Moving course1 to course1 subdir.

machine_learning/course1/mlclass-ex3-008/mlclass-ex3/lrCostFunction.m

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+function [J, grad] = lrCostFunction(theta, X, y, lambda)
+%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. 
+
+% Initialize some useful values
+m = length(y); % number of training examples
+
+% You need to return the following variables correctly 
+J = 0;
+grad = zeros(size(theta));
+
+% ====================== YOUR CODE HERE ======================
+% Instructions: Compute the cost of a particular choice of theta.
+%               You should set J to the cost.
+%               Compute the partial derivatives and set grad to the partial
+%               derivatives of the cost w.r.t. each parameter in theta
+%
+% Hint: The computation of the cost function and gradients can be
+%       efficiently vectorized. For example, consider the computation
+%
+%           sigmoid(X * 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. 
+%
+% 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  
+%           grad = grad + YOUR_CODE_HERE (using the temp variable)
+%
+
+
+h = sigmoid( X*theta );
+o = ones(size(y));
+reg = theta;
+reg(1) = 0;
+J = sum( y .* log(h) + (o-y) .* log(o-h) ) / (-m) + (lambda/(2*m))*sum(reg.^2);
+grad = (X'*(h - y)) / m + (lambda/m) * reg;
+
+
+
+
+
+
+
+
+% =============================================================
+
+grad = grad(:);
+
+end