# reduce方法
def reduce(user_num, time_num, poi_num):
res = [[[0 for i in range(poi_num)] for j in range(time_num)]
for k in range(user_num)]
for i in range(user_num):
for j in range(time_num):
for k in range(poi_num):
file_name = str(i) + "_" + str(j) + "_" + str(k) + ".db"
file_path = "data/step_two/" + file_name
print file_path
tensor_tensor_db = open(file_path, 'r')
tensor_tensor = pickle.load(tensor_tensor_db)
res = three_order_tensor_add(res, tensor_tensor)
return res
if __name__ == '__main__':
# beijing = (39.433333, 41.05, 115.416667, 117.5)
# haidian = (39.883333, 40.15, 116.05, 116.383333)
region = (39.883333, 40.05, 116.05, 116.383333)
filter_count = 600
zero_adjustment = True
time_num = settings.TIME_SLICE
data, axis_users, axis_pois, check_data = init_data(region, filter_count)
user_num = len(axis_users)
poi_num = len(axis_pois)
map(user_num, time_num, poi_num)
for k in range(poi_num):
res_tensor[i][j][k] /= sum
elif strategy == "all":
res_tensor = three_tensor_hadarmard(1/three_order_tensor_first_norm(res_tensor), res_tensor)
else:
raise
return res_tensor
if __name__ == '__main__':
# beijing = (39.433333, 41.05, 115.416667, 117.5)
# haidian = (39.883333, 40.15, 116.05, 116.383333)
region = (39.883333, 40.05, 116.05, 116.383333)
filter_count = 600
data, axis_users, axis_pois = init_data(region, filter_count)
for key in data.keys():
print "用户" + str(key) + "序列为" + str(data[key])
tensor_list = {}
poi_num = len(axis_pois)
for index in range(len(data.keys())):
temp = data[index]
tensor_list[index] = build_fouth_order_transition_tensor(temp, poi_num)
for user_index in range(len(axis_users)):
print sparsity(tensor_list[user_index])
print check_fourth_order_transition_tensor(tensor_list[user_index])
# 等价关系
print "transition: ", sparsity(build_fouth_order_transition_tensor(data[1], poi_num))
for j in range(time_num):
for k in range(poi_num):
tensor[i][j][k] /= sum
return tensor
if __name__ == '__main__':
# beijing = (39.433333, 41.05, 115.416667, 117.5)
# haidian = (39.883333, 40.15, 116.05, 116.383333)
region = (39.883333, 40.05, 116.05, 116.383333)
filter_count = 600
alpha = 0.8
alpha_shift = 0.1
data, axis_users, axis_pois, check_data = init_data(region, filter_count)
user_num = len(axis_users)
time_num = settings.TIME_SLICE
poi_num = len(axis_pois)
transition_tensor = mtt(data, user_num, poi_num)
transition_tensor2 = inreducible_tensor(transition_tensor, user_num, time_num, poi_num, alpha)
transition_tensor3 = mtt(data, user_num, poi_num, zero_adjustment=False)
# equal_all_sum_one: equal
# init_tensor1 = [[[1/(poi_num * time_num * user_num) for i in range(poi_num)] for j in range(time_num)] for k in range(user_num)]
# # random_all_sum_one: no zero element
# temp_tensor = [[[random.choice([1, 2, 3, 100]) for i in range(poi_num)] for j in range(time_num)] for k in range(user_num)]
# init_tensor2 = three_tensor_hadarmard(1/three_order_tensor_first_norm(temp_tensor), temp_tensor)
#
# init_tensor3 = [[[0 for i in range(poi_num)] for j in range(time_num)] for k in range(user_num)]
res_tensor[i][j][k] /= sum
elif strategy == "all":
res_tensor = three_tensor_hadarmard(
1 / three_order_tensor_first_norm(res_tensor), res_tensor)
else:
raise
return res_tensor
if __name__ == '__main__':
# beijing = (39.433333, 41.05, 115.416667, 117.5)
# haidian = (39.883333, 40.15, 116.05, 116.383333)
region = (39.883333, 40.05, 116.05, 116.383333)
filter_count = 600
data, axis_users, axis_pois = init_data(region, filter_count)
for key in data.keys():
print "用户" + str(key) + "序列为" + str(data[key])
tensor_list = {}
poi_num = len(axis_pois)
for index in range(len(data.keys())):
temp = data[index]
tensor_list[index] = build_fouth_order_transition_tensor(temp, poi_num)
for user_index in range(len(axis_users)):
print sparsity(tensor_list[user_index])
print check_fourth_order_transition_tensor(tensor_list[user_index])
# 等价关系
print "transition: ", sparsity(