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
		'''
		n_i, n_j = y_i.sum(), y_j.sum()
		prior_scale_j = prior_scale_i = 1
		if self._scale_type == 'class-size':
			prior_scale_i = ((n_i) * self._scale * 1. / np.sum(self._priors))
			prior_scale_j = ((n_j) * self._scale * 1. / np.sum(self._priors))
		elif self._scale_type == 'corpus-size':
			prior_scale_j = prior_scale_i = ((n_i + n_j) * self._scale * 1. / np.sum(self._priors))
		elif self._scale_type == 'word':
			prior_scale_j = prior_scale_i = self._scale / np.sum(self._priors)
		elif self._scale_type == 'background-corpus-size':
			prior_scale_j = prior_scale_i = self._scale
		a_wj = (self._priors * prior_scale_j) ** self._prior_power
		a_0j = np.sum(a_wj)
		a_wi = (self._priors * prior_scale_i) ** self._prior_power
		a_0i = np.sum(a_wi)

		delta_i_j = (np.log((y_i + a_wi) / (n_i + a_0i - y_i - a_wi))
		             - np.log((y_j + a_wj) / (n_j + a_0j - y_j - a_wj)))
		var_delta_i_j = (1. / (y_i + a_wi)
		                 + 1. / (n_i + a_0i - y_i - a_wi)
		                 + 1. / (y_j + a_wj)
		                 + 1. / (n_j + a_0j - y_j - a_wj))
		zeta_i_j = delta_i_j / np.sqrt(var_delta_i_j)
		return zeta_i_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
		
scattertext/termsignificance/LogOddsRatioInformativeDirichletPiror.py