%% 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');

pause;

%% ============ 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');

pause;

%% ============== 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');

pause;

%% ========= 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');

pause;

%% ======= 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;

%% ============== Part 6: Entering ratings for a new user ===============

% Before we will train the collaborative filtering model, we will first

% add ratings that correspond to a new user that we just observed. This

% part of the code will also allow you to put in your own ratings for the

% movies in our dataset!

%

movieList = loadMovieList();

% Initialize my ratings

my_ratings = zeros(1682, 1);

% Check the file movie_idx.txt for id of each movie in our dataset

% For example, Toy Story (1995) has ID 1, so to rate it "4", you can set

my_ratings(1) = 4;

% Or suppose did not enjoy Silence of the Lambs (1991), you can set

my_ratings(98) = 2;

% We have selected a few movies we liked / did not like and the ratings we

% gave are as follows:

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;

fprintf('\n\nNew user ratings:\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

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

pause;

%% ================== Part 7: Learning Movie Ratings ====================

% Now, you will train the collaborative filtering model on a movie rating

% dataset of 1682 movies and 943 users

%

fprintf('\nTraining collaborative filtering...\n');

% Load data

load('ex8_movies.mat');

% Y is a 1682x943 matrix, containing ratings (1-5) of 1682 movies by

% 943 users

%

% R is a 1682x943 matrix, where R(i,j) = 1 if and only if user j gave a

% rating to movie i

% Add our own ratings to the data matrix

Y = [my_ratings Y];

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

% Normalize Ratings

[Ynorm, Ymean] = normalizeRatings(Y, R);

% Useful Values

num_users = size(Y, 2);

num_movies = size(Y, 1);

num_features = 10;

% 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 = 10;

theta = fmincg (@(t)(cofiCostFunc(t, Y, 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