Moving course1 to course1 subdir.

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

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+function [J, grad] = costFunction(theta, X, y)
+%COSTFUNCTION Compute cost and gradient for logistic regression
+%   J = COSTFUNCTION(theta, X, y) computes the cost of using theta as the
+%   parameter for 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
+%
+% Note: grad should have the same dimensions as theta
+%
+
+h = sigmoid( X*theta );
+o = ones(size(y));
+J = sum( y .* log(h) + (o-y) .* log(o-h) ) / (-m);
+grad = (X'*(h - y)) / m;
+
+% =============================================================
+
+end