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https://github.com/mayank-02/minimum-edit-distance
Implementation of Wagner–Fischer algorithm for Levenshtein distance between two strings
https://github.com/mayank-02/minimum-edit-distance
dynamic-programming edit-distance levenshtein-distance python wagner-fischer-algorithm
Last synced: about 1 month ago
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Implementation of Wagner–Fischer algorithm for Levenshtein distance between two strings
- Host: GitHub
- URL: https://github.com/mayank-02/minimum-edit-distance
- Owner: mayank-02
- License: mit
- Created: 2020-12-15T18:38:58.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2020-12-15T18:48:35.000Z (about 4 years ago)
- Last Synced: 2023-08-15T06:38:32.206Z (over 1 year ago)
- Topics: dynamic-programming, edit-distance, levenshtein-distance, python, wagner-fischer-algorithm
- Language: Python
- Homepage:
- Size: 3.91 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Minimum Edit Distance
The similarity between two strings may be measured in many ways. One of such a string metric is known as the Levenshtein distance, which is a type of edit distance.
The edit distance between two strings is the minimum number of single-character insertions, deletions, or substitutions required to change one string into the other.
The class `Levenshtein.py` implements the **[Wagner–Fischer algorithm](https://en.wikipedia.org/wiki/Wagner%E2%80%93Fischer_algorithm)** allowing to pass **varying costs** for insertion, deletion and substitution.
For more info on Levenshtein distance, refer [wiki](https://en.wikipedia.org/wiki/Levenshtein_distance).
## Requirements
- Python 3.6+
- No dependencies on any other library
## Usage
```python
from Levenshtein import Levenshtein
source = "hello"
target = "world"
l = Levenshtein(source, target, costs=(1, 1, 2))
min_distance = l.distance()
# min_distance = 4
operations = l.edit_ops()
# operations =
# [{'type': 'Substitution', 'i': 0, 'j': 0},
# {'type': 'Substitution', 'i': 1, 'j': 1},
# {'type': 'Substitution', 'i': 2, 'j': 2},
# {'type': 'Match', 'i': 3, 'j': 3},
# {'type': 'Substitution', 'i': 4, 'j': 4}]
l.print_distance_matrix()
# Distance Matrix:
# - - w o r l d
# - 0 2 4 6 8 10
# h 2 1 3 5 7 9
# e 4 3 2 4 6 8
# l 6 5 4 3 4 6
# l 8 7 6 5 3 5
# o 10 9 7 7 5 4
l.print_edit_ops()
# Edit Operations:
# Type i j
# --------------------
# Substitution 0 0
# Substitution 1 1
# Substitution 2 2
# Match 3 3
# Substitution 4 4
```
Check out `Levenshtein.py` for more details.
## Authors
[Mayank Jain](https://github.com/mayank-02)
## License
MIT