/**
* PMX cross operator
*
* @param poblacion Population of chromosomes
* @param newPob New population
* @param sel1 First parent
* @param sel2 Second parent
*/
public void crucePMX(Cromosoma poblacion[], Cromosoma newPob[], int sel1, int sel2) {
int e1, e2;
int limSup, limInf;
int i;
boolean temp[];
temp = new boolean[datosTrain.length];
e1 = Randomize.Randint(0, datosTrain.length - 1);
e2 = Randomize.Randint(0, datosTrain.length - 1);
if (e1 > e2) {
limSup = e1;
limInf = e2;
} else {
limSup = e2;
limInf = e1;
}
for (i = 0; i < datosTrain.length; i++) {
if (i < limInf || i > limSup) temp[i] = poblacion[sel1].getGen(i);
else temp[i] = poblacion[sel2].getGen(i);
}
newPob[0] = new Cromosoma(temp);
for (i = 0; i < datosTrain.length; i++) {
if (i < limInf || i > limSup) temp[i] = poblacion[sel2].getGen(i);
else temp[i] = poblacion[sel1].getGen(i);
}
newPob[1] = new Cromosoma(temp);
} // end-method
/** Applies a tournament selection, with tournament size of 2 */
public void tournament_selection() {
int i, j, k, mejor_torneo;
int tam_torneo = 2;
int Torneo[] = new int[tam_torneo];
boolean repetido;
RuleSet sample[] = new RuleSet[long_poblacion];
for (i = 0; i < long_poblacion; i++) {
Torneo[0] = Randomize.Randint(0, long_poblacion);
mejor_torneo = Torneo[0];
for (j = 1; j < tam_torneo; j++) {
do {
Torneo[j] = Randomize.Randint(0, long_poblacion);
repetido = false;
k = 0;
while ((k < j) && (!repetido)) {
if (Torneo[j] == Torneo[k]) repetido = true;
else k++;
}
} while (repetido);
if (poblacion[Torneo[j]].fitness > poblacion[mejor_torneo].fitness)
mejor_torneo = Torneo[j];
}
sample[i] = new RuleSet(poblacion[mejor_torneo]);
}
previousPob = poblacion;
poblacion = sample;
}
/**
* It calculates the cross HUX
*
* @param Padre1 it is a father to cross
* @param Padre2 it is the other father to do the cross
* @param Hijo1 the son obtained
* @param Hijo2 the other obtained
*/
void HUX(char[] Padre1, char[] Padre2, char[] Hijo1, char[] Hijo2) {
int j, distintos, pos, intercambios;
char px, py, temp;
if (flag3 == 1) {
posiciones = new int[n_reglas_total];
flag3 = 0;
}
distintos = 0;
for (j = 0; j < n_reglas_total; j++) {
px = Padre1[j];
py = Padre2[j];
Hijo1[j] = px;
Hijo2[j] = py;
if (px != py) {
posiciones[distintos] = j;
distintos++;
}
}
intercambios = distintos / 2;
if (distintos > 0 && intercambios == 0) intercambios = 1;
for (j = 0; j < intercambios; j++) {
pos = Randomize.Randint(0, --distintos);
temp = Hijo1[posiciones[pos]];
Hijo1[posiciones[pos]] = Hijo2[posiciones[pos]];
Hijo2[posiciones[pos]] = temp;
posiciones[pos] = posiciones[distintos];
}
}
public void TournamentSelection() {
int i, j, winner, candidate;
for (i = 0; i < popSize; i++) {
// There can be only one
winner = Randomize.Randint(0, popSize);
for (j = 1; j < tournamentSize; j++) {
candidate = Randomize.Randint(0, popSize);
if (population[candidate].compareToIndividual(
population[winner], Parameters.optimizationMethod)
> 0) {
winner = candidate;
}
}
offspringPopulation[i] = cf.cloneClassifier(population[winner]);
}
}
/**
* Shuffles a vector of differences
*
* @param diff Vector of differences
* @param index Final position of the vector
*/
private void shuffleDiff(int diff[], int index) {
int pos, tmp;
for (int i = 0; i < index; i++) {
pos = Randomize.Randint(0, index);
tmp = diff[i];
diff[i] = diff[pos];
diff[pos] = tmp;
}
} // end-method
/**
* Constructor with parameters. It creates a random general rule
*
* @param database Data Base associated to the general rule base that includes this rule
* @param n_classes Number of classes in the training set
* @param tnorm T-norm used to compute the compatibility degree
* @param tconorm T-conorm used to compute the compatibility degree
* @param rule_weight Way of computing the rule weight
*/
public Rule(DataBase database, int n_classes, int tnorm, int tconorm, int rule_weight) {
int length_antecedent;
boolean[] selected_antecedent = new boolean[database.numVariables()];
antecedent = new ArrayList<FuzzyAntecedent>(database.numVariables());
for (int i = 0; i < database.numVariables(); i++) {
selected_antecedent[i] = false;
}
do {
// Select randomly some variables of the database and create randomly its antecedent
