm class implements a simple part of the IterativeAlgorithm interface that manages the listeners for the algorithm.
class	AnytimeAlgorithmWrapper<ResultType,InternalAlgorithm extends AnytimeAlgorithm<?>> Wraps an AnytimeAlgorithm.

Methods in gov.sandia.cognition.algorithm with parameters of type IterativeAlgorithm

Modifier and Type	Method and Description
void	AnytimeAlgorithmWrapper.algorithmEnded(IterativeAlgorithm algorithm)
void	IterativeAlgorithmListener.algorithmEnded(IterativeAlgorithm algorithm) This method is called when the algorithm has ended, after the last step of the algorithm.
void	AnytimeAlgorithmWrapper.algorithmStarted(IterativeAlgorithm algorithm)
void	IterativeAlgorithmListener.algorithmStarted(IterativeAlgorithm algorithm) This method is called when a algorithm has started, before the first step of the algorithm.
void	AnytimeAlgorithmWrapper.stepEnded(IterativeAlgorithm algorithm)
void	IterativeAlgorithmListener.stepEnded(IterativeAlgorithm algorithm) This method is called when the algorithm has ended a step of its execution.
void	AnytimeAlgorithmWrapper.stepStarted(IterativeAlgorithm algorithm)
void	IterativeAlgorithmListener.stepStarted(IterativeAlgorithm algorithm) This method is called when the algorithm has started a step in its execution.

Uses of IterativeAlgorithm in gov.sandia.cognition.algorithm.event

Methods in gov.sandia.cognition.algorithm.event with parameters of type IterativeAlgorithm

Modifier and Type	Method and Description
void	AbstractIterativeAlgorithmListener.algorithmEnded(IterativeAlgorithm algorithm)
void	AbstractIterativeAlgorithmListener.algorithmStarted(IterativeAlgorithm algorithm)
void	AbstractIterativeAlgorithmListener.stepEnded(IterativeAlgorithm algorithm)
void	IterationMeasurablePerformanceReporter.stepEnded(IterativeAlgorithm algorithm)
void	AbstractIterativeAlgorithmListener.stepStarted(IterativeAlgorithm algorithm)
void	IterationStartReporter.stepStarted(IterativeAlgorithm algorithm)

Uses of IterativeAlgorithm in gov.sandia.cognition.learning.algorithm

Subinterfaces of IterativeAlgorithm in gov.sandia.cognition.learning.algorithm

Modifier and Type	Interface and Description
interface	AnytimeBatchLearner<DataType,ResultType> A batch learner that is also and Anytime algorithm.

Classes in gov.sandia.cognition.learning.algorithm that implement IterativeAlgorithm

Modifier and Type	Class and Description
class	AbstractAnytimeBatchLearner<DataType,ResultType> The AbstractAnytimeBatchLearner abstract class implements a standard method for conforming to the BatchLearner and AnytimeLearner (IterativeAlgorithm and StoppableAlgorithm) interfaces.
class	AbstractAnytimeSupervisedBatchLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>> The AbstractAnytimeSupervisedBatchLearner abstract class extends the AbstractAnytimeBatchLearner to implement the SupervisedBatchLearner interface.

Uses of IterativeAlgorithm in gov.sandia.cognition.learning.algorithm.annealing

Classes in gov.sandia.cognition.learning.algorithm.annealing that implement IterativeAlgorithm

Modifier and Type	Class and Description
class	SimulatedAnnealer<CostParametersType,AnnealedType> The SimulatedAnnealer class implements the simulated annealing algorithm using the provided cost function and perturbation function.

