45 lines
1.5 KiB
Python
45 lines
1.5 KiB
Python
import numpy as np
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import jax
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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 algorithms.neat.function_factory import FunctionFactory
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from problems import EnhanceLogic
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import time
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def evaluate(problem, func):
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inputs = problem.ask_for_inputs()
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pop_predict = jax.device_get(func(inputs))
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# print(pop_predict)
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fitnesses = []
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for predict in pop_predict:
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f = problem.evaluate_predict(predict)
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fitnesses.append(f)
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return np.array(fitnesses)
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# @using_cprofile
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# @partial(using_cprofile, root_abs_path='/mnt/e/neatax/', replace_pattern="/mnt/e/neat-jax/")
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def main():
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tic = time.time()
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config = Configer.load_config()
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problem = EnhanceLogic("xor", n=3)
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problem.refactor_config(config)
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function_factory = FunctionFactory(config)
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evaluate_func = lambda func: evaluate(problem, func)
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pipeline = Pipeline(config, function_factory, seed=33413)
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print("start run")
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pipeline.auto_run(evaluate_func)
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total_time = time.time() - tic
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compile_time = pipeline.function_factory.compile_time
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total_it = pipeline.generation
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mean_time_per_it = (total_time - compile_time) / total_it
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evaluate_time = pipeline.evaluate_time
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print(f"total time: {total_time:.2f}s, compile time: {compile_time:.2f}s, real_time: {total_time - compile_time:.2f}s, evaluate time: {evaluate_time:.2f}s")
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print(f"total it: {total_it}, mean time per it: {mean_time_per_it:.2f}s")
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if __name__ == '__main__':
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main()
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