📘 多层次模糊综合评价模型/模糊综合评价.ipynb

import numpy as np
# 1、一级模糊综合评判
# 影响运行费用的各因素的单因素评价矩阵为:
R23 = np.array([
[0.18,0.14,0.18,0.14,0.13,0.23],
[0.15,0.20,0.15,0.25,0.10,0.15],
[0.25,0.12,0.13,0.12,0.18,0.20],
[0.16,0.15,0.21,0.11,0.20,0.17],
[0.23,0.18,0.17,0.16,0.15,0.11],
[0.19,0.13,0.12,0.12,0.11,0.33],
[0.17,0.16,0.15,0.08,0.25,0.19]])
# 权重分配为
A23 = np.array([0.20,0.15,0.10,0.10,0.20,0.15,0.10])
# 评价结果
# np.dot是Numpy库中的一个函数，用于计算两个数组的点积。对于一维数组，它计算的是这两个效组的内积。
# 对于二维效组(矩阵)，它计算的是阵乘法。
B23 = np.dot(A23,R23)
print('B23=',B23)
# 2、二级模糊综合评判
# 产品情况的二级评判如下，
R1 = np.array([
[0.12,0.18,0.17,0.23,0.13,0.17],
[0.15,0.13,0.18,0.25,0.12,0.17],
[0.14,0.13,0.16,0.18,0.20,0.19],
[0.12,0.14,0.15,0.17,0.19,0.23],
[0.16,0.12,0.13,0.2,0.18,0.16]])
A1 = np.array([0.15,0.40,0.25,0.10,0.10])
B1 = np.dot(A1,R1)
print('B1=',B1)
# 销售能力二级评判如下，
R2 = np.array([
[0.13,0.15,0.14,0.18,0.16,0.25],
[0.12,0.16,0.13,0.17,0.19,0.23],
B23,
[0.14,0.13,0.15,0.16,0.18,0.24],
[0.16,0.15,0.15,0.17,0.18,0.19]])
A2 = np.array([0.2,0.15,0.25,0.25,0.15])
B2 = np.dot(A2, R2)
print('B2=',B2)
# 市场需求的二级评判
R3 = np.array([
[0.15,0.14,0.13,0.18,0.14,0.26],
[0.16,0.15,0.18,0.14,0.16,0.21]])
A3 = np.array([0.55,0.45])
B3 = np.dot(A3,R3)
print('B3=',B3)
# 3、三级模糊综合评判
R = np.array([B1,B2,B3])
A = np.array([0.4,0.3,0.3])
B = np.dot(A,R)
print('B=',B)

B23= [0.191  0.1565 0.1595 0.1465 0.1505 0.196 ]
B1= [0.141  0.1375 0.1655 0.2165 0.1545 0.18  ]
B2= [0.15075  0.148125 0.147375 0.163625 0.170125 0.222   ]
B3= [0.1545 0.1445 0.1525 0.162  0.149  0.2375]
B= [0.147975  0.1427875 0.1561625 0.1842875 0.1575375 0.20985  ]