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Diffstat (limited to 'RModel/main.r')
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1 files changed, 107 insertions, 0 deletions
diff --git a/RModel/main.r b/RModel/main.r new file mode 100755 index 0000000..4279252 --- /dev/null +++ b/RModel/main.r @@ -0,0 +1,107 @@ +#!/usr/bin/env Rscript + +my_kmeans <- function(data, k, max_iters = 1000) { + data <- as.matrix(na.omit(data)) + n <- nrow(data) + p <- ncol(data) + + centroids <- data[sample(n, k), , drop = FALSE] + rownames(centroids) <- 1:k + + clusters <- integer(n) + prev_centroids <- matrix(0, nrow = k, ncol = p) + iter <- 0 + converged <- FALSE + + while (iter < max_iters && !converged) { + distances <- matrix(0, nrow = n, ncol = k) + + # find distance of cluster centers to a each point + for (i in 1:k) { + # sweep by cols + diff <- sweep(data, 2, centroids[i, ], "-") + distances[, i] <- sqrt(rowSums(diff^2)) + } + + # assign to closest centroid by rows + clusters <- apply(distances, 1, which.min) + + prev_centroids <- centroids + + for (i in 1:k) { + if (sum(clusters == i) > 0) { + centroids[i, ] <- colMeans(data[clusters == i, , drop = FALSE]) + } + } + + converged <- sqrt(sum((centroids - prev_centroids)^2)) < 1e-4 + + iter <- iter + 1 + } + + wss <- 0 + for (i in 1:k) { + cluster_points <- data[clusters == i, , drop = FALSE] + if (nrow(cluster_points) > 0) { + cluster_center <- centroids[i, ] + differences <- sweep(cluster_points, 2, cluster_center, "-") + wss <- wss + sum(differences^2) + } + } + + return(list( + clusters = clusters, + centers = centroids, + withinss = wss, + iter = iter, + converged = converged + )) +} + + +load("income_elec_state.rdata") + +head(income_elec_state) + +income_elec_state <- log10(income_elec_state) +income_elec_state <- income_elec_state[income_elec_state$elec > 2.83, ] + +k <- 3 +km <- my_kmeans(income_elec_state, k) + +km_centers <- data.frame(km$centers) +head(km_centers) + +if (!require(ggplot2)) install.packages("ggplot2", repos = "https://cran.r-project.org/") +library(ggplot2) + +ggplot( + data = income_elec_state, + mapping = aes(x = income, y = elec, color = factor(km$cluster)), +) + + labs(x = "income", y = "electricity usage") + + geom_point(shape = 1) + + geom_point( + data = km_centers, + mapping = aes( + x = income, + y = elec, + color = factor(rownames(km_centers)), + label = NULL + ), + shape = 13, + size = 4 + ) + + +wss <- NULL +range <- 1:10 +for (i in range) { + res <- my_kmeans(income_elec_state, i) + wss <- c(wss, res$withinss) +} +wss_df <- data.frame(wss) +ggplot(wss_df, aes(x = range, y = wss)) + + geom_path() + + geom_point() + + scale_x_continuous(breaks = range)
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