Thanks for the compliments. Just to make sure the history books get it right: I saw this thing over here, and this other thing over there, and I decided to bring them together. All credit goes to Felix for creating the thing. It's awesome having this tool that was shaped by tasks like implementing HTM <https://github.com/nupic-community/comportex/>, using it <https://nupic-community.github.io/sanity/>, and studying it <http://floybix.github.io/>.
Chetan: I'm glad you like it, thanks for diving in. Regarding higher-level statistics: you, Felix, and Bill Atkinson <https://youtu.be/OHSuydq2OW4?t=7m36s> have all thought of this. :) So I added it. Here's the commit <https://github.com/nupic-community/sanity-nupic/commit/2e18722a7abb4cda5f095e205fddbbe76efb15d1>. It was already there in the client, it just needed the HTM server to participate. Here's a TM receiving predictable sequences with resets at the beginning. Time is on the left-right axis, and number-of-columns is on the top-bottom axis. [image: Inline image 1] Here's the hotgym example, *using temporal_memory.py*: [image: Inline image 2] Here's the hotgym example, *using TP10X2.py* [image: Inline image 3] On Mon, Nov 30, 2015 at 9:40 AM, Matthew Taylor <[email protected]> wrote: > What Marcus has done for HTM visualizations is truly amazing. If this > type of thing had existed when I first started working with HTM, the > learning curve would have been significantly decreased. > > On Wed, Nov 25, 2015 at 3:40 PM, Chetan Surpur <[email protected]> > wrote: > > A nice little project for an interested community member might be to > create > > an example of high-order sequence learning, visualize it with Sanity, and > > make a screencast or an interactive document that demonstrates how the > > temporal memory learns high order sequences. > > If anyone does this, I will send that person a box of goodies. :) > > --------- > Matt Taylor > OS Community Flag-Bearer > Numenta > >
