Iteration() 공개 최종 메소드

Perform either a train or a cross validation. If the folds property is greater than 1 then cross validation will be done. Cross validation does not produce a usable model, but it does set the error. If you are cross validating try C and Gamma values until you have a good error rate. Then use those values to train, producing the final model.
public final Iteration ( ) : void
리턴 void
SVMTrain 1 문서
예제 #1
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 private static SupportVectorMachine Create(IMLDataSet theset, int inputs)
 {
     IMLDataSet training = new BasicMLDataSet(theset);
     SupportVectorMachine result = new SupportVectorMachine(inputs, SVMType.EpsilonSupportVectorRegression, KernelType.Sigmoid);
     SVMTrain train = new SVMTrain(result, training);
     train.Iteration();
     return result;
 }
예제 #2
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 private SupportVectorMachine Create()
 {
     IMLDataSet training = new BasicMLDataSet(XOR.XORInput, XOR.XORIdeal);
     SupportVectorMachine result = new SupportVectorMachine(2, SVMType.EpsilonSupportVectorRegression, KernelType.RadialBasisFunction);
     SVMTrain train = new SVMTrain(result, training);
     train.Iteration();
     return result;
 }
예제 #3
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        public static double TrainNetworks(SupportVectorMachine network, MarketMLDataSet training)
        {
            // train the neural network
            SVMTrain trainMain = new SVMTrain(network, training);

            StopTrainingStrategy stop = new StopTrainingStrategy(0.0001, 200);
            trainMain.AddStrategy(stop);


            var sw = new Stopwatch();
            sw.Start();
            while (!stop.ShouldStop())
            {
                trainMain.PreIteration();


                trainMain.Iteration();
                trainMain.PostIteration();

                Console.WriteLine(@"Iteration #:" + trainMain.IterationNumber + @" Error:" + trainMain.Error);
            }
            sw.Stop();
            Console.WriteLine("SVM Trained in :" + sw.ElapsedMilliseconds + "For error:" + trainMain.Error + " Iterated:" + trainMain.IterationNumber);
            return trainMain.Error;
        }
예제 #4
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 /// <inheritdoc/>
 public override sealed void FinishTraining()
 {
     _internalTrain.Gamma = BestGamma;
     _internalTrain.C     = BestConst;
     _internalTrain.Iteration();
 }
예제 #5
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 public static void train(SupportVectorMachine network,IMLDataSet training)
 {
     SVMTrain train = new SVMTrain(network, training);
     train.Iteration();
 }
예제 #6
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 public static double  train(SupportVectorMachine network, IMLDataSet training)
 {
     SVMTrain train = new SVMTrain(network, training);
     train.Iteration();
     return train.Error;
 }
예제 #7
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 public static double TrainSVM(SVMTrain train, SupportVectorMachine machine)
 {
    
     StopTrainingStrategy stop = new StopTrainingStrategy(0.0001, 200);
     train.AddStrategy(stop);
     var sw = new Stopwatch();
     sw.Start();
     while (!stop.ShouldStop())
     {
         train.PreIteration();
         
         train.Iteration();
         train.PostIteration();
         Console.WriteLine(@"Iteration #:" + train.IterationNumber + @" Error:" + train.Error +" Gamma:"+train.Gamma);
     }
     sw.Stop();
     Console.WriteLine(@"SVM Trained in :" + sw.ElapsedMilliseconds);
     return train.Error;
 }