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：
