李宏毅机器学习代码笔记-Regression

该代码仅仅是本人在看学习视频时的记录，可以直接运行的。没有详细解说，大家可以去b站上看李宏毅机器学习的视频。

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) #bias
y = np.arange(-5,5,0.1) #weight
Z = np.zeros((len(x),len(y)))
for i in range(len(x)):
    for j in range(len(y)):
        b = x[i]
        w = y[j]
        Z[j][i] = 0
        for n in range(len(x_data)): # 0-9
            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 = 100000

# Store initial values for plotting.
b_history = [b]
w_history = [w]

lr_b = 0
lr_w = 0

# iterations
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]) * 1.0
        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

    # Update parameters.
    b = b - lr/np.sqrt(lr_b) * b_grad
    w = w - lr/np.sqrt(lr_w) * w_grad

    # Store parameters for plotting
    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)
# xlim，ylim指定坐标轴的取值范围
plt.xlabel(r'$b$',fontsize=16)
plt.xlabel(r'$b$',fontsize=16)
plt.show()