我们先演示支持向量机的基础使用,完整演示代码请见本书GitHub上的9-1.py。
导入库文件:
print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn import svm
创建40个随机点:
np.random.seed(0) X = np.r_[np.random.randn(20, 2) - [2, 2], np.random.randn(20, 2) + [2, 2]] Y = [0] * 20 + [1] * 20 # fit the model clf = svm.SVC(kernel='linear') clf.fit(X, Y)
构造超平面:
w = clf.coef_[0] a = -w[0] / w[1] xx = np.linspace(-5, 5) yy = a * xx - (clf.intercept_[0]) / w[1] # plot the parallels to the separating hyperplane that pass through the # support vectors b = clf.support_vectors_[0] yy_down = a * xx + (b[1] - a * b[0]) b = clf.support_vectors_[-1] yy_up = a * xx + (b[1] - a * b[0])
调用matplotlib画图:
plt.plot(xx, yy, 'k-') plt.plot(xx, yy_down, 'k--') plt.plot(xx, yy_up, 'k--') plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=80, facecolors='none') plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired) plt.axis('tight') plt.show()
运行代码:
localhost:code maidou$ python 9-1.py None localhost:code maidou$
SVM hello word代码如图9-9所示。
图9-9 SVM hello word代码