def main():
# initialize the problems
problem_1 = Problem1('L1.txt')
problem_2 = Problem1('L2.txt')
problem_3 = Problem1('L3.txt')
# create the graphs based on the text file
problem_1.create_graph()
problem_2.create_graph()
problem_3.create_graph()
# create the actual problem to search
problem_1 = InstrumentedProblem(
GraphProblem(problem_1.start_coordinates, problem_1.end_coordinates,
problem_1.graph))
problem_2 = InstrumentedProblem(
GraphProblem(problem_2.start_coordinates, problem_2.end_coordinates,
problem_2.graph))
problem_3 = InstrumentedProblem(
GraphProblem(problem_3.start_coordinates, problem_3.end_coordinates,
problem_3.graph))
#compare the search algorithms on all problems
compare_searchers(problems=[problem_1, problem_2, problem_3],
header=[
'Algorithm', 'L1 ((1,1), (14,19))',
'L2 ((1,1), (12,11))', 'L3 ((1,1), (13,4))'
],
searchers=[
uniform_cost_search, greedy_best_first_graph_search,
astar_search
])
def test_bidirectional_breadth_first_search_vs_depth_first_graph_search():
eight_puzzle = EightPuzzle(initial=instance_three, goal=goal)
eight_puzzle_reversed = EightPuzzle(initial=goal, goal=instance_three)
ins_problem = InstrumentedProblem(eight_puzzle)
res = instrumented_bidirectional_breadth_first_search(eight_puzzle, eight_puzzle_reversed, True)
depth_first_graph_search(ins_problem)
assert (res[1]) <= ins_problem.explored
def main():
game = solitaire([['O', '_', '_', 'O', '_'], ['O', '_', 'O', '_', 'O'],
['_', 'O', '_', 'O', '_'], ['O', '_', 'O', '_', '_'],
['_', 'O', '_', '_', '_']])
p = InstrumentedProblem(game)
resultBreadthFirstSearch = breadth_first_search(p)
print(resultBreadthFirstSearch.solution())
print(resultBreadthFirstSearch.path()[0].state.board)
def test_knapsack_simulated_annealing_low_dimensional_2():
params = open_file("../dataset/low-dimensional/f2_l-d_kp_20_878")
problem = KnapsackProblem(params[0], params[1])
ins_problem = InstrumentedProblem(problem)
result = simulated_annealing(ins_problem,
exp_schedule(2000, 0.00005, 200000))
optimum = int((open(
"../dataset/low-dimensional-optimum/f2_l-d_kp_20_878").readline()))
assert optimum == 1024
assert result.value == optimum
def test_knapsack_hill_climbing_large_scale_3():
params = open_file("../dataset/large_scale/knapPI_1_500_1000_1")
problem = KnapsackProblem(params[0], params[1])
ins_problem = InstrumentedProblem(problem)
result = hill_climbing(ins_problem)
optimum = int((
open("../dataset/large_scale-optimum/knapPI_1_500_1000_1").readline()))
assert optimum == 28857
assert result.value <= 10000
assert result.value >= 9800
def test_knapsack_simulated_annealing_plot_large_scale_6_4():
params = open_file("../dataset/large_scale/knapPI_1_5000_1000_1")
problem = KnapsackProblem(params[0], params[1])
ins_problem = InstrumentedProblem(problem)
result = simulated_annealing_plot(ins_problem, (800, 0.0005, 4000))
optimum = int((open(
"../dataset/large_scale-optimum/knapPI_1_5000_1000_1").readline()))
assert optimum == 276457
assert result.value <= 230000
assert result.value >= 190000
def test_knapsack_hill_climbing_rr_large_scale_5():
params = open_file("../dataset/large_scale/knapPI_1_2000_1000_1")
problem = KnapsackProblem(params[0], params[1])
ins_problem = InstrumentedProblem(problem)
result = hill_climbing_random_restart(ins_problem, 100)
optimum = int((open(
"../dataset/large_scale-optimum/knapPI_1_2000_1000_1").readline()))
assert optimum == 110625
assert result.value <= 30000
assert result.value >= 27000
def test_knapsack_simulated_annealing_large_scale_5():
params = open_file("../dataset/large_scale/knapPI_1_2000_1000_1")
problem = KnapsackProblem(params[0], params[1])
ins_problem = InstrumentedProblem(problem)
result = simulated_annealing(ins_problem)
optimum = int((open(
"../dataset/large_scale-optimum/knapPI_1_2000_1000_1").readline()))
assert optimum == 110625
assert result.value <= 14200
assert result.value >= 8500
def main():
L1 = graphHelper()
L2 = graphHelper()
L3 = graphHelper()
print("Manhattan")
compare_searchers(problems=[
InstrumentedProblem(ManhattanProblem(L1[0], L1[1], L1[2])),
InstrumentedProblem(ManhattanProblem(L2[0], L2[1], L2[2])),
InstrumentedProblem(ManhattanProblem(L3[0], L3[1], L3[2]))
],
header=['Algorithm', "L1", "L2", "L3"],
searchers=[
uniform_cost_search, greedy_best_first_graph_search,
astar_search
])
print("Euclidean")
compare_searchers(problems=[
InstrumentedProblem(GraphProblem(L1[0], L1[1], L1[2])),
InstrumentedProblem(GraphProblem(L2[0], L2[1], L2[2])),
InstrumentedProblem(GraphProblem(L3[0], L3[1], L3[2]))
],
header=['Algorithm', "L1", "L2", "L3"],
searchers=[
uniform_cost_search, greedy_best_first_graph_search,
astar_search
])
'hill_climbing': lambda x: hill_climbing(x),
'annealing': lambda x: simulated_annealing(x, schedule=sim)
}
# print(sys.argv)
searchers = sys.argv[1:]
lewisProblems = map(LewisSudokuProblem, sudokus)
naiveProblems = map(SudokuProblem, sudokus)
#csv header
print("problem,searcher,explored,states,tests,solution,value")
for (i, (p1, p2)) in enumerate(zip(lewisProblems, naiveProblems)):
#print("Initial state:")
for s in searchers:
if s in ['hill_climbing', 'annealing']:
ip = InstrumentedProblem(p1)
else:
ip = InstrumentedProblem(p2)
#print(ip.initial)
result = choices[s](ip)
print("%d,%s,%d,%d,%d,%s,%s" %
(i, s, ip.succs, ip.states, ip.goal_tests,
bool(result) and true_goal_test(result.state),
ip.value(result.state)) if result else "")
# if result:
# print("Stats: %s"%(ip))
# print("Result:\n %s"%(result.state))
# print("Is a solution: %s"%(ip.goal_test(result.state)))
def test_astar_3():
problem = InstrumentedProblem(SixteenPuzzle(initial=instance_three, goal=goal))
result = astar_search(problem, problem.gaschnig_index)
assert result.state == goal
def test_iterative_deepening_astar_2():
problem = InstrumentedProblem(SixteenPuzzle(initial=instance_two, goal=goal))
result = iterative_deepening_astar_search(problem, problem.gaschnig_index)
assert result.state == goal
