import numpy as np
X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
y = np.array([1, 1, 2, 2])
from sklearn.svm import SVC
clf = SVC()
clf.fit(X, y) 
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,
    gamma=0.0, kernel='rbf', max_iter=-1, probability=False,
    random_state=None, shrinking=True, tol=0.001, verbose=False)
print(clf.predict([[-0.8, -1]]))

import numpy as np
X = np.array([[-1, 1], [-2, -1], [1, 1], [2, 1]])
y = np.array([1, 1, 2, 2])
from sklearn.svm import SVC
clf = SVC()
clf.fit(X, y) 
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,
    gamma=0.0, kernel='rbf', max_iter=-1, probability=False,
    random_state=None, shrinking=True, tol=0.001, verbose=False)
print(clf.predict([[-0.8, -1]]))

Test commit