def optimize(self, cid, other_champion_stats):
champion = Champion.objects(champion_id=cid).get()
self.input_array[0] = cid
self.input_array[1:7] = np.array(champion.class_data)
self.input_array[7:self.start_ind] = np.array(other_champion_stats)
x0 = [random.choice(self.item_ids) for x in range(12)]
ret = basinhopping(self.objective_function,
x0,
take_step=self.TakeStep(self.item_ids,
self.stepsize),
minimizer_kwargs=self.minimizer_kwargs,
niter=100,
stepsize=100,
T=10.0)
return ret
def optimize(self, cid, other_champion_stats):
champion = Champion.objects(champion_id=cid).get()
self.input_array[0] = cid
self.input_array[1:7] = np.array(champion.class_data)
self.input_array[7:self.start_ind] = np.array(other_champion_stats)
x0 = [random.choice(self.item_ids) for x in range(12)]
ret = basinhopping(
self.objective_function,
x0,
take_step=self.TakeStep(self.item_ids, self.stepsize),
minimizer_kwargs=self.minimizer_kwargs,
niter=100,
stepsize=100,
T=10.0
)
return ret
def _load_champions(self, w):
static_champ_list = w.static_get_champion_list(champ_data='tags')
for cname in static_champ_list['data']:
champion = Champion.from_dict(static_champ_list['data'][cname])
champion.save()