Examples of org.encog.mathutil.randomize.FanInRandomizer

org.encog.mathutil.randomize.FanInRandomizer
A randomizer that attempts to create starting weight values that are conducive to propagation training. This is one of the best randomizers offered in Encog, however, the Nguyen Widrow method generally performs better. From: Neural Networks - A Comprehensive Foundation, Haykin, chapter 6.7 @author jheaton


  public static void main(final String args[]) {


    RangeRandomizer rangeRandom = new RangeRandomizer(-1, 1);
    NguyenWidrowRandomizer nwrRandom = new NguyenWidrowRandomizer(-1, 1);
    FanInRandomizer fanRandom = new FanInRandomizer();
    GaussianRandomizer gaussianRandom = new GaussianRandomizer(0, 1);


    System.out.println("Error improvement, higher is better.");
    BasicMLDataSet training = new BasicMLDataSet(XOR_INPUT,
        XOR_IDEAL);

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  public void testFanIn()
  {    
    BasicNetwork network = EncogUtility.simpleFeedForward(5,10,15,20, true);
    double[] weights = network.getStructure().getFlat().getWeights();
    EngineArray.fill(weights, 100);
    (new FanInRandomizer()).randomize(network);
    System.out.println(network.dumpWeights());
    for(int i=0;i<weights.length;i++ )
    {
      Assert.assertTrue(weights[i]<10);
    }

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          network, new PilotScore(), 10, 2, 100);
    }
    else
    {
      train = new NeuralGeneticAlgorithm(
          network, new FanInRandomizer(), 
          new PilotScore(),500, 0.1, 0.25);
    }
    
    int epoch = 1;

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    case 1: // Nguyen-Widrow
      r = new NguyenWidrowRandomizer(dialog.getLow().getValue(), dialog
          .getHigh().getValue());
      break;
    case 2: // Fan in
      r = new FanInRandomizer(dialog.getLow().getValue(), dialog
          .getHigh().getValue(), false);
      break;
    }


    if (r != null) {

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  public void testFanIn()
  {    
    BasicNetwork network = EncogUtility.simpleFeedForward(5,10,15,20, true);
    double[] weights = network.getStructure().getFlat().getWeights();
    EngineArray.fill(weights, 100);
    (new FanInRandomizer()).randomize(network);
    System.out.println(network.dumpWeights());
    for(int i=0;i<weights.length;i++ )
    {
      Assert.assertTrue(weights[i]<10);
    }

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