def show_statistics(matrix, data, subjects_order, groups_empty_space,
teachers_empty_space):
"""
Prints statistics.
"""
cost_hard = check_hard_constraints(matrix, data)
if cost_hard == 0:
print('Hard constraints satisfied: 100.00 %')
else:
print('Hard constraints NOT satisfied, cost: {}'.format(cost_hard))
print('Soft constraints satisfied: {:.02f} %\n'.format(
subjects_order_cost(subjects_order)))
empty_groups, max_empty_group, average_empty_groups = empty_space_groups_cost(
groups_empty_space)
print('TOTAL empty space for all GROUPS and all days: ', empty_groups)
print('MAX empty space for GROUP in day: ', max_empty_group)
print('AVERAGE empty space for GROUPS per week: {:.02f}\n'.format(
average_empty_groups))
empty_teachers, max_empty_teacher, average_empty_teachers = empty_space_teachers_cost(
teachers_empty_space)
print('TOTAL empty space for all TEACHERS and all days: ', empty_teachers)
print('MAX empty space for TEACHER in day: ', max_empty_teacher)
print('AVERAGE empty space for TEACHERS per week: {:.02f}\n'.format(
average_empty_teachers))
def write_solution_to_file(matrix, data, filled, filepath, groups_empty_space,
teachers_empty_space, subjects_order):
"""
Writes statistics and schedule to file.
"""
f = open('solution_files/sol_' + filepath, 'w')
f.write(
'-------------------------- STATISTICS --------------------------\n')
cost_hard = check_hard_constraints(matrix, data)
if cost_hard == 0:
f.write('\nHard constraints satisfied: 100.00 %\n')
else:
f.write('Hard constraints NOT satisfied, cost: {}\n'.format(cost_hard))
f.write('Soft constraints satisfied: {:.02f} %\n\n'.format(
subjects_order_cost(subjects_order)))
empty_groups, max_empty_group, average_empty_groups = empty_space_groups_cost(
groups_empty_space)
f.write('TOTAL empty space for all GROUPS and all days: {}\n'.format(
empty_groups))
f.write('MAX empty space for GROUP in day: {}\n'.format(max_empty_group))
f.write('AVERAGE empty space for GROUPS per week: {:.02f}\n\n'.format(
average_empty_groups))
empty_teachers, max_empty_teacher, average_empty_teachers = empty_space_teachers_cost(
teachers_empty_space)
f.write('TOTAL empty space for all TEACHERS and all days: {}\n'.format(
empty_teachers))
f.write(
'MAX empty space for TEACHER in day: {}\n'.format(max_empty_teacher))
f.write('AVERAGE empty space for TEACHERS per week: {:.02f}\n\n'.format(
average_empty_teachers))
# f_hour = free_hour(matrix)
# if f_hour != -1:
# f.write('Free term -> {}\n'.format(f_hour))
# else:
# f.write('NO hours without classes.\n')
groups_dict = {}
for group_name, group_index in data.groups.items():
if group_index not in groups_dict:
groups_dict[group_index] = group_name
days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']
hours = [7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]
f.write(
'\n--------------------------- SCHEDULE ---------------------------')
for class_index, times in filled.items():
c = data.classes[class_index]
groups = ' '
for g in c.groups:
groups += groups_dict[g] + ', '
f.write('\n\nClass {}\n'.format(class_index))
f.write(
'Teacher: {} \nSubject: {} \nGroups:{} \nType: {} \nDuration: {} hour(s)'
.format(c.teacher, c.subject, groups[:len(groups) - 2], c.type,
c.duration))
room = str(data.classrooms[times[0][1]])
f.write('\nClassroom: {:2s}\nTime: {}'.format(room[:room.rfind('-')],
days[times[0][0] // 12]))
for time in times:
f.write(' {}'.format(hours[time[0] % 12]))
f.close()
def evolutionary_algorithm(matrix, data, free, filled, groups_empty_space,
teachers_empty_space, subjects_order):
"""
Evolutionary algorithm that tires to find schedule such that hard constraints are satisfied.
It uses (1+1) evolutionary strategy with Stifel's notation.
"""
n = 3
sigma = 2
run_times = 5
max_stagnation = 200
for run in range(run_times):
print('Run {} | sigma = {}'.format(run + 1, sigma))
t = 0
stagnation = 0
cost_stats = 0
while stagnation < max_stagnation:
# check if optimal solution is found
loss_before, cost_classes, cost_teachers, cost_classrooms, cost_groups = hard_constraints_cost(
matrix, data)
if loss_before == 0 and check_hard_constraints(matrix, data) == 0:
print('Found optimal solution: \n')
show_timetable(matrix)
break
# sort classes by their loss, [(loss, class index)]
costs_list = sorted(cost_classes.items(),
key=itemgetter(1),
reverse=True)
# 10*n
for i in range(len(costs_list) // 4):
# mutate one to its ideal spot
if random.uniform(0, 1) < sigma and costs_list[i][1] != 0:
mutate_ideal_spot(matrix, data, costs_list[i][0], free,
filled, groups_empty_space,
teachers_empty_space, subjects_order)
# else:
# # exchange two who have the same duration
# r = random.randrange(len(costs_list))
# c1 = data.classes[costs_list[i][0]]
# c2 = data.classes[costs_list[r][0]]
# if r != i and costs_list[r][1] != 0 and costs_list[i][1] != 0 and c1.duration == c2.duration:
# exchange_two(matrix, filled, costs_list[i][0], costs_list[r][0])
loss_after, _, _, _, _ = hard_constraints_cost(matrix, data)
if loss_after < loss_before:
stagnation = 0
cost_stats += 1
else:
stagnation += 1
t += 1
# Stifel for (1+1)-ES
if t >= 10 * n and t % n == 0:
s = cost_stats
if s < 2 * n:
sigma *= 0.85
else:
sigma /= 0.85
cost_stats = 0
print(
'Number of iterations: {} \nCost: {} \nTeachers cost: {} | Groups cost: {} | Classrooms cost:'
' {}'.format(t, loss_after, cost_teachers, cost_groups,
cost_classrooms))