虽然xor问题还是跑不出来，但至少已经确定不是distance的错了

examples/distance_test.py

@@ -0,0 +1,84 @@
+from typing import Callable, List
+from functools import partial
+
+import numpy as np
+
+from utils import Configer
+from algorithms.neat.genome.numpy import analysis, distance
+from algorithms.neat.genome.numpy import create_initialize_function, create_mutate_function
+
+
+def real_distance(nodes1, connections1, nodes2, connections2, input_idx, output_idx):
+    nodes1, connections1 = analysis(nodes1, connections1, input_idx, output_idx)
+    nodes2, connections2 = analysis(nodes2, connections2, input_idx, output_idx)
+    compatibility_coe = 0.5
+    disjoint_coe = 1.
+    node_distance = 0.0
+    if nodes1 or nodes2:  # otherwise, both are empty
+        disjoint_nodes = 0
+        for k2 in nodes2:
+            if k2 not in nodes1:
+                disjoint_nodes += 1
+
+        for k1, n1 in nodes1.items():
+            n2 = nodes2.get(k1)
+            if n2 is None:
+                disjoint_nodes += 1
+            else:
+                if n1[0] is None:
+                    continue
+                d = abs(n1[0] - n2[0]) + abs(n1[1] - n2[1])
+                d += 1 if n1[2] != n2[2] else 0
+                d += 1 if n1[3] != n2[3] else 0
+                node_distance += d
+
+        max_nodes = max(len(nodes1), len(nodes2))
+        node_distance = (compatibility_coe * node_distance + disjoint_coe * disjoint_nodes) / max_nodes
+
+    connection_distance = 0.0
+    if connections1 or connections2:
+        disjoint_connections = 0
+        for k2 in connections2:
+            if k2 not in connections1:
+                disjoint_connections += 1
+
+        for k1, c1 in connections1.items():
+            c2 = connections2.get(k1)
+            if c2 is None:
+                disjoint_connections += 1
+            else:
+                # Homologous genes compute their own distance value.
+                d = abs(c1[0] - c2[0])
+                d += 1 if c1[1] != c2[1] else 0
+                connection_distance += d
+        max_conn = max(len(connections1), len(connections2))
+        connection_distance = (compatibility_coe * connection_distance + disjoint_coe * disjoint_connections) / max_conn
+
+    return node_distance + connection_distance
+
+
+def main():
+    config = Configer.load_config()
+    keys_idx = config.basic.num_inputs + config.basic.num_outputs
+    pop_size = config.neat.population.pop_size
+    init_func = create_initialize_function(config)
+    pop_nodes, pop_connections, input_idx, output_idx = init_func()
+
+    mutate_func = create_mutate_function(config, input_idx, output_idx, batch=True)
+
+    while True:
+        pop_nodes, pop_connections = mutate_func(pop_nodes, pop_connections, list(range(keys_idx, keys_idx + pop_size)))
+        keys_idx += pop_size
+        for i in range(pop_size):
+            for j in range(pop_size):
+                nodes1, connections1 = pop_nodes[i], pop_connections[i]
+                nodes2, connections2 = pop_nodes[j], pop_connections[j]
+                numpy_d = distance(nodes1, connections1, nodes2, connections2)
+                real_d = real_distance(nodes1, connections1, nodes2, connections2, input_idx, output_idx)
+                assert np.isclose(numpy_d, real_d), f'{numpy_d} != {real_d}'
+                print(numpy_d, real_d)
+
+
+if __name__ == '__main__':
+    np.random.seed(0)
+    main()

examples/xor.py

@@ -1,7 +1,6 @@
 from typing import Callable, List
 from functools import partial
 
-import jax
 import numpy as np
 
 from utils import Configer
@@ -18,8 +17,7 @@ def evaluate(forward_func: Callable) -> List[float]:
     :return:
     """
     outs = forward_func(xor_inputs)
-    outs = jax.device_get(outs)
-    fitnesses = -np.mean((outs - xor_outputs) ** 2, axis=(1, 2))
+    fitnesses = np.mean((outs - xor_outputs) ** 2, axis=(1, 2))
     # print(fitnesses)
     return fitnesses.tolist()  # returns a list