虽然xor问题还是跑不出来，但至少已经确定不是distance的错了

algorithms/neat/genome/numpy/__init__.py

@@ -1,4 +1,4 @@
-from .genome import create_initialize_function, expand, expand_single
+from .genome import create_initialize_function, expand, expand_single, analysis
 from .distance import distance
 from .mutate import create_mutate_function
 from .forward import create_forward_function

algorithms/neat/genome/numpy/distance.py

@@ -5,6 +5,9 @@ from numpy.typing import NDArray
 
 from algorithms.neat.genome.utils import flatten_connections, set_operation_analysis
 
+EMPTY_NODE = np.full((1, 5), np.nan)
+EMPTY_CON = np.full((1, 4), np.nan)
+
 
 def distance(nodes1: NDArray, connections1: NDArray, nodes2: NDArray, connections2: NDArray) -> NDArray:
     """
@@ -13,15 +16,70 @@ def distance(nodes1: NDArray, connections1: NDArray, nodes2: NDArray, connection
     connections are a 3-d array with shape (2, N, N), axis 0 means [weights, enable]
     """
 
-    node_distance = gene_distance(nodes1, nodes2, 'node')
+    nd = node_distance(nodes1, nodes2)  # node distance
 
     # refactor connections
     keys1, keys2 = nodes1[:, 0], nodes2[:, 0]
     cons1 = flatten_connections(keys1, connections1)
     cons2 = flatten_connections(keys2, connections2)
+    cd = connection_distance(cons1, cons2)  # connection distance
+    return nd + cd
 
-    connection_distance = gene_distance(cons1, cons2, 'connection')
-    return node_distance + connection_distance
+
+def node_distance(nodes1, nodes2, disjoint_coe=1., compatibility_coe=0.5):
+    node_cnt1 = np.sum(~np.isnan(nodes1[:, 0]))
+    node_cnt2 = np.sum(~np.isnan(nodes2[:, 0]))
+    max_cnt = np.maximum(node_cnt1, node_cnt2)
+
+    nodes = np.concatenate((nodes1, nodes2), axis=0)
+    keys = nodes[:, 0]
+    sorted_indices = np.argsort(keys, axis=0)
+    nodes = nodes[sorted_indices]
+    nodes = np.concatenate([nodes, EMPTY_NODE], axis=0)  # add a nan row to the end
+    fr, sr = nodes[:-1], nodes[1:]  # first row, second row
+    nan_mask = np.isnan(nodes[:, 0])
+
+    intersect_mask = (fr[:, 0] == sr[:, 0]) & ~nan_mask[:-1]
+
+    non_homologous_cnt = node_cnt1 + node_cnt2 - 2 * np.sum(intersect_mask)
+    nd = batch_homologous_node_distance(fr, sr)
+    nd = np.where(np.isnan(nd), 0, nd)
+    homologous_distance = np.sum(nd * intersect_mask)
+
+    val = non_homologous_cnt * disjoint_coe + homologous_distance * compatibility_coe
+
+    if max_cnt == 0:  # consider the case that both genome has no gene
+        return 0
+    else:
+        return val / max_cnt
+
+
+def connection_distance(cons1, cons2, disjoint_coe=1., compatibility_coe=0.5):
+    con_cnt1 = np.sum(~np.isnan(cons1[:, 2]))  # weight is not nan, means the connection exists
+    con_cnt2 = np.sum(~np.isnan(cons2[:, 2]))
+    max_cnt = np.maximum(con_cnt1, con_cnt2)
+
+    cons = np.concatenate((cons1, cons2), axis=0)
+    keys = cons[:, :2]
+    sorted_indices = np.lexsort(keys.T[::-1])
+    cons = cons[sorted_indices]
+    cons = np.concatenate([cons, EMPTY_CON], axis=0)  # add a nan row to the end
+    fr, sr = cons[:-1], cons[1:]  # first row, second row
+
+    # both genome has such connection
+    intersect_mask = np.all(fr[:, :2] == sr[:, :2], axis=1) & ~np.isnan(fr[:, 2]) & ~np.isnan(sr[:, 2])
+
+    non_homologous_cnt = con_cnt1 + con_cnt2 - 2 * np.sum(intersect_mask)
+    cd = batch_homologous_connection_distance(fr, sr)
+    cd = np.where(np.isnan(cd), 0, cd)
+    homologous_distance = np.sum(cd * intersect_mask)
+
+    val = non_homologous_cnt * disjoint_coe + homologous_distance * compatibility_coe
+
+    if max_cnt == 0:  # consider the case that both genome has no gene
+        return 0
+    else:
+        return val / max_cnt
 
 
 def gene_distance(ar1, ar2, gene_type, compatibility_coe=0.5, disjoint_coe=1.):

algorithms/neat/species.py

@@ -319,6 +319,10 @@ def o2m_distance(r_nodes, r_connections, pop_nodes, pop_connections):
     distances = []
     for nodes, connections in zip(pop_nodes, pop_connections):
         d = distance(r_nodes, r_connections, nodes, connections)
+        if d < 0:
+            d = distance(r_nodes, r_connections, nodes, connections)
+            print(d)
+            assert False
         distances.append(d)
     distances = np.stack(distances, axis=0)
     return distances