42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
from functools import partial
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from utils import Configer
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from algorithms.neat import Pipeline
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from time_utils import using_cprofile
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from problems import Sin, Xor, DIY
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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]])
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#
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#
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# def evaluate(forward_func: Callable) -> List[float]:
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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.tolist() # returns a list
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# @using_cprofile
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@partial(using_cprofile, root_abs_path='/mnt/e/neat-jax/', replace_pattern="/mnt/e/neat-jax/")
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def main():
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config = Configer.load_config()
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# config.neat.population.pop_size = 50
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problem = Xor()
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# problem = Sin()
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# problem = DIY(func=lambda x: (np.sin(x) + np.exp(x) - x ** 2) / (np.cos(x) + np.sqrt(x)) - np.log(x + 1))
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problem.refactor_config(config)
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pipeline = Pipeline(config, seed=0)
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best_nodes, best_connections = pipeline.auto_run(problem.evaluate)
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# print(best_nodes, best_connections)
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# func = pipeline.function_factory.ask_batch_forward(best_nodes, best_connections)
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# problem.print(func)
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if __name__ == '__main__':
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main()
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