diff --git a/utils/utils.py b/utils/utils.py
index 57dea31..f4db6f2 100644
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -2,16 +2,10 @@ import numpy as np
 from utils.plog import INFO
 from collections import OrderedDict
 
-
 # for multiple predictions, modify from `learn_add.py`
 def flatten(l):
-    return (
-        [item for sublist in l for item in flatten(sublist)]
-        if isinstance(l, list)
-        else [l]
-    )
-
-
+    return [item for sublist in l for item in flatten(sublist)] if isinstance(l, list) else [l]
+    
 # for multiple predictions, modify from `learn_add.py`
 def reform_idx(flatten_pred_res, save_pred_res):
     re = []
@@ -26,6 +20,22 @@ def reform_idx(flatten_pred_res, save_pred_res):
         i = i + j
     return re
 
+def hamming_dist(A, B):
+    B = np.array(B)
+    A = np.expand_dims(A, axis = 0).repeat(axis=0, repeats=(len(B)))
+    return np.sum(A != B, axis = 1)
+
+def confidence_dist(A, B):
+    B = np.array(B)
+    A = np.clip(A, 1e-9, 1)
+    A = np.expand_dims(A, axis=0)
+    A = A.repeat(axis=0, repeats=(len(B)))
+    rows = np.array(range(len(B)))
+    rows = np.expand_dims(rows, axis = 1).repeat(axis = 1, repeats = len(B[0]))
+    cols = np.array(range(len(B[0])))
+    cols = np.expand_dims(cols, axis = 0).repeat(axis = 0, repeats = len(B))
+    return 1 - np.prod(A[rows, cols, B], axis = 1)
+
 
 def block_sample(X, Z, Y, sample_num, epoch_idx):
     part_num = len(X) // sample_num
@@ -40,28 +50,25 @@ def block_sample(X, Z, Y, sample_num, epoch_idx):
     return X, Z, Y
 
 
-def hamming_dist(A, B):
-    B = np.array(B)
-    A = np.expand_dims(A, axis=0).repeat(axis=0, repeats=(len(B)))
-    return np.sum(A != B, axis=1)
-
-
-def confidence_dist(A, B):
-    B = np.array(B)
-    A = np.clip(A, 1e-9, 1)
-    A = np.expand_dims(A, axis=0)
-    A = A.repeat(axis=0, repeats=(len(B)))
-    rows = np.array(range(len(B)))
-    rows = np.expand_dims(rows, axis=1).repeat(axis=1, repeats=len(B[0]))
-    cols = np.array(range(len(B[0])))
-    cols = np.expand_dims(cols, axis=0).repeat(axis=0, repeats=len(B))
-    return 1 - np.prod(A[rows, cols, B], axis=1)
+def gen_mappings(chars, symbs):
+	n_char = len(chars)
+	n_symbs = len(symbs)
+	if n_char != n_symbs:
+		print('Characters and symbols size dosen\'t match.')
+		return
+	from itertools import permutations
+	mappings = []
+	# returned mappings
+	perms = permutations(symbs)
+	for p in perms:
+		mappings.append(dict(zip(chars, list(p))))
+	return mappings
 
+def mapping_res(original_pred_res, m):
+    return [[m[symbol] for symbol in formula] for formula in original_pred_res]
 
-def copy_state_dict(state_dict):
-    new_state_dict = OrderedDict()
-    for k, v in state_dict.items():
-        if k.startswith('base_model'):
-            name = ".".join(k.split(".")[1:])
-            new_state_dict[name] = v
-    return new_state_dict
+def remapping_res(pred_res, m):
+    remapping = {}
+    for key, value in m.items():
+        remapping[value] = key
+    return [[remapping[symbol] for symbol in formula] for formula in pred_res]