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)
import preparation
import fitness_function
import random
global crossover_rate
global mutation_rate
global repairs
# define constants
crossover_rate = 0.7
mutation_rate = 0.1
population_size = 10
number_generations = 1000
# instantiates the framework of the schedule
prep_schedule, slot_list = preparation.main()
# creates 100 random schedules
old_generation = []
for i in range(population_size):
prep_schedule, slot_list = preparation.main()
rand_schedule = random_schedule(prep_schedule, slot_list)
old_generation.append(rand_schedule)
generation = 0
fitness_total = []
# print results of algo
for i in range(number_generations):
new_generation = next_generation(old_generation, slot_list)
def main(data):
prep_schedule, slot_list, course_list = preparation.main(data)
for i in range(100):
table_data = hill_climber(data, prep_schedule, slot_list, course_list)
if table_data != False:
return table_data
import preparation
import score_function
import schedule_maker
# 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]
number_courses = len(course_list)
total_students = 0
for course in course_list:
number_students = len(course.students)
total_students += number_students
avg_students = total_students / number_courses
print avg_students
# # create 100 schedules
# for i in range (100):
# # create schedule
# 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]
#
import preparation
import fitness_function
import random
global crossover_rate
global mutation_rate
global repairs
# define constants
crossover_rate = 0.7
mutation_rate = 0.1
population_size = 10
number_generations = 1000
# instantiates the framework of the schedule
prep_schedule, slot_list = preparation.main()
# creates 100 random schedules
old_generation = []
for i in range(population_size):
prep_schedule, slot_list = preparation.main()
rand_schedule = random_schedule(prep_schedule, slot_list)
old_generation.append(rand_schedule)
generation = 0
fitness_total = []
# print results of algo
for i in range(number_generations):
new_generation = next_generation(old_generation, slot_list)
def main(data):
prep_schedule, slot_list, course_list = preparation.main(data)
for i in range(100):
table_data = hill_climber(data, prep_schedule, slot_list, course_list)
if table_data != False:
return table_data
