import csv
import numpy as np
import tensorflow as tf
from sklearn.preprocessing import MinMaxScaler
from numpy import *
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 print(n)
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]
d = dict(zip(n1,n2))
print("该矩阵在一致性检测时采用的RI值为：", d[n])
return d[n]
def test_consitstence(self, max_val, RI): # 测试一致性 CI = (max_val - self.row) / (self.row - 1)
if RI == 0:
print("判断矩阵的RI值为 " + str(0) + " 通过一致性检验")
return True else:
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 main():
weigh_matrix = []
num_of_standard = []
l0 = []
l1 = []
def input_data(f1): # 对输入的判断矩阵进行处理 l0 = []
l1 = []
li = []
with open(f1) as f:
reader = csv.reader(f)
for line in reader:
print(line)
if line == []:
l1.append(l0)
l0 = []
continue for i in line:
if "/" in i:
b = i.split("/")
li.append(int(b[0]) / float(b[1]))
else:
li.append(float(i))
l0.append(li)
li = []
l1.append(l0)
print(l1)
return l1
l1= input_data(r"C:\Users\adm\Desktop\data11.txt")
print("l1",l1)
def normalize_vector(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))
file = open(r"C:\Users\adm\Desktop\data000112.txt", 'a')
for i in vector_after_normalization:
file.write(str(i))
file.write("\n")
return vector_after_normalization
def to_input_matrix():#判别矩阵的一致性检验 for i in l1:
length = len(l1)
print(i)
a = AHP(i)
max_val, max_vector = a.get_tezheng(i)
record_max_vector = max_vector
RI = a.RImatrix(len(i))
flag = a.test_consitstence(max_val, RI)
while not flag:
print("对比矩阵未通过一致性检验，请重新输入对比矩阵！")
break #flag = to_input_matrix(length) weight = normalize_vector(record_max_vector) # 返回权重[[0.5, 0.5], [0.5, 0.5], [0.5, 0.5]] weigh_matrix.append(weight)
print("最终的权重矩阵为：", weigh_matrix)
return weigh_matrix
w00=to_input_matrix()
print("w00",w00)
def construct_weightmatrix(weight):
data = []
l = []
for i in weight[0]:
data.append(i.real)
#print(data) for i in weight[1:4]:
for j in i:
data.append(j.real)
for i in weight[4:]:
for j in i:
data.append(j.real)
print(data)
data2 = data
print(len(data2))
return data2
d = construct_weightmatrix(w00)
print(d)
def get_index(data ): # 第一个矩阵1*3，第二个矩阵7*3。第三个矩阵16*7 row = [3, 7, 16]
column = [1, 3, 7]
x1 = [0, 1, 2]
y1 = [0, 0, 0]
x2 = [0, 1, 2, 3, 4, 5, 6]
y2 = [0, 0, 1, 1, 1, 2, 2]
x3 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, ]
y3 = [0, 0, 1, 1, 2, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 6]
weight = []
def transform(x, y, data, row, col):
h = list(zip(x, y))
print(h)
mat1 = zeros((row, col))
for i in range(len(data)):
mat1[h[i]] = data[i]
weight.append(mat1)
print(weight)
transform(x1, y1, data[:3], 3, 1)
transform(x2, y2, data[3:10], 7, 3)
transform(x3, y3, data[10:], 16, 7)
return weight
w1 = get_index(d)#返回组织好的权重矩阵d for i in w1:
print(i)
# print(w1) def get_finalweight(w):#矩阵相乘 j = 0
s = np.array([1])
print(np.dot(w[1], w[0]))
while j < len(w):
print("weight0[i]", w[j])
s = np.dot(w[j], s)
print("s", s)
j += 1
print("s", s)
return s
s1=get_finalweight(w1)
def data_process():
i = 0
data = []
global name
name = []
with open(r'C:\Users\adm\Desktop\数据.csv') as f:
reader = csv.reader(f)
for row in reader:
if i == 0:
i += 1
continue else:
data.append(row[:])
name.append(row[1])
data = np.asarray(data)
data1 = data[:, 3:].astype('float64')
scaler = MinMaxScaler(feature_range=(0, 1))
data1 = scaler.fit_transform(data1)
print(len(data1))
return data1
def ScoreCalculator():
SCORE = []
score = []
value = data_process()
print(len(value))
for i in range(len(value)):
#print("i",len(value[i])) #print(len(s1)) SCORE = np.sum([x * y for x, y in zip(value[i], s1)]) * 100
score.append(SCORE)
score = [i / np.max(score) * 100 for i in score]
print(score)
d = dict(zip(name, score))
d1 = sorted(d.items(), key=lambda items: items[1], reverse=True)
# print(d1) for key, value in d1[:80]:
if value > 80:
print(str(key) + " 最终得到的评分为： " + str(value) + " 合理")
elif 70 < value < 80:
print(str(key) + " 最终得到的评分为： " + str(value) + " 较为合理")
elif 60 < value < 70:
print(str(key) + " 最终得到的评分为： " + str(value) + " 一般合理")
else:
print(str(key) + " 最终得到的评分为： " + str(value) + " 不合理")
print("所有指标权重的和", np.sum(s1))
ScoreCalculator()
if __name__ == "__main__":
main()
