Create utils.py

utils/utils.py

@@ -0,0 +1,35 @@
+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)