gov.sandia.cognition.util
Class AbstractCloneableSerializable

java.lang.Object
  extended by gov.sandia.cognition.util.AbstractCloneableSerializable
All Implemented Interfaces:
CloneableSerializable, Serializable, Cloneable
Direct Known Subclasses:
AbstractBatchAndIncrementalLearner, AbstractBatchLearnerContainer, AbstractBinaryCategorizer, AbstractCategorizer, AbstractClusterHierarchyNode, AbstractCogxelConverter, AbstractConfidenceStatistic, AbstractConfusionMatrix, AbstractCostFunction, AbstractDataConverter, AbstractDistribution, AbstractDocumentExtractor, AbstractFileSerializationHandler, AbstractInputOutputPair, AbstractIterativeAlgorithm, AbstractIterativeAlgorithmListener, AbstractLearningExperiment, AbstractLineBracketInterpolator, AbstractMultipleHypothesisComparison, AbstractMultipleHypothesisComparison.Statistic, AbstractNamed, AbstractOccurrenceInText, AbstractParallelAlgorithm, AbstractPrincipalComponentsAnalysis, AbstractRandomized, AbstractRelation, AbstractRing, AbstractScalarFunction, AbstractScalarMap, AbstractSingleTermFilter, AbstractStatefulEvaluator, AbstractSufficientStatistic, AbstractSupervisedPerformanceEvaluator, AbstractTargetEstimatePair, AbstractTemporal, AbstractTerm, AbstractTermIndex, AbstractTermWeightNormalizer, AbstractTextual, AbstractTextualConverter, AbstractTokenizer, AbstractUnweightedEnsemble, AbstractValueDiscriminantPair, AbstractVectorSpaceModel, AbstractVectorThresholdMaximumGainLearner, AbstractWeighted, AbstractWeightedEnsemble, AdaptiveRejectionSampling, AnalysisOfVarianceOneWay, BayesianLinearRegression, BayesianLinearRegression.PredictiveDistribution, BayesianRobustLinearRegression, BayesianRobustLinearRegression.PredictiveDistribution, BernoulliConfidence, BetaBinomialDistribution.MomentMatchingEstimator, BetaDistribution.MomentMatchingEstimator, BetaDistribution.WeightedMomentMatchingEstimator, BinaryCategorizerSelector, BinomialDistribution.MaximumLikelihoodEstimator, ChebyshevDistanceMetric, ChebyshevInequality, ChineseRestaurantProcess, ChiSquareConfidence, CholeskyDecompositionMTJ, ClusterDistortionMeasure, CognitiveModelLiteState, CognitiveModuleStateWrapper, CogxelStateLite, CompositeBatchLearnerPair, CompositeCategorizer, CompositeEvaluatorList, CompositeLocalGlobalTermWeighter, ConfidenceInterval, ConstantEvaluator, ConstantLearner, ConvexReceiverOperatingCharacteristic, CosineDistanceMetric, CosineSimilarityFunction, DecisionTree, DefaultBinaryConfusionMatrix.ActualPredictedPairSummarizer, DefaultBinaryConfusionMatrix.CombineSummarizer, DefaultBinaryConfusionMatrixConfidenceInterval.Summary, DefaultCluster, DefaultClusterCreator, DefaultCogxel, DefaultComparator, DefaultConfusionMatrix.ActualPredictedPairSummarizer, DefaultConfusionMatrix.CombineSummarizer, DefaultConfusionMatrix.Factory, DefaultDataDistribution.DefaultFactory, DefaultDivergenceFunctionContainer, DefaultDuration, DefaultFactory, DefaultIdentifiedValue, DefaultIndexedTerm, DefaultIndexer, DefaultKernelContainer, DefaultKernelsContainer, DefaultKeyValuePair, DefaultPair, DefaultPrecisionRecallPair, DefaultSemanticIdentifierMap, DefaultStopList, DefaultTermCounts, DefaultTriple, DefaultVectorFactoryContainer, DefaultWeightedValue.WeightComparator, DirectSampler, DirichletProcessMixtureModel.DPMMLogConditional, DirichletProcessMixtureModel.MultivariateMeanCovarianceUpdater, DirichletProcessMixtureModel.MultivariateMeanUpdater, DirichletProcessMixtureModel.Sample, DiscreteNaiveBayesCategorizer, DiscreteNaiveBayesCategorizer.Learner, DistributionParameterEstimator.DistributionWrapper, EigenvectorPowerIteration, ElementWiseVectorFunction, EntropyEvaluator, EuclideanDistanceCostFunction, EuclideanDistanceMetric, EuclideanDistanceSquaredMetric, ExponentialDistribution.MaximumLikelihoodEstimator, ExponentialDistribution.WeightedMaximumLikelihoodEstimator, FeedforwardNeuralNetwork, FisherLinearDiscriminantBinaryCategorizer.ClosedFormSolver, FisherSignConfidence, ForwardReverseEvaluatorPair, FourierTransform, FourierTransform.Inverse, FriedmanConfidence, GammaDistribution.MomentMatchingEstimator, GammaDistribution.WeightedMomentMatchingEstimator, GaussianClusterCreator, GaussianClusterDivergenceFunction, GaussianConfidence, GaussianContextRecognizer, GaussianProcessRegression.PredictiveDistribution, GeneralizedLinearModel, GeometricDistribution.MaximumLikelihoodEstimator, GradientDescendableApproximator, IdentityDataConverter, IdentityDistanceMetric, IdentityEvaluator, IdentityLearner, ImportanceSampler, ImportanceSampling.DefaultUpdater, KDTree.Neighborhood.Neighbor, KDTree.PairFirstVectorizableIndexComparator, KNearestNeighborExhaustive.Neighbor, KolmogorovSmirnovConfidence, LaplaceDistribution.MaximumLikelihoodEstimator, LaplaceDistribution.WeightedMaximumLikelihoodEstimator, LatentDirichletAllocationVectorGibbsSampler.Result, LatentSemanticAnalysis, LatentSemanticAnalysis.Transform, LinearBasisRegression, LinearCombinationFunction, LinearKernel, LinearMultiCategorizer, LinearRegression, LinearVectorFunction, LineBracket, LocallyWeightedFunction.Learner, LogNormalDistribution.MaximumLikelihoodEstimator, LogNormalDistribution.WeightedMaximumLikelihoodEstimator, ManhattanDistanceMetric, MannWhitneyUConfidence, MarkovChain, MarkovInequality, MaximumAPosterioriCategorizer.Learner, MaximumLikelihoodDistributionEstimator.DistributionEstimationTask, MeanLearner, MinkowskiDistanceMetric, MostFrequentLearner, MostFrequentSummarizer, MultinomialDistribution.Domain.MultinomialIterator, MultipleComparisonExperiment, MultipleComparisonExperiment.Statistic, MultivariateDecorrelator, MultivariateDecorrelator.DiagonalCovarianceLearner, MultivariateDecorrelator.FullCovarianceLearner, MultivariateDiscriminant, MultivariateGaussian.MaximumLikelihoodEstimator, MultivariateGaussian.WeightedMaximumLikelihoodEstimator, MultivariateLinearRegression, MultivariateMonteCarloIntegrator, NegativeBinomialDistribution.MaximumLikelihoodEstimator, NegativeBinomialDistribution.WeightedMaximumLikelihoodEstimator, NGramFilter, NumberAverager, NumberComparator, NumericalDifferentiator, ParallelBaumWelchAlgorithm.DistributionEstimatorTask, ParallelDirichletProcessMixtureModel.ClusterUpdaterTask, ParallelDirichletProcessMixtureModel.ObservationAssignmentTask, ParallelHiddenMarkovModel.ComputeTransitionsTask, ParallelHiddenMarkovModel.LogLikelihoodTask, ParallelHiddenMarkovModel.NormalizeTransitionTask, ParallelHiddenMarkovModel.ObservationLikelihoodTask, ParallelHiddenMarkovModel.StateObservationLikelihoodTask, ParallelHiddenMarkovModel.ViterbiTask, ParallelLatentDirichletAllocationVectorGibbsSampler.DocumentSampleTask, ParameterDerivativeFreeCostMinimizer.ParameterCostEvaluatorDerivativeFree, ParameterDifferentiableCostMinimizer.ParameterCostEvaluatorDerivativeBased, PIDController.State, PoissonDistribution.MaximumLikelihoodEstimator, PoissonDistribution.WeightedMaximumLikelihoodEstimator, PolynomialFunction.Regression, PolynomialKernel, PrincipalComponentsAnalysisFunction, ProbabilisticLatentSemanticAnalysis.Result, PrototypeFactory, Quadtree, Quadtree.Node, RadialBasisKernel, ReceiverOperatingCharacteristic, ReceiverOperatingCharacteristic.DataPoint, ReceiverOperatingCharacteristic.DataPoint.Sorter, RejectionSampling.DefaultUpdater, RingAverager, ScalarBasisSet, ScalarFunctionKernel, SharedSemanticMemoryLiteFactory, SharedSemanticMemoryLiteSettings, SigmoidKernel, SimplePatternRecognizer, SimplePatternRecognizerState, SimpleStatisticalSpellingCorrector, StandardDistributionNormalizer.Learner, StudentTConfidence, StudentTConfidence.Summary, StudentTDistribution.MaximumLikelihoodEstimator, StudentTDistribution.WeightedMaximumLikelihoodEstimator, SumSquaredErrorCostFunction.Cache, TermVectorSimilarityNetworkCreator, TimeSeriesPredictionLearner, TreeSetBinner, UniformDistribution.MaximumLikelihoodEstimator, UnivariateGaussian.MaximumLikelihoodEstimator, UnivariateGaussian.WeightedMaximumLikelihoodEstimator, UnivariateLinearRegression, UnivariateMonteCarloIntegrator, UnivariateSummaryStatistics, ValueClamper, ValueMapper, VectorBasedCognitiveModelInput, VectorFunctionToScalarFunction.Learner, VectorizableIndexComparator, VectorMeanCentroidClusterCreator, VectorNaiveBayesCategorizer, VectorNaiveBayesCategorizer.BatchGaussianLearner, VectorNaiveBayesCategorizer.Learner, VectorThresholdVarianceLearner, WeightedEuclideanDistanceMetric, WeightedMeanLearner, WeightedMostFrequentLearner, WeightedNumberAverager, WeightedRingAverager, WilcoxonSignedRankConfidence, WolfeConditions, ZeroKernel

