disable activation in the output node of network;

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

tensorneat/algorithm/hyperneat/hyperneat.py

@@ -1,3 +1,5 @@
+from typing import Callable
+
 import jax, jax.numpy as jnp
 
 from utils import State, Act, Agg
@@ -18,6 +20,7 @@ class HyperNEAT(BaseAlgorithm):
             activation=Act.sigmoid,
             aggregation=Agg.sum,
             activate_time: int = 10,
+            output_transform: Callable = Act.sigmoid,
     ):
         assert substrate.query_coors.shape[1] == neat.num_inputs, \
             "Substrate input size should be equal to NEAT input size"
@@ -34,6 +37,7 @@ class HyperNEAT(BaseAlgorithm):
             node_gene=HyperNodeGene(activation, aggregation),
             conn_gene=HyperNEATConnGene(),
             activate_time=activate_time,
+            output_transform=output_transform
         )
 
     def setup(self, randkey):
@@ -102,11 +106,13 @@ class HyperNodeGene(BaseNodeGene):
         self.activation = activation
         self.aggregation = aggregation
 
-    def forward(self, attrs, inputs):
-        return self.activation(
-            self.aggregation(inputs)
-        )
+    def forward(self, attrs, inputs, is_output_node=False):
+        return jax.lax.cond(
+            is_output_node,
+            lambda: self.aggregation(inputs),  # output node does not need activation
+            lambda: self.activation(self.aggregation(inputs))
 
+        )
 
 class HyperNEATConnGene(BaseConnGene):
     custom_attrs = ['weight']