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
