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
def serialization(data, user_num, poi_num):
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
for i in range(user_num):
for j in range(user_num):
if zero_adjustment:
temp_tensor = build_fouth_order_influence_tensor(data[i], data[j], poi_num, True)
else:
temp_tensor = build_fouth_order_influence_tensor(data[i], data[j], poi_num, False)
print str(i)+"对"+str(j)+"是否满足随机性条件", check_fourth_order_transition_tensor(temp_tensor)
fouth_influence_tensor = fouth_tensor_hadarmard(nor_cor_matrix[i][j], temp_tensor)
file_name = str(i)+"_"+str(j)+".db"
file_path = "data/step_one/"+file_name
print file_path
transition_tensor_db = open(file_path, 'w')
pickle.dump(fouth_influence_tensor, transition_tensor_db)
transition_tensor_db.close()
