public void ejecutar() {
int i, j, l, m;
double alfai;
int nClases;
int claseObt;
boolean marcas[];
boolean notFound;
int init;
int clasSel[];
int baraje[];
int pos, tmp;
String instanciasIN[];
String instanciasOUT[];
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++;
/* Shuffle the train set */
baraje = new int[datosTrain.length];
Randomize.setSeed(semilla);
for (i = 0; i < datosTrain.length; i++) baraje[i] = i;
for (i = 0; i < datosTrain.length; i++) {
pos = Randomize.Randint(i, datosTrain.length - 1);
tmp = baraje[i];
baraje[i] = baraje[pos];
baraje[pos] = tmp;
}
/*
* Inicialization of the flagged instaces vector for a posterior
* elimination
*/
marcas = new boolean[datosTrain.length];
for (i = 0; i < datosTrain.length; i++) marcas[i] = false;
if (datosTrain.length > 0) {
// marcas[baraje[0]] = true; //the first instance is included always
nSel = n_p;
if (nSel < nClases) nSel = nClases;
} else {
System.err.println("Input dataset is empty");
nSel = 0;
}
clasSel = new int[nClases];
System.out.print("Selecting initial neurons... ");
// at least, there must be 1 neuron of each class at the beginning
init = nClases;
for (i = 0; i < nClases && i < datosTrain.length; i++) {
pos = Randomize.Randint(0, datosTrain.length - 1);
tmp = 0;
while ((clasesTrain[pos] != i || marcas[pos]) && tmp < datosTrain.length) {
pos = (pos + 1) % datosTrain.length;
tmp++;
}
if (tmp < datosTrain.length) marcas[pos] = true;
else init--;
// clasSel[i] = i;
}
for (i = init; i < Math.min(nSel, datosTrain.length); i++) {
tmp = 0;
pos = Randomize.Randint(0, datosTrain.length - 1);
while (marcas[pos]) {
pos = (pos + 1) % datosTrain.length;
tmp++;
}
// if(i<nClases){
// notFound = true;
// do{
// for(j=i-1;j>=0 && notFound;j--){
// if(clasSel[j] == clasesTrain[pos])
// notFound = false;
// }
// if(!notFound)
// pos = Randomize.Randint (0, datosTrain.length-1);
// }while(!notFound);
// }
// clasSel[i] = clasesTrain[pos];
marcas[pos] = true;
init++;
}
nSel = init;
System.out.println("Initial neurons selected: " + nSel);
/* Building of the S set from the flags */
conjS = new double[nSel][datosTrain[0].length];
clasesS = new int[nSel];
for (m = 0, l = 0; m < datosTrain.length; m++) {
if (marcas[m]) { // the instance must be copied to the solution
for (j = 0; j < datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[m][j];
}
clasesS[l] = clasesTrain[m];
l++;
}
}
alfai = alpha;
boolean change = true;
/* Body of the LVQ algorithm. */
// Train the network
for (int it = 0; it < T && change; it++) {
change = false;
alpha = alfai;
for (i = 1; i < datosTrain.length; i++) {
// search for the nearest neuron to training instance
pos = NN(nSel, conjS, datosTrain[baraje[i]]);
// nearest neuron labels correctly the class of training
// instance?
