Package org.encog.neural.som.training.basic.neighborhood

Examples of org.encog.neural.som.training.basic.neighborhood.NeighborhoodSingle

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    BasicTrainSOM train = new BasicTrainSOM(
        network,
        0.7,
        training,
        new NeighborhoodSingle());
       
    int iteration = 0;
   
    for(iteration = 0;iteration<=10;iteration++)
    {
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      nf = new NeighborhoodRBF(size, t);
    } else if (neighborhoodStr.equalsIgnoreCase("rbf1d")) {
      nf = new NeighborhoodRBF1D(t);
    }
    if (neighborhoodStr.equalsIgnoreCase("single")) {
      nf = new NeighborhoodSingle();
    }

    final BasicTrainSOM result = new BasicTrainSOM((SOM) method,
        learningRate, training, nf);
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      case 3:
        return new NeighborhoodRBF1D( new InverseMultiquadricFunction(0,1,this.rbfWidth.getValue()));
      case 4:
        return new NeighborhoodBubble( (int)this.rbfWidth.getValue() );
      case 5:
        return new NeighborhoodSingle( );
      case 6:
        return new NeighborhoodRBF(RBFEnum.Gaussian, this.cols.getValue(), this.rows.getValue());
      case 7:
        return new NeighborhoodRBF(RBFEnum.MexicanHat, this.cols.getValue(), this.rows.getValue());
      case 8:
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    // Create the neural network.
    SOM network = new SOM(4,2);   
    network.setWeights(new Matrix(MATRIX_ARRAY));

    final BasicTrainSOM train = new BasicTrainSOM(network, 0.4,
        training, new NeighborhoodSingle());
    train.setForceWinner(true);
    int iteration = 0;

    for (iteration = 0; iteration <= 100; iteration++) {
      train.iteration();
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    Assert.assertEquals(1.0, bubble.function(5, 4),0.1);
    Assert.assertEquals(1.0, bubble.function(5, 5),0.1);
  }
 
  public void testSingle() throws Throwable {
    NeighborhoodSingle bubble = new NeighborhoodSingle();
    Assert.assertEquals(0.0, bubble.function(5, 0),0.1);
    Assert.assertEquals(1.0, bubble.function(5, 5),0.1); 
  }
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      nf = new NeighborhoodRBF(size, t);
    } else if (neighborhoodStr.equalsIgnoreCase("rbf1d")) {
      nf = new NeighborhoodRBF1D(t);
    }
    if (neighborhoodStr.equalsIgnoreCase("single")) {
      nf = new NeighborhoodSingle();
    }

    final BasicTrainSOM result = new BasicTrainSOM((SOM) method,
        learningRate, training, nf);
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    // Create the neural network.
    SOM network = new SOM(4,2);   
    network.setWeights(new Matrix(MATRIX_ARRAY));

    final BasicTrainSOM train = new BasicTrainSOM(network, 0.4,
        training, new NeighborhoodSingle());
    train.setForceWinner(true);
    int iteration = 0;

    for (iteration = 0; iteration <= 100; iteration++) {
      train.iteration();
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    Assert.assertEquals(1.0, bubble.function(5, 4),0.1);
    Assert.assertEquals(1.0, bubble.function(5, 5),0.1);
  }
 
  public void testSingle() throws Throwable {
    NeighborhoodSingle bubble = new NeighborhoodSingle();
    Assert.assertEquals(0.0, bubble.function(5, 0),0.1);
    Assert.assertEquals(1.0, bubble.function(5, 5),0.1); 
  }
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Related Classes of org.encog.neural.som.training.basic.neighborhood.NeighborhoodSingle

  • org.encog.examples.neural.som.SimpleSOM
  • org.encog.ml.factory.train.NeighborhoodSOMFactory
  • org.encog.neural.networks.training.competitive.neighborhood.TestNeighborhood
  • org.encog.neural.networks.training.competitive.TestCompetitive
  • org.encog.workbench.dialogs.training.methods.InputSOM
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