another thing:
if i put some pattern with None as predicted field, as suggested here (
https://github.com/numenta/nupic/wiki/Spatial-Classification), the model is
actually modified and None is threated as a category/value... and the
output is "None".

thanks again

Alessandro



On Wed, Sep 4, 2013 at 9:16 AM, Alessandro Bardelli <
[email protected]> wrote:

> hi everyone,
> I am new to CLA and nupic and I have some troubles in getting started,
> understand how to build a model and set the parameters correctly.
> my questions:
>
> 1) Is there a tutorial or a reasoned example (i.e. highlighting the main
> implementation/options choices.) where i can start from? for now I have
> started reading the examples and going up in the "hierarchy", trying to
> understand the source code documentation...but it's almost a reverse
> engineering approach :). I have found the slides of the osconlecture...is 
> there also a video?
>
> 2) I have found the description_template.py but it seems to me that has a
> quite different syntax respect to the others description files (e.g. the
> encoders). am i missing something?
>
> 3) in the scalar_1 example:
>     the randomData feature is ignored, isn't it? how the "n" and "w" are
> chosen? is there a rule?
>
>     why is the SP disabled?
>
>     i have tried to change this example to classify the patterns according
> to the sum of two features, i.e.
>     class, field1, field2, randomData
>     float,float,float,float
>     ,,,
>     3,1,2, something
>     7,3,4, something
>     7,1,6, something
>     1,0,1, something
>     5,1,4, something
>     ....
>     .....
>     to do this i've just added an encoder for field2 and randomData...but
> i am unable to get a decent result. where i am wrong?
>
>
> 4) is there a way to save the model produced and use it again both to make
> inferences and to do some more train over new data?
>     As what concerning the latter i have hacked the OpfRunExperiment.py
> file with a for cycle
>       model=runExperiment(sys.argv[1:])
>       for c in range (0,9):
>          runExperiment(sys.argv[1:],model)
>
>     but i am quite sure that there is a cleaner and smarter way of doing
> this
>
> thanks
>
> Alessandro
>
> p.s. sorry for my bad english
>
>
>
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