dy'ddlmZddlmZmZmZddlmZddlm Z m Z dddddZ dd dZ dddddZ dddd!dZdddd!dZddd"dZddd#dZdS)$) annotations)CallableHashableSequence)is_none)EditopsOpcodesN) processor score_cutoffs1Sequence[Hashable]s2r (Callable[..., Sequence[Hashable]] | Noner int | Nonereturnintc||||}||}|sdSdt|zdz }i}|j}d}|D]}||d|z||<|dz}|D]} || d} || z} || z|| z z}t|t| dd} || |kr| ndS)a Calculates the length of the longest common subsequence Parameters ---------- s1 : Sequence[Hashable] First string to compare. s2 : Sequence[Hashable] Second string to compare. processor: callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. score_cutoff : int, optional Maximum distance between s1 and s2, that is considered as a result. If the similarity is smaller than score_cutoff, 0 is returned instead. Default is None, which deactivates this behaviour. Returns ------- similarity : int similarity between s1 and s2 Nr0lengetbincount) r rr r Sblock block_getxch1ch2Matchesuress `/home/feoh/.local/pipx/venvs/poetry/lib/python3.11/site-packages/rapidfuzz/distance/LCSseq_py.py similarityr% s < Yr]] Yr]] q c"ggA!#E I AYsA&&*c  a)C## K Uq1u  a&&#b''  " "3 ' 'C'3,+>+>33QFrdict[Hashable, int]c |sdSdt|zdz }|j}|D]}||d}||z}||z||z z}t|t| dd} || |kr| ndS)Nrrrr) rr rr rrr r!r"r#s r$_block_similarityr)Cs q c"ggA I)C## K Uq1u  a&&#b''  " "3 ' 'C'3,+>+>33QFr&c|||}||}tt|t|}t||}||z }|||kr|n|dzS)a Calculates the LCS distance in the range [0, max]. This is calculated as ``max(len1, len2) - similarity``. Parameters ---------- s1 : Sequence[Hashable] First string to compare. s2 : Sequence[Hashable] Second string to compare. processor: callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. score_cutoff : int, optional Maximum distance between s1 and s2, that is considered as a result. If the distance is bigger than score_cutoff, score_cutoff + 1 is returned instead. Default is None, which deactivates this behaviour. Returns ------- distance : int distance between s1 and s2 Examples -------- Find the LCS distance between two strings: >>> from rapidfuzz.distance import LCSseq >>> LCSseq.distance("lewenstein", "levenshtein") 2 Setting a maximum distance allows the implementation to select a more efficient implementation: >>> LCSseq.distance("lewenstein", "levenshtein", score_cutoff=1) 2 Nr)maxrr%)r rr r maximumsimdists r$distancer/Ysw^ Yr]] Yr]]#b''3r77##G R  C S=D (DL,@,@44|VWGWWr& float | Nonefloatct|st|rdS|||}||}|r|sdStt|t|}t|||z }|||kr|ndS)a2 Calculates a normalized LCS similarity in the range [1, 0]. This is calculated as ``distance / max(len1, len2)``. Parameters ---------- s1 : Sequence[Hashable] First string to compare. s2 : Sequence[Hashable] Second string to compare. processor: callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. score_cutoff : float, optional Optional argument for a score threshold as a float between 0 and 1.0. For norm_dist > score_cutoff 1.0 is returned instead. Default is 1.0, which deactivates this behaviour. Returns ------- norm_dist : float normalized distance between s1 and s2 as a float between 0 and 1.0 ?Nrr)rr+rr/)r rr r r,norm_sims r$normalized_distancer5s>r{{gbkks Yr]] Yr]] Rq#b''3r77##GB')H$,L0H0H88qPr&ct|st|rdS|||}||}dt||z }|||kr|ndS)a Calculates a normalized LCS similarity in the range [0, 1]. This is calculated as ``1 - normalized_distance`` Parameters ---------- s1 : Sequence[Hashable] First string to compare. s2 : Sequence[Hashable] Second string to compare. processor: callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. score_cutoff : float, optional Optional argument for a score threshold as a float between 0 and 1.0. For norm_sim < score_cutoff 0 is returned instead. Default is 0, which deactivates this behaviour. Returns ------- norm_sim : float normalized similarity between s1 and s2 as a float between 0 and 1.0 Examples -------- Find the normalized LCS similarity between two strings: >>> from rapidfuzz.distance import LCSseq >>> LCSseq.normalized_similarity("lewenstein", "levenshtein") 0.8181818181818181 Setting a score_cutoff allows the implementation to select a more efficient implementation: >>> LCSseq.normalized_similarity("lewenstein", "levenshtein", score_cutoff=0.9) 0.0 When a different processor is used s1 and s2 do not have to be strings >>> LCSseq.normalized_similarity(["lewenstein"], ["levenshtein"], processor=lambda s: s[0]) 0.81818181818181 gNr3r)rr5)r rr r r4s r$normalized_similarityr7ssdr{{gbkks Yr]] Yr]](R000H$,L0H0H88qPr&r rct)uc Return Editops describing how to turn s1 into s2. Parameters ---------- s1 : Sequence[Hashable] First string to compare. s2 : Sequence[Hashable] Second string to compare. processor: callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. Returns ------- editops : Editops edit operations required to turn s1 into s2 Notes ----- The alignment is calculated using an algorithm of Heikki Hyyrö, which is described [6]_. It has a time complexity and memory usage of ``O([N/64] * M)``. References ---------- .. [6] Hyyrö, Heikki. "A Note on Bit-Parallel Alignment Computation." Stringology (2004). Examples -------- >>> from rapidfuzz.distance import LCSseq >>> for tag, src_pos, dest_pos in LCSseq.editops("qabxcd", "abycdf"): ... print(("%7s s1[%d] s2[%d]" % (tag, src_pos, dest_pos))) delete s1[0] s2[0] delete s1[3] s2[2] insert s1[4] s2[2] insert s1[6] s2[5] )NotImplementedErrorr rr s r$editopsr<s X r&r cJt|||S)u Return Opcodes describing how to turn s1 into s2. Parameters ---------- s1 : Sequence[Hashable] First string to compare. s2 : Sequence[Hashable] Second string to compare. processor: callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. Returns ------- opcodes : Opcodes edit operations required to turn s1 into s2 Notes ----- The alignment is calculated using an algorithm of Heikki Hyyrö, which is described [7]_. It has a time complexity and memory usage of ``O([N/64] * M)``. References ---------- .. [7] Hyyrö, Heikki. "A Note on Bit-Parallel Alignment Computation." Stringology (2004). Examples -------- >>> from rapidfuzz.distance import LCSseq >>> a = "qabxcd" >>> b = "abycdf" >>> for tag, i1, i2, j1, j2 in LCSseq.opcodes(a, b): ... print(("%7s a[%d:%d] (%s) b[%d:%d] (%s)" % ... (tag, i1, i2, a[i1:i2], j1, j2, b[j1:j2]))) delete a[0:1] (q) b[0:0] () equal a[1:3] (ab) b[0:2] (ab) delete a[3:4] (x) b[2:2] () insert a[4:4] () b[2:3] (y) equal a[4:6] (cd) b[3:5] (cd) insert a[6:6] () b[5:6] (f) r8)r< as_opcodesr;s r$opcodesr?,s&d 2rY / / / : : < <rEs#"""""//////////$$$$$$;;;;;;;;;?# 4G4G4G4G4G4Gv $ GGGGG4;?# 6X6X6X6X6X6Xz;?!% +Q+Q+Q+Q+Q+Qd;?!% :Q:Q:Q:Q:Q:QB;? ,,,,,,f;? 2=2=2=2=2=2=2=2=r&