adjust default parameter; successful run recurrent-xor example

examples/func_fit/xor.py

@@ -1,43 +1,30 @@
 from tensorneat.pipeline import Pipeline
 from tensorneat.algorithm.neat import NEAT
-from tensorneat.genome import DefaultGenome, DefaultNodeGene, DefaultMutation
+from tensorneat.genome import DefaultGenome
 from tensorneat.problem.func_fit import XOR3d
-from tensorneat.common import Act, Agg
+from tensorneat.common import Act
 
 if __name__ == "__main__":
     pipeline = Pipeline(
         algorithm=NEAT(
             pop_size=10000,
             species_size=20,
-            compatibility_threshold=2,
             survival_threshold=0.01,
             genome=DefaultGenome(
                 num_inputs=3,
                 num_outputs=1,
                 init_hidden_layers=(),
-                node_gene=DefaultNodeGene(
-                    activation_default=Act.tanh,
-                    activation_options=Act.tanh,
-                    aggregation_default=Agg.sum,
-                    aggregation_options=Agg.sum,
-                ),
-                output_transform=Act.standard_sigmoid,  # the activation function for output node
-                mutation=DefaultMutation(
-                    node_add=0.1,
-                    conn_add=0.1,
-                    node_delete=0,
-                    conn_delete=0,
-                ),
+                output_transform=Act.standard_sigmoid,
             ),
         ),
         problem=XOR3d(),
         generation_limit=500,
-        fitness_target=-1e-8,
+        fitness_target=-1e-6,  # float32 precision
+        seed=42,
     )
 
     # initialize state
     state = pipeline.setup()
-    # print(state)
     # run until terminate
     state, best = pipeline.auto_run(state)
     # show result