finish all refactoring

algorithm/hyperneat/hyperneat.py

@@ -0,0 +1,116 @@
+import jax, jax.numpy as jnp
+
+from utils import State, Act, Agg
+from .. import BaseAlgorithm, NEAT
+from ..neat.gene import BaseNodeGene, BaseConnGene
+from ..neat.genome import RecurrentGenome
+from .substrate import *
+
+
+class HyperNEAT(BaseAlgorithm):
+
+    def __init__(
+            self,
+            substrate: BaseSubstrate,
+            neat: NEAT,
+            below_threshold: float = 0.3,
+            max_weight: float = 5.,
+            activation=Act.sigmoid,
+            aggregation=Agg.sum,
+            activate_time: int = 10,
+    ):
+        assert substrate.query_coors.shape[1] == neat.num_inputs, \
+            "Substrate input size should be equal to NEAT input size"
+
+        self.substrate = substrate
+        self.neat = neat
+        self.below_threshold = below_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(activation, aggregation),
+            conn_gene=HyperNEATConnGene(),
+            activate_time=activate_time,
+        )
+
+    def setup(self, randkey):
+        return State(
+            neat_state=self.neat.setup(randkey)
+        )
+
+    def ask(self, state: State):
+        return self.neat.ask(state.neat_state)
+
+    def tell(self, state: State, fitness):
+        return state.update(
+            neat_state=self.neat.tell(state.neat_state, fitness)
+        )
+
+    def transform(self, individual):
+        transformed = self.neat.transform(individual)
+        query_res = jax.vmap(self.neat.forward, in_axes=(0, None))(self.substrate.query_coors, transformed)
+
+        # mute the connection with weight below threshold
+        query_res = jnp.where(
+            (-self.below_threshold < query_res) & (query_res < self.below_threshold),
+            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 = jnp.where(query_res < 0, query_res + self.below_threshold, query_res)
+        query_res = query_res / (1 - self.below_threshold) * self.max_weight
+
+        h_nodes, h_conns = self.substrate.make_nodes(query_res), self.substrate.make_conn(query_res)
+        return self.hyper_genome.transform(h_nodes, h_conns)
+
+    def forward(self, inputs, transformed):
+        # add bias
+        inputs_with_bias = jnp.concatenate([inputs, jnp.array([1])])
+        return self.hyper_genome.forward(inputs_with_bias, transformed)
+
+    @property
+    def num_inputs(self):
+        return self.substrate.num_inputs - 1  # remove bias
+
+    @property
+    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.neat_state)
+
+    def generation(self, state: State):
+        return self.neat.generation(state.neat_state)
+
+
+class HyperNodeGene(BaseNodeGene):
+
+    def __init__(self,
+                 activation=Act.sigmoid,
+                 aggregation=Agg.sum,
+                 ):
+        super().__init__()
+        self.activation = activation
+        self.aggregation = aggregation
+
+    def forward(self, attrs, inputs):
+        return self.activation(
+            self.aggregation(inputs)
+        )
+
+
+class HyperNEATConnGene(BaseConnGene):
+    custom_attrs = ['weight']
+
+    def forward(self, attrs, inputs):
+        weight = attrs[0]
+        return inputs * weight