def main():
# creates a list of student and course objects in preparation file
lists = preparation.main()
student_list = lists[0]
course_list = lists[1]
session_list = lists[2]
room_list = lists[3]
score_max = 0
# searches 5 times for a local maximum and visualizes the best score
for i in range(5):
print i
# executes simulated annealing
local_max_schedule = simulated_annealing(student_list, course_list, session_list, room_list)
schedule_room_list = local_max_schedule[0]
schedule_student_list = local_max_schedule[1]
score_list = score_function.main(schedule_room_list, schedule_student_list, course_list)
score_local_max = score_list[0]
# when new hillclimber score is better
if score_local_max > score_max:
max_schedule = local_max_schedule
score_max = score_local_max
data = max_schedule[2]
# writes data of schedule to csv
with open("data_simulated_annealing.csv", "wb") as f:
writer = csv.writer(f)
writer.writerow(["Iteration", "Score", "malus_conflict", "malus_capacity", "malus_spread", "bonus_spread"])
for row in data:
writer.writerow(row)
# visualizes room schedules
schedule_room_list = max_schedule[0]
schedule_student_list = max_schedule[1]
for schedule_room in schedule_room_list:
visualize.visualize(schedule_room)
# visualize students schedules
for schedule_student in schedule_student_list[151:152]:
visualize.visualize(schedule_student)
for schedule_student in schedule_student_list[155:156]:
visualize.visualize(schedule_student)
def simulated_annealing(student_list, course_list, session_list, room_list):
"""
Starts at random schedule and climbs the hill.
Probability of acceptance reduces when temperature cools down
"""
# creates schedule with schedules for room and students
schedule = schedule_maker.main(student_list, course_list, session_list, room_list)
schedule_room_list = schedule[0]
schedule_student_list = schedule[1]
course_list = schedule[2]
time_slot_list = schedule[3]
new_score_list = score_function.main(schedule_room_list, schedule_student_list, course_list)
score = new_score_list[0]
malus_conflict = new_score_list[1]
malus_capacity = new_score_list[2]
malus_spread = new_score_list[3]
bonus_spread = new_score_list[4]
# sets simulated annaeling to True
sim = True
# initiates temperature (certain acceptance prob of 0.8)
temperature = 1000
# initiates cooling_rate
cooling_rate = 0.0001
# were tries and scores per step are saved
data = []
iteration = 0
while True:
# only change score when new_score is higher
tries = 0
while True:
# saves the data for this iteration in data list
data_iteration = []
data_iteration.append(iteration)
data_iteration.append(score)
# data.append(data_iteration)
data_iteration.append(malus_conflict)
data_iteration.append(malus_capacity)
data_iteration.append(malus_spread)
data_iteration.append(bonus_spread)
data.append(data_iteration)
# select 2 random timeslots from random
random_1 = random.choice(time_slot_list)
random_2 = random.choice(time_slot_list)
tries += 1
iteration += 1
# Switches two random timeslots in all relevant schedules
switch(random_1, random_2, schedule_student_list, schedule_room_list)
# calculates new score of incremental change
new_score_list = score_function.main(schedule_room_list, schedule_student_list, course_list)
new_score = new_score_list[0]
# calculates probability of acceptance
prob = acceptance_probability(score, new_score, temperature)
# if simulated annaeling is on
if sim:
# cools the temperature
temperature *= 1 - cooling_rate
# when probability of acceptance is higher then random number between 0 and 1 take a step
if prob > random.random():
score = new_score
malus_conflict = new_score_list[1]
malus_capacity = new_score_list[2]
malus_spread = new_score_list[3]
bonus_spread = new_score_list[4]
break
# when the score is not better switch back
else:
switch(random_2, random_1, schedule_student_list, schedule_room_list)
# when simulated annaeling is over start hillclimber
else:
# when new score is better take the step
if new_score > score:
score = new_score
break
# when the score is not better switch back
else:
switch(random_2, random_1, schedule_student_list, schedule_room_list)
new_score = score_function.main(schedule_room_list, schedule_student_list, course_list)
# ends function if he tries more then 5000 steps
if tries > 10000:
return [schedule_room_list, schedule_student_list, data]
# ends function if temperature is below 1
if temperature < 1:
sim = False