length_antecedent = Randomize.RandintClosed(1, database.numVariables());
for (int i = 0; i < length_antecedent; i++) {
int var_selected;
FuzzyAntecedent new_antecedent;
do {
var_selected = Randomize.Randint(0, database.numVariables());
} while (selected_antecedent[var_selected]);
new_antecedent = new FuzzyAntecedent(database, var_selected);
if (new_antecedent.isValid()) {
selected_antecedent[var_selected] = true;
antecedent.add((FuzzyAntecedent) new_antecedent);
}
}
} while (antecedent.size() == 0);
t_norm = tnorm;
t_conorm = tconorm;
ruleWeight = rule_weight;
level = 1;
clas = Randomize.Randint(0, n_classes);
n_e = true;
}
private void interpola(
double ra[],
double rb[],
int na[],
int nb[],
boolean ma[],
boolean mb[],
double resS[],
double resR[],
int resN[],
boolean resM[]) {
int i;
double diff;
double gap;
int suerte;
for (i = 0; i < ra.length; i++) {
if (ma[i] == true && mb[i] == true) {
resM[i] = true;
resS[i] = 0;
} else {
resM[i] = false;
if (entradas[i].getType() == Attribute.REAL) {
diff = rb[i] - ra[i];
gap = Randomize.Rand();
resR[i] = ra[i] + gap * diff;
resS[i] =
(ra[i] + entradas[i].getMinAttribute())
/ (entradas[i].getMaxAttribute() - entradas[i].getMinAttribute());
} else if (entradas[i].getType() == Attribute.INTEGER) {
diff = rb[i] - ra[i];
gap = Randomize.Rand();
resR[i] = Math.round(ra[i] + gap * diff);
resS[i] =
(ra[i] + entradas[i].getMinAttribute())
/ (entradas[i].getMaxAttribute() - entradas[i].getMinAttribute());
} else {
suerte = Randomize.Randint(0, 2);
if (suerte == 0) {
resN[i] = na[i];
} else {
resN[i] = nb[i];
}
resS[i] = (double) resN[i] / (double) (entradas[i].getNominalValuesList().size() - 1);
}
}
}
}
/** Deletes a variable from the fuzzy antecedent set of this rule */
public void deleteVar() {
int selected_var;
// Select a variable in the fuzzy antecedent
selected_var = Randomize.Randint(0, antecedent.size());
// Delete this label from the Fuzzy set
antecedent.remove(selected_var);
weight = 0.0;
raw_fitness = 0.0;
penalized_fitness = -1.0;
n_e = true;
ideal = 0;
level = 1;
}
/**
* One-point crossover
*
* @param cr1 index of parent 1 in poblation
* @param cr2 index of parent 2 in poblation
*/
public void onePointCrossover(int cr1, int cr2) {
RuleSet rule1 = poblacion[cr1];
RuleSet rule2 = poblacion[cr2];
// there are 3*number of attribute elements, plus class value in each cromosome
int cutpoint = Randomize.Randint(0, n_genes);
int cutpoint_rule = cutpoint / (3 * nAtt + 1);
int cutpoint_variable = cutpoint % (3 * nAtt + 1);
// rule1 is replaced from cutpoint (inclusive) to the end of his rule set
rule1.copyFromPointtoEnd(rule2, cutpoint_rule, cutpoint_variable);
// rule2 is replaced from the begining of his rule set to cutpoint (not inclusive)
rule2.copyFromBegintoPoint(rule1, cutpoint_rule, cutpoint_variable);
// childs must be evaluated
rule1.setEvaluated(false);
rule2.setEvaluated(false);
}
/**
* Adds a new variable to the fuzzy antecedent set of this rule
*
* @param data Data Base associated to the general rule base that includes this rule
*/
public void addVar(DataBase data) {
boolean found_var = false;
int selected_var;
if (antecedent.size() >= data.numVariables()) {
System.err.println(
"We cannot add a new var to this rule since it has all the possible vars in it");
System.exit(-1);
}
// Find a label that we do not have in the label set yet
do {
selected_var = Randomize.Randint(0, data.numVariables());
found_var = false;
for (int i = 0; i < antecedent.size() && !found_var; i++) {
if (((FuzzyAntecedent) antecedent.get(i)).getAttribute() == selected_var) {
found_var = true;
}
}
} while (found_var);
// Add this variable to the Fuzzy set
FuzzyAntecedent new_antecedent;
do {
new_antecedent = new FuzzyAntecedent(data, selected_var);
} while (!new_antecedent.isValid());
antecedent.add(new_antecedent);
weight = 0.0;
raw_fitness = 0.0;
penalized_fitness = -1.0;
n_e = true;
ideal = 0;
level = 1;
}
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());
}
/**
* The main method of the class that includes the operations of the algorithm. It includes all the
* operations that the algorithm has and finishes when it writes the output information into
* files.
