train_MR.m

fprintf('Loading movie ratings dataset.\n\n');

%  Load data
load ('Y_matrix1.mat');
Y=Y(1:500,1:500);
no_movies = size(Y,1);
no_users = size(Y, 2);
fprintf("no of movies = %d\n",size(Y,1));
fprintf("no of users = %d\n",size(Y,2))

R=Y>0;

fprintf("no of rows in R = %d\n",size(R,1));
fprintf("no of colms in R = %d\n",size(R,2))

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');
pause;

my_ratings = zeros(no_movies, 1);

my_ratings(1) = 4;
my_ratings(98) = 2;
my_ratings(7) = 3;
my_ratings(12)= 5;
my_ratings(54) = 4;
my_ratings(64)= 5;
my_ratings(66)= 3;
my_ratings(69) = 5;
my_ratings(183) = 4;
my_ratings(226) = 5;
my_ratings(355)= 5;

Y = [my_ratings Y];
R = [(my_ratings ~= 0) R];

%  Normalize Ratings
[Ynorm, Ymean] = normalizeRatings(Y, R);
fprintf("Ymean = %f",Ymean)
fprintf("normalization done \n" );
%  Useful Values
num_users = size(Y, 2);
num_movies = size(Y, 1);
num_features = 19;

% Set Initial Parameters (Theta, X)
X = randn(num_movies, num_features);
Theta = randn(num_users, num_features);

initial_parameters = [X(:); Theta(:)];

% Set options for fmincg
options = optimset('GradObj', 'on', 'MaxIter', 100);

% Set Regularization
lambda = 2;

theta = fmincg (@(t)(cofiCostFunc1(t, Ynorm, R, num_users, num_movies, ...
                                num_features, lambda)), ...
                initial_parameters, options);

% Unfold the returned theta back into U and W
X = reshape(theta(1:num_movies*num_features), num_movies, num_features);
Theta = reshape(theta(num_movies*num_features+1:end), ...
                num_users, num_features);

fprintf('Recommender system learning completed.\n');

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

%% ================== Part 8: Recommendation for you ====================
%  After training the model, you can now make recommendations by computing
%  the predictions matrix.
%

p = X * Theta';
my_predictions = p(:,1) + Ymean;

movieList = loadMovieList();

[r, ix] = sort(my_predictions, 'descend');
fprintf('\nTop recommendations for you:\n');
for i=1:10
    j = ix(i);
    fprintf('Predicting rating %.1f for movie %s\n', my_predictions(j), ...
            movieList{j});
end

fprintf('\n\nOriginal ratings provided:\n');
for i = 1:length(my_ratings)
    if my_ratings(i) > 0 
        fprintf('Rated %d for %s\n', my_ratings(i), ...
                 movieList{i});
    end
end

save X_mov_feat.mat X