50 lines
1.3 KiB
Python
50 lines
1.3 KiB
Python
import jax
|
|
import numpy as np
|
|
|
|
from config import Config, BasicConfig, NeatConfig
|
|
from pipeline import Pipeline
|
|
from algorithm import NEAT, RecurrentGene, RecurrentGeneConfig
|
|
from algorithm import HyperNEAT, NormalSubstrate, NormalSubstrateConfig
|
|
|
|
|
|
xor_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
|
|
xor_outputs = np.array([[0], [1], [1], [0]], dtype=np.float32)
|
|
|
|
|
|
def evaluate(forward_func):
|
|
"""
|
|
:param forward_func: (4: batch, 2: input size) -> (pop_size, 4: batch, 1: output size)
|
|
:return:
|
|
"""
|
|
outs = forward_func(xor_inputs)
|
|
outs = jax.device_get(outs)
|
|
fitnesses = 4 - np.sum((outs - xor_outputs) ** 2, axis=(1, 2))
|
|
return fitnesses
|
|
|
|
|
|
if __name__ == '__main__':
|
|
config = Config(
|
|
basic=BasicConfig(
|
|
fitness_target=3.99999,
|
|
pop_size=100
|
|
),
|
|
neat=NeatConfig(
|
|
network_type="recurrent",
|
|
maximum_nodes=50,
|
|
maximum_conns=100,
|
|
inputs=4,
|
|
outputs=1
|
|
|
|
),
|
|
gene=RecurrentGeneConfig(
|
|
activation_default="tanh",
|
|
activation_options=("tanh", ),
|
|
),
|
|
substrate=NormalSubstrateConfig(),
|
|
)
|
|
neat = NEAT(config, RecurrentGene)
|
|
hyperNEAT = HyperNEAT(config, neat, NormalSubstrate)
|
|
|
|
pipeline = Pipeline(config, hyperNEAT)
|
|
pipeline.auto_run(evaluate)
|