*/
public void run() {
int nPos = 0;
int nNeg = 0;
int i, j, l, m;
int tmp;
int posID;
int positives[];
int overs[];
double conjS[][];
int clasesS[];
int tamS;
long tiempo = System.currentTimeMillis();
/*Count of number of positive and negative examples*/
for (i = 0; i < clasesTrain.length; i++) {
if (clasesTrain[i] == 0) nPos++;
else nNeg++;
}
if (nPos > nNeg) {
tmp = nPos;
nPos = nNeg;
nNeg = tmp;
posID = 1;
} else {
posID = 0;
}
/*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++;
}
}
/*Obtain the oversampling array taking account the previous array*/
overs = new int[nNeg - nPos];
Randomize.setSeed(semilla);
for (i = 0; i < overs.length; i++) {
tmp = Randomize.Randint(0, nPos - 1);
overs[i] = positives[tmp];
}
tamS = 2 * nNeg;
/*Construction of the S set from the previous vector S*/
conjS = new double[tamS][datosTrain[0].length];
clasesS = new int[tamS];
for (j = 0; j < datosTrain.length; j++) {
for (l = 0; l < datosTrain[0].length; l++) conjS[j][l] = datosTrain[j][l];
clasesS[j] = clasesTrain[j];
}
for (m = 0; j < tamS; j++, m++) {
for (l = 0; l < datosTrain[0].length; l++) conjS[j][l] = datosTrain[overs[m]][l];
clasesS[j] = clasesTrain[overs[m]];
}
System.out.println(
"RandomOverSampling "
+ relation
+ " "
+ (double) (System.currentTimeMillis() - tiempo) / 1000.0
+ "s");
OutputIS.escribeSalida(ficheroSalida[0], conjS, clasesS, entradas, salida, nEntradas, relation);
OutputIS.escribeSalida(ficheroSalida[1], test, entradas, salida, nEntradas, relation);
}
/**
* The main method of the class that includes the operations of the algorithm. It includes all the
* operations that the algorithm has and finishes when it writes the output information into
* files.
*/
public void run() {
int S[];
int i, j, l, m;
int nPos = 0, nNeg = 0;
int posID;
int nClases;
int pos;
int baraje[];
int tmp;
double conjS[][];
int clasesS[];
int tamS = 0;
int claseObt;
int cont;
int busq;
boolean marcas[];
int nSel;
double conjS2[][];
int clasesS2[];
double minDist, dist;
long tiempo = System.currentTimeMillis();
/*CNN PART*/
/*Count of number of positive and negative examples*/
for (i = 0; i < clasesTrain.length; i++) {
if (clasesTrain[i] == 0) nPos++;
else nNeg++;
}
if (nPos > nNeg) {
tmp = nPos;
nPos = nNeg;
nNeg = tmp;
posID = 1;
} else {
posID = 0;
}
/*Inicialization of the candidates set*/
S = new int[datosTrain.length];
for (i = 0; i < S.length; i++) S[i] = Integer.MAX_VALUE;
/*Inserting an element of mayority class*/
Randomize.setSeed(semilla);
pos = Randomize.Randint(0, clasesTrain.length - 1);
while (clasesTrain[pos] == posID) pos = (pos + 1) % clasesTrain.length;
S[tamS] = pos;
tamS++;
/*Insert all subset of minority class*/
for (i = 0; i < clasesTrain.length; i++) {
if (clasesTrain[i] == posID) {
S[tamS] = i;
tamS++;
}
}
/*Algorithm body. We resort randomly the instances of T and compare with the rest of S.
