def test3a_0():
profile = util.Profile(bulletin, 'profile3a.txt')
cspConstructor = submission.SchedulingCSPConstructor(bulletin, copy.deepcopy(profile))
csp = cspConstructor.get_basic_csp()
cspConstructor.add_quarter_constraints(csp)
alg = submission.BacktrackingSearch()
alg.solve(csp)
# Verify correctness.
grader.requireIsEqual(3, alg.numOptimalAssignments)
sol = util.extract_course_scheduling_solution(profile, alg.optimalAssignment)
for assignment in alg.allAssignments:
sol = util.extract_course_scheduling_solution(profile, assignment)
grader.requireIsTrue(verify_schedule(bulletin, profile, sol, False))
def test3b_4():
profile = util.Profile(bulletin, 'profile3b3.txt')
cspConstructor = submission.SchedulingCSPConstructor(
bulletin, copy.deepcopy(profile))
csp = cspConstructor.get_basic_csp()
cspConstructor.add_all_additional_constraints(csp)
alg = submission.BacktrackingSearch()
alg.solve(csp, mcv=True, ac3=True)
for assignment in alg.allAssignments:
sol = util.extract_course_scheduling_solution(profile, assignment)
print sol
def test_0(self):
"""3b-0-basic: Basic test for add_unit_constraints"""
profile = util.Profile(bulletin, 'profile3b.txt')
cspConstructor = submission.SchedulingCSPConstructor(
bulletin, copy.deepcopy(profile))
csp = cspConstructor.get_basic_csp()
cspConstructor.add_unit_constraints(csp)
alg = submission.BacktrackingSearch()
alg.solve(csp)
# Verify correctness.
self.assertEqual(15, alg.numOptimalAssignments)
for assignment in alg.allAssignments:
sol = util.extract_course_scheduling_solution(profile, assignment)
self.assertTrue(verify_schedule(bulletin, profile, sol))
if len(sys.argv) < 2:
print "Usage: %s <profile file (e.g., profile3d.txt)>" % sys.argv[0]
sys.exit(1)
profilePath = sys.argv[1]
bulletin = util.CourseBulletin('courses.json')
profile = util.Profile(bulletin, profilePath)
profile.print_info()
cspConstructor = submission.SchedulingCSPConstructor(bulletin, profile)
csp = cspConstructor.get_basic_csp()
cspConstructor.add_all_additional_constraints(csp)
alg = submission.BacktrackingSearch()
alg.solve(csp, mcv=True, ac3=True)
if alg.optimalAssignment:
print alg.optimalWeight
for key, value in alg.optimalAssignment.items():
print key, '=', value
print alg.numOptimalAssignments
if alg.numOptimalAssignments > 0:
print "solution========================="
solution = util.extract_course_scheduling_solution(profile,
alg.optimalAssignment)
util.print_course_scheduling_solution(solution)
for assignment in alg.allAssignments:
solution = util.extract_course_scheduling_solution(profile, assignment)
util.print_course_scheduling_solution(solution)
import util, submission, sys
if len(sys.argv) < 2:
print(("Usage: %s <profile file (e.g., profile3d.txt)>" % sys.argv[0]))
sys.exit(1)
profilePath = sys.argv[1]
bulletin = util.CourseBulletin('courses.json')
profile = util.Profile(bulletin, profilePath)
profile.print_info()
cspConstructor = submission.SchedulingCSPConstructor(bulletin, profile)
csp = cspConstructor.get_basic_csp()
cspConstructor.add_all_additional_constraints(csp)
alg = submission.BacktrackingSearch()
alg.solve(csp, mcv = True, ac3 = True)
if alg.optimalAssignment:
print((alg.optimalWeight))
for key, value in list(alg.optimalAssignment.items()):
print((key, '=', value))
for assignment in alg.allOptimalAssignments:
solution = util.extract_course_scheduling_solution(profile, assignment)
if solution:
# displays one of the best assignments
util.print_course_scheduling_solution(solution)
break
elif alg.numOptimalAssignments == 1:
print("The best schedule is to take 0 units every quarter.")
import util, submission, sys
if len(sys.argv) < 2:
print "Usage: %s <profile file (e.g., profile3d.txt)>" % sys.argv[0]
sys.exit(1)
profilePath = sys.argv[1]
bulletin = util.CourseBulletin('courses.json')
profile = util.Profile(bulletin, profilePath)
profile.print_info()
cspConstructor = submission.SchedulingCSPConstructor(bulletin, profile)
csp = cspConstructor.get_basic_csp()
cspConstructor.add_all_additional_constraints(csp)
alg = submission.BacktrackingSearch()
alg.solve(csp, mcv = True, mac = True)
if alg.optimalAssignment:
print alg.optimalWeight
for key, value in alg.optimalAssignment.items():
print key, '=', value
if alg.numOptimalAssignments > 0:
solution = util.extract_course_scheduling_solution(profile, alg.optimalAssignment)
util.print_course_scheduling_solution(solution)
