Examples of CalculateScore

org.encog.ml.CalculateScore
Used by simulated annealing and genetic algorithms to calculate the score for a machine learning method. This allows networks to be ranked. We may be seeking a high or a low score, depending on the value the shouldMinimize method returns.
org.encog.neural.networks.training.CalculateScore
Used by simulated annealing and genetic algorithms to calculate the score for a neural network. This allows networks to be ranked. We may be seeking a high or a low score, depending on the value the shouldMinimize method returns.

Examples of org.encog.ml.CalculateScore

    MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
    NEATPopulation pop = new NEATPopulation(2,1,1000);
    pop.setInitialConnectionDensity(1.0);// not required, but speeds training
    pop.reset();


    CalculateScore score = new TrainingSetScore(trainingSet);
    // train the neural network
    
    final EvolutionaryAlgorithm train = NEATUtil.constructNEATTrainer(pop,score);
    
    do {

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Examples of org.encog.ml.CalculateScore

    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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Examples of org.encog.ml.CalculateScore

    if (!(method instanceof MLEncodable)) {
      throw new TrainingError(
          "Invalid method type, requires an encodable MLMethod");
    }


    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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Examples of org.encog.ml.CalculateScore

   * @return The newly created trainer.
   */
  public MLTrain create(final MLMethod method,
      final MLDataSet training, final String argsStr) {


    final CalculateScore score = new TrainingSetScore(training);    
    final TrainEA train = NEATUtil.constructNEATTrainer((NEATPopulation)method, score);


    return train;
  }

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Examples of org.encog.ml.CalculateScore

  public MLTrain create(final MLMethod method,
      final MLDataSet training, final String argsStr) {
    
    PrgPopulation pop = (PrgPopulation)method;
    
    final CalculateScore score = new TrainingSetScore(training);    
    TrainEA train = new TrainEA(pop, score);
    train.getRules().addRewriteRule(new RewriteConstants());
    train.getRules().addRewriteRule(new RewriteAlgebraic());
    train.setCODEC(new PrgCODEC());
    train.addOperation(0.8, new SubtreeCrossover());

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Examples of org.encog.ml.CalculateScore

  
  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);


    EvolutionaryAlgorithm train = NEATUtil.constructNEATTrainer(

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Examples of org.encog.ml.CalculateScore

    Assert.assertEquals(10,pop.getPopulationSize());
    Assert.assertEquals(0.2,pop.getSurvivalRate());
    
    // 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);
    EvolutionaryAlgorithm train = NEATUtil.constructNEATTrainer(pop, score);
    train.iteration();


  }

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Examples of org.encog.ml.CalculateScore

{
  private static double FAKE_DATA[][] = { { 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0 } };


  public void testNPE() throws Exception
  {
    final CalculateScore score = new TrainingSetScore(new BasicMLDataSet(FAKE_DATA, FAKE_DATA));


    // create a new random population and train it
    NEATPopulation pop = new NEATPopulation(FAKE_DATA[0].length, 1, 50);
    pop.reset();
    EvolutionaryAlgorithm training1 = NEATUtil.constructNEATTrainer(pop, score);

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Examples of org.encog.ml.CalculateScore

    new PersistNEATPopulation().save(serialized3, training3.getPopulation());
  }


  public void testSaveRead() throws Exception
  {
    final CalculateScore score = new TrainingSetScore(new BasicMLDataSet(FAKE_DATA, FAKE_DATA));
    NEATPopulation pop = new NEATPopulation(FAKE_DATA[0].length, 1, 50);
    pop.reset();
    // create a new random population and train it
    EvolutionaryAlgorithm training1 = NEATUtil.constructNEATTrainer(pop, score);
    training1.iteration();

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Examples of org.encog.ml.CalculateScore

  @Test
  public void testAnneal() throws Throwable
  {
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);    
    BasicNetwork network = NetworkUtil.createXORNetworkUntrained();
    CalculateScore score = new TrainingSetScore(trainingData);
    NeuralSimulatedAnnealing anneal = new NeuralSimulatedAnnealing(network,score,10,2,100);
    NetworkUtil.testTraining(trainingData,anneal,0.01);
  }

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