remove create_func....

algorithm/hyperneat/hyperneat.py

@@ -0,0 +1,116 @@
+from typing import Type
+
+import jax
+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 utils import Activation, Aggregation
+from algorithm.neat import NEAT
+from .substrate import analysis_substrate
+
+
+class HyperNEAT(Algorithm):
+
+    def __init__(self, config: Config, neat: NEAT, substrate: Type[Substrate]):
+        self.config = config
+        self.neat = neat
+        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,
+        )
+        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]
+
+        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]
+        h_conns = np.zeros((correspond_keys.shape[0], 3), dtype=np.float32)
+        h_conns[:, 0:2] = correspond_keys
+
+        state = state.update(
+            h_input_idx=h_input_idx,
+            h_output_idx=h_output_idx,
+            h_hidden_idx=h_hidden_idx,
+            h_nodes=h_nodes,
+            h_conns=h_conns,
+            query_coors=query_coors,
+        )
+
+        return state
+
+    def ask_algorithm(self, state: State):
+        return state.pop_genomes
+
+    def tell_algorithm(self, state: State, fitness):
+        return self.neat.tell(state, fitness)
+
+    def forward(self, state, inputs: Array, transformed: Array):
+        return HyperNEATGene.forward(self.config.hyper_neat, state, inputs, transformed)
+
+    def forward_transform(self, state: State, genome: Genome):
+        t = self.neat.forward_transform(state, genome)
+        query_res = vmap(self.neat.forward, in_axes=(None, 0, None))(state, state.query_coors, t)
+
+        # mute the connection with weight below threshold
+        query_res = jnp.where((-state.below_threshold < query_res) & (query_res < state.below_threshold), 0., query_res)
+
+        # make query res in range [-max_weight, max_weight]
+        query_res = jnp.where(query_res > 0, query_res - state.below_threshold, query_res)
+        query_res = jnp.where(query_res < 0, query_res + state.below_threshold, query_res)
+        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))
+
+
+class HyperNEATGene:
+    node_attrs = []  # no node attributes
+    conn_attrs = ['weight']
+
+    @staticmethod
+    def forward_transform(genome: Genome):
+        N = genome.nodes.shape[0]
+        u_conns = jnp.zeros((N, N), dtype=jnp.float32)
+
+        in_keys = jnp.asarray(genome.conns[:, 0], jnp.int32)
+        out_keys = jnp.asarray(genome.conns[:, 1], jnp.int32)
+        weights = genome.conns[:, 2]
+
+        u_conns = u_conns.at[in_keys, out_keys].set(weights)
+        return genome.nodes, u_conns
+
+    @staticmethod
+    def forward(config: HyperNeatConfig, state: State, inputs, transformed):
+        act = Activation.name2func[config.activation]
+        agg = Aggregation.name2func[config.aggregation]
+
+        batch_act, batch_agg = jax.vmap(act), jax.vmap(agg)
+
+        nodes, weights = transformed
+
+        inputs_with_bias = jnp.concatenate((inputs, jnp.ones((1,))), axis=0)
+
+        input_idx = state.h_input_idx
+        output_idx = state.h_output_idx
+
+        N = nodes.shape[0]
+        vals = jnp.full((N,), 0.)
+
+        def body_func(i, values):
+            values = values.at[input_idx].set(inputs_with_bias)
+            nodes_ins = values * weights.T
+            values = batch_agg(nodes_ins)  # z = agg(ins)
+            values = values * nodes[:, 2] + nodes[:, 1]  # z = z * response + bias
+            values = batch_act(values)  # z = act(z)
+            return values
+
+        vals = jax.lax.fori_loop(0, config.activate_times, body_func, vals)
+        return vals[output_idx]