def mtt(data, user_num, poi_num, zero_adjustment=True):
print "共有" + str(user_num) + "个用户"
for key in data.keys():
print "用户" + str(key) + "序列为" + str(data[key])
correlation_matrix = get_correlation_matrix(data, user_num)
nor_cor_matrix = normalize(correlation_matrix)
print "归一化相关系数矩阵: ", nor_cor_matrix
transition_tensor = build_six_order_transition_tensor(data, poi_num, nor_cor_matrix, zero_adjustment)
print "转移张量非零元素占比:", sparsity(transition_tensor)
print "转移张量是否满足随机性:", check_six_order_transition_tensor(transition_tensor)
return transition_tensor
def mtt(data, user_num, poi_num, zero_adjustment=True):
print "共有" + str(user_num) + "个用户"
for key in data.keys():
print "用户" + str(key) + "序列为" + str(data[key])
correlation_matrix = get_correlation_matrix(data, user_num)
nor_cor_matrix = normalize(correlation_matrix)
print "归一化相关系数矩阵: ", nor_cor_matrix
transition_tensor = build_six_order_transition_tensor(
data, poi_num, nor_cor_matrix, zero_adjustment)
print "转移张量非零元素占比:", sparsity(transition_tensor)
print "转移张量是否满足随机性:", check_six_order_transition_tensor(transition_tensor)
return transition_tensor
#
# 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)
res3, iterator_values3 = tensor_three_mode_product(transition_tensor, temp_tensor_user_equal)
res4, iterator_values4 = tensor_three_mode_product(transition_tensor, temp_tensor_user_ramdom)
print analysis_eigen_tensor(res1, "user")
print analysis_eigen_tensor(res1, "time")
print analysis_eigen_tensor(res1, "poi")
print "\n"
