solution with one line

mlclass-ex1/gradientDescent.m

@@ -18,19 +18,10 @@
     %
 
 
-    x = X(:,2);
-    h = theta(1) + (theta(2)*x);
-
-    theta_zero = theta(1) - alpha * (1/m) * sum(h-y);
-    theta_one  = theta(2) - alpha * (1/m) * sum((h - y) .* x);
-
-    theta = [theta_zero; theta_one];
-    % ============================================================
-
-    % Save the cost J in every iteration
-    J_history(iter) = computeCost(X, y, theta);
-    % disp(J_history(iter));
+     %this is also possible in one line
+     theta = theta - alpha * (1/m) * sum((X * theta - y) .* X)';
+
 end
 
   disp(min(J_history));
-end
+end