for board_name in board_names:
# print("Hello testing for loop")
if board_name[7:-4] in noSolution_board:
print('unsolvable board ' + str(board_name[7:]))
continue
print("Loading board file %s" % (board_name, ))
with open(board_name) as file:
board = Board.read_board(file)
tableFormat3["Board"].append(board_name[7:])
bp0 = BloxorzProblem(board)
# bp0.heuristic = bp0.heuristic1
searcher = searchGeneric.AStarSearcher(bp0)
result = searcher.search()
if result is None:
print("For board %s, found no solution!" % (board_name, ))
tableFormat3['AStar_Length'].append(0)
tableFormat3['Astar_expansions'].append(0)
else:
sequence = [arc.action for arc in result.arcs()]
#print("For board %s, found solution with length %d using %d expansions" % (board_name, len(sequence), searcher.num_expanded))
tableFormat3['AStar_Length'].append(len(sequence))
tableFormat3['Astar_expansions'].append(searcher.num_expanded)
continue
#dataframe and converion to xml and html file with a average of the lengths and expansions
df3 = pd.DataFrame(tableFormat3)
time = Time[line[-1]]
cons.append(Constraint((line[1],), end_before_time_(time)))
elif "ends-after" in line and len(line) == 4:
time = Time[line[-1]]
cons.append(Constraint((line[1],), end_after_time_(time)))
# print(task)
# print(cons)
# print(soft_cons)
# print(cost_unit)
# get the csp
csp = Extendedcsp(task, cons, soft_cons)
# print(csp)
# search for path
searcher = searchGeneric.AStarSearcher(Search_with_AC_from_cost_CSP(csp))
result = searcher.search()
# print("-----------")
if result and min(searcher.problem.cost_list)<= MAX_cost:
# print(solution and cost)
ans = {m: cspConsistency.select(n) for (m, n) in result.end().items()}
for x in ans:
start, _ = ans[x]
date, time = start
print(f'{x}:{get_key(Date,date)} {get_key(Time,time)}')
print(f'cost:{min(searcher.problem.cost_list)}')
else:
print('No solution')