scikit-learn versions of libsvm has been extended to be able to work with training data represented as dense arrays (directly addressing the memory of a floating point numpy array) in addition to the native sparse data reprenentation of the original libsvm. So presumably you might have to convert your support vectors from one representation to the other but that should not be very complicated.
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