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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
- %
-
- %sum(R .* (((X * Theta') .- Y) .^ 2)) 1*4 matrix
- J = 1 / 2 * sum(sum(R .* (((X * Theta') .- Y) .^ 2)));
- J = J + lambda / 2 * sum(sum(X .^ 2)) + lambda / 2 * sum(sum(Theta .^ 2));
-
- % (((X * Theta') .- Y) * Theta)
-
- X_grad = (R .* ((X * Theta') .- Y)) * Theta;
- X_grad = X_grad + lambda .* X;
- Theta_grad = (R .* ((X * Theta') .- Y))' * X;
- Theta_grad = Theta_grad + lambda .* Theta;
-
-
-
-
-
- % =============================================================
-
- grad = [X_grad(:); Theta_grad(:)];
-
- end
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