96 lines
3.0 KiB
Python
96 lines
3.0 KiB
Python
from typing import Callable
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import jax, jax.numpy as jnp
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from utils import unflatten_conns, topological_sort, I_INF
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from . import BaseGenome
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from ..gene import BaseNodeGene, BaseConnGene, DefaultNodeGene, DefaultConnGene
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from ..ga import BaseMutation, BaseCrossover, DefaultMutation, DefaultCrossover
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class DefaultGenome(BaseGenome):
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"""Default genome class, with the same behavior as the NEAT-Python"""
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network_type = "feedforward"
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def __init__(
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self,
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num_inputs: int,
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num_outputs: int,
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max_nodes=5,
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max_conns=4,
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node_gene: BaseNodeGene = DefaultNodeGene(),
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conn_gene: BaseConnGene = DefaultConnGene(),
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mutation: BaseMutation = DefaultMutation(),
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crossover: BaseCrossover = DefaultCrossover(),
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output_transform: Callable = None,
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):
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super().__init__(
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num_inputs,
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num_outputs,
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max_nodes,
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max_conns,
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node_gene,
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conn_gene,
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mutation,
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crossover,
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)
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if output_transform is not None:
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try:
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_ = output_transform(jnp.zeros(num_outputs))
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except Exception as e:
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raise ValueError(f"Output transform function failed: {e}")
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self.output_transform = output_transform
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def transform(self, state, nodes, conns):
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u_conns = unflatten_conns(nodes, conns)
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conn_enable = u_conns[0] == 1
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# remove enable attr
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u_conns = jnp.where(conn_enable, u_conns[1:, :], jnp.nan)
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seqs = topological_sort(nodes, conn_enable)
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return seqs, nodes, u_conns
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def forward(self, state, inputs, transformed):
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cal_seqs, nodes, conns = transformed
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N = nodes.shape[0]
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ini_vals = jnp.full((N,), jnp.nan)
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ini_vals = ini_vals.at[self.input_idx].set(inputs)
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nodes_attrs = nodes[:, 1:]
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def cond_fun(carry):
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values, idx = carry
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return (idx < N) & (cal_seqs[idx] != I_INF)
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def body_func(carry):
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values, idx = carry
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i = cal_seqs[idx]
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def hit():
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ins = jax.vmap(self.conn_gene.forward, in_axes=(None, 1, 0))(
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state, conns[:, :, i], values
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)
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z = self.node_gene.forward(
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state,
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nodes_attrs[i],
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ins,
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is_output_node=jnp.isin(i, self.output_idx),
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)
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new_values = values.at[i].set(z)
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return new_values
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# the val of input nodes is obtained by the task, not by calculation
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values = jax.lax.cond(jnp.isin(i, self.input_idx), lambda: values, hit)
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return values, idx + 1
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vals, _ = jax.lax.while_loop(cond_fun, body_func, (ini_vals, 0))
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if self.output_transform is None:
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return vals[self.output_idx]
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else:
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return self.output_transform(vals[self.output_idx])
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