docstr: Return the label probability distribution for classifying a string
  of text.
  
  Example:
  ::
  
      >>> classifier = MaxEntClassifier(train_data)
      >>> prob_dist = classifier.prob_classify("I feel happy this morning.")
      >>> prob_dist.max()
      'positive'
      >>> prob_dist.prob("positive")
      0.7
  
  :rtype: nltk.probability.DictionaryProbDist
docstr_tok: ["Return","the","label","probability","distribution","for","classifying","a","string","of","text",".","Example",":",":",":",">",">",">","classifier","=","MaxEntClassifier","(","train_data",")",">",">",">","prob_dist","=","classifier.prob_classify","(","``","I","feel","happy","this","morning",".","''",")",">",">",">","prob_dist.max","(",")","'positive'",">",">",">","prob_dist.prob","(","``","positive","''",")","0.7",":","rtype",":","nltk.probability.DictionaryProbDist"]
code: def prob_classify(self, text):
      """Return the label probability distribution for classifying a string
      of text.
  
      Example:
      ::
  
          >>> classifier = MaxEntClassifier(train_data)
          >>> prob_dist = classifier.prob_classify("I feel happy this morning.")
          >>> prob_dist.max()
          'positive'
          >>> prob_dist.prob("positive")
          0.7
  
      :rtype: nltk.probability.DictionaryProbDist
      """
      feats = self.extract_features(text)
      return self.classifier.prob_classify(feats)
  
imports: from __future__ import absolute_import
  from itertools import chain
  import nltk
  from textblob.compat import basestring
  from textblob.decorators import cached_property
  from textblob.exceptions import FormatError
  from textblob.tokenizers import word_tokenize
  from textblob.utils import strip_punc, is_filelike
  import textblob.formats
label_desc: Use image node_id 1 for calling the MaxEntClassifier obj's underlying member method code with example usage: obj.prob_classify(text) and returns: self