A Markov model trained on Internet Archive text files.
Project description
Create Markov models trained on Internet Archive text files.
Free software: BSD license
Installation
pip install ia-markov
Quick Start
from ia_markov import MarkovModel
m = MarkovModel()
m.train_model('FuturistManifesto')
m.model.make_sentence()
'Courage, audacity, and revolt will be drunk with love and admiration for us.'
Documentation
Development
To run the all tests run:
tox
Note, to combine the coverage data from all the tox environments run:
Windows |
set PYTEST_ADDOPTS=--cov-append tox |
|---|---|
Other |
PYTEST_ADDOPTS=--cov-append tox |
Changelog
0.3.1 (2018-10-14)
Added tests to sentence_split method - @danhenriquesc
0.3.0 (2018-10-05)
Added MarkovModel.make_sentence - @Abelarm
Use Spacy instead of NLTK for POS - @ex00
Added pipenv for dependency management - @alxwrd
Removed breaking coverage combine call in Travis CI build - @accraze
0.2.1 (2018-9-01)
Updated NLTK to v3.3
Updated markovify to v0.7.1
Updated internetarchive to 1.8.1
0.2.0 (2018-7-29)
Added ability to set custom hidden state sizes
Fix flaky model test
0.1.3 (2018-7-22)
EOL Py2.7 and Windows support
Fix docs CI build
0.1.2 (2018-7-21)
Test mocks when downloading corpus
Deprecate Windows/appveyor support
0.1.1 (2018-7-14)
Fixed failing flake8 check tests
Updated travis CI build config
0.1.0 (2016-11-27)
First release on PyPI.
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Source Distribution
Built Distribution
Hashes for ia_markov-0.3.1-py2.py3-none-any.whl
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| SHA256 | 7810bbeaa4bca4154175f420d3dc11588674eecd28799b529abd1d54cea7849e |
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| MD5 | 8b6d7e7ff2225bcc20480ac46d110d92 |
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| BLAKE2b-256 | ab94bb3eb358ea1be646c4bc6dc1fbc3fc4f0cd2473d1d5ffe971ccd2cac4dca |
