def balance_scores_and_dont_scale(cat_scores, not_cat_scores):
		'''
		median = np.median(cat_scores)
		scores = np.zeros(len(cat_scores)).astype(np.float)
		scores[cat_scores > median] = cat_scores[cat_scores > median]
		not_cat_mask = cat_scores < median if median != 0 else cat_scores <= median
		scores[not_cat_mask] = -not_cat_scores[not_cat_mask]
		'''
		scores = np.zeros(len(cat_scores)).astype(np.float)
		scores[cat_scores > not_cat_scores] = cat_scores[cat_scores > not_cat_scores]
		scores[cat_scores < not_cat_scores] = -not_cat_scores[cat_scores < not_cat_scores]
		return scores
median = np.median(cat_scores)
		scores = np.zeros(len(cat_scores)).astype(np.float)
		scores[cat_scores > median] = cat_scores[cat_scores > median]
		not_cat_mask = cat_scores < median if median != 0 else cat_scores <= median
		scores[not_cat_mask] = -not_cat_scores[not_cat_mask]