If an instance doesn´t classified correctly, it is inserted in S*/
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, clasesTrain.length - 1);
tmp = baraje[i];
baraje[i] = baraje[pos];
baraje[pos] = tmp;
}
for (i = 0; i < datosTrain.length; i++) {
if (clasesTrain[i] != posID) { // only for mayority class instances
/*Construction of the S set from the previous vector S*/
conjS = new double[tamS][datosTrain[0].length];
clasesS = new int[tamS];
for (j = 0; j < tamS; j++) {
for (l = 0; l < datosTrain[0].length; l++) conjS[j][l] = datosTrain[S[j]][l];
clasesS[j] = clasesTrain[S[j]];
}
/*Do KNN to the instance*/
claseObt = KNN.evaluacionKNN(k, conjS, clasesS, datosTrain[baraje[i]], 2);
if (claseObt != clasesTrain[baraje[i]]) { // fail in the class, it is included in S
Arrays.sort(S);
busq = Arrays.binarySearch(S, baraje[i]);
if (busq < 0) {
S[tamS] = baraje[i];
tamS++;
}
}
}
}
/*Construction of the S set from the previous vector S*/
conjS = new double[tamS][datosTrain[0].length];
clasesS = new int[tamS];
for (j = 0; j < tamS; j++) {
for (l = 0; l < datosTrain[0].length; l++) conjS[j][l] = datosTrain[S[j]][l];
clasesS[j] = clasesTrain[S[j]];
}
/*TOMEK LINKS PART*/
/*Inicialization of the instance flagged vector of the S set*/
marcas = new boolean[conjS.length];
for (i = 0; i < conjS.length; i++) {
marcas[i] = true;
}
nSel = conjS.length;
for (i = 0; i < conjS.length; i++) {
minDist = Double.POSITIVE_INFINITY;
pos = 0;
for (j = 0; j < conjS.length; j++) {
if (i != j) {
dist = KNN.distancia(conjS[i], conjS[j]);
if (dist < minDist) {
minDist = dist;
pos = j;
}
}
}
if (clasesS[i] != clasesS[pos]) {
if (clasesS[i] != posID) {
if (marcas[i] == true) {
marcas[i] = false;
nSel--;
}
} else {
if (marcas[pos] == true) {
marcas[pos] = false;
nSel--;
}
}
}
}
/*Construction of the S set from the flags*/
conjS2 = new double[nSel][conjS[0].length];
clasesS2 = new int[nSel];
for (m = 0, l = 0; m < conjS.length; m++) {
if (marcas[m]) { // the instance will evaluate
for (j = 0; j < conjS[0].length; j++) {
conjS2[l][j] = conjS[m][j];
}
clasesS2[l] = clasesS[m];
l++;
}
}
System.out.println(
"CNN_TomekLinks "
+ relation
+ " "
+ (double) (System.currentTimeMillis() - tiempo) / 1000.0
+ "s");
OutputIS.escribeSalida(
ficheroSalida[0], conjS2, clasesS2, entradas, salida, nEntradas, relation);
OutputIS.escribeSalida(ficheroSalida[1], test, entradas, salida, nEntradas, relation);
}
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);
}
/** Executes the algorithm */
public void ejecutar() {
int i, j, l;
int nClases;
double conjS[][];
double conjR[][];
int conjN[][];
boolean conjM[][];
int clasesS[];
int nSel = 0;
Cromosoma poblacion[];
int ev = 0;
double prob[];
double NUmax = 1.5;
double NUmin = 0.5; // used for lineal ranking
double aux;
double pos1, pos2;
int sel1, sel2, comp1, comp2;
Cromosoma newPob[];
long tiempo = System.currentTimeMillis();
/*Getting the number of different clases*/
nClases = 0;
for (i = 0; i < clasesTrain.length; i++) if (clasesTrain[i] > nClases) nClases = clasesTrain[i];
nClases++;
/*Random inicialization of the population*/
Randomize.setSeed(semilla);
poblacion = new Cromosoma[tamPoblacion];
for (i = 0; i < tamPoblacion; i++) poblacion[i] = new Cromosoma(datosTrain.length);
/*Initial evaluation of the population*/
for (i = 0; i < tamPoblacion; i++)
poblacion[i].evalua(
datosTrain,
realTrain,
nominalTrain,
nulosTrain,
clasesTrain,
alfa,
kNeigh,
nClases,
distanceEu);
if (torneo) {
while (ev < nEval) {
newPob = new Cromosoma[2];
/*Binary tournament selection*/
