matplotlib使用

import numpy as np
import matplotlib.pyplot as plt

生成数据

 mean1=[5,5]
 cov1=[[1,1],[1,1.5]]
 data=np.random.multivariate_normal(mean1,cov1,100)
 x1,y1=data.T
 x2=np.random.choice(10,10)
 y2=x2

画图

 plt.plot(x1,y1,c='b')
 plt.scatter(x2,y2,'r-')
 plt.xlabel('x')
 plt.ylabel('y')
 new_xtick=[1,2,3,4,5]
 new_ytick=[1,2,3,4,5]
 plt.xticks(new_xtick)
 plt.yticks(new_ytick)
 ax=plt.gca()
 ax = plt.gca()
 ax.spines['right'].set_color('none')
 ax.spines['top'].set_color('none')
13 plt.show()

注：在使用matplotlib的时候四个边框都出现了刻度，可以设置顶部和左部的刻度分别和底部和右部刻度重叠。

 ax.spines['left'].set_position(('data',0))
 ax.spines['right'].set_position(('data',0))
 ax.spines['bottom'].set_position(('data',0))
 ax.spines['top'].set_position(('data',0))

pca应用

 dot_in=np.array([[2**(0.5)/2,-2**(0.5)/2],[2**(0.5)/2,2**(0.5)/2]])

 dot_inv=np.linalg.inv(dot_in)

 x3,y3=x1-mean1[0],y1-mean1[1]

 x4,y4=np.dot(dot_inv,np.array([x3,y3]))

 plt.scatter(x1,y1,c='r')

 plt.scatter(x4,y4,c='b')

 ax=plt.gca()

 ax.spines['bottom'].set_position(('data',0))

 ax.spines['top'].set_position(('data',0))

 ax.spines['left'].set_position(('data',0))

 ax.spines['right'].set_position(('data',0))

 ax.spines['right'].set_color(('none'))

 ax.spines['top'].set_color(('none'))

  plt.show()