Created
April 17, 2023 08:18
-
-
Save samionb/03e0fc30b03cd0bff184ca24b4b51b0a to your computer and use it in GitHub Desktop.
KMeans test
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| #include <iostream> | |
| #include <vector> | |
| #include <cstdlib> | |
| #include <ctime> | |
| #include <cassert> | |
| // KMeans function here | |
| int main() | |
| { | |
| // Set up random data | |
| const int num_points = 100; | |
| const int num_dimensions = 2; | |
| std::vector<std::vector<float>> data(num_points, std::vector<float>(num_dimensions)); | |
| std::srand(std::time(nullptr)); | |
| for (int i = 0; i < num_points; i++) { | |
| for (int j = 0; j < num_dimensions; j++) { | |
| data[i][j] = std::rand() / static_cast<float>(RAND_MAX); | |
| } | |
| } | |
| // Run KMeans | |
| const int k = 3; | |
| const int max_iterations = 100; | |
| const auto centroids = KMeans(data, k, max_iterations); | |
| // Ensure that the number of centroids is correct | |
| assert(centroids.size() == k); | |
| // Ensure that each centroid has the correct number of dimensions | |
| for (const auto& centroid : centroids) { | |
| assert(centroid.size() == num_dimensions); | |
| } | |
| // Ensure that each data point is assigned to the correct cluster | |
| for (int i = 0; i < num_points; i++) { | |
| int closest_cluster = 0; | |
| float min_distance = std::numeric_limits<float>::max(); | |
| for (int j = 0; j < k; j++) { | |
| float distance = 0.0f; | |
| for (int d = 0; d < num_dimensions; d++) { | |
| distance += std::pow(data[i][d] - centroids[j][d], 2); | |
| } | |
| distance = std::sqrt(distance); | |
| if (distance < min_distance) { | |
| min_distance = distance; | |
| closest_cluster = j; | |
| } | |
| } | |
| assert(closest_cluster == cluster_assignment[i]); | |
| } | |
| std::cout << "All tests passed!\n"; | |
| return 0; | |
| } |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment