python实现梯度下降算法
本帖最后由 zhoufanking 于 2013-05-14 00:24:24 编辑 刚开始学习机器学习，想自己用python实现课件中的算法，加深认识。下面是我写的使用随机梯度下降计算线性回归模型参数的代码，但是得到的结果不对，请帮忙看看！
import sys
#Training data set
#each element in x represents (x0,x1,x2)
x = [(1,2104,3) , (1,1600,3) ,(1,2400,3), (1,1416,2) , (1,3000,4)]
#y[i] is the output of y = theta0 * x[0] + theta1 * x[1] +theta2 * x[2]
y = [400,330,369,232,540]
epsilon = 0.00000001
#learning rate
alpha = 0.0001
diff = [0,0]
max_itor = 1000
error1 = 0
error0 =0
cnt = 0
m = len(x)
#init the parameters to zero
theta0 = 0
theta1 = 0
theta2 = 0
while cnt < max_itor:
cnt = cnt + 1
#calculate the parameters
for i in range(m):
diff[0] = y[i]-( theta0 + 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
for lp in range(len(x)):
error1 += ( y[i]-( theta0 + theta1 * x[i][1] + theta2 * x[i][2] ) )**2/2
if abs(error1-error0) < epsilon:
break
else:
error0 = error1
print ' theta0 : %f, theta1 : %f, theta2 : %f, error1 : %f'%(theta0,theta1,theta2,error1)
print ' theta0 : %f, theta1 : %f, theta2 : %f'%(theta0,theta1,theta2)
