Examples of iteration()

Examples of org.encog.neural.cpn.training.TrainOutstar.iteration()

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  {
    int epoch = 1;

    MLTrain train = new TrainOutstar(network,training,0.1);
    do {
      train.iteration();
      System.out
          .println("Training outstar, Epoch #" + epoch + ", error=" + train.getError() );
      epoch++;
    } while(train.getError()>0.01);
  }
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Examples of org.encog.neural.flat.train.prop.TrainFlatNetworkBackPropagation.iteration()

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    Stopwatch sw = new Stopwatch();
    sw.start();
    // run epoch of learning procedure
    for (int i = 0; i < ITERATIONS; i++) {
      train.iteration();
    }
    sw.stop();

    return sw.getElapsedMilliseconds();
  }
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Examples of org.encog.neural.flat.train.prop.TrainFlatNetworkResilient.iteration()

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    final TrainFlatNetworkResilient train = new TrainFlatNetworkResilient(
        network, trainingSet);

    for (int iteration = 1; iteration <= ITERATIONS; iteration++) {
      train.iteration();

      System.out.println();
      System.out.println("*** Iteration #" + iteration);
      System.out.println("Error: " + train.getError());
      evaluate(network,trainingSet);
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Examples of org.encog.neural.flat.train.prop.TrainFlatNetworkResilient.iteration()

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    //train.setTargetDevice(Encog.getInstance().getCL().getDevices().get(0));
   
    int epoch = 1;

    do {
      train.iteration();
      System.out
          .println("Epoch #" + epoch + " Error:" + train.getError());
      epoch++;
    } while(train.getError() > 0.01 );
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Examples of org.encog.neural.neat.training.NEATTraining.iteration()

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    // see if the population can actually be used to train
    MLDataSet trainingSet = new BasicMLDataSet(XOR.XOR_INPUT, XOR.XOR_IDEAL);   
    CalculateScore score = new TrainingSetScore(trainingSet);
    NEATTraining train = new NEATTraining(score,pop);
    train.iteration();

  }
 
  @Override
  protected void tearDown() throws Exception {
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Examples of org.encog.neural.networks.training.Train.iteration()

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    private int trainNeuralNetwork() {
        final Train train = new ResilientPropagation(network, trainingData);

        int epoch = 1;
        do {
            train.iteration();
            //System.out.println("Epoch #" + epoch + " Error: " + train.getError());
            epoch++;
            if (epoch > 500) {
                return 1;
            }
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Examples of org.encog.neural.networks.training.cross.CrossValidationKFold.iteration()

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    final CrossValidationKFold trainFolded = new CrossValidationKFold(train,4);

    int epoch = 1;

    do {
      trainFolded.iteration();
      System.out
          .println("Epoch #" + epoch + " Error:" + trainFolded.getError());
      epoch++;
    } while (trainFolded.getError() > MAX_ERROR);
  }
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Examples of org.encog.neural.networks.training.pnn.TrainBasicPNN.iteration()

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        XOR.XOR_IDEAL);

    System.out.println("Learning...");

    TrainBasicPNN train = new TrainBasicPNN(network, trainingSet);
    train.iteration();
    XOR.verifyXOR(network, 0.001);
    return network;   
  }
 
  public void testPersistEG()
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Examples of org.encog.neural.networks.training.propagation.Propagation.iteration()

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      train.addStrategy(strat);
      train.setNumThreads(1); // force single thread mode

      for (int i = 0; (i < this.iterations) && !getShouldStop()
          && !strat.shouldStop(); i++) {
        train.iteration();
      }

      error = Math.min(error, train.getError());
    }
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Examples of org.encog.neural.networks.training.propagation.Propagation.iteration()

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      //Propagation train = new ResilientPropagation((ContainsFlat)method, trainingData);
      ((Propagation)train).fixFlatSpot(true);
     
      int iteration = 0;
      do {
        train.iteration();
       
        iteration++;
      } while( train.getError()>0.01 );
      count[i] = iteration;
      System.out.println("Begin Try #" + (i+1) + ", took " + iteration + " iterations.");     
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