virtual void solve() {
auto lines = paracel_load(input);
local_parser(item_vects, lines);
std::cout << "local parser done" << std::endl;
auto handler = [&](const std::vector<std::string> & linelst) {
std::unordered_map<std::string, std::vector<double> > other_item_vects;
local_parser(other_item_vects, linelst);
local_learning(other_item_vects);
};
paracel_sequential_loadall(input, handler);
select_top();
std::cout << "learning done" << std::endl;
}
void predict(const std::string & pred_fn) {
auto lines = pt->paracel_load(pred_fn);
local_parser(lines);
for(size_t i = 0; i < samples.size(); ++i) {
predv.push_back(lr_hypothesis(samples[i]));
}
}
virtual void solve() {
auto lines = paracel_load(input);
local_parser(item_vects, lines);
std::cout << "parser done" << std::endl;
if(learning_method == "default") {
auto all_lines = paracel_loadall(input);
local_parser(all_item_vects, all_lines);
std::cout << "loadall done" << std::endl;
normalize(item_vects);
normalize(all_item_vects);
std::cout << "normalize done" << std::endl;
sync();
learning();
} else if(learning_method == "limit_storage") {
normalize(item_vects); // normalize here to reduce calculation
init_paras();
sync();
mls_learning();
} else {}
}
void logistic_regression::solve() {
auto lines = paracel_load(input);
local_parser(lines); // init data
sync();
if(learning_method == "dgd") {
dgd_learning();
} else if(learning_method == "ipm") {
ipm_learning();
} else if(learning_method == "agd") {
agd_learning();
} else {
std::cout << "learning method not supported." << std::endl;
return;
}
sync();
//print(theta);
}
void test(const std::string & test_fn) {
auto lines = pt->paracel_load(test_fn);
local_parser(lines);
std::cout << "loss in test dataset is:" << calc_loss() << std::endl;
}
void solve() {
auto lines = pt->paracel_load(input);
local_parser(lines);
learning();
}
void logistic_regression::predict(const std::string & pred_fn) {
auto lines = paracel_load(input);
local_parser(lines); // re-init samples, labels
std::cout << "mean loss" << calc_loss() << std::endl;
}
