public void ejecutar() {
int i, j, l, m, o;
int nClases;
int claseObt;
boolean marcas[];
double conjS[][];
int clasesS[];
int eleS[], eleT[];
int bestAc, aciertos;
int temp[];
int pos, tmp;
long tiempo = System.currentTimeMillis();
/*Getting the number of different classes*/
nClases = 0;
for (i = 0; i < clasesTrain.length; i++) if (clasesTrain[i] > nClases) nClases = clasesTrain[i];
nClases++;
/*Inicialization of the flagged instance vector of the S set*/
marcas = new boolean[datosTrain.length];
for (i = 0; i < datosTrain.length; i++) marcas[i] = false;
/*Allocate memory for the random selection*/
m = (int) ((porcentaje * datosTrain.length) / 100.0);
eleS = new int[m];
eleT = new int[datosTrain.length - m];
temp = new int[datosTrain.length];
for (i = 0; i < datosTrain.length; i++) temp[i] = i;
/** Random distribution of elements in each set */
Randomize.setSeed(semilla);
for (i = 0; i < eleS.length; i++) {
pos = Randomize.Randint(i, datosTrain.length - 1);
tmp = temp[i];
temp[i] = temp[pos];
temp[pos] = tmp;
eleS[i] = temp[i];
}
for (i = 0; i < eleT.length; i++) {
pos = Randomize.Randint(m + i, datosTrain.length - 1);
tmp = temp[m + i];
temp[m + i] = temp[pos];
temp[pos] = tmp;
eleT[i] = temp[m + i];
}
for (i = 0; i < eleS.length; i++) marcas[eleS[i]] = true;
/*Building of the S set from the flags*/
conjS = new double[m][datosTrain[0].length];
clasesS = new int[m];
for (o = 0, l = 0; o < datosTrain.length; o++) {
if (marcas[o]) { // the instance will be evaluated
for (j = 0; j < datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[o][j];
}
clasesS[l] = clasesTrain[o];
l++;
}
}
/*Evaluation of the S set*/
bestAc = 0;
for (i = 0; i < datosTrain.length; i++) {
claseObt = KNN.evaluacionKNN2(k, conjS, clasesS, datosTrain[i], nClases);
if (claseObt == clasesTrain[i]) // correct clasification
bestAc++;
}
/*Body of the ENNRS algorithm. Change the S set in each iteration for instances
of the T set until get a complete sustitution*/
for (i = 0; i < n; i++) {
/*Preparation the set to interchange*/
for (j = 0; j < eleS.length; j++) {
pos = Randomize.Randint(j, eleT.length - 1);
tmp = eleT[j];
eleT[j] = eleT[pos];
eleT[pos] = tmp;
}
/*Interchange of instances*/
for (j = 0; j < eleS.length; j++) {
tmp = eleS[j];
eleS[j] = eleT[j];
eleT[j] = tmp;
marcas[eleS[j]] = true;
marcas[eleT[j]] = false;
}
/*Building of the S set from the flags*/
for (o = 0, l = 0; o < datosTrain.length; o++) {
if (marcas[o]) { // the instance will evaluate
for (j = 0; j < datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[o][j];
}
clasesS[l] = clasesTrain[o];
l++;
}
}
/*Evaluation of the S set*/
aciertos = 0;
for (j = 0; j < datosTrain.length; j++) {
claseObt = KNN.evaluacionKNN2(k, conjS, clasesS, datosTrain[j], nClases);
if (claseObt == clasesTrain[j]) // correct clasification
aciertos++;
}
if (aciertos > bestAc) { // keep S
bestAc = aciertos;
} else { // undo changes
for (j = 0; j < eleS.length; j++) {
tmp = eleS[j];
eleS[j] = eleT[j];
eleT[j] = tmp;
marcas[eleS[j]] = true;
marcas[eleT[j]] = false;
}
}
}
/*Building of the S set from the flags*/
/*Building of the S set from the flags*/
for (o = 0, l = 0; o < datosTrain.length; o++) {
if (marcas[o]) { // the instance will evaluate
for (j = 0; j < datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[o][j];
}
clasesS[l] = clasesTrain[o];
l++;
}
}
System.out.println(
"ENNRS " + relation + " " + (double) (System.currentTimeMillis() - tiempo) / 1000.0 + "s");
// COn conjS me vale.
int trainRealClass[][];
int trainPrediction[][];
trainRealClass = new int[datosTrain.length][1];
trainPrediction = new int[datosTrain.length][1];
// Working on training
for (i = 0; i < datosTrain.length; i++) {
trainRealClass[i][0] = clasesTrain[i];
trainPrediction[i][0] = KNN.evaluate(datosTrain[i], conjS, nClases, clasesS, this.k);
}
KNN.writeOutput(ficheroSalida[0], trainRealClass, trainPrediction, entradas, salida, relation);
// Working on test
int realClass[][] = new int[datosTest.length][1];
int prediction[][] = new int[datosTest.length][1];
// Check time
for (i = 0; i < realClass.length; i++) {
realClass[i][0] = clasesTest[i];
prediction[i][0] = KNN.evaluate(datosTest[i], conjS, nClases, clasesS, this.k);
}
KNN.writeOutput(ficheroSalida[1], realClass, prediction, entradas, salida, relation);
}