gov.sandia.cognition.algorithm

Interface AnytimeAlgorithm<ResultType>

Type Parameters:

ResultType - The type of the result generated by the algorithm.

All Superinterfaces:

IterativeAlgorithm, StoppableAlgorithm

All Known Subinterfaces:

AnytimeBatchLearner<DataType,ResultType>, BagBasedCategorizerEnsembleLearner<InputType,CategoryType>, FunctionMinimizer<InputType,OutputType,EvaluatorType>, LineMinimizer<EvaluatorType>, MarkovChainMonteCarlo<ObservationType,ParameterType>, ParameterCostMinimizer<ResultType>, RootBracketer, RootFinder

All Known Implementing Classes:

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

@CodeReview(reviewer="Kevin R. Dixon",
            date="2008-02-08",
            changesNeeded=false,
            comments="Interface looks fine.")
 @PublicationReference(author="Shlomo Zilberstein",
                      title="Using Anytime Algorithms in Intelligent Systems",
                      type=Journal,
                      publication="AI Magazine",
                      year=1996,
                      pages={73,83},
                      url="http://anytime.cs.umass.edu/~shlomo/papers/aimag96.pdf")
public interface AnytimeAlgorithm<ResultType>
extends IterativeAlgorithm, StoppableAlgorithm

The AnytimeAlgorithm interface defines the functionality of an iterative algorithm that is stoppable and can return intermediate results.

Since:

2.0

Author:

Justin Basilico, Kevin R. Dixon

See Also:

IterativeAlgorithm, StoppableAlgorithm

Method Summary

Modifier and Type	Method and Description
int	getMaxIterations() Gets the maximum number of total iterations before stopping.
ResultType	getResult() Gets the current result of the algorithm.
void	setMaxIterations(int maxIterations) Sets the maximum number of total iterations before stopping.

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

addIterativeAlgorithmListener, getIteration, removeIterativeAlgorithmListener

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

isResultValid, stop

Method Detail

getMaxIterations

int getMaxIterations()

Gets the maximum number of total iterations before stopping.

Returns:

Maximum number of total iterations before stopping. Must be greater than zero.

setMaxIterations

void setMaxIterations(int maxIterations)

Sets the maximum number of total iterations before stopping.

Parameters:

maxIterations - Maximum number of iterations before stopping. Must be greater than zero.

getResult

ResultType getResult()

Gets the current result of the algorithm.

Returns:

Current result of the algorithm.