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40 lines
1.6 KiB
Python
40 lines
1.6 KiB
Python
# directly running the DOE because existing surrogates can be explored with another workflow
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from numpy import random as r
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import time
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import importlib.util
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import multiprocessing
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# initialise the logic helpers
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r.seed(int(time.time()))
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heuristicpath = "/home/remi/Documents/MDAF-GitLAB/SourceCode/SampleAlgorithms/SimmulatedAnnealing.py"
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heuristic_name = "SimmulatedAnnealing"
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testfunctionpaths = ["/home/remi/Documents/MDAF-GitLAB/SourceCode/TestFunctions/Bukin2.py"]
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funcnames = ["Bukin2"]
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objs = 0
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args = {"high": 200, "low": -200, "t": 0.01, "p": 0.8}
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scale = 62
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def doe(heuristicpath, heuristic_name, testfunctionpaths, funcnames, objs, args, scale):
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spec = importlib.util.spec_from_file_location(heuristic_name, heuristicpath)
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heuristic = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(heuristic)
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proc = list()
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#heuristic.MyClass()
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for idx, funcpath in enumerate(testfunctionpaths):
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testspec = importlib.util.spec_from_file_location(funcnames[idx], funcpath)
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func = importlib.util.module_from_spec(testspec)
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testspec.loader.exec_module(func)
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#func.MyClass()
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initpoint = [r.random() * scale, r.random() * scale]
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proc.append(multiprocessing.Process(target=heuristic.main, name=funcnames[idx], args=(func, objs, initpoint, args)))
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best = proc[idx].run()
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print("started :" + str(initpoint) + "\nEnded :" + str(best))
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# simulatedAnnealing(S = [9.00,4.00],y = 0,high = 10,low = -8,t =0.01,p = 0.8)
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# proc = subprocess.call(["python", heuristic, "arg-15", "arg2", "argN"])
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doe (heuristicpath, heuristic_name, testfunctionpaths, funcnames, objs, args, scale) |