comp1 = Randomize.Randint(0, tamPoblacion - 1);
do {
comp2 = Randomize.Randint(0, tamPoblacion - 1);
} while (comp2 == comp1);
if (poblacion[comp1].getCalidad() > poblacion[comp2].getCalidad()) sel1 = comp1;
else sel1 = comp2;
comp1 = Randomize.Randint(0, tamPoblacion - 1);
do {
comp2 = Randomize.Randint(0, tamPoblacion - 1);
} while (comp2 == comp1);
if (poblacion[comp1].getCalidad() > poblacion[comp2].getCalidad()) sel2 = comp1;
else sel2 = comp2;
if (Randomize.Rand() < pCruce) { // there is cross
crucePMX(poblacion, newPob, sel1, sel2);
} else { // there is not cross
newPob[0] = new Cromosoma(datosTrain.length, poblacion[sel1]);
newPob[1] = new Cromosoma(datosTrain.length, poblacion[sel2]);
}
/*Mutation of the cromosomes*/
for (i = 0; i < 2; i++) newPob[i].mutacion(pMutacion1to0, pMutacion0to1);
/*Evaluation of the population*/
for (i = 0; i < 2; i++)
if (!(newPob[i].estaEvaluado())) {
newPob[i].evalua(
datosTrain,
realTrain,
nominalTrain,
nulosTrain,
clasesTrain,
alfa,
kNeigh,
nClases,
distanceEu);
ev++;
}
/*Replace the two worst*/
Arrays.sort(poblacion);
poblacion[tamPoblacion - 2] = new Cromosoma(datosTrain.length, newPob[0]);
poblacion[tamPoblacion - 1] = new Cromosoma(datosTrain.length, newPob[1]);
}
} else {
/*Get the probabilities of lineal ranking in case of not use binary tournament*/
prob = new double[tamPoblacion];
for (i = 0; i < tamPoblacion; i++) {
aux = (double) (NUmax - NUmin) * ((double) i / (tamPoblacion - 1));
prob[i] = (double) (1.0 / (tamPoblacion)) * (NUmax - aux);
}
for (i = 1; i < tamPoblacion; i++) prob[i] = prob[i] + prob[i - 1];
while (ev < nEval) {
/*Sort the population by quality criterion*/
Arrays.sort(poblacion);
newPob = new Cromosoma[2];
pos1 = Randomize.Rand();
pos2 = Randomize.Rand();
for (j = 0; j < tamPoblacion && prob[j] < pos1; j++) ;
sel1 = j;
for (j = 0; j < tamPoblacion && prob[j] < pos2; j++) ;
sel2 = j;
if (Randomize.Rand() < pCruce) { // there is cross
crucePMX(poblacion, newPob, sel1, sel2);
} else { // there is not cross
newPob[0] = new Cromosoma(datosTrain.length, poblacion[sel1]);
newPob[1] = new Cromosoma(datosTrain.length, poblacion[sel2]);
}
/*Mutation of the cromosomes*/
for (i = 0; i < 2; i++) newPob[i].mutacion(pMutacion1to0, pMutacion0to1);
/*Evaluation of the population*/
for (i = 0; i < 2; i++)
if (!(newPob[i].estaEvaluado())) {
newPob[i].evalua(
datosTrain,
realTrain,
nominalTrain,
nulosTrain,
clasesTrain,
alfa,
kNeigh,
nClases,
distanceEu);
ev++;
}
/*Replace the two worst*/
poblacion[tamPoblacion - 2] = new Cromosoma(datosTrain.length, newPob[0]);
poblacion[tamPoblacion - 1] = new Cromosoma(datosTrain.length, newPob[1]);
}
}
nSel = poblacion[0].genesActivos();
/*Building of S set from the best cromosome obtained*/
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 (poblacion[0].getGen(i)) { // the instance must 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(
"SGA " + relation + " " + (double) (System.currentTimeMillis() - tiempo) / 1000.0 + "s");
OutputIS.escribeSalida(
ficheroSalida[0], conjR, conjN, conjM, clasesS, entradas, salida, nEntradas, relation);
OutputIS.escribeSalida(ficheroSalida[1], test, entradas, salida, nEntradas, relation);
} // end-method
/** Function which cross the population */
public void Cruce() {
int i, j;
int temp, mom, dad;
int numCruces = 0;
double distancia;
int aux;
aux = (Genes * BITS_GEN) + n_reglas_total;
if (flag2 == 1) {
String1 = new char[aux];
String2 = new char[aux];
flag2 = 0;
}
for (i = 0; i < Popsize; i++) sample[i] = i;
