Package org.encog.engine.network.activation

Examples of org.encog.engine.network.activation.ActivationStep

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  {
    MLDataSet trainingSet = new BasicMLDataSet(XOR.XOR_INPUT, XOR.XOR_IDEAL);
   
    CalculateScore score = new TrainingSetScore(trainingSet);
    // train the neural network
    ActivationStep step = new ActivationStep();
    step.setCenter(0.5);
   
    NEATTraining train = new NEATTraining(
        score, 2, 1, 10);
    //train.setOutputActivationFunction(step);
   
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  private NEATNetwork create()
  {
    List<NEATNeuron> neurons = new ArrayList<NEATNeuron>();
    ActivationFunction afSigmoid = new ActivationSigmoid();
    ActivationFunction afStep = new ActivationStep();
   
    // create the neurons
    NEATNeuron input1 = new NEATNeuron(
        NEATNeuronType.Input,
        1,
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    if (name.equalsIgnoreCase(MLActivationFactory.AF_SOFTMAX)) {
      return new ActivationSoftMax();
    }

    if (name.equalsIgnoreCase(MLActivationFactory.AF_STEP)) {
      return new ActivationStep();
    }

    if (name.equalsIgnoreCase(MLActivationFactory.AF_TANH)) {
      return new ActivationTANH();
    }
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    MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
    NEATPopulation pop = new NEATPopulation(2,1,1000);
    CalculateScore score = new TrainingSetScore(trainingSet);
    // train the neural network
    ActivationStep step = new ActivationStep();
    step.setCenter(0.5);
    pop.setOutputActivationFunction(step);
   
    final NEATTraining train = new NEATTraining(score,pop);
   
    EncogUtility.trainToError(train, 0.01);
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      break;
    case 7:
      newActivation = new ActivationSoftMax();
      break;
    case 8:
      newActivation = new ActivationStep();
      break;           
    case 9:
      newActivation = new ActivationTANH();
      break;
    case 10:
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    if (name.equalsIgnoreCase(MLActivationFactory.AF_SOFTMAX)) {
      return new ActivationSoftMax();
    }

    if (name.equalsIgnoreCase(MLActivationFactory.AF_STEP)) {
      return new ActivationStep();
    }

    if (name.equalsIgnoreCase(MLActivationFactory.AF_TANH)) {
      return new ActivationTANH();
    }
View Full Code Here
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  {
    MLDataSet trainingSet = new BasicMLDataSet(XOR.XOR_INPUT, XOR.XOR_IDEAL);
   
    CalculateScore score = new TrainingSetScore(trainingSet);
    // train the neural network
    ActivationStep step = new ActivationStep();
    step.setCenter(0.5);

    EvolutionaryAlgorithm train = NEATUtil.constructNEATTrainer(
        score, 2, 1, 10);
    //train.setOutputActivationFunction(step);
   
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Related Classes of org.encog.engine.network.activation.ActivationStep

  • org.encog.examples.neural.xor.XORNEAT
  • org.encog.persist.TestPersistNEAT
  • org.encog.persist.TestPersistPopulation
  • org.encog.plugin.system.SystemActivationPlugin
  • org.encog.workbench.dialogs.activation.ActivationDialog
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