a v5`n^@s8ddlmZddlmZddlmZmZz ddl Z Wne yJdZ Yn0gdZ GdddeZ Gd d d eZ Gd d d eZGd ddeZGdddeZGdddeZGdddeZGdddeZGdddeZGdddeZe Ze ZeZZeZeZeZeZeZeZeZ dS)) defaultdict) zip_longest)BaseBaseSimilarityN)HammingMLIPNS LevenshteinDamerauLevenshteinJaro JaroWinklerStrCmp95NeedlemanWunschGotoh SmithWatermanhammingmlipns levenshteindamerau_levenshteinjaro jaro_winklerstrcmp95needleman_wunschgotohsmith_watermanc@s"eZdZdZd ddZdd ZdS) rz Compute the Hamming distance between the two or more sequences. The Hamming distance is the number of differing items in ordered sequences. https://en.wikipedia.org/wiki/Hamming_distance rNFTcCs"||_|p|j|_||_||_dSN)qval_ident test_functruncateexternal)selfrrrr r"Alib/python3.9/site-packages/textdistance/algorithms/edit_based.py__init__#s zHamming.__init__csHj|}j|}|dur |Sjr*tnt}tfdd||DS)Ncsg|]}j| qSr")r).0esr!r"r# 1z$Hamming.__call__..)_get_sequences quick_answerrziprsum)r! sequencesresult_zipr"r'r#__call__)s   zHamming.__call__)rNFT)__name__ __module__ __qualname____doc__r$r1r"r"r"r#rs rc@s2eZdZdZd ddZddZd d Zd d ZdS)r a Compute the absolute Levenshtein distance between the two sequences. The Levenshtein distance is the minimum number of edit operations necessary for transforming one sequence into the other. The edit operations allowed are: * deletion: ABC -> BC, AC, AB * insertion: ABC -> ABCD, EABC, AEBC.. * substitution: ABC -> ABE, ADC, FBC.. https://en.wikipedia.org/wiki/Levenshtein_distance TODO: https://gist.github.com/kylebgorman/1081951/9b38b7743a3cb5167ab2c6608ac8eea7fc629dca rNTcCs||_|p|j|_||_dSrrrrr r!rrr r"r"r#r$As zLevenshtein.__init__cCs|r|st|t|S||d|drF||dd|ddSt||dd||||dd}||dd|dd}t||dS)Nr)lenrmin)r!s1s2dsr"r"r# _recursiveFszLevenshtein._recursivec Cst|d}t|d}d}tr,t|}nt|}td|D]}||gdg|d}}td|D]^}||d} ||dd} |||d||d} ||d| } t| | | ||<qfq>|dS)zp source: https://github.com/jamesturk/jellyfish/blob/master/jellyfish/_jellyfish.py#L18 rNrr8)r9numpyZarangerangerr:) r!r;r<ZrowsZcolsprevZcurrcZdeletionZ insertionZdistZeditr"r"r#_cicledWs    zLevenshtein._cicledcCs4|||\}}|||}|dur(|S|||Sr)r*r+rEr!r;r<r/r"r"r#r1ns  zLevenshtein.__call__)rNT)r2r3r4r5r$r?rEr1r"r"r"r#r 4s  r c@s2eZdZdZd ddZddZd d Zd d ZdS)r a Compute the absolute Damerau-Levenshtein distance between the two sequences. The Damerau-Levenshtein distance is the minimum number of edit operations necessary for transforming one sequence into the other. The edit operations allowed are: * deletion: ABC -> BC, AC, AB * insertion: ABC -> ABCD, EABC, AEBC.. * substitution: ABC -> ABE, ADC, FBC.. * transposition: ABC -> ACB, BAC https://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance rNTcCs||_|p|j|_||_dSrr6r7r"r"r#r$s zDamerauLevenshtein.