Great!  I'm glad you gave it a try Matt.

I ran into that JPEG decoder issue on the Amazon EC2 image that Ian
put together.  It looks like PIL and pillow depend on libjpeg-dev for
this so it has to be installed when PIL / pillow is compiled.

http://stackoverflow.com/questions/8915296/python-image-library-fails-with-message-decoder-jpeg-not-available-pil





On Mon, Jul 7, 2014 at 8:41 AM, Matthew Taylor <[email protected]> wrote:
> Thanks Jim. I was able to run your demo, but I had to recompile PIL because
> the "jpeg decoder" was missing.
>
> ---------
> Matt Taylor
> OS Community Flag-Bearer
> Numenta
>
>
> On Sun, Jul 6, 2014 at 11:09 PM, Jim Bridgewater <[email protected]> wrote:
>>
>> Here's the version of this experiment using an SP with 1024 columns
>> and 16 active columns on a data set that contains 16 characters (0-9,
>> A-F).
>>
>> The results are similar in that setting increment for active columns,
>> synPermActiveInc, equal to the connection threshold, synPermConnected,
>> tends to produce the best results.  However, one exception was that
>> setting both increment and decrement values to 0.1*synPermConnected
>> produced 100% accuracies for all but the highest threshold values.
>> This was not the case for the SP with only one active column.
>>
>> This time I included pictures of the permanences for the best and
>> worst configurations for comparison.  For the worst configuration many
>> columns have permanences far above zero for  inputs that are not
>> active for any of the input images.  Maybe these columns were never
>> active and therefore those permanences were never decremented?  I
>> guess that is another experiment to run...recording active columns to
>> see how many get used.
>>
>> On Sat, Jul 5, 2014 at 8:23 AM, Ian Danforth <[email protected]>
>> wrote:
>> >
>> >
>> >
>> > On Fri, Jul 4, 2014 at 9:31 PM, Jim Bridgewater <[email protected]>
>> > wrote:
>> >>
>> >> Hi Ian,
>> >>
>> >> You are correct.  Thanks for the feedback, I've updated the
>> >> introduction and attached the revised document.
>> >>
>> >> There is still the question of whether these results will hold with
>> >> more active columns and I plan to run that experiment and post the
>> >> results soon.  I am planning to use your Amazon EC2 image for this
>> >> since these jobs can take some time for large numbers of columns.
>> >>
>> >
>> > Awesome! Let me know if you end up installing tools that you think
>> > should be
>> > part of the default AMI.
>> >
>> >>
>> >> For pooling, I want to duplicate the characters in the data set using
>> >> different fonts and see if I can get the spatial pooler to generalize
>> >> between the different fonts.
>> >>
>> >>
>> >
>> > Looking forward to those results!
>> >
>> > Ian
>> >
>> >
>> >
>> >>
>> >>
>> >> On Fri, Jul 4, 2014 at 6:43 PM, Ian Danforth <[email protected]>
>> >> wrote:
>> >> > Jim,
>> >> >
>> >> >  To be clear you have 186 training examples and 256 columns, 100%
>> >> > potential
>> >> > pool and you have 1 column on at a time, so you would expect 100%
>> >> > accuracy.
>> >> > Correct? You might want to note this up front. Something like "this
>> >> > should
>> >> > be an easy task for the spatial pooler, however certain parameter
>> >> > configurations were found to be problematic."
>> >> >
>> >> > Also I know you know this but for others reading along, to do
>> >> > "pooling"
>> >> > the
>> >> > number of training examples needs to be at least > than number of
>> >> > columns
>> >> > and preferably >>. Otherwise each column will perfectly fit a single
>> >> > example. Also you need to test on a separate set of data to see how
>> >> > well
>> >> > the
>> >> > model generalizes.
>> >> >
>> >> > Ian
>> >> >
>> >> >
>> >> > On Fri, Jul 4, 2014 at 12:29 AM, Jim Bridgewater <[email protected]>
>> >> > wrote:
>> >> >>
>> >> >> Hi everyone,
>> >> >>
>> >> >> I'm working on a NuPIC vision toolkit for the Season of NuPIC this
>> >> >> summer and I've created a GitHub repo for it called nupic.vision.
>> >> >> The
>> >> >> URL is:
>> >> >>
>> >> >> https://github.com/baroobob/nupic.vision
>> >> >>
>> >> >> There is a demo for those who want to try it out.
>> >> >>
>> >> >> I've attached the write up of results from using this toolkit to
>> >> >> investigate the effects of the synapse connection threshold,
>> >> >> synPermConnected, the permanence increment for active synapses,
>> >> >> synPermActiveInc, and the permanence decrement for inactive
>> >> >> synapses,
>> >> >> synPermInactiveDec, on image recognition accuracy and the amount of
>> >> >> training required.
>> >> >>
>> >> >> --
>> >> >> Jim Bridgewater, PhD
>> >> >> Arizona State University
>> >> >> 480-227-9592
>> >> >>
>> >> >> _______________________________________________
>> >> >> nupic mailing list
>> >> >> [email protected]
>> >> >> http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org
>> >> >>
>> >> >
>> >> >
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>> >> >
>> >>
>> >>
>> >>
>> >> --
>> >> Jim Bridgewater, PhD
>> >> Arizona State University
>> >> 480-227-9592
>> >>
>> >> _______________________________________________
>> >> nupic mailing list
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>> >> http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org
>> >>
>> >
>> >
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>>
>>
>>
>> --
>> Jim Bridgewater, PhD
>> Arizona State University
>> 480-227-9592
>>
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>
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-- 
Jim Bridgewater, PhD
Arizona State University
480-227-9592

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