编辑FunnyMap对象

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# -*- coding: utf-8 -*-

import csv

import copy

import numpy as np
import pandas as pd

import sys

import matplotlib as mpl
mpl.use('Agg')

import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc  ###计算roc和auc
#from sklearn import cross_validation

y1=[]
y2=[]
file='/root/happyli/Data/5716.data'

index1=0;
index2=1;

if len(sys.argv)>3:
    file=sys.argv[1]
    index1=int(sys.argv[2])
    index2=int(sys.argv[3])
    
with open(file,'r') as csvfile:
    reader = csv.reader(csvfile)
    y1 = [row[index1] for row in reader]
    
    
with open(file,'r') as csvfile:
    reader = csv.reader(csvfile)
    y2 = [row[index2] for row in reader]

print(len(y1))

print(len(y2))

print(y1[0:100])
print(y2[0:100])

x1=[]
for a in y1:
    x1.append(float(a))

x2=[]
for a in y2:
    x2.append(float(a))
    
  
print(np.mean(np.multiply((x1-np.mean(x1)),(x2-np.mean(x2))))/(np.std(x1)*np.std(x2)))
  
#corrcoef得到相关系数矩阵（向量的相似程度）
  
#print(np.corrcoef(vc,vb))

ID=5766 corrcoef.py 相关系数
# -- coding: utf-8 -- import csv import copy import numpy as np import pandas as pd import sys import matplotlib as mpl mpl.use('Agg') import matplotlib.pyplot as plt from sklearn import svm, datasets from sklearn.metrics import roc_curve, auc ###计算roc和auc #from sklearn import cross_validation y1=[] y2=[] file='/root/happyli/Data/5716.data' index1=0; index2=1; if len(sys.argv)>3: file=sys.argv[1] index1=int(sys.argv[2]) index2=int(sys.argv[3]) with open(file,'r') as csvfile: reader = csv.reader(csvfile) y1 = [row[index1] for row in reader] with open(file,'r') as csvfile: reader = csv.reader(csvfile) y2 = [row[index2] for row in reader] print(len(y1)) print(len(y2)) print(y1[0:100]) print(y2[0:100]) x1=[] for a in y1: x1.append(float(a)) x2=[] for a in y2: x2.append(float(a)) print(np.mean(np.multiply((x1-np.mean(x1)),(x2-np.mean(x2))))/(np.std(x1)*np.std(x2))) #corrcoef得到相关系数矩阵（向量的相似程度） #print(np.corrcoef(vc,vb))