for (i = 0; i < Popsize; i++) {
j = Randomize.Randint(i, Popsize - 1);
temp = sample[j];
sample[j] = sample[i];
sample[i] = temp;
}
for (i = 0; i < 2 * Popsize; i++) Poblacion[i].set_entrado(0);
/** ********************************************************************* */
/* DISPARAR CRUCE */
POPSIZE = Popsize;
noCruce = 0;
for (i = 0; i < Popsize / 2; i++) {
mom = sample[2 * i];
dad = sample[2 * i + 1];
/* COMPROBAR DISTANCIA HAMMING ENTRE LOS PADRES */
String1 = StringRep(Poblacion[mom].Gene(), Genes);
String2 = StringRep(Poblacion[dad].Gene(), Genes);
distancia = DistHam(String1, String2, Genes * BITS_GEN);
String1 = StringRep(Poblacion[mom].GeneA(), GenesA);
String2 = StringRep(Poblacion[dad].GeneA(), GenesA);
double aux1 = DistHam(String1, String2, GenesA * BITS_GEN);
distancia += aux1;
distancia /= 2.0;
if (distancia > (THRESHOLD)) {
xPC_BLX(
0.8,
Poblacion[mom].Gene(),
Poblacion[dad].Gene(),
Poblacion[POPSIZE].Gene(),
Poblacion[POPSIZE + 1].Gene(),
Genes);
xPC_BLX(
0.8,
Poblacion[mom].GeneA(),
Poblacion[dad].GeneA(),
Poblacion[POPSIZE].GeneA(),
Poblacion[POPSIZE + 1].GeneA(),
GenesA);
HUX(
Poblacion[mom].GeneR(),
Poblacion[dad].GeneR(),
Poblacion[POPSIZE].GeneR(),
Poblacion[POPSIZE + 1].GeneR());
Poblacion[POPSIZE].set_entrado(1);
Poblacion[POPSIZE + 1].set_entrado(1);
POPSIZE = POPSIZE + 2;
numCruces++;
} else noCruce++;
}
}
/**
* 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];
}
}
/** It runs the Qstatistic */
public void runAlgorithm() {
int i, j, l, h;
double conjS[][];
double conjR[][];
int conjN[][];
boolean conjM[][];
int clasesS[];
int nSel = 0;
Chromosome poblacion[];
int ev = 0;
Chromosome C[];
int baraje[];
int pos, tmp;
Chromosome newPob[];
int d;
int tamC;
Chromosome pobTemp[];
int nPos = 0, nNeg = 0, posID, negID;
double datosArt[][];
double realArt[][];
int nominalArt[][];
boolean nulosArt[][];
int clasesArt[];
int tamS;
long tiempo = System.currentTimeMillis();
// Randomize.setSeed (semilla);
posID = clasesTrain[0];
negID = -1;
for (i = 0; i < clasesTrain.length; i++) {
if (clasesTrain[i] != posID) {
negID = clasesTrain[i];
break;
}
}
/* Count of number of positive and negative examples */
for (i = 0; i < clasesTrain.length; i++) {
if (clasesTrain[i] == posID) nPos++;
else nNeg++;
}
if (nPos > nNeg) {
tmp = nPos;
nPos = nNeg;
nNeg = tmp;
tmp = posID;
posID = negID;
negID = tmp;
} else {
/*
* tmp = posID; posID = negID; negID = tmp;
*/
}
if (hybrid.equalsIgnoreCase("smote + eus")) {
if (balance) {
tamS = 2 * nNeg;
} else {
tamS = nNeg + nPos + (int) (nPos * smoting);
}
datosArt = new double[tamS][datosTrain[0].length];
realArt = new double[tamS][datosTrain[0].length];
nominalArt = new int[tamS][datosTrain[0].length];
nulosArt = new boolean[tamS][datosTrain[0].length];
clasesArt = new int[tamS];
SMOTE(
datosTrain,
realTrain,
nominalTrain,
nulosTrain,
clasesTrain,
datosArt,
realArt,
nominalArt,
nulosArt,
clasesArt,
kSMOTE,
ASMO,
smoting,
balance,
nPos,
posID,
nNeg,
negID,
distanceEu);
} else {
datosArt = new double[datosTrain.length][datosTrain[0].length];
realArt = new double[datosTrain.length][datosTrain[0].length];
nominalArt = new int[datosTrain.length][datosTrain[0].length];
nulosArt = new boolean[datosTrain.length][datosTrain[0].length];
clasesArt = new int[clasesTrain.length];
for (i = 0; i < datosTrain.length; i++) {
for (j = 0; j < datosTrain[i].length; j++) {
datosArt[i][j] = datosTrain[i][j];
realArt[i][j] = realTrain[i][j];
nominalArt[i][j] = nominalTrain[i][j];
nulosArt[i][j] = nulosTrain[i][j];
}
