Moving course1 to course1 subdir.

machine_learning/course1/mlclass-ex2-008/mlclass-ex2/costFunctionReg.m

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+function [J, grad] = costFunctionReg(theta, X, y, lambda)
+%COSTFUNCTIONREG Compute cost and gradient for logistic regression with regularization
+%   J = COSTFUNCTIONREG(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
+
+
+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;
+
+% =============================================================
+
+end