Add CustomFuncFit into problem; Add related examples

tensorneat/genome/gene/conn/default.py

@@ -17,6 +17,8 @@ class DefaultConn(BaseConn):
         weight_mutate_power: float = 0.15,
         weight_mutate_rate: float = 0.2,
         weight_replace_rate: float = 0.015,
+        weight_lower_bound: float = -5.0,
+        weight_upper_bound: float = 5.0,
     ):
         super().__init__()
         self.weight_init_mean = weight_init_mean
@@ -24,6 +26,9 @@ class DefaultConn(BaseConn):
         self.weight_mutate_power = weight_mutate_power
         self.weight_mutate_rate = weight_mutate_rate
         self.weight_replace_rate = weight_replace_rate
+        self.weight_lower_bound = weight_lower_bound
+        self.weight_upper_bound = weight_upper_bound
+
 
     def new_zero_attrs(self, state):
         return jnp.array([0.0])  # weight = 0
@@ -36,6 +41,7 @@ class DefaultConn(BaseConn):
             jax.random.normal(randkey, ()) * self.weight_init_std
             + self.weight_init_mean
         )
+        weight = jnp.clip(weight, self.weight_lower_bound, self.weight_upper_bound)
         return jnp.array([weight])
 
     def mutate(self, state, randkey, attrs):
@@ -49,7 +55,7 @@ class DefaultConn(BaseConn):
             self.weight_mutate_rate,
             self.weight_replace_rate,
         )
-
+        weight = jnp.clip(weight, self.weight_lower_bound, self.weight_upper_bound)
         return jnp.array([weight])
 
     def distance(self, state, attrs1, attrs2):