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the multiprocessing workflows as well as comms pipelines are operational
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@@ -14,8 +14,8 @@ 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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args = {"high": 200, "low": -200, "t": 100, "p": 0.8}
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scale = 3
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def doe(heuristicpath, heuristic_name, testfunctionpaths, funcnames, objs, args, scale):
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@@ -23,6 +23,7 @@ def doe(heuristicpath, heuristic_name, testfunctionpaths, funcnames, objs, args,
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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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connections = []
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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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@@ -30,11 +31,14 @@ def doe(heuristicpath, heuristic_name, testfunctionpaths, funcnames, objs, args,
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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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connections.append(multiprocessing.Pipe())
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proc.append(multiprocessing.Process(target=heuristic.main, name=funcnames[idx], args=(func, objs, initpoint, args, connections[idx][0])))
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responses = []
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for idx,process in enumerate(proc):
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process.start()
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responses.append(connections[idx][1].recv())
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print("started :" + str(initpoint) + "\nEnded :" + str(responses[0]))
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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)
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