clasesArt[i] = clasesTrain[i];
}
}
/* Count of number of positive and negative examples */
nPos = nNeg = 0;
for (i = 0; i < clasesArt.length; i++) {
if (clasesArt[i] == posID) nPos++;
else nNeg++;
}
if (majSelection) d = nNeg / 4;
else d = datosArt.length / 4;
/* Random initialization of the population */
poblacion = new Chromosome[popSize];
baraje = new int[popSize];
for (i = 0; i < popSize; i++)
if (majSelection) poblacion[i] = new Chromosome(nNeg);
else poblacion[i] = new Chromosome(datosArt.length);
/* Initial evaluation of the population */
for (i = 0; i < popSize; i++)
poblacion[i].evalua(
datosTrain,
realTrain,
nominalTrain,
nulosTrain,
clasesTrain,
datosArt,
realArt,
nominalArt,
nulosArt,
clasesArt,
wrapper,
k,
evMeas,
majSelection,
pFactor,
P,
posID,
nPos,
distanceEu,
entradas,
anteriores,
salidasAnteriores);
/* Until stop condition */
while (ev < nEval) {
C = new Chromosome[popSize];
/* Selection(r) of C(t) from P(t) */
for (i = 0; i < popSize; i++) baraje[i] = i;
for (i = 0; i < popSize; i++) {
pos = Randomize.Randint(i, popSize - 1);
tmp = baraje[i];
baraje[i] = baraje[pos];
baraje[pos] = tmp;
}
for (i = 0; i < popSize; i++)
if (majSelection) C[i] = new Chromosome(nNeg, poblacion[baraje[i]]);
else C[i] = new Chromosome(datosArt.length, poblacion[baraje[i]]);
/* Structure recombination in C(t) constructing C'(t) */
tamC = recombinar(C, d, nNeg, nPos, majSelection);
newPob = new Chromosome[tamC];
for (i = 0, l = 0; i < C.length; i++) {
if (C[i].esValido()) { // the cromosome must be copied to the
// new poblation C'(t)
if (majSelection) newPob[l] = new Chromosome(nNeg, C[i]);
else newPob[l] = new Chromosome(datosArt.length, C[i]);
l++;
}
}
/* Structure evaluation in C'(t) */
for (i = 0; i < newPob.length; i++) {
newPob[i].evalua(
datosTrain,
realTrain,
nominalTrain,
nulosTrain,
clasesTrain,
datosArt,
realArt,
nominalArt,
nulosArt,
clasesArt,
wrapper,
k,
evMeas,
majSelection,
pFactor,
P,
posID,
nPos,
distanceEu,
entradas,
anteriores,
salidasAnteriores);
ev++;
}
/* Selection(s) of P(t) from C'(t) and P(t-1) */
Arrays.sort(poblacion);
Arrays.sort(newPob);
/*
* If the best of C' is worse than the worst of P(t-1), then there
* will no changes
*/
if (tamC == 0 || newPob[0].getCalidad() < poblacion[popSize - 1].getCalidad()) {
d--;
} else {
pobTemp = new Chromosome[popSize];
for (i = 0, j = 0, l = 0; i < popSize && l < tamC; i++) {
if (poblacion[j].getCalidad() > newPob[l].getCalidad()) {
if (majSelection) pobTemp[i] = new Chromosome(nNeg, poblacion[j]);
else pobTemp[i] = new Chromosome(datosArt.length, poblacion[j]);
j++;
} else {
if (majSelection) pobTemp[i] = new Chromosome(nNeg, newPob[l]);
else pobTemp[i] = new Chromosome(datosArt.length, newPob[l]);
l++;
}
}
if (l == tamC) { // there are cromosomes for copying
for (; i < popSize; i++) {
if (majSelection) pobTemp[i] = new Chromosome(nNeg, poblacion[j]);
else pobTemp[i] = new Chromosome(datosArt.length, poblacion[j]);
j++;
}
}
poblacion = pobTemp;
}
/* Last step of the algorithm */
if (d <= 0) {
for (i = 1; i < popSize; i++) {
poblacion[i].divergeCHC(r, poblacion[0], prob0to1Div);
}
for (i = 0; i < popSize; i++)
if (!(poblacion[i].estaEvaluado())) {
poblacion[i].evalua(
datosTrain,
realTrain,
nominalTrain,
nulosTrain,
clasesTrain,
datosArt,
realArt,
nominalArt,
nulosArt,
clasesArt,
wrapper,
k,
evMeas,
majSelection,
pFactor,
P,
posID,
nPos,
distanceEu,
entradas,
anteriores,
salidasAnteriores);
ev++;
}
/* Reinicialization of d value */
if (majSelection) d = (int) (r * (1.0 - r) * (double) nNeg);
