Random initialization

ex7/ex7.m

@@ -86,6 +86,7 @@ max_iters = 10;
 % settings them to be random examples (as can be seen in
 % kMeansInitCentroids).
 initial_centroids = [3 3; 6 2; 8 5];
+%initial_centroids = kMeansInitCentroids(X, K);
 
 % Run K-Means algorithm. The 'true' at the end tells our function to plot
 % the progress of K-Means

ex7/kMeansInitCentroids.m

@@ -1,5 +1,5 @@
 function centroids = kMeansInitCentroids(X, K)
-%KMEANSINITCENTROIDS This function initializes K centroids that are to be 
+%KMEANSINITCENTROIDS This function initializes K centroids that are to be
 %used in K-Means on the dataset X
 %   centroids = KMEANSINITCENTROIDS(X, K) returns K initial centroids to be
 %   used with the K-Means on the dataset X
@@ -13,12 +13,10 @@ centroids = zeros(K, size(X, 2));
 %               the dataset X
 %
 
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+% Randomly reorder the indices of examples
+randidx = randperm(size(X, 1));
+% Take the first K examples as centroids
+centroids = X(randidx(1:K), :);
 
 % =============================================================