Oh by the way, keep in mind that I'm still a python novice. Improvements, clarifications, and pull requests are welcome! --------- Matt Taylor OS Community Flag-Bearer Numenta
On Thu, Oct 3, 2013 at 9:59 AM, Matthew Taylor <[email protected]> wrote: > I've been putting together some experiments with NLP and CEPT's word > SDRs. Thanks to Subutai and Francisco for your help with this. > > I've got some initial decent results, at least proving that we can > take CEPT's SDRs as input for the CLA and get predicted SDRs back out > and get the "similar terms" for the SDR from CEPT's API. > > https://github.com/rhyolight/nupic_nlp > > The README on that repo is extensive, so if you are interested, please > get a CEPT API key[1] and try it out with your own word associations. > Here is an example (from the README): > > $ ./run_association_experiment.py resources/animals.txt > resources/vegetables.txt -p 100 -t 1000 > Prediction output for 1000 pairs of terms > > #COUNT TERM ONE TERM TWO | TERM TWO PREDICTION > -------------------------------------------------------------------- > # 100 salmon endive | lentil > # 101 crocodile borage | > # 102 wolf turmeric | amaranth > # 103 termite chickweed | > # 104 quail poke | > # 105 woodpecker shallot | > # 106 echidna caper | tomato > # 107 panther guar | > # 108 ape tomatillo | chrysanthemum > # 109 bee cabbage | > # 110 seahorse sorrel | > # 111 camel tomatillo | lemongrass > # 112 rat chives | > # 113 crab yam | turnip > > This script takes a random term from the first file and a random term > from the second. It converts each term to an SDR through the CEPT API > and feeds term #1 and term #2 into NuPIC, bypassing the spacial pooler > and sending it right into the TP (as described in the hello_tp > example[2]). The next prediction after feeding in term #1 is preserved > and printed to the console. Then it resets the TP so that it can only > learn that simple one->two relationship. In the sample above, NuPIC > should only be predicting plants or vegatables, given that the > association I'm training it on is "animal" --> "vegetable". > > This trivial example seems to be working rather well, although NuPIC > doesn't always have a valid SDR prediction. The predictions it does > create almost always seem to be some sort of plant. Even more > interesting is that sometimes NuPIC predicts SDRs that resolve to > words outside the range of the input values. > > Happy hacking! > --------- > Matt Taylor > OS Community Flag-Bearer > Numenta > > [1] https://cept.3scale.net/signup (YOU MUST upgrade your account to > use the API endpoints this project requires, email [email protected] > and tell him you're working on NuPIC NLP tasks and he'll upgrade you.) > [2] https://github.com/numenta/nupic/blob/master/examples/tp/hello_tp.py _______________________________________________ nupic mailing list [email protected] http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org