Uses of IterativeAlgorithm in gov.sandia.cognition.learning.algorithm.clustering

Classes in gov.sandia.cognition.learning.algorithm.clustering that implement IterativeAlgorithm

Modifier and Type	Class and Description
class	AffinityPropagation<DataType> The AffinityPropagation algorithm requires three parameters: a divergence function, a value to use for self-divergence, and a damping factor (called lambda in the paper; 0.5 is the default).
class	AgglomerativeClusterer<DataType,ClusterType extends Cluster<DataType>> The AgglomerativeClusterer implements an agglomerative clustering algorithm, which is a type of hierarchical clustering algorithm.
class	DBSCANClusterer<DataType extends Vectorizable,ClusterType extends Cluster<DataType>> The DBSCAN algorithm requires three parameters: a distance metric, a value for neighborhood radius, and a value for the minimum number of surrounding neighbors for a point to be considered non-noise.
class	DirichletProcessClustering Clustering algorithm that wraps Dirichlet Process Mixture Model.
class	KMeansClusterer<DataType,ClusterType extends Cluster<DataType>> The KMeansClusterer class implements the standard k-means (k-centroids) clustering algorithm.
class	KMeansClustererWithRemoval<DataType,ClusterType extends Cluster<DataType>> Creates a k-means clustering algorithm that removes clusters that do not have sufficient membership to pass a simple statistical significance test.
class	MiniBatchKMeansClusterer<DataType extends Vector> Approximates k-means clustering by working on random subsets of the data.
class	OptimizedKMeansClusterer<DataType> This class implements an optimized version of the k-means algorithm that makes use of the triangle inequality to compute the same answer as k-means while using less distance calculations.
class	ParallelizedKMeansClusterer<DataType,ClusterType extends Cluster<DataType>> This is a parallel implementation of the k-means clustering algorithm.
class	PartitionalClusterer<DataType,ClusterType extends Cluster<DataType>> The PartitionalClusterer implements a partitional clustering algorithm, which is a type of hierarchical clustering algorithm.

Uses of IterativeAlgorithm in gov.sandia.cognition.learning.algorithm.ensemble

Subinterfaces of IterativeAlgorithm in gov.sandia.cognition.learning.algorithm.ensemble

Modifier and Type	Interface and Description
interface	BagBasedCategorizerEnsembleLearner<InputType,CategoryType> Interface for a bag-based ensemble learner.

Classes in gov.sandia.cognition.learning.algorithm.ensemble that implement IterativeAlgorithm

Modifier and Type	Class and Description
class	AbstractBaggingLearner<InputType,OutputType,MemberType,EnsembleType extends Evaluator<? super InputType,? extends OutputType>> Learns an 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.
class	AdaBoost<InputType> The AdaBoost class implements the Adaptive Boosting (AdaBoost) algorithm formulated by Yoav Freund and Robert Shapire.
class	BaggingCategorizerLearner<InputType,CategoryType> 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.
class	BaggingRegressionLearner<InputType> 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.
class	BinaryBaggingLearner<InputType> The BinaryBaggingLearner implements the Bagging learning algorithm.
class	CategoryBalancedBaggingLearner<InputType,CategoryType> An extension of the basic bagging learner that attempts to sample bags that have equal numbers of examples from every category.
class	CategoryBalancedIVotingLearner<InputType,CategoryType> An extension of IVoting for dealing with skew problems that makes sure that there are an equal number of examples from each category in each sample that an ensemble member is trained on.
class	IVotingCategorizerLearner<InputType,CategoryType> Learns an ensemble in a method similar to bagging except that on each iteration the bag is built from two parts, each sampled from elements from disjoint sets.
class	MultiCategoryAdaBoost<InputType,CategoryType> An implementation of a multi-class version of the Adaptive Boosting (AdaBoost) algorithm, known as AdaBoost.M1.

Methods in gov.sandia.cognition.learning.algorithm.ensemble with parameters of type IterativeAlgorithm

Modifier and Type	Method and Description
void	AbstractCategorizerOutOfBagStoppingCriteria.algorithmEnded(IterativeAlgorithm algorithm)
void	BaggingCategorizerLearner.OutOfBagErrorStoppingCriteria.algorithmEnded(IterativeAlgorithm algorithm)
void	AbstractCategorizerOutOfBagStoppingCriteria.algorithmStarted(IterativeAlgorithm algorithm)
void	BaggingCategorizerLearner.OutOfBagErrorStoppingCriteria.algorithmStarted(IterativeAlgorithm algorithm)
void	IVotingCategorizerLearner.OutOfBagErrorStoppingCriteria.algorithmStarted(IterativeAlgorithm algorithm)
void	AbstractCategorizerOutOfBagStoppingCriteria.stepEnded(IterativeAlgorithm algorithm)
void	BaggingCategorizerLearner.OutOfBagErrorStoppingCriteria.stepEnded(IterativeAlgorithm algorithm)

