def num_multiple_fuzzy(n, f):
n = int(n)
num_multiple_res = []
for i in f:
# num_multiple_res.append(n*i)
num_multiple_res.append(precision(n * i, 2))
return num_multiple_res
def alphaIteration(risks):
'''
:param risks: risks = [[风险名称,[prob1，prob2...],[impact1,impact2...]],[风险名称,[prob1，prob2...],[impact1,impact2...]],[],... ]
:return: aggregationResult = [[风险名称, 聚合风险值], [风险名称, 聚合风险值], ... ]
'''
aggregationResult = []
# A = [0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]
# alpha是8个权值更新迭代
A = [0.0, 0.07, 0.23, 0.42, 0.65, 0.86, 0.97, 1.0]
# A = [0.0,0.5]
low = 0.000
high = 0.000
weight = 0.000
for risk in risks:
'''
针对当前风险获得它的模糊风险值 result
'''
R = risk[1]
W = risk[2]
for alpha in A:
# 每个alpha计算一个大轮
# print 'alpha:',alpha
a, b = PFWA(R, W, alpha)
# a, b = PFWA([[0.200, 0.384, 0.536,0.867],[0.133, 0.323, 0.485, 0.733],[0.443, 0.581, 0.667, 0.800]],
# [[0.323, 0.485, 0.605,0.867],[0.528, 0.629, 0.667, 0.733],[0.491, 0.613, 0.658, 0.800]],alpha)
weight = alpha
if alpha == 0.0:
low = a
high = b
if a == b:
break
medium = (a + b) / 2
# 适应目前模糊数的输入格式
result = [
precision(low, 3),
precision(medium, 3),
precision(high, 3),
precision(weight, 3)
]
aggregationResult.append([risk[0], result])
return aggregationResult
def fuzzywith2heights_plus(f1, f2):
'''
:param f: 若干个模糊数（列表）组成的列表
:return: 列表
'''
plus_res = []
for i, j in zip(f1, f2):
plus_res.append(precision(i + j, 2))
plus_res.append(min(f1[4], f2[4]))
plus_res.append(min(f1[5], f2[5]))
return plus_res
def fuzzy_plus(f):
'''
:param f: 若干个模糊数（列表）组成的列表
:return: 列表
'''
plus_res = []
for j in range(size):
tmp = 0
for i in f:
tmp = tmp + i[j]
plus_res.append(precision(tmp, 2))
return plus_res
def getRiskscore(self):
'''
模糊风险值去模糊化得到单值
确定模糊数的实际模型
:return: self.risk_score
'''
# self.setFuzzynum()
self.setFuzzyscore()
fuzzy = []
for i in self.fuzzy_score:
if i not in fuzzy:
fuzzy.append(i)
fuzzy_len = len(fuzzy)
if fuzzy_len == 3:
self.risk_score = Defuzzification.triangle_defuzzification(fuzzy)
elif fuzzy_len == 4:
self.risk_score = Defuzzification.trapezoidal_defuzzification(
fuzzy)
self.risk_score = precision(self.risk_score, 3)
return self.risk_score
def fuzzy_division(f1, f2):
division_res = []
for i in range(len(f1)):
division_res.append(precision(f1[i] / f2[i], 3))
return division_res
def fuzzy_multiple(f1, f2):
multiple_res = []
for i in range(len(f1)):
multiple_res.append(precision(f1[i] * f2[i], 2))
return multiple_res
