/** It applies the migration stage */
void Migration() {
int i, j, num;
double u;
/* First, the individual in the population are ordered according to their fitness */
Collections.sort(Poblacion);
/* The second half (the worst one) is radomly initialized again */
for (j = (tamPoblacion / 2); j < tamPoblacion; j++) {
/* First, the antecedent */
for (i = 0; i < entradas; i++) {
Poblacion.get(j).antecedente[i].m =
Randomize.RanddoubleClosed(train.getMin(i), train.getMax(i));
Poblacion.get(j).antecedente[i].sigma = Randomize.RandintClosed(1, 4);
}
/* Secondly, the consequent */
do {
num = 0;
for (i = 0; i < entradas; i++) {
u = Randomize.RandClosed();
/* The term is used in the consequent */
if (u < 0.5) {
Poblacion.get(j).consecuente[i] = Randomize.RanddoubleClosed(-1.0, 1.0);
// Poblacion.get(j).consecuente[entradas] =
// Randomize.RanddoubleClosed(-45.0, 45.0);
if (Poblacion.get(j).consecuente[i] != 0.0) {
num++;
}
}
/* The term is NOT used in the consequent */
else {
Poblacion.get(j).consecuente[i] = 0.0;
}
}
u = Randomize.RandClosed();
/* The term is used in the consequent */
if (u < 0.5) {
Poblacion.get(j).consecuente[entradas] =
Randomize.RanddoubleClosed(
-1.0 * ((train.getMax(entradas) - train.getMin(entradas)) / 2.0),
((train.getMax(entradas) - train.getMin(entradas)) / 2.0));
// Poblacion.get(j).consecuente[entradas] =
// Randomize.RanddoubleClosed(-45.0, 45.0);
// Poblacion.get(j).consecuente[entradas] =
// Randomize.RanddoubleClosed(train.getMin(entradas), train.getMax(entradas));
if (Poblacion.get(j).consecuente[entradas] != 0.0) {
num++;
}
}
/* The term is NOT used in the consequent */
else {
Poblacion.get(j).consecuente[entradas] = 0.0;
}
} while (num == 0);
}
}
/** It initializes each individual in the population */
void initializePopulation() {
int i, j, num;
double u;
for (j = 0; j < tamPoblacion; j++) {
/* First, the antecedent */
for (i = 0; i < entradas; i++) {
Poblacion.get(j).antecedente[i].m =
Randomize.RanddoubleClosed(train.getMin(i), train.getMax(i));
Poblacion.get(j).antecedente[i].sigma = Randomize.RandintClosed(1, 4);
}
/* Secondly, the consequent */
do {
num = 0;
for (i = 0; i < entradas; i++) {
u = Randomize.RandClosed();
/* The term is used in the consequent */
if (u < 0.5) {
Poblacion.get(j).consecuente[i] = Randomize.RanddoubleClosed(-1.0, 1.0);
// Poblacion.get(j).consecuente[i] =
// Randomize.RanddoubleClosed(train.getMin(i) - 45.0, train.getMax(i) + 45.0);
// Poblacion.get(j).consecuente[i] =
// Randomize.RanddoubleClosed(-45.0, 45.0);
if (Poblacion.get(j).consecuente[i] != 0.0) {
num++;
}
}
/* The term is NOT used in the consequent */
else {
Poblacion.get(j).consecuente[i] = 0.0;
}
}
u = Randomize.RandClosed();
/* The term is used in the consequent */
if (u < 0.5) {
// Poblacion.get(j).consecuente[entradas] = Randomize.RanddoubleClosed(
// - 45.0, 45.0);
Poblacion.get(j).consecuente[entradas] =
Randomize.RanddoubleClosed(
-1.0 * ((train.getMax(entradas) - train.getMin(entradas)) / 2.0),
((train.getMax(entradas) - train.getMin(entradas)) / 2.0));
if (Poblacion.get(j).consecuente[entradas] != 0.0) {
num++;
}
}
/* The term is NOT used in the consequent */
else {
Poblacion.get(j).consecuente[entradas] = 0.0;
}
} while (num == 0);
}
}
/**
* It calculate the output of the fuzzy system for a given example
*
* @param ejemplo double [] A given example
* @return double The output value obtained as output of the fuzzy system for a given example
*/
double Output_fuzzy_system(double[] ejemplo) {
int i;
double result, suma1, suma2, omega, y;
suma1 = suma2 = 0.0;
for (i = 0; i < Nr; i++) {
omega = Matching_degree(SistemaDifuso.get(i), ejemplo);
y = Output_value(SistemaDifuso.get(i), ejemplo);
suma1 += (omega * y);
suma2 += omega;
}
if (suma2 != 0.0) {
result = suma1 / suma2;
} else {
// result = 0.0;
result = ((train.getMax(entradas) - train.getMin(entradas)) / 2.0);
