Package org.encog.neural.networks.training

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

67
68
69
70
71
72
73
74
75
  @Test
  public void testAnneal() throws Throwable
  {
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);   
    FreeformNetwork network = NetworkUtil.createXORFreeformNetworkUntrained();
    CalculateScore score = new TrainingSetScore(trainingData);
    NeuralSimulatedAnnealing anneal = new NeuralSimulatedAnnealing(network,score,10,2,100);
    NetworkUtil.testTraining(trainingData,anneal,0.01);
  }
View Full Code Here
76
77
78
79
80
81
82
83
84
85
86
 
  @Test
  public void testGenetic() throws Throwable
  {
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);   
    CalculateScore score = new TrainingSetScore(trainingData);
    MLMethodGeneticAlgorithm genetic = new MLMethodGeneticAlgorithm(new MethodFactory(){
      @Override
      public MLMethod factor() {
        FreeformNetwork network = NetworkUtil.createXORFreeformNetworkUntrained();
        network.reset();
View Full Code Here
48
49
50
51
52
53
54
55
56
57
58
  public void testSort1() {
   
    MLDataSet trainingSet = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
    NEATPopulation pop = new NEATPopulation(2,1,100);
    pop.reset();
    CalculateScore score = new TrainingSetScore(trainingSet);
    final EvolutionaryAlgorithm train = NEATUtil.constructNEATTrainer(pop,score);
       
    NEATGenome genome1 = new NEATGenome();
    genome1.setAdjustedScore(3.0);
    NEATGenome genome2 = new NEATGenome();
View Full Code Here
75
76
77
78
79
80
81
82
83
84
85
  public void testSort2() {
   
    MLDataSet trainingSet = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
    NEATPopulation pop = new NEATPopulation(2,1,100);
    pop.reset();
    CalculateScore score = new TrainingSetScore(trainingSet);
    final EvolutionaryAlgorithm train = NEATUtil.constructNEATTrainer(pop,score);
       
    NEATGenome genome1 = new NEATGenome();
    genome1.setAdjustedScore(3.0);
    NEATGenome genome2 = new NEATGenome();
View Full Code Here
156
157
158
159
160
161
162
     * the same topology as this network.
     * @param trainingSet The training set.
     */
    public NeuralPSO(BasicNetwork network, MLDataSet trainingSet)       
    {  
      this(network, new NguyenWidrowRandomizer(), new TrainingSetScore(trainingSet), 20);
    }
View Full Code Here
62
63
64
65
66
67
68
69
70
71
72
    final ParamsHolder holder = new ParamsHolder(args);

    final int particles = holder.getInt(
        MLTrainFactory.PROPERTY_PARTICLES, false, 20);
   
    CalculateScore score = new TrainingSetScore(training);
    Randomizer randomizer = new NguyenWidrowRandomizer();
   
    final MLTrain train = new NeuralPSO((BasicNetwork)method,randomizer,score,particles);
   
    return train;
View Full Code Here
TOP

Related Classes of org.encog.neural.networks.training.TrainingSetScore

  • org.encog.examples.neural.recurrent.elman.ElmanXOR
  • org.encog.examples.neural.recurrent.jordan.JordanXOR
  • org.encog.examples.neural.xor.XORNEAT
  • org.encog.ml.ea.train.basic.TrainEA
  • org.encog.ml.factory.train.AnnealFactory
  • org.encog.ml.factory.train.EPLGAFactory
  • org.encog.ml.factory.train.GeneticFactory
  • org.encog.ml.factory.train.NEATGAFactory
  • org.encog.ml.factory.train.PSOFactory
  • org.encog.neural.freeform.TestFreeformTraining
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.