灰色关联度算法

转自https://blog.csdn.net/fontthrone/article/details/80607794 Font Tian同学

#coding:utf-8
import sys
import pandas as pd
import numpy as np
from numpy import *
import matplotlib.pyplot as plt



def GRA_ONE(gray, m=0):
    # 读取为df格式
    gray = (gray - gray.min()) / (gray.max() - gray.min())
    # 标准化
    std = gray.iloc[:, m] # 为标准要素
    ce = gray.iloc[:, 0:] # 为比较要素
    n, m = ce.shape[0], ce.shape[1] # 计算行列
    # 与标准要素比较，相减
    a = zeros([m, n])
    for i in range(m):
        for j in range(n):
            a[i, j] = abs(ce.iloc[j, i] - std[j])
    # 取出矩阵中最大值与最小值
    c, d = amax(a), amin(a)
    # 计算值
    result = zeros([m, n])
    for i in range(m):
        for j in range(n):
            ss = a[i, j] + 0.5 * c
            if ss ==0:
                result[i, j] =1
            else:
                result[i, j] = (d + 0.5 * c) / (a[i, j] + 0.5 * c)

    # 求均值，得到灰色关联值,并返回
    return pd.DataFrame([mean(result[i, :]) for i in range(m)])


def GRA(DataFrame):
    list_columns = [
        str(s) for s in range(len(DataFrame.columns)) if s not in [None]
    ]
    df_local = pd.DataFrame(columns=list_columns)
    for i in range(len(DataFrame.columns)):
        df_local.iloc[:, i] = GRA_ONE(DataFrame, m=i)[0]
    return df_local

wine = pd.read_csv("h2.csv")
wine.head()
print "----------------------------"
data_wine_gra = GRA(wine)

print data_wine_gra

附件
h.csv

a,b,c ,d
1,2,1,1
2,2,2,1
3,3,3,2
4,2,4,2
5,4,5,3
6,1,6,3

h2.csv

a,b
1,100
2,200
3,300
4,400
5,500
6,491

该输出为矩阵 [i,j] 表示第i列和第j列关联度