使用numpy，scipy库用最小二乘法拟合曲线

#coding=gbk
import numpy as np
from scipy.optimize import leastsq
import pylab as pl

Yi=np.array([7.01,2.78,6.47,6.71,4.1,4.23,4.05])
xi=np.array([8.19,2.72,6.39,8.71,4.7,2.66,3.78])

def func(p,x):
	k,b=p
	return k*x+b

def error(p,x,y,s):
	print(s)
	return func(p,x)-y

 #TEST
p0=[100,2]
#print( error(p0,Xi,Yi) )
s="Test the number of iteration" #试验最小二乘法函数leastsq得调用几次error函数才能找到使得均方误差之和最小的k、b
Para=leastsq(error,p0,args=(xi,Yi,s)) #把error函数中除了p以外的参数打包到args中
k,b=Para[0]
print("k=",k,'\n',"b=",b)

###绘图，看拟合效果###
import matplotlib.pyplot as plt

plt.figure(figsize=(10,1))
plt.scatter(xi,Yi,color="red",label="Sample Point",linewidth=3) #画样本点
x=np.linspace(0,10,1000)
y=k*x+b
plt.plot(x,y,color="orange",label="Fitting Line",linewidth=2) #画拟合直线
plt.legend
plt.show()#coding=gbk
import numpy as np
from scipy.optim