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 allowing to pass varying costs for insertion, deletion and substitution.
For more info on Levenshtein distance, refer wiki.
- Python 3.6+
- No dependencies on any other library
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 4Check out Levenshtein.py for more details.
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