class_docstr: Term-Frequency document model (term = word).
class_docstr_tok: ["Term-Frequency","document","model","(","term","=","word",")","."]
docstr: Returns normalized frequency of term in document.
  http://nlp.stanford.edu/IR-book/html/htmledition/maximum-tf-normalization-1.html
  
  :parameter float smooth:
      0.0 <= smooth <= 1.0, generally set to 0.4, although some
      early work used the value 0.5. The term is a smoothing term
      whose role is to damp the contribution of the second term.
      It may be viewed as a scaling down of TF by the largest TF
      value in document.
  :returns float:
      0.0 <= frequency <= 1.0, where 0 means no occurrence in document
      and 1 the most frequent term in document.
docstr_tok: ["Returns","normalized","frequency","of","term","in","document",".","http",":","\/\/nlp.stanford.edu\/IR-book\/html\/htmledition\/maximum-tf-normalization-1.html",":","parameter","float","smooth",":","0.0","<","=","smooth","<","=","1.0",",","generally","set","to","0.4",",","although","some","early","work","used","the","value","0.5",".","The","term","is","a","smoothing","term","whose","role","is","to","damp","the","contribution","of","the","second","term",".","It","may","be","viewed","as","a","scaling","down","of","TF","by","the","largest","TF","value","in","document",".",":","returns","float",":","0.0","<","=","frequency","<","=","1.0",",","where","0","means","no","occurrence","in","document","and","1","the","most","frequent","term","in","document","."]
code: def normalized_term_frequency(self, term, smooth=0.0):
      """
      Returns normalized frequency of term in document.
      http://nlp.stanford.edu/IR-book/html/htmledition/maximum-tf-normalization-1.html
  
      :parameter float smooth:
          0.0 <= smooth <= 1.0, generally set to 0.4, although some
          early work used the value 0.5. The term is a smoothing term
          whose role is to damp the contribution of the second term.
          It may be viewed as a scaling down of TF by the largest TF
          value in document.
      :returns float:
          0.0 <= frequency <= 1.0, where 0 means no occurrence in document
          and 1 the most frequent term in document.
      """
      frequency = self.term_frequency(term) / self._max_frequency
      return smooth + (1.0 - smooth) * frequency
  
file_path: sumy/sumy/models/tf.py
imports: from __future__ import absolute_import
  from __future__ import division, print_function, unicode_literals
  import math
  from collections import Counter
  from pprint import pformat
  from .._compat import to_unicode, unicode, string_types, Sequence
label_desc: Use image node_id 6 for calling the TfDocumentModel obj's underlying member method code with example usage: obj.normalized_term_frequency(term, smooth) and returns: unknown