print()
if 'y' in shared:
shared2 = True
else:
shared2 = False
if 'y' in save:
save2 = True
else:
save2 = False
start2 = time()
if algo2 == 'hc':
hc = hillclimber.HillClimber(algo)
if version is not None:
hc.run(shared2, save2, version)
else:
hc.run(shared2, save2, None)
else:
sa = simulatedannealing.SimulatedAnnealing(algo)
if version is not None:
sa.run(0.005, 30, shared2, save2, version)
else:
sa.run(0.005, 30, shared2, save2, None)
end2 = time()
print(f"\nThe second algorithm found a solution in {round(end2 - start2, 3)} seconds.")
# depth = df.DepthFirst(test_graph, transmitters.get_scheme(1)[0:4])
# depth.run()
#
# print(f"Value of the configuration after Depth First: "
# f"{depth.graph.calculate_value()}")
# --------------------------- Breadth First --------------------------------
# breadth = bf.BreadthFirst(test_graph, transmitters.get_scheme(1)[0:4])
# breadth.run()
#
# print(f"Value of the configuration after Breadth First: "
# f"{breadth.graph.calculate_value()}")
# --------------------------- Hill Climber ---------------------------------
print("Setting up Hill Climber...")
climber = hc.HillClimber(random_graph, transmitters.get_scheme(1))
print("Running Hill Climber...")
climber.run(2000, verbose=True)
print(f"Value of the configuration after Hill Climber: "
f"{climber.graph.calculate_value()}")
# --------------------------- Simulated Annealing --------------------------
# It is very difficult to find a good starting temperature for SA. A rule to
# help you find one is to use the maximum change in score that could happen
# when mutating your state. In our case, this is 19, because the transmitter
# maximum difference in score between the most expensive and the cheapest
# transmitter is 19.
print("Setting up Simulated Annealing...")
for i in range(helper.repeats):
if i % 10 == 0:
print(f"{i}/{helper.repeats}")
# run the random algorithm once for each iteration
input_files = Map(helper.stations_data_file,
helper.connections_data_file)
random = rd.Random(input_files, helper.duration,
helper.max_num_trajects,
helper.total_connections)
random.run(1)
# ---------------Hill climber---------------------
# run the hill climber for each iteration of the random algorithm
hillclimber = hc.HillClimber(random, input_files,
helper.total_connections)
hillclimber.run(100)
# compare the outcomes of the hill climber and save the best outcome
score_list.append(hillclimber.highscore)
if hillclimber.highscore > best_score:
best_score = hillclimber.highscore
best_traject = hillclimber.hillclimber_solution
print(f"Highscore: {best_score}, Solution: {best_traject}")
# create the output files: optimal solution and list of scores, and visualisation
helper.output(score_list, helper.map_size, helper.user_algorithm,
best_traject, best_score)
vis.visualise(input_files, best_traject, helper.score_csv)