def map(distribution_tensor, user_num, time_num, poi_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, 'rw')
tensor_tensor = pickle.load(tensor_tensor_db)
res = three_tensor_hadarmard(distribution_tensor[i][j][k], tensor_tensor)
pickle.dump(res, tensor_tensor_db)
tensor_tensor_db.close()
# init_tensor6 = user_slice_sum_one(temp_tensor)
# res1, iterator_values1 = tensor_three_mode_product(transition_tensor, init_tensor1)
# res2, iterator_values2 = tensor_three_mode_product(transition_tensor, init_tensor2)
# res3, iterator_values3 = tensor_three_mode_product(transition_tensor, init_tensor3)
#
# print analysis_eigen_tensor(init_tensor1)
# print analysis_eigen_tensor(init_tensor2)
# print analysis_eigen_tensor(init_tensor3)
#
# print analysis_eigen_tensor(res1)
# print analysis_eigen_tensor(res2)
# print analysis_eigen_tensor(res3)
temp_tensor = [[[random.choice([0, 1, 1000]) for i in range(poi_num)] for j in range(time_num)] for k in range(user_num)]
temp_tensor = three_tensor_hadarmard(1/three_order_tensor_first_norm(temp_tensor), temp_tensor)
print "初始张量性质:\n"
print analysis_eigen_tensor(temp_tensor, "user")
print analysis_eigen_tensor(temp_tensor, "time")
print analysis_eigen_tensor(temp_tensor, "poi")
print "\n"
temp_tensor1 = temp_tensor[:]
temp_tensor2 = temp_tensor[:]
temp_tensor3 = temp_tensor[:]
temp_tensor4 = temp_tensor[:]
res1, iterator_values1 = tensor_three_mode_product(transition_tensor, temp_tensor1)
res2, iterator_values2 = tensor_three_mode_product(transition_tensor2, temp_tensor2)
# res3, iterator_values3 = shifted_tensor_three_mode_product(transition_tensor3, temp_tensor3, alpha_shift)
# res1, iterator_values1 = tensor_three_mode_product(transition_tensor, init_tensor1)
# res2, iterator_values2 = tensor_three_mode_product(transition_tensor, init_tensor2)
# res3, iterator_values3 = tensor_three_mode_product(transition_tensor, init_tensor3)
#
# print analysis_eigen_tensor(init_tensor1)
# print analysis_eigen_tensor(init_tensor2)
# print analysis_eigen_tensor(init_tensor3)
#
# print analysis_eigen_tensor(res1)
# print analysis_eigen_tensor(res2)
# print analysis_eigen_tensor(res3)
temp_tensor = [[[random.choice([0, 1, 1000]) for i in range(poi_num)]
for j in range(time_num)] for k in range(user_num)]
temp_tensor = three_tensor_hadarmard(
1 / three_order_tensor_first_norm(temp_tensor), temp_tensor)
print "初始张量性质:\n"
print analysis_eigen_tensor(temp_tensor, "user")
print analysis_eigen_tensor(temp_tensor, "time")
print analysis_eigen_tensor(temp_tensor, "poi")
print "\n"
temp_tensor1 = temp_tensor[:]
temp_tensor2 = temp_tensor[:]
temp_tensor3 = temp_tensor[:]
temp_tensor4 = temp_tensor[:]
res1, iterator_values1 = tensor_three_mode_product(transition_tensor,
temp_tensor1)
res2, iterator_values2 = tensor_three_mode_product(transition_tensor2,
# res1, iterator_values1 = tensor_three_mode_product(transition_tensor, init_tensor1)
# res2, iterator_values2 = tensor_three_mode_product(transition_tensor, init_tensor2)
# res3, iterator_values3 = tensor_three_mode_product(transition_tensor, init_tensor3)
#
# print analysis_eigen_tensor(init_tensor1)
# print analysis_eigen_tensor(init_tensor2)
# print analysis_eigen_tensor(init_tensor3)
#
# print analysis_eigen_tensor(res1)
# print analysis_eigen_tensor(res2)
# print analysis_eigen_tensor(res3)
temp_tensor_all_equal = [[[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)]
temp_tensor = [[[random.choice([0, 1, 2, 3, 4]) for i in range(poi_num)] for j in range(time_num)] for k in range(user_num)]
temp_tensor_all_ramdom = three_tensor_hadarmard(1/three_order_tensor_first_norm(temp_tensor), temp_tensor)
temp_tensor_user_equal = [[[1/(poi_num * time_num) for i in range(poi_num)] for j in range(time_num)] for k in range(user_num)]
temp_tensor = [[[random.choice([0, 1, 2, 3, 4]) for i in range(poi_num)] for j in range(time_num)] for k in range(user_num)]
temp_tensor_user_ramdom = user_slice_sum_one(temp_tensor)
print check_six_order_transition_tensor(transition_tensor)
# print "初始张量性质:\n"
# print analysis_eigen_tensor(temp_tensor, "user")
# print analysis_eigen_tensor(temp_tensor, "time")
# print analysis_eigen_tensor(temp_tensor, "poi")
# print "\n"
res1, iterator_values1 = tensor_three_mode_product(transition_tensor, temp_tensor_all_equal)
res2, iterator_values2 = tensor_three_mode_product(transition_tensor, temp_tensor_all_ramdom)
