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>, Cloneable, CloneableSerializable, IterativeAlgorithm, Serializable, StoppableAlgorithm
All Known Implementing Classes:
AbstractAnytimeBatchLearner, AbstractAnytimeFunctionMinimizer, AbstractAnytimeLineMinimizer, AbstractAnytimeSupervisedBatchLearner, AbstractBaggingLearner, AbstractBaumWelchAlgorithm, AbstractBracketedRootFinder, AbstractMarkovChainMonteCarlo, AbstractParameterCostMinimizer, AbstractRootFinder, AdaBoost, AffinityPropagation, AgglomerativeClusterer, BaggingCategorizerLearner, BaggingRegressionLearner, BatchMultiPerceptron, BaumWelchAlgorithm, BinaryBaggingLearner, CategoryBalancedBaggingLearner, CategoryBalancedIVotingLearner, DirichletProcessClustering, DirichletProcessMixtureModel, 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, 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
 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.