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https://github.com/roy-ht/editdistance
Fast implementation of the edit distance(Levenshtein distance)
https://github.com/roy-ht/editdistance
Last synced: 1 day ago
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Fast implementation of the edit distance(Levenshtein distance)
- Host: GitHub
- URL: https://github.com/roy-ht/editdistance
- Owner: roy-ht
- License: mit
- Created: 2013-09-13T09:08:53.000Z (about 11 years ago)
- Default Branch: master
- Last Pushed: 2024-02-16T14:46:53.000Z (9 months ago)
- Last Synced: 2024-10-30T00:35:57.862Z (16 days ago)
- Language: C++
- Size: 158 KB
- Stars: 661
- Watchers: 18
- Forks: 62
- Open Issues: 26
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
README
# editdistance
Fast implementation of the edit distance (Levenshtein distance).
This library simply implements [Levenshtein distance](http://en.wikipedia.org/wiki/Levenshtein_distance) with C++ and Cython.
The algorithm used in this library is proposed by
[Heikki Hyyrö, "Explaining and extending the bit-parallel approximate string matching algorithm of Myers", (2001)](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.19.7158&rep=rep1&type=pdf)
## Binary wheels
Thanks to [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel)
There are binary wheels on Linux, Mac OS, and Windows.
## Install
You can install via pip:
```bash
pip install editdistance
```
## Usage
It's quite simple:
```python
import editdistance
editdistance.eval('banana', 'bahama')
# 2L
```
# Simple Benchmark
With IPython, I tried several libraries:
* [pyxDamerauLevenshtein](https://pypi.python.org/pypi/pyxDamerauLevenshtein)
* [pylev](https://pypi.python.org/pypi/pylev)
* [python-Levenshtein](https://pypi.python.org/pypi/python-Levenshtein)
On Python 2.7.5:
```python
a = 'fsffvfdsbbdfvvdavavavavavava'
b = 'fvdaabavvvvvadvdvavavadfsfsdafvvav'
import pylev
timeit pylev.levenshtein(a, b)
# 100 loops, best of 3: 7.48 ms per loop
from pyxdameraulevenshtein import damerau_levenshtein_distance
timeit damerau_levenshtein_distance(a, b)
# 100000 loops, best of 3: 11.4 µs per loop
timeit editdistance.eval(a, b) # my library
# 100000 loops, best of 3: 3.5 µs per loop
import Levenshtein
timeit Levenshtein.distance(a, b)
# 100000 loops, best of 3: 3.21 µs per loop
```
## Distance with Any Object
Above libraries only support strings.
But Sometimes other type of objects such as list of strings(words).
I support any iterable, only requires hashable object of it:
```python
Levenshtein.distance(['spam', 'egg'], ['spam', 'ham'])
# ---------------------------------------------------------------------------
# TypeError Traceback (most recent call last)
# in ()
# ----> 1 Levenshtein.distance(['spam', 'egg'], ['spam', 'ham'])
#
# TypeError: distance expected two Strings or two Unicodes
editdistance.eval(['spam', 'egg'], ['spam', 'ham'])
# 1L
```
So if object's hash is same, it's same.
You can provide `__hash__` method to your object instances.
Enjoy!
## License
It is released under the MIT license.
```
Copyright (c) 2013 Hiroyuki Tanaka
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
```