def invite_friends_to_restaurant(self):
shape = self.properties.risk_tolerance * get_parameters().get('typical_restaurant_event_size')
event_size = np.random.gamma(shape, 1)
logger().debug(f"Restaurant event size of {self} is {event_size}")
accepted = [self]
for human in self.tribe[TribeSelector.FRIEND]:
if human != self and human.personal_decision(Dilemma.ACCEPT_FRIEND_INVITATION_TO_RESTAURANT):
accepted.append(human)
if len(accepted) >= event_size:
break
if len(accepted) == 1:
return
outdoor = flip_coin(linear_rescale(self.properties.risk_tolerance, 0, 0.5))
if flip_coin(linear_rescale(self.work_info.base_income, 0, 1 / 5)):
restaurant_type = RestaurantType.FANCY
else:
restaurant_type = RestaurantType.FAST_FOOD
event = self.work_district.get_available_restaurant(len(accepted), outdoor, restaurant_type)
if event is not None and not outdoor:
event = self.work_district.get_available_restaurant(len(accepted), True, restaurant_type)
if event is None:
return
event.available -= len(accepted)
for human in accepted:
human.social_event = (self, event)
def build_tribe(self, human, humans, mininum, maximum, temperature = -0.9):
n = int(round(linear_rescale(human.properties.extroversion, mininum, maximum)))
if n >= len(humans):
n = len(humans) - 1
if n <= 0:
return [human]
tribe_candidates = humans.copy()
tribe_candidates.remove(human)
tribe = self.find_friends(human, tribe_candidates,n,temperature)
tribe.append(human)
return tribe
def build_tribe(self, human, humans, mininum, maximum):
n = linear_rescale(human.properties.extroversion, mininum, maximum)
w = [self.similarity[human][h] for h in humans]
tribe = [human]
count = 0
while n > 0 and count < len(humans):
count += 1
selected = roulette_selection(humans, w)
if selected not in tribe:
tribe.append(selected)
n -= 1
return tribe
def build_tribe(self, human, humans, mininum, maximum):
n = int(round(linear_rescale(human.properties.extroversion, mininum, maximum)))
if n >= len(humans):
n = len(humans) - 1
if n <= 0:
return [human]
tribe_candidates = humans.copy()
tribe_candidates.remove(human)
w = [self.similarity[human][h] for h in tribe_candidates]
tribe = roulette_selection(tribe_candidates, w, num_selections=n)
tribe.append(human)
return tribe
def _compute_similarity(self, h1, h2):
assert len(self.feature_vector[h1]) == len(self.feature_vector[h2])
d = np.linalg.norm(self.feature_vector[h1] - self.feature_vector[h2])
return linear_rescale(d, 1, 0, 0,
math.sqrt(len(self.feature_vector[h1])))
