/** Method interface for Automatic Branch and Bound */
public void ejecutar() {
String resultado;
int i, numFeatures;
Date d;
d = new Date();
resultado =
"RESULTS generated at "
+ String.valueOf((Date) d)
+ " \n--------------------------------------------------\n";
resultado += "Algorithm Name: " + params.nameAlgorithm + "\n";
/* call of ABB algorithm */
runABB();
resultado += "\nPARTITION Filename: " + params.trainFileNameInput + "\n---------------\n\n";
resultado += "Features selected: \n";
for (i = numFeatures = 0; i < features.length; i++)
if (features[i] == true) {
resultado += Attributes.getInputAttribute(i).getName() + " - ";
numFeatures++;
}
resultado +=
"\n\n"
+ String.valueOf(numFeatures)
+ " features of "
+ Attributes.getInputNumAttributes()
+ "\n\n";
resultado +=
"Error in test (using train for prediction): "
+ String.valueOf(data.validacionCruzada(features))
+ "\n";
resultado +=
"Error in test (using test for prediction): "
+ String.valueOf(data.LVOTest(features))
+ "\n";
resultado += "---------------\n";
System.out.println("Experiment completed successfully");
/* creates the new training and test datasets only with the selected features */
Files.writeFile(params.extraFileNameOutput, resultado);
data.generarFicherosSalida(params.trainFileNameOutput, params.testFileNameOutput, features);
}
/** Recursive method for ABB */
private void abb(boolean feat[]) {
boolean[] child;
double measure;
threshold = data.measureIEP(feat);
for (int i = 0; i < cardinalidadCto(feat); i++) {
child = removeOne(feat, i);
measure = data.measureIEP(child);
if (legitimate(child) && measure < threshold) {
if (measure < data.measureIEP(features)) {
// we keep the best found in 'features'
System.arraycopy(child, 0, features, 0, child.length);
}
abb(child);
} else { // we prune this node
pruned.add(child);
}
}
}
/** Creates a new instance of ABB */
public ABB(String ficParametros) {
/* loads the parameter file */
params = new Parametros(ficParametros);
Randomize.setSeed(params.seed);
/* loads both of training and test datasets */
data = new Datos(params.trainFileNameInput, params.testFileNameInput, params.paramKNN);
features = new boolean[data.returnNumFeatures()];
pruned = new Vector<boolean[]>();
}