__init__c Cs^tjt|dt|dgtjd}tdt|dD]}|d||d<q6tdt|dD]}|d|d|<q^t|D]\}}t|D]\}}t||| }t||d|d|||dd||d|d||||<|r|sq||||dsqt|||||d|d||||<qq||t|dt|dS)NrZdtyper8)r@zerosr9intrA enumeraterr: r!r;r<r=ijZcs1Zcs2Zcostr"r"r#_numpys,$  zDamerauLevenshtein._numpyc CsTi}tdt|dD]}|d||df<qtdt|dD]}|d|d|f<q>t|D]\}}t|D]\}}t||| }t||d|fd|||dfd||d|df||||f<|rl|sql||||dsql|||d|sqlt|||f||d|df||||f<qlq\|t|dt|dfS)za https://www.guyrutenberg.com/2008/12/15/damerau-levenshtein-distance-in-python/ r8rrH)rAr9rKrJrr:rLr"r"r# _pure_pythons0  zDamerauLevenshtein._pure_pythoncCs4|||\}}|||}|dur(|S|||Sr)r*r+rPrFr"r"r#r1s  zDamerauLevenshtein.__call__)rNT)r2r3r4r5r$rOrPr1r"r"r"r#r xs  !%r c@s,eZdZdZd ddZddZdd d Zd S)r a Computes the Jaro-Winkler measure between two strings. The Jaro-Winkler measure is designed to capture cases where two strings have a low Jaro score, but share a prefix. and thus are likely to match. https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance https://github.com/Yomguithereal/talisman/blob/master/src/metrics/distance/jaro.js https://github.com/Yomguithereal/talisman/blob/master/src/metrics/distance/jaro-winkler.js FTrcCs||_||_||_||_dSr)rlong_tolerance winklerizer )r!rQrRrr r"r"r#r$szJaroWinkler.__init__cGsdSNrr"r!r.r"r"r#maximumszJaroWinkler.maximum皙?cCs|||\}}|||}|dur(|St|}t|}|r@|sDdSt||}|dd}|dkrfd}dg|} dg|} d} t|D]l\} } td| |}t| ||d}t||dD]4}| |s||| krd| | <| |<| d7} qqq| sdSd}}t| D]T\} }|r t||D]}| |r$|d}qDq$|| ||kr |d7}q |d}| || |}|| || 7}|d}|js|S|dks|dks|dkr|St|d }d} | |kr|| || kr|| r| d7} q| r|| |d |7}|jr,|d kr0|S| | dksPd| || krT|S| | d||| dd}|d ||7}|S) NrHrrFTffffff??) r*r+r9maxrKr:rArRrQ)r!r;r<Z prefix_weightr/Zs1_lenZs2_lenZmin_len search_rangeZs1_flagsZs2_flagsZ common_charsrMZs1_chZlowhirNkZ trans_countZs1_fweighttmpr"r"r#r1sn         &   zJaroWinkler.__call__N)FTrT)rVr2r3r4r5r$rUr1r"r"r"r#r s r cseZdZdfdd ZZS)r FrTcstj|d||ddS)NF)rQrRrr )superr$)r!rQrr  __class__r"r#r$Bs z Jaro.