/**
* It launches the algorithm
*
* @param lanzar Chc execute the algorithm class
* @param fich String file's name
*/
public void execute(Chc lanzar, String fich) {
if (somethingWrong) { // We do not execute the program
System.err.println(
"An error was found, either the data-set have numerical values or missing values.");
System.err.println("Aborting the program");
// We should not use the statement: System.exit(-1);
} else {
// We do here the algorithm's operations
// nClasses = train.getnOutputs();
// Finally we should fill the training and test output files
doOutput(this.train, this.outputTr, lanzar);
doOutput(this.test, this.outputTst, lanzar);
EscribeBCLing e = new EscribeBCLing();
e.write(
fich, lanzar.getEc_tra(), lanzar.getEc_tst(), lanzar.getP().getE().base(), lanzar.getP());
System.out.println("Algorithm Finished");
}
}
/**
* It returns the algorithm regresion output given an input example
*
* @param example double[] The input example
* @param lanzar
* @return double the output generated by the algorithm
*/
private double regressionOutput(double[] example, Chc lanzar) {
double output = 0.0;
output = lanzar.getP().getE().base().FLC(example);
// }
// FLC();
/**
* Here we should include the algorithm directives to generate the classification output from
* the input example
*/
return output;
}
/**
* It returns the algorithm regresion output given an input example
*
* @param example double[] The input example
* @param lanzar Chc the algorithm class
* @return double the output generated by the algorithm
*/
private double regressionOutput(double[] example, Chc lanzar) {
double output = 0.0;
// for(int i=0;i<example.length;i++){
output = lanzar.getP().getE().base().FLC(example);
/*fichero = "tunlatgcomunTRA.txt";
String sal2 = new String("");
sal= lanzar.Ectra()+"\n";
Fichero.escribeFichero(fichero, sal2);
fichero = "tunlatgcomunTST.txt";
String sal3 = new String("");
sal= lanzar.Ectst()+"\n";
Fichero.escribeFichero(fichero, sal3);
//}
//FLC();
/**
Here we should include the algorithm directives to generate the
classification output from the input example
*/
return output;
}