def __top_n_movies_for_user_id(self, knn_dict):
# compute the predicted ratings of user_id for each movie, and select top
# n movies which have got higher prediction ratings.
mat = self.__data.um_mat
n_cols = self.__data.n_cols
mat_t = np.matrix.transpose(mat)
m = 0
m_dict = {}
for movie in mat_t:
pr = 0.0
for u, v in knn_dict:
pr += movie[u] * v
m_dict[m] = pr
m += 1
top_n_list = compute.get_top_n_movies(m_dict, mat, self.__data.user_id, self.__data.top_n)
return top_n_list
def __top_n_movies_for_user_id(self, knn_list):
# compute the frequency count of each movie for top k nearest neighbors
mat = self.__data.um_mat
n_cols = self.__data.n_cols
movie_arr = np.zeros(n_cols)
for k in knn_list:
movie_arr = np.add(movie_arr, mat[k])
# create a dictionary from movie frequency count array
m_dict = {}
for i in range(0, n_cols):
m_dict[i] = movie_arr[i]
# select top n movies for users whose frequency counts is higher
top_n_list = compute.get_top_n_movies(m_dict, mat, self.__data.user_id, \
self.__data.top_n)
return top_n_list
def __top_n_movies_for_user_id(self, knn_list):
# compute the frequency count of each movie for top k nearest neighbors
mat = self.__data.um_mat
n_cols = self.__data.n_cols
movie_arr = np.zeros(n_cols)
for k in knn_list:
movie_arr = np.add(movie_arr, mat[k])
# create a dictionary from movie frequency count array
m_dict = {}
for i in range(0, n_cols):
m_dict[i] = movie_arr[i]
# select top n movies for users whose frequency counts is higher
top_n_list = compute.get_top_n_movies(m_dict, mat, self.__data.user_id, \
self.__data.top_n)
return top_n_list