python随机梯度下降多项回归

import matplotlib.pyplot as plt
import numpy as np
x=[[400],[450],[484],[500],[510],[525],[540],[549],[558],[590],[610],[640],[680],[750],[900]]
y=[[80],[89],[92],[102],[121],[160],[180],[189],[199],[203],[247],[250],[259],[289],[356]]
x2=[]
plt.plot(x,y,'ks')
for i in range(len(x)):
    x2.append(x[i][0]*x[i][0])
def polynomial_regression(x,x2,y):
    alpha=0.00000000001
    m=len(x)
    w0=0
    w1=0
    w2=0
    count=0
    error2=0
    while 1:
        finish=0
        w=[0,0,0]
        for i in range(m):
            count+=1
            w[0]=w0+w1*x[i][0]+w2*x2[i]-y[i][0]
            w[1]=(w0+w1*x[i][0]+w2*x2[i]-y[i][0])*x[i][0]
            w[2]=(w0+w1*x[i][0]+w2*x2[i]-y[i][0])*x2[i]
            w0=w0-alpha*w[0]
            w1=w1-alpha*w[1]
            w2=w2-alpha*w[2]
            error1=abs(w0+w1*x[i][0]+w2*x2[i]-y[i][0])
            if abs(error2-error1)<1:#收敛程度
                finish=1
                break
            if(count>15):
                finish=1
                break
            error2=error1
            print('w0:%f,w1:%f,w2:%f,error:%f'%(w0,w1,w2,error1))
        if(finish==1):
            break
    y=[]
    for i in range(len(x)):
        y.append(w0+w1*x[i][0]+w2*x2[i])
    plt.plot(x,y,'g')
polynomial_regression(x,x2,y)
plt.show()