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When two Myanmar strings look similar but differ by a character or two, the library needs a way to quantify that difference. mySpellChecker implements three edit distance variants — classic Levenshtein, Damerau-Levenshtein with transpositions, and a weighted version tuned for common Myanmar character confusions — each backed by optional Cython acceleration.

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Overview

Edit distance measures how many operations are needed to transform one string into another. mySpellChecker implements several algorithms optimized for Myanmar text.

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Algorithms

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Levenshtein Distance

The classic edit distance algorithm with three operations:
  • Insertion: Add a character
  • Deletion: Remove a character
  • Substitution: Replace a character
from myspellchecker.algorithms.distance.edit_distance import levenshtein_distance

distance = levenshtein_distance("မြန်", "မြမ်")
# Result: 1 (one substitution: န် → မ်)
Note: Always import from edit_distance (the Python wrapper), not edit_distance_c (the Cython module) directly. The wrapper automatically uses the Cython implementation when available and falls back to a pure Python implementation when Cython extensions are not compiled. This ensures your code works on all platforms.

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Damerau-Levenshtein Distance

Extends Levenshtein with transposition (swap adjacent characters): 
from myspellchecker.algorithms.distance.edit_distance import damerau_levenshtein_distance

# Transposition example
distance = damerau_levenshtein_distance("AB", "BA")
# Result: 1 (one transposition)

# Without transposition (Levenshtein)
distance = levenshtein_distance("AB", "BA")
# Result: 2 (substitute A→B, substitute B→A)

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Weighted Damerau-Levenshtein

Uses custom costs for Myanmar-specific character confusions: 
from myspellchecker.algorithms.distance.edit_distance import (
    weighted_damerau_levenshtein_distance,
)

# Myanmar-specific substitution costs are loaded automatically at import time.
# Common medial confusion: ြ (U+103C) vs ျ (U+103B)
# Cost is 0.3 instead of 1.0
distance = weighted_damerau_levenshtein_distance("ကြ", "ကျ")
# Result: 0.3

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Myanmar-Specific Substitution Costs

The weighted algorithm uses reduced costs for commonly confused characters:
Confusion TypeCharactersCost
NYA variantsည ↔ ဉ0.1
Vowel lengthိ ↔ ီ, ု ↔ ူ0.2
Ra/Ya confusionရ ↔ ယ0.3
Medial confusionျ ↔ ြ0.3
Medial confusionွ ↔ ှ0.4
Aspirated pairsက ↔ ခ, စ ↔ ဆ, etc.0.4

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Setting Custom Costs

Custom substitution costs are defined in the MYANMAR_SUBSTITUTION_COSTS dictionary within edit_distance.py and applied automatically at module import time via the Cython extension.

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Implementation Details

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UTF-8 Handling

The Cython implementation properly handles Myanmar Unicode: 
# UTF-8 to codepoints conversion
cdef vector[int] utf8_to_codepoints(string s):
    # Handles 1-4 byte UTF-8 sequences
    # Myanmar characters are 3-byte sequences (U+1000-U+109F)

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Memory Optimization

Uses row-based dynamic programming for O(min(m,n)) space: 
# Space: O(n) where n = len(shorter string)
# Time: O(m*n)

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Performance

The Cython implementation provides significant speedup:
ImplementationSpeedNotes
Pure Python~100μs/pairReference implementation
Cython~10μs/pair10x faster, uses C++ unordered_map

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Benchmark

import time
from myspellchecker.algorithms.distance.edit_distance import levenshtein_distance

# Benchmark 10,000 comparisons
start = time.time()
for _ in range(10000):
    levenshtein_distance("မြန်မာနိုင်ငံ", "မြနမ်ာနိုင်ငံ")
elapsed = time.time() - start
print(f"10K comparisons: {elapsed:.3f}s")
# Typical result: ~0.1s (Cython) vs ~1.0s (Python)

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Integration with SymSpell

Edit distance is used by SymSpell for suggestion ranking: 
from myspellchecker.algorithms.symspell import SymSpell

symspell = SymSpell(provider, max_edit_distance=2)

# Suggestions are ranked by edit distance
suggestions = symspell.lookup("မြနမ်ာ", level='word')
for suggestion in suggestions:
    print(f"{suggestion.term}: distance={suggestion.edit_distance}")

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Pure Python Fallback

If Cython extensions aren’t available, pure Python is used: 
from myspellchecker.algorithms.distance.edit_distance import (
    levenshtein_distance,
    damerau_levenshtein_distance,
)

# Same API, slower performance
distance = levenshtein_distance("မြန်", "မြမ်")

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See Also