import csv
import numpy as np
import tensorflow as tf
#大概的思路是：
#1.首先输入每个指标下面对应的对比矩阵，每个这个矩阵是由专家比较每两个
#指标之间的相对性重要性形成的，将这个矩阵作为输入，首先计算这个矩阵是否能
#通过一致性的检验。在计算一致性时，要先求出该矩阵的特征值和特征向量。
'''c1,c2 = np.linalg.eig(C)
print (c1)
# [ 2. 1.]
print (c2)
#[[ 0.89442719 0.70710678]
# [ 0.4472136 0.70710678]]'''
class AHP:
def __init__(self,array):
self.row = len(array)
self.col = len(array[0])
def get_tezheng(self,array):#获取特征值和特征向量
te_val ,te_vector = np.linalg.eig(array)
list1=list(te_val)
print("特征值为：",te_val)
print("特征向量为：",te_vector)
#得到最大特征值对应的特征向量
max_val = np.max(list1)
index = list1.index(max_val)
max_vector = te_vector[:,index]
print("最大的特征值:"+str(max_val)+" 对应的特征向量为："+str(max_vector))
return max_val,max_vector
def RImatrix(self,n):#建立RI矩阵
d = {}
n1 = [1,2,3,4,5,6,7,8,9]
n2 = [0,0,0.58,0.90,1.12,1.24,1.32,1.41,1.45]
for i in range(n):
d[n1[n]] = n2[n]
print("该矩阵在一致性检测时采用的RI值为：",d[n1[n]])
return d[n1[n]]
def test_consitstence(self,max_val,RI):#测试一致性
CI = (max_val-self.row)/(self.row-1)
CR = CI/RI
if CR < 0.10 :
print("判断矩阵的CR值为 "+str(CR) + "通过一致性检验")
return True
else:
print("判断矩阵的CR值为 "+str(CR) + "判断矩阵未通过一致性检验，请重新输入判断矩阵")
return False
def normalize_vector(self,max_vector):#特征向量归一化
vector_after_normalization=[]
sum0 = np.sum(max_vector)
for i in range(len(max_vector)):
vector_after_normalization.append(max_vector[i]/sum0)
print("该级指标的权重权重矩阵为： "+str(vector_after_normalization))
return vector_after_normalization
def weightCalculator(self, normalMatrix):#计算最终指标对应的权重值
#layers weight calculations.
listlen = len(normalMatrix) -1
layerWeights = list()
while listlen > -1:
sum = float()
for i in normalMatrix:
sum+= i[listlen]
sumAverage = round(sum / len(normalMatrix),3)
layerWeights.append(sumAverage)
listlen-=1
return layerWeights
import csv
import numpy as np
import tensorflow as tf
def main():#这里需要确定指标的规模即多少个一级指标，多少个二级指标，这样才能确定要计算多少个对比矩阵
array1=[]
array2=[]
def define_structure():
level_structure = []
level = int(input("请输入指标的级数："))#输入比如说这是个三级指标体系
level0 = input("请输入一级下指标的个数：")
level.append(level0)
level2 = []
for i in range(level):#每一级指标下有多少具体的指标个数
rate_num = input("请输入" +str(i) "层下指标的个数：")
#level2.append(rate_num)
for j in range(rate_num ):
two_level_for_one = int(input("请输入第" +str(i)+ " 个一级指标对应的下级指标的个数："))
level_structure.append(two_level_for_one )
return level_structure
n = level_structure
def creat_matrix(n):
for i in n:
length = input("请输入指标对比矩阵的阶数：")
length = int(length)
count=0
for i in range(length):
for j in range(length):
count += 1
x = input("请输入指标对比矩阵的第"+str(count)+ " 个元素：")
x = float(x)
array1.append(x)
for i in range(length*length):
if (i+1)%length==0:
array2.append(array1[i-length+1:i+1])
print(array2)
array2=np.mat(array2)
a=AHP(array2)
max_val,max_vector = a.get_tezheng(array2)
RI= a.RImatrix(length)
flag = a.test_consitstence(max_val,RI)
if flag:
weight = a.normalize_vector(max_vector)
main()
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