__init__)FrT)r2r3r4r$ __classcell__r"r"rdr#r Asr c@sNeZdZdZdZdddZd d Zd d Zd dZddZ ddZ ddZ dS)ra0 Computes the Needleman-Wunsch measure between two strings. The Needleman-Wunsch generalizes the Levenshtein distance and considers global alignment between two strings. Specifically, it is computed by assigning a score to each alignment between two input strings and choosing the score of the best alignment, that is, the maximal score. An alignment between two strings is a set of correspondences between the characters of between them, allowing for gaps. https://en.wikipedia.org/wiki/Needleman%E2%80%93Wunsch_algorithm Fr[NrTcCs*||_||_|r||_n|j|_||_dSr)rgap_costsim_funcrr r!rgrhrr r"r"r#r$Ys zNeedlemanWunsch.__init__cGsttt| |jSr)r\mapr9rgrTr"r"r#minimumbszNeedlemanWunsch.minimumcGsttt|Sr)r\rjr9rTr"r"r#rUeszNeedlemanWunsch.maximumcGsd|j|S)z'Get distance between sequences r8) similarityrTr"r"r#distancehszNeedlemanWunsch.distancecGs6|j|}|j|}|dkr dS|j||||S)!Get distance from 0 to 1 r)rkrUrmr!r.rkrUr"r"r#normalized_distancems   z#NeedlemanWunsch.normalized_distancecGs6|j|}|j|}|dkr dS|j|||dS)rnrrrH)rkrUrlror"r"r#normalized_similarityvs   z%NeedlemanWunsch.normalized_similarityc Cs<ts td|||\}}tjt|dt|dftjd}tt|dD]}||j ||df<qPtt|dD]}||j |d|f<qzt|dD]|\}}t|dD]h\}}||d|df| ||}||d|f|j} |||df|j} t || | |||f<qq||j dd|j ddfS)Nz2Please, install numpy for Needleman-Wunsch measurerrGr) r@ ImportErrorr*rIr9floatrArgrKrhr\shape) r!r;r<dist_matrMrNc1c2matchdeleteinsertr"r"r#r1s$ zNeedlemanWunsch.__call__)r[NrT) r2r3r4r5Zpositiver$rkrUrmrprqr1r"r"r"r#rKs    rc@s*eZdZdZd ddZdd Zd d ZdS) ra  Computes the Smith-Waterman measure between two strings. The Smith-Waterman algorithm performs local sequence alignment; that is, for determining similar regions between two strings. Instead of looking at the total sequence, the Smith-Waterman algorithm compares segments of all possible lengths and optimizes the similarity measure. https://en.wikipedia.org/wiki/Smith%E2%80%93Waterman_algorithm https://github.com/Yomguithereal/talisman/blob/master/src/metrics/distance/smith-waterman.js r[NrTcCs"||_||_|p|j|_||_dSr)rrgrrhr rir"r"r#r$s zSmithWaterman.__init__cGsttt|Srr:rjr9rTr"r"r#rUszSmithWaterman.maximumc Csts td|||\}}|||}|dur4|Stjt|dt|dftjd}t|ddD]\}}t|ddD]j\}}||d|df|||} ||d|f|j } |||df|j } t d| | | |||f<qxqd||j dd|j ddfS)Nz0Please, install numpy for Smith-Waterman measurerrGstartr) r@rrr*r+rIr9rsrKrhrgr\rt) r!r;r<r/rurMsc1rNsc2rxryrzr"r"r#r1s"  zSmithWaterman.__call__)r[NrTrbr"r"r"r#rs rc@s2eZdZdZdddZdd Zd d Zd d ZdS)rzGotoh score Gotoh's algorithm is essentially Needleman-Wunsch with affine gap penalties: https://www.cs.umd.edu/class/spring2003/cmsc838t/papers/gotoh1982.pdf r皙?NTcCs0||_||_||_|r||_n|j|_||_dSr)rgap_opengap_extrhrr )r!rrrhrr r"r"r#r$szGotoh.