李宏毅机器学习p4 regression demo

李宏毅机器学习p4 regression demo

import numpy as np
import matplotlib.pyplot as plt

x_data = [338, 333, 328, 207, 226, 25, 179, 60, 208, 606]
y_data = [640, 633,619, 393, 428, 27, 193, 66, 226, 1591]

x = np.arange(-200,-100,1)
y = np.arange(-5,5,0.1)
Z = np.zeros((len(x),len(y)))

X,Y = np.meshgrid(x,y)
for i in range(len(x)):
    for j in range(len(y)):
        b = x[i]
        w = y[i]
        Z[j][i] = 0
        for n in range(len(x_data)):
            Z[j][i] = Z[j][i]+(y_data[n]-b - w*x_data[n])**2
        Z[j][i]= Z[j][i]/len(x_data)
b=-120
w=-4
lr = 1
iteration = 1000000

b_history = [b]
w_history = [w]

lr_b = 0
lr_w = 0

for i in range(iteration):
    
    b_grad = 0.0
    w_grad = 0.0
    for n in range(len(x_data)):
         b_grad = b_grad - 2.0*(y_data[n] - b - w*x_data[n])*1
         w_grad =w_grad -2.0*(y_data[n] - b - w*x_data[n])*x_data[n]
        
    lr_b = lr_b +b_grad**2
    lr_w = lr_w +w_grad**2
    #更新参数
    b = b - lr/np.sqrt(lr_b)*b_grad
    w = w - lr/np.sqrt(lr_w)*w_grad
    
    b_history.append(b)
    w_history.append(w)
    

# plot the figure
plt.contourf(x, y, Z, 50, alpha=0.5, cmap=plt.get_cmap('jet'))
plt.plot([-188.4], [2.67], 'x', ms=12, markeredgewidth=3, color='orange')
plt.plot(b_history, w_history, 'o-', ms=3, lw=1.5, color='black')
plt.xlim(-200, -100)
plt.ylim(-5, 5)
plt.xlabel(r'$b$', fontsize=16)
plt.ylabel(r'$w$', fontsize=16)
plt.show()