Examples of SVDTraining

Examples of org.encog.neural.rbf.training.SVDTraining

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  public void testPersistNetworkRBF()
  {
    MLDataSet trainingSet = XOR.createXORDataSet();
    RBFNetwork network = new RBFNetwork(2,4,1, RBFEnum.Gaussian);

    SVDTraining training = new SVDTraining(network,trainingSet);
    training.iteration();
    XOR.verifyXOR(network, 0.1);
   
    EncogDirectoryPersistence.saveObject(EG_FILENAME, network);
    RBFNetwork network2 = (RBFNetwork)EncogDirectoryPersistence.loadObject(EG_FILENAME);
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Examples of org.encog.neural.rbf.training.SVDTraining

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        //Smooth training data provides true values for the provided input dimensions.
        create2DSmoothTainingDataGit();

        //Create the training set and train.
        MLDataSet trainingSet = new BasicMLDataSet(INPUT, IDEAL);
        MLTrain train = new SVDTraining(network, trainingSet);

        //SVD is a single step solve
        int epoch = 1;
        do
        {
            train.iteration();
            System.out.println("Epoch #" + epoch + " Error:" + train.getError());
            epoch++;
        } while ((epoch < 1) && (train.getError() > 0.001));

        // test the neural network
        System.out.println("Neural Network Results:");

        //Create a testing array which may be to a higher resoltion than the original training data
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Examples of org.encog.neural.rbf.training.SVDTraining

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      throw new EncogError(
          "RBF-SVD training cannot be used on a method of type: "
              + method.getClass().getName());
    }

    return new SVDTraining((RBFNetwork) method, training);
  }
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Examples of org.encog.neural.rbf.training.SVDTraining

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      throw new EncogError(
          "RBF-SVD training cannot be used on a method of type: "
              + method.getClass().getName());
    }

    return new SVDTraining((RBFNetwork) method, training);
  }
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Examples of org.encog.neural.rbf.training.SVDTraining

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  public void testPersistNetworkRBF()
  {
    MLDataSet trainingSet = XOR.createXORDataSet();
    RBFNetwork network = new RBFNetwork(2,4,1, RBFEnum.Gaussian);

    SVDTraining training = new SVDTraining(network,trainingSet);
    training.iteration();
    XOR.verifyXOR(network, 0.1);
   
    EncogDirectoryPersistence.saveObject(EG_FILENAME, network);
    RBFNetwork network2 = (RBFNetwork)EncogDirectoryPersistence.loadObject(EG_FILENAME);
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