ML之SVM：随机产生100个点，建立SVM模型，找出超平面方程
目录
实现结果
代码实例
import numpy as np
import pylab as pl
from sklearn import svm
X = np.r_[np.random.randn(100, 2) - [2, 2], np.random.randn(100, 2) + [2, 2]]
Y = [0]*100 +[1]*100
clf = svm.SVC(kernel='linear')
clf.fit(X, Y)
w = clf.coef_[0]
a = -w[0]/w[1]
xx = np.linspace(-5, 5)
yy = a*xx - (clf.intercept_[0])/w[1]
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_)
# 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()
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ML之SVM：随机产生100个点，建立SVM模型，找出超平面方程