65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
# SPDX-License-Identifier: MIT
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# Copyright (C) 2022 Max Bachmann
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"""
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The Levenshtein (edit) distance is a string metric to measure the
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difference between two strings/sequences s1 and s2.
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It's defined as the minimum number of insertions, deletions or
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substitutions required to transform s1 into s2.
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"""
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from __future__ import annotations
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from typing import Callable, Hashable, Sequence
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from rapidfuzz.distance import Editops, Opcodes
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def distance(
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s1: Sequence[Hashable],
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s2: Sequence[Hashable],
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*,
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weights: tuple[int, int, int] | None = (1, 1, 1),
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processor: Callable[..., Sequence[Hashable]] | None = None,
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score_cutoff: int | None = None,
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score_hint: int | None = None,
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) -> int: ...
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def normalized_distance(
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s1: Sequence[Hashable],
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s2: Sequence[Hashable],
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*,
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weights: tuple[int, int, int] | None = (1, 1, 1),
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processor: Callable[..., Sequence[Hashable]] | None = None,
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score_cutoff: float | None = 0,
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score_hint: float | None = 0,
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) -> float: ...
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def similarity(
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s1: Sequence[Hashable],
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s2: Sequence[Hashable],
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*,
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weights: tuple[int, int, int] | None = (1, 1, 1),
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processor: Callable[..., Sequence[Hashable]] | None = None,
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score_cutoff: int | None = None,
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score_hint: int | None = None,
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) -> int: ...
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def normalized_similarity(
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s1: Sequence[Hashable],
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s2: Sequence[Hashable],
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*,
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weights: tuple[int, int, int] | None = (1, 1, 1),
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processor: Callable[..., Sequence[Hashable]] | None = None,
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score_cutoff: float | None = 0,
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score_hint: float | None = 0,
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) -> float: ...
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def editops(
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s1: Sequence[Hashable],
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s2: Sequence[Hashable],
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*,
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processor: Callable[..., Sequence[Hashable]] | None = None,
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score_hint: int | None = None,
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) -> Editops: ...
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def opcodes(
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s1: Sequence[Hashable],
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s2: Sequence[Hashable],
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*,
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processor: Callable[..., Sequence[Hashable]] | None = None,
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score_hint: int | None = None,
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) -> Opcodes: ...
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