void test_network(Network* network){
char* input_line = malloc(50*sizeof(char));
Vector* testing_point_in = new_vec(DIMENSION_INPUT+1);
Vector* testing_point_out = new_vec(DIMENSION_OUTPUT);
size_t test_set_size = 0;
while (scanf("%s\n", input_line) != EOF) {
test_set_size++;
#if REGRESSION
sscanf(input_line, "%lf\n", &testing_point_in->scalars[0]);
testing_point_in->scalars[0] = normalise_data(testing_point_in->scalars[0], MAX_VALUES_INPUT[0], MIN_VALUES_INPUT[0]);
testing_point_in->scalars[DIMENSION_INPUT] = BIAS;
testing_point_out = compute_output_network(network, testing_point_in);
printf("%.7lf\n", denormalise_data(testing_point_out->scalars[0], MAX_VALUES_INPUT[1], MIN_VALUES_INPUT[1]));
#elif CLASSIFICATION
sscanf(input_line, "%lf,%lf\n", &testing_point_in->scalars[0], &testing_point_in->scalars[1]);
testing_point_in->scalars[DIMENSION_INPUT] = BIAS;
testing_point_out = compute_output_network(network, testing_point_in);
if (testing_point_out->scalars[0] >= 0) {
printf("+1\n");
}else{
printf("-1\n");
}
#endif
delete_vec(testing_point_out);
}
delete_vec(testing_point_in);
free(input_line);
}
size_t read_input(Vector* training_data[], Vector* teaching_data[]){
size_t input_size = 0;
char* input_line = malloc(50*sizeof(char));
Vector* not_normalised_training[MAX_INPUT_LENGHT];
Vector* not_normalised_teaching[MAX_INPUT_LENGHT];
for (int i = 0; i < MAX_INPUT_LENGHT; i++) {
not_normalised_training[i] = new_vec(DIMENSION_INPUT+1); // +bias
not_normalised_teaching[i] = new_vec(DIMENSION_OUTPUT);
}
for (int i = 0; i < MAX_INPUT_LENGHT; i++) {
if (scanf("%s\n", input_line) == EOF){
break;
}
if (!strncmp(input_line, TERMINATING_STR, (int)strlen(TERMINATING_STR)-1)) {
break;
}
#if REGRESSION
sscanf(input_line, "%lf,%lf\n",
¬_normalised_training[i]->scalars[0],
¬_normalised_teaching[i]->scalars[0]);
not_normalised_training[i]->scalars[1] = BIAS;
#elif CLASSIFICATION
sscanf(input_line, "%lf,%lf,%lf\n",
¬_normalised_training[i]->scalars[0],
¬_normalised_training[i]->scalars[1],
¬_normalised_teaching[i]->scalars[0]);
not_normalised_training[i]->scalars[2] = BIAS;
#endif
input_size++;
}
double min = FLOAT_MAX;
double max = FLOAT_MIN;
for (int i = 0; i < input_size; i++) {
if (not_normalised_training[i]->scalars[0] > max) {
max = not_normalised_training[i]->scalars[0];
}
if (not_normalised_training[i]->scalars[0] < min) {
min = not_normalised_training[i]->scalars[0];
}
}
MAX_VALUES_INPUT[0] = max;
MIN_VALUES_INPUT[0] = min;
#if REGRESSION
min = FLOAT_MAX;
max = FLOAT_MIN;
for (int i = 0; i < input_size; i++) {
if (not_normalised_teaching[i]->scalars[0] > max) {
max = not_normalised_teaching[i]->scalars[0];
}
if (not_normalised_teaching[i]->scalars[0] < min) {
min = not_normalised_teaching[i]->scalars[0];
}
}
MAX_VALUES_INPUT[1] = max;
MIN_VALUES_INPUT[1] = min;
#elif CLASSIFICATION
min = FLOAT_MAX;
max = FLOAT_MIN;
for (int i = 0; i < input_size; i++) {
if (not_normalised_training[i]->scalars[1] > max) {
