/**
* Main Function
*
* @param args the Command line arguments. Only one is processed: the name of the file containing
* the
* <p>parameters
*/
public static void main(String[] args) throws IOException {
double[][] X;
double[][] Y;
int nInpt, nOutpl, ndata, i, j;
Rbfn net;
try {
// Help required
if (args.length > 0) {
if (args[0].equals("--help")
|| args[0].equals("-help")
|| args[0].equals("-h")
|| args[0].equals("-?")) {
doHelp();
return;
}
}
System.out.println("- Executing doRbfnDec " + args.length);
// Reading parameters
String paramFile = (args.length > 0) ? args[0] : "parameters.txt";
setParameters(paramFile);
System.out.println(" - Parameters file: " + paramFile);
// Random generator setup
if (reallySeed) {
Randomize.setSeed((long) seed);
}
// Reading Training dataset
ProcDataset Dtrn = new ProcDataset(trnFile, true);
// Training
System.out.println("Modeling Dataset");
Dtrn.processModelDataset();
nInpt = Dtrn.getninputs();
nOutpl = 1; // PD.getnvariables()-nInpt;
ndata = Dtrn.getndata();
Y = new double[ndata][1];
X = Dtrn.getX();
double[] auxY;
auxY = Dtrn.getY();
for (i = 0; i < ndata; i++) Y[i][0] = auxY[i];
// Building and training the net
net = new Rbfn(nNeuronsIni, X, ndata, nInpt, nOutpl);
net.decremental(X, Y, ndata, percent, alfa);
double[] obtained = new double[ndata];
net.testModeling(X, ndata, obtained);
Dtrn.generateResultsModeling(outTrnFile, auxY, obtained);
// TEST
ProcDataset Dtst = new ProcDataset(tstFile, false);
Dtst.processModelDataset();
nInpt = Dtst.getninputs();
nOutpl = 1; // PD.getnvariables()-nInpt;
ndata = Dtst.getndata();
X = Dtst.getX();
auxY = Dtst.getY();
Y = new double[ndata][1];
for (i = 0; i < ndata; i++) Y[i][0] = auxY[i];
obtained = new double[ndata];
net.testModeling(X, ndata, obtained);
Dtst.generateResultsModeling(outTstFile, auxY, obtained);
RBFUtils.createOutputFile(trnFile, outRbfFile);
net.printRbfn(outRbfFile);
if (Dtrn.datasetType() == 2) System.out.println("This is not a clustering algorithm");
System.out.println(
"- End of doRbfnDec. See results in output files named according to "
+ paramFile
+ " parameters file.");
} catch (Exception e) {
throw new InternalError(e.toString());
}
}
/**
* Reads parameters from parameter file.
*
* @param _fileName Name of file with parameters.
* @return True if everything goes right. False otherwise.
*/
private static boolean setParameters(String fileName) {
Hashtable parameters = RBFUtils.parameters(fileName);
RBFUtils.setVerbosity(parameters);
String tmp;
tmp = ((String) ((Vector) parameters.get("inputData")).get(0));
trnFile = tmp.substring(1, tmp.length() - 1); // Character " must be removed.
tmp = (String) ((Vector) parameters.get("inputData")).get(2);
tstFile = tmp.substring(1, tmp.length() - 1); // Character " must be removed.
tmp = (String) ((Vector) parameters.get("outputData")).get(0);
outTrnFile = tmp.substring(1, tmp.length() - 1); // Character " must be removed.
tmp = (String) ((Vector) parameters.get("outputData")).get(1);
outTstFile = tmp.substring(1, tmp.length() - 1); // Character " must be removed.
tmp = (String) ((Vector) parameters.get("outputData")).get(2);
outRbfFile = tmp.substring(1, tmp.length() - 1); // Character " must be removed.
percent = Double.parseDouble((String) ((Vector) parameters.get("percent")).get(0));
alfa = Double.parseDouble((String) ((Vector) parameters.get("alfa")).get(0));
nNeuronsIni =
(int) Double.parseDouble((String) ((Vector) parameters.get("nNeuronsIni")).get(0));
if (parameters.containsKey("seed")) {
reallySeed = true;
seed = (double) Double.parseDouble((String) ((Vector) parameters.get("seed")).get(0));
} else {
reallySeed = false;
}
RBFUtils.verboseln("Training file : " + trnFile);
RBFUtils.verboseln("Test file : " + tstFile);
RBFUtils.verboseln("Ouput Training file: " + outTrnFile);
RBFUtils.verboseln("Ouput Test file : " + outTstFile);
RBFUtils.verboseln("Ouput RBF file : " + outRbfFile);
RBFUtils.verboseln("percent : " + percent);
RBFUtils.verboseln("alfa : " + alfa);
RBFUtils.verbose("Seed : ");
if (reallySeed) {
RBFUtils.verboseln("" + seed);
} else {
RBFUtils.verboseln("No seed, i.e., pure random execution");
}
RBFUtils.verboseln("Verbosity : " + RBFUtils.getVerbosity());
return (trnFile != ""
&& tstFile != ""
&& outTrnFile != ""
&& outTstFile != ""
&& percent > 0
&& alfa > 0);
}