/**
* Function which evaluates the population
*
* @param inicio it contains the beginning of the evaluation
* @param fin it contains the end of the evaluation
*/
public void Evaluate(int inicio, int fin) {
int i;
double performance = 0.;
for (i = inicio; i < fin; i++) {
Poblacion[i].set_perf(
E.eval_EC(
Poblacion[i].Gene(),
Poblacion[i].GeneA(),
Poblacion[i].GeneR(),
n_reglas_total)); // eval(Poblacion.elementAt(i).Gene);
performance = Poblacion[i].perf();
Trials++;
if (Trials == 1) this.Best = this.Worst = performance;
if (Better(performance, Best)) this.Best = performance;
if (Better(Worst, performance)) this.Worst = performance;
}
}
/**
* It initialize the population
*
* @param tam1 It contains the size of the table of training
* @param v1 It contains the training input values
* @param s1 It contains the training output values
* @param tam2 It contains the size of the table of test
* @param v2 It contains the test input values
* @param s2 It contains the test output values
* @param n_variables It contains the number of variables
* @param reglas It contains the number of rules
* @param var It contains the number of state variables
* @param sal It contains the exit value
* @param v It contains the values of data base
* @param semilla It contains the value of the seed
*/
public void Initialize(
int tam1,
double[][] v1,
double[] s1,
int tam2,
double[][] v2,
double[] s2,
int n_variables,
int reglas,
int var,
double sal,
double[] v,
long semilla) {
/* INICIALIZAR VARIABLES */
int i, j;
Genes = 0;
contador = contador2 = 0;
Reiniciado = 0;
Randomize.setSeed(semilla);
E = new Ecm(tam1, v1, s1, tam2, v2, s2, n_variables, reglas, var, sal, v);
Poblacion = new Cromosoma[2 * Popsize];
Trials = 0;
for (i = 0; i < n_variables; i++) {
Genes += E.base().getN_etiquetas(i);
}
Ajuste = 1;
Gene = new Gen[Genes];
GenesA = Genes - E.base().getN_etiquetas(E.base().getN_var_estado());
THRESHOLD = (double) ((Genes + GenesA) * BITS_GEN / 4.0);
reduccionIni = THRESHOLD * 0.001;
n_reglas_total = reglas;
Gen aux = new Gen();
aux.set_min(0.0);
aux.set_max(1.0);
for (i = 0; i < Genes; i++) {
Gene[i] = aux;
}
for (i = 0; i < 2 * Popsize; i++) {
Poblacion[i] = new Cromosoma(Genes, GenesA, n_reglas_total);
}
sample = new int[Popsize];
BEST_CROM = new Cromosoma(Genes, Genes, n_reglas_total);
for (j = 0; j < Genes; j++) {
BEST_CROM.set_gene(j, Gene[j].min() + (Gene[j].max() - Gene[j].min()) / 2.);
Poblacion[0].set_gene(j, BEST_CROM.gene(j));
}
for (j = 0; j < GenesA; j++) {
BEST_CROM.set_geneA(j, Gene[j].min() + (Gene[j].max() - Gene[j].min()) / 2.);
Poblacion[0].set_geneA(j, BEST_CROM.gene(j));
}
for (j = 0; j < n_reglas_total; j++) {
BEST_CROM.set_geneR(j, (char) 1);
Poblacion[0].set_geneR(j, (char) 1);
}
for (i = 1; i < Popsize; i++) {
for (j = 0; j < Genes; j++)
Poblacion[i].set_gene(
j, Gene[j].min() + (Gene[j].max() - Gene[j].min()) * Randomize.Rand());
for (j = 0; j < GenesA; j++)
Poblacion[i].set_geneA(
j, Gene[j].min() + (Gene[j].max() - Gene[j].min()) * Randomize.Rand());
for (j = 0; j < n_reglas_total; j++) Poblacion[i].set_geneR(j, (char) 1);
}
F = new Funciones();
}