sklearn SVM 支持向量机

2018-01-22



In [1]:

    
from sklearn import svm

分类svc



In [6]:

    
x = [[0,0], [1,1], [1,0]]
y = [0,1,1]



In [7]:

    
clf = svm.SVC()
clf.fit(x,y)









    Out[7]:





SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False)



In [12]:

    
z = [2,2]
result = clf.predict([[2,2]])
print(result)

[1]



In [14]:

    
print(clf.support_vectors_)
print(clf.support_)
print(clf.n_support_)









    



[[ 0.  0.]
 [ 1.  1.]
 [ 1.  0.]]
[0 1 2]
[1 2]

回归



In [15]:

    
x = [[0, 0], [1, 1]]  
y = [0.5, 1.5]   
clf = svm.SVR()   
clf.fit(x, y)









    Out[15]:





SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
  kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)



In [17]:

    
z = [[2,2]]
result = clf.predict([[2, 2]])   
print(result)









    



[ 1.22120072]



In [ ]: