65 lines
1.9 KiB
Mathematica
65 lines
1.9 KiB
Mathematica
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function [centroids, idx] = runkMeans(X, initial_centroids, ...
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max_iters, plot_progress)
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%RUNKMEANS runs the K-Means algorithm on data matrix X, where each row of X
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%is a single example
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% [centroids, idx] = RUNKMEANS(X, initial_centroids, max_iters, ...
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% plot_progress) runs the K-Means algorithm on data matrix X, where each
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% row of X is a single example. It uses initial_centroids used as the
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% initial centroids. max_iters specifies the total number of interactions
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% of K-Means to execute. plot_progress is a true/false flag that
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% indicates if the function should also plot its progress as the
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% learning happens. This is set to false by default. runkMeans returns
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% centroids, a Kxn matrix of the computed centroids and idx, a m x 1
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% vector of centroid assignments (i.e. each entry in range [1..K])
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%
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% Set default value for plot progress
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if ~exist('plot_progress', 'var') || isempty(plot_progress)
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plot_progress = false;
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end
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% Plot the data if we are plotting progress
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if plot_progress
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figure;
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hold on;
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end
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% Initialize values
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[m n] = size(X);
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K = size(initial_centroids, 1);
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centroids = initial_centroids;
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previous_centroids = centroids;
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idx = zeros(m, 1);
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% Run K-Means
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for i=1:max_iters
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% Output progress
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fprintf('K-Means iteration %d/%d...\n', i, max_iters);
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if exist('OCTAVE_VERSION')
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fflush(stdout);
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end
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% For each example in X, assign it to the closest centroid
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idx = findClosestCentroids(X, centroids);
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% Optionally, plot progress here
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if plot_progress
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plotProgresskMeans(X, centroids, previous_centroids, idx, K, i);
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previous_centroids = centroids;
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fprintf('Press enter to continue.\n');
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pause;
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end
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% Given the memberships, compute new centroids
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centroids = computeCentroids(X, idx, K);
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end
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% Hold off if we are plotting progress
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if plot_progress
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hold off;
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end
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end
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