/**
* Knn evaluation for classification
*
* @return
*/
public static int evaluacionKNNClass(
int nvec,
double conj[][],
double real[][],
int nominal[][],
boolean nulos[][],
int clases[],
double ejemplo[],
double ejReal[],
int ejNominal[],
boolean ejNulos[],
int nClases,
boolean distance,
int vecinos[],
int clase) {
int i, j, l;
boolean parar = false;
int vecinosCercanos[];
double minDistancias[];
int votos[];
double dist;
int votada, votaciones;
if (nvec > conj.length) nvec = conj.length;
votos = new int[nClases];
vecinosCercanos = new int[nvec];
minDistancias = new double[nvec];
for (i = 0; i < nvec; i++) {
vecinosCercanos[i] = -1;
minDistancias[i] = Double.POSITIVE_INFINITY;
}
for (i = 0; i < conj.length; i++) {
dist =
KNN.distancia(
conj[i],
real[i],
nominal[i],
nulos[i],
ejemplo,
ejReal,
ejNominal,
ejNulos,
distance);
if (dist > 0 && clases[i] == clase) {
parar = false;
for (j = 0; j < nvec && !parar; j++) {
if (dist < minDistancias[j]) {
parar = true;
for (l = nvec - 1; l >= j + 1; l--) {
minDistancias[l] = minDistancias[l - 1];
vecinosCercanos[l] = vecinosCercanos[l - 1];
}
minDistancias[j] = dist;
vecinosCercanos[j] = i;
}
}
}
}
for (j = 0; j < nClases; j++) {
votos[j] = 0;
}
for (j = 0; j < nvec; j++) {
if (vecinosCercanos[j] >= 0) votos[clases[vecinosCercanos[j]]]++;
}
votada = 0;
votaciones = votos[0];
for (j = 1; j < nClases; j++) {
if (votaciones < votos[j]) {
votaciones = votos[j];
votada = j;
}
}
for (i = 0; i < vecinosCercanos.length; i++) vecinos[i] = vecinosCercanos[i];
return votada;
}
/**
* SMOTE preprocessing procedure
*
* @param datosTrain input training dta
* @param realTrain actual training data
* @param nominalTrain nominal attribute values
* @param nulosTrain null values
* @param clasesTrain training classes
* @param datosArt synthetic instances
*/
public void SMOTE(
double datosTrain[][],
double realTrain[][],
int nominalTrain[][],
boolean nulosTrain[][],
int clasesTrain[],
double datosArt[][],
double realArt[][],
int nominalArt[][],
boolean nulosArt[][],
int clasesArt[],
int kSMOTE,
int ASMO,
double smoting,
boolean balance,
int nPos,
int posID,
int nNeg,
int negID,
boolean distanceEu) {
int i, j, l, m;
int tmp, pos;
int positives[];
int neighbors[][];
double genS[][];
double genR[][];
int genN[][];
boolean genM[][];
int clasesGen[];
int nn;
/* Localize the positive instances */
positives = new int[nPos];
for (i = 0, j = 0; i < clasesTrain.length; i++) {
if (clasesTrain[i] == posID) {
positives[j] = i;
j++;
}
}
/* Randomize the instance presentation */
for (i = 0; i < positives.length; i++) {
tmp = positives[i];
pos = Randomize.Randint(0, positives.length - 1);
positives[i] = positives[pos];
positives[pos] = tmp;
}
/* Obtain k-nearest neighbors of each positive instance */
neighbors = new int[positives.length][kSMOTE];
for (i = 0; i < positives.length; i++) {
switch (ASMO) {
case 0:
KNN.evaluacionKNN2(
kSMOTE,
datosTrain,
realTrain,
nominalTrain,
nulosTrain,
clasesTrain,
datosTrain[positives[i]],
realTrain[positives[i]],
nominalTrain[positives[i]],
nulosTrain[positives[i]],
Math.max(posID, negID) + 1,
distanceEu,
neighbors[i]);
break;
case 1:
evaluacionKNNClass(
kSMOTE,
datosTrain,
realTrain,
nominalTrain,
nulosTrain,
clasesTrain,
datosTrain[positives[i]],
realTrain[positives[i]],
nominalTrain[positives[i]],
nulosTrain[positives[i]],
Math.max(posID, negID) + 1,
distanceEu,
neighbors[i],
posID);
break;
case 2:
evaluacionKNNClass(
kSMOTE,
datosTrain,
realTrain,
nominalTrain,
nulosTrain,
clasesTrain,
datosTrain[positives[i]],
realTrain[positives[i]],
nominalTrain[positives[i]],
nulosTrain[positives[i]],
Math.max(posID, negID) + 1,
distanceEu,
neighbors[i],
negID);
break;
}
}
/* Interpolation of the minority instances */
if (balance) {
genS = new double[nNeg - nPos][datosTrain[0].length];
genR = new double[nNeg - nPos][datosTrain[0].length];
genN = new int[nNeg - nPos][datosTrain[0].length];
genM = new boolean[nNeg - nPos][datosTrain[0].length];
clasesGen = new int[nNeg - nPos];
} else {
genS = new double[(int) (nPos * smoting)][datosTrain[0].length];
genR = new double[(int) (nPos * smoting)][datosTrain[0].length];
genN = new int[(int) (nPos * smoting)][datosTrain[0].length];
genM = new boolean[(int) (nPos * smoting)][datosTrain[0].length];
clasesGen = new int[(int) (nPos * smoting)];
}
for (i = 0; i < genS.length; i++) {
clasesGen[i] = posID;
nn = Randomize.Randint(0, kSMOTE - 1);
interpola(
realTrain[positives[i % positives.length]],
realTrain[neighbors[i % positives.length][nn]],
nominalTrain[positives[i % positives.length]],
nominalTrain[neighbors[i % positives.length][nn]],
nulosTrain[positives[i % positives.length]],
nulosTrain[neighbors[i % positives.length][nn]],
genS[i],
genR[i],
genN[i],
genM[i]);
}
for (j = 0; j < datosTrain.length; j++) {
for (l = 0; l < datosTrain[0].length; l++) {
datosArt[j][l] = datosTrain[j][l];
realArt[j][l] = realTrain[j][l];
nominalArt[j][l] = nominalTrain[j][l];
nulosArt[j][l] = nulosTrain[j][l];
}
clasesArt[j] = clasesTrain[j];
}
for (m = 0; j < datosArt.length; j++, m++) {
for (l = 0; l < datosTrain[0].length; l++) {
datosArt[j][l] = genS[m][l];
realArt[j][l] = genR[m][l];
nominalArt[j][l] = genN[m][l];
nulosArt[j][l] = genM[m][l];
}
clasesArt[j] = clasesGen[m];
}
}
