def test_consecutive_course_scheduleing3(self):
# when one of the course were assigned at [2,4]
consecutive_courses = []
for C in self.status.list_of_unassigned_Courses:
if C.name in ['G5_5_自然實驗_1', 'G5_5_自然實驗_2']:
consecutive_courses.append(C)
bm.Course.assign_Rooms_and_Teacher_to_Course(
self.status, consecutive_courses[0].name, '自然教室(五)', 'G5_5', '秀玲')
bm.Course.assign_Course_period(self.status,
consecutive_courses[0].name,
('Monday', 2))
scheduling_results = cs.repeated_course_assignments_until_feasibility(
self.status, [consecutive_courses[1]], max_count=1)
self.assertEqual(consecutive_courses[1].period, ('Monday', 1))
self.assertTrue(scheduling_results)
self.assertEqual(consecutive_courses[0].Room.name,
consecutive_courses[1].Room.name)
self.assertEqual(len(consecutive_courses[0].Room.schedule_in_tuples),
2)
def test_consecutive_course_scheduleing1(self):
# when none of the course were assigned
consecutive_courses = []
for C in self.status.list_of_unassigned_Courses:
if C.name in ['G5_5_自然實驗_1', 'G5_5_自然實驗_2']:
consecutive_courses.append(C)
scheduling_results = cs.repeated_course_assignments_until_feasibility(
self.status, consecutive_courses, max_count=1)
room = consecutive_courses[0].Room
s1 = room.schedule_in_tuples[0]
s2 = room.schedule_in_tuples[1]
self.assertEqual(s1[0], s2[0])
if s1[1] > s2[1]:
self.assertTrue(s1[1] == s2[1] + 1)
else:
self.assertTrue(s1[1] == s2[1] - 1)
self.assertTrue(scheduling_results)
self.assertEqual(consecutive_courses[0].Room.name,
consecutive_courses[1].Room.name)
self.assertEqual(len(consecutive_courses[0].Room.schedule_in_tuples),
2)
def solving_zhes_with_sequencial_LNS(seed=None, n_iteration=100):
# random.seed()
subject_set = {
'閱讀', '彈性英語', '電腦', '綜合', '數', '彈性', '生活', '音樂', '健康', '閩南語', '英語',
'自然', '社會', '彈性音樂', '國', '自然實驗', '體育', '美勞', '英語12a', '彈性英語12a',
'英語12b', '彈性英語12b', '英語12c', '彈性英語12c', '英語34a', '彈性英語34a', '英語34b',
'彈性英語34b', '英語34c', '彈性英語34c', '英語56a', '彈性英語56a', '英語56b', '彈性英語56b',
'英語56c', '彈性英語56c'
}
best_status = zhes_scheduling_problem.initate_zhes_status()
def only_keep_subjects(best_status, list_of_subjects):
for C in best_status.list_of_unassigned_Courses.copy():
if (C.subject not in list_of_subjects): #
best_status.change_unassigned_Course_to_stalled_list(C)
only_keep_subjects(best_status, ["自然", "自然實驗"])
best_status, score = cs.large_neighbourhood_search_with_scoring(
best_status, max_iteration=n_iteration)
if not best_status.list_of_unassigned_Courses == []:
return best_status, None
best_status.fix_assigned_Courses()
# best_status.free_stalled_Courses()
# only_keep_subjects(best_status, ['英語12a', '彈性英語12a','英語12b', '彈性英語12b', '英語12c', '彈性英語12c',
# '英語34a', '彈性英語34a','英語34b', '彈性英語34b', '英語34c', '彈性英語34c',
# '英語56a', '彈性英語56a','英語56b', '彈性英語56b','英語56c', '彈性英語56c',"英語", "彈性英語"])
#
# best_status , score = cs.large_neighbourhood_search_with_scoring(best_status, max_iteration = n_iteration)
# if not best_status.list_of_unassigned_Courses == []:
# return best_status, None
# best_status.fix_assigned_Courses()
# best_status.free_stalled_Courses()
# only_keep_subjects(best_status, ["音樂", "彈性音樂", '體育','美勞'])
# best_status , score = cs.large_neighbourhood_search_with_scoring(best_status, max_iteration = n_iteration)
# if not best_status.list_of_unassigned_Courses == []:
# return best_status, None
# best_status.fix_assigned_Courses()
# best_status.free_stalled_Courses()
# only_keep_subjects(best_status, ['閩南語','社會'])
# best_status , score = cs.large_neighbourhood_search_with_scoring(best_status, max_iteration = n_iteration)
# if not best_status.list_of_unassigned_Courses == []:
# print([c.name for c in best_status.list_of_unassigned_Courses])
# return best_status, None
# best_status.fix_assigned_Courses()
# best_status.free_stalled_Courses()
#
# only_keep_subjects(best_status, subject_set - {'國', '數', '綜合', '彈性', '生活'})
# best_status , score = cs.large_neighbourhood_search_with_scoring(best_status, max_iteration = n_iteration)
# if not best_status.list_of_unassigned_Courses == []:
# print([c.name for c in best_status.list_of_unassigned_Courses])
# return best_status, None
# best_status.fix_assigned_Courses()
# best_status.free_stalled_Courses()
#
#
# best_status, scoring = cs.large_neighbourhood_search_with_scoring(best_status, max_iteration = n_iteration)
# if not best_status.list_of_unassigned_Courses == []:
# print([c.name for c in best_status.list_of_unassigned_Courses])
# return best_status, None
# print("not assigned: "+ str([C.name for C in best_status.list_of_unassigned_Courses]))
print('Assigned {} out of {} courses'.format(
len(best_status.list_of_assigned_Courses +
best_status.list_of_fixed_Courses),
len(best_status.list_of_Courses) -
len(best_status.list_of_stalled_Courses)))
return best_status, score
__author__ = "Jenny Zeng"
__email__ = "*****@*****.**"
if __name__ == '__main__':
start_time = time.time()
# config upper standing units
upper_units = 90
# load taken info
startQ, applied_units, taken = DataLoading.load_taken(
filename="info/taken2.txt")
# load avoid info
avoid = DataLoading.load_avoid(filename="info/avoid.txt")
# load graph, config if user is upper standing
G = DataLoading.load_courses(prereq_filename="info/fullcourses_new.txt",
show_upper=cs.is_upper_standing(
applied_units, upper_units))
# load requirement sheet
R_detail, R = DataLoading.load_requirements(
requirements=[
"University", "GEI", "GEII", "GEIII", "GEIV", "GEV", "GEVI",
"GEVII", "GEVIII", "CS-Lower-division", "CS-Upper-division",
"Intelligent Systems"
],
filename="info/specializations.txt")
# update requirement table based on the taken information
cs.update_requirements(R_detail, R, taken)
print(R_detail)
print(R)
# load max width for each quarter
max_widths = DataLoading.load_width_func_table("info/widthFunc.txt")