void *staff_runner(void *arg){
Runner_Arg *rarg = (Runner_Arg *) arg;
int read;
int fd_is_close;
while(1){
// lock ; read file;
int locked = pthread_mutex_lock(rarg->read_fd_lock);
if (locked!=0) return error_lock("Could not lock on file : ", rarg->read_fd_lock);
char *line = read_line_from_input(rarg->fd, &fd_is_close);
if (!line){
if (fd_is_close){
pthread_mutex_unlock(rarg->read_fd_lock);
//TODO - next step.
return NULL;
}
return error_lock("Strange... could not allocate memory? : " , rarg->read_fd_lock);
}
if (pthread_mutex_unlock(rarg->read_fd_lock) != 0){
perror("Unlock :");
}
char **arguments = split(line, &read);
// Check arguments is clean:
if (arguments == NULL ) return error_("Could not allocate in split of input line: ");
if (read != 6 ){
fprintf(stderr, "Ignoring input line: %s | %d.", line, read);
freeArray(arguments);
continue;
}
//Create new Staff entity.
if (strcasecmp(arguments[2], "teacher") == 0){
if (read_teacher(arg, arguments, read) != 0){
return NULL;
}
}
else{
fprintf(stderr, "EntityType %s is not valid\n", arguments[2]);
}
freeArray(arguments);
}
}
int main(int argc, char *argv[])
{
if (argc != 2) {
std::cerr << "usage: " << argv[0] << " inputfile" << std::endl;
exit(1);
}
double teacher[MAX_TEACHERS][IN_SIZE];
if (!read_teacher(argv[1], teacher)) {
std::cerr << "read_teacher failed" << std::endl;
exit(1);
}
double in[IN_SIZE], out[OUT_SIZE];
double weights[IN_SIZE][OUT_SIZE];
// initialize weights
for (int i = 0; i < IN_SIZE; i++) {
for (int j = 0; j < OUT_SIZE; j++) {
weights[i][j] = 0; // works only for perceptron ? (should be random numbers)
}
}
// learning
bool is_learning = true;
int num_loops = 0;
while (is_learning) {
for (int j = 0; j < MAX_TEACHERS; j++) {
for (int k = 0; k < IN_SIZE - 1; k++) {
in[k] = teacher[j][k];
}
in[IN_SIZE-1] = 1;
for (int k = 0; k < OUT_SIZE; k++) {
// calculating the current output
double o = 0;
for (int l = 0; l < IN_SIZE; l++) {
o += weights[l][k] * in[l];
}
// check if it fires or not
out[k] = step_func(o);
}
if (is_learned(teacher, in, out, weights)) {
is_learning = false;
break;
}
// update weights
for (int k = 0; k < OUT_SIZE; k++) {
for (int l = 0; l < IN_SIZE; l++) {
weights[l][k] += ETA * (teacher[j][IN_SIZE-1] - out[k]) * in[l];
}
}
}
if (++num_loops == MAX_LOOPS) {
std::cout << "not solved" << std::endl;
break;
}
}
std::cout << "num_loops: " << num_loops << std::endl;
for (int k = 0; k < OUT_SIZE; k++) {
for (int l = 0; l < IN_SIZE; l++) {
std::cout << weights[l][k] << " ";
}
std::cout << std::endl;
}
// check if learning works
for (int j = 0; j < MAX_TEACHERS; j++) {
for (int k = 0; k < IN_SIZE - 1; k++) {
in[k] = teacher[j][k];
}
for (int k = 0; k < OUT_SIZE; k++) {
// calculating the current output
double o = 0;
for (int l = 0; l < IN_SIZE; l++) {
o += weights[l][k] * in[l];
}
std::cout << "in: " << in[0] << ", " << in[1] << " - o: " << o << std::endl;
}
}
return 0;
}
