Parameters
		----------
		cat_word_counts : np.array
			category counts
		not_cat_word_counts : np.array
			not category counts

		Returns
		-------
		np.array
			scores

def get_scores_for_category(self, cat_word_counts, not_cat_word_counts):
		'''
		Parameters
		----------
		cat_word_counts : np.array
			category counts
		not_cat_word_counts : np.array
			not category counts

		Returns
		-------
		np.array
			scores
		'''
		beta = self.beta_
		assert len(cat_word_counts) == len(not_cat_word_counts)
		if self.priors_ is not None:
			p = self.priors_
			assert len(p) == len(cat_word_counts)
			precision = ((cat_word_counts + p * 1.) /
			             (cat_word_counts + not_cat_word_counts + 2 * p))
			recall = (cat_word_counts + p) * 1. / (cat_word_counts.sum() + p.sum())
		else:
			precision = (cat_word_counts * 1. / (cat_word_counts + not_cat_word_counts))
			recall = cat_word_counts * 1. / cat_word_counts.sum()
		precision_normcdf = ScaledFScore._safe_scaler(self.scaler_algo_, precision)
		recall_normcdf = ScaledFScore._safe_scaler(self.scaler_algo_, recall)
		scores = self._weighted_h_mean(precision_normcdf, recall_normcdf)
		scores[np.isnan(scores)] = 0.
		return scores