line = line.split(' ')
tasks = line[1]
day = week_to_num[line[3]]
time = time_to_num[line[4]]
soft_cost[tasks] = int(line[-1])
soft_constraint[tasks] = day * 10 + time
# print(soft_constraint)
# print(hard_constraint)
# print(task_domain)
csp = New_CSP(task_domain, hard_constraint, soft_constraint, soft_cost)
problem = Search_with_AC_from_Cost_CSP(csp)
solution = AStarSearcher(problem).search()
# print(solution)
if solution:
solution = solution.end()
for task in solution:
for item in week_to_num:
if week_to_num[item] == list(solution[task])[0][0] // 10:
day = item
for item in time_to_num:
if time_to_num[item] == list(solution[task])[0][0] % 10:
time = item
print(f'{task}:{day} {time}')
print(f'cost:{problem.heuristic(solution)}')
else:
print('No solution')
domain_num(' '.join(line[-1].split()[1:])))
fp.close()
for k in domains.keys():
domains[k] = domains[k] & set(domain_duration(k, domains[k]))
# calling CSP class with domains, constraints and cost
csp = CSP(domains, constraints, cost)
# calling Search_with_AC_from_Cost_CSP class with csp
search_from_cost_csp = Search_with_AC_from_Cost_CSP(csp)
# calling search funstion of AStarSearcher class with search_from_cost_csp
path = AStarSearcher(search_from_cost_csp).search()
if path != None:
final_cost = 0
# sorting the path dictionary in increasing order of the tasknames
path = {k: path.end().to_node[k] for k in sorted(path.end().to_node)}
# printing out the output
for v in path:
print(v,
':',
working_days[path[v] // 8],
' ',
working_hours[path[v] % 8],
sep='')
# calculating the final_cost
final_cost += cost[(v, path[v])]
print('cost:', final_cost, sep='')
else:
print('No Solution')