yes that is one of the documents I currently read. I have to be very careful because there were alot of stuff I missed on the first read, so I'm rereading. -------| http://ifni.co
On Thu, Dec 3, 2015 at 5:22 PM, Matthew Taylor <[email protected]> wrote: > Have you read this yet? It should provide a very long-winded answer to your > question about synapses. > > http://arxiv.org/pdf/1511.00083.pdf > > --------- > Matt Taylor > OS Community Flag-Bearer > Numenta > > On Thu, Dec 3, 2015 at 12:45 PM, mraptor <[email protected]> wrote: >> >> > >> The model keeps trying to learn sequences and is creating an >> increasing number of synapses and segments containing the new random >> transitions that it sees. >> > >> >> What is the mechanism/algorithm for creating/destroying segments ? And >> the "attachment" of synapses ! >> Couldn't find explanation for this process in the docs. >> >> thanks >> >> -------| http://ifni.co >> >> >> On Thu, Dec 3, 2015 at 2:25 PM, Subutai Ahmad <[email protected]> wrote: >> > Roberto, >> > >> > This is not unexpected if you are feeding in random data all the time. >> > The >> > model keeps trying to learn sequences and is creating an increasing >> > number >> > of synapses and segments containing the new random transitions that it >> > sees. >> > If you feed in more predictable data (e.g. self.amplitude = >> > (self.amplitude >> > + 1)%200 ) you should not see such a large increase in time. If you >> > still >> > see a big increase with predictable data then there might indeed be some >> > memory issue. >> > >> > --Subutai >> > >> > On Thu, Dec 3, 2015 at 9:34 AM, Roberto Becerra >> > <[email protected]> >> > wrote: >> >> >> >> Hi community! >> >> >> >> So, I have built a very simple script just to try the speed of >> >> execution >> >> of NuPIC, because I am seeing that it slows down a lot after a few >> >> hours of >> >> execution, I wonder if you have observed this or have any comments on >> >> something weird I might be doing. The script goes like this: >> >> >> >> self.model = ModelFactory.create(model_params.MODEL_PARAMS) >> >> self.model.enableInference({'predictedField': 'binAmplitude'}) >> >> self.likelihood= AnomalyLikelihood() >> >> self.startTime = time.time() >> >> while True: >> >> self.amplitude = random.randint(0,200) >> >> self.result = self.model.run({"binAmplitude" : >> >> self.amplitude}) >> >> self.anomaly = self.result.inferences['anomalyScore'] >> >> self.likelihood = >> >> self.likelihood.anomalyProbability(self.amplitude, self.anomaly) >> >> print 'Loop Period: ' + format(time.time() - self.startTime) >> >> self.startTime = time.time() >> >> >> >> It is creating one model and running forever with random inputs. In >> >> the >> >> beginning the Loop Period is around 0.01 seconds, or 100 Hz, but as >> >> time >> >> goes on (I left it running over night) the period increased to values >> >> that >> >> are not constant, but reaching up to 4 secods, 10 seconds or even 128 >> >> seconds! >> >> >> >> I am running quite a limited computer, but I don´t think this is the >> >> cause, maybe some memory leak? or resources that are available for >> >> python? >> >> >> >> OSX El Capitan (but it was happening in Yosemite as well) >> >> Mac Mini Intel Core 2 Duo 2.0GHz , A1283 2GB 250GB >> >> >> >> What do you think of this? Thanks! >> >> >> >> Roberto Becerra >> >> https://iobridger.wordpress.com/ >> > >> > >> >
