伯努利朴素贝叶斯方法适合于伯努利分布（即二项分布或0-1分布）的数据集。
import numpy as npfrom sklearn.naive_bayes import BernoulliNB#导入数据集生成工具from sklearn.datasets import make_blobs#导入数据集拆分工具from sklearn.model_selection import train_test_splitX,y=make_blobs(n_samples=500,centers=5,random_state=8)X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=8)nb=BernoulliNB()nb.fit(X_train,y_train)print('模型得分：{:.3f}'.format(nb.score(X_test, y_test)))#导入画图工具import matplotlib.pyplot as plt#限定坐标轴范围，X共两列x_min,x_max=X[:,0].min()-0.5,X[:,0].max()+0.5y_min,y_max=X[:,1].min()-0.5,X[:,1].max()+0.5#使用不同的背景色表示不同的分类xx,yy = np.meshgrid(np.arange(x_min, x_max,.02),np.arange(y_min, y_max, .02))z = nb.predict(np.c_[(xx.ravel(),yy.ravel())]).reshape(xx.shape)plt.pcolormesh(xx,yy,z,cmap=plt.cm.Pastel1)plt.scatter(X_train[:,0],X_train[:,1],c=y_train,cmap=plt.cm.cool,edgecolor='k')plt.scatter(X_test[:,0],X_test[:,1],c=y_test,cmap=plt.cm.cool,marker='*',edgecolor='k')plt.xlim(xx.min(),xx.max())plt.ylim(yy.min(),yy.max())plt.title('Classifier: BernoulliNB')plt.show()
