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);
}
public void ejecutar() {
int i, j, l;
boolean marcas[];
boolean marcas2[];
boolean marcastmp[];
boolean incorrect[];
int nSel;
double conjS[][];
double conjR[][];
int conjN[][];
boolean conjM[][];
int clasesS[];
Vector<Integer> vecinos[];
int next;
int maxneigh;
int pos;
int borrado;
int claseObt;
int nClases;
long tiempo = System.currentTimeMillis();
/*Getting the number of differents classes*/
nClases = 0;
for (i = 0; i < clasesTrain.length; i++) if (clasesTrain[i] > nClases) nClases = clasesTrain[i];
nClases++;
/*Inicialization of the flagged instances vector for a posterior copy*/
marcas = new boolean[datosTrain.length];
marcas2 = new boolean[datosTrain.length];
incorrect = new boolean[datosTrain.length];
marcastmp = new boolean[datosTrain.length];
Arrays.fill(marcas, true);
Arrays.fill(marcas2, true);
Arrays.fill(incorrect, false);
Arrays.fill(marcastmp, true);
vecinos = new Vector[datosTrain.length];
for (i = 0; i < datosTrain.length; i++) vecinos[i] = new Vector<Integer>();
for (i = 0; i < datosTrain.length; i++) {
next = nextNeighbour(marcas, datosTrain, i, vecinos[i]);
for (j = 0; j < datosTrain.length; j++) marcastmp[j] = marcas[j];
while (next >= 0 && clasesTrain[next] == clasesTrain[i]) {
vecinos[i].add(new Integer(next));
marcastmp[next] = false;
next = nextNeighbour(marcastmp, datosTrain, i, vecinos[i]);
}
}
maxneigh = vecinos[0].size();
pos = 0;
for (i = 1; i < datosTrain.length; i++) {
if (vecinos[i].size() > maxneigh) {
maxneigh = vecinos[i].size();
pos = i;
}
}
while (maxneigh > 0) {
for (i = 0; i < vecinos[pos].size(); i++) {
borrado = vecinos[pos].elementAt(i).intValue();
marcas[borrado] = false;
for (j = 0; j < datosTrain.length; j++) {
vecinos[j].removeElement(new Integer(borrado));
}
vecinos[borrado].clear();
}
vecinos[pos].clear();
maxneigh = vecinos[0].size();
pos = 0;
for (i = 1; i < datosTrain.length; i++) {
if (vecinos[i].size() > maxneigh) {
maxneigh = vecinos[i].size();
pos = i;
}
}
}
/*Building of the S set from the flags*/
nSel = 0;
for (i = 0; i < datosTrain.length; i++) if (marcas[i]) nSel++;
conjS = new double[nSel][datosTrain[0].length];
conjR = new double[nSel][datosTrain[0].length];
conjN = new int[nSel][datosTrain[0].length];
conjM = new boolean[nSel][datosTrain[0].length];
clasesS = new int[nSel];
for (i = 0, l = 0; i < datosTrain.length; i++) {
if (marcas[i]) { // the instance will be copied to the solution
for (j = 0; j < datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[i][j];
conjR[l][j] = realTrain[i][j];
conjN[l][j] = nominalTrain[i][j];
conjM[l][j] = nulosTrain[i][j];
}
clasesS[l] = clasesTrain[i];
l++;
}
}
for (i = 0; i < datosTrain.length; i++) {
/*Apply 1-NN to the instance*/
claseObt =
KNN.evaluacionKNN2(
1,
conjS,
conjR,
conjN,
conjM,
clasesTrain,
datosTrain[i],
realTrain[i],
nominalTrain[i],
nulosTrain[i],
nClases,
true);
if (claseObt != clasesTrain[i]) {
incorrect[i] = true;
}
}
for (i = 0; i < datosTrain.length; i++) vecinos[i] = new Vector<Integer>();
for (i = 0; i < datosTrain.length; i++) {
if (incorrect[i]) {
next = nextNeighbour(marcas2, datosTrain, i, vecinos[i]);
for (j = 0; j < datosTrain.length; j++) marcastmp[j] = marcas2[j];
while (next >= 0 && clasesTrain[next] == clasesTrain[i]) {
vecinos[i].add(new Integer(next));
marcastmp[next] = false;
next = nextNeighbour(marcastmp, datosTrain, i, vecinos[i]);
}
}
}
maxneigh = vecinos[0].size();
pos = 0;
for (i = 1; i < datosTrain.length; i++) {
if (vecinos[i].size() > maxneigh) {
maxneigh = vecinos[i].size();
pos = i;
}
}
while (maxneigh > 0) {
for (i = 0; i < vecinos[pos].size(); i++) {
borrado = vecinos[pos].elementAt(i).intValue();
marcas2[borrado] = false;
for (j = 0; j < datosTrain.length; j++) {
vecinos[j].removeElement(new Integer(borrado));
}
vecinos[borrado].clear();
}
vecinos[pos].clear();
maxneigh = vecinos[0].size();
pos = 0;
for (i = 1; i < datosTrain.length; i++) {
if (vecinos[i].size() > maxneigh) {
maxneigh = vecinos[i].size();
pos = i;
}
}
}
for (i = 0; i < marcas.length; i++) marcas[i] |= (marcas2[i] & incorrect[i]);
/*Building of the S set from the flags*/
nSel = 0;
for (i = 0; i < datosTrain.length; i++) if (marcas[i]) nSel++;
conjS = new double[nSel][datosTrain[0].length];
conjR = new double[nSel][datosTrain[0].length];
conjN = new int[nSel][datosTrain[0].length];
conjM = new boolean[nSel][datosTrain[0].length];
clasesS = new int[nSel];
for (i = 0, l = 0; i < datosTrain.length; i++) {
if (marcas[i]) { // the instance will be copied to the solution
for (j = 0; j < datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[i][j];
conjR[l][j] = realTrain[i][j];
conjN[l][j] = nominalTrain[i][j];
conjM[l][j] = nulosTrain[i][j];
}
clasesS[l] = clasesTrain[i];
l++;
}
}
System.out.println(
"Reconsistent "
+ 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, 1);
}
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, 1);
}
KNN.writeOutput(ficheroSalida[1], realClass, prediction, entradas, salida, relation);
}
