fix bugs

tensorneat/examples/func_fit/xor.py

@@ -8,7 +8,7 @@ if __name__ == "__main__":
     pipeline = Pipeline(
         algorithm=NEAT(
             species=DefaultSpecies(
-                genome=DefaultGenome(
+                genome=DenseInitialize(
                     num_inputs=3,
                     num_outputs=1,
                     max_nodes=50,
@@ -21,7 +21,7 @@ if __name__ == "__main__":
                         # aggregation_options=(Agg.sum,),
                         aggregation_options=AGG_ALL,
                     ),
-                    output_transform=Act.sigmoid,  # the activation function for output node
+                    output_transform=Act.standard_sigmoid,  # the activation function for output node
                     mutation=DefaultMutation(
                         node_add=0.1,
                         conn_add=0.1,
@@ -29,7 +29,7 @@ if __name__ == "__main__":
                         conn_delete=0,
                     ),
                 ),
-                pop_size=100000,
+                pop_size=10000,
                 species_size=20,
                 compatibility_threshold=2,
                 survival_threshold=0.01,  # magic
@@ -37,7 +37,7 @@ if __name__ == "__main__":
         ),
         problem=XOR3d(),
         generation_limit=10000,
-        fitness_target=-1e-8,
+        fitness_target=-1e-3,
     )
 
     # initialize state

tensorneat/examples/gymnax/cartpole.py

@@ -6,7 +6,7 @@ from algorithm.neat import *
 from problem.rl_env import GymNaxEnv
 
 
-def action_policy(forward_func, obs):
+def action_policy(randkey, forward_func, obs):
     return jnp.argmax(forward_func(obs))
 
 
@@ -27,7 +27,9 @@ if __name__ == "__main__":
                 species_size=10,
             ),
         ),
-        problem=GymNaxEnv(env_name="CartPole-v1", repeat_times=5, action_policy=action_policy),
+        problem=GymNaxEnv(
+            env_name="CartPole-v1", repeat_times=5, action_policy=action_policy
+        ),
         generation_limit=10000,
         fitness_target=500,
     )

tensorneat/examples/interpret_visualize/genome_sympy.py

@@ -1,14 +1,14 @@
 import jax, jax.numpy as jnp
 
 from algorithm.neat import *
-from algorithm.neat.genome.hidden import AdvanceInitialize
+from algorithm.neat.genome.dense import DenseInitialize
 from utils.graph import topological_sort_python
+from utils import *
 
-if __name__ == '__main__':
-    genome = AdvanceInitialize(
-        num_inputs=17,
-        num_outputs=6,
-        hidden_cnt=8,
+if __name__ == "__main__":
+    genome = DenseInitialize(
+        num_inputs=3,
+        num_outputs=1,
         max_nodes=50,
         max_conns=500,
     )
@@ -19,16 +19,19 @@ if __name__ == '__main__':
     nodes, conns = genome.initialize(state, randkey)
 
     network = genome.network_dict(state, nodes, conns)
-    print(set(network["nodes"]), set(network["conns"]))
-    order, _ = topological_sort_python(set(network["nodes"]), set(network["conns"]))
-    print(order)
 
     input_idx, output_idx = genome.get_input_idx(), genome.get_output_idx()
-    print(input_idx, output_idx)
 
-    print(genome.repr(state, nodes, conns))
-    print(network)
+    res = genome.sympy_func(state, network, sympy_input_transform=lambda x: 999999999*x, sympy_output_transform=SympyStandardSigmoid)
+    (symbols,
+    args_symbols,
+    input_symbols,
+    nodes_exprs,
+    output_exprs,
+    forward_func,) = res
 
-    res = genome.sympy_func(state, network, precision=3)
-    print(res)
+    print(symbols)
+    print(output_exprs[0].subs(args_symbols))
 
+    inputs = jnp.zeros(3)
+    print(forward_func(inputs))