max = not_normalised_training[i]->scalars[1];
}
if (not_normalised_training[i]->scalars[1] < min) {
min = not_normalised_training[i]->scalars[1];
}
}
MAX_VALUES_INPUT[1] = max;
MIN_VALUES_INPUT[1] = min;
#endif
for (int i = 0; i < input_size; i++) {
#if REGRESSION
training_data[i]->scalars[0] = normalise_data(not_normalised_training[i]->scalars[0], MAX_VALUES_INPUT[0], MIN_VALUES_INPUT[0]);
training_data[i]->scalars[1] = BIAS;//normalised_training->scalars[1];
teaching_data[i]->scalars[0] = normalise_data(not_normalised_teaching[i]->scalars[0], MAX_VALUES_INPUT[1], MIN_VALUES_INPUT[1]);
#elif CLASSIFICATION
training_data[i]->scalars[0] = normalise_data(not_normalised_training[i]->scalars[0], MAX_VALUES_INPUT[0], MIN_VALUES_INPUT[0]);
training_data[i]->scalars[1] = normalise_data(not_normalised_training[i]->scalars[1], MAX_VALUES_INPUT[1], MIN_VALUES_INPUT[1]);
training_data[i]->scalars[2] = BIAS;//normalised_training->scalars[2];
teaching_data[i]->scalars[0] = not_normalised_teaching[i]->scalars[0];
#endif
}
for (int i = 0; i < MAX_INPUT_LENGHT; i++) {
delete_vec(not_normalised_training[i]);
delete_vec(not_normalised_teaching[i]);
}
free(input_line);
return input_size;
}
/****************
*
* Programme principal
*
****************/
int main(int argc, char **argv)
{
/* infos des différentes touches */
usage();
/* création d'une mémoire pour stocker */
/* les triangles de la surface iso-valeur */
/* -> tableau lT de dimension maximale nTmax */
/* la variable nT donne le nombre de triangles calculés */
/* (cf. routine calculIsoSurface) */
initBufferTriangles();
/* création d'une grille de données 3D de dimensions (sx,sy,sz) */
/* -> tableau buffer_vol */
/* buffer_vol[i+sx*j+sx*sy*k] contient la valeur du sommet de */
/* coordonnées (i,j,k), la valeur entre un réel entre 0.0 et 1.0 */
if (argc==1)
creer_data_f_implicite(); /* données à partir d'une fonction */
else
lire_fichier_data(argc, argv); /* données à partir d'un fichier */
normalise_data();
/*creation de la fenetre*/
glutInit(&argc, argv);
glutInitDisplayMode(GLUT_DOUBLE | GLUT_RGB | GLUT_DEPTH);
glutInitWindowSize(500, 500);
glutCreateWindow("Marching Tetrahedra");
/* volume englobant pour la vue 3D */
glMatrixMode(GL_PROJECTION);
glOrtho(-(-xmin+xmax)*.7*eX, (-xmin+xmax)*.7*eX,
-(-ymin+ymax)*.7*eY, (-ymin+ymax)*.7*eY,
-(-zmin+zmax)*10*eZ, (-zmin+zmax)*10*eZ);
glMatrixMode(GL_MODELVIEW);
/* initialisation des parametres de vue */
initialisation();
/* fonction affichage */
glutDisplayFunc (display);
/* gestion touche clavier */
glutKeyboardFunc(clavier);
/* function de gestion des moments où rien ne se passe */
glutIdleFunc(display);
/* fonctions pour la gestion de la traqueboule */
glutMouseFunc(tbMouseFunc);
glutMotionFunc(tbMotionFunc);
/* initialisation lumière et materiaux */
initLumiere();
initMatiere();
glClearColor( 1., 1., 1., 0.);
/* cache les elements invisibles */
glEnable(GL_DEPTH_TEST);
glEnable(GL_NORMALIZE);
/* boucle d'evenements GLUT */
glutMainLoop();
return 0;
}
