/hBpSrSSKrSSKJr SSKrSqS SjrS SjrS Sjr Sr Sr g!\a SrN"f=f) aP A set of functions used to interface with the external megam_ maxent optimization package. Before megam can be used, you should tell NLTK where it can find the megam binary, using the ``config_megam()`` function. Typical usage: >>> from nltk.classify import megam >>> megam.config_megam() # pass path to megam if not found in PATH # doctest: +SKIP [Found megam: ...] Use with MaxentClassifier. Example below, see MaxentClassifier documentation for details. nltk.classify.MaxentClassifier.train(corpus, 'megam') .. _megam: https://www.umiacs.umd.edu/~hal/megam/index.html N) find_binaryc$[SUS//SQSS9qg)a% Configure NLTK's interface to the ``megam`` maxent optimization package. :param bin: The full path to the ``megam`` binary. If not specified, then nltk will search the system for a ``megam`` binary; and if one is not found, it will raise a ``LookupError`` exception. :type bin: str megamMEGAM)z megam.optr megam_686zmegam_i686.optz0https://www.umiacs.umd.edu/~hal/megam/index.html)env_vars binary_namesurlN)r _megam_bin)bins K/var/www/auris/envauris/lib/python3.13/site-packages/nltk/classify/megam.py config_megamr)s J > Jc ^^^ TR5n[U5VVs0sHupgXv_M nnnUHum m[TS5(a-URSR UU U4SjU555 OURSUT-5 U(d[ TR T T5X#5 O6UH0n URS5 [ TR T U 5X#5 M2 URS5 M gs snnf)a Generate an input file for ``megam`` based on the given corpus of classified tokens. :type train_toks: list(tuple(dict, str)) :param train_toks: Training data, represented as a list of pairs, the first member of which is a feature dictionary, and the second of which is a classification label. :type encoding: MaxentFeatureEncodingI :param encoding: A feature encoding, used to convert featuresets into feature vectors. May optionally implement a cost() method in order to assign different costs to different class predictions. :type stream: stream :param stream: The stream to which the megam input file should be written. :param bernoulli: If true, then use the 'bernoulli' format. I.e., all joint features have binary values, and are listed iff they are true. Otherwise, list feature values explicitly. If ``bernoulli=False``, then you must call ``megam`` with the ``-fvals`` option. :param explicit: If true, then use the 'explicit' format. I.e., list the features that would fire for any of the possible labels, for each token. If ``explicit=True``, then you must call ``megam`` with the ``-explicit`` option. cost:c3\># UH!n[TRTTU55v M# g7fN)strr).0lencoding featuresetlabels r #write_megam_file..is&R6aX]]:ua@AA6s),z%dz # N)labels enumeratehasattrwritejoin_write_megam_featuresencode) train_toksrstream bernoulliexplicitrirlabelnumrrs ` ` @r write_megam_filer+Bs>__ F+4V+<=+sDcD[c [S5eU(dS5eUR5RS5n[R"US5nUHCnUR5(dMUR5upg[ U5U[ U5'ME U$)z Given the stdout output generated by ``megam`` when training a model, return a ``numpy`` array containing the corresponding weight vector. This function does not currently handle bias features. z.This function requires that numpy be installedznon-explicit not supported yetrd)numpy ValueErrorstripsplitzerosfloatint)sfeatures_countr(linesweightslinefidweights r parse_megam_weightsr<~s  }IJJ 5558 GGIOOD !Ekk.#.G ::<<**,KC %f GCH  NrcU(d [S5eUHRup4U(a/US:XaURSU-5 M(US:wa [S5eM;URSUSU35 MT g)Nz:MEGAM classifier requires the use of an always-on feature.z %srz3If bernoulli=True, then allfeatures must be binary. )r/r!)vectorr&r'r:fvals r r#r#st  K    qy US[) L LL1SE4&) *rc[U[5(a [S5e[c [ 5 [/U-n[ R "U[ RS9nUR5up4URS:wa [5 [U5 [S5e[U[5(aU$URS5$)z5 Call the ``megam`` binary with the given arguments. z args should be a list of strings)stdoutrzmegam command failed!zutf-8) isinstancer TypeErrorr r subprocessPopenPIPE communicate returncodeprintOSErrordecode)argscmdprCstderrs r call_megamrRs$:;; , CZ__5A}}V ||q  f -..&# }}W%%rr)TT)T) __doc__rFnltk.internalsrr. ImportErrorr rr+r<r#rRrr rWsR"& 29x$+"&E Es *55