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0.0.1

PyPI

AHP

AHP in Python

Published23 Feb 2023

Direct dependencies2

Transitive dependenciesNot calculated

README

Version 0.0.1 documentation

AHP

层次分析法

How to use

Install

pip install ahp

use

from AHP import AHP
import numpy as np

# 准则重要性矩阵
criteria = np.array([[1, 2, 7, 5, 5],
                     [1 / 2, 1, 4, 3, 3],
                     [1 / 7, 1 / 4, 1, 1 / 2, 1 / 3],
                     [1 / 5, 1 / 3, 2, 1, 1],
                     [1 / 5, 1 / 3, 3, 1, 1]])

# 对每个准则，方案优劣排序
b1 = np.array([[1, 1 / 3, 1 / 8], [3, 1, 1 / 3], [8, 3, 1]])
b2 = np.array([[1, 2, 5], [1 / 2, 1, 2], [1 / 5, 1 / 2, 1]])
b3 = np.array([[1, 1, 3], [1, 1, 3], [1 / 3, 1 / 3, 1]])
b4 = np.array([[1, 3, 4], [1 / 3, 1, 1], [1 / 4, 1, 1]])
b5 = np.array([[1, 4, 1 / 2], [1 / 4, 1, 1 / 4], [2, 4, 1]])

b = [b1, b2, b3, b4, b5]
a = AHP(criteria, b).run()

打印：

==========准则层==========
最大特征值5.072084,CR=0.014533,检验通过
准则层权重 = [0.47583538 0.26360349 0.0538146  0.09806829 0.10867824]

==========方案层==========
          方案0       方案1       方案2     最大特征值            CR  一致性检验
准则0  0.081935  0.236341  0.681725  3.001542  8.564584e-04   True
准则1  0.595379  0.276350  0.128271  3.005535  3.075062e-03   True
准则2  0.428571  0.428571  0.142857  3.000000 -4.934325e-16   True
准则3  0.633708  0.191921  0.174371  3.009203  5.112618e-03   True
准则4  0.344545  0.108525  0.546931  3.053622  2.978976e-02   True

==========目标层==========
[[0.318586   0.23898522 0.44242878]]
最优选择是方案2

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Declared dependencies 2

Native requirements

2 direct targetsNot calculated transitive count

Package / target	Requirement	Relationship
numpy Environment { "extras": "", "marker": null, "requires_python": ">=3.5", "evaluation": "NOT_EVALUATED" }	`numpy`	runtime
pandas Environment { "extras": "", "marker": null, "requires_python": ">=3.5", "evaluation": "NOT_EVALUATED" }	`pandas`	runtime