def get_scores(self, *args):
		'''
		In this case, parameters a and b aren't used, since this information is taken
		directly from the corpus categories.

		Returns
		-------

		'''

		def jelinek_mercer_smoothing(cat):
			p_hat_w = self.tdf_[cat] * 1. / self.tdf_[cat].sum()
			c_hat_w = (self.smoothing_lambda_) * self.tdf_.sum(axis=1) * 1. / self.tdf_.sum().sum()
			return (1 - self.smoothing_lambda_) * p_hat_w + self.smoothing_lambda_ * c_hat_w

		p_w = jelinek_mercer_smoothing('cat')
		q_w = jelinek_mercer_smoothing('ncat')

		kl_divergence = p_w * np.log(p_w / q_w) / np.log(2)

		tt, pvals = self.get_t_statistics()

		return kl_divergence * (pvals < self.min_p_)

		In this case, parameters a and b aren't used, since this information is taken
		directly from the corpus categories.

		Returns
		-------

		
scattertext/termscoring/RelativeEntropy.py