/**
* This private method extract the dataset and the method's parameters from the KEEL environment,
* calculates the centroids using the KMeans class and print out the results with the validation
* dataset.
*
* @param tty unused boolean parameter, kept for compatibility
* @param pc ProcessConfig object to obtain the train and test datasets and the method's
* parameters.
*/
private void clustering_kmeans(boolean tty, ProcessConfig pc) {
try {
String linea;
ProcessDataset pd = new ProcessDataset();
linea = (String) pc.parInputData.get(ProcessConfig.IndexTrain);
if (pc.parNewFormat) pd.processClusterDataset(linea, true);
else pd.procesa_clustering_old(linea);
int ndatos = pd.getNdata(); // Number of examples
int nvariables = pd.getNvariables(); // Number of variables
int nentradas = pd.getNinputs(); // Number of inputs
pd.showDatasetStatistics();
System.out.println("Number of examples=" + ndatos);
System.out.println("Number of inputs=" + nentradas);
double[][] X = pd.getX(); // Input data
double[] emaximo = pd.getImaximum(); // Maximum and Minimum for input data
double[] eminimo = pd.getIminimum();
int[] neparticion = new int[nentradas];
int s;
s = pc.parNClusters;
KMeans KM = new KMeans(X, s, rand);
double fallos = 0;
try {
for (int i = 0; i < X.length; i++) {
int clase = KM.nearestCentroid(X[i]);
// System.out.println("pattern="+i+" cluster="+clase);
}
} catch (Exception e) {
System.out.println(e.toString());
}
// Clusters in the test set
ProcessDataset pdt = new ProcessDataset();
int nprueba, npentradas, npvariables;
linea = (String) pc.parInputData.get(ProcessConfig.IndexTestKMeans);
if (pc.parNewFormat) pdt.processClusterDataset(linea, false);
else pdt.procesa_clustering_old(linea);
nprueba = pdt.getNdata();
npvariables = pdt.getNvariables();
npentradas = pdt.getNinputs();
pdt.showDatasetStatistics();
if (npentradas != nentradas) throw new IOException("Error in test file");
double[][] Xp = pdt.getX();
int[] Co = new int[Xp.length];
// Test set is classified
try {
for (int i = 0; i < Xp.length; i++) {
Co[i] = KM.nearestCentroid(Xp[i]);
// System.out.println("pattern test="+i+" cluster="+Co[i]);
}
} catch (Exception e) {
System.out.println(e.toString());
}
// Output format for clustering algorithms
pc.results(Xp, Co);
KM.print();
} catch (FileNotFoundException e) {
System.err.println(e + " Training data not found");
} catch (IOException e) {
System.err.println(e + " Read error");
}
}
/**
* 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());
}
}