corrected for delta match

tests/test_methods.py

@@ -122,7 +122,10 @@ def f(t):
                     )
                     dissimilarity_matrix[i, j] = 1 / (1 + time_array[i, j])
     best_match_n1 = np.argmax(dissimilarity_matrix, axis=1)
-    best_match_n2 = np.argmax(dissimilarity_matrix, axis=0)
+    n2_match_matrix = np.zeros((ts.num_nodes, other.num_nodes))
+    for i, j in enumerate(best_match_n1):
+        n2_match_matrix[i, j] = dissimilarity_matrix[i, j]
+    best_match_n2 = np.argmax(n2_match_matrix, axis=0)
     # find non-unque bins for nodes in n_2 then find max of node span from that bin.
     best_match_n1_spans = np.zeros((ts.num_nodes,))
     best_match_n2_spans = np.zeros((other.num_nodes,))

tscompare/methods.py

@@ -444,7 +444,12 @@ def f(t):
     # n2_match is finds the max match between nodes in N2 and their
     # best match in N1 based on max-span (for tpr, inverse_dissimilarity)
     best_n1_match = best_match_matrix.argmax(axis=1).A1
-    best_n2_match = best_match_matrix.argmax(axis=0).A1
+    n2_match_matrix = scipy.sparse.lil_matrix((ts.num_nodes, other.num_nodes))
+    n2_match_matrix[np.arange(ts.num_nodes), best_n1_match] = best_match_matrix.tocsr()[
+        np.arange(ts.num_nodes), best_n1_match
+    ]
+    n2_match_matrix = n2_match_matrix.tocsr()
+    best_n2_match = n2_match_matrix.argmax(axis=0).A1
     n2_match_mask = best_n1_match[best_n2_match] == np.arange(other.num_nodes)
     best_match_n1_spans = shared_spans[np.arange(ts.num_nodes), best_n1_match].reshape(
         -1