+ hBtRt^RIt^RIHt^RItRsRRltR RltR Rlt Rt Rt R# ]dRtL$i;i) 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,\RVR..RORR7sR#)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 megamMEGAMz0https://www.umiacs.umd.edu/~hal/megam/index.html)env_vars binary_namesurlN)z megam.optr megam_686zmegam_i686.opt)r _megam_bin)bins&5/usr/lib/python3/dist-packages/nltk/classify/megam.py config_megamr )s J > Jc aaa SP4p\V4UUu/uFwrgWvbK pppVFwo o\SR4'd.VPRP VV V3RlV444MVPRVS,,4V'g\ SP S S4W#4M6VF0p VPR4\ SP S V 4W#4K2 VPR4K R#uuppi)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\<"TF!p\SPSSV44xK# R#5iN)strr).0lencoding featuresetlabels& r #write_megam_file..is&R6aX]]:ua@AA6s),z%dz # N)labels enumeratehasattrwritejoin_write_megam_featuresencode) train_toksrstream bernoulliexplicitrirlabelnumrrs &f&&& ` @r write_megam_filer*Bs>__ F+4V+<=+s D cJ\f \R4hV'gQR4hVP4PR4p\P!VR4pVFDpVP4'gKVP4wrg\ V4V\ V4&KF V#)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  NrcV'g \R4hVFYwr4V'd6V^8XdVPRV,4K.V^8wd \R4hKBVPRV RV 24K[ R#)z:MEGAM classifier requires the use of an always-on feature.z %sz3If bernoulli=True, then allfeatures must be binary. N)r.r )vectorr%r&r9fvals&&& r r"r"st  K    qy US[) L LL1SE4&) *rc\V\4'd \R4h\f \ 4\.V,p\ P !V\ PR7pVP4wr4VP^8wd!\4\V4\R4h\V\4'dV#VPR4#)z5 Call the ``megam`` binary with the given arguments. z args should be a list of strings)stdoutzmegam command failed!zutf-8) isinstancer TypeErrorr r subprocessPopenPIPE communicate returncodeprintOSErrordecode)argscmdprAstderrs& r call_megamrPs$:;; , CZ__5A}}V ||q  f -..&# }}W%%rr)TT)T) __doc__rDnltk.internalsrr- ImportErrorr r r*r;r"rPrr rUsR"& 29x$+"&E Es + 77