update functions. Visualize, Interpretable and with evox

examples/func_fit/custom_func_fit.py

@@ -6,7 +6,7 @@ from tensorneat.genome import DefaultGenome, DefaultNode, DefaultMutation, BiasN
 from tensorneat.problem.func_fit import CustomFuncFit
 from tensorneat.common import ACT, AGG
 
-
+# define a custom function fit problem
 def pagie_polynomial(inputs):
     x, y = inputs
     res = 1 / (1 + jnp.pow(x, -4)) + 1 / (1 + jnp.pow(y, -4))
@@ -14,9 +14,12 @@ def pagie_polynomial(inputs):
     # important! returns an array, NOT a scalar
     return jnp.array([res])
 
+# define custom activate function and register it
+def square(x):
+    return x ** 2
+ACT.add_func("square", square)
 
 if __name__ == "__main__":
-
     custom_problem = CustomFuncFit(
         func=pagie_polynomial,
         low_bounds=[-1, -1],

examples/func_fit/xor.py

@@ -14,7 +14,7 @@ if __name__ == "__main__":
                 num_inputs=3,
                 num_outputs=1,
                 init_hidden_layers=(),
-                output_transform=ACT.standard_sigmoid,
+                output_transform=ACT.sigmoid,
             ),
         ),
         problem=XOR3d(),

examples/func_fit/xor_hyperneat.py

@@ -22,12 +22,12 @@ if __name__ == "__main__":
                     num_inputs=4,  # size of query coors
                     num_outputs=1,
                     init_hidden_layers=(),
-                    output_transform=ACT.standard_tanh,
+                    output_transform=ACT.tanh,
                 ),
             ),
             activation=ACT.tanh,
             activate_time=10,
-            output_transform=ACT.standard_sigmoid,
+            output_transform=ACT.sigmoid,
         ),
         problem=XOR3d(),
         generation_limit=300,

examples/func_fit/xor_recurrent.py

@@ -14,7 +14,7 @@ if __name__ == "__main__":
                 num_inputs=3,
                 num_outputs=1,
                 init_hidden_layers=(),
-                output_transform=ACT.standard_sigmoid,
+                output_transform=ACT.sigmoid,
                 activate_time=10,
             ),
         ),