void adaboost(struct list_haar *larray, int nbex, int T)
{
//printf("******lancement d'adaboost****\n\n");
float alpha = 1;
long Et = 0;
float *w = malloc(nbex * sizeof(float));
*w = (1/(float)nbex);
int i, j;
struct stump *h;
for (int t = 1; t <= T; t++)
{
//struct list_haar *haar_tmp = larray;
//float *tmp = w;
h = best_stump(larray, w, nbex, 162336);
for (i = 0; i < nbex; i++)
{
Et += w[i];
}
if (Et == 0 && t == 1)
write_data(h, 1);
else
{
alpha = 0.5 * log ((1 - Et) / Et);
for(j = 0; j < nbex; j++ )
w[j] = (w[j] / 2) * ((1 / Et) + (1 / (1 - Et)));
}
write_data(h, alpha);
}
}
void adaboost(t_couple_image *img_set, int T){
int count = 0;
float error = 0;
FILE *file = fopen("strong_classifier", "w");
for(int t = 0; t < T; t++){
t_dec_stump *ht = best_stump(img_set);
t_feature *f = malloc(sizeof(t_feature));
f->i=ht->i; f->j=ht->j, f->w=ht->w; f->h=ht->h; f->type=ht->type;
for(int i = 0; i<PICT_WITH_FACE+PICT_WITH_NO_FACE; i++)
{
feature_scaling(img_set[i].img, f);
if(f->param<ht->threshold-ht->margin)
{
error+=img_set[i].weight;
count++;
}
}
count = 0;
free(f);
if(error == 0 && t==0)
{
fprintf(file, "%lf %d %d %d %d %d %d %d %d\n", ht->error, ht->threshold, ht->margin, ht->toggle, ht->i, ht->j, ht->w, ht->h, ht->type);
free(ht);
break;
}
else
{
for(int i = 0; i<PICT_WITH_FACE + PICT_WITH_NO_FACE; i++)
{
img_set[i].weight = (img_set[i].weight/2);
if(ht->toggle!=img_set[i].face)
img_set[i].weight*=1.0/error;
else
img_set[i].weight*=1.0/(1-error);
}
}
fprintf(file, "%lf %d %d %d %d %d %d %d %d\n", ht->error, ht->threshold, ht->margin, ht->toggle, ht->i, ht->j, ht->w, ht->h, ht->type);
printf("%lf %d %d %d %d %d %d %d %d\n", ht->error, ht->threshold, ht->margin, ht->toggle, ht->i, ht->j, ht->w, ht->h, ht->type);
free(ht);
}
fclose(file);
}
