>>> from sklearn import svm
>>> X = [[0, 0], [1, 1]]
>>> y = [0, 1]
>>> clf = svm.SVC()
>>> clf.fit(X, y)  
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)

>>> clf.predict([[2., 2.]])
array([1])

from sklearn import svm
clf = svm.SVC(kernel = 'linear')
clf.fit(features_train, labels_train)
pred = clf.predict(features_test)
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(pred, labels_test)
print accuracy