class_docstr: Implements the log-odds-ratio with an uninformative dirichlet prior from
          Monroe, B. L., Colaresi, M. P., & Quinn, K. M. (2008). Fightin' words: Lexical feature selection and evaluation for identifying the content of political conflict. Political Analysis, 16(4), 372–403.
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docstr: Parameters
  ----------
  y_i, np.array(int)
          Arrays of word counts of words occurring in positive class
  y_j, np.array(int)
  
  Returns
  -------
  np.array of z-scores
docstr_tok: ["Parameters","--","--","--","--","--","y_i",",","np.array","(","int",")","Arrays","of","word","counts","of","words","occurring","in","positive","class","y_j",",","np.array","(","int",")","Returns","--","--","--","-","np.array","of","z-scores"]
code: def get_zeta_i_j_given_separate_counts(self, y_i, y_j):
      """
      Parameters
      ----------
      y_i, np.array(int)
              Arrays of word counts of words occurring in positive class
      y_j, np.array(int)
  
      Returns
      -------
      np.array of z-scores
      """
      yp_i = y_i + self.alpha_w
      yp_j = y_j + self.alpha_w
      np_i = np.sum(yp_i)
      np_j = np.sum(yp_j)
  
      delta_i_j = np.log(yp_i / (np_i - yp_i)) - np.log(
          yp_j / (np_j - yp_j)
      )
      var_delta_i_j = (
          1.0 / (yp_i)
          + 1.0 / (np_i - yp_i)
          + 1.0 / (yp_j)
          + 1.0 / (np_j - yp_j)
      )
      zeta_i_j = delta_i_j / np.sqrt(var_delta_i_j)
      return zeta_i_j
  
file_path: scattertext/scattertext/termsignificance/LogOddsRatioUninformativeDirichletPrior.py
imports: import numpy
  from scipy.special import ndtr
  from scattertext.termranking import AbsoluteFrequencyRanker
  from scattertext.termsignificance.TermSignificance import TermSignificance
label_desc: Use image node_id 5 for calling the LogOddsRatioUninformativeDirichletPrior obj's underlying member method code with example usage: obj.get_zeta_i_j_given_separate_counts(y_i, y_j) and returns: zeta_i_j