from math import *
import
numpy as np
import pandas as pd
import os
#权重文件位置
WEIGHTS_JSON="
../result/weight
.json"
#采样次数
SAMPLE_TIMES=100
#Fbwm输入目录
FBWM_INPUT_DIR="
../input/fuzzy_bwm_input/"
#Fbwm输入文件
FBWM_INPUT_PATH=os
.path
.join
(FBWM_INPUT_DIR,"c4_c5_fuzzy_bwm
.xlsx"
)
#权重方法
WEIGHTMETHOD="bwm"
#结果目录
RESULT_DIR="
../result/analysis/mc/tri_dis/"
#攻击数量
#初始化结果目录
os
.makedirs
(RESULT_DIR,exist_ok=True
)#递归创建，已存在则跳过
#清空目录文件
for file in os
.listdir
(RESULT_DIR
):
file_path=os
.path
.join
(RESULT_DIR,file
)
try:
os
.remove
(file_path
)
except OSError as e:
print
(f"无法删除旧文件{file_path},原因{e}"
)
#逆变换采样函数
def c_tfn
(tfn
):
l,m,u=tfn
if not
(l<m<u
):
raise ValueError
("tfn格式错误"
)
r=np
.random
.rand
()#生成[0,1]之间的随机数
p=
(m-l
)/
(u-l
)
if r<p:
return l+sqrt
(r*
(u-l
)*
(m-l
))
else:
return u-sqrt
((1-r
)(u-m
)(u-l
))
#读取权重评估数据
address=FBWM_INPUT_PATH
df=pd
.read_excel
(address, sheet_name = "Sheet1", skiprows = 2, nrows= 9, usecols=[1,2,3],header=None
)
df
.columns=["Criteria","Best","Worst"]
Best = df["Criteria"][df[df['Best'] == "Equally importance"]
.index
.tolist
()[0]]
Worst = df["Criteria"][df[df['Worst'] == "Equally importance"]
.index
.tolist
()[0]]
df
.columns = ["Criteria", Best, Worst]
Cnum = df
.shape[0]
df
.set_index
(df["Criteria"], inplace=True
)
# Fuzzification
Fuzzy = {"Equally importance": [1, 1, 1],
"Weakly important": [2 / 3, 1, 3 / 2],
"Fairly important": [3 / 2, 2, 5 / 2],
"Very important": [5 / 2, 3, 7 / 2],
"Absolutely important": [7 / 2, 4, 9 / 2]}
#确认输出
mc_bwminput_output_path=os
.path
.join
(RESULT_DIR,"mc_bwm_input
.xlsx"
)
result={}
#蒙特卡洛循环
for sample_idx in range
(SAMPLE_TIMES
):
for i in [Best, Worst]:
for j in range
(df
.shape[0]
):
df[i][j] =c_tfn
(Fuzzy[df[i][j]]
)#随机采样
df
.to_excel
(mc_bwminput_output_path,index=False
)#随机采样后的bwm输入
weightmethod=WEIGHTMETHOD
#权重计算
os
.system
('/usr/bin/time -f"%e %M" -a -o
../result/tmp/time
.log python3
../
lib/Weight/{}/{}
.py {} {}'
.format
(weightmethod , weightmethod , mc_bwminput_output_path, '
../result/weight
.json'
))
#指标计算
#os
.system
('/usr/bin/time -f"%e %M" -a -o
../result/tmp/time
.log python3
../
lib/cal_metrics
.py {} {}'
.format
('
../result/tmp/topo
.json', '
../result/path_metrics
.csv'
))
#排名计算
os
.system
('/usr/bin/time -f"%e %M" -a -o
../result/tmp/time
.log python3
../
lib/Rank/{}/{}
.py {} {} {} {} {}'
.format
('mabac' , 'mabac' , '
../result/tmp/topo
.json',WEIGHTS_JSON, '
../result/tmp/data
.csv', '
../result/rank
.json', '
../result/path_metrics
.csv'
))
try:
with open
('
../result/rank
.json',"r"
) as f:
rank=json
.load
(f
)#读取排名文件
with open
(WEIGHTS_JSON,"r"
) as m:
weight=json
.load
(m
)#读取权重文件
except Exception as e:
print
(f"读取出错：{e}"
)
#整合输出
result[sample_idx]={
"weight":weight[weight],
"TFN":weight[TFN],
"ksi":weight[ksi],
"CR":weight[CR],
"ranking":rank[ranking]
}
#输出排序结果
output_path=os
.path
.join
(RESULT_DIR,"monte_carlo_tfn
.json"
)
with open
(output_path,"w"
) as f:
json
.dump
(result,f,indent=2
) 我的代码功能可以实现嘛
本文介绍了numpy.emath和numpy.lib.scimath模块，这两个模块提供了自动处理复数域的数学函数。通过示例，解释了在实数域中对负数开平方会得到复数结果，帮助理解数学函数如何在不同域中转换。同时，讨论了函数的‘分支切割’概念，即函数在复数域的行为，建议通过绘制复数域图像以增进理解。
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