/* Main function with command line parameters - Validates parameters & calls
necessary functions to support the parameters*/
int main(int argc, char *argv[])
{
int func_num;
if (argc >= 2) {
if (streq(argv[1], "rootap_addr")) {
get_set_addr(1);
} else if (streq(argv[1], "rpt_addr")) {
get_set_addr(2);
} else if (streq(argv[1], "sta_addr")) {
get_set_addr(3);
} else if(streq(argv[1], "rec_msg")) {
if (argc != 4) {
printf("Missing argument : msg \n");
usage();
exit(0);
}
if (atoi(argv[2]) == 3)
func_num = 1;
else if (atoi(argv[2]) == 6)
func_num = 2;
else if (atoi(argv[2]) == 7)
func_num = 3;
else {
printf("Invalid func: msg \n");
exit(0);
}
rpttoolListen(func_num, 0, atoi(argv[3]));
} else if (streq(argv[1], "rec_gput")) {
if (argc != 4) {
printf("Missing argument : mode \n");
usage();
exit(0);
}
rpttoolListen(0,atoi(argv[2]), atoi(argv[3]));
} else if (streq(argv[1], "node_blk")) {
if (argc != 3) {
printf("Missing argument : mode \n");
usage();
exit(0);
}
block_nodes(atoi(argv[2]));
} else if (streq(argv[1], "master")) {
master = atoi(argv[2]);
if (master < 0 || master > 4 ) {
printf("Invalid mode. Exiting...\n");
usage();
}
mode = master >> 1;
interface = atoi(argv[3]);
init_training();
if (master%2 == 1) {
master_sm();
} else {
slave_sm();
}
} else if (streq(argv[1],"-h")) {
typename rbm_t::weight train(RBM& rbm, IIterator ifirst, IIterator ilast, EIterator efirst, EIterator elast, std::size_t max_epochs) {
dll::auto_timer timer("rbm_trainer:train");
//In case of shuffle, we don't want to shuffle the input, therefore create a copy and shuffle it
std::vector<typename std::iterator_traits<IIterator>::value_type> input_copy;
std::vector<typename std::iterator_traits<EIterator>::value_type> expected_copy;
auto iterators = prepare_it(ifirst, ilast, efirst, elast, input_copy, expected_copy);
decltype(auto) input_first = std::get<0>(iterators);
decltype(auto) input_last = std::get<1>(iterators);
decltype(auto) expected_first = std::get<2>(iterators);
decltype(auto) expected_last = std::get<3>(iterators);
//Initialize RBM and trainign parameters
init_training(rbm, input_first, input_last);
//Some RBM may init weights based on the training data
//Note: This can't be done in init_training, since it will
//sometimes be called with the wrong input values
init_weights(rbm, input_first, input_last);
//Allocate the trainer
auto trainer = get_trainer(rbm);
//Train for max_epochs epoch
for (std::size_t epoch = 0; epoch < max_epochs; ++epoch) {
//Shuffle if necessary
shuffle(input_first, input_last, expected_first, expected_last);
//Create a new context for this epoch
rbm_training_context context;
//Start a new epoch
init_epoch();
//Train on all the data
train_sub(input_first, input_last, expected_first, trainer, context, rbm);
//Finalize the current epoch
finalize_epoch(epoch, context, rbm);
}
return finalize_training(rbm);
}
