remove create_func....

algorithm/hyperneat/hyperneat.py

@@ -5,30 +5,30 @@ from jax import numpy as jnp, Array, vmap
 import numpy as np
 
 from config import Config, HyperNeatConfig
-from core import Algorithm, Substrate, State, Genome
+from core import Algorithm, Substrate, State, Genome, Gene
 from utils import Activation, Aggregation
-from algorithm.neat import NEAT
 from .substrate import analysis_substrate
+from algorithm import NEAT
 
 
 class HyperNEAT(Algorithm):
 
-    def __init__(self, config: Config, neat: NEAT, substrate: Type[Substrate]):
+    def __init__(self, config: Config, gene: Type[Gene], substrate: Type[Substrate]):
         self.config = config
-        self.neat = neat
+        self.neat = NEAT(config, gene)
         self.substrate = substrate
 
     def setup(self, randkey, state=State()):
         neat_key, randkey = jax.random.split(randkey)
         state = state.update(
-            below_threshold=self.config.hyper_neat.below_threshold,
-            max_weight=self.config.hyper_neat.max_weight,
+            below_threshold=self.config.hyperneat.below_threshold,
+            max_weight=self.config.hyperneat.max_weight,
         )
         state = self.neat.setup(neat_key, state)
         state = self.substrate.setup(self.config.substrate, state)
 
-        assert self.config.hyper_neat.inputs + 1 == state.input_coors.shape[0]  # +1 for bias
-        assert self.config.hyper_neat.outputs == state.output_coors.shape[0]
+        assert self.config.hyperneat.inputs + 1 == state.input_coors.shape[0]  # +1 for bias
+        assert self.config.hyperneat.outputs == state.output_coors.shape[0]
 
         h_input_idx, h_output_idx, h_hidden_idx, query_coors, correspond_keys = analysis_substrate(state)
         h_nodes = np.concatenate((h_input_idx, h_output_idx, h_hidden_idx))[..., np.newaxis]
@@ -53,7 +53,7 @@ class HyperNEAT(Algorithm):
         return self.neat.tell(state, fitness)
 
     def forward(self, state, inputs: Array, transformed: Array):
-        return HyperNEATGene.forward(self.config.hyper_neat, state, inputs, transformed)
+        return HyperNEATGene.forward(self.config.hyperneat, state, inputs, transformed)
 
     def forward_transform(self, state: State, genome: Genome):
         t = self.neat.forward_transform(state, genome)
@@ -68,6 +68,7 @@ class HyperNEAT(Algorithm):
         query_res = query_res / (1 - state.below_threshold) * state.max_weight
 
         h_conns = state.h_conns.at[:, 2:].set(query_res)
+
         return HyperNEATGene.forward_transform(Genome(state.h_nodes, h_conns))