I am trying to train an SVM in scikit. I am following the example and tried to adjust it to my 3d feature vectors. I tried the example from the page http://scikit-learn.org/stable/modules/svm.html and it ran through. While bugfixing I came back to the tutorial setup and found this:
X = [[0, 0], [1, 1],[2,2]]
y = [0, 1,1]
clf = svm.SVC()
clf.fit(X, y)
works while
X = [[0, 0,0], [1, 1,1],[2,2,2]]
y = [0, 1,1]
clf = svm.SVC()
clf.fit(X, y)
fails with:
ValueError: X.shape[1] = 2 should be equal to 3, the number of features at training time
what is wrong here? It's only one additional dimension... Thanks, El
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?