from eight import state
from bfs import *
from timeit import Timer
initial = state(None, [8,1,3,2,4,5,state.space,7,6], 0)
goal = state(None, [1,2,3,8,state.space,4,7,6,5])
print(initial)
f = lambda: bfs(initial, goal)
res = f()
print('has decision :', res[0])
print('open states :', res[2])
print('closed states :', res[3])
print('result path :')
for m in res[1]:
print(m)
t = Timer(f)
print("Time = ", t.timeit(number=100))
from eight import state, fair_evaluator, good_evaluator, weak_evaluator, bad_evaluator
from asearch import a_search
from timeit import Timer
initial = state(None, [8,1,3,0,4,5,2,7,6], 0)
goal = state(None, [1,2,3,8,0,4,7,6,5])
print('fair =', fair_evaluator(initial, goal))
print('good =', good_evaluator(initial, goal))
print('weak =', weak_evaluator(initial, goal))
# print('bad =', bad_evaluator(initial, goal))
f = lambda: a_search(initial, goal, fair_evaluator)
# f = lambda: a_search(initial, goal, good_evaluator)
# f = lambda: a_search(initial, goal, weak_evaluator)
# f = lambda: a_search(initial, goal, bad_evaluator)
res = f()
print('has decision :', res['solved'])
print('open states :', res['openstates'])
print('closed states :', res['closedstates'])
print('result path :')
for m in res['path']:
print(m)
t = Timer(f)
print("Time = ", t.timeit(number=1))
from eight import state
s = state(None, [1, 2, 3, 4, 5, 6, 7, 8, 0])
print(f"Initial state:{s}")
moves = s.get_moves()
for move in moves:
print(f"Possible moves: {move}")
from eight import state
from bfs import *
from timeit import Timer
initial = state(None, [[8, 1, 3], [2, 4, 5], [state.space, 7, 6]], 0)
goal = state(None, [[1, 2, 3], [8, state.space, 4], [7, 6, 5]])
print(initial)
f = lambda: bfs(initial, goal)
res = f()
print('has decision :', res[0])
print('open states :', res[2])
print('closed states :', res[3])
print('result path :')
for m in res[1]:
print(m)
t = Timer(f)
print("Time = ", t.timeit(number=100))