'[f ,ddlmZGddZdS)) defaultdictc<eZdZdZd dZdZd dZd dZd Zd Z dS) MinimalSeta Find contexts where more than one possible target value can appear. E.g. if targets are word-initial letters, and contexts are the remainders of words, then we would like to find cases like "fat" vs "cat", and "training" vs "draining". If targets are parts-of-speech and contexts are words, then we would like to find cases like wind (noun) 'air in rapid motion', vs wind (verb) 'coil, wrap'. Nct|_t|_tt|_i|_|r |D]\}}}||||dSdS)z Create a new minimal set. :param parameters: The (context, target, display) tuples for the item :type parameters: list(tuple(str, str, str)) N)set_targets _contextsr_seen _displaysadd)self parameterscontexttargetdisplays H/var/www/piapp/venv/lib/python3.11/site-packages/nltk/misc/minimalset.py__init__zMinimalSet.__init__s{  %%   3,6 3 3(&'2222 3 3 3 3c|j|||j||j|||j||f<dS)a Add a new item to the minimal set, having the specified context, target, and display form. :param context: The context in which the item of interest appears :type context: str :param target: The item of interest :type target: str :param display: The information to be reported for each item :type display: str N)r r r rr )r rrrs rr zMinimalSet.add&sf 7''' 7### &!!!-4()))rc.fdjDS)z Determine which contexts occurred with enough distinct targets. :param minimum: the minimum number of distinct target forms :type minimum: int :rtype: list cPg|]"}tj|k |#S)lenr ).0cminimumr s r z'MinimalSet.contexts..Ds1KKKaSA-?-?7-J-J-J-J-Jr)r )r rs``rcontextszMinimalSet.contexts<s&LKKKK4>KKKKrc:||f|jvr|j||fS|SN)r )r rrdefaults rrzMinimalSet.displayFs+ V  . .>7F"34 4Nrcxg}|jD]/}|||}|r||0|Sr")rrappend)r rresultrxs r display_allzMinimalSet.display_allLsKm ! !F Wf--A ! a    rc|jSr")r)r s rtargetszMinimalSet.targetsTs }rr")r)r ) __name__ __module__ __qualname____doc__rr rrr(r*rrrrr s3333 444,LLLL rrN) collectionsrrrrrr0sU$#####JJJJJJJJJJr