支持向量机 人脸识别（SVM）SKLearn

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
import pylab as pl
from sklearn import svm

# we create 40 separable points
np.random.seed(0)#每次运行结果不变
X = np.r_[np.random.randn(20, 2) - [2, 2], np.random.randn(20, 2) + [2, 2]]
#randn20,0   产生20个点每个点两维
#-+[2,2]正态分布范围
Y = [0]*20 +[1]*20
print(X,Y)

#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]#斜率
xx = np.linspace(-5, 5)
yy = a*xx - (clf.intercept_[0])/w[1]#clf.intercept_[0]bias偏置

# 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])

print "w: ", w
print "a: ", a

# print "xx: ", xx
# print "yy: ", yy
print "support_vectors_: ", clf.support_vectors_
print "clf.coef_: ", clf.coef_

# switching to the generic n-dimensional parameterization of the hyperplan to the 2D-specific equation
# of a line y=a.x +b: the generic w_0x + w_1y +w_3=0 can be rewritten y = -(w_0/w_1) x + (w_3/w_1)


# plot the line, the points, and the nearest vectors to the plane
pl.plot(xx, yy, 'k-')
pl.plot(xx, yy_down, 'k--')
pl.plot(xx, yy_up, 'k--')

pl.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],
          s=80, facecolors='none')
pl.scatter(X[:, 0], X[:, 1], c=Y, cmap=pl.cm.Paired)

pl.axis('tight')
pl.show()
#!/usr/bin/env python
# -*- coding: utf-8 -