Implements means computation

machine-learning-ex7/ex7/computeCentroids.m

@@ -26,12 +26,20 @@ centroids = zeros(K, n);
 % Note: You can use a for-loop over the centroids to compute this.
 %
 
-
-
-
-
-
-
+% this could be vectorized but today is Christmas and I have family talking
+% loud everywhere around.
+for i = 1:K
+    temp = [];
+    for j = 1:m
+        if idx(j) == i
+            temp = [temp; X(j, :)];
+        end
+    end
+    
+    s = size(temp, 1);
+    centroids(i, :) = sum(temp)./s;
+    
+end
 
 % =============================================================
 

machine-learning-ex7/ex7/ex7.m

@@ -44,7 +44,7 @@ fprintf(' %d', idx(1:3));
 fprintf('\n(the closest centroids should be 1, 3, 2 respectively)\n');
 
 fprintf('Program paused. Press enter to continue.\n');
-pause;
+% pause;
 
 %% ===================== Part 2: Compute Means =========================
 %  After implementing the closest centroids function, you should now
@@ -63,7 +63,7 @@ fprintf('   [ 5.813503 2.633656 ]\n');
 fprintf('   [ 7.119387 3.616684 ]\n\n');
 
 fprintf('Program paused. Press enter to continue.\n');
-pause;
+% pause;
 
 
 %% =================== Part 3: K-Means Clustering ======================
@@ -93,7 +93,7 @@ initial_centroids = [3 3; 6 2; 8 5];
 fprintf('\nK-Means Done.\n\n');
 
 fprintf('Program paused. Press enter to continue.\n');
-pause;
+% pause;
 
 %% ============= Part 4: K-Means Clustering on Pixels ===============
 %  In this exercise, you will use K-Means to compress an image. To do this,