The main thing is that we still want templated specialisations of the train methods. Not sure that works with this other approach? But would have to check... I think we could think of the proposed solution as a mix of the double dispatching (for feature type, string, dense, etc) and the mix ins for the template method overloading...or?
On Tue, 26 Jun 2018 at 12:07, Fernando J. Iglesias García < fernando.iglesi...@gmail.com> wrote: > A different idea to using mix-ins for dispatching: > https://en.wikipedia.org/wiki/Double_dispatch#Double_dispatch_in_C++ > In a nutshell the idea is that on calling CMahine::train(CFeatures* f) we > use some method in the hierarchy of CFeatures that works out the > "downcast". It feels a bit like Shogun's obtain_from_generic, though a key > difference is that obtain_from_generic is static. > > What do you think? > > On Mon, 25 Jun 2018 at 17:55, Heiko Strathmann <heiko.strathm...@gmail.com> > wrote: > >> feedback welcome. >> >> https://gist.github.com/karlnapf/95a9c72a642d61ec268a39407f8761b2 >> >> Problem: >> currently, dispatching happens inside train_machine of algorithm >> specializations: redundant code, error prone, might be forgotten at all >> Actually, only LARS, LDA do this, most other classes do nothing, i.e. >> crash/error when something else than float64 is passed (bad) >> LARS/LDA solved this via making the train method templated and then >> dispatch inside train_machine. >> >> Solution we propose (result of a discussion with Viktor last week, >> refined in a meeting with Giovanni and Shubhab today): Use mixins to keep >> dispatching code in algorithm specializations, this allows for templated >> train methods and gives a compile error if they are not implemented by the >> algorithm author. Yet we can centralize the code to make algorithm >> specialisations nicer and less error prone. See gist. >> We will have to think how all this works with multiple feature types >> (string, etc), and also how multiple mix-ins can be combined (e.g. LARS is >> a LinearMachine, IterativeMixIn, DenseTrainMixIn, and it would be the >> 'iteration' method that would be templated. >> Shubham will draft a compiling minimal example for this. >> >> >> First attempt (doesnt work) >> Move dispatching into base class CMachine. Call templated train methods >> there which are overloaded in subclass. BUT cannot have virtual templated >> methods, so this wont fly. >> >> >> H >> > _______________________________________________ >> shogun-team mailing list >> shogun-t...@shogun-toolbox.org >> https://nn7.de/cgi-bin/mailman/listinfo/shogun-team >> > -- Sent from my phone
