def main(args):
# Read the board from standard in
board = sys.stdin.read().splitlines()
for i in range(len(board)):
board[i] = list(board[i])
# Define the heuristics
def manhatten_heuristic(state, goal_state):
dr = abs(state[0] - goal_state[0])
dc = abs(state[1] - goal_state[1])
return dr + dc
def no_heuristic(state, goal_state):
return 0
# A*
astar_problem = GridProblem(board, manhatten_heuristic)
astar_result = uniform_cost.search(astar_problem)
make_graphics('astar_', args.image_destination, args.postfix,
board, astar_result)
# Dijsktra
dijkstra_problem = GridProblem(board, no_heuristic)
dijkstra_result = uniform_cost.search(dijkstra_problem)
make_graphics('dijkstra_', args.image_destination, args.postfix,
board, dijkstra_result)
# BFS
bfs_problem = GridProblem(board, no_heuristic)
bfs_result = breadth_first.search(bfs_problem)
make_graphics('bfs_', args.image_destination, args.postfix,
board, bfs_result)
def main(args):
# Reading input
rows, cols = [int(x) for x in sys.stdin.readline().split(' ')]
cars = []
for line in sys.stdin:
line = line.strip('()\n')
parts = line.split(',')
parts = [int(part) for part in parts]
cars.append(tuple(parts))
# Making a 2D-representation
board = [[-1 for c in range(cols)] for r in range(rows)]
for ci in range(len(cars)):
(ori, c, r, size) = cars[ci]
dr = [0, 1][ori]
dc = [1, 0][ori]
for i in range(size):
board[r + (dr * i)][c + (dc * i)] = ci
# Solving
heuristic = str_to_heuristic(args.heuristic, cars, board)
problem = RushHourProblem(cars, board, heuristic)
(end_state, parent, explored, _) = uniform_cost.search(problem)
# Results
print('Explored states:'.rjust(17), len(explored))
move = 0
while end_state in parent:
if args.image_destination:
make_graphics(end_state, args.image_destination, move)
move += 1
end_state = parent[end_state]
if args.image_destination:
make_graphics(end_state, args.image_destination, move)
print('Moves:'.rjust(17), move)
print('Branching factor:', '{:1.3f}'.format(
exp(log(len(explored)) / move)))
