随机梯度下降法

import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt

alpha = 0.01
a0 = np.random.uniform(0, 1)
a1 = np.random.uniform(0, 1)
a2 = np.random.uniform(0, 1)
a = np.array([a1, a2])

eps = 1e-4
e0 = 1
e1 = 1
e2 = 1

x = np.array([[2, 3], [4, 6], [7, 8], [12, 15]])
t = np.array([5, 8, 14, 17])

cnt = 0  
while (abs(e0) >= eps or abs(e1) >= eps or abs(e2) >= eps): 
    cnt += 1
    i = np.random.randint(0,4)
    e0 = (np.sum((x[i] * a)) + a0 * 1 - t[i])
    e1 = (np.sum((x[i] * a)) + a0 * 1 - t[i]) * x[i][0]
    e2 = (np.sum((x[i] * a)) + a0 * 1 - t[i]) * x[i][1]
    i = np.random.randint(0,4)
    e0 += (np.sum((x[i] * a)) + a0 * 1 - t[i])
    e1 += (np.sum((x[i] * a)) + a0 * 1 - t[i]) * x[i][0]
    e2 += (np.sum((x[i] * a)) + a0 * 1 - t[i]) * x[i][1]
    e0 /= 5
    e1 /= 5
    e2 /= 5

    a0 = a0 - alpha * e0 
    a1 = a1 - alpha * e1
    a2 = a2 - alpha * e2
    a = np.array([a1, a2])  
print(cnt)
print(a0, a1, a2)

fig = plt.figure()
ax = Axes3D(fig)
a = np.arange(0, 20) 
b = np.arange(0, 20)
A, B = np.meshgrid(a, b) 

Z = a0 + a1 * A + a2 * B 
plt.xlabel('x1')
plt.ylabel('x2')
ax.plot_surface(A, B, Z, rstride=3, cstride=3, cmap='rainbow')  
plt.show()

193320
4.7166585579851406 3.3440500715117722 -1.7656272392230077