def multiple_start(func,
penalty_func,
track,
func_type="original",
init_points_num=5,
show_info=True):
a = 10
b = 35
init_speeds = [
b - a + (b - a) / init_points_num * i
for i in range(init_points_num + 1)
]
init_vector_pool = [[speed] * len(track.sections) for speed in init_speeds]
track = Track(max_section_length=30000, max_section_slope_angle=0.15)
track.load_track_from_csv("data/track_Australia.csv")
track.combine_points_to_sections()
results = []
for i, init_vector in enumerate(range(len(init_vector_pool))):
track.compute_arrival_times(START_DATETIME, DRIVE_TIME_BOUNDS,
init_vector)
track.compute_weather_params()
optimal_speeds = optimization_methods.penalty_method(
func=func,
penalty_func=penalty_func,
x0=init_vector,
args=track,
func_type=func_type,
eps=1,
tol=1e-3,
max_step=20,
show_info=True)
loss = compute_loss_func(optimal_speeds, track)
penalty = compute_total_penalty(optimal_speeds,
track,
func_type="original")
results.append([loss, penalty, optimal_speeds])
results.sort(key=lambda res: (res[1], res[0]))
if show_info:
print("Best result Loss: {} Penalty {}".format(results[0][0],
results[0][1]))
return results[0][2]
def main(init_precision="3dim", func_type="original"):
if not os.path.isdir("logs"):
os.mkdir("logs")
# Load race track
track = Track(max_section_length=30000, max_section_slope_angle=0.15)
track.load_track_from_csv("data/track_Australia.csv")
# track.preprocess_track()
# track.draw_track_altitudes("График высот маршрута до предобработки")
track.combine_points_to_sections()
# track.draw_track_altitudes("График высот маршрута после предобработки")
init_speeds = [INIT_SPEED] * len(track.sections)
track.compute_arrival_times(START_DATETIME, DRIVE_TIME_BOUNDS, init_speeds)
track.compute_weather_params()
# track.sections.solar_radiation = track.sections.solar_radiation * 1.18 # Simulate October conditions
# track.draw_track_features("Ключевые параметры маршрута")
# track.sections = track.sections[:10] # Taking only small part of track for speeding up tests
# Test that optimization is possible
test_speeds = [1] * len(track.sections)
energy_levels_test = energy_manager.compute_energy_levels(
track, test_speeds)
assert (energy_levels_test[-1] >=
0), "Too little energy to cover the distance!"
# Find initial approximation
args = [compute_loss_func, compute_total_penalty, track]
if init_precision == "1dim":
kwargs = dict(func_type="original",
low_speed_range=np.linspace(start=15, stop=25, num=40))
elif init_precision == "3dim":
kwargs = dict(func_type="original",
low_speed_range=range(15, 20),
high_speed_range=range(25, 36),
n_range=range(1,
int(np.ceil(len(track.sections) * 0.1))))
else:
assert False, "Not implemented such type of initial precision"
base_speed_vector = optimization_methods.grid_search(*args, **kwargs)
draw_solution(base_speed_vector, title="Начальное приближение")
track.compute_arrival_times(START_DATETIME, DRIVE_TIME_BOUNDS,
base_speed_vector)
track.compute_weather_params()
# Optimize speed
print(
"Init loss:", compute_loss_func(base_speed_vector, track),
"\nInit penalty:",
compute_total_penalty(base_speed_vector, track, func_type="original"))
optimal_speeds = optimization_methods.penalty_method(
func=compute_loss_func,
penalty_func=compute_total_penalty,
x0=base_speed_vector,
args=track,
func_type=func_type,
eps=1,
tol=1e-3,
max_step=20,
show_info=True)
# print("Optimization result:", optimal_speeds)
print("Final loss:", compute_loss_func(optimal_speeds, track),
"\nFinal penalty:",
compute_total_penalty(optimal_speeds, track, func_type="original"))
print("Total travel time:",
round(compute_loss_func(optimal_speeds, track) / 3600, 2), "hours")
# Try to find better solution in point vicinity
optimization_methods.random_change_method(optimal_speeds,
compute_loss_func,
compute_total_penalty,
track,
change_koef=1,
iter_num=100)
# Solution visualisation
draw_solution(optimal_speeds)
# Find relation between solar radiation and vehicle speed
solar_radiation_levels = track.sections.solar_radiation
draw_speed_solar_radiation_relation(optimal_speeds, solar_radiation_levels)
# Save params about model
model_data = energy_manager.compute_energy_levels_full(
track, optimal_speeds)
energy_manager.draw_energy_levels(energy_levels=model_data["levels"],
energy_incomes=model_data["incomes"],
energy_outcomes=model_data["outcomes"])
model_params = model_data["params"]
model_params.to_csv(
"logs/model_params " +
str(datetime.datetime.today().strftime("%Y-%m-%d %H-%M-%S")) + ".csv",
sep=";")
