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 _______________________________________________ mlpack mailing list [email protected] http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack
