本文整理匯總了Python中expWorkbench.ModelEnsemble._generate_samples方法的典型用法代碼示例。如果您正苦於以下問題：Python ModelEnsemble._generate_samples方法的具體用法？Python ModelEnsemble._generate_samples怎麽用？Python ModelEnsemble._generate_samples使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類expWorkbench.ModelEnsemble的用法示例。
在下文中一共展示了ModelEnsemble._generate_samples方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: DummyModel
# 需要導入模塊: from expWorkbench import ModelEnsemble [as 別名]
# 或者: from expWorkbench.ModelEnsemble import _generate_samples [as 別名]
if __name__ == "__main__":
ema_logging.log_to_stderr(ema_logging.INFO)
model = DummyModel(r"", "dummy")
np.random.seed(123456789)
ensemble = ModelEnsemble()
ensemble.set_model_structure(model)
policy_levers = {'Trigger a': {'type':'list', 'values':[0, 0.25, 0.5, 0.75, 1]},
'Trigger b': {'type':'list', 'values':[0, 0.25, 0.5, 0.75, 1]},
'Trigger c': {'type':'list', 'values':[0, 0.25, 0.5, 0.75, 1]}}
cases = ensemble._generate_samples(10, UNION)[0]
ensemble.add_policy({"name":None})
experiments = [entry for entry in ensemble._generate_experiments(cases)]
for entry in experiments:
entry.pop("model")
entry.pop("policy")
cases = experiments
stats, pop = ensemble.perform_robust_optimization(cases=cases,
reporting_interval=100,
obj_function=obj_func,
policy_levers=policy_levers,
weights = (MINIMIZE,)*2,
nr_of_generations=20,
algorithm=epsNSGA2,
pop_size=4,