disable activation in the output node of network;

we recommend to use output_transform;
change hyperparameters (strong) in XOR example;

tensorneat/algorithm/neat/genome/recurrent.py

@@ -1,3 +1,5 @@
+from typing import Callable
+
 import jax, jax.numpy as jnp
 from utils import unflatten_conns
 
@@ -18,10 +20,18 @@ class RecurrentGenome(BaseGenome):
                  node_gene: BaseNodeGene = DefaultNodeGene(),
                  conn_gene: BaseConnGene = DefaultConnGene(),
                  activate_time: int = 10,
+                 output_transform: Callable = None
                  ):
         super().__init__(num_inputs, num_outputs, max_nodes, max_conns, node_gene, conn_gene)
         self.activate_time = activate_time
 
+        if output_transform is not None:
+            try:
+                _ = output_transform(jnp.zeros(num_outputs))
+            except Exception as e:
+                raise ValueError(f"Output transform function failed: {e}")
+        self.output_transform = output_transform
+
     def transform(self, nodes, conns):
         u_conns = unflatten_conns(nodes, conns)
 
@@ -52,7 +62,11 @@ class RecurrentGenome(BaseGenome):
             )(conns, values)
 
             # calculate nodes
-            values = jax.vmap(self.node_gene.forward)(nodes_attrs, node_ins.T)
+            is_output_nodes = jnp.isin(
+                jnp.arange(N),
+                self.output_idx
+            )
+            values = jax.vmap(self.node_gene.forward)(nodes_attrs, node_ins.T, is_output_nodes)
             return values
 
         vals = jax.lax.fori_loop(0, self.activate_time, body_func, vals)