#include #include #include #include #include #include #include #include #include

// A struct to represent a rating given by a user to an item struct Rating { int user_id; int item_id; float rating; };

// A struct to represent a predicted rating for an item struct PredictedRating { int id; int user_id; int item_id; float rating; };

// Reads the ratings from a .csv file and returns them as a vector std::vector read_ratings(const std::string& filename) { std::vector ratings;

std::ifstream file(filename); if (file.is_open()) { std::string line; // Skip the first line (header) std::getline(file, line); while (std::getline(file, line)) { Rating rating; std::sscanf(line.c_str(), "%d,%d,%f", &rating.user_id, &rating.item_id, &rating.rating); ratings.push_back(rating); } file.close(); }

return ratings; }

// Reads the test cases from a .csv file and returns them as a vector std::vector read_test_cases(const std::string& filename) { std::vector test_cases;

std::ifstream file(filename); if (file.is_open()) { std::string line; // Skip the first line (header) std::getline(file, line); while (std::getline(file, line)) { PredictedRating test_case; std::sscanf(line.c_str(), "%d,%d,%d", &test_case.id, &test_case.user_id, &test_case.item_id); test_cases.push_back(test_case); } file.close(); }

return test_cases; }

// Calculates the root mean squared error between the predicted ratings and the actual ratings float calculate_rmse(const std::vector& predicted_ratings, const std::vector& actual_ratings) { float sum_squared_error = 0.0f; for (const auto& predicted_rating : predicted_ratings) { auto actual_rating_iter = std::find_if(actual_ratings.begin(), actual_ratings.end(), [&](const Rating& r) { return r.user_id == predicted_rating.user_id && r.item_id == predicted_rating.item_id; }); if (actual_rating_iter != actual_ratings.end()) { sum_squared_error += std::pow(predicted_rating.rating - actual_rating_iter->rating, 2); } } return std::sqrt(sum_squared_error / predicted_ratings.size()); }

float predict_rating_mean_item(int user_id, int item_id, const std::unordered_map>& item_ratings) { // If the item has not been rated by any users, return the mean rating of all items if (item_ratings.count(item_id) == 0) { float sum_ratings = 0.0f; int num_ratings = 0; for (const auto& [_, ratings] : item_ratings) { for (const auto& rating : ratings) { sum_ratings += rating.rating; num_ratings++; } } return sum_ratings / num_ratings; } else { // If the item has been rated by at least one user, return the mean rating of the item float sum_ratings = 0.0f; int num_ratings = 0; for (const auto& rating : item_ratings.at(item_id)) { sum_ratings += rating.rating; num_ratings++; } return sum_ratings / num_ratings; } }

int main() { // Read in the training and test sets std::vector training_set = read_ratings("train.csv"); std::vector test_set = read_test_cases("test.csv");

// Initialize a map to store the ratings received by each item std::unordered_map> item_ratings; for (const auto& rating : training_set) { item_ratings[rating.item_id].push_back(rating); }

std::fstream fout;

// opens an existing csv file or creates a new file. fout.open("report.csv", std::ios::out | std::ios::app);

// Predict the ratings for the test set using the mean rating of the item for (auto& predicted_rating : test_set) { predicted_rating.rating = predict_rating_mean_item(predicted_rating.user_id, predicted_rating.item_id, item_ratings); fout << std::to_string(predicted_rating.id) << "," << std::to_string(predicted_rating.rating) << " "; }

// Calculate the RMSE between the predicted ratings and the actual ratings float rmse = calculate_rmse(test_set, training_set); std::cout << "RMSE: " << rmse << std::endl;

return 0; }

I want to be able to lower the RMSE value even more by using matrix factorization. Can you rewrite this code accordingly? Understand my question thoroughly, do not write nonsense answers. Please DO NOT spam.