Hi everyone!
Right now, I am trying to port Jun Zhu's MedLDA model in Python, which is a
deterministic topic model.
Right now, I am using 20 newsgroup data crawled by scikit-learn's provided
script.
In the original code, the author used SVMLight to preform large-margin
learning, while scikit-learn uses LibSVM, so I have some problems get
things done.
1. Is there any way in scikit-learn to get the document number for the
support vectors? Since classification is not my sole purpose, document
number is quite curcial for the topic modeling stage. I looked over the
reference section on the scikit-learn website, but couldn't find how to get
the doc number for support vectors.
2. Another problem concerns the dual coefficients, in the documents, *
dual_coef_* which holds the product *y_i * alpha_i*. Right now, I want
to get *alpha_i*, is it OK that i just use *dual_coef_ / y_i* to get*alpha_i
*?
Thank you very much!
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