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

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