def get_lake_model():
"""Returns a fully formulated model of the lake problem."""
# instantiate the model
lake_model = Model('lakeproblem', function=lake_problem)
lake_model.time_horizon = 100
# specify uncertainties
lake_model.uncertainties = [
RealParameter('b', 0.1, 0.45),
RealParameter('q', 2.0, 4.5),
RealParameter('mean', 0.01, 0.05),
RealParameter('stdev', 0.001, 0.005),
RealParameter('delta', 0.93, 0.99)
]
# set levers, one for each time step
lake_model.levers = [
RealParameter(str(i), 0, 0.1) for i in range(lake_model.time_horizon)
]
# specify outcomes
lake_model.outcomes = [
ScalarOutcome('max_P', ),
ScalarOutcome('utility'),
ScalarOutcome('inertia'),
ScalarOutcome('reliability')
]
# override some of the defaults of the model
lake_model.constants = [Constant('alpha', 0.41), Constant('nsamples', 150)]
return lake_model
def lake_model():
lake_model_actual()
if __name__ == '__main__':
freeze_support()
lake_model_actual()
#####################################################################################################
# now connect the model with the workbench
from ema_workbench import Model, RealParameter, ScalarOutcome, Constant
model = Model('lakeproblem', function=lake_model_actual)
model.time_horizon = 100
# specify uncertainties
model.uncertainties = [
RealParameter('b', 0.1, 0.45),
RealParameter('q', 2.0, 4.5),
RealParameter('mean', 0.01, 0.05),
RealParameter('stdev', 0.001, 0.005),
RealParameter('delta', 0.93, 0.99)
]
# specify outcomes
model.outcomes = [
ScalarOutcome('max_P'),
ScalarOutcome('utility'),
ScalarOutcome('inertia'),
reliability += np.sum(X < Pcrit) / float(nsamples * nvars)
max_P = np.max(average_daily_P)
utility = np.sum(alpha * decisions * np.power(delta, np.arange(nvars)))
inertia = np.sum(np.diff(decisions) > -0.02) / float(nvars - 1)
return max_P, utility, inertia, reliability
if __name__ == "__main__":
ema_logging.log_to_stderr(ema_logging.INFO)
# instantiate the model
lake_model = Model("lakeproblem", function=lake_problem)
lake_model.time_horizon = 100
# specify uncertainties
lake_model.uncertainties = [
RealParameter("b", 0.1, 0.45),
RealParameter("q", 2.0, 4.5),
RealParameter("mean", 0.01, 0.05),
RealParameter("stdev", 0.001, 0.005),
RealParameter("delta", 0.93, 0.99),
]
# set levers, one for each time step
lake_model.levers = [RealParameter(str(i), 0, 0.1) for i in range(lake_model.time_horizon)]
# specify outcomes
lake_model.outcomes = [
