Package org.encog.neural.pattern

Examples of org.encog.neural.pattern.JordanPattern


    return (BasicNetwork)pattern.generate();
  }
   
  public BasicNetwork createJordan()
  {
    JordanPattern pattern = new JordanPattern();
    pattern.setInputNeurons(this.input[0].length);
    pattern.setOutputNeurons(this.ideal[0].length);
    pattern.addHiddenLayer(16);
    pattern.setActivationFunction(new ActivationSigmoid());
    return (BasicNetwork)pattern.generate();
  }
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*/
public class JordanXOR {

  static BasicNetwork createJordanNetwork() {
    // construct an Jordan type network
    JordanPattern pattern = new JordanPattern();
    pattern.setActivationFunction(new ActivationSigmoid());
    pattern.setInputNeurons(1);
    pattern.addHiddenLayer(6);
    pattern.setOutputNeurons(1);
    return (BasicNetwork)pattern.generate();
  }
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  }
 
  public void performJordanTest(int input, int hidden, int ideal)
  {
    // we are really just making sure no array out of bounds errors occur
    JordanPattern jordanPattern = new JordanPattern();
    jordanPattern.setInputNeurons(input);
    jordanPattern.addHiddenLayer(hidden);
    jordanPattern.setOutputNeurons(ideal);
    BasicNetwork network = (BasicNetwork)jordanPattern.generate();
    MLDataSet training = RandomTrainingFactory.generate(1000, 5, network.getInputCount(), network.getOutputCount(), -1, 1);
    ResilientPropagation prop = new ResilientPropagation(network,training);
    prop.iteration();
    prop.iteration();   
  }
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  private static MLMethod createJordan() {
    CreateJordanDialog dialog = new CreateJordanDialog(EncogWorkBench
        .getInstance().getMainWindow());
    if (dialog.process()) {
      JordanPattern jordan = new JordanPattern();
      jordan.setInputNeurons(dialog.getInputCount().getValue());
      jordan.addHiddenLayer(dialog.getHiddenCount().getValue());
      jordan.setOutputNeurons(dialog.getOutputCount().getValue());
      jordan.setActivationFunction(new ActivationTANH());
      return jordan.generate();
    } else
      return null;

  }
View Full Code Here

  }
 
  public void performJordanTest(int input, int hidden, int ideal)
  {
    // we are really just making sure no array out of bounds errors occur
    JordanPattern jordanPattern = new JordanPattern();
    jordanPattern.setInputNeurons(input);
    jordanPattern.addHiddenLayer(hidden);
    jordanPattern.setOutputNeurons(ideal);
    BasicNetwork network = (BasicNetwork)jordanPattern.generate();
    MLDataSet training = RandomTrainingFactory.generate(1000, 5, network.getInputCount(), network.getOutputCount(), -1, 1);
    ResilientPropagation prop = new ResilientPropagation(network,training);
    prop.iteration();
    prop.iteration();   
  }
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