Running your latter code works for me:
>>> X = [[0,0,0], [1,1,1], [2,2,2]]
>>> y = [0,1,1]
>>> clf = svm.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, shrinking=True, tol=0.001,
verbose=False)
That error message seems like it should actually happen when you're calling .predict()
on an SVM object with kernel="precomputed"
. Is that the case?