Uses of IterativeAlgorithm in gov.sandia.cognition.learning.algorithm.factor.machine

Classes in gov.sandia.cognition.learning.algorithm.factor.machine that implement IterativeAlgorithm

Modifier and Type	Class and Description
class	AbstractFactorizationMachineLearner An abstract class for learning FactorizationMachines.
class	FactorizationMachineAlternatingLeastSquares Implements an Alternating Least Squares (ALS) algorithm for learning a Factorization Machine.
class	FactorizationMachineStochasticGradient Implements a Stochastic Gradient Descent (SGD) algorithm for learning a Factorization Machine.

Uses of IterativeAlgorithm in gov.sandia.cognition.learning.algorithm.genetic

Classes in gov.sandia.cognition.learning.algorithm.genetic that implement IterativeAlgorithm

Modifier and Type	Class and Description
class	GeneticAlgorithm<CostParametersType,GenomeType> The GeneticAlgorithm class implements a generic genetic algorithm that uses a given cost function to minimize and a given reproduction function for generating the population.
class	ParallelizedGeneticAlgorithm<CostParametersType,GenomeType> This is a parallel implementation of the genetic algorithm.

Uses of IterativeAlgorithm in gov.sandia.cognition.learning.algorithm.hmm

Classes in gov.sandia.cognition.learning.algorithm.hmm that implement IterativeAlgorithm

Modifier and Type	Class and Description
class	AbstractBaumWelchAlgorithm<ObservationType,DataType> Partial implementation of the Baum-Welch algorithm.
class	BaumWelchAlgorithm<ObservationType> Implements the Baum-Welch algorithm, also known as the "forward-backward algorithm", the expectation-maximization algorithm, etc for Hidden Markov Models (HMMs).
class	ParallelBaumWelchAlgorithm<ObservationType> A Parallelized implementation of some of the methods of the Baum-Welch Algorithm.

Uses of IterativeAlgorithm in gov.sandia.cognition.learning.algorithm.minimization

Subinterfaces of IterativeAlgorithm in gov.sandia.cognition.learning.algorithm.minimization

Modifier and Type	Interface and Description
interface	FunctionMinimizer<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>> Interface for unconstrained minimization of nonlinear functions.

Classes in gov.sandia.cognition.learning.algorithm.minimization that implement IterativeAlgorithm

Modifier and Type	Class and Description
class	AbstractAnytimeFunctionMinimizer<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>> A partial implementation of a minimization algorithm that is iterative, stoppable, and approximate.
class	FunctionMinimizerBFGS Implementation of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) Quasi-Newton nonlinear minimization algorithm.
class	FunctionMinimizerConjugateGradient Conjugate gradient method is a class of algorithms for finding the unconstrained local minimum of a nonlinear function.
class	FunctionMinimizerDFP Implementation of the Davidon-Fletcher-Powell (DFP) formula for a Quasi-Newton minimization update.
class	FunctionMinimizerDirectionSetPowell Implementation of the derivative-free unconstrained nonlinear direction-set minimization algorithm called "Powell's Method" by Numerical Recipes.
class	FunctionMinimizerFletcherReeves This is an implementation of the Fletcher-Reeves conjugate gradient minimization procedure.
class	FunctionMinimizerGradientDescent This is an implementation of the classic Gradient Descent algorithm, also known as Steepest Descent, Backpropagation (for neural nets), or Hill Climbing.
class	FunctionMinimizerLiuStorey This is an implementation of the Liu-Storey conjugate gradient minimization procedure.
class	FunctionMinimizerNelderMead Implementation of the Downhill Simplex minimization algorithm, also known as the Nelder-Mead method.
class	FunctionMinimizerPolakRibiere This is an implementation of the Polack-Ribiere conjugate gradient minimization procedure.
class	FunctionMinimizerQuasiNewton This is an abstract implementation of the Quasi-Newton minimization method, sometimes called "Variable-Metric methods." This family of minimization algorithms uses first-order gradient information to find a locally minimum to a scalar function.

