I noticed a few errors when I ran the code below. Can you correct the wrong parts and repost the code? Also, I would be happy if you could guide me on how to use the second code below in the first code.

Here is a minimal C++ code that reads in two .csv files, one for training and one for testing, and estimates the ratings for the items in the test set using either user-based collaborative filtering (UBCF) or item-based collaborative filtering (IBCF). The code also calculates the root mean squared error (RMSE) between the predicted ratings and the actual ratings (which are hidden in the test set). The RMSE should be less than 1.0 in order to meet the requirement. This code reads in the training and test sets, stores the ratings given by each user and received by each item in maps, and then uses either user-based collaborative filtering (UBCF) or item-based collaborative filtering (IBCF) to calculate the predicted ratings for the items in the test set. The code then calculates the root mean squared error (RMSE) between the predicted ratings and the actual ratings, and writes the predicted ratings to a .csv file.

#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;

}

Here are implementations of the calculate_predicted_rating_using_ubcf and calculate_predicted_rating_using_ibcf functions using user-based collaborative filtering (UBCF) and item-based collaborative filtering (IBCF), respectively:

(These functions both take in the user_id and item_id of the user and item for which the rating is being predicted, as well as the user_ratings map and the all_ratings vector. They use these inputs to calculate the predicted rating using UBCF or IBCF, respectively.)

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

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

// Get the ratings given by the user to other items

std::vector user_item_ratings = user_ratings.at(user_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 item rated by the user

for (const auto& rating : user_item_ratings) {

// Calculate the similarity between the current item and the item being rated

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

numerator += similarity * rating.rating;

denominator += similarity;

}

// Return the predicted rating

return numerator / denominator;

}

// 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;

}