edfb0596e7
change the args for genome.forward. Origin: (state, inputs, transformed) New: (state, transformed, inputs)
132 lines
4.0 KiB
Python
132 lines
4.0 KiB
Python
from typing import Callable
|
|
|
|
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 = 5.0,
|
|
aggregation=Agg.sum,
|
|
activation=Act.sigmoid,
|
|
activate_time: int = 10,
|
|
output_transform: Callable = Act.sigmoid,
|
|
):
|
|
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(aggregation, activation),
|
|
conn_gene=HyperNEATConnGene(),
|
|
activate_time=activate_time,
|
|
output_transform=output_transform,
|
|
)
|
|
|
|
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):
|
|
return self.neat.ask(state)
|
|
|
|
def tell(self, state: 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))(
|
|
state, transformed, self.substrate.query_coors
|
|
)
|
|
# mute the connection with weight below threshold
|
|
query_res = jnp.where(
|
|
(-self.below_threshold < query_res) & (query_res < self.below_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 = 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(state, h_nodes, h_conns)
|
|
|
|
def forward(self, state, inputs, transformed):
|
|
# add bias
|
|
inputs_with_bias = jnp.concatenate([inputs, jnp.array([1])])
|
|
|
|
res = self.hyper_genome.forward(state, inputs_with_bias, transformed)
|
|
return res
|
|
|
|
@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)
|
|
|
|
def generation(self, state: State):
|
|
return self.neat.generation(state)
|
|
|
|
|
|
class HyperNodeGene(BaseNodeGene):
|
|
def __init__(
|
|
self,
|
|
aggregation=Agg.sum,
|
|
activation=Act.sigmoid,
|
|
):
|
|
super().__init__()
|
|
self.aggregation = aggregation
|
|
self.activation = activation
|
|
|
|
def forward(self, state, attrs, inputs, is_output_node=False):
|
|
return jax.lax.cond(
|
|
is_output_node,
|
|
lambda: self.aggregation(inputs), # output node does not need activation
|
|
lambda: self.activation(self.aggregation(inputs)),
|
|
)
|
|
|
|
|
|
class HyperNEATConnGene(BaseConnGene):
|
|
custom_attrs = ["weight"]
|
|
|
|
def forward(self, state, attrs, inputs):
|
|
weight = attrs[0]
|
|
return inputs * weight
|