docstr: Parameters
  ----------
  X : np.array
          Array of word counts, shape (N, 2) where N is the vocab size.  X[:,0] is the
          positive class, while X[:,1] is the negative class. None by default
  
  Returns
  -------
  np.array of z-scores
docstr_tok: ["Parameters","--","--","--","--","--","X",":","np.array","Array","of","word","counts",",","shape","(","N",",","2",")","where","N","is","the","vocab","size",".","X","[",":",",0","]","is","the","positive","class",",","while","X","[",":",",1","]","is","the","negative","class",".","None","by","default","Returns","--","--","--","-","np.array","of","z-scores"]
code: def get_zeta_i_j(self, X):
      """
      Parameters
      ----------
      X : np.array
              Array of word counts, shape (N, 2) where N is the vocab size.  X[:,0] is the
              positive class, while X[:,1] is the negative class. None by default
  
      Returns
      -------
      np.array of z-scores
      """
      y_i, y_j = X.T[0], X.T[1]
      return self.get_zeta_i_j_given_separate_counts(y_i, y_j)
  
file_path: scattertext/scattertext/termsignificance/LogOddsRatioSmoothed.py
imports: import numpy
  from scipy.special import ndtr
  from scattertext.Scalers import scale_neg_1_to_1_with_zero_mean_abs_max
  from scattertext.termranking import AbsoluteFrequencyRanker
  from scattertext.termsignificance.TermSignificance import TermSignificance
label_desc: Use image node_id 7 for calling the LogOddsRatioSmoothed obj's underlying member method code with example usage: obj.get_zeta_i_j(X) and returns: self