def run_once():
model = DiffusionModel(n, m, ep, p, q)
while not model.has_stabilized():
observed_infected, resistant = model.get_observed_infected(), model.get_resistant()
if model.has_stabilized(observed_infected, resistant):
# Observed model has stabilized; take no action for one round
target = None
else:
if strategy == 1:
target = random.choice(tuple(observed_infected))
elif strategy == 2:
target = get_best_target(model, observed_infected, resistant)
else:
target = random.choice(tuple(set(range(n)).difference(resistant)))
model.step(target)
return model.get_value()
def run_once():
model = DiffusionModel(n, m, ep, p, q)
search_tree = {}
nsim_total = 0
while not model.has_stabilized():
best_target, nsim = search(model, search_tree)
model.step(best_target)
nsim_total += nsim
return model.get_value(), nsim_total