gistfile1.txt

def predict(ratings, similarity, type='user'):
    
    if type == 'user':
        mean_user_rating = ratings.mean(axis=1).reshape(-1,1)
        #We use np.newaxis so that mean_user_rating has same format as ratings
        
        ratings_diff = (ratings - mean_user_rating)
        pred = mean_user_rating + similarity.dot(ratings_diff) / np.array([np.abs(similarity).sum(axis=1)]).T
    
    elif type == 'item':
        pred = ratings.dot(similarity) / np.array([np.abs(similarity).sum(axis=1)])
    
    return pred