Python实现熵值法确定权重.pdfVIP

Python实实现现熵熵值值法法确确定定权权重重 本本⽂⽂从从以以下下四四个个⽅⽅⾯⾯，，介介绍绍⽤⽤Python实实现现熵熵值值法法确确定定权权重重：： ⼀⼀. 熵熵值值法法介介绍绍 熵值法是计算指标权重的经 算法之⼀，它是指⽤来判断某个指标的离散程度的数学⽅法。离散程度越⼤，即信息量越⼤，不确定性就越 ⼩，熵也就越⼩；信息量越⼩，不确定性越⼤，熵也越⼤。根据熵的特性，我们可以通过计算熵值来判断⼀个事件的随机性及⽆序程度，也 可以⽤熵值来判断某个指标的离散程度，指标的离散程度越⼤，该指标对综合评价的影响越⼤。 ⼆⼆. 熵熵值值法法实实现现 1.假设数据有n⾏记录，m个变量，数据可以⽤⼀个n*m的矩阵A表⽰(n⾏m列，即n⾏记录数，m个特征列) 2.数据的归⼀化处理 xij 表⽰矩阵A 的第i⾏j列元素。 3.计算第j项指标下第i个记录所占⽐重 4.计算第j项指标的熵值 5.计算第j项指标的差异系数 6.计算第j项指标的权重 三三. Python实实现现熵熵值值法法⽰⽰例例1 样例数据1 .csv格式数据内容 var1,var2,var3,var4,var5,var6 171.33,151.33,0.28,0,106.36,0.05 646.66,370,1.07,61,1686.79,1.64 533.33,189.66,0.59,0,242.31,0.57 28.33,0,0.17,0,137.85,2.29 620,234,0.88,41.33,428.33,0.13 192.33,177.66,0.16,0,128.68,1.07 111,94,0.18,0,234.27,0.22 291,654,1.21,65.66,2.26,0 421.33,247,0.7,0,0.4,0 193,288.66,0.16,0,0,0 82.33,118,0.11,0,758.41,0.24 649.66,648.66,0.54,0,13.35,0.11 37.66,103.33,0.12,0,1133.51,1.1 183.33,282.33,0.55,0,624.73,1.04 1014.66,1264.66,5.07,814.66,0,0 90.66,134,0.3,0,0.15,0 200.66,98.33,0.33,0,681.54,0.51 540.66,558.66,1.08,62,2.71,0.09 80,60.66,0.13,0,910.19,0.88 530.66,281.33,0.88,36,743.21,0.72 166,133,0.13,0,246.88,2.05 377.66,310.33,0.57,0,102.89,0.57 143.33,73,0.23,0,103.94,0.1 394.66,473.66,0.56,0,1.06,0.03 535.66,447.33,0.44,0,10.59,0.08 52.66,56.66,0.52,0,0,0 1381.66,760.66,2.3,781.66,248.71,0.13 44.33,42.33,0.07,0,0.66,0 71.66,62.66,0.11,0,535.26,0.52 148.33,56.66,0.24,0,173.83,0.16 Pyth n代码: # !/ usr/ bin/ pyth n # -*- c ding: utf-8 -*- """ Created n Fri Mar 23 10:48:36 2018 @auth r: Big Teacher Br ther """ imp rt pandas as pd imp rt numpy as np imp rt math fr m numpy imp rt array # 1读取数据 df = pd.read_csv ('E:\\text .csv', enc ding='gb2312') # 2数据预处理 ,去除空值的记录 df .dr pna() #定义熵值法函数 def cal_weight(x): '''熵值法计算变量的权重''' # 标准化 x = x .apply (lambda x : ((x - np.min(x)) / (np.max (x)