add sympy support; which can transfer your network into sympy expression;

add visualize in genome;
add related tests.

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

@@ -47,3 +47,12 @@ class BaseNodeGene(BaseGene):
         return "{}(idx={:<{idx_width}})".format(
             self.__class__.__name__, idx, idx_width=idx_width
         )
+
+    def to_dict(self, state, node):
+        idx = node[0]
+        return {
+            "idx": int(idx),
+        }
+
+    def sympy_func(self, state, node_dict, inputs, is_output_node=False, precision=None):
+        raise NotImplementedError

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

@@ -1,8 +1,18 @@
 from typing import Tuple
 
+import numpy as np
 import jax, jax.numpy as jnp
 
-from utils import Act, Agg, act_func, agg_func, mutate_int, mutate_float
+from utils import (
+    Act,
+    Agg,
+    act_func,
+    agg_func,
+    mutate_int,
+    mutate_float,
+    convert_to_sympy,
+)
+
 from . import BaseNodeGene
 
 
@@ -45,12 +55,12 @@ class DefaultNodeGene(BaseNodeGene):
 
         self.aggregation_default = aggregation_options.index(aggregation_default)
         self.aggregation_options = aggregation_options
-        self.aggregation_indices = jnp.arange(len(aggregation_options))
+        self.aggregation_indices = np.arange(len(aggregation_options))
         self.aggregation_replace_rate = aggregation_replace_rate
 
         self.activation_default = activation_options.index(activation_default)
         self.activation_options = activation_options
-        self.activation_indices = jnp.arange(len(activation_options))
+        self.activation_indices = np.arange(len(activation_options))
         self.activation_replace_rate = activation_replace_rate
 
     def new_identity_attrs(self, state):
@@ -145,5 +155,38 @@ class DefaultNodeGene(BaseNodeGene):
             act_func.__name__,
             idx_width=idx_width,
             float_width=precision + 3,
-            func_width=func_width
+            func_width=func_width,
         )
+
+    def to_dict(self, state, node):
+        idx, bias, res, agg, act = node
+        return {
+            "idx": int(idx),
+            "bias": float(bias),
+            "res": float(res),
+            "agg": self.aggregation_options[int(agg)].__name__,
+            "act": self.activation_options[int(act)].__name__,
+        }
+
+    def sympy_func(
+        self, state, node_dict, inputs, is_output_node=False, precision=None
+    ):
+
+        bias = node_dict["bias"]
+        res = node_dict["res"]
+        agg = node_dict["agg"]
+        act = node_dict["act"]
+
+        if precision is not None:
+            bias = round(bias, precision)
+            res = round(res, precision)
+
+        z = convert_to_sympy(agg)(inputs)
+        z = bias + z * res
+
+        if is_output_node:
+            return z
+        else:
+            z = convert_to_sympy(act)(z)
+
+        return z

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

@@ -2,7 +2,16 @@ from typing import Tuple
 
 import jax, jax.numpy as jnp
 
-from utils import Act, Agg, act_func, agg_func, mutate_int, mutate_float
+from utils import (
+    Act,
+    Agg,
+    act_func,
+    agg_func,
+    mutate_int,
+    mutate_float,
+    convert_to_sympy,
+)
+
 from . import BaseNodeGene
 
 
@@ -121,3 +130,33 @@ class NodeGeneWithoutResponse(BaseNodeGene):
             float_width=precision + 3,
             func_width=func_width,
         )
+
+    def to_dict(self, state, node):
+        idx, bias, agg, act = node
+        return {
+            "idx": int(idx),
+            "bias": float(bias),
+            "agg": self.aggregation_options[int(agg)].__name__,
+            "act": self.activation_options[int(act)].__name__,
+        }
+
+    def sympy_func(
+        self, state, node_dict, inputs, is_output_node=False, precision=None
+    ):
+
+        bias = node_dict["bias"]
+        agg = node_dict["agg"]
+        act = node_dict["act"]
+
+        if precision is not None:
+            bias = round(bias, precision)
+
+        z = convert_to_sympy(agg)(inputs)
+        z = bias + z
+
+        if is_output_node:
+            return z
+        else:
+            z = convert_to_sympy(act)(z)
+
+        return z

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

@@ -25,8 +25,3 @@ class KANNode(BaseNodeGene):
 
     def forward(self, state, attrs, inputs, is_output_node=False):
         return Agg.sum(inputs)
-
-    def repr(self, state, node, precision=2):
-        idx = node[0]
-        idx = int(idx)
-        return "{}(idx: {})".format(self.__class__.__name__, idx)