struct network BA_algorithm (int init_node_nb, int incr_edge_nb, int total){
int i, k, done;
struct node * nodeptr;
struct network n;
n = new_network(init_node_nb);
for(i = 0; i <= init_node_nb - 1; i ++){
add_default_node(&n);
if (i) add_edge(&n.nodes[i - 1], &n.nodes[i]);
}
for(i = init_node_nb; i <= total - 1; i++){
add_default_node(&n);
for (k = 0; k <= incr_edge_nb - 1; k++){
done = 0;
do {
nodeptr = &n.nodes[(int)(rand() / (double)RAND_MAX * (n.node_nb - 1))];
if ((rand() / (double)RAND_MAX < nodeptr->adj_node_nb / (double) n.node_nb) && nodeptr->id != i) { //Nodes are going to be linked
add_edge(&n.nodes[i], nodeptr);
done = 1;
}
} while (!done);
}
}
return n;
}
// ------------------- MAIN ----------------- //
// ------------------------------------------ //
int main(){
srand((unsigned int)time(NULL));
Vector* training_data[MAX_INPUT_LENGHT];
Vector* teaching_data[MAX_INPUT_LENGHT];
for (int i = 0; i < MAX_INPUT_LENGHT; i++) {
training_data[i] = new_vec(DIMENSION_INPUT+1);
teaching_data[i] = new_vec(DIMENSION_OUTPUT);
}
size_t TRAINING_SET_SIZE = 0;
TRAINING_SET_SIZE = read_input(training_data, teaching_data);
// in_layer, out_layer, hid_layer_count, hid_layers
Network* network = new_network(DIMENSION_INPUT, DIMENSION_OUTPUT, 2, 4, 4);
// print_network(network);
Vector*** best_weights = malloc((network->hidden_layers_count+1) * sizeof(Vector**));
for (size_t layer = 0; layer < network->hidden_layers_count; layer++) {
best_weights[layer] = malloc(network->hidden_layers[layer]->size * sizeof(Vector*));
for (size_t neuron_id = 0; neuron_id < network->hidden_layers[layer]->size; neuron_id++) {
best_weights[layer][neuron_id] = new_vec(network->hidden_layers[layer]->neurons[neuron_id]->weights->length);
}
}
best_weights[network->hidden_layers_count] = malloc(network->output_layer->size * sizeof(Vector*));
for (size_t neuron_id = 0; neuron_id < network->output_layer->size; neuron_id++) {
best_weights[network->hidden_layers_count][neuron_id] = new_vec(network->output_layer->neurons[neuron_id]->weights->length);
}
time_t time_at_beginning = time(0);
double total_error_old = FLOAT_MAX;
double total_error = 1.0;
double minimum_error_achieved = FLOAT_MAX;
double epsilon = 0.0001;
size_t epoch_count = 0;
while ((time(0) - time_at_beginning) < 30 && (total_error = error_total(network, training_data, teaching_data, TRAINING_SET_SIZE)) > epsilon) {
if (minimum_error_achieved > total_error) {
minimum_error_achieved = total_error;
dump_weights(network, best_weights);
// print_detailed_layer(network->hidden_layers[1]);
}
for (size_t i = 0; i < TRAINING_SET_SIZE; i++) {
train_network_with_backprop(network, training_data[i], teaching_data[i]);
}
if (epoch_count % 1000 == 0) {
// printf("Epochs count: %ld\n",epoch_count);
if (fabs(total_error - total_error_old) < 0.001) {
// printf("Shaking Weights!\n");
shake_weights(network);
}
total_error_old = total_error;
// printf("Total error: %.15lf\n", total_error);
}
update_learning_rate(network, ++epoch_count);
scramble_data(training_data, teaching_data, TRAINING_SET_SIZE);
}
// printf("Network training finished with a total error: %.15lf\n", total_error);
// printf("Network training achieved a minimum total error: %.15lf\n", minimum_error_achieved);
// print_detailed_layer(network->hidden_layers[1]);
load_weights(network, best_weights);
// print_detailed_layer(network->input_layer);
// print_detailed_layer(network->hidden_layers[0]);
// print_detailed_layer(network->hidden_layers[1]);
// print_detailed_layer(network->output_layer);
test_network(network);
for (size_t layer = 0; layer < network->hidden_layers_count; layer++) {
for (size_t neuron_id = 0; neuron_id < network->hidden_layers[layer]->size; neuron_id++) {
delete_vec(best_weights[layer][neuron_id]);
}
}
for (size_t neuron_id = 0; neuron_id < network->output_layer->size; neuron_id++) {
delete_vec(best_weights[network->hidden_layers_count][neuron_id]);
}
delete_network(network);
for (int i = 0; i < MAX_INPUT_LENGHT; i++) {
delete_vec(training_data[i]);
delete_vec(teaching_data[i]);
}
return EXIT_SUCCESS;
}
