import numpy as np import matplotlib.pyplot as plt def sigmod(x): return 1/(1+np.exp(-x)) label = np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1], np.float32).reshape(16, 1) data = np.array( [(0.5, 0.5), (0.5, 1.0), (0.5, 1.5), (0.8, 2.0), (0.6, 1.2), (0.9, 1.3), (1.2, 0.9), (1.5, 0.5) , (1.1, 2.9), (1.5, 3.0), (1.6, 2.5), (1.8, 2.0), (1.9, 3.1), (2.3, 2.8), (2.3, 1.6), (2.9, 1.5)], np.float32) data_num,data_dim = data.shape new_data = np.ones((data_num,data_dim+1)) new_data[:,:2] =data theta = np.random.normal(size=(data_dim+1,1)) lr = 1e-1 for i in range(10000): prdict = new_data@theta prdict = sigmod(prdict) loss = -np.sum(label*np.log(predict)+(1-label)*np.log(1-prdict)) # loss = -np.sum(label * np.log(predict) + (1 - label) * np.log(1 - predict)) d_theta = new_data.T@(prdict-label) theta -= lr*d_theta if i%1000==0: print(f"loss{loss}")
标签:逻辑,回归,0.5,label,prdict,np,theta,data From: https://www.cnblogs.com/xiaoruirui/p/16815666.html