def main():
#with open(RESULTS_FILE, 'rb') as cached_pcd_file:
# cache_data = pickle.load(cached_pcd_file)
# pprint.pprint(cache_data)
#return
#with open(RESULTS_FILE, 'rb') as cached_pcd_file:
# cache_data = pickle.load(cached_pcd_file)
# for alg in ALGORITHMS:
# if ALGORITHMS[alg]["do_hyper"]:
# ALGORITHMS[alg]["opt_param"] = cache_data[alg]["opt_param"]
#### STEP 1 - Get classified pointcloud ####
environment = PointCloudEnvironment(my_print, TERRAIN_ASSESSMENT_FILE,
POINTCLOUD_FILE)
coverable_points = environment.coverable_pcd.points
traversable_points = environment.traversable_pcd.points
motion_planner = MotionPlanner(my_print, environment.traversable_pcd)
#If from terrain assessment file:
#with open(TERRAIN_ASSESSMENT_FILE, 'rb') as cached_pcd_file:
# cache_data = pickle.load(cached_pcd_file)
# coverable_points = cache_data["coverable_points"]
# traversable_points = cache_data["traversable_points"]
#traversable_pcd = PointCloud(my_print, points= traversable_points)
#motion_planner = MotionPlanner(my_print, traversable_pcd)
#### STEP 2 - Hyper parameters ####
for algorithm_key, algorithm in ALGORITHMS.items():
if algorithm["do_hyper"]:
trials = Trials()
hyper_optimizer = HyptoOptimizer(save_data, algorithm, my_print,
HYPER_START_POS, motion_planner,
coverable_points)
if algorithm_key == "BA*":
opt_param = fmin(hyper_optimizer.hyper_test_bastar,
space=(hp.uniform('angle_offset', 0,
np.pi * 2),
hp.uniform('step_size', 0.5, 1),
hp.uniform('visited_threshold', 0.25,
0.5)),
algo=tpe.suggest,
max_evals=HYPER_MAX_EVAL,
trials=trials)
elif algorithm_key == "Inward Spiral":
opt_param = fmin(hyper_optimizer.hyper_test_inward_spiral,
space=(hp.uniform('step_size', 0.5, 1),
hp.uniform('visited_threshold', 0.25,
0.5)),
algo=tpe.suggest,
max_evals=HYPER_MAX_EVAL,
trials=trials)
elif algorithm_key == "Sampled BA*":
coverage_2 = algorithm["hyper_min_coverage"] / 100
opt_param = fmin(
hyper_optimizer.hyper_test_sampled_bastar_param,
space=(hp.uniform('coverage_1', 0.25, coverage_2),
hp.uniform('coverage_2', coverage_2 - 0.025,
coverage_2),
hp.uniform('max_distance', 1, 10),
hp.uniform('max_distance_part_II', 1,
20), hp.uniform('max_iterations', 30,
150),
hp.uniform('min_bastar_coverage', 0.005, 0.05),
hp.uniform('min_spiral_length', 2, 100),
hp.uniform('nbr_of_angles', 0.6,
8.4), hp.uniform('step_size', 0.66,
1.33),
hp.uniform('visited_threshold', 0.25, 0.5)),
algo=tpe.suggest,
max_evals=HYPER_MAX_EVAL,
trials=trials)
print(trials.statuses())
algorithm["opt_param"] = opt_param
algorithm["hyper_data"] = trials.trials
ALGORITHMS[algorithm_key] = algorithm
save_data(ALGORITHMS)
#### STEP 3 - Full tests ####
for start_point_nr in range(NUMBER_OF_START_POINTS):
#start_point = get_random_point(traversable_points)
start_point = start_points[start_point_nr]
print("Start point " + str(start_point_nr) + ": " + str(start_point))
for algorithm_key, algorithm in ALGORITHMS.items():
if algorithm["do_experiment"]:
experimenter = Experimenter(algorithm, print)
parameters = None
if "opt_param" in algorithm:
parameters = algorithm["opt_param"]
cpp = algorithm["cpp"](my_print, motion_planner,
coverable_points,
algorithm["experiment_time_limit"],
parameters)
if "sample_specific_stats" in algorithm:
experimenter.perform_sample_cpp(cpp, start_point,
start_point_nr)
