def test_compute_position_return_values(swarm, bounds, static):
"""Test if compute_position() gives the expected shape and range"""
topology = Ring(static=static)
p = topology.compute_position(swarm, bounds)
assert p.shape == swarm.velocity.shape
if bounds is not None:
assert (bounds[0] <= p).all() and (bounds[1] >= p).all()
def __fitNBeta(self, dim, n_particles, itera, options, objetive_function,
BetaChange, bound):
my_topology = Ring()
my_swarm = P.create_swarm(n_particles=n_particles,
dimensions=dim,
options=options,
bounds=bound)
my_swarm.pbest_cost = np.full(n_particles, np.inf)
my_swarm.best_cost = np.inf
for i in range(itera):
for a in range(n_particles):
my_swarm.position[a][0:BetaChange] = sorted(
my_swarm.position[a][0:BetaChange])
for c in range(1, self.BetaChange):
if my_swarm.position[a][c -
1] + 5 >= my_swarm.position[a][c]:
my_swarm.position[a][c] = my_swarm.position[a][c] + 5
my_swarm.current_cost = objetive_function(my_swarm.position)
my_swarm.pbest_pos, my_swarm.pbest_cost = P.operators.compute_pbest(
my_swarm)
#my_swarm.current_cost[np.isnan(my_swarm.current_cost)]=np.nanmax(my_swarm.current_cost)
#my_swarm.pbest_cost = objetive_function(my_swarm.pbest_pos)
my_swarm.best_pos, my_swarm.best_cost = my_topology.compute_gbest(
my_swarm, options['p'], options['k'])
if i % 20 == 0:
print(
'Iteration: {} | my_swarm.best_cost: {:.4f} | days: {}'.
format(
i + 1, my_swarm.best_cost,
str(my_swarm.pbest_pos[my_swarm.pbest_cost.argmin()])))
my_swarm.velocity = my_topology.compute_velocity(my_swarm,
bounds=bound)
my_swarm.position = my_topology.compute_position(my_swarm,
bounds=bound)
final_best_cost = my_swarm.best_cost.copy()
final_best_pos = my_swarm.pbest_pos[
my_swarm.pbest_cost.argmin()].copy()
return final_best_pos, final_best_cost