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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