class_docstr: Phrase-based stack decoder for machine translation
  
  >>> from nltk.translate import PhraseTable
  >>> phrase_table = PhraseTable()
  >>> phrase_table.add(('niemand',), ('nobody',), log(0.8))
  >>> phrase_table.add(('niemand',), ('no', 'one'), log(0.2))
  >>> phrase_table.add(('erwartet',), ('expects',), log(0.8))
  >>> phrase_table.add(('erwartet',), ('expecting',), log(0.2))
  >>> phrase_table.add(('niemand', 'erwartet'), ('one', 'does', 'not', 'expect'), log(0.1))
  >>> phrase_table.add(('die', 'spanische', 'inquisition'), ('the', 'spanish', 'inquisition'), log(0.8))
  >>> phrase_table.add(('!',), ('!',), log(0.8))
  
  >>> #  nltk.model should be used here once it is implemented
  >>> from collections import defaultdict
  >>> language_prob = defaultdict(lambda: -999.0)
  >>> language_prob[('nobody',)] = log(0.5)
  >>> language_prob[('expects',)] = log(0.4)
  >>> language_prob[('the', 'spanish', 'inquisition')] = log(0.2)
  >>> language_prob[('!',)] = log(0.1)
  >>> language_model = type('',(object,),{'probability_change': lambda self, context, phrase: language_prob[phrase], 'probability': lambda self, phrase: language_prob[phrase]})()
  
  >>> stack_decoder = StackDecoder(phrase_table, language_model)
  
  >>> stack_decoder.translate(['niemand', 'erwartet', 'die', 'spanische', 'inquisition', '!'])
  ['nobody', 'expects', 'the', 'spanish', 'inquisition', '!']
class_docstr_tok: ["Phrase-based","stack","decoder","for","machine","translation",">",">",">","from","nltk.translate","import","PhraseTable",">",">",">","phrase_table","=","PhraseTable","(",")",">",">",">","phrase_table.add","(","(","'niemand","'",",",")",",","(","'nobody","'",",",")",",","log","(","0.8",")",")",">",">",">","phrase_table.add","(","(","'niemand","'",",",")",",","(","'no","'",",","'one","'",")",",","log","(","0.2",")",")",">",">",">","phrase_table.add","(","(","'erwartet","'",",",")",",","(","'expects","'",",",")",",","log","(","0.8",")",")",">",">",">","phrase_table.add","(","(","'erwartet","'",",",")",",","(","'expecting","'",",",")",",","log","(","0.2",")",")",">",">",">","phrase_table.add","(","(","'niemand","'",",","'erwartet","'",")",",","(","'one","'",",","'does","'",",","'not","'",",","'expect","'",")",",","log","(","0.1",")",")",">",">",">","phrase_table.add","(","(","'die","'",",","'spanische","'",",","'inquisition","'",")",",","(","'the","'",",","'spanish","'",",","'inquisition","'",")",",","log","(","0.8",")",")",">",">",">","phrase_table.add","(","(","'","!","'",",",")",",","(","'","!","'",",",")",",","log","(","0.8",")",")",">",">",">","#","nltk.model","should","be","used","here","once","it","is","implemented",">",">",">","from","collections","import","defaultdict",">",">",">","language_prob","=","defaultdict","(","lambda",":","-999.0",")",">",">",">","language_prob","[","(","'nobody","'",",",")","]","=","log","(","0.5",")",">",">",">","language_prob","[","(","'expects","'",",",")","]","=","log","(","0.4",")",">",">",">","language_prob","[","(","'the","'",",","'spanish","'",",","'inquisition","'",")","]","=","log","(","0.2",")",">",">",">","language_prob","[","(","'","!","'",",",")","]","=","log","(","0.1",")",">",">",">","language_model","=","type","(","``",",","(","object",",",")",",","{","'probability_change","'",":","lambda","self",",","context",",","phrase",":","language_prob","[","phrase","]",",","'probability","'",":","lambda","self",",","phrase",":","language_prob","[","phrase","]","}",")","(",")",">",">",">","stack_decoder","=","StackDecoder","(","phrase_table",",","language_model",")",">",">",">","stack_decoder.translate","(","[","'niemand","'",",","'erwartet","'",",","'die","'",",","'spanische","'",",","'inquisition","'",",","'","!","'","]",")","[","'nobody","'",",","'expects","'",",","'the","'",",","'spanish","'",",","'inquisition","'",",","'","!","'","]"]
docstr: float: Amount of reordering of source phrases.
      Lower values favour monotone translation, suitable when
      word order is similar for both source and target languages.
      Value between 0.0 and 1.0. Default 0.5.
docstr_tok: ["float",":","Amount","of","reordering","of","source","phrases",".","Lower","values","favour","monotone","translation",",","suitable","when","word","order","is","similar","for","both","source","and","target","languages",".","Value","between","0.0","and","1.0",".","Default","0.5","."]
code: def distortion_factor(self):
      """
      float: Amount of reordering of source phrases.
          Lower values favour monotone translation, suitable when
          word order is similar for both source and target languages.
          Value between 0.0 and 1.0. Default 0.5.
      """
      return self.__distortion_factor
  
imports: import warnings
  from collections import defaultdict
  from math import log
label_desc: Use image node_id 2 for calling the StackDecoder obj's underlying member method code with example usage: obj.distortion_factor() and returns: self