1 | from FramsticksLib import FramsticksLib
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2 | from .structures.individual import Individual
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3 |
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4 | from .frams_base.experiment_frams_niching import ExperimentFramsNiching
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5 |
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6 |
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7 | def main():
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8 | # random.seed(123) # see FramsticksLib.DETERMINISTIC below, set to True if you want full determinism
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9 | # must be set before FramsticksLib() constructor call
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10 | FramsticksLib.DETERMINISTIC = False
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11 | parsed_args = ExperimentFramsNiching.get_args_for_parser().parse_args()
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12 | Individual.fitness_set_negative_to_zero = parsed_args.fitness_set_negative_to_zero # setting the "static" field once
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13 | print("Argument values:", ", ".join(
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14 | ['%s=%s' % (arg, getattr(parsed_args, arg)) for arg in vars(parsed_args)]))
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15 | # multiple criteria not supported here. If needed, use FramsticksEvolution.py
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16 | opt_criteria = parsed_args.opt.split(",")
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17 | framsLib = FramsticksLib(
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18 | parsed_args.path, parsed_args.lib, parsed_args.sim)
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19 | constrains = {"max_numparts": parsed_args.max_numparts,
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20 | "max_numjoints": parsed_args.max_numjoints,
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21 | "max_numneurons": parsed_args.max_numneurons,
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22 | "max_numconnections": parsed_args.max_numconnections,
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23 | "max_numgenochars": parsed_args.max_numgenochars,
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24 | }
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25 |
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26 | experiment = ExperimentFramsNiching(frams_lib=framsLib,
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27 | optimization_criteria=opt_criteria,
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28 | hof_size=parsed_args.hof_size,
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29 | constraints=constrains,
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30 | normalize=parsed_args.normalize,
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31 | dissim=parsed_args.dissim,
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32 | fit=parsed_args.fit,
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33 | genformat=parsed_args.genformat,
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34 | popsize=parsed_args.popsize,
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35 | archive_size=parsed_args.archive,
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36 | save_only_best=parsed_args.save_only_best,
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37 | knn_niching=parsed_args.knn_niching,
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38 | knn_nslc=parsed_args.knn_nslc)
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39 |
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40 | experiment.evolve(hof_savefile=parsed_args.hof_savefile,
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41 | generations=parsed_args.generations,
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42 | initialgenotype=parsed_args.initialgenotype,
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43 | pmut=parsed_args.pmut,
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44 | pxov=parsed_args.pxov,
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45 | tournament_size=parsed_args.tournament)
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46 |
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47 |
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48 | if __name__ == "__main__":
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49 | main()
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