algorithm["sample_specific_stats"].append(
experimenter.sample_specific_stats)
else:
experimenter.perform_cpp(cpp, start_point, start_point_nr)
algorithm["experiment_results"].append(experimenter.results)
ALGORITHMS[algorithm_key] = algorithm
save_data(ALGORITHMS)
def main():
#with open(RESULTS_FILE, 'rb') as cached_pcd_file:
# cache_data = pickle.load(cached_pcd_file)
# pprint.pprint(cache_data)
#return
with open(RESULTS_FILE, 'rb') as cached_pcd_file:
cache_data = pickle.load(cached_pcd_file)
ALGORITHMS = deepcopy(cache_data)
for alg in ALGORITHMS:
ALGORITHMS[alg]["do_hyper"] = False
ALGORITHMS[alg][
"cpp"] = lambda print, motion_planner, cov_points, time_limit, parameters: RandomBAstar3(
print, motion_planner, PointCloud(
print, points=cov_points), time_limit, parameters)
#### STEP 1 - Get classified pointcloud ####
environment = PointCloudEnvironment(my_print, TERRAIN_ASSESSMENT_FILE,
POINTCLOUD_FILE)
coverable_points = environment.coverable_pcd.points
traversable_points = environment.traversable_pcd.points
motion_planner = MotionPlanner(my_print, environment.traversable_pcd)
#If from terrain assessment file:
#with open(TERRAIN_ASSESSMENT_FILE, 'rb') as cached_pcd_file:
# cache_data = pickle.load(cached_pcd_file)
# coverable_points = cache_data["coverable_points"]
# traversable_points = cache_data["traversable_points"]
#traversable_pcd = PointCloud(my_print, points= traversable_points)
#motion_planner = MotionPlanner(my_print, traversable_pcd)
#### STEP 2 - Hyper parameters ####
for algorithm_key, algorithm in ALGORITHMS.items():
if algorithm["do_hyper"]:
trials = Trials()
hyper_optimizer = HyptoOptimizer(save_data, algorithm, my_print,
HYPER_START_POS, motion_planner,
coverable_points)
opt_param = fmin(
hyper_optimizer.hyper_test_newest_sampled_bastar_param,
space=(hp.uniform('ba_exploration', 0.75,
0.95), hp.uniform('max_distance', 1, 5),
hp.uniform('max_distance_part_II', 4, 10),
hp.uniform('min_bastar_cost_per_coverage', 5000, 10000),
hp.uniform('min_spiral_cost_per_coverage', 10000,
20000), hp.uniform('step_size', 0.5, 1.0),
hp.uniform('visited_threshold', 0.25, 0.5)),
algo=tpe.suggest,
max_evals=HYPER_MAX_EVAL,
trials=trials)
print(trials.statuses())
algorithm["opt_param"] = opt_param
algorithm["hyper_data"] = trials.trials
ALGORITHMS[algorithm_key] = algorithm
save_data(ALGORITHMS)
#### STEP 3 - Full tests ####
for start_point_nr in range(NUMBER_OF_START_POINTS):
#start_point = get_random_point(traversable_points)
start_point = start_points[start_point_nr]
print("Start point " + str(start_point_nr) + ": " + str(start_point))
for algorithm_key, algorithm in ALGORITHMS.items():
if algorithm["do_experiment"]:
experimenter = Experimenter(algorithm, print)
parameters = None
if "opt_param" in algorithm:
parameters = algorithm["opt_param"]
cpp = algorithm["cpp"](my_print, motion_planner,
coverable_points,
algorithm["experiment_time_limit"],
parameters)
if "sample_specific_stats" in algorithm:
experimenter.perform_sample_cpp(cpp, start_point,
start_point_nr)
algorithm["sample_specific_stats"].append(
experimenter.sample_specific_stats)
else:
experimenter.perform_cpp(cpp, start_point, start_point_nr)
algorithm["experiment_results"].append(experimenter.results)
ALGORITHMS[algorithm_key] = algorithm
save_data(ALGORITHMS)
