def ucs_runtime():
arbitrary_solvable_problems = get_100_solvable_search_problems()
for problem in arbitrary_solvable_problems:
source = problem[0]
target = problem[1]
problem = RoutingProblem(source, target, MapData.get_instance())
best_first_graph_search(problem, cost_function, f=g)
def idastar_runtime():
arbitrary_solvable_problems = get_100_solvable_search_problems()
for i in range(0, 5):
source = arbitrary_solvable_problems[i][0]
target = arbitrary_solvable_problems[i][1]
problem = RoutingProblem(source, target, MapData.get_instance())
idastar(problem)
def find_astar_route(source, target):
problem = RoutingProblem(source, target, MapData.get_instance())
# Use best first graph search algorithm when function f defined as g (the path cost) + h (the heuristic function)
# and return the solution path.
return best_first_graph_search(
problem,
cost_function,
f=lambda n: g(n) + heuristic_function(n, Node(problem.goal)))[0]
def astar_runtime():
arbitrary_solvable_problems = get_100_solvable_search_problems()
for problem in arbitrary_solvable_problems:
source = problem[0]
target = problem[1]
problem = RoutingProblem(source, target, MapData.get_instance())
best_first_graph_search(
problem,
cost_function,
f=lambda n: g(n) + heuristic_function(n, Node(problem.goal)))
def ucs_solution_for_100_problems():
ucs_results_file = open('results/UCSRuns.txt', "w")
problems_list = load_data()
for problem in problems_list:
source = problem[0]
target = problem[1]
problem = RoutingProblem(int(source), int(target),
MapData.get_instance())
ucs_results_file.write(
str(best_first_graph_search(problem, cost_function, f=g)[1]) +
"\n")
ucs_results_file.close()
def idastar_solution_for_5_problems():
astar_results_file = open('results/IDAStarRuns.txt', "w")
problems_list = load_data()
for line in range(0, 5):
source = problems_list[line][0]
target = problems_list[line][1]
problem = RoutingProblem(int(source), int(target),
MapData.get_instance())
path = idastar(problem)
cost = 0
for i in range(len(path) - 1):
links = MapData.get_instance().get(path[i]).links
for link in links:
if link.target == path[i + 1]:
cost += cost_function(link)
break
astar_results_file.write("heuristic cost: " + str(
heuristic_function(Node(problem.start_state), Node(problem.goal)))
+ ", actual cost: " + str(cost) + "\n")
astar_results_file.close()
def astar_solution_for_100_problems():
astar_results_file = open('results/AStarRuns.txt', "w")
problems_list = load_data()
counter = 0
for problem in problems_list:
source = problem[0]
target = problem[1]
problem = RoutingProblem(int(source), int(target),
MapData.get_instance())
path, cost = best_first_graph_search(
problem,
cost_function,
f=lambda n: g(n) + heuristic_function(n, Node(problem.goal)))
astar_results_file.write("heuristic cost: " + str(
heuristic_function(Node(problem.start_state), Node(problem.goal)))
+ ", actual cost: " + str(cost) + "\n")
# We want to draw solution maps only for 10 problems as required.
if counter < 10:
draw_solution_map(path)
counter += 1
astar_results_file.close()
def find_ucs_route(source, target):
problem = RoutingProblem(source, target, MapData.get_instance())
# Use best first graph search algorithm when function f defined as g (the path cost), and return the solution path.
return best_first_graph_search(problem, cost_function, f=g)[0]
def find_idastar_route(source, target):
problem = RoutingProblem(source, target, MapData.get_instance())
return idastar(problem)