50 lines
1.4 KiB
Python
50 lines
1.4 KiB
Python
import jax
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import numpy as np
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from config import Config, BasicConfig, NeatConfig
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from pipeline import Pipeline
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from algorithm import NEAT, HyperNEAT
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from algorithm.neat.gene import RecurrentGene, RecurrentGeneConfig
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from algorithm.hyperneat.substrate import NormalSubstrate, NormalSubstrateConfig
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xor_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
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xor_outputs = np.array([[0], [1], [1], [0]], dtype=np.float32)
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def evaluate(forward_func):
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"""
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:param forward_func: (4: batch, 2: input size) -> (pop_size, 4: batch, 1: output size)
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:return:
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"""
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outs = forward_func(xor_inputs)
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outs = jax.device_get(outs)
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fitnesses = 4 - np.sum((outs - xor_outputs) ** 2, axis=(1, 2))
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return fitnesses
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if __name__ == '__main__':
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config = Config(
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basic=BasicConfig(
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fitness_target=3.99999,
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pop_size=10000
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),
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neat=NeatConfig(
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network_type="recurrent",
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maximum_nodes=50,
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maximum_conns=100,
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inputs=4,
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outputs=1
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),
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gene=RecurrentGeneConfig(
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activation_default="tanh",
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activation_options=("tanh",),
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),
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substrate=NormalSubstrateConfig(),
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)
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neat = NEAT(config, RecurrentGene)
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hyperNEAT = HyperNEAT(config, neat, NormalSubstrate)
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pipeline = Pipeline(config, hyperNEAT)
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pipeline.auto_run(evaluate)
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