Esempio n. 1
0
 private FileInputSynapse createInput(String name, int firstRow, int firstCol, int lastCol) {
   FileInputSynapse input = new FileInputSynapse();
   input.setInputFile(new File(name));
   input.setFirstRow(firstRow);
   if (firstCol != lastCol)
     input.setAdvancedColumnSelector(
         (new StringBuilder(String.valueOf(firstCol))).append("-").append(lastCol).toString());
   else input.setAdvancedColumnSelector(Integer.toString(firstCol));
   NormalizerPlugIn norm = new NormalizerPlugIn();
   if (firstCol != lastCol)
     norm.setAdvancedSerieSelector(
         (new StringBuilder("1-")).append(Integer.toString((lastCol - firstCol) + 1)).toString());
   else norm.setAdvancedSerieSelector("1");
   input.addPlugIn(norm);
   return input;
 }
Esempio n. 2
0
  /** @param args */
  public static void main(String[] args) {
    // TODO Auto-generated method stub
    int trainingRows = 80;
    int testingIndex = 82;
    int endTestingIndex = 125;
    //		 Prepare the training and testing data set
    FileInputSynapse fileIn = new FileInputSynapse();
    fileIn.setInputFile(new File("data/JOONE-TEST2.DATA"));
    fileIn.setAdvancedColumnSelector("1-35");

    // Input data normalized between -1 and +1
    NormalizerPlugIn normIn = new NormalizerPlugIn();
    normIn.setAdvancedSerieSelector("1-34");
    normIn.setMin(-1);
    normIn.setMax(1);
    fileIn.addPlugIn(normIn);

    // Target data normalized between 0 and 1
    NormalizerPlugIn normDes = new NormalizerPlugIn();
    normDes.setAdvancedSerieSelector("35");
    fileIn.addPlugIn(normDes);

    // Extract the training data
    double[][] inputTrain = JooneTools.getDataFromStream(fileIn, 3, trainingRows, 1, 34);
    double[][] desiredTrain = JooneTools.getDataFromStream(fileIn, 3, trainingRows, 35, 35);

    // Extract the testing data
    double[][] inputTest =
        JooneTools.getDataFromStream(fileIn, testingIndex, endTestingIndex, 1, 34);
    double[][] desiredTest =
        JooneTools.getDataFromStream(fileIn, testingIndex, endTestingIndex, 35, 35);

    //		 Line 1: Create an MLP network with 3 layers [2,2,1 nodes] with a logistic output layer
    NeuralNet nnet = JooneTools.create_standard(new int[] {34, 15, 1}, JooneTools.LOGISTIC);

    //		 Line 2: Train the network for 5000 epochs, or until the rmse < 0.01
    double rmse =
        JooneTools.train(
            nnet,
            inputTrain,
            desiredTrain,
            5000, // Max epoch
            0.01, // Min RMSE
            0, // Epochs between ouput reports
            null, // Std Output
            false // Asynchronous mode
            );
    System.out.println("Training complete.");

    //		 Line 3: Interrogate the network
    double[][] out = JooneTools.compare(nnet, inputTest, desiredTest);
    System.out.println("Comparion of the last " + out.length + " rows:");
    int cols = out[0].length / 2;
    for (int i = 0; i < out.length; ++i) {
      System.out.print("\nOutput: ");
      for (int x = 0; x < cols; ++x) {
        System.out.print(out[i][x] + " ");
      }
      System.out.print("\tTarget: ");
      for (int x = cols; x < cols * 2; ++x) {
        System.out.print(out[i][x] + " ");
      }
    }
  }