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;
}
/** @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] + " ");
}
}
}