李宏毅-機器學習&深度學習-筆記-第一練習-線性迴歸

import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm_notebook

x_data = [338.,333.,328.,207.,226.,25.,170.,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)
X, Y = np.meshgrid(x, y)

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)):
            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 # initial b
w = -4   # initial w
lr = 1 # learning rate
iteration = 1000000
# store initial value for plotting
b_history = [b]
w_history = [w]

llr_b = 0
lr_w = 0


# iteration
for i in tqdm_notebook(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]
    
    # AdaGrad
    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)
print("b--->", b)
print("w--->", 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')  #這個點是根據上面遍歷後，得到的結果選取的
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()