Uses of Interface
gov.sandia.cognition.util.Randomized

Packages that use Randomized

Package	Description
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.clustering.initializer	Provides implementations of methods for selecting initial clusters.
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.reproducer	Provides reproduction functions for use with a Genetic Algorithm.
gov.sandia.cognition.learning.algorithm.genetic.selector	Provides selection functions for a Genetic Algorithm.
gov.sandia.cognition.learning.algorithm.perceptron.kernel
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.data	Provides data set utilities for learning.
gov.sandia.cognition.learning.data.feature	Provides data feature extractors.
gov.sandia.cognition.learning.experiment	Provides experiments for validating the performance of learning algorithms.
gov.sandia.cognition.learning.function.vector	Provides functions that output vectors.
gov.sandia.cognition.statistics	Provides the inheritance hierarchy for general statistical methods and distributions.
gov.sandia.cognition.statistics.bayesian	Provides algorithms for computing Bayesian estimates of parameters.
gov.sandia.cognition.statistics.distribution	Provides statistical distributions.
gov.sandia.cognition.text.topic	Provides topic modeling algorithms.
gov.sandia.cognition.util	Provides general utility classes.

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

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

Modifier and Type	Class and Description
class	VectorizablePerturber The VectorizablePerturber implements a Perturber for Vectorizable objects.

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

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

Modifier and Type	Class and Description
class	DirichletProcessClustering Clustering algorithm that wraps Dirichlet Process Mixture Model.
class	KMeansFactory Creates a parallelized version of the k-means clustering algorithm for the typical use: clustering vector data with a Euclidean distance metric.
class	MiniBatchKMeansClusterer<DataType extends Vector> Approximates k-means clustering by working on random subsets of the data.
class	PartitionalClusterer<DataType,ClusterType extends Cluster<DataType>> The PartitionalClusterer implements a partitional clustering algorithm, which is a type of hierarchical clustering algorithm.

Uses of Randomized in gov.sandia.cognition.learning.algorithm.clustering.initializer

Classes in gov.sandia.cognition.learning.algorithm.clustering.initializer that implement Randomized

Modifier and Type	Class and Description
class	AbstractMinDistanceFixedClusterInitializer<ClusterType extends Cluster<DataType>,DataType> Implements an abstract FixedClusterInitializer that works by using the minimum distance from a point to the cluster.
class	DistanceSamplingClusterInitializer<ClusterType extends Cluster<DataType>,DataType> Implements FixedClusterInitializer that initializes clusters by first selecting a random point for the first cluster and then randomly sampling each successive cluster based on the squared minimum distance from the point to the existing selected clusters.
class	GreedyClusterInitializer<ClusterType extends Cluster<DataType>,DataType> Implements a FixedClusterInitializer that greedily attempts to create the initial clusters.
class	NeighborhoodGaussianClusterInitializer Creates GaussianClusters near existing, but not on top of, data points.
class	RandomClusterInitializer<ClusterType extends Cluster<DataType>,DataType> Creates initial clusters by selecting random data points as singleton clusters.

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

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

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	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	OnlineBaggingCategorizerLearner<InputType,CategoryType,MemberType extends Evaluator<? super InputType,? extends CategoryType>> An implementation of an online version of the Bagging algorithm for learning an ensemble of categorizers.

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

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

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 Randomized in gov.sandia.cognition.learning.algorithm.genetic.reproducer

Classes in gov.sandia.cognition.learning.algorithm.genetic.reproducer that implement Randomized

Modifier and Type	Class and Description
class	VectorizableCrossoverFunction The VectorizableCrossoverFunction class is a CrossoverFunction that takes two Vectorizable.

Uses of Randomized in gov.sandia.cognition.learning.algorithm.genetic.selector

Classes in gov.sandia.cognition.learning.algorithm.genetic.selector that implement Randomized

Modifier and Type	Class and Description
class	TournamentSelector<GenomeType> The TournamentSelector class implements a Selector that uses tournament selection to create a new population.

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

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

Modifier and Type	Class and Description
class	OnlineKernelRandomizedBudgetPerceptron<InputType> An implementation of a fixed-memory kernel Perceptron algorithm.

