https://github.com/bradmontgomery/shaney
A copy of of the Mark V. Shaney markov chain.
https://github.com/bradmontgomery/shaney
markov-chain python python-script
Last synced: 11 months ago
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A copy of of the Mark V. Shaney markov chain.
- Host: GitHub
- URL: https://github.com/bradmontgomery/shaney
- Owner: bradmontgomery
- Created: 2015-01-17T17:44:51.000Z (about 11 years ago)
- Default Branch: main
- Last Pushed: 2024-09-15T19:56:24.000Z (over 1 year ago)
- Last Synced: 2025-04-18T09:34:34.321Z (11 months ago)
- Topics: markov-chain, python, python-script
- Language: Python
- Size: 25.4 KB
- Stars: 7
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
Mark V. Shaney
==============
_SLIDES: I've given a few talks at meetups and conferences on Markov Chains,
most recently at DevSpace 2016. You can find [those slides on speakerdeck](https://speakerdeck.com/bkmontgomery/fun-with-markov-chains-devspace-2016)_.
----
This started out as a copy of the `shaney.py` Markov Chain implementation taken
from [Yisong Yue's website](http://www.yisongyue.com/shaney/).
I've refactored the code a bit, but the important parts are still pretty much
the same. This version works with Python 3.
That site describes shaney as:
> Mark V. Shaney is a Python script which takes in a typically fairly large body
> of text and generates another [smaller] body of text which resembles the
> original, usually with hilarious side-effects. This page is a web version of
> the script for all those people fortunate enough to have never acquired a
> savviness for nerdy coding. Mark V. Shaney was first written by Bruce Ellis.
According to comments in the file, this python script was written by Greg McFarlane
with some editing by Joe Strout.
I've also cleaned it up a bit.
Usage
-----
Run the file, and pass it a path to some training text. This should work fine
with a default installation of Python 3.
python shaney.py /path/to/text.txt -v
For additional options, run `python shaney.py -h`. Notably, this script
implements both a 2nd-order (the original) and 3rd-order markov chain. To
run the 3rd-order chain, use:
python shaney.py /path/to/text.txt -v -o 3
Web App
-------
There's also a simple web app that will display quotes generated from training
data. This requires a few additional dependences (see `requirements.txt`).
pip install -r requirements.txt
Run the we application with:
python web.py /path/to/text.txt