import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

# 定義下載數(shù)據(jù)的函數(shù)
def ReadAndSaveDataByPandas(target_url = None,file_save_path = None ,save=False):
    if target_url !=None:
        target_url = ("http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv")   
    if file_save_path != None:
        file_save_path = "/home/fonttian/Data/UCI/Glass/glass.csv"
    wine = pd.read_csv(target_url, header=0, sep=";")
    if save == True:
        wine.to_csv(file_save_path, index=False)
    return wine
# 從硬盤(pán)讀取數(shù)據(jù)進(jìn)入內(nèi)存
wine = pd.read_csv("/home/font/Data/UCI/WINE/wine.csv")
wine.head()

import pandas as pd
from numpy import *
def GRA_ONE(DataFrame,m=0):
    gray= DataFrame
    #讀取為df格式
    gray=(gray - gray.min()) / (gray.max() - gray.min())
    #標(biāo)準(zhǔn)化
    std=gray.iloc[:,m]#為標(biāo)準(zhǔn)要素
    ce=gray.iloc[:,0:]#為比較要素
    n=ce.shape[0]
    m=ce.shape[1]#計(jì)算行列

    #與標(biāo)準(zhǔn)要素比較，相減
    a=zeros([m,n])
    for i in range(m):
        for j in range(n):
            a[i,j]=abs(ce.iloc[j,i]-std[j])
    #取出矩陣中最大值與最小值
    c=amax(a)
    d=amin(a)
    #計(jì)算值
    result=zeros([m,n])
            result[i,j]=(d+0.5*c)/(a[i,j]+0.5*c)
    #求均值，得到灰色關(guān)聯(lián)值
    result2=zeros(m)
            result2[i]=mean(result[i,:])
    RT=pd.DataFrame(result2)
    return RT
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

data_wine_gra = GRA(wine)
# data_wine_gra.to_csv(path+"GRA.csv") 存儲(chǔ)結(jié)果到硬盤(pán)
data_wine_gra

Empty DataFrame
Columns: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
Index: []

# 灰色關(guān)聯(lián)結(jié)果矩陣可視化
import seaborn as sns
%matplotlib inline
def ShowGRAHeatMap(DataFrame):
    import matplotlib.pyplot as plt
    import seaborn as sns
    %matplotlib inline
    colormap = plt.cm.RdBu
    plt.figure(figsize=(14,12))
    plt.title('Pearson Correlation of Features', y=1.05, size=15)
    sns.heatmap(DataFrame.astype(float),linewidths=0.1,vmax=1.0, square=True, cmap=colormap, linecolor='white', annot=True)
    plt.show()
ShowGRAHeatMap(data_wine_gra)