Package org.encog.ml.svm.training

Examples of org.encog.ml.svm.training.SVMTrain

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  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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    return network;
  }
 
  public void train(SVM network,MLDataSet training)
  {
    final SVMTrain train = new SVMTrain(network, training);
    train.iteration();
  }
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      final MLDataSet dataSet, final double error) {

    MLTrain train;

    if (method instanceof SVM) {
      train = new SVMTrain((SVM)method, dataSet);
    } else {
      train = new ResilientPropagation((ContainsFlat)method, dataSet);
    }
    EncogUtility.trainToError(train, error);
  }
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    if (dialog.process()) {
      double c = dialog.getC().getValue();
      double g = dialog.getGamma().getValue();
      SVM method = (SVM) file.getObject();
      SVMTrain train = new SVMTrain((SVM) method, trainingData);
      train.setC(c);
      train.setGamma(g);
      train.iteration();
      double error = method.calculateError(trainingData);
      if (EncogWorkBench.askQuestion("Training Done",
          "Error: " + Format.formatPercent(error)
              + "\nSave training?")) {
        file.save();
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    final double gamma = holder.getDouble(MLTrainFactory.PROPERTY_GAMMA,
        false, defaultGamma);
    final double c = holder.getDouble(MLTrainFactory.PROPERTY_C, false,
        defaultC);

    final SVMTrain result = new SVMTrain((SVM) method, training);
    result.setGamma(gamma);
    result.setC(c);
    return result;
  }
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    return network;
  }
 
  public void train(SVM network,MLDataSet training)
  {
    final SVMTrain train = new SVMTrain(network, training);
    train.iteration();
  }
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    final double gamma = holder.getDouble(MLTrainFactory.PROPERTY_GAMMA,
        false, defaultGamma);
    final double c = holder.getDouble(MLTrainFactory.PROPERTY_C, false,
        defaultC);

    final SVMTrain result = new SVMTrain((SVM) method, training);
    result.setGamma(gamma);
    result.setC(c);
    return result;
  }
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      final MLDataSet dataSet, final double error) {

    MLTrain train;

    if (method instanceof SVM) {
      train = new SVMTrain((SVM)method, dataSet);
    } if(method instanceof FreeformNetwork ) {
      train = new FreeformResilientPropagation((FreeformNetwork) method, dataSet);
    } else {
      train = new ResilientPropagation((ContainsFlat)method, dataSet);
    }
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  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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Related Classes of org.encog.ml.svm.training.SVMTrain

  • org.encog.EncogError
  • org.encog.examples.neural.predict.sunspot.PredictSunspotSVM
  • org.encog.mathutil.error.ErrorCalculation
  • org.encog.ml.factory.train.SVMFactory
  • org.encog.persist.TestPersistSVM
  • org.encog.util.simple.EncogUtility
  • org.encog.workbench.process.TrainBasicNetwork
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