Files
tensorneat-mend/examples/xor_test.py
2023-07-19 15:43:49 +08:00

51 lines
1.5 KiB
Python

import jax
import numpy as np
from algorithm.config import Configer
from algorithm.neat import NEAT, NormalGene, RecurrentGene, Pipeline
from algorithm.neat.genome import create_mutate
xor_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
def single_genome(func, nodes, conns):
t = RecurrentGene.forward_transform(nodes, conns)
out1 = func(xor_inputs[0], t)
out2 = func(xor_inputs[1], t)
out3 = func(xor_inputs[2], t)
out4 = func(xor_inputs[3], t)
print(out1, out2, out3, out4)
def batch_genome(func, nodes, conns):
t = NormalGene.forward_transform(nodes, conns)
out = jax.vmap(func, in_axes=(0, None))(xor_inputs, t)
print(out)
def pop_batch_genome(func, pop_nodes, pop_conns):
t = jax.vmap(NormalGene.forward_transform)(pop_nodes, pop_conns)
func = jax.vmap(jax.vmap(func, in_axes=(0, None)), in_axes=(None, 0))
out = func(xor_inputs, t)
print(out)
if __name__ == '__main__':
config = Configer.load_config("xor.ini")
# neat = NEAT(config, NormalGene)
neat = NEAT(config, RecurrentGene)
randkey = jax.random.PRNGKey(42)
state = neat.setup(randkey)
forward_func = RecurrentGene.create_forward(config)
mutate_func = create_mutate(config, RecurrentGene)
nodes, conns = state.pop_nodes[0], state.pop_conns[0]
single_genome(forward_func, nodes, conns)
# batch_genome(forward_func, nodes, conns)
nodes, conns = mutate_func(state, randkey, nodes, conns, 10000)
single_genome(forward_func, nodes, conns)
# batch_genome(forward_func, nodes, conns)
#