# mlclass / exercise-7 / octave / runkMeans.m

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64``` ```function [centroids, idx] = runkMeans(X, initial_centroids, ... max_iters, plot_progress) %RUNKMEANS runs the K-Means algorithm on data matrix X, where each row of X %is a single example % [centroids, idx] = RUNKMEANS(X, initial_centroids, max_iters, ... % plot_progress) runs the K-Means algorithm on data matrix X, where each % row of X is a single example. It uses initial_centroids used as the % initial centroids. max_iters specifies the total number of interactions % of K-Means to execute. plot_progress is a true/false flag that % indicates if the function should also plot its progress as the % learning happens. This is set to false by default. runkMeans returns % centroids, a Kxn matrix of the computed centroids and idx, a m x 1 % vector of centroid assignments (i.e. each entry in range [1..K]) % % Set default value for plot progress if ~exist('plot_progress', 'var') || isempty(plot_progress) plot_progress = false; end % Plot the data if we are plotting progress if plot_progress figure; hold on; end % Initialize values [m n] = size(X); K = size(initial_centroids, 1); centroids = initial_centroids; previous_centroids = centroids; idx = zeros(m, 1); % Run K-Means for i=1:max_iters % Output progress fprintf('K-Means iteration %d/%d...\n', i, max_iters); if exist('OCTAVE_VERSION') fflush(stdout); end % For each example in X, assign it to the closest centroid idx = findClosestCentroids(X, centroids); % Optionally, plot progress here if plot_progress plotProgresskMeans(X, centroids, previous_centroids, idx, K, i); previous_centroids = centroids; fprintf('Press enter to continue.\n'); pause; end % Given the memberships, compute new centroids centroids = computeCentroids(X, idx, K); end % Hold off if we are plotting progress if plot_progress hold off; end end ```