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 p-values
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","p-values"]
code: def get_p_vals_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 p-values
      """
      return z_to_p_val(
          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 5 for calling the LogOddsRatioSmoothed obj's underlying member method code with example usage: obj.get_p_vals_given_separate_counts(y_i, y_j) and returns: z_to_p_val