Vlad Niculae
Vlad Niculae
Hi, thanks a lot! First of all, I agree this is a feature that should be implemented, and it should not be too difficult. Would you be interested in contributing...
The first step should be figuring out what objective function we want, so we can work out the intercept upgrades. Then, writing some failing unit tests.
If i'm not mistaken, if you use `fit_lower='augment', fit_linear=True`, you will (indirectly) be learning an intercept; check the dimensionality of the learned weight vectors and matrices: they will be greater...
Thanks for pointing that out, you are not doing anything wrong. Indeed, `fit_lower='augment'` was designed with lower degrees in mind, not with linear terms in mind. If you set `fit_linear=False,...
(I had some typos in the comment above. If viewing this by e-mail, please visit the updated comment on github)
Hi Anmol, This is a very interesting question. Usually, it's the **weights** of a model that are constrained to be non negative, not the outputs. In the case of factorization...
This is interesting, thank you @equialgo!
Thanks for the find! We'll look into it. Has this caused memory issues for you?
At a glance I think you're correct: `active_set_[k]` and `configuration` are the same configuration, but different in-memory objects, so I think the highlighted line should go. @andre-martins do you agree?
hmm, I don't think `decode_matrix_tree` belongs in ad3, what is the case for adding it?