Hey guys,
atleast something:
class InferenceType(Enum("TemporalNextStep",
"TemporalClassification",
"NontemporalClassification",
"TemporalAnomaly",
"NontemporalAnomaly",
"TemporalMultiStep",
"NontemporalMultiStep")):
from py/nupic/frameworks/opf/opfutils.py
On Mon, Nov 25, 2013 at 6:48 PM, Aaron Zimmerman <
[email protected]> wrote:
> Is there any documentation or resources to help understand the available
> runtime configuration options for the OPF? Specifically, I'm not clear on
> the inference types etc.
>
>
> On Sun, Nov 24, 2013 at 12:04 PM, Doug King <[email protected]> wrote:
>
>> Hi Mark and Aaron, and welcome to the NuPic community.
>>
>> I few tips/pointers:
>>
>> - hierarchy: we are not doing this yet, NuPic is working with one region
>> for now.
>>
>> - sensible defaults and components:
>> https://github.com/numenta/nupic/wiki/Running-Swarms will explain some
>> of this. If you use the tool-set that we call OPF (Online Prediction
>> Framework) you will be able to run a swarm that should optimize for you.
>>
>> - music analysis: a couple of things you might be interested in. And
>> impressive first hackathon demo was note prediction for a song. This was
>> based on midi sequence of notes for training. Timing was not predicted or
>> accounted for, only the sequence, but it was still impressive.
>>
>> - audio analysis: converting raw audio waveform via FFT into a spectrum
>> and convert the frequency scalar values. Your in luck - Stewart just posted
>> yesterday on this list a simple python file that does this I think.
>>
>> I find a large portion of software engineers have a fascination with
>> music and sound recording and production, myself included. I expect there
>> will be quite a bit of interest in music, speech and audio applications of
>> NuPic.
>>
>> -Doug
>>
>>
>> On Sun, Nov 24, 2013 at 7:59 AM, Mark Wilson <[email protected]> wrote:
>>
>>> I'm also a lurker and also interested in signal processing (not music,
>>> but probably near enough).
>>>
>>> I'm a bit overwhelmed by lots of information about what all the
>>> different parts of the Nupic system do. I have read the CLA Whitepaper and
>>> have a bit of a grasp on the concepts. What I am having difficulty with is
>>> bridging the gap between that and the signal I would like to infer and
>>> predict.
>>>
>>> Can some one help outline what steps should be taken to set up a
>>> "suitable" HTM? Things I'm thinking about:
>>>
>>> - HTM regions and hierarchy
>>> - Which Nupic libraries should be used (OPF, more direct CLA
>>> libraries?)
>>> - Some sensible defaults for the multitude of tunables.
>>> - Maybe there is another project that is similar enough to be used?
>>>
>>> Any help or suggestions would be great.
>>>
>>> Thanks
>>>
>>> Mark
>>>
>>>
>>> On 14 November 2013 14:07, Aaron Zimmerman
>>> <[email protected]>wrote:
>>>
>>>> Hi all,
>>>>
>>>> I’ve been a lurker for a while, excited to try my hand at some CLA
>>>> applications, especially toward music analysis and creation.
>>>>
>>>> Is there any documentation of the different types of inferences and the
>>>> requirements to use them?
>>>>
>>>> The hot gym example uses “TemporalMultiStep”. While I can infer some
>>>> meaning from the name, it isn’t clear to me what the technical difference
>>>> is between this and other.
>>>>
>>>>
>>>> Thanks,
>>>>
>>>> Aaron Zimmerman
>>>> Platform Engineer
>>>> Sprout Social
>>>> 773.227.7528
>>>> @apzimmerman <https://twitter.com/apzimmerman>
>>>> sproutsocial.com<http://www.google.com/url?q=http%3A%2F%2Fsproutsocial.com%2F&sa=D&sntz=1&usg=AFrqEzcFZhOCY3atDfnGIaMM9Kza3cB4Zw>
>>>>
>>>> _______________________________________________
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>>>>
>>>>
>>>
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>>>
>>
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>
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>
--
Marek Otahal :o)
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