Try to reduce difference between sympy formula and network.

Spend a whole night. Failed;
I'll never try it anymore.

tensorneat/algorithm/neat/gene/conn/default.py

@@ -82,7 +82,7 @@ class DefaultConnGene(BaseConnGene):
         return {
             "in": int(conn[0]),
             "out": int(conn[1]),
-            "weight": np.array(conn[2], dtype=np.float32),
+            "weight": jnp.float32(conn[2]),
         }
 
     def sympy_func(self, state, conn_dict, inputs, precision=None):

tensorneat/algorithm/neat/gene/node/default.py

@@ -163,8 +163,8 @@ class DefaultNodeGene(BaseNodeGene):
         idx, bias, res, agg, act = node
 
         idx = int(idx)
-        bias = np.array(bias, dtype=np.float32)
+        bias = jnp.float32(bias)
-        res = np.array(res, dtype=np.float32)
+        res = jnp.float32(res)
         agg = int(agg)
         act = int(act)
 
@@ -186,7 +186,7 @@ class DefaultNodeGene(BaseNodeGene):
         res = sp.symbols(f"n_{nd['idx']}_r")
 
         z = convert_to_sympy(nd["agg"])(inputs)
-        z = bias + z * res
+        z = bias + res * z
 
         if is_output_node:
             pass

tensorneat/algorithm/neat/gene/node/default_without_response.py

@@ -137,7 +137,7 @@ class NodeGeneWithoutResponse(BaseNodeGene):
 
         idx = int(idx)
 
-        bias = np.array(bias, dtype=np.float32)
+        bias = jnp.float32(bias)
         agg = int(agg)
         act = int(act)
 

tensorneat/algorithm/neat/genome/default.py

@@ -216,7 +216,6 @@ class DefaultGenome(BaseGenome):
                 symbols[i] = sp.Symbol(f"h{i}")
 
         nodes_exprs = {}
-
         args_symbols = {}
         for i in order:
 

tensorneat/utils/aggregation/agg_jnp.py

@@ -9,23 +9,19 @@ class Agg:
 
     @staticmethod
     def sum(z):
-        z = jnp.where(jnp.isnan(z), 0, z)
+        return jnp.sum(z, axis=0, where=~jnp.isnan(z))
-        return jnp.sum(z, axis=0)
 
     @staticmethod
     def product(z):
-        z = jnp.where(jnp.isnan(z), 1, z)
+        return jnp.prod(z, axis=0, where=~jnp.isnan(z))
-        return jnp.prod(z, axis=0)
 
     @staticmethod
     def max(z):
-        z = jnp.where(jnp.isnan(z), -jnp.inf, z)
+        return jnp.max(z, axis=0, where=~jnp.isnan(z))
-        return jnp.max(z, axis=0)
 
     @staticmethod
     def min(z):
-        z = jnp.where(jnp.isnan(z), jnp.inf, z)
+        return jnp.min(z, axis=0, where=~jnp.isnan(z))
-        return jnp.min(z, axis=0)
 
     @staticmethod
     def maxabs(z):

tensorneat/utils/aggregation/agg_sympy.py

@@ -7,6 +7,10 @@ class SympySum(sp.Function):
     def eval(cls, z):
         return sp.Add(*z)
 
+    @classmethod
+    def numerical_eval(cls, z, backend=np):
+        return backend.sum(z)
+
 
 class SympyProduct(sp.Function):
     @classmethod