FIRST CODE

How can I use the second code in the first code, if you can type the code in a way that will run without errors, I would appreciate it.

#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;

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());

}

int main() {

// Read in the training and test sets

std::vector training_set = read_ratings("training.csv");

std::vector test_set = read_test_cases("test.csv");

// Initialize a map to store the ratings given by each user

std::unordered_map> user_ratings;

for (const auto& rating : training_set) {

user_ratings[rating.user_id].push_back(rating);

}

// 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);

}

// Initialize a vector to store the predicted ratings

std::vector predicted_ratings;

for (const auto& test_case : test_set) {

// Calculate the predicted rating using either UBCF or IBCF

float rating;

if (use_ubcf) {

// Calculate the predicted rating using UBCF

rating = calculate_predicted_rating_using_ubcf(test_case.user_id, test_case.item_id, user_ratings, training_set);

} else {

// Calculate the predicted rating using IBCF

rating = calculate_predicted_rating_using_ibcf(test_case.user_id, test_case.item_id, item_ratings, training_set);

}

// Add the predicted rating to the vector

predicted_ratings.push_back({ test_case.id, rating });

}

// Calculate the RMSE between the predicted ratings and the actual ratings

float rmse = calculate_rmse(predicted_ratings, training_set);

std::cout << "RMSE: " << rmse << std::endl;

// Write the predicted ratings to a .csv file

std::ofstream output_file("predicted_ratings.csv");

output_file << "ID,Rating" << std::endl;

for (const auto& predicted_rating : predicted_ratings) {

output_file << predicted_rating.id << "," << predicted_rating.rating << std::endl;

}

output_file.close();

return 0;

}

SECOND CODE

// Calculates the predicted rating for an item using item-based collaborative filtering (IBCF)

float calculate_predicted_rating_using_ibcf(int user_id, int item_id, const std::unordered_map>& item_ratings, const std::vector& all_ratings) {

// Get the ratings received by the item from other users

std::vector item_user_ratings = item_ratings.at(item_id);

// Initialize variables to store the numerator and denominator of the prediction formula

float numerator = 0.0f;

float denominator = 0.0f;

// Calculate the numerator and denominator for each user who rated the item

for (const auto& rating : item_user_ratings) {

// Calculate the similarity between the current user and the user who rated the item

float similarity = calculate_similarity(user_id, rating.user_id, all_ratings);

numerator += similarity * rating.rating;

denominator += similarity;

}

// Return the predicted rating

return numerator / denominator;

}