coursera/machine learning/machine-learning-ex8/ex8/myex8.m

%% Machine Learning Online Class
%  Exercise 8 | Anomaly Detection and Collaborative Filtering
%
%  Instructions
%  ------------
%
%  This file contains code that helps you get started on the
%  exercise. You will need to complete the following functions:
%
%     estimateGaussian.m
%     selectThreshold.m
%     cofiCostFunc.m
%
%  For this exercise, you will not need to change any code in this file,
%  or any other files other than those mentioned above.
%

%% =============== Part 1: Loading movie ratings dataset ================
%  You will start by loading the movie ratings dataset to understand the
%  structure of the data.
%  
fprintf('Loading movie ratings dataset.\n\n');

%  Load data
load ('ex8_movies.mat');

%  Y is a 1682x943 matrix, containing ratings (1-5) of 1682 movies on 
%  943 users
%
%  R is a 1682x943 matrix, where R(i,j) = 1 if and only if user j gave a
%  rating to movie i

%  From the matrix, we can compute statistics like average rating.
fprintf('Average rating for movie 1 (Toy Story): %f / 5\n\n', ...
        mean(Y(1, R(1, :))));

%  We can "visualize" the ratings matrix by plotting it with imagesc
imagesc(Y);
ylabel('Movies');
xlabel('Users');

fprintf('\nProgram paused. Press enter to continue.\n');


%% ============ Part 2: Collaborative Filtering Cost Function ===========
%  You will now implement the cost function for collaborative filtering.
%  To help you debug your cost function, we have included set of weights
%  that we trained on that. Specifically, you should complete the code in 
%  cofiCostFunc.m to return J.

%  Load pre-trained weights (X, Theta, num_users, num_movies, num_features)
load ('ex8_movieParams.mat');

%  Reduce the data set size so that this runs faster
num_users = 4; num_movies = 5; num_features = 3;
X = X(1:num_movies, 1:num_features);
Theta = Theta(1:num_users, 1:num_features);
Y = Y(1:num_movies, 1:num_users);
R = R(1:num_movies, 1:num_users);

%  Evaluate cost function
J = cofiCostFunc([X(:) ; Theta(:)], Y, R, num_users, num_movies, ...
               num_features, 0);
           
fprintf(['Cost at loaded parameters: %f '...
         '\n(this value should be about 22.22)\n'], J);

fprintf('\nProgram paused. Press enter to continue.\n');



%% ============== Part 3: Collaborative Filtering Gradient ==============
%  Once your cost function matches up with ours, you should now implement 
%  the collaborative filtering gradient function. Specifically, you should 
%  complete the code in cofiCostFunc.m to return the grad argument.
%  
fprintf('\nChecking Gradients (without regularization) ... \n');

%  Check gradients by running checkNNGradients
checkCostFunction;

fprintf('\nProgram paused. Press enter to continue.\n');




%% ========= Part 4: Collaborative Filtering Cost Regularization ========
%  Now, you should implement regularization for the cost function for 
%  collaborative filtering. You can implement it by adding the cost of
%  regularization to the original cost computation.
%  

%  Evaluate cost function
J = cofiCostFunc([X(:) ; Theta(:)], Y, R, num_users, num_movies, ...
               num_features, 1.5);
           
fprintf(['Cost at loaded parameters (lambda = 1.5): %f '...
         '\n(this value should be about 31.34)\n'], J);

fprintf('\nProgram paused. Press enter to continue.\n');

%% ======= Part 5: Collaborative Filtering Gradient Regularization ======
%  Once your cost matches up with ours, you should proceed to implement 
%  regularization for the gradient. 
%

%  
fprintf('\nChecking Gradients (with regularization) ... \n');

%  Check gradients by running checkNNGradients
checkCostFunction(1.5);

fprintf('\nProgram paused. Press enter to continue.\n');
pause;