3 d @stdZddlZddlmZy ddlZWnek r<dZYnXdadddZdddZdd d Z d d Z d dZ dS)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_binarycCs td|dgddddgddadS) aA 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 ZmegamZMEGAMz megam.optZ megam_686zmegam_i686.optz0https://www.umiacs.umd.edu/~hal/megam/index.html)Zenv_varsZ binary_namesurlN)r _megam_bin)binr3/tmp/pip-build-v9q4h5k9/nltk/nltk/classify/megam.py config_megam)s  rTcsj}ddt|D}x|D]\tdrV|jdjfdd|Dn|jd||stj||n,x*|D]"}|jdtj|||qW|jd q Wd S) 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. cSsi|]\}}||qSrr).0ilabelrrr bsz$write_megam_file..cost:c3s |]}tj|VqdS)N)strr )r l)encoding featuresetr rr isz#write_megam_file..z%dz # N)labels enumeratehasattrwritejoin_write_megam_featuresencode)Z train_toksrstream bernoulliexplicitrZlabelnumrr)rrr rwrite_megam_fileBs    rcCsltdkrtd|std|jjd}tj|d}x0|D](}|jr<|j\}}t||t|<qs     <