Moving course1 to course1 subdir.

machine_learning/course1/mlclass-ex5-008/mlclass-ex5/linearRegCostFunction.m

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+function [J, grad] = linearRegCostFunction(X, y, theta, lambda)
+%LINEARREGCOSTFUNCTION Compute cost and gradient for regularized linear 
+%regression with multiple variables
+%   [J, grad] = LINEARREGCOSTFUNCTION(X, y, theta, lambda) computes the 
+%   cost of using theta as the parameter for linear regression to fit the 
+%   data points in X and y. Returns the cost in J and the gradient in grad
+
+% 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 and gradient of regularized linear 
+%               regression for a particular choice of theta.
+%
+%               You should set J to the cost and grad to the gradient.
+%
+
+reg = theta;
+reg(1) = 0;
+
+J = sum( (X*theta - y) .^ 2 ) / (2*m) + (lambda/(2*m))*sum(reg.^2);
+grad = (X' * (X*theta-y) / m) + (lambda/m) * reg;
+
+
+% =========================================================================
+
+grad = grad(:);
+
+end