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)
import searchProblem
from searchTest import run
if __name__ == "__main__":
run(searchProblem.problem1,"Problem 1")
# run(searchProblem.acyclic_delivery_problem,"Acyclic Delivery")
# run(searchProblem.cyclic_delivery_problem,"Cyclic Delivery")
# also test some graphs with cycles, and some with multiple least-cost paths
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("\nBFS: (Use ^C if it goes on forever)")
bfs = BFS(problem)
print("Path found:", bfs.search(), " cost=", bfs.solution.cost)
print("\nDepth-first search: (Use ^C if it goes on forever)")
#tsearcher = Searcher(problem)
print("Path found:", tsearcher.search(), " cost=",
tsearcher.solution.cost)
import searchProblem
from searchTest import run
if __name__ == "__main__":
#run(searchProblem.problem2,"problem2")
run(searchProblem.Romania_map, "Romania_map")
# run(searchProblem.acyclic_delivery_problem,"Acyclic Delivery")
# run(searchProblem.cyclic_delivery_problem,"Cyclic Delivery")
# also test some graphs with cycles, and some with multiple least-cost paths
# 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)
# print("\nBreadth-first search: ")
# tsearcher = BFSearcher(problem)
# print("Path found:", tsearcher.search(),
# " cost=", tsearcher.solution.cost)
if __name__ == "__main__":
run(searchProblem.romania_map, "romania_map")
# run(searchProblem.acyclic_delivery_problem,"Acyclic Delivery")
# run(searchProblem.cyclic_delivery_problem,"Cyclic Delivery")
# also test some graphs with cycles, and some with multiple least-cost paths
def run(problem, name):
print("\n\n*******", name)
print("\nA*:")
tsearcher = Searcher(problem)
print("Path found:", tsearcher.search(), " cost=",
tsearcher.solution.cost)
print("there are", tsearcher.frontier.count(tsearcher.solution.cost),
"elements remaining on the queue with f-value=",
tsearcher.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)
print("\nBranch and bound (with too-good initial bound):")
tbb = DF_branch_and_bound(problem,
tsearcher.solution.cost + 0.1) # cheating!!!!
print("Path found:", tbb.search(), " cost=", tbb.solution.cost)
if __name__ == "__main__":
run(searchProblem.search_simple1, "Problem 1")
# run(searchProblem.search_acyclic_delivery,"Acyclic Delivery")
# run(searchProblem.search_cyclic_delivery,"Cyclic Delivery")
# also test some graphs with cycles, and some with multiple least-cost paths