Thanks Marcus and Ryan for the feedback. I will look into some recent publications for some better methods and see if I can find something nice to work on.
Thanks, Rishabh Garg On Mon, Mar 15, 2021 at 9:13 PM Marcus Edel <[email protected]> wrote: > Personally, I would check some recent publications first and see if there > is a > method out there that performs better than other methods; maybe it makes > sense to add that to mlpack as part of the project; if it uses the basic > building > blocks you mentioned, great, we can use that as a foundation to think about > the interface that actually reuses some of the building blocks to build > the more > complex method. > > > On 14. Mar 2021, at 14:49, Ryan Curtin <[email protected]> wrote: > > > > On Sun, Mar 14, 2021 at 10:19:24PM +0530, RISHABH GARG wrote: > >> Hello Marcus and Ryan, I did a bit of research and found a few pitfalls > in > >> the statsmodels library :- > >> 1. The algorithms written in it are in-memory algorithms, so it is > >> incapable of handling large datasets. > >> 2. It does not have very good documentation. > >> > >> We can easily beat it in terms of documentation, but I am not sure about > >> the external memory algorithms. Also, I would like to know if the > >> algorithms implemented in mlpack are in-memory or external memory? > > > > All mlpack models use Armadillo, which only supports in-memory > > computation, but the algorithms themselves are implemented in a generic > > way, so with a little bit of work and hacking it is possible to use > > external memory for mlpack computations (but I think nobody is really > > doing this). > > > > -- > > Ryan Curtin | "Hey, tell me the truth... are we still in the > > [email protected] | game?" - The Chinese Waiter > >
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