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

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

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.

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

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

Modifier and Type	Class and Description
class	RandomSubVectorThresholdLearner<OutputType> Learns a decision function by taking a randomly sampling a subspace from a given set of input vectors and then learning a threshold function by passing the subspace vectors to a sublearner.

Uses of Randomized in gov.sandia.cognition.learning.data

Subinterfaces of Randomized in gov.sandia.cognition.learning.data

Modifier and Type	Interface and Description
interface	RandomizedDataPartitioner<DataType> The RandomizedDataPartitioner extends a DataPartitioner to indicate that is it is randomized, which means that its partitions are based (at least in part) on an underlying random number generator.

Classes in gov.sandia.cognition.learning.data that implement Randomized

Modifier and Type	Class and Description
class	RandomDataPartitioner<DataType> The RandomDataPartitioner class implements a randomized data partitioner that takes a collection of data and randomly splits it into training and testing sets based on a fixed percentage of training data.

Uses of Randomized in gov.sandia.cognition.learning.data.feature

Classes in gov.sandia.cognition.learning.data.feature that implement Randomized

Modifier and Type	Class and Description
class	RandomSubspace Selects a random subspace from the given vector, which is a random set of indices.

Uses of Randomized in gov.sandia.cognition.learning.experiment

Classes in gov.sandia.cognition.learning.experiment that implement Randomized

Modifier and Type	Class and Description
class	CrossFoldCreator<DataType> The CrossFoldCreator implements a validation fold creator that creates folds for a typical k-fold cross-validation experiment.
class	RandomByTwoFoldCreator<DataType> A validation fold creator that takes a given collection of data and randomly splits it in half a given number of times, returning two folds for each split, using one half as training and the other half as testing.

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

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

Modifier and Type	Class and Description
class	ThreeLayerFeedforwardNeuralNetwork This is a "standard" feedforward neural network with a single hidden layer.

Uses of Randomized in gov.sandia.cognition.statistics

Classes in gov.sandia.cognition.statistics that implement Randomized

Modifier and Type	Class and Description
class	AbstractRandomVariable<DataType> Partial implementation of RandomVariable.
class	UnivariateRandomVariable This is an implementation of a RandomVariable for scalar distributions.

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

Subinterfaces of Randomized 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.
interface	ParticleFilter<ObservationType,ParameterType> A particle filter aims to estimate a sequence of hidden parameters based on observed data using point-mass estimates of the posterior distribution.

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

Modifier and Type	Class and Description
class	AbstractMarkovChainMonteCarlo<ObservationType,ParameterType> Partial abstract implementation of MarkovChainMonteCarlo.
class	AbstractParticleFilter<ObservationType,ParameterType> Partial abstract implementation of ParticleFilter.
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	ImportanceSampling<ObservationType,ParameterType> Importance sampling is a Monte Carlo inference technique where we sample from an easy distribution over the hidden variables (parameters) and then weight the result by the ratio of the likelihood of the parameters given the evidence and the likelihood of generating the parameters.
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.
class	RejectionSampling<ObservationType,ParameterType> Rejection sampling is a method of inferring hidden parameters by using an easy-to-sample-from distribution (times a scale factor) that envelopes another distribution that is difficult to sample from.
class	SamplingImportanceResamplingParticleFilter<ObservationType,ParameterType> An implementation of the standard Sampling Importance Resampling particle filter.

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

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

Modifier and Type	Class and Description
static class	MixtureOfGaussians.EMLearner An Expectation-Maximization based "soft" assignment learner.
static class	ScalarMixtureDensityModel.EMLearner An EM learner that estimates a mixture model from data
class	StudentizedRangeDistribution Implementation of the Studentized Range distribution, which defines the population correction factor when performing multiple comparisons.
static class	StudentizedRangeDistribution.CDF CDF of the StudentizedRangeDistribution

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

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

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.

Uses of Randomized in gov.sandia.cognition.util

Classes in gov.sandia.cognition.util that implement Randomized

Modifier and Type	Class and Description
class	AbstractRandomized The AbstractRandomized abstract class implements the Randomized interface by containing the random object in a protected field.