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: Same function as get_zeta_i_j_given_separate_counts
  
  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: ["Same","function","as","get_zeta_i_j_given_separate_counts","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_scores(self, y_i, y_j):
      """
      Same function as get_zeta_i_j_given_separate_counts
  
      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
      """
      z_scores = self.get_zeta_i_j_given_separate_counts(y_i, y_j)
      # scaled_scores = scale_neg_1_to_1_with_zero_mean_abs_max(z_scores)
      return z_scores
  
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 9 for calling the LogOddsRatioUninformativeDirichletPrior obj's underlying member method code with example usage: obj.get_scores(y_i, y_j) and returns: z_scores