[FIX] fix bug in confidence dist calculation

abl/utils/utils.py

@@ -46,9 +46,9 @@ def reform_idx(flattened_list, structured_list):
     list
         A reformed list that mimics the structure of structured_list.
     """
-    if not isinstance(flattened_list, list):
-        raise TypeError("Input must be of type list.")
-    
+    # if not isinstance(flattened_list, list):
+    #     raise TypeError("Input must be of type list.")
+
     if not isinstance(structured_list[0], (list, tuple)):
         return flattened_list
 
@@ -107,8 +107,8 @@ def confidence_dist(pred_prob, candidates):
         Confidence distances computed for each candidate.
     """
     pred_prob = np.clip(pred_prob, 1e-9, 1)
-    rows, cols = np.indices((len(candidates), len(candidates[0])))
-    return 1 - np.prod(pred_prob[rows, cols, candidates], axis=1)
+    _, cols = np.indices((len(candidates), len(candidates[0])))
+    return 1 - np.prod(pred_prob[cols, candidates], axis=1)
 
 
 def block_sample(X, Z, Y, sample_num, seg_idx):
@@ -248,3 +248,60 @@ def calculate_revision_num(parameter, total_length):
         if parameter < 0:
             raise ValueError("If parameter is an int, it must be non-negative.")
         return parameter
+
+
+if __name__ == "__main__":
+    A = np.array(
+        [
+            [
+                0.18401675,
+                0.06797526,
+                0.06797541,
+                0.06801736,
+                0.06797528,
+                0.06797526,
+                0.06818808,
+                0.06797527,
+                0.06800033,
+                0.06797526,
+                0.06797526,
+                0.06797526,
+                0.06797526,
+            ],
+            [
+                0.07223161,
+                0.0685229,
+                0.06852708,
+                0.17227574,
+                0.06852163,
+                0.07018146,
+                0.06860291,
+                0.06852849,
+                0.06852163,
+                0.0685216,
+                0.0685216,
+                0.06852174,
+                0.0685216,
+            ],
+            [
+                0.06794382,
+                0.0679436,
+                0.06794395,
+                0.06794346,
+                0.06794346,
+                0.18467231,
+                0.06794345,
+                0.06794871,
+                0.06794345,
+                0.06794345,
+                0.06794345,
+                0.06794345,
+                0.06794345,
+            ],
+        ],
+        dtype=np.float32,
+    )
+    B = [[0, 9, 3], [0, 11, 4]]
+
+    print(ori_confidence_dist(A, B))
+    print(confidence_dist(A, B))