吴恩达Coursera, 机器学习专项课程, Machine Learning：Unsupervised Learning, Recommenders, Reinforcement Learning第...

吴恩达Coursera, 机器学习专项课程, Machine Learning：Unsupervised Learning, Recommenders, Reinforcement Learning第二周所有jupyter notebook文件1：

吴恩达Coursera, 机器学习专项课程, Machine Learning：Unsupervised Learning, Recommenders, Reinforcement Learning第二周所有jupyter notebook文件(包括实验室练习文件)1

本次作业

Exercise 1

# 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 ###
    error = 0.5 * (np.square(X @ W.T+b - Y) * R).sum()
    reg1 = 0.5 * lambda_ * np.square(X).sum()
    reg2 = 0.5 * lambda_ * np.square(W).sum()
    J = error + reg1 + reg2      
    
    ### END CODE HERE ### 

    return J