This repository contains a small, simple and efficient implementation of the Lempel-Ziv complexity algorithm.
If the lempel_ziv_complexity.py
file is accessible in your PATH or in Python's path:
>>> from lempel_ziv_complexity import lempel_ziv_complexity
>>> s = '1001111011000010'
>>> lempel_ziv_complexity(s) # 1 / 0 / 01 / 11 / 10 / 110 / 00 / 010
8
See this file.
If you are really interested about the details of how the algorithm works, please see this GIF screencast from PythonTutor.com.
- Reference: this short lecture note by Peter Shor (Oct.2005).
If the lempel_ziv_complexity.so
file is accessible in your PATH or in Python's path:
There is also a Cython version, to have a faster implementation:
$ ipython
...
>>> s = '1001111011000010'
>>> %timeit lempel_ziv_complexity(s)
6.1 µs ± 33.6 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
>>> %timeit lempel_ziv_complexity_cython(s)
132 ns ± 2.55 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
The speedup is typically between ×50 and ×100.
Demo on a Jupyter notebook
See this notebook: on nbviewever, which also compares with the Julia version.
Easy!
Clone this repository, go in the folder, compile, test, and if it works, install it.
cd /tmp/
git clone https://GitHub.com/Naereen/Lempel-Ziv_Complexity
cd Lempel-Ziv_Complexity/src/
make build
make test # should pass
make install # mv the build/lib*/*.so files where you need them
Be sure to include the dynamic library when you need it, or in a folder accessible by your Python interpreter (somewhere in sys.path
).
The file is lempel_ziv_complexity_cython.so
(for Python 2) or the lempel_ziv_complexity_cython.cpython-35m-x86_64-linux-gnu.so
(for Python 3.5, or higher, adapt the name).
This project is hosted on the Pypi package repository.
sudo pip install lempel_ziv_complexity
# test it
python -c "from lempel_ziv_complexity import lempel_ziv_complexity; print(lempel_ziv_complexity('1001111011000010') == 8)" # test
I was curious and wanted to write the same algorithm in Julia. Here it is: lempel_ziv_complexity.jl. See this part of the notebook for a benchmark between Julia and Python.
The Julia package is published here: Naereen/LempelZiv.jl, and see here for its documentation.
Python v2.7+ or Python v3.1+.
- Numba can be used to speed up the pure Python version.
- Cython is needed to build the C extension (faster).
MIT Licensed (file LICENSE). © Lilian Besson, 2017-2019.