本文整理汇总了Python中expWorkbench.ModelEnsemble.add_policy方法的典型用法代码示例。如果您正苦于以下问题：Python ModelEnsemble.add_policy方法的具体用法？Python ModelEnsemble.add_policy怎么用？Python ModelEnsemble.add_policy使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类expWorkbench.ModelEnsemble的用法示例。
在下文中一共展示了ModelEnsemble.add_policy方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: outcome_optimize
# 需要导入模块: from expWorkbench import ModelEnsemble [as 别名]
# 或者: from expWorkbench.ModelEnsemble import add_policy [as 别名]
def outcome_optimize():
ema_logging.log_to_stderr(ema_logging.INFO)
model = TestModel("", 'simpleModel') #instantiate the model
ensemble = ModelEnsemble() #instantiate an ensemble
ensemble.set_model_structure(model) #set the model on the ensemble
policy = {"name": "test",
"L1": 1,
"L2": 1}
ensemble.add_policy(policy)
def obj_func(results):
return results['y']
results = ensemble.perform_outcome_optimization(obj_function=obj_func,
minimax = 'minimize',
nrOfGenerations = 1000,
nrOfPopMembers = 10)
graph_errorbars_raw(results['stats'])
plt.show()示例2: DummyModel
# 需要导入模块: from expWorkbench import ModelEnsemble [as 别名]
# 或者: from expWorkbench.ModelEnsemble import add_policy [as 别名]
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,
crossover_rate=0.5,