__init__cGsttt| Srr{rTr"r"r#rksz Gotoh.minimumcGsttt|Srr{rTr"r"r#rUsz Gotoh.maximumc Csts td|||\}}t|}t|}tj|d|dftjd}tj|d|dftjd}tj|d|dftjd}d|d<td|d<td|d<td|dD]R}td||df<|j |j|d||df<td||df<|j ||df<qtd|dD]T} td|d| f<td|d| f<|j |d| f<|j |j| d|d| f<qt |ddD]\}} t |ddD]\} } | | | } t ||d| df| ||d| df| ||d| df| ||| f<t ||d| f|j||d| f|j||| f<t ||| df|j||| df|j||| f<qqrdd |j D\}} t ||| f||| f||| fS) Nz'Please, install numpy for Gotoh measurerrGr)rrz-infr|css|]}|dVqdS)rNr")r%nr"r"r# r)z!Gotoh.__call__..) r@rrr*r9rIrsrArrrKrhr\rt) r!r;r<len_s1len_s2Zd_matZp_matZq_matrMrNr~rZsim_valr"r"r#r1sN  "   zGotoh.__call__)rrNrT)r2r3r4r5r$rkrUr1r"r"r"r#rs  rc@s:eZdZdZdZdddZddZed d Zd d Z d S)r z`strcmp95 similarity http://cpansearch.perl.org/src/SCW/Text-JaroWinkler-0.1/strcmp95.c )$)AE)rI)rO)rU)BV)rr)rr)rr)rr)rr)rr)rY)rr)CG)rF)Wr)rr)XK)SZ)rr)Qr)rr)MN)Lr)rr)PR)rJ)2r)5r)8r)1r)rr)0r)rr)rr)rrFTcCs||_||_dSr) long_stringsr )r!rr r"r"r#r$#szStrCmp95.__init__cGsdSrSr"rTr"r"r#rU'szStrCmp95.maximumcCsdt|kodkSS)Nr[)ord)charr"r"r# _in_range*szStrCmp95._in_rangecCs|}|}|||}|dur0|St|}t|}tt}|jD] \}}d|||f<d|||f<qN||kr|} |} n|} |} dg| } dg| } td| dd} d} |d}t|D]l\}}t|| d}t || |}t ||dD]8}| |dkr|||krd| |<d| |<| d7} qqq| dkr@dSd}}t|D]\\}}| |sfqPt ||D]"}| |dkrp|d}qqp|||krP|d7}qP|d}d}| | krpt |D]}| |dkrq| ||sqt |D]l}| |dkrq| ||s(q||||f|vrBq||||||f7}d| |<q̐qq|d| }||||}|| || 7}|d}|dkr|St | d }d}t ||D]B\}}||krq||krq| rq|d7}q|r(||d d |7}|js4|S| d krB|S| |dksbd| | |krf|S|d rx|S| |d|||dd}|d ||7}|S) NrXrrHrrWg$@g@rYrZrVr[)stripupperr+r9rrJsp_mxr\rKr:rArr,isdigitr)r!r;r<r/rrZadjwtrvrwr]ZminvZs1_flagZs2_flagZnum_comZyl1rMr~ZlowlimZhilimrNr_Zn_transZn_simiZnum_simr`rresr"r"r#r1.s                       zStrCmp95.__call__N)FT) r2r3r4r5rr$rU staticmethodrr1r"r"r"r#r s  r c@s*eZdZdZd ddZdd Zd d Zd S)ra* Compute the Hamming distance between the two or more sequences. The Hamming distance is the number of differing items in ordered sequences. http://www.sial.iias.spb.su/files/386-386-1-PB.pdf https://github.com/Yomguithereal/talisman/blob/master/src/metrics/distance/mlipns.js ?rHrTcCs||_||_||_||_dSr)r threshold maxmismatchesr )r!rrrr r"r"r#r$szMLIPNS.__init__cGsdSrSr"rTr"r"r#rUszMLIPNS.maximumcGs|j|}|j|}|dur |Sd}t|}ttt|}t|r||jkr|sVdSd||||jkrpdS|d7}|d8}|d8}q<|sdSdS)Nrr) r*r+rr\rjr9allrr)r!r.r/Z mismatchesZhammaxlenr"r"r#r1s$    zMLIPNS.__call__N)rrHrTrbr"r"r"r#rs r)! collectionsr itertoolsrbaserZ_BaserZ_BaseSimilarityr@rr__all__rr r r r rrrr rrrZdameraurrrrrrrrr"r"r"r#s8      Ded R/I)