if (clasesS[pos] != clasesTrain[baraje[i]]) { // NO - repel
// the neuron
for (j = 0; j < conjS[pos].length; j++) {
conjS[pos][j] = conjS[pos][j] - alpha * (datosTrain[baraje[i]][j] - conjS[pos][j]);
}
change = true;
} else { // YES - migrate the neuron towards the input vector
for (j = 0; j < conjS[pos].length; j++) {
conjS[pos][j] = conjS[pos][j] + alpha * (datosTrain[baraje[i]][j] - conjS[pos][j]);
}
}
alpha = nu * alpha;
}
// Shuffle again the training partition
baraje = new int[datosTrain.length];
for (i = 0; i < datosTrain.length; i++) baraje[i] = i;
for (i = 0; i < datosTrain.length; i++) {
pos = Randomize.Randint(i, datosTrain.length - 1);
tmp = baraje[i];
baraje[i] = baraje[pos];
baraje[pos] = tmp;
}
}
System.out.println(
"LVQ " + relation + " " + (double) (System.currentTimeMillis() - tiempo) / 1000.0 + "s");
// Classify the train data set
instanciasIN = new String[datosReferencia.length];
instanciasOUT = new String[datosReferencia.length];
for (i = 0; i < datosReferencia.length; i++) {
/* Classify the instance selected in this iteration */
Attribute a = Attributes.getOutputAttribute(0);
int tipo = a.getType();
claseObt = KNN.evaluacionKNN2(1, conjS, clasesS, datosReferencia[i], nClases);
if (tipo != Attribute.NOMINAL) {
instanciasIN[i] = new String(String.valueOf(clasesReferencia[i]));
instanciasOUT[i] = new String(String.valueOf(claseObt));
} else {
instanciasIN[i] = new String(a.getNominalValue(clasesReferencia[i]));
instanciasOUT[i] = new String(a.getNominalValue(claseObt));
}
}
escribeSalida(
ficheroSalida[0], instanciasIN, instanciasOUT, entradas, salida, nEntradas, relation);
// Classify the test data set
normalizarTest();
instanciasIN = new String[datosTest.length];
instanciasOUT = new String[datosTest.length];
for (i = 0; i < datosTest.length; i++) {
/* Classify the instance selected in this iteration */
Attribute a = Attributes.getOutputAttribute(0);
int tipo = a.getType();
claseObt = KNN.evaluacionKNN2(1, conjS, clasesS, datosTest[i], nClases);
if (tipo != Attribute.NOMINAL) {
instanciasIN[i] = new String(String.valueOf(clasesTest[i]));
instanciasOUT[i] = new String(String.valueOf(claseObt));
} else {
instanciasIN[i] = new String(a.getNominalValue(clasesTest[i]));
instanciasOUT[i] = new String(a.getNominalValue(claseObt));
}
}
escribeSalida(
ficheroSalida[1], instanciasIN, instanciasOUT, entradas, salida, nEntradas, relation);
// Print the network to a file
printNetworkToFile(ficheroSalida[2], referencia.getHeader());
}
/**
* Evaluates a chromosome
*
* @param datos Reference to the training set
* @param real Reference to the training set (real valued)
* @param nominal Reference to the training set (nominal valued)
* @param nulos Reference to the training set (null values)
* @param clases Output attribute of each instance
* @param alfa Alpha value of the fitness function
* @param kNeigh Number of neighbors for the KNN algorithm
* @param nClases Number of classes of the problem
* @param distanceEu True= Euclidean distance; False= HVDM
*/
public void evalua(
double datos[][],
double real[][],
int nominal[][],
boolean nulos[][],
int clases[],
double alfa,
int kNeigh,
int nClases,
boolean distanceEu) {
int i, j, l, m;
int aciertos = 0;
double M, s;
double conjS[][];
double conjR[][];
int conjN[][];
boolean conjM[][];
int clasesS[];
int vecinos[];
int claseObt;
int vecinoCercano;
double dist, minDist;
M = (double) datos.length;
s = (double) genesActivos();
if (kNeigh > 1) {
vecinos = new int[kNeigh];
conjS = new double[(int) s][datos[0].length];
conjR = new double[(int) s][datos[0].length];
conjN = new int[(int) s][datos[0].length];
conjM = new boolean[(int) s][datos[0].length];
clasesS = new int[(int) s];
for (j = 0, l = 0; j < datos.length; j++) {
if (cuerpo[j]) { // the instance must be copied to the solution
for (m = 0; m < datos[j].length; m++) {
conjS[l][m] = datos[j][m];
conjR[l][m] = real[j][m];
conjN[l][m] = nominal[j][m];
conjM[l][m] = nulos[j][m];
}
clasesS[l] = clases[j];
l++;
}
}
for (i = 0; i < datos.length; i++) {
claseObt =
KNN.evaluacionKNN2(
kNeigh,
conjS,
conjR,
conjN,
conjM,
clasesS,
datos[i],
real[i],
nominal[i],
nulos[i],
nClases,
distanceEu,
vecinos);
if (claseObt >= 0) if (clases[i] == claseObt) aciertos++;
}
} else {
for (i = 0; i < datos.length; i++) {
vecinoCercano = -1;
minDist = Double.POSITIVE_INFINITY;
for (j = 0; j < datos.length; j++) {
if (cuerpo[j]) { // It is in S
dist =
KNN.distancia(
datos[i],
real[i],
nominal[i],
nulos[i],
datos[j],
real[j],
nominal[j],
nulos[j],
distanceEu);
if (dist < minDist && dist != 0) {
minDist = dist;
vecinoCercano = j;
}
}
}
if (vecinoCercano >= 0) if (clases[i] == clases[vecinoCercano]) aciertos++;
}
}
calidad = ((double) (aciertos) / M) * alfa * 100.0;
calidad += ((1.0 - alfa) * 100.0 * (M - s) / M);
cruzado = false;
} // end-method
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);
}