else d = (int) (r * (1.0 - r) * (double) datosArt.length);
}
}
Arrays.sort(poblacion);
if (majSelection) {
nSel = poblacion[0].genesActivos() + nPos;
/* Construction of S set from the best cromosome */
conjS = new double[nSel][datosArt[0].length];
conjR = new double[nSel][datosArt[0].length];
conjN = new int[nSel][datosArt[0].length];
conjM = new boolean[nSel][datosArt[0].length];
clasesS = new int[nSel];
h = 0;
for (i = 0, l = 0; i < nNeg; i++, h++) {
for (; clasesArt[h] == posID && h < clasesArt.length; h++) ;
if (poblacion[0].getGen(i)) { // the instance must be copied to
// the solution
for (j = 0; j < datosArt[h].length; j++) {
conjS[l][j] = datosArt[h][j];
conjR[l][j] = realArt[h][j];
conjN[l][j] = nominalArt[h][j];
conjM[l][j] = nulosArt[h][j];
}
clasesS[l] = clasesArt[h];
l++;
}
}
for (i = 0; i < datosArt.length; i++) {
if (clasesArt[i] == posID) {
for (j = 0; j < datosArt[i].length; j++) {
conjS[l][j] = datosArt[i][j];
conjR[l][j] = realArt[i][j];
conjN[l][j] = nominalArt[i][j];
conjM[l][j] = nulosArt[i][j];
}
clasesS[l] = clasesArt[i];
l++;
}
}
} else {
nSel = poblacion[0].genesActivos();
/* Construction of S set from the best cromosome */
conjS = new double[nSel][datosArt[0].length];
conjR = new double[nSel][datosArt[0].length];
conjN = new int[nSel][datosArt[0].length];
conjM = new boolean[nSel][datosArt[0].length];
clasesS = new int[nSel];
for (i = 0, l = 0; i < datosArt.length; i++) {
if (poblacion[0].getGen(i)) { // the instance must be copied to
// the solution
for (j = 0; j < datosArt[i].length; j++) {
conjS[l][j] = datosArt[i][j];
conjR[l][j] = realArt[i][j];
conjN[l][j] = nominalArt[i][j];
conjM[l][j] = nulosArt[i][j];
}
clasesS[l] = clasesArt[i];
l++;
}
}
}
if (hybrid.equalsIgnoreCase("eus + smote")) {
nPos = nNeg = 0;
for (i = 0; i < clasesS.length; i++) {
if (clasesS[i] == posID) nPos++;
else nNeg++;
}
if (nPos < nNeg) {
if (balance) {
tamS = 2 * nNeg;
} else {
tamS = nNeg + nPos + (int) (nPos * smoting);
}
datosArt = new double[tamS][datosTrain[0].length];
realArt = new double[tamS][datosTrain[0].length];
nominalArt = new int[tamS][datosTrain[0].length];
nulosArt = new boolean[tamS][datosTrain[0].length];
clasesArt = new int[tamS];
SMOTE(
conjS,
conjR,
conjN,
conjM,
clasesS,
datosArt,
realArt,
nominalArt,
nulosArt,
clasesArt,
kSMOTE,
ASMO,
smoting,
balance,
nPos,
posID,
nNeg,
negID,
distanceEu);
nSel = datosArt.length;
/* Construction of S set from the best cromosome */
conjS = new double[nSel][datosArt[0].length];
conjR = new double[nSel][datosArt[0].length];
conjN = new int[nSel][datosArt[0].length];
conjM = new boolean[nSel][datosArt[0].length];
clasesS = new int[nSel];
for (i = 0; i < datosArt.length; i++) {
for (j = 0; j < datosArt[i].length; j++) {
conjS[i][j] = datosArt[i][j];
conjR[i][j] = realArt[i][j];
conjN[i][j] = nominalArt[i][j];
conjM[i][j] = nulosArt[i][j];
}
clasesS[i] = clasesArt[i];
}
}
}
/*
* for (i = 0; i < poblacion.length; i++){ for (j = 0; j <
* poblacion[0].cuerpo.length; j++){
* System.out.print((poblacion[i].cuerpo[j] ? 1 : 0)); }
* System.out.println(" Calidad: " + poblacion[i].calidad); }
*/
best = poblacion[0].cuerpo.clone();
bestOutputs = poblacion[0].prediction.clone();
System.out.println(
"QstatEUSCHC "
+ relation
+ " "
+ (double) (System.currentTimeMillis() - tiempo) / 1000.0
+ "s");
OutputIS.escribeSalida(
ficheroSalida[0], conjR, conjN, conjM, clasesS, entradas, salida, nEntradas, relation);
// OutputIS.escribeSalida(ficheroSalida[1], test, entradas, salida,
// nEntradas, relation);
}
