针对二分类问题的线性判别分析模型

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针对二分类问题的线性判别分析模型

以下代码片内容为周志华著《机器学习》习题3.5的程序（关于二分类问题的线性判别分析模型）。

# 周志华，机器学习，习题3.5，线性判别分类# 导入库import numpy as npimport matplotlib.pyplot as plt# 存入训练集AttrSet = np.matrix([[0.697,0.460],[0.774,0.376],[0.634,0.264],[0.608,0.318],                  [0.556,0.215],[0.403,0.237],[0.481,0.149],[0.437,0.211],                  [0.666,0.091],[0.243,0.267],[0.245,0.057],[0.343,0.099],                  [0.639,0.161],[0.657,0.198],[0.360,0.370],[0.593,0.042],[0.719,0.103]]).TFlagSet = np.matrix([np.concatenate((np.ones(8),np.zeros(9)))]).T# 训练集分类NumAttr = AttrSet.shape[0]NumSam = AttrSet.shape[1]AttrSetGood,AttrSetBad = np.matrix([[] for m in range(NumAttr)]),np.matrix([[] for m in range(NumAttr)])for m in range(NumSam):    if FlagSet[m,0]==1:        AttrSetGood = np.c_[AttrSetGood,AttrSet[:,m]]    else:        AttrSetBad = np.c_[AttrSetBad,AttrSet[:,m]]# 求解线性判别分类的系数UGood,UBad = np.mean(AttrSetGood,1),np.mean(AttrSetBad,1)# np.cov(A)函数将A的每一个列向量当做随机变量列向量的样本，计算其协方差矩阵，需要注意的是计算结果除了样本的个数-1SW = np.cov(AttrSetGood)*(AttrSetGood.shape[1]-1) + np.cov(AttrSetBad)*(AttrSetBad.shape[1]-1)WBest = np.linalg.pinv(SW)*(UGood-UBad)# 观察习得模型的准确性plt.close('all')plt.figure(1)plt.scatter(np.array(AttrSetGood[0,:]).reshape(AttrSetGood.shape[1],order='C'),np.array(AttrSetGood[1,:]).reshape(AttrSetGood.shape[1],order='C'),marker='o',color='k',label='EsGood')plt.scatter(np.array(AttrSetBad[0,:]).reshape(AttrSetBad.shape[1],order='C'),np.array(AttrSetBad[1,:]).reshape(AttrSetBad.shape[1],order='C'),marker='o',color='r',label='EsBad')plt.xlabel('Density')plt.ylabel('Sugar')plt.legend(loc='upper left')plt.title('Exercise Set')plt.figure(2)IndexGood,IndexBad = [],[]for m in range(NumSam):    IndexGood.append(m) if WBest.T*(AttrSet[:,m]-UGood)*(AttrSet[:,m].T-UGood.T)*WBest

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