Uses of IterativeAlgorithm in gov.sandia.cognition.learning.algorithm.minimization.line

Subinterfaces of IterativeAlgorithm in gov.sandia.cognition.learning.algorithm.minimization.line

Modifier and Type	Interface and Description
interface	LineMinimizer<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>> Defines the functionality of a line-minimization algorithm, often called a "line search" algorithm.

Classes in gov.sandia.cognition.learning.algorithm.minimization.line that implement IterativeAlgorithm

Modifier and Type	Class and Description
class	AbstractAnytimeLineMinimizer<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>> Partial AnytimeAlgorithm implementation of a LineMinimizer.
class	LineMinimizerBacktracking Implementation of the backtracking line-minimization algorithm.
class	LineMinimizerDerivativeBased This is an implementation of a line-minimization algorithm proposed by Fletcher that makes extensive use of first-order derivative information.
class	LineMinimizerDerivativeFree This is an implementation of a LineMinimizer that does not require derivative information.

Uses of IterativeAlgorithm in gov.sandia.cognition.learning.algorithm.minimization.matrix

Classes in gov.sandia.cognition.learning.algorithm.minimization.matrix that implement IterativeAlgorithm

Modifier and Type	Class and Description
class	ConjugateGradientMatrixSolver Implements a matrix solver using Conjugate Gradient.
class	ConjugateGradientWithPreconditionerMatrixSolver Implements a matrix solver using Conjugate Gradient with a preconditioner.
class	IterativeMatrixSolver<Operator extends MatrixVectorMultiplier> Base class for all iterative matrix solvers that takes care of most of the basic iterative logic and the function minimizer interface.
class	OverconstrainedConjugateGradientMatrixMinimizer Implements a overconstrained conjugate gradient matrix optimizer.
class	SteepestDescentMatrixSolver Implements a basic Steepest Descent iterative solver for linear systems of equations.

Uses of IterativeAlgorithm in gov.sandia.cognition.learning.algorithm.pca

Classes in gov.sandia.cognition.learning.algorithm.pca that implement IterativeAlgorithm

Modifier and Type	Class and Description
class	GeneralizedHebbianAlgorithm Implementation of the Generalized Hebbian Algorithm, also known as Sanger's Rule, which is a generalization of Oja's Rule.

Uses of IterativeAlgorithm in gov.sandia.cognition.learning.algorithm.perceptron

Classes in gov.sandia.cognition.learning.algorithm.perceptron that implement IterativeAlgorithm

Modifier and Type	Class and Description
class	BatchMultiPerceptron<CategoryType> Implements a multi-class version of the standard batch Perceptron learning algorithm.
class	Perceptron The Perceptron class implements the standard Perceptron learning algorithm that learns a binary classifier based on vector input.

Uses of IterativeAlgorithm in gov.sandia.cognition.learning.algorithm.perceptron.kernel

Classes in gov.sandia.cognition.learning.algorithm.perceptron.kernel that implement IterativeAlgorithm

Modifier and Type	Class and Description
class	KernelAdatron<InputType> The KernelAdatron class implements an online version of the Support Vector Machine learning algorithm.
class	KernelPerceptron<InputType> The KernelPerceptron class implements the kernel version of the Perceptron algorithm.

Uses of IterativeAlgorithm in gov.sandia.cognition.learning.algorithm.regression

Subinterfaces of IterativeAlgorithm in gov.sandia.cognition.learning.algorithm.regression

Modifier and Type	Interface and Description
interface	ParameterCostMinimizer<ResultType extends VectorizableVectorFunction> A anytime algorithm that is used to estimate the locally minimum-cost parameters of an object.

