[人工智能]监督学习之梯度下降法的应用

import numpy as np
import random
import math

theta0 = np.random.random()
theta1 = np.random.random()
theta2 = np.random.random()
theta = np.array([theta1, theta2])
alpha = 0.1
x = np.array([[2104,3],[1600,3],[2400,3],[1416,3],[3000,3]])
t = np.array([400,330,369,232,540])
eps = 1e-4
e0 = 99
e1 = 99
e2 = 99
i = 0
while e0 >= eps or e1 >= eps or e2 >= eps :
    e0 = (np.sum((x[i] * theta)) + theta0 - t[i])
    e1 = e0 * x[i,0]
    e2 = e0 * x[i,1]
    theta0 = theta0 - alpha * e0
    theta1 = theta1 - alpha * e1
    theta2 = theta2 - alpha * e2
    i+=1
    if i >= 5 :
        i = 0
print(theta0,theta1,theta2)

运行结果如下图所示