Ok I think I see now. So you are training the model that certain sequences of words are associated with certain event types. And you are asking it to predict event types based on what it has already seen.
Correct me if I am wrong, but it seems that your goal is to have a system that generalizes on the input text. I don't think the approach you are taking is going to work well at generalization unless you train it on hundreds of different phrases over and over. It has to learn that terms like "upwards" and "up" and "forward" and "north" have similar meanings, and there is no semantic meaning encoded in those category terms until the model has seen enough to know that a certain event type will follow it. --------- Matt Taylor OS Community Flag-Bearer Numenta On Tue, Jan 12, 2016 at 10:34 AM, Sebastián Narváez <[email protected]> wrote: > Both Events and Words are strings, and both are encoder with the same > nupic built-in Category Encoder. You could say my current model is learning > a sequence of strings, it makes no differentiation whatsoever between an > Event and a Word. The CLA Classifier does a multistep prediction, of 1 and > 2 steps ahead. When I'm training the model, I pass the entire sequence, > words and events. Then, when I'm testing, I only pass the words and let the > CLA Classifier predict the next to elements, which should be the events. > > On Tue, Jan 12, 2016 at 1:02 PM, Matthew Taylor <[email protected]> wrote: > >> So you have a NuPIC model accepting sentences like "move to the left", >> and it is classifying them into event types? If so, how are you doing that? >> Are the words treated as string categories? Is each sentence a sequence? >> How is a prediction being translated into an event type? I'm really >> confused. :? >> >> --------- >> Matt Taylor >> OS Community Flag-Bearer >> Numenta >> >> On Mon, Jan 11, 2016 at 12:39 PM, Sebastián Narváez <[email protected]> >> wrote: >> >>> Ok, sorry for the inaccuracy, I was trying to explain it in a quick >>> manner. I'm making a task execution software for a virtual environment >>> (It's like a game. So far the character can move up, down, left and right) >>> with nupic. It takes a sentence as a parameter (which is treated as a >>> sequence of words) and outputs an event on the virtual environment (i.e.: >>> the movement of the character). So for example, an input can be ['move', >>> 'to', 'the', 'left'], and it should output ['event-move', 'event-left']. >>> >>> I've tried various encoders and structures. So far I've got the best >>> results using the same Category Encoder to encode the words of the input >>> sentence, and the output events, and passing them trough a SP, a TM all the >>> way to the CLA Classifier. I train the model by passing a sequence of words >>> and then it's corresponding sequence of events, then reset()ing the TM and >>> passing another pair of sequences. >>> >>> After roughly 50 iterations, the model correctly predicts the sequence >>> of events 77.77% of the time, given a sequence of words (or input >>> sentence). Technically, it should always succesfully predict the sentences, >>> as I'm currently testing with the exact same training set (there is no >>> noise whatsoever), so I thought it might improve if I adjusted the SP >>> and/or TM parameters. In fact, before I played with them a little, my >>> succes rate was of about 66.66%. What I've found funny, however, is that it >>> almost always predict correctly if the sentence ends with the direction >>> (e.g. ['move', 'it', 'upwards']), but fails if it doesn't (e.g. ['could', >>> 'you', 'move', 'it', 'upwards', '?']). >>> >>> I hope I explained myself clearly. Please tell me if there's something >>> weird in my explanation; english is not my main language. >>> >>> On Mon, Jan 11, 2016 at 1:36 PM, Matthew Taylor <[email protected]> >>> wrote: >>> >>>> Sebastian, is it possible to talk about this data without abstraction? >>>> I'm having a hard time understanding what you mean by "fixed size >>>> sequence". It would help if you could talk about the actual sensors >>>> producing this data? >>>> >>>> Either way, if you swarm for a model with two data sources in an >>>> attempt to optimize the predictions of one of these fields, many times the >>>> swarm exposes an unexpected lack of correlation between the two fields. >>>> Sometimes it is simply that to predict field A, the value of field B simply >>>> does not matter enough to keep track of it. >>>> >>>> If both sequences are independent of each other like you say, I would >>>> not expect the value of one field to affect the value of another field at >>>> all. >>>> >>>> >>>> --------- >>>> Matt Taylor >>>> OS Community Flag-Bearer >>>> Numenta >>>> >>>> On Fri, Jan 8, 2016 at 7:26 PM, Sebastián Narváez <[email protected]> >>>> wrote: >>>> >>>>> Sure. I have two inputs from different sensors. Having a sequence from >>>>> the first sensor (which isn't of a fixed size), I want to predict a >>>>> sequence from the second sensor (which right now is of a fixed size of >>>>> two, >>>>> but it would be nice to generalize to non-fixed size later). When a >>>>> sequence from the first sensor is passed to Nupic and it returns a >>>>> prediction, another sequence from the first sensor is then passed, that >>>>> have no relation whatsoever with the previous sequence. That's what I >>>>> meant >>>>> with non-streaming data. Each sequence is independent from the other. >>>>> >>>>> >>>>> On Wed, Jan 6, 2016 at 2:04 PM, Matthew Taylor <[email protected]> >>>>> wrote: >>>>> >>>>>> Hi Sebastian, >>>>>> >>>>>> We recommend you use the swarming library to identify the best SP and >>>>>> TM parameters. See >>>>>> https://github.com/numenta/nupic/wiki/Running-Swarms for details. >>>>>> >>>>>> I'm not sure what you mean by non-streaming data, can you elaborate? >>>>>> >>>>>> >>>>>> --------- >>>>>> Matt Taylor >>>>>> OS Community Flag-Bearer >>>>>> Numenta >>>>>> >>>>>> On Tue, Jan 5, 2016 at 3:42 PM, Sebastián Narváez < >>>>>> [email protected]> wrote: >>>>>> >>>>>>> Hey guys. Is there any recommendations for the SP and TM parameteres >>>>>>> based on the type of inputs or the encoder used? Also, how does swarming >>>>>>> behave with non-streming data? (i.e.: A dataset of independent >>>>>>> sequences). >>>>>>> >>>>>>> Thanks in advance! >>>>>>> >>>>>> >>>>>> >>>>> >>>> >>> >> >
