python 梯度下降應用於線性迴歸

import os
import os.path
def lineLogist():
    #train data
    x = [(1,104,3),(1,100,3),(1,200,3),(1,216,2),(1,300,4)]
    #y[i] is the output of y = theta0 * x[0] + theta1 * x[1] +theta2 * x[2]
    y=[400,330,369,232,540]
    epsilom=0.0000000001
    #learning rate
    alpha=0.0001
    diff=[0,0]
    max_iter=1000
    error1=0
    error0=0
    cnt=0
    m=len(x)
    #init the parameters to zero
    theta0=0
    theta1=0
    theta2=0
    while cnt<max_iter:
        cnt+=1
        #calculate the parameters
        for i in range(m):
                
            diff[0]=(y[i])-(theta0*x[i][0]+theta1*x[i][1]+theta2*x[i][2])
       
            theta0=theta0+alpha*diff[0]*x[i][0]
            theta1=theta1+alpha*diff[0]*x[i][1]
            theta2=theta2+alpha*diff[0]*x[i][2]
            #calculate the cost function
        error1=0
        for lp in range(m):
            error1+=( y[lp]-( theta0*x[lp][0] + theta1 * x[lp][1] + theta2 * x[lp][2] ) )**2/2
        if abs(error1-error0)<epsilom:
            break
        else:
            error0=error1
            print 'theta0:%f,theta1:%f,theta2:%f,error1:%f'%(theta0,theta1,theta2,error1)
if __name__=="__main__":
    lineLogist()