}
return (result);
}
/**
* It calculate the matching degree between the antecedent of the rule and a given example
*
* @param indiv Individual The individual representing a fuzzy rule
* @param ejemplo double [] A given example
* @return double The matching degree between the example and the antecedent of the rule
*/
double Matching_degree(Individual indiv, double[] ejemplo) {
int i, sig;
double result, suma, numerador, denominador, sigma, ancho_intervalo;
suma = 0.0;
for (i = 0; i < entradas; i++) {
ancho_intervalo = train.getMax(i) - train.getMin(i);
sigma = -1.0;
sig = indiv.antecedente[i].sigma;
switch (sig) {
case 1:
sigma = 0.3;
break;
case 2:
sigma = 0.4;
break;
case 3:
sigma = 0.5;
break;
case 4:
sigma = 0.6;
break;
}
sigma *= ancho_intervalo;
numerador = Math.pow((ejemplo[i] - indiv.antecedente[i].m), 2.0);
denominador = Math.pow(sigma, 2.0);
suma += (numerador / denominador);
}
suma *= -1.0;
result = Math.exp(suma);
return (result);
}
/**
* It prints the current population as a String
*
* @return String The current population as a String
*/
public String Print_Population() {
int i, j, sig;
double sigma, ancho_intervalo;
boolean anterior_nulo;
String output = new String("");
output += "Rule Base with " + Nr + " rules\n\n";
for (i = 0; i < Nr; i++) {
output += "Rule " + (i + 1) + ": IF ";
for (j = 0; j < entradas; j++) {
ancho_intervalo = train.getMax(j) - train.getMin(j);
sigma = -1.0;
sig = BestSistemaDifuso.get(i).antecedente[j].sigma;
switch (sig) {
case 1:
sigma = 0.3;
break;
case 2:
sigma = 0.4;
break;
case 3:
sigma = 0.5;
break;
case 4:
sigma = 0.6;
break;
}
sigma *= ancho_intervalo;
output +=
"X("
+ (j + 1)
+ ") is Gaussian("
+ BestSistemaDifuso.get(i).antecedente[j].m
+ ", "
+ sigma
+ ")";
if (j != (entradas - 1)) {
output += " and ";
}
}
output += " THEN Y = ";
anterior_nulo = true;
if (BestSistemaDifuso.get(i).consecuente[entradas] != 0.0) {
anterior_nulo = false;
output += "(" + BestSistemaDifuso.get(i).consecuente[entradas] + ")";
}
for (j = 0; j < entradas; j++) {
if (BestSistemaDifuso.get(i).consecuente[j] != 0.0) {
if (anterior_nulo == false) {
output += " + ";
}
anterior_nulo = false;
output += "(" + BestSistemaDifuso.get(i).consecuente[j] + " * X(" + (j + 1) + "))";
}
}
output += "\n\n";
}
return (output);
}
/** It applies mutation genetic operator */
void Mutation() {
int i, j, aux1, num;
double u, u2;
for (j = 0; j < tamPoblacion; j++) {
/* First, the antecedent */
for (i = 0; i < entradas; i++) {
u = Randomize.RandClosed();
if (u < probMut) {
Poblacion.get(j).antecedente[i].m =
Randomize.RanddoubleClosed(train.getMin(i), train.getMax(i));
}
u = Randomize.RandClosed();
if (u < probMut) {
aux1 = Poblacion.get(j).antecedente[i].sigma;
do {
Poblacion.get(j).antecedente[i].sigma = Randomize.RandintClosed(1, 4);
} while (aux1 == Poblacion.get(j).antecedente[i].sigma);
}
}
/* Secondly, the consequent */
num = 0;
for (i = 0; i <= entradas; i++) {
if (Poblacion.get(j).consecuente[i] != 0.0) {
num++;
}
}
for (i = 0; i < entradas; i++) {
u = Randomize.RandClosed();
if (u < probMut) {
u2 = Randomize.RandClosed();
if (u2 < 0.5) {
Poblacion.get(j).consecuente[i] = Randomize.RanddoubleClosed(-1.0, 1.0);
}
/* The term is NOT used in the consequent */
else {
if (num != 1) {
Poblacion.get(j).consecuente[i] = 0.0;
num--;
}
}
}
}
u = Randomize.RandClosed();
if (u < probMut) {
u2 = Randomize.RandClosed();
if (u2 < 0.5) {
Poblacion.get(j).consecuente[entradas] =
Randomize.RanddoubleClosed(
-1.0 * ((train.getMax(entradas) - train.getMin(entradas)) / 2.0),
((train.getMax(entradas) - train.getMin(entradas)) / 2.0));
// Poblacion.get(j).consecuente[entradas] =
// Randomize.RanddoubleClosed(-45.0, 45.0);
}
/* The term is NOT used in the consequent */
else {
if (num != 1) {
Poblacion.get(j).consecuente[entradas] = 0.0;
num--;
}
}
}
}
}