def run(problem,name):
print("\n\n*******",name)
print("\nA*:")
asearcher = AStarSearcher(problem)
print("Path found:",asearcher.search()," cost=",asearcher.solution.cost)
print("there are",asearcher.frontier.count(asearcher.solution.cost),
"elements remaining on the queue with f-value=",asearcher.solution.cost)
print("\nA* with MPP:"),
msearcher = SearcherMPP(problem)
print("Path found:",msearcher.search()," cost=",msearcher.solution.cost)
print("there are",msearcher.frontier.count(msearcher.solution.cost),
"elements remaining on the queue with f-value=",msearcher.solution.cost)
bound = asearcher.solution.cost+0.01
print("\nBranch and bound (with too-good initial bound of", bound,")")
tbb = DF_branch_and_bound(problem,bound) # cheating!!!!
print("Path found:",tbb.search()," cost=",tbb.solution.cost)
print("Rerunning B&B")
print("Path found:",tbb.search())
bbound = asearcher.solution.cost*2+10
print("\nBranch and bound (with not-very-good initial bound of", bbound, ")")
tbb2 = DF_branch_and_bound(problem,bbound) # cheating!!!!
print("Path found:",tbb2.search()," cost=",tbb2.solution.cost)
print("Rerunning B&B")
print("Path found:",tbb2.search())
print("\nDepth-first search: (Use ^C if it goes on forever)")
tsearcher = Searcher(problem)
print("Path found:",tsearcher.search()," cost=",tsearcher.solution.cost)
def main():
prob = Tile_problem(4, [1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0])
#prob = Tile_problem(3, [1,1,0, 1,0,0, 1,0,1])
s = AStarSearcher(prob)
path = s.search()
if path:
print(f"Path found (cost = {path.cost})\n{path}")
return path
def main():
smoke_test()
prob = River_problem()
s = AStarSearcher(prob)
path = s.search()
if path:
print("Path found (cost=%s)\n%s" % (path.cost, path))
return path
if __name__ == '__main__':
main()
task = var[0]
duration = var[1]
domains[task] = sorted({(i * 10 + j, i * 10 + j + duration) for i in range(1, 6) for j in range(1, 10 - duration)})
# print("variables = ", variables)
# print("domains = ", domains)
# print("constraints", constraints)
# print("soft_constraints", soft_constraints)
# print("soft_cost", soft_cost)
# which adds soft_constraints and soft_cost compared with origin
my_csp = My_CSP(domains, constraints, soft_constraints, soft_cost)
my_problem = Search_with_AC_from_Cost_CSP(my_csp)
my_searcher = AStarSearcher(my_problem)
my_solution = my_searcher.search()
# print(my_solution)
if my_solution is not None: # there is a solution if finding a path
final_schedule = my_solution.end() # find the end node(result) in our solution path
if final_schedule is not None:
my_cost = my_problem.heuristic(final_schedule)
for task in final_schedule:
start_time = list(final_schedule[task])[0][0]
day = start_time // 10
time = start_time % 10
output_day = ""
output_time = ""
for key in day2num:
if day2num[key] == day:
output_day = key
for key in time2num: