def solve():
"""
Solve the puzzle by getting the required parameters from the request args.
"""
algorithm = request.args['algorithm']
arr = list(map(int, request.args['input[]'][1:-1].split(',')))
print(algorithm)
agent = None
if algorithm == "BFS":
agent = BFS()
elif algorithm == "DFS":
agent = DFS()
elif algorithm == "A start (Euclidean)":
agent = AStar(Euclidean)
elif algorithm == "A start (Manhatten)":
agent = AStar(Manhatten)
else:
return arr
start = timeit.default_timer()
res = agent.search(arr)
end = timeit.default_timer()
res['time'] = end - start
ret = jsonify(res)
return ret
from bfs import BFS
from romenia_map import ROMENIA
bfs = BFS(ROMENIA, start='Arad')
print bfs.search('Bucharest') == 'Bucharest'
def main():
pygame.init()
# Taking input from user
n = int(input("Enter grid size:"))
Sx, Sy = [int(x) for x in input("Enter knight coords:").split()]
Tx, Ty = [int(x) for x in input("Enter queen coords:").split()]
# Setting up the screen and images
res = (n * pixels, n * pixels)
gameDisplay = pygame.display.set_mode(res)
queen = pygame.image.load("queen.png")
knight = pygame.image.load("knight.png")
# Initializing the board and the algo
createChessboard(gameDisplay, n)
placeEntity(queen, Ty, Tx, gameDisplay)
b = BFS(n, Sx, Sy, Tx, Ty)
# game loop
running = True
while running:
if b.q:
# returns current node and previous node
t, prev = b.search()
placeEntity(knight, t[1], t[0], gameDisplay)
markVisited(prev[1], prev[0], gameDisplay)
pygame.display.update()
time.sleep(1)
if t == (Tx, Ty):
createChessboard(gameDisplay, n)
placeEntity(queen, Ty, Tx, gameDisplay)
placeEntity(knight, Sy, Sx, gameDisplay)
path = b.createPath()
for i, j in path[1:]:
markVisited(j, i, gameDisplay)
pygame.display.update()
continue
if b.q == []:
createChessboard(gameDisplay, n)
cross = pygame.image.load("cross.jpg")
placeEntity(cross, Ty, Tx, gameDisplay)
placeEntity(knight, Sy, Sx, gameDisplay)
pygame.display.update()
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
break
print("Target position ", (Tx, Ty), " reached: ", b.visited[Tx][Ty])
pygame.quit()
def run(self):
R = len(self.content['starts']) # robots
N = self.content['N'] # grid size
# We use a breadth-first search to determine the horizon, i.e.,
# upper complexity bound, of the problem, based on the maximum distance
# of a goal from the start positions of the paths.
distance_to_target = []
bfs = BFS(N, self.content['starts'], self.content['obstacles'])
for i, source in enumerate(bfs.starts):
bfs.search(source)
distance_to_target.append(bfs.G[tuple(
self.content['goals'][i])]['d'])
H_MIN = max(distance_to_target) # horizon
H_MAX = int(H_MIN * self.h_mult)
H_MAX_ORIG = H_MAX
logging.log(logging.INFO, 'BFS lower bound: {}.'.format(H_MIN))
logging.log(logging.INFO,
'Setting H_MAX to h_mult * {}: {}'.format(H_MIN, H_MAX))
if self.search == 'binary':
H = (H_MAX - H_MIN) // 2 + H_MIN
elif self.search == 'linear':
H = H_MIN
else:
logging.log(logging.ERROR,
'Search method is not one of "linear", "binary".')
exit()
logging.log(logging.INFO,
'Beginning search with horizon: {}'.format(H))
# H denotes the horizon (makespan) we are currently checking.
# If H is too small, there will be no solution and the solver will
# return *unsat*. H_MAX is the maximum possible horizon of the problem,
# as computed above using BFS.
while H < H_MAX:
# make the EF-SMT instance here
plan = [[[
Symbol('p_%d^%d-0' % (i, j), INT),
Symbol('p_%d^%d-1' % (i, j), INT)
] for j in range(H)] for i in range(R)]
attack = [[Symbol('a_%d-0' % i, INT),
Symbol('a_%d-1' % i, INT)] for i in range(H)]
# There is an implicit "exists" quantifier out front on the plan
# variables. We are asking the solver to determine if there exists a
# model for the plan variables s.t. the plan is a valid MAPF
# solution, and s.t. forall choices (in (Z^2)^H) for the attack
# variables, the attack is not a valid attack on the plan.
formula = ForAll(
[symvar for coordinate in attack for symvar in coordinate],
And(
IsPlan(plan, N, self.content['starts'],
self.content['goals'],
self.content['safes'] + self.content['obstacles']),
Not(
IsAttack(attack, plan, N, self.content['obstacles'],
self.content['safes']))))
try:
model = self.get_model(formula)
except timeout_decorator.TimeoutError:
model = None
# If the solver returns a sat result, we ask for a satisfying model.
# The makespan of the MAPF solution will be H (<= H_MAX).
if model:
control = [[[
model.get_py_value(plan[i][j][0]),
model.get_py_value(plan[i][j][1])
] for j in range(H)] for i in range(R)]
self.content['control'] = list(zip(*control))
logging.log(logging.INFO,
'Found solution for horizon: {}'.format(H))
if self.search == 'linear':
break
else:
H_MAX = H
H = (H_MAX - H_MIN) // 2 + H_MIN
else:
logging.log(logging.INFO,
'UNSAT for H={}, updating H.'.format(H))
if self.search == 'linear':
H += 1
else:
H_MIN = H + 1
H = (H_MAX - H_MIN) // 2 + H_MIN
if 'control' not in self.content:
logging.log(logging.INFO, 'UNSAT for H_MAX={}.'.format(H_MAX_ORIG))
b_map2 = "generatedpaths/bfs/map2.txt" b_map3 = "generatedpaths/bfs/map3.txt" d_map1 = "generatedpaths/dfs/map1.txt" d_map2 = "generatedpaths/dfs/map2.txt" d_map3 = "generatedpaths/dfs/map3.txt" print "\nBFS - Map1" world = World(Util.read_file(map1), b_map1) bfs_search = BFS(world) print timeit.Timer(bfs_search.search).timeit(1) print "\nBFS - Map2" world = World(Util.read_file(map2), b_map2) bfs_search = BFS(world) bfs_search.search() print timeit.Timer(bfs_search.search).timeit(1) print "\nBFS - Map3" world = World(Util.read_file(map3), b_map3) bfs_search = BFS(world) bfs_search.search() print timeit.Timer(bfs_search.search).timeit(1) print "\nDFS - Map1" world = World(Util.read_file(map1), d_map1) dfs_search = DFS(world) dfs_search.search() print timeit.Timer(dfs_search.search).timeit(1) print "\nDFS - Map2"
from bfs import BFS
from romenia_map import ROMENIA
bfs = BFS(ROMENIA, start="Arad")
print bfs.search("Bucharest") == "Bucharest"
def solveBFS(rubik):
aux = time.time()
solution_BFS = BFS.search(rubik, lambda x: x == RubikPuzzle())
t["BFS: "] = (time.time() - aux) * 1000
aux = 0
return solution_BFS
def test_bfs(self):
agent = BFS()
steps = agent.search([1, 2, 5, 3, 4, 0, 6, 7, 8])
print("Solved in {} steps".format(len(steps)))
print(steps)
