def run():
f = open('./config.yml')
config = yaml.load(f)
roi_shape = config['roi_shape']
dead_zone = config['dead_zone']
cube_resolution = config['cube_resolution']
laser_num = config['laser_num']
lidar_num = config['lidar_num']
beam_angle = config['beam_angle']
max_itrs = config['max_itrs']
numb_bees = config['numb_bees']
lower_bound = config['lower_bound'] * lidar_num
upper_bound = config['upper_bound'] * lidar_num
estimate_solve_time = config['estimate_solve_time']
save_result_to_json = config['save_result_to_json']
assert (len(beam_angle) == laser_num
), "Please check the number of beams' angles of pitch"
eval.logConfig(config=config)
VSR_solver = eval.Evaluator(roi_shape=roi_shape,
dead_zone=dead_zone,
cube_resolution=cube_resolution,
laser_num=laser_num,
lidar_num=lidar_num,
pitch_angle=beam_angle)
# creates model
if estimate_solve_time:
eval.estimateTime(config=config, VSR_solver=VSR_solver, Hive=Hive)
now = time.time()
model = Hive.BeeHive(lower=lower_bound,
upper=upper_bound,
fun=VSR_solver.solve,
numb_bees=numb_bees,
max_itrs=max_itrs,
verbose=True)
cost = model.run()
total_time = time.time() - now
print("Total time consume: ", total_time)
print("Solution: ", model.solution)
# prints out best solution
print("Fitness Value ABC: {0}".format(model.best))
if save_result_to_json:
eval.saveResults(config=config,
model=model,
total_time=total_time,
cost=cost)
# plots convergence
Utilities.ConvergencePlot(cost)
def run():
ndim = int(10)
model = Hive.BeeHive(
lower=[-5.12] * ndim,
upper=[5.12] * ndim,
fun=evaluator,
numb_bees=50,
max_itrs=100,
)
cost = model.run()
Utilities.ConvergencePlot(cost)
print("Fitness Value ABC: {0}".format(model.best))
def run():
# Crea el modelo
ndim = int(2)
model = Hive.BeeHive(lower = [0 for i in range(ndim)],
upper = [10 for i in range(ndim)],
fun = Rosenbrock ,
numb_bees = 50 ,
max_itrs = 60 ,)
# Corre el modelo
cost = model.run()
# Grafica de convergencia
Utilities.ConvergencePlot(cost)
# Prints
print("Fitness Value ABC: {0}".format(model.best))
print("Solution: {0}".format(cost["solution"]))
def run():
# creates model
ndim = int(2)
model = Hive.BeeHive(
lower=[0] * ndim,
upper=[10] * ndim,
fun=evaluator,
numb_bees=10,
max_itrs=50,
)
# runs model
cost = model.run()
# plots convergence
Utilities.ConvergencePlot(cost)
# prints out best solution
print("Fitness Value ABC: {0}".format(model.best))
def runAlgos(runs,
lower,
upper,
d,
numb_bees,
fun,
name,
means=True,
bests=False,
draw=True):
print("\n\n-----------------------\nRunning algorithms using ", name,
" for fitness")
with concurrent.futures.ThreadPoolExecutor() as executor:
future1 = executor.submit(
convertResult,
lower,
upper,
d,
fun,
numb_bees,
runs,
0,
"ABC",
name,
)
future2 = executor.submit(
convertResult,
lower,
upper,
d,
fun,
numb_bees,
runs,
1,
"sABC",
name,
)
future3 = executor.submit(
convertResult,
lower,
upper,
d,
fun,
numb_bees,
runs,
2,
"qABC",
name,
)
# future4 = executor.submit(convertResult,lower, upper, d, fun, numb_bees, runs, 3, "qsABC", name,)
future5 = executor.submit(
convertResult,
lower,
upper,
d,
fun,
numb_bees,
runs,
4,
"iqABC",
name,
)
future6 = executor.submit(
convertResult,
lower,
upper,
d,
fun,
numb_bees,
runs,
5,
"iqsABC",
name,
)
cost1, ends1, avg1 = future1.result()
cost2, ends2, avg2 = future2.result()
cost3, _, _ = future3.result()
# cost4, _, _ = future4.result()
cost5, ends5, avg5 = future5.result()
cost6, ends6, avg6 = future6.result()
ends5 = np.array(ends5)
ends6 = np.array(ends6)
if len(ends5) >= 20:
stat, p = wilcoxon(ends5, ends6)
print('Alg %s Statistics=%.3f, p=%.3f' % (name, stat, p))
f = open(filename, "a")
resultString = "iqsabc"
if avg5 < avg6: resultString = "iqabc"
if p >= 0.05: resultString = "-"
f.write('Zres&%s&%s&%f&p=%.4f\\\\\n' % (name, resultString,
(avg5 - avg6), p))
f.close()
if len(ends1) >= 20:
stat, p = wilcoxon(ends1, ends2)
print('Alg %s Statistics=%.3f, p=%.3f' % (name, stat, p))
f = open(filename, "a")
resultString = "sabc"
if avg1 < avg2: resultString = "abc"
if p >= 0.05: resultString = "-"
f.write('Yres&%s&%s&%f&p=%.4f\\\\\n' % (name, resultString,
(avg1 - avg2), p))
f.close()
if draw:
Utilities.ConvergencePlots(
name, [cost1, cost2, cost3, cost5, cost6],
['blue', 'magenta', 'green', 'red', 'black'],
['ABC', 'sABC', 'qABC', 'iqABC', 'iqsABC'],
means=means,
bests=bests,
right=int(3000))
pass