gov.sandia.cognition.learning.algorithm

Interface AnytimeBatchLearner<DataType,ResultType>

Type Parameters:

ResultType - The type of object created by the learning algorithm. For example, a FeedforwardNeuralNetwork.

DataType - The type of the data that the algorithm uses to perform the learning. For example, a Collection<InputOutputPair<Vector, Double>> or String.

All Superinterfaces:

AnytimeAlgorithm<ResultType>, BatchLearner<DataType,ResultType>, java.lang.Cloneable, CloneableSerializable, IterativeAlgorithm, java.io.Serializable, StoppableAlgorithm

All Known Subinterfaces:

BagBasedCategorizerEnsembleLearner<InputType,CategoryType>

All Known Implementing Classes:

AbstractAnytimeBatchLearner, AbstractAnytimeFunctionMinimizer, AbstractAnytimeLineMinimizer, AbstractAnytimeSupervisedBatchLearner, AbstractBaggingLearner, AbstractBaumWelchAlgorithm, AbstractBracketedRootFinder, AbstractFactorizationMachineLearner, AbstractLogisticRegression, AbstractMarkovChainMonteCarlo, AbstractParameterCostMinimizer, AbstractRootFinder, AdaBoost, AffinityPropagation, AgglomerativeClusterer, BaggingCategorizerLearner, BaggingRegressionLearner, BatchMultiPerceptron, BaumWelchAlgorithm, BinaryBaggingLearner, CategoryBalancedBaggingLearner, CategoryBalancedIVotingLearner, DBSCANClusterer, DirichletProcessClustering, DirichletProcessMixtureModel, FactorizationMachineAlternatingLeastSquares, FactorizationMachineStochasticGradient, FletcherXuHybridEstimation, FunctionMinimizerBFGS, FunctionMinimizerConjugateGradient, FunctionMinimizerDFP, FunctionMinimizerDirectionSetPowell, FunctionMinimizerFletcherReeves, FunctionMinimizerGradientDescent, FunctionMinimizerLiuStorey, FunctionMinimizerNelderMead, FunctionMinimizerPolakRibiere, FunctionMinimizerQuasiNewton, GaussianContextRecognizer.Learner, GaussNewtonAlgorithm, GeneralizedHebbianAlgorithm, GeneticAlgorithm, IVotingCategorizerLearner, KernelAdatron, KernelBasedIterativeRegression, KernelPerceptron, KernelWeightedRobustRegression, KMeansClusterer, KMeansClustererWithRemoval, LatentDirichletAllocationVectorGibbsSampler, LeastSquaresEstimator, LevenbergMarquardtEstimation, LineMinimizerBacktracking, LineMinimizerDerivativeBased, LineMinimizerDerivativeFree, LogisticRegression, MetropolisHastingsAlgorithm, MiniBatchKMeansClusterer, MixtureOfGaussians.EMLearner, MultiCategoryAdaBoost, OptimizedKMeansClusterer, ParallelBaumWelchAlgorithm, ParallelDirichletProcessMixtureModel, ParallelizedGeneticAlgorithm, ParallelizedKMeansClusterer, ParallelLatentDirichletAllocationVectorGibbsSampler, PartitionalClusterer, Perceptron, PrimalEstimatedSubGradient, ProbabilisticLatentSemanticAnalysis, RootBracketExpander, RootFinderBisectionMethod, RootFinderFalsePositionMethod, RootFinderNewtonsMethod, RootFinderRiddersMethod, RootFinderSecantMethod, ScalarMixtureDensityModel.EMLearner, SequentialMinimalOptimization, SimulatedAnnealer, SuccessiveOverrelaxation

public interface AnytimeBatchLearner<DataType,ResultType>
extends AnytimeAlgorithm<ResultType>, BatchLearner<DataType,ResultType>

A batch learner that is also and Anytime algorithm.

Since:

3.0

Author:

Kevin R. Dixon

Method Summary

Modifier and Type	Method and Description
DataType	getData() Gets the data to use for learning.
boolean	getKeepGoing() Gets the keep going value, which indicates if the algorithm should continue on to another step.

Methods inherited from interface gov.sandia.cognition.algorithm.AnytimeAlgorithm

getMaxIterations, getResult, setMaxIterations

Methods inherited from interface gov.sandia.cognition.algorithm.IterativeAlgorithm

addIterativeAlgorithmListener, getIteration, removeIterativeAlgorithmListener

Methods inherited from interface gov.sandia.cognition.algorithm.StoppableAlgorithm

isResultValid, stop

Methods inherited from interface gov.sandia.cognition.learning.algorithm.BatchLearner

learn

Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable

clone

Method Detail

getKeepGoing

boolean getKeepGoing()

Gets the keep going value, which indicates if the algorithm should continue on to another step.

Returns:

The keep going value.

getData

DataType getData()

Gets the data to use for learning. This is set when learning starts and then cleared out once learning is finished.

Returns:

The data to use for learning.