/** * 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); }