/**
* Main function
*
* @param args Arguments to the main method
* @throws FileNotFoundException
* @throws IOException
*/
public static void main(String[] args) throws FileNotFoundException, IOException {
if (args.length <= 0) {
System.err.println("No parameters file");
System.exit(1);
}
SetupParameters global = new SetupParameters();
global.LoadParameters(args[0]);
OpenDataset train = new OpenDataset();
OpenDataset test = null;
OpenDataset validation = null;
boolean isTrain;
train.processClassifierDataset(global.train_file, true);
global.n_test_patterns = 0;
global.n_train_patterns = train.getndatos();
if (global.test_data) {
test = new OpenDataset();
test.processClassifierDataset(global.test_file, false);
global.n_test_patterns = test.getndatos();
}
global.n_val_patterns = 0;
if (global.val_data) {
validation = new OpenDataset();
validation.processClassifierDataset(global.val_file, false);
global.n_val_patterns = validation.getndatos();
}
// Assign data and parameters to internal variables
// Number of inputs
global.Ninputs = 0;
for (int i = 0; i < train.getnentradas(); i++) {
if (train.getTiposAt(i) == 0) {
Vector in_values = train.getRangosVar(i);
global.Ninputs += in_values.size();
} else {
global.Ninputs++;
}
}
// Number of outputs
if (train.getTiposAt(train.getnentradas()) != 0) {
global.Noutputs = train.getnsalidas();
} else {
Vector out_values = train.getRangosVar(train.getnentradas());
global.Noutputs = out_values.size();
}
Data data =
new Data(
global.Ninputs + global.Noutputs, global.n_train_patterns, global.n_test_patterns, 0);
Genesis.DatasetToArray(data.train, train);
if (global.test_data) {
Genesis.DatasetToArray(data.test, test);
}
if (global.val_data) {
Genesis.DatasetToArray(data.validation, validation);
}
if (global.tipify_inputs == true) {
double mean, sigma, sq_sum; /* Tipify input data. */
/* Scale input. */
for (int i = 0; i < global.Ninputs; i++) {
/* Get the mean and variance. */
mean = sigma = sq_sum = 0.;
for (int j = 0; j < global.n_train_patterns; j++) {
mean += data.train[j][i];
sq_sum += data.train[j][i] * data.train[j][i];
}
mean /= global.n_train_patterns;
sigma = Math.sqrt(sq_sum / global.n_train_patterns - mean * mean);
/* Tipify: z = (x - mean)/std. dev. */
/* If std. dev. is 0 do nothing. */
if (sigma > 0.000001) {
for (int j = 0; j < global.n_train_patterns; j++) {
data.train[j][i] = (data.train[j][i] - mean) / sigma;
}
for (int j = 0; j < global.n_test_patterns; j++) {
data.test[j][i] = (data.test[j][i] - mean) / sigma;
}
}
}
}
sonn SelfOrganizingNetwork = new sonn(global, data);
SelfOrganizingNetwork.SaveNetwork("SONN_Network", global.seed, false);
if (global.problem.compareToIgnoreCase("Classification") == 0) {
double result =
SelfOrganizingNetwork.TestSONNInClassification(
global, data.train, global.n_train_patterns);
System.out.print("Train accuracy: " + result + "\t");
result =
SelfOrganizingNetwork.TestSONNInClassification(global, data.test, global.n_test_patterns);
System.out.println("Test accuracy: " + result);
} else {
double result =
SelfOrganizingNetwork.TestSONNInRegression(global, data.train, global.n_train_patterns);
System.out.print("Train accuracy: " + result + "\t");
result =
SelfOrganizingNetwork.TestSONNInRegression(global, data.test, global.n_test_patterns);
System.out.println("Test accuracy: " + result);
}
SelfOrganizingNetwork.SaveOutputFile(
global.train_output, data.train, global.n_train_patterns, global);
