Issue in Programming Assignment: Collaborative Filtering Recommender Systems

Course Q&A Machine Learning Specialization Unsupervised Learning, Recommenders, Reinforcement
1

I am facing issue in with regularization code . exactly where and how to put the with regularization code . I checked the hints also but not getting it. please anyone solve my problm.

GRADED FUNCTION: cofi_cost_func

UNQ_C1

def cofi_cost_func(X, W, b, Y, R, lambda_):
“”"
Returns the cost for the content-based filtering
Args:
X (ndarray (num_movies,num_features)): matrix of item features
W (ndarray (num_users,num_features)) : matrix of user parameters
b (ndarray (1, num_users) : vector of user parameters
Y (ndarray (num_movies,num_users) : matrix of user ratings of movies
R (ndarray (num_movies,num_users) : matrix, where R(i, j) = 1 if the i-th movies was rated by the j-th user
lambda_ (float): regularization parameter
Returns:
J (float) : Cost
“”"
nm, nu = Y.shape
J = 0
### START CODE HERE ###

for j in range(nu):
    w = W[j,:]
    b_j = b[0,j]
    for i in range(nm):
        x = X[i,:]
        y = Y[i,j]
        r = R[i,j]
        # J += np.square(r * (np.dot(w,x) + b_j - y ) ) ## without regularization
        # J += (lambda_/2) * (np.sum(np.square(W)) + np.sum(np.square(X))) ## with regularization
J = J/2 


### END CODE HERE ### 

return J
2

Hi @Shagun_jain, when implementing the non-vectorized implementation of this exercise, you must add the regularization term outside the for loop. Otherwise, you would not satisfy the correctness of the given cost function.

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To refactor the code, you need to fix the following line by adding the corresponding term.

J = J/2 + (lambda_/2) * (np.sum(np.square(W)) + np.sum(np.square(X)))

I hope you find this answer useful.

Cheers,

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3

@Enzo_Faliveni_Alzuet Thankyou for the help. Now I am able to resolve the issue .

1 Like