def __init__(self, params=None, strategy=None):
super().__init__(params)
self.strategy = strategy if strategy else [
rand_bound(0,
(self.BOUNDS[i][1] - CitizenStrat.BOUNDS[i][0]) * 0.05)
for i in range(self.P_SIZE)
]
def __init__(self):
super().__init__()
self.velocity = [rand_bound(self.VEL_BOUNDS[i][0],
self.VEL_BOUNDS[i][1])
for i in range(self.P_SIZE)]
self.best_cost = self.error
self.best_pos = self.parameters.copy()
def set_value(self, param, value=None):
if value:
if value < self.BOUNDS[param][0]:
value = self.BOUNDS[param][0]
if value > self.BOUNDS[param][1]:
value = self.BOUNDS[param][1]
self.parameters[param] = value
else:
self.parameters[param] = rand_bound(*self.BOUNDS[param])
def create_harmony(self):
harmony = CitizenHar()
for i in range(CitizenHar.P_SIZE):
if rand() < self.consid_r:
pos = randint_bound(self.pop_size)
value = self.population[pos].get_value(i)
if rand() < self.adjust_r:
value = value + self.ran * rand_bound(-1, 1)
harmony.set_value(i, value)
else:
harmony.set_value(i)
return harmony
def take_step(self, current, step_size):
position = Citizen()
for i in range(Citizen.P_SIZE):
minimum = min(
[self.bounds[i][0],
current.get_value(i) - step_size[i]])
maximum = max(
[self.bounds[i][1],
current.get_value(i) + step_size[i]])
value = rand_bound(minimum, maximum)
position.set_value(i, value)
return position
def generate_sample(self):
sample = Citizen()
for i in range(Citizen.P_SIZE):
aux = rand_bound(self.means[i], self.stdevs[i])
sample.set_value(i, aux)
return sample
def create_means(self):
return [rand_bound(*Citizen.BOUNDS[i]) for i in range(Citizen.P_SIZE)]
def __init__(self, params=None):
self.parameters = params.copy() if params \
else [rand_bound(*self.BOUNDS[i]) for i in range(self.P_SIZE)]
self.error = np.inf