docstr: Quoted from https://arxiv.org/pdf/1904.12848.pdf.
  // We set a high probability for replacing words with low TF-IDF scores and
  // set a low probability for replacing words with high TF-IDF scores
docstr_tok: ["Quoted","from","https",":","\/\/arxiv.org\/pdf\/1904.12848.pdf",".","\/\/","We","set","a","high","probability","for","replacing","words","with","low","TF-IDF","scores","and","\/\/","set","a","low","probability","for","replacing","words","with","high","TF-IDF","scores"]
code: def _normalize(cls, data):
      """
      Quoted from https://arxiv.org/pdf/1904.12848.pdf.
      // We set a high probability for replacing words with low TF-IDF scores and
      // set a low probability for replacing words with high TF-IDF scores
      """
      data = data.max() - np.copy(data)
      if data.sum() == 0:
          return [0]
      return data / data.sum()
  
file_path: nlpaug/nlpaug/model/word_stats/tfidf.py
imports: import os
  import math
  import numpy
  from nlpaug.model.word_stats import WordStatistics
label_desc: Use image node_id 2 for calling the TfIdf obj's underlying member method code with example usage: obj._normalize(cls, data) and returns: unknown, list