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- 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(0.5 * (((X * Theta' - Y) .* R) .^ 2))) + 0.5 * lambda * (sum(sum(Theta .^ 2)) + sum(sum(X .^ 2)));
- for i = 1:size(R, 1)
- idx = find(R(i,:) == 1);
- Theta_tmp = Theta(idx, :);
- Y_tmp = Y(i, idx);
- X_grad(i,:) = (X(i,:) * Theta_tmp' - Y_tmp) * Theta_tmp + lambda * X(i,:);
- end
- for j = 1:size(R, 2)
- idx = find(R(:,j) == 1);
- X_tmp = X(idx,:);
- Y_tmp = Y(idx,j);
- Theta_grad(j,:) = (Theta(j,:) * X_tmp' - Y_tmp') * X_tmp + lambda * Theta(j,:);
- end
- % =============================================================
- grad = [X_grad(:); Theta_grad(:)];
- end
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