Add string-distance similarity metrics#307
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fuzzy exposes only difflib's gestalt ratio. Add the edit-distance and token-set metrics it lacks: levenshtein, damerau_levenshtein (transposition -aware), jaro / jaro_winkler, char-n-gram jaccard / dice, and a unified similarity() that normalises every metric to [0, 1]. Pairs with normalize_text. Wired through facade, executor (AC_text_similarity), MCP, and the Script Builder with a headless test batch and EN/Zh docs.
Up to standards ✅🟢 Issues
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| Metric | Results |
|---|---|
| Complexity | 68 |
| Duplication | 0 |
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What
fuzzyexposed only difflib's gestalt ratio. This adds the edit-distance and token-set metrics it lacks.levenshtein/damerau_levenshtein(adjacent-transposition aware) — integer edit distances.jaro/jaro_winkler—[0,1](Jaro-Winkler boosts a common prefix; standard for short labels).jaccard/dice— character-n-gram set similarity.similarity(a, b, *, metric)— unified entry; normalizes edit distances to1 - d/max_len.Pairs with
normalize_text(round-10). Helper extraction keeps Jaro/Damerau CC low.Layers
utils/text_similarity/(pure stdlib, zero PySide6).__all__.AC_text_similarity.ac_text_similarity(read-only).test/unit_test/headless/test_text_similarity_batch.py(9 tests; known Jaro/Jaro-Winkler vectors).v99_features_doc.rst(EN + Zh) + toctrees + 3 README What's-new sections.Verification
pytest test/unit_test/headless/test_text_similarity_batch.py→ 9 passed.ruff check je_auto_control/clean; pylint 10.00/10; bandit clean; radon CC clean.