Classes in gov.sandia.cognition.learning.algorithm.regression that implement IterativeAlgorithm

Modifier and Type	Class and Description
class	AbstractLogisticRegression<InputType,OutputType,FunctionType extends Evaluator<? super InputType,OutputType>> Abstract partial implementation for logistic regression classes.
class	AbstractMinimizerBasedParameterCostMinimizer<ResultType extends VectorizableVectorFunction,EvaluatorType extends Evaluator<? super Vector,? extends java.lang.Double>> Partial implementation of ParameterCostMinimizer, based on the algorithms from the minimization package.
class	AbstractParameterCostMinimizer<ResultType extends VectorizableVectorFunction,CostFunctionType extends SupervisedCostFunction<Vector,Vector>> Partial implementation of ParameterCostMinimizer.
class	FletcherXuHybridEstimation The Fletcher-Xu hybrid estimation for solving the nonlinear least-squares parameters.
class	GaussNewtonAlgorithm Implementation of the Gauss-Newton parameter-estimation procedure.
class	KernelBasedIterativeRegression<InputType> The KernelBasedIterativeRegression class implements an online version of the Support Vector Regression algorithm.
class	KernelWeightedRobustRegression<InputType,OutputType> KernelWeightedRobustRegression takes a supervised learning algorithm that operates on a weighted collection of InputOutputPairs and modifies the weight of a sample based on the dataset output and its corresponding estimate from the Evaluator from the supervised learning algorithm at each iteration.
class	LeastSquaresEstimator Abstract implementation of iterative least-squares estimators.
class	LevenbergMarquardtEstimation Implementation of the nonlinear regression algorithm, known as Levenberg-Marquardt Estimation (or LMA).
class	LogisticRegression Performs Logistic Regression by means of the iterative reweighted least squares (IRLS) algorithm, where the logistic function has an explicit bias term, and a diagonal L2 regularization term.
class	ParameterDerivativeFreeCostMinimizer Implementation of a class of objects that uses a derivative-free minimization algorithm.
class	ParameterDifferentiableCostMinimizer This class adapts the unconstrained nonlinear minimization algorithms in the "minimization" package to the task of estimating locally optimal (minimum-cost) parameter sets.

Uses of IterativeAlgorithm in gov.sandia.cognition.learning.algorithm.root

Subinterfaces of IterativeAlgorithm in gov.sandia.cognition.learning.algorithm.root

Modifier and Type	Interface and Description
interface	RootBracketer Defines the functionality of a algorithm that finds a bracket of a root from an initial guess.
interface	RootFinder Defines the functionality of a root-finding algorithm.

Classes in gov.sandia.cognition.learning.algorithm.root that implement IterativeAlgorithm

Modifier and Type	Class and Description
class	AbstractBracketedRootFinder Partial implementation of RootFinder that maintains a bracket on the root.
class	AbstractRootFinder Partial implementation of RootFinder.
class	MinimizerBasedRootFinder A root finder that uses minimization techniques to find the roots (zero-crossings).
class	RootBracketExpander The root-bracketing expansion algorithm.
class	RootFinderBisectionMethod Bisection algorithm for root finding.
class	RootFinderFalsePositionMethod The false-position algorithm for root finding.
class	RootFinderNewtonsMethod Newton's method, sometimes called Newton-Raphson method, uses first-order derivative information to iteratively locate a root.
class	RootFinderRiddersMethod The root-finding algorithm due to Ridders.
class	RootFinderSecantMethod The secant algorithm for root finding.

Uses of IterativeAlgorithm in gov.sandia.cognition.learning.algorithm.svm

Classes in gov.sandia.cognition.learning.algorithm.svm that implement IterativeAlgorithm

Modifier and Type	Class and Description
class	PrimalEstimatedSubGradient An implementation of the Primal Estimated Sub-Gradient Solver (PEGASOS) algorithm for learning a linear support vector machine (SVM).
class	SequentialMinimalOptimization<InputType> An implementation of the Sequential Minimal Optimization (SMO) algorithm for training a Support Vector Machine (SVM), which is a kernel-based binary categorizer.
class	SuccessiveOverrelaxation<InputType> The SuccessiveOverrelaxation class implements the Successive Overrelaxation (SOR) algorithm for learning a Support Vector Machine (SVM).

Uses of IterativeAlgorithm in gov.sandia.cognition.learning.algorithm.tree

Classes in gov.sandia.cognition.learning.algorithm.tree that implement IterativeAlgorithm

Modifier and Type	Class and Description
class	AbstractDecisionTreeLearner<InputType,OutputType> The AbstractDecisionTreeLearner implements common functionality for learning algorithms that learn a decision tree.
class	CategorizationTreeLearner<InputType,OutputType> The CategorizationTreeLearner class implements a supervised learning algorithm for learning a categorization tree.
class	RegressionTreeLearner<InputType> The RegressionTreeLearner class implements a learning algorithm for a regression tree that makes use of a decider learner and a regression learner.

