Package org.encog.ml.data.basic

Examples of org.encog.ml.data.basic.BasicMLDataSet$BasicMLIterator

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  public final File EG_FILENAME = TEMP_DIR.createFile("encogtest.eg");
  public final File SERIAL_FILENAME = TEMP_DIR.createFile("encogtest.ser");
 
  private SVM create()
  {
    MLDataSet training = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
    SVM result = new SVM(2,SVMType.EpsilonSupportVectorRegression,KernelType.RadialBasisFunction);
    final SVMTrain train = new SVMTrain(result, training);
    train.iteration();
    return result;
  }
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  @Test
  public void testRPROP() throws Throwable
  {
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
   
    BasicNetwork network = NetworkUtil.createXORNetworkUntrained();
    MLTrain rprop = new ResilientPropagation(network, trainingData);
    NetworkUtil.testTraining(trainingData,rprop,0.03);
  }
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  }
 
  @Test
  public void testLMA() throws Throwable
  {
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
   
    BasicNetwork network = NetworkUtil.createXORNetworkUntrained();
    MLTrain rprop = new LevenbergMarquardtTraining(network, trainingData);
    NetworkUtil.testTraining(trainingData,rprop,0.03);
  }
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  }
 
  @Test
  public void testBPROP() throws Throwable
  {
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
   
    BasicNetwork network = NetworkUtil.createXORNetworkUntrained();

    MLTrain bprop = new Backpropagation(network, trainingData, 0.7, 0.9);
    NetworkUtil.testTraining(trainingData,bprop,0.01);
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  }
 
  @Test
  public void testManhattan() throws Throwable
  {
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
   
    BasicNetwork network = NetworkUtil.createXORNetworkUntrained();
    MLTrain bprop = new ManhattanPropagation(network, trainingData, 0.01);
    NetworkUtil.testTraining(trainingData,bprop,0.01);
  }
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  }
 
  @Test
  public void testSCG() throws Throwable
  {
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
   
    BasicNetwork network = NetworkUtil.createXORNetworkUntrained();
    MLTrain bprop = new ScaledConjugateGradient(network, trainingData);
    NetworkUtil.testTraining(trainingData,bprop,0.04);
  }
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  }
 
  @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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  }
 
  @Test
  public void testMLMethodGenetic() 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() {
        BasicNetwork network = NetworkUtil.createXORNetworkUntrained();
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  {
   
    PNNOutputMode mode = PNNOutputMode.Regression;
    BasicPNN network = new BasicPNN(PNNKernelType.Gaussian, mode, 2, 1);

    BasicMLDataSet trainingSet = new BasicMLDataSet(XOR.XOR_INPUT,
        XOR.XOR_IDEAL);

    TrainBasicPNN train = new TrainBasicPNN(network, trainingSet);
    train.iteration();
   
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  {
   
    PNNOutputMode mode = PNNOutputMode.Classification;
    BasicPNN network = new BasicPNN(PNNKernelType.Gaussian, mode, 2, 2);

    BasicMLDataSet trainingSet = new BasicMLDataSet(XOR.XOR_INPUT,
        XOR.XOR_IDEAL);

    TrainBasicPNN train = new TrainBasicPNN(network, trainingSet);
    train.iteration();
   
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Related Classes of org.encog.ml.data.basic.BasicMLDataSet$BasicMLIterator

  • com.wiieditor.neuralnetwork.NeuralNetworkWrapper
  • org.encog.app.analyst.csv.AnalystClusterCSV
  • org.encog.app.analyst.util.AnalystUtility
  • org.encog.EncogError
  • org.encog.examples.clustering.kmeans.SimpleKMeans
  • org.encog.examples.neural.activation.CustomActivation
  • org.encog.examples.neural.adaline.AdalineDigits
  • org.encog.examples.neural.benchmark.AverageBenchmark
  • org.encog.examples.neural.benchmark.BinaryVsMemory
  • org.encog.examples.neural.benchmark.FahlmanEncoder
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