Hi,
I'm just starting out with nupic, nd would really appreciate a pointer or
two on which inference model is best for my task.
I'm trying to train a model to to perform a specific feature detection task
on discrete data. My Input data is:
* S1 a set of N discrete category values representing features derived from
raw spatial-domain data
* S2 a set of N feature categorisations of S1 sequences, where element S2_i
can -- broadly speaking -- be determined from { S1_(i-n) ... S1_(i+n) }
(although exact value of n is unknown)
I would like to train a model to learn to perform the S1->S2 feature
categorisation. For a given sequence S1, the model should give a best guess
at the likely feature for each element.
My initial attempt, based on the one_gym and opf examples, is to treat the
data as a temporal sequence and train a TemporalMultiStep on each
{S1_i,S2_i} tuple (using the "string" type for the data).
I don't know how successful this is going to be (still swarming...) , but I
can already see some problems. My questions are:
* Is TemporalMultiStep the right inference type for this? (I don't need
prediction at future i, just an inference of S2_i). Would
TemporalClassifier be more appropriate?
* In the case where all of the S1 set is known in advance, treating the
data as temporal input means that the model is unable to learn from
'future' input, which is certainly going to make the feature learning
harder. (Though I could combine results inference from running S1 both
forwards and backwards through the model?)
Would it instead be better to try a classification of intervals over S1, eg
giving as inputs { {S1_i-n .. S1_i+n}, S2_i } ?. In that case, would it be
better to treat this with a non-temporal model? (NontemporalClassifier?)
I'd be most grateful any opinions or advice.
Cheers,
Matt
