附上代码：
# -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt data = [[0.697,0.460,1], [0.774,0.376,1], [0.634,0.264,1], [0.608,0.318,1], [0.556,0.215,1], [0.403,0.237,1], [0.481,0.149,1], [0.437,0.211,1], [0.666,0.091,0], [0.243,0.267,0], [0.245,0.057,0], [0.343,0.099,0], [0.639,0.161,0], [0.657,0.198,0], [0.360,0.370,0], [0.593,0.042,0], [0.719,0.103,0]] # 2 attributes(column),17 instances(row) data = np.array([i[:-1] for i in data]) X0 = np.array(data[:8]) X1 = np.array(data[8:]) miu0 = np.mean(X0,axis=0).reshape((-1,1)) miu1 = np.mean(X1,axis=0).reshape((-1,1)) cov0 = np.cov(X0,rowvar=False) cov1 = np.cov(X1,rowvar=False) scatter_w = np.mat(cov0 + cov1) omiga = scatter_w.I * (miu0-miu1) plt.scatter(X0[:,0],X0[:,1],label='+') plt.scatter(X1[:,0],X1[:,1],label='-') plt.plot([0,1],[0,-omiga[0]/omiga[1]],label='y') plt.legend() plt.show()下图为所得到的超平面（本题为二维数据，因而超平面为线），由此可见，LDA对上述数据集的分类错误率为3/17。
