Re: Twitter

On Thu, Sep 27, 2012 at 2:41 PM, Chris Babcock <[email protected]> wrote:
> On Wed, Sep 19, 2012 at 5:44 AM,  <[email protected]> wrote:
>> Chris Babcock <[email protected]> writes:
>>> I tried correlate on the bios only. It was predictably a failure. Then
>>> I found that I could request an arbitrary number of tweets with the
>>> bio. The default of 30 tweets trained the correlator almost
>>> satisfactorily, but I decided to try hyperspace.
>>>
>>> I'm getting reasonable results with the hyperspace classifier using
>>> TOE, but I'm wondering how it would do with SSTTT. Are there any good
>>> training examples that someone could put up on Pastebin? I tried
>>> Google, but all I got was mailing list articles. The one post
>>> containing code was too complex to serve as a general example.
>>>
>>> Here's my TOE code:
>>>
>>> http://pastebin.com/jCDshUn5
>>>
>>> I'm probably just one loop away from SSTTT...
>>
>> Less than that, actually.  Right now, if I read it correctly out of
>> pastebin, the built-in test in CLASSIFY is thresholding at a pR of 0.0
>> and you do this test at lines 35 and 51
>>
>> The difference is to grab the output of the classify in line 35 and 51,
>> and then check it to see whether the pR listed is sufficiently high
>> and in the right direction - if insufficient, then train.
>>
>> It takes about two or three lines in each place, which you can
>> crib from the mailing list articles.
>>
>> Here's the relevant part of one that looks nice:
>>
>>        {  do your classify here, capture the result as :stats: }
>>        match (:: :pR:) [:stats:] /Best match.*: (.*)  \nTotal/
>>        {
>>            {
>>                 eval /:@::*:pR: > 0.5:/ #SSTTT
>>                 {
>>                         [[[ do your learn here... ]]]
>>                 }
>>            }
>>        }
>>
>>
>>
>>
>> (note: we wrap the CLASSIFY in {} so that the built-in FAIL doesn't
>> cause a skip if it fails)
>>
>>       - Bill Yerazunis
>
> I had some difficulty bootstrapping the code with your simple
> recommendation. When there's an error, like a missing CSS file,
> nothing is placed in the stats variable. My solution is probably more
> complicated than it needs to be:
>
> http://pastebin.com/Bnqwfkb5
>
> I'd be grateful if you'd take a glance at my code and make sure my
> logic is correct, please. The training code looks at the bio to see
> whether I'm following that user and, depending on the result, checks
> the tweet history to see whether the user is already correctly
> classified. If the classification fails then the case is trained. If
> the classification succeeds then we check the pR and train if it's
> lower than 0.5. If the stats variable isn't set due to an error then
> we assume the pR is 0 and train that case.
>
> The advantage of training on the tweets without the bio attached is
> that I can use the same CSS files for other purposes, like following
> hash tags.
>
> Chris
> --
> "Nothing says, 'I love you,' like 200 feet of parachute cord and a cargo net."
>    - Fred, Scooby-Doo! Mystery Incorporated

Here... this is better:

http://pastebin.com/wmdq9bLW

Chris
--
"Nothing says, 'I love you,' like 200 feet of parachute cord and a cargo net."
   - Fred, Scooby-Doo! Mystery Incorporated

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