Vectorized regularized logistic regression, again

ex3/lrCostFunction.m

@@ -27,6 +27,10 @@ grad = zeros(size(theta));
 %       prediction for that example. You can make use of this to vectorize
 %       the cost function and gradient computations.
 %
+
+J = 1/m * (-y'*log(sigmoid(X*theta)) - (1-y)'*log(1-sigmoid(X*theta))) ...
+    + lambda/(2*m) * theta(2:end)' * theta(2:end);
+
 % Hint: When computing the gradient of the regularized cost function,
 %       there're many possible vectorized solutions, but one solution
 %       looks like:
@@ -36,14 +40,8 @@ grad = zeros(size(theta));
 %           grad = grad + YOUR_CODE_HERE (using the temp variable)
 %
 
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+regularization_term = lambda/m * vertcat([0], theta(2:end));
+grad = 1/m * X' * (sigmoid(X*theta) - y) + regularization_term;
 
 % =============================================================