# ===================== Part 5: Collaborative Filtering Gradient Regularization ===================== # Once your cost matches up with ours, you should proceed to implement # regularization for the gradient. print('Checking Gradients (with regularization) ...') # Check gradients by running check_cost_function check_cost_function(1.5) input('Program paused. Press ENTER to continue') # ===================== 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! movie_list = load_movie_list() # Initialize my ratings my_ratings = np.zeros(len(movie_list)) # Check the file movie_ids.txt for id of each movie in our dataset # For example, Toy Story (1995) has ID 0, so to rate it "4", you can set my_ratings[0] = 4 # Or suppose did not enjoy Silence of the lambs (1991), you can set my_ratings[97] = 2 # We have selected a few movies we liked / did not like and the ratings we # gave are as follows: my_ratings[6] = 3 my_ratings[11] = 5
# print('Checking Gradients (with regularization) ...') # Check gradients by running check_cost_function cf.check_cost_function(1.5) input('Program paused. Press ENTER to continue') # ===================== 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! # movie_list = lm.load_movie_list() # Initialize my ratings my_ratings = np.zeros(len(movie_list)) # Check the file movie_ids.txt for id of each movie in our dataset # For example, Toy Story (1995) has ID 0, so to rate it "4", you can set my_ratings[0] = 4 # Or suppose did not enjoy Silence of the lambs (1991), you can set my_ratings[97] = 2 # We have selected a few movies we liked / did not like and the ratings we # gave are as follows: my_ratings[6] = 3 my_ratings[11] = 5
print('Cost at loaded parameters (lambda = 1.5): {} \n (this value should be \ about 31.34)'.format(J)) input('\nProgram paused. Press enter to continue.\n') # ======= Part 5: Collaborative Filtering Gradient Regularization ====== print('\nChecking Gradients (with regularization) ... ') # Check gradients by running checkNNGradients check_cost_function(1.5) input('\nProgram paused. Press enter to continue.\n') # ============== Part 6: Entering ratings for a new user =============== movie_list = load_movie_list() # Initialize my ratings my_ratings = np.zeros(1682) my_ratings[0] = 4 my_ratings[97] = 2 my_ratings[6] = 3 my_ratings[11] = 5 my_ratings[53] = 4 my_ratings[63] = 5 my_ratings[65] = 3 my_ratings[68] = 5 my_ratings[182] = 4 my_ratings[225] = 5