diff --git a/utils/utils.py b/utils/utils.py
deleted file mode 100644
index 742dc03..0000000
--- a/utils/utils.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import numpy as np
-
-# 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]
-    
-# for multiple predictions, modify from `learn_add.py`
-def _reform_ids(flatten_pred_res, save_pred_res):
-    re = []
-    i = 0
-    for e in save_pred_res:
-        j = 0
-        ids = []
-        while j < len(e):
-            ids.append(flatten_pred_res[i + j])
-            j += 1
-        re.append(ids)
-        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)