gov.sandia.cognition.algorithm
Interface MeasurablePerformanceAlgorithm
- All Known Subinterfaces:
- ParameterCostMinimizer<ResultType>
- All Known Implementing Classes:
- AbstractBaumWelchAlgorithm, AbstractBracketedRootFinder, AbstractMinimizerBasedParameterCostMinimizer, AbstractParameterCostMinimizer, AbstractRootFinder, AffinityPropagation, BatchMultiPerceptron, BaumWelchAlgorithm, DirichletProcessClustering, DistributionParameterEstimator, FletcherXuHybridEstimation, GaussianContextRecognizer.Learner, GaussNewtonAlgorithm, GeneralizedHebbianAlgorithm, GeneticAlgorithm, KernelAdatron, KernelBasedIterativeRegression, KernelPerceptron, KMeansClusterer, KMeansClustererWithRemoval, LeastSquaresEstimator, LevenbergMarquardtEstimation, MetropolisHastingsAlgorithm, MixtureOfGaussians.EMLearner, MixtureOfGaussians.Learner, OptimizedKMeansClusterer, ParallelBaumWelchAlgorithm, ParallelizedGeneticAlgorithm, ParallelizedKMeansClusterer, ParameterDerivativeFreeCostMinimizer, ParameterDifferentiableCostMinimizer, Perceptron, RootFinderBisectionMethod, RootFinderFalsePositionMethod, RootFinderNewtonsMethod, RootFinderRiddersMethod, RootFinderSecantMethod, ScalarMixtureDensityModel.EMLearner, SequentialMinimalOptimization, SimulatedAnnealer, SuccessiveOverrelaxation
@CodeReview(reviewer="Kevin R. Dixon",
date="2008-12-02",
changesNeeded=false,
comments={"I do wonder if we should return a Collection of NamedValues here.","Otherwise, looks good."})
public interface MeasurablePerformanceAlgorithm
An interface for an algorithm that has a measurable quantity of performance
that can be retrieved. Typically, this is used after each iteration, in
conjunction with the IterativeAlgorithm interface, but it can also
be used on its own to determine the performance of the algorithm after it
has completed.
- Since:
- 2.1
- Author:
- Justin Basilico
- See Also:
IterativeAlgorithm
getPerformance
NamedValue<? extends Number> getPerformance()
- Gets the name-value pair that describes the current performance of the
algorithm. For most algorithms, this is the value that they are
attempting to optimize.
- Returns:
- The name-value pair that describes the current performance
of the algorithm.