Hi, On Wed, May 11, 2016 at 4:20 AM, 박진만 <[email protected]> wrote:
> Thank you all. I think that you two are talking about the same version. > > > I'm not familiar with coding C++, so I prefer to use python language. > > Then can I use the fastest TP if I include temporal_memory.py like this: > > from nupic.research.temporal_memory import TemporalMemory > not exactly. Either you work with a more high-level OPF and in the description.py you will use `"implementation": "cpp"` (or "tm_cpp", to be exactly sure search the examples or the code). Or you work with the TM,SP,.. classes directly, then you'll need to import `from nupic.bindings.... import ...` All in all, the API is the same, so you can write your code using the Py versions and then switch to the C++ wrappers. I'd say this is a recommended devel approach. > > Or Should I use C++ language to use the fastest TP? > My unconfirmed experiment suggests using C++ directly, see the PR for "low level anomaly example" in nupic.core is even significantly faster then using the C++/swig wrappers for python, but you've said your not very familiar with CPP so the wrappers should be a good choice. > (Can you tell me how to include the fastest TP version?) > > > Also, can you tell me the fastest SP and CLA version and how to include > them? > As above, use the "cpp" implementation, I think it's a default in OPF description.py experiments. Cheers, > > Thank you for answering my questions so kindly. > > > > > 2016-05-11 0:27 GMT+09:00 Matthew Taylor <[email protected]>: > >> Marcus has been making some really great performance improvements to >> the C++ TM, and claims it is now faster than the fastest version of >> the old TP algorithms. >> >> If you want speed, I believe your best bet is to use NuPIC and the >> tm_cpp implementation. >> --------- >> Matt Taylor >> OS Community Flag-Bearer >> Numenta >> >> >> On Tue, May 10, 2016 at 6:57 AM, cogmission (David Ray) >> <[email protected]> wrote: >> > That would be here: HTM.java ! :-) >> > >> > Just being funny, but quite possibly true! >> > >> > I think the temporal_memory.py runs the new C++ TM underneath, and so >> for >> > Python - it might be the fastest because the algorithm has just been >> updated >> > to process things more efficiently... But the Numenta engineers can >> confirm >> > this. >> > >> > Cheers, >> > David >> > >> > On Tue, May 10, 2016 at 7:36 AM, 박진만 <[email protected]> wrote: >> >> >> >> Hello Nupic, I saw that there are many versions of SP, TP and CLA in >> nupic >> >> library. >> >> >> >> For example, there are many TP versions; TP.py, TP10X2.py, >> >> temporal_memory.py.. etc. >> >> >> >> What I want to know is that which one is the fastest one among those TP >> >> implementations. >> >> >> >> I want to use the fastest one because I want to deal with a big amount >> of >> >> data. >> >> >> >> Can you tell me the most useful and the fastest SP, TP, and CLA >> >> implementation codes? >> >> >> >> thank you. >> >> >> >> >> > >> > >> > >> > -- >> > With kind regards, >> > >> > David Ray >> > Java Solutions Architect >> > >> > Cortical.io >> > Sponsor of: HTM.java >> > >> > [email protected] >> > http://cortical.io >> >> > -- Marek Otahal :o)
