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Improvement in sceduler #5

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Improvement in sceduler #5

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84 changes: 67 additions & 17 deletions scheduler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -245,7 +245,6 @@ def evolutionary_algorithm(matrix, data, free, filled, groups_empty_space, teach
print('Number of iterations: {} \nCost: {} \nTeachers cost: {} | Groups cost: {} | Classrooms cost:'
' {}'.format(t, loss_after, cost_teachers, cost_groups, cost_classrooms))


def simulated_hardening(matrix, data, free, filled, groups_empty_space, teachers_empty_space, subjects_order, file):
"""
Algorithm that uses simulated hardening with geometric decrease of temperature to optimize timetable by satisfying
Expand All @@ -257,30 +256,27 @@ def simulated_hardening(matrix, data, free, filled, groups_empty_space, teachers
t = 0.5
_, _, curr_cost_group = empty_space_groups_cost(groups_empty_space)
_, _, curr_cost_teachers = empty_space_teachers_cost(teachers_empty_space)
curr_cost = curr_cost_group # + curr_cost_teachers
curr_cost = curr_cost_group + curr_cost_teachers
if free_hour(matrix) == -1:
curr_cost += 1

for i in range(iter_count):
rt = random.uniform(0, 1)
t *= 0.99 # geometric decrease of temperature
t *= 0.99 # geometric decrease of temperature

# save current results
old_matrix = copy.deepcopy(matrix)
old_free = copy.deepcopy(free)
old_filled = copy.deepcopy(filled)
old_groups_empty_space = copy.deepcopy(groups_empty_space)
old_teachers_empty_space = copy.deepcopy(teachers_empty_space)
old_subjects_order = copy.deepcopy(subjects_order)
old_state = (copy.deepcopy(matrix), copy.deepcopy(free), copy.deepcopy(filled),
copy.deepcopy(groups_empty_space), copy.deepcopy(teachers_empty_space),
copy.deepcopy(subjects_order))

# try to mutate 1/4 of all classes
for j in range(len(data.classes) // 4):
for _ in range(len(data.classes) // 4):
index_class = random.randrange(len(data.classes))
mutate_ideal_spot(matrix, data, index_class, free, filled, groups_empty_space, teachers_empty_space,
subjects_order)
_, _, new_cost_groups = empty_space_groups_cost(groups_empty_space)
_, _, new_cost_teachers = empty_space_teachers_cost(teachers_empty_space)
new_cost = new_cost_groups # + new_cost_teachers
new_cost = new_cost_groups + new_cost_teachers
if free_hour(matrix) == -1:
new_cost += 1

Expand All @@ -289,12 +285,8 @@ def simulated_hardening(matrix, data, free, filled, groups_empty_space, teachers
curr_cost = new_cost
else:
# return to previously saved data
matrix = copy.deepcopy(old_matrix)
free = copy.deepcopy(old_free)
filled = copy.deepcopy(old_filled)
groups_empty_space = copy.deepcopy(old_groups_empty_space)
teachers_empty_space = copy.deepcopy(old_teachers_empty_space)
subjects_order = copy.deepcopy(old_subjects_order)
matrix, free, filled, groups_empty_space, teachers_empty_space, subjects_order = old_state

if i % 100 == 0:
print('Iteration: {:4d} | Average cost: {:0.8f}'.format(i, curr_cost))

Expand All @@ -304,6 +296,64 @@ def simulated_hardening(matrix, data, free, filled, groups_empty_space, teachers
show_statistics(matrix, data, subjects_order, groups_empty_space, teachers_empty_space)
write_solution_to_file(matrix, data, filled, file, groups_empty_space, teachers_empty_space, subjects_order)

# def simulated_hardening(matrix, data, free, filled, groups_empty_space, teachers_empty_space, subjects_order, file):
# """
# Algorithm that uses simulated hardening with geometric decrease of temperature to optimize timetable by satisfying
# soft constraints as much as possible (empty space for groups and existence of an hour in which there is no classes).
# """
# # number of iterations
# iter_count = 2500
# # temperature
# t = 0.5
# _, _, curr_cost_group = empty_space_groups_cost(groups_empty_space)
# _, _, curr_cost_teachers = empty_space_teachers_cost(teachers_empty_space)
# curr_cost = curr_cost_group # + curr_cost_teachers
# if free_hour(matrix) == -1:
# curr_cost += 1

# for i in range(iter_count):
# rt = random.uniform(0, 1)
# t *= 0.99 # geometric decrease of temperature

# # save current results
# old_matrix = copy.deepcopy(matrix)
# old_free = copy.deepcopy(free)
# old_filled = copy.deepcopy(filled)
# old_groups_empty_space = copy.deepcopy(groups_empty_space)
# old_teachers_empty_space = copy.deepcopy(teachers_empty_space)
# old_subjects_order = copy.deepcopy(subjects_order)

# # try to mutate 1/4 of all classes
# for j in range(len(data.classes) // 4):
# index_class = random.randrange(len(data.classes))
# mutate_ideal_spot(matrix, data, index_class, free, filled, groups_empty_space, teachers_empty_space,
# subjects_order)
# _, _, new_cost_groups = empty_space_groups_cost(groups_empty_space)
# _, _, new_cost_teachers = empty_space_teachers_cost(teachers_empty_space)
# new_cost = new_cost_groups # + new_cost_teachers
# if free_hour(matrix) == -1:
# new_cost += 1

# if new_cost < curr_cost or rt <= math.exp((curr_cost - new_cost) / t):
# # take new cost and continue with new data
# curr_cost = new_cost
# else:
# # return to previously saved data
# matrix = copy.deepcopy(old_matrix)
# free = copy.deepcopy(old_free)
# filled = copy.deepcopy(old_filled)
# groups_empty_space = copy.deepcopy(old_groups_empty_space)
# teachers_empty_space = copy.deepcopy(old_teachers_empty_space)
# subjects_order = copy.deepcopy(old_subjects_order)
# if i % 100 == 0:
# print('Iteration: {:4d} | Average cost: {:0.8f}'.format(i, curr_cost))

# print('TIMETABLE AFTER HARDENING')
# show_timetable(matrix)
# print('STATISTICS AFTER HARDENING')
# show_statistics(matrix, data, subjects_order, groups_empty_space, teachers_empty_space)
# write_solution_to_file(matrix, data, filled, file, groups_empty_space, teachers_empty_space, subjects_order)


def main():
"""
Expand Down