Uses of IterativeAlgorithm in gov.sandia.cognition.learning.function.vector

Classes in gov.sandia.cognition.learning.function.vector that implement IterativeAlgorithm

Modifier and Type	Class and Description
static class	GaussianContextRecognizer.Learner Creates a GaussianContextRecognizer from a Dataset[Vector] using a BatchClusterer

Uses of IterativeAlgorithm in gov.sandia.cognition.learning.performance

Methods in gov.sandia.cognition.learning.performance with parameters of type IterativeAlgorithm

Modifier and Type	Method and Description
void	AnytimeBatchLearnerValidationPerformanceReporter.stepEnded(IterativeAlgorithm algorithm)

Uses of IterativeAlgorithm in gov.sandia.cognition.math

Classes in gov.sandia.cognition.math that implement IterativeAlgorithm

Modifier and Type	Class and Description
class	LentzMethod This class implements Lentz's method for evaluating continued fractions.

Uses of IterativeAlgorithm in gov.sandia.cognition.statistics.bayesian

Subinterfaces of IterativeAlgorithm in gov.sandia.cognition.statistics.bayesian

Modifier and Type	Interface and Description
interface	MarkovChainMonteCarlo<ObservationType,ParameterType> Defines the functionality of a Markov chain Monte Carlo algorithm.

Classes in gov.sandia.cognition.statistics.bayesian that implement IterativeAlgorithm

Modifier and Type	Class and Description
class	AbstractMarkovChainMonteCarlo<ObservationType,ParameterType> Partial abstract implementation of MarkovChainMonteCarlo.
class	DirichletProcessMixtureModel<ObservationType> An implementation of Dirichlet Process clustering, which estimates the number of clusters and the centroids of the clusters from a set of data.
class	MetropolisHastingsAlgorithm<ObservationType,ParameterType> An implementation of the Metropolis-Hastings MCMC algorithm, which is the most general formulation of MCMC but can be slow.
class	ParallelDirichletProcessMixtureModel<ObservationType> A Parallelized version of vanilla Dirichlet Process Mixture Model learning.

Uses of IterativeAlgorithm in gov.sandia.cognition.statistics.distribution

Classes in gov.sandia.cognition.statistics.distribution that implement IterativeAlgorithm

Modifier and Type	Class and Description
static class	MixtureOfGaussians.EMLearner An Expectation-Maximization based "soft" assignment learner.
static class	MixtureOfGaussians.Learner A hard-assignment learner for a MixtureOfGaussians
static class	ScalarMixtureDensityModel.EMLearner An EM learner that estimates a mixture model from data

Uses of IterativeAlgorithm in gov.sandia.cognition.statistics.method

Classes in gov.sandia.cognition.statistics.method that implement IterativeAlgorithm

Modifier and Type	Class and Description
class	DistributionParameterEstimator<DataType,DistributionType extends ClosedFormDistribution<? extends DataType>> A method of estimating the parameters of a distribution using an arbitrary CostFunction and FunctionMinimizer algorithm.

Uses of IterativeAlgorithm in gov.sandia.cognition.text.topic

Classes in gov.sandia.cognition.text.topic that implement IterativeAlgorithm

Modifier and Type	Class and Description
class	LatentDirichletAllocationVectorGibbsSampler A Gibbs sampler for performing Latent Dirichlet Allocation (LDA).
class	ParallelLatentDirichletAllocationVectorGibbsSampler A parallel implementation of LatentDirichletAllocationVectorGibbsSampler.
class	ProbabilisticLatentSemanticAnalysis An implementation of the Probabilistic Latent Semantic Analysis (PLSA) algorithm.

Methods in gov.sandia.cognition.text.topic with parameters of type IterativeAlgorithm

Modifier and Type	Method and Description
void	ProbabilisticLatentSemanticAnalysis.StatusPrinter.stepEnded(IterativeAlgorithm algorithm)
void	ProbabilisticLatentSemanticAnalysis.StatusPrinter.stepStarted(IterativeAlgorithm algorithm)