线性回归 Python实现

 1 import numpy as np
 2 import pylab
 3 
 4 
 5 def plot_data(data, b, m):
 6     x = data[:, 0]
 7     y = data[:, 1]
 8     y_predict = m*x + b
 9     pylab.plot(x, y_predict, 'k-')
10     pylab.plot(x, y, 'o')
11     pylab.show()
12 
13 
14 def gradient(data, initial_b, initial_m, learning_rate, num_iter):
15     b = initial_b
16     m = initial_m
17     x = data[:, 0]
18     y = data[:, 1]
19     n = float(len(data))
20     for i in range(num_iter):
21         b_gradient = -(1/n)*(y - m*x-b)
22         b_gradient = np.sum(b_gradient, axis=0)
23         m_gradient = -(1/n)*x*(y - m*x - b)
24         m_gradient = np.sum(m_gradient)
25         theta0 = b - (learning_rate*b_gradient)
26         theta1 = m - (learning_rate*m_gradient)
27         b = theta0
28         m = theta1
29         if i % 100 == 0:
30             j = (np.sum((y - m*x - b)**2))/n
31             print("参数b:{},参数m:{},损失值：{}".format(b, m, j))
32     return [b, m]
33 
34 
35 def linear_regression():
36     # 导入数据
37     data = np.loadtxt('data.csv', delimiter=',')
38 
39     # 线性回归参数设定
40     learning_rate = 0.001
41     initial_b = 0.0
42     initial_m = 0.0
43     num_iter = 1000
44 
45     b, m = gradient(data, initial_b, initial_m, learning_rate, num_iter)
46     plot_data(data, b, m)
47 
48 if __name__ == '__main__':
49     linear_regression()

测试结果：