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

Packages that use Randomized
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 ensmble methods. 
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
 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
 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 PartitionalClusterer<DataType,ClusterType extends Cluster<DataType>>
          The PartitionClusterer 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
 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.
 

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

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

Classes in gov.sandia.cognition.learning.algorithm.genetic.reproducer that implement Randomized
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
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
 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
 class AbstractRandomized
          The AbstractRandomized abstract class implements the Randomized interface by containing the random object in a protected field.