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/ > > > > > >
