灰色关联分析算法,灰色关联分析怎么操作

import pandas as pd

import numpy as np

# x = pd.DataFrame([[2000,0.732,0.836,0.628,0.743], [2001,0.758,0.883,0.688,0.787], [2002,0.859,0.914,0.781,0.929],[2003,1.0125,1.0440,1.0237,0.9847],[2004,1.2356,1.1069,1.2833,1.2363],[2005,1.4013,1.2152,1.5405,1.3182]])

# x=pd.read_excel('D:datewinequalitywinequality-red1.xlsx')

csv_file = "D:datewinequalitywinequality-red1.csv"

csv_data = pd.read_csv(csv_file, low_memory=False) # 防止弹出警告

x= pd.DataFrame(csv_data)

x=x.iloc[:,1:].T

# 1、数据均值化处理

x_mean=x.mean(axis=1)

for i in range(x.index.size):

x.iloc[i,:] = x.iloc[i,:]/x_mean[i]

# x.iloc[i,:] = x.iloc[i,:]/x_mean[i]  #i或者i+1不太明晰

# 2、提取参考队列和比较队列

ck=x.iloc[0,:]

cp=x.iloc[1:,:]

# 比较队列与参考队列相减

t=pd.DataFrame()

for j in range(cp.index.size):

temp=pd.Series(cp.iloc[j,:]-ck)

t=t.append(temp,ignore_index=True)

#求最大差和最小差

mmax=t.abs().max().max()

mmin=t.abs().min().min()

rho=0.5

#3、求关联系数

ksi=((mmin+rho*mmax)/(abs(t)+rho*mmax))

#4、求关联度

r=ksi.sum(axis=1)/ksi.columns.size

#5、关联度排序，得到结果r3>r2>r1

result=r.sort_values(ascending=False)

print(result)