# mlclass / exercise-8 / octave / cofiCostFunc.m

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73``` ```function [J, grad] = cofiCostFunc(params, Y, R, num_users, num_movies, ... num_features, lambda) %COFICOSTFUNC Collaborative filtering cost function % [J, grad] = COFICOSTFUNC(params, Y, R, num_users, num_movies, ... % num_features, lambda) returns the cost and gradient for the % collaborative filtering problem. % % Unfold the U and W matrices from params X = reshape(params(1:num_movies*num_features), num_movies, num_features); Theta = reshape(params(num_movies*num_features+1:end), ... num_users, num_features); % You need to return the following values correctly J = 0; X_grad = zeros(size(X)); Theta_grad = zeros(size(Theta)); % ====================== YOUR CODE HERE ====================== % Instructions: Compute the cost function and gradient for collaborative % filtering. Concretely, you should first implement the cost % function (without regularization) and make sure it is % matches our costs. After that, you should implement the % gradient and use the checkCostFunction routine to check % that the gradient is correct. Finally, you should implement % regularization. % % Notes: X - num_movies x num_features matrix of movie features % Theta - num_users x num_features matrix of user features % Y - num_movies x num_users matrix of user ratings of movies % R - num_movies x num_users matrix, where R(i, j) = 1 if the % i-th movie was rated by the j-th user % % You should set the following variables correctly: % % X_grad - num_movies x num_features matrix, containing the % partial derivatives w.r.t. to each element of X % Theta_grad - num_users x num_features matrix, containing the % partial derivatives w.r.t. to each element of Theta % J = sum(sum(((X * Theta' - Y) .^2) .* R /2)) + lambda * (sum(sum(Theta .^ 2)) + sum(sum(X .^ 2))) / 2; for i=1:size(X,1) idx = find(R(i,:)==1); Theta_t = Theta(idx,:); X_grad(i,:) = (X(i,:) * Theta_t' - Y(i,idx)) * Theta_t + lambda * X(i,:); end for i = 1:size(Theta,1) idx = find(R(:,i)==1); Theta_grad(i,:) = (X(idx,:)' * (X(idx,:) * Theta(i,:)' - Y(idx,i)))' + lambda * Theta(i,:); end % ============================================================= grad = [X_grad(:); Theta_grad(:)]; end ```