SelfOrganizingNetwork.SaveOutputFile(
global.test_output, data.test, global.n_test_patterns, global);
}
/**
* Constructor that takes only the setup parameters (NOT USED)
*
* @param global Global Definition parameters
* @throws FileNotFoundException
* @throws IOException
*/
public Data(SetupParameters global) throws FileNotFoundException, IOException {
String line;
int pos1, pos2;
try {
// Training data
FileInputStream file = new FileInputStream(global.train_file);
BufferedReader f = new BufferedReader(new InputStreamReader(file));
// Number of patterns
line = f.readLine();
global.n_train_patterns = Integer.parseInt(line);
// Number of inputs
line = f.readLine();
global.Ninputs = Integer.parseInt(line);
// Number of outputs
line = f.readLine();
global.Noutputs = Integer.parseInt(line);
// Read data
train = new double[global.n_train_patterns][global.Ninputs + global.Noutputs];
for (int i = 0; i < global.n_train_patterns; i++) {
line = f.readLine();
pos1 = 0;
for (int j = 0; j < global.Ninputs + global.Noutputs - 1; j++) {
pos2 = line.indexOf(" ", pos1);
train[i][j] = Double.parseDouble(line.substring(pos1, pos2));
pos1 = pos2 + 1;
}
train[i][global.Ninputs + global.Noutputs - 1] = Double.parseDouble(line.substring(pos1));
}
file.close();
} catch (FileNotFoundException e) {
System.err.println("Training file does not exist");
System.exit(-1);
}
if (global.test_data) {
try {
// Training data
FileInputStream file = new FileInputStream(global.test_file);
BufferedReader f = new BufferedReader(new InputStreamReader(file));
// Number of patterns
line = f.readLine();
global.n_test_patterns = Integer.parseInt(line);
// Number of inputs
line = f.readLine();
global.Ninputs = Integer.parseInt(line);
// Number of outputs
line = f.readLine();
global.Noutputs = Integer.parseInt(line);
// Read data
test = new double[global.n_test_patterns][global.Ninputs + global.Noutputs];
for (int i = 0; i < global.n_test_patterns; i++) {
line = f.readLine();
pos1 = 0;
for (int j = 0; j < global.Ninputs + global.Noutputs - 1; j++) {
pos2 = line.indexOf(" ", pos1);
test[i][j] = Double.parseDouble(line.substring(pos1, pos2));
pos1 = pos2 + 1;
}
test[i][global.Ninputs + global.Noutputs - 1] = Double.parseDouble(line.substring(pos1));
}
file.close();
} catch (FileNotFoundException f) {
System.err.println("Testing file does not exist");
System.exit(-1);
}
}
if (global.val_data) {
try {
// Training data
FileInputStream file = new FileInputStream(global.val_file);
BufferedReader f = new BufferedReader(new InputStreamReader(file));
// Number of patterns
line = f.readLine();
global.n_val_patterns = Integer.parseInt(line);
// Number of inputs
line = f.readLine();
global.Ninputs = Integer.parseInt(line);
// Number of outputs
line = f.readLine();
global.Noutputs = Integer.parseInt(line);
global.Nhidden[global.Nhidden_layers] = global.Noutputs;
// Read data
validation = new double[global.n_val_patterns][global.Ninputs + global.Noutputs];
for (int i = 0; i < global.n_val_patterns; i++) {
line = f.readLine();
pos1 = 0;
for (int j = 0; j < global.Ninputs + global.Noutputs - 1; j++) {
pos2 = line.indexOf(" ", pos1);
validation[i][j] = Double.parseDouble(line.substring(pos1, pos2));
pos1 = pos2 + 1;
}
validation[i][global.Ninputs + global.Noutputs - 1] =
Double.parseDouble(line.substring(pos1));
}
file.close();
} catch (FileNotFoundException e) {
System.err.println("Validation file does not exist");
System.exit(-1);
}
}
}