I have trained & validated a SVM using the scikit-learn SVC (RBF) class without problems, but my final deployment of the classifier is to be in a body of C++ code using libsvm only, no Python, and this is an existing system reading the libsvm .model sparse format text file. To save retraining using the libsvm command line tools, I'm considering implementing a SVC method to write the equivalent libsvm .model file from the SVC attributes such as support_vectors_ etc. I wanted to ask the experts about the feasibility, particularly if there are pitfalls I'm not aware of? That does assume that the support vectors would themselves be compatible, given libsvm as the common code base?
Leigh M. Smith