public abstract class AbstractCloneableSerializable
extends Object
implements CloneableSerializable

The AbstractCloneableserializable abstract class implements a default version of the clone method that calls the Object clone method, but traps the exception that can be thrown.

Since:
2.1
Author:
Justin Basilico
See Also:
Serialized Form

Constructor Summary
AbstractCloneableSerializable()
          Creates a new instance of AbstractCloneableSerializable.
 
Method Summary
 CloneableSerializable clone()
          This makes public the clone method on the Object class and removes the exception that it throws.
 
Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Constructor Detail

AbstractCloneableSerializable

public AbstractCloneableSerializable()
Creates a new instance of AbstractCloneableSerializable.

Method Detail

clone

public CloneableSerializable clone()
This makes public the clone method on the Object class and removes the exception that it throws. Its default behavior is to automatically create a clone of the exact type of object that the clone is called on and to copy all primitives but to keep all references, which means it is a shallow copy. Extensions of this class may want to override this method (but call super.clone() to implement a "smart copy". That is, to target the most common use case for creating a copy of the object. Because of the default behavior being a shallow copy, extending classes only need to handle fields that need to have a deeper copy (or those that need to be reset). Some of the methods in ObjectUtil may be helpful in implementing a custom clone method. Note: The contract of this method is that you must use super.clone() as the basis for your implementation.

Specified by:
clone in interface CloneableSerializable
Overrides:
clone in class Object
Returns:
A clone of this object.