python 画支持向量机分类图（svm）

import numpy as np
import pylab as pl   #画图用
from sklearn import svm
import matplotlib.pyplot as plt
from sklearn import svmnp.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)# get the separating hyperplane
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])# plot the line, the points, and the nearest vectors to the plane
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()

画出的图结果如下：

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