-
-
Save TurRil/6883f26678e8ad5fdba8924ef2641bbf to your computer and use it in GitHub Desktop.
This file contains 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
| my_kmeans <- function(data, k, n_iterations) { | |
| # Helper function for euclidean distance | |
| euclidean_distance <- function(p1, p2) { | |
| dist <- sqrt(sum((p1-p2)^2)) | |
| return(dist) | |
| } | |
| # Initialize centers randomly | |
| centers <- df[sample(nrow(df), k, replace = FALSE), ] | |
| # Perform n iterations | |
| iteration <- 1 | |
| while(iteration < n_iterations) { | |
| # Calculate distance of each point to each center | |
| distances <- matrix(Inf, nrow = nrow(df), ncol = k) | |
| for (i in seq_len(nrow(df))) { | |
| for (j in seq_len(k)) { | |
| distances[i, j] <- euclidean_distance(df[i, ], centers[j, ]) | |
| } | |
| } | |
| # Assign each point to the closest center | |
| cluster_id <- apply(distances, 1, which.min) | |
| # Calculate new centers | |
| for (i in seq_len(k)) { | |
| this_cluster <- df[cluster_id == i,] | |
| centers[k, ] <- colMeans(this_cluster) | |
| } | |
| iteration <- iteration + 1 | |
| } | |
| cluster_id | |
| } |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment