| 1 | from FramsticksLib import FramsticksLib
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| 2 |
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| 3 | from .frams_base.experiment_frams_niching import ExperimentFramsNiching
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| 4 |
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| 5 |
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| 6 | def main():
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| 7 | # random.seed(123) # see FramsticksLib.DETERMINISTIC below, set to True if you want full determinism
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| 8 | # must be set before FramsticksLib() constructor call
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| 9 | FramsticksLib.DETERMINISTIC = False
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| 10 | parsed_args = ExperimentFramsNiching.get_args_for_parser().parse_args()
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| 11 | print("Argument values:", ", ".join(
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| 12 | ['%s=%s' % (arg, getattr(parsed_args, arg)) for arg in vars(parsed_args)]))
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| 13 | # multiple criteria not supported here. If needed, use FramsticksEvolution.py
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| 14 | opt_criteria = parsed_args.opt.split(",")
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| 15 | framsLib = FramsticksLib(
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| 16 | parsed_args.path, parsed_args.lib, parsed_args.sim)
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| 17 | constrains = {"max_numparts": parsed_args.max_numparts,
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| 18 | "max_numjoints": parsed_args.max_numjoints,
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| 19 | "max_numneurons": parsed_args.max_numneurons,
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| 20 | "max_numconnections": parsed_args.max_numconnections,
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| 21 | "max_numgenochars": parsed_args.max_numgenochars,
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| 22 | }
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| 23 |
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| 24 | print('Best individuals:')
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| 25 | experiment = ExperimentFramsNiching(frams_lib=framsLib,
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| 26 | optimization_criteria=opt_criteria,
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| 27 | hof_size=parsed_args.hof_size,
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| 28 | constraints=constrains,
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| 29 | normalize=parsed_args.normalize,
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| 30 | dissim=parsed_args.dissim,
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| 31 | fit=parsed_args.fit,
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| 32 | genformat=parsed_args.genformat,
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| 33 | popsize=parsed_args.popsize,
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| 34 | archive_size=parsed_args.archive,
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| 35 | save_only_best=parsed_args.save_only_best)
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| 36 |
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| 37 | experiment.evolve(hof_savefile=parsed_args.hof_savefile,
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| 38 | generations=parsed_args.generations,
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| 39 | initialgenotype=parsed_args.initialgenotype,
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| 40 | pmut=parsed_args.pmut,
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| 41 | pxov=parsed_args.pxov,
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| 42 | tournament_size=parsed_args.tournament)
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| 43 |
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| 44 |
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| 45 | if __name__ == "__main__":
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| 46 | main()
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