def __init__(self, spec):
Method.__init__(self)
plan = ExperimentPlan(Context.SimulationChain)
print "MethodDOE: ", spec
plan.Import(open(spec["Plan"]))
self.Plan = plan
#self._Event = threading.Event()
#self._Event.set()
self._Sem = threading.Semaphore(0)
def __init__(self, spec):
Method.__init__(self)
plan = ExperimentPlan(Context.SimulationChain)
print "MethodDOE: ", spec
plan.Import(open(spec["Plan"]))
self.Plan = plan
if spec.has_key("InfillCriteriaFunction"):
self.InfillCriteriaFunction = spec["InfillCriteriaFunction"]
else:
self.InfillCriteriaFunction = AdaptiveDOECriteria.SimpleLOO
self.MaxIterations = spec["Iterations"]
self.NewSitesPerIteration = spec["NewSitesPerIteration"]
self._Sem = threading.Semaphore(0)
def __init__(self, spec):
Method.__init__(self)
self.Chromosome = Factory.Chromosome(Context.SimulationChain)
self.ResEval = ChainResponseEvaluator()
self.ResEval.Start()
self.PenEval = Factory.CreatePenaltyEvaluator(spec)
self.FitnessEval = EA.FitnessEvaluator()
optiConfig = spec["OptimizerConfig"]
popSize = optiConfig["PopulationSize"]
self.OptimizerConfig = optiConfig
postIterCallback = lambda gen, pop: self.PostIteration(gen, pop)
self.BestExperiment = None
if spec["Optimizer"] == "GA":
self.Optimizer = EA.MonoObjectiveGA(self.Chromosome, self.ResEval,
self.PenEval, self.FitnessEval,
popSize, 2)
self.Optimizer.PostIterationCallback = postIterCallback
self.HistoryFile = open("%s.history.dat" % Context.Name, "w")
elif spec["Optimizer"] == "SurrogateGA":
Context.Database.Import(open(optiConfig["DOE"]))
pop = Factory.PopulationFromPlan(self.Chromosome, Context.Database)
self.Optimizer = EA.MonoObjectiveSurrogateGA(
self.Chromosome, self.ResEval, self.PenEval, self.FitnessEval,
pop)
elif spec["Optimizer"] == "ParetoGA":
self.Optimizer = EA.SPEA2(self.Chromosome, self.ResEval,
self.PenEval,
optiConfig["PopulationSize"],
optiConfig["ArchiveSize"])
elif spec["Optimizer"] == "ParetoSurrogateGA":
Context.Database.Import(open(optiConfig["DOE"]))
pop = Factory.PopulationFromPlan(self.Chromosome, Context.Database)
self.Optimizer = EA.ParetoSurrogateGA(
self.Chromosome, self.ResEval, self.PenEval, pop, popSize,
optiConfig["ArchiveSize"], optiConfig["Update"],
optiConfig["UpdateTruncation"])
