- https://jamesthornton.com#EXIWlRrkjKE
- https://twitter.com/Rangers/status/1641554510605611011
- https://twitter.com/astros/status/1641996751618940936
- https://www.youtube.com/watch?v=S7c3lzpDkvQ#7SlILk2WMTI
- https://gist.github.com/espeed/dd3026536ec3d38afea1072941670f52#3ch6eXkQWU8-C5TDc46E1G4-trIjpVH8h88-UcW056B9jeQ-LvG_M_fAkXU
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https://www.youtube.com/watch?v=5mcPOd28oN0#D9fpFW_uXjA ZGCjx20hMbE LY6Rp9OdoO0 | |
https://www.youtube.com/watch?v=Z-Z0jkeoAkA |
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691136 705691 704201 710345 710477 702539 719740 733465 730899 735195 724022 736272 |
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### Keybase proof | |
I hereby claim: | |
* I am espeed on github. | |
* I am espeed (https://keybase.io/espeed) on keybase. | |
* I have a public key whose fingerprint is 626C 87CB 743D 0402 F487 91A8 5BC5 5A31 5C09 DCA1 | |
To claim this, I am signing this object: |
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1 |
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{"0.8782798733075599":"-----BEGIN PGP PUBLIC KEY BLOCK-----\r\nCharset: UTF-8\r\n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\r\nW4Uyq2H+b1+iBriO9LDIs1pS2CGKGEKDPg==\r\n=qJ/3\r\n-----END PGP PUBLIC KEY BLOCK-----\r\n"} |
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Verifying that +jamesthornton is my blockchain ID. https://onename.com/jamesthornton |
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Verifying that +espeed is my blockchain ID. https://onename.com/espeed |
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python ./nltk-trainer/train_classifier.py ./data/googleNews --instances files --fraction 0.75 --min_score 2 --ngrams 1 2 3 --show-most-informative 10 --classifier NaiveBayes | |
loading ./data/googleNews | |
2 labels: ['neg', 'pos'] | |
calculating word scores | |
using bag of words from known set feature extraction | |
1682507 words meet min_score and/or max_feats | |
37116 training feats, 12371 testing feats | |
training NaiveBayes classifier | |
accuracy: 0.582815 | |
neg precision: 0.659028 |
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import timeit | |
import pickle | |
import nltk.data | |
from nltk.util import ngrams | |
t0 = timeit.default_timer() |
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$ python | |
Python 2.7.10 (default, Jul 5 2015, 14:15:43) | |
[GCC 5.1.1 20150618 (Red Hat 5.1.1-4)] on linux2 | |
Type "help", "copyright", "credits" or "license" for more information. | |
>>> import scipy | |
>>> scipy.__version__ | |
'0.14.1' | |
>>> import numpy | |
>>> numpy.__version__ | |
'1.9.2' |