Examples of iteration()

Examples of org.encog.ml.fitting.linear.TrainLinearRegression.iteration()

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    double[][] IDEAL = { {0}, {-3}, {-1}, {-2} };
   
    MLDataSet data = new BasicMLDataSet(INPUT,IDEAL);
    LinearRegression lin = new LinearRegression(1);
    TrainLinearRegression train = new TrainLinearRegression(lin,data);
    train.iteration();
   
    System.out.println("w0 = " + Format.formatDouble(lin.getWeights()[0],2));
    System.out.println("w1 = " + Format.formatDouble(lin.getWeights()[1],2));
    System.out.println("Error: " + lin.calculateError(data));
  }
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Examples of org.encog.ml.genetic.BasicGeneticAlgorithm.iteration()

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    int iteration = 0;
   
    while(!done)
    {
      iteration++;
      genetic.iteration();
      double thisSolution = genetic.getPopulation().getBest().getScore();
      System.out.println(iteration + ": " + thisSolution);
    }
  }
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Examples of org.encog.ml.genetic.GeneticAlgorithm.iteration()

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    int iteration = 0;
   
    while(!done)
    {
      iteration++;
      genetic.iteration();
      double thisSolution = genetic.getPopulation().getBest().getScore();
      System.out.println(iteration + ": " + thisSolution);
    }
  }
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Examples of org.encog.ml.hmm.train.bw.TrainBaumWelch.iteration()

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    TrainBaumWelch bwl = new TrainBaumWelch(learntHmm,training);
   
    KullbackLeiblerDistanceCalculator klc =
      new KullbackLeiblerDistanceCalculator();
   
    bwl.iteration(5);
   
    learntHmm = (HiddenMarkovModel)bwl.getMethod();
   
    double e = klc.distance(learntHmm, hmm);
    Assert.assertTrue(e<0.01);
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Examples of org.encog.ml.hmm.train.kmeans.TrainKMeans.iteration()

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    HiddenMarkovModel learntHmm = buildDiscInitHMM();
   
    KullbackLeiblerDistanceCalculator klc =
      new KullbackLeiblerDistanceCalculator();
   
    trainer.iteration(5);
    learntHmm = (HiddenMarkovModel)trainer.getMethod();
    double e = klc.distance(learntHmm, hmm);
    Assert.assertTrue(e<0.05);
  }
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Examples of org.encog.ml.kmeans.KMeansClustering.iteration()

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      set.add(new BasicMLData(element));
    }

    final KMeansClustering kmeans = new KMeansClustering(2, set);

    kmeans.iteration(100);
    System.out.println("Final WCSS: " + kmeans.getWCSS());

    // Display the cluster
    int i = 1;
    for (final MLCluster cluster : kmeans.getClusters()) {
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Examples of org.encog.ml.svm.training.SVMTrain.iteration()

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  private SVM create()
  {
    MLDataSet training = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
    SVM result = new SVM(2,SVMType.EpsilonSupportVectorRegression,KernelType.RadialBasisFunction);
    final SVMTrain train = new SVMTrain(result, training);
    train.iteration();
    return result;
  }
 
  public void testPersistEG()
  {
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Examples of org.encog.ml.svm.training.SVMTrain.iteration()

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  }
 
  public void train(SVM network,MLDataSet training)
  {
    final SVMTrain train = new SVMTrain(network, training);
    train.iteration();
  }
 
  public void predict(SVM network)
  {
    NumberFormat f = NumberFormat.getNumberInstance();
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Examples of org.encog.ml.train.MLTrain.iteration()

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    trainMain.addStrategy(new HybridStrategy(trainAlt));
    trainMain.addStrategy(stop);

    int epoch = 0;
    while (!stop.shouldStop()) {
      trainMain.iteration();
      System.out.println("Training " + what + ", Epoch #" + epoch
          + " Error:" + trainMain.getError());
      epoch++;
    }
    return trainMain.getError();
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Examples of org.encog.ml.train.MLTrain.iteration()

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      this.currentJob.createTrainer(this.manager.isSingleThreaded());
      final MLTrain train = this.currentJob.getTrain();
      int interation = 1;

      while (this.currentJob.shouldContinue()) {
        train.iteration();
        interation++;
      }
      watch.stop();
    } catch (final Throwable t) {
      this.currentJob.setError(t);
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