Vectorized regularized logistic regression, again

ex3/lrCostFunction.m

@@ -1,14 +1,14 @@
 function [J, grad] = lrCostFunction(theta, X, y, lambda)
-%LRCOSTFUNCTION Compute cost and gradient for logistic regression with 
+%LRCOSTFUNCTION Compute cost and gradient for logistic regression with
 %regularization
 %   J = LRCOSTFUNCTION(theta, X, y, lambda) computes the cost of using
 %   theta as the parameter for regularized logistic regression and the
-%   gradient of the cost w.r.t. to the parameters. 
+%   gradient of the cost w.r.t. to the parameters.
 
 % Initialize some useful values
 m = length(y); % number of training examples
 
-% You need to return the following variables correctly 
+% You need to return the following variables correctly
 J = 0;
 grad = zeros(size(theta));
 
@@ -25,25 +25,23 @@ grad = zeros(size(theta));
 %
 %       Each row of the resulting matrix will contain the value of the
 %       prediction for that example. You can make use of this to vectorize
-%       the cost function and gradient computations. 
+%       the cost function and gradient computations.
 %
-% Hint: When computing the gradient of the regularized cost function, 
+
+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:
 %           grad = (unregularized gradient for logistic regression)
-%           temp = theta; 
-%           temp(1) = 0;   % because we don't add anything for j = 0  
+%           temp = theta;
+%           temp(1) = 0;   % because we don't add anything for j = 0
 %           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;
 
 % =============================================================