def manhattan_dist_sum(p, tiles = None):
if not tiles:
tiles = p.flat
solved = puzzle.solved(p)
s = 0
for t in tiles:
s_pos = puzzle.get_position(p, t)
t_pos = puzzle.get_position(solved, t)
s += u.manhattan_distance(*s_pos, *t_pos)
return s
def rec_a_star(p, selector = SubSelect()):
solved = puzzle.solved(p)
path = []
while selector.applicable(p):
tiles_to_solve = [t for t in solved.flat if t not in selector.apply(solved)]
heuristic = lambda puz: manhattan_dist_sum(puz, tiles_to_solve)
new_path = ida_star(p, heuristic)
path += new_path
p = selector.apply(puzzle.apply_actions(p, new_path))
solved = selector.apply(solved)
path += a_star(p, manhattan_dist_sum)
return path
def __init__(self, p, moves, transp = False):
self.locked = set()
self.transposed = transp
self.p = p.transpose() if transp else p
self.s = puz.solved(p).transpose() if transp else puz.solved(p)
self._moves = [transpose_action(m) for m in moves] if transp else moves
