Package org.encog.neural.networks.training

Examples of org.encog.neural.networks.training.CalculateScore

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  private NEATPopulation generate()
  {
    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(
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    Assert.assertEquals(10,pop.getYoungBonusAgeThreshold());
    Assert.assertEquals(0.3,pop.getYoungScoreBonus());
   
    // see if the population can actually be used to train
    MLDataSet trainingSet = new BasicMLDataSet(XOR.XOR_INPUT, XOR.XOR_IDEAL);   
    CalculateScore score = new TrainingSetScore(trainingSet);
    NEATTraining train = new NEATTraining(score,pop);
    train.iteration();

  }
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  }

  public static double trainNetwork(final String what,
      final BasicNetwork network, final MLDataSet trainingSet) {
    // train the neural network
    CalculateScore score = new TrainingSetScore(trainingSet);
    final MLTrain trainAlt = new NeuralSimulatedAnnealing(
        network, score, 10, 2, 100);

    final MLTrain trainMain = new Backpropagation(network, trainingSet,0.000001, 0.0);
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  public static void main(final String args[]) {

    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);
   
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  }

  public static double trainNetwork(final String what,
      final BasicNetwork network, final MLDataSet trainingSet) {
    // train the neural network
    CalculateScore score = new TrainingSetScore(trainingSet);
    final MLTrain trainAlt = new NeuralSimulatedAnnealing(
        network, score, 10, 2, 100);

    final MLTrain trainMain = new Backpropagation(network, trainingSet,0.000001, 0.0);
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      if (dialog.getLoadToMemory().getValue()) {
        training = ((BufferedNeuralDataSet) training).loadToMemory();
      }

      CalculateScore score = new TrainingSetScore(training);
      NEATTraining train = new NEATTraining(score, pop);

      BasicTrainingProgress tab = new BasicTrainingProgress(train,
          popFile, train.getTraining());
      tab.setMaxError(dialog.getMaxError().getValue() / 100);
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    if (dialog.process()) {
      final double startTemp = dialog.getStartTemp().getValue();
      final double stopTemp = dialog.getStartTemp().getValue();
      final int cycles = dialog.getCycles().getValue();

      CalculateScore score = new TrainingSetScore(trainingData);
      final NeuralSimulatedAnnealing train = new NeuralSimulatedAnnealing(
          (BasicNetwork) file.getObject(), score, startTemp,
          stopTemp, cycles);
      train.setTraining(trainingData);
      startup(file, train, dialog.getMaxError().getValue() / 100.0);
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      final int populationSize = dialog.getPopulationSize().getValue();
      final double mutationPercent = dialog.getMutationPercent()
          .getValue();
      final double percentToMate = dialog.getPercentToMate().getValue();

      CalculateScore score = new TrainingSetScore(trainingData);
      final NeuralGeneticAlgorithm train = new NeuralGeneticAlgorithm(
          (BasicNetwork) file.getObject(),
          new RangeRandomizer(-1, 1), score, populationSize,
          mutationPercent, percentToMate);
      train.setTraining(trainingData);
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    if (!(method instanceof BasicNetwork)) {
      throw new TrainingError(
          "Invalid method type, requires BasicNetwork");
    }

    final CalculateScore score = new TrainingSetScore(training);

    final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
    final ParamsHolder holder = new ParamsHolder(args);
    final double startTemp = holder.getDouble(
        MLTrainFactory.PROPERTY_TEMPERATURE_START, false, 10);
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    if (!(method instanceof BasicNetwork)) {
      throw new TrainingError(
          "Invalid method type, requires BasicNetwork");
    }

    final CalculateScore score = new TrainingSetScore(training);

    final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
    final ParamsHolder holder = new ParamsHolder(args);
    final int populationSize = holder.getInt(
        MLTrainFactory.PROPERTY_POPULATION_SIZE, false, 5000);
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Related Classes of org.encog.neural.networks.training.CalculateScore

  • org.encog.examples.neural.recurrent.elman.ElmanXOR
  • org.encog.examples.neural.recurrent.jordan.JordanXOR
  • org.encog.examples.neural.xor.XORNEAT
  • org.encog.ml.factory.train.AnnealFactory
  • org.encog.ml.factory.train.GeneticFactory
  • org.encog.persist.TestPersistPopulation
  • org.encog.workbench.process.TrainBasicNetwork
  • org.encog.workbench.tabs.population.neat.NEATPopulationTab
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