update hyperneat and related examples

tensorneat/algorithm/hyperneat/hyperneat.py

@@ -1,12 +1,12 @@
 from typing import Callable
 
-import jax, jax.numpy as jnp
+import jax
+from jax import vmap, numpy as jnp
 
-from tensorneat.common import State, Act, Agg
-from .. import BaseAlgorithm, NEAT
-from ..neat.gene import BaseNodeGene, BaseConnGene
-from ..neat.genome import RecurrentGenome
 from .substrate import *
+from tensorneat.common import State, Act, Agg
+from tensorneat.algorithm import BaseAlgorithm, NEAT
+from tensorneat.genome import BaseNode, BaseConn, RecurrentGenome
 
 
 class HyperNEAT(BaseAlgorithm):
@@ -14,64 +14,65 @@ class HyperNEAT(BaseAlgorithm):
         self,
         substrate: BaseSubstrate,
         neat: NEAT,
-        below_threshold: float = 0.3,
+        weight_threshold: float = 0.3,
         max_weight: float = 5.0,
-        aggregation=Agg.sum,
-        activation=Act.sigmoid,
+        aggregation: Callable = Agg.sum,
+        activation: Callable = Act.sigmoid,
         activate_time: int = 10,
-        output_transform: Callable = Act.sigmoid,
+        output_transform: Callable = Act.standard_sigmoid,
     ):
         assert (
             substrate.query_coors.shape[1] == neat.num_inputs
-        ), "Substrate input size should be equal to NEAT input size"
-
+        ), "Query coors of Substrate should be equal to NEAT input size"
+        
         self.substrate = substrate
         self.neat = neat
-        self.below_threshold = below_threshold
+        self.weight_threshold = weight_threshold
         self.max_weight = max_weight
         self.hyper_genome = RecurrentGenome(
             num_inputs=substrate.num_inputs,
             num_outputs=substrate.num_outputs,
             max_nodes=substrate.nodes_cnt,
             max_conns=substrate.conns_cnt,
-            node_gene=HyperNodeGene(aggregation, activation),
-            conn_gene=HyperNEATConnGene(),
+            node_gene=HyperNEATNode(aggregation, activation),
+            conn_gene=HyperNEATConn(),
             activate_time=activate_time,
             output_transform=output_transform,
         )
+        self.pop_size = neat.pop_size
 
     def setup(self, state=State()):
         state = self.neat.setup(state)
         state = self.substrate.setup(state)
         return self.hyper_genome.setup(state)
 
-    def ask(self, state: State):
+    def ask(self, state):
         return self.neat.ask(state)
 
-    def tell(self, state: State, fitness):
+    def tell(self, state, fitness):
         state = self.neat.tell(state, fitness)
         return state
 
     def transform(self, state, individual):
         transformed = self.neat.transform(state, individual)
-        query_res = jax.vmap(self.neat.forward, in_axes=(None, None, 0))(
+        query_res = vmap(self.neat.forward, in_axes=(None, None, 0))(
             state, transformed, self.substrate.query_coors
         )
-        # mute the connection with weight below threshold
+        # mute the connection with weight weight threshold
         query_res = jnp.where(
-            (-self.below_threshold < query_res) & (query_res < self.below_threshold),
+            (-self.weight_threshold < query_res) & (query_res < self.weight_threshold),
             0.0,
             query_res,
         )
 
         # make query res in range [-max_weight, max_weight]
         query_res = jnp.where(
-            query_res > 0, query_res - self.below_threshold, query_res
+            query_res > 0, query_res - self.weight_threshold, query_res
         )
         query_res = jnp.where(
-            query_res < 0, query_res + self.below_threshold, query_res
+            query_res < 0, query_res + self.weight_threshold, query_res
         )
-        query_res = query_res / (1 - self.below_threshold) * self.max_weight
+        query_res = query_res / (1 - self.weight_threshold) * self.max_weight
 
         h_nodes, h_conns = self.substrate.make_nodes(
             query_res
@@ -79,11 +80,11 @@ class HyperNEAT(BaseAlgorithm):
 
         return self.hyper_genome.transform(state, h_nodes, h_conns)
 
-    def forward(self, state, inputs, transformed):
+    def forward(self, state, transformed, inputs):
         # add bias
         inputs_with_bias = jnp.concatenate([inputs, jnp.array([1])])
 
-        res = self.hyper_genome.forward(state, inputs_with_bias, transformed)
+        res = self.hyper_genome.forward(state, transformed, inputs_with_bias)
         return res
 
     @property
@@ -94,18 +95,11 @@ class HyperNEAT(BaseAlgorithm):
     def num_outputs(self):
         return self.substrate.num_outputs
 
-    @property
-    def pop_size(self):
-        return self.neat.pop_size
-
-    def member_count(self, state: State):
-        return self.neat.member_count(state)
-
-    def generation(self, state: State):
-        return self.neat.generation(state)
+    def show_details(self, state, fitness):
+        return self.neat.show_details(state, fitness)
 
 
-class HyperNodeGene(BaseNodeGene):
+class HyperNEATNode(BaseNode):
     def __init__(
         self,
         aggregation=Agg.sum,
@@ -123,7 +117,7 @@ class HyperNodeGene(BaseNodeGene):
         )
 
 
-class HyperNEATConnGene(BaseConnGene):
+class HyperNEATConn(BaseConn):
     custom_attrs = ["weight"]
 
     def forward(self, state, attrs, inputs):