remove create_func....

algorithm/neat/gene/recurrent.py

@@ -1,84 +0,0 @@
-from dataclasses import dataclass
-
-import jax
-from jax import Array, numpy as jnp, vmap
-
-from .normal import NormalGene, NormalGeneConfig
-from core import State, Genome
-from utils import Activation, Aggregation, unflatten_conns
-
-
-@dataclass(frozen=True)
-class RecurrentGeneConfig(NormalGeneConfig):
-    activate_times: int = 10
-
-    def __post_init__(self):
-        super().__post_init__()
-        assert self.activate_times > 0
-
-
-class RecurrentGene(NormalGene):
-
-    @staticmethod
-    def forward_transform(state: State, genome: Genome):
-        u_conns = unflatten_conns(genome.nodes, genome.conns)
-
-        # remove un-enable connections and remove enable attr
-        conn_enable = jnp.where(~jnp.isnan(u_conns[0]), True, False)
-        u_conns = jnp.where(conn_enable, u_conns[1:, :], jnp.nan)
-
-        return genome.nodes, u_conns
-
-    @staticmethod
-    def create_forward(state: State, config: RecurrentGeneConfig):
-        activation_funcs = [Activation.name2func[name] for name in config.activation_options]
-        aggregation_funcs = [Aggregation.name2func[name] for name in config.aggregation_options]
-
-        def act(idx, z):
-            """
-            calculate activation function for each node
-            """
-            idx = jnp.asarray(idx, dtype=jnp.int32)
-            # change idx from float to int
-            res = jax.lax.switch(idx, activation_funcs, z)
-            return res
-
-        def agg(idx, z):
-            """
-            calculate activation function for inputs of node
-            """
-            idx = jnp.asarray(idx, dtype=jnp.int32)
-
-            def all_nan():
-                return 0.
-
-            def not_all_nan():
-                return jax.lax.switch(idx, aggregation_funcs, z)
-
-            return jax.lax.cond(jnp.all(jnp.isnan(z)), all_nan, not_all_nan)
-
-        batch_act, batch_agg = vmap(act), vmap(agg)
-
-        def forward(inputs, transform) -> Array:
-            nodes, cons = transform
-
-            input_idx = state.input_idx
-            output_idx = state.output_idx
-
-            N = nodes.shape[0]
-            vals = jnp.full((N,), 0.)
-
-            weights = cons[0, :]
-
-            def body_func(i, values):
-                values = values.at[input_idx].set(inputs)
-                nodes_ins = values * weights.T
-                values = batch_agg(nodes[:, 4], nodes_ins)  # z = agg(ins)
-                values = values * nodes[:, 2] + nodes[:, 1]  # z = z * response + bias
-                values = batch_act(nodes[:, 3], values)  # z = act(z)
-                return values
-
-            vals = jax.lax.fori_loop(0, config.activate_times, body_func, vals)
-            return vals[output_idx]
-
-        return forward