This commit is contained in:
parent
683cac334c
commit
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@ -1,7 +1,95 @@
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from typing import Any, Iterator
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import itertools
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from collections import Counter
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from typing import Any, Callable, Iterable, Iterator, Sequence, TypeAlias
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from ..base import BaseSolver
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from ..base import BaseSolver
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from ..tools.graphs import dijkstra, make_neighbors_grid_fn
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Node: TypeAlias = tuple[int, int]
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def make_neighbors_fn(grid: list[str], cheat_length: int):
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n_rows, n_cols = len(grid), len(grid[0])
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def _fn(node: Node):
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row, col = node
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return (
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((row_n, col_n), abs(row_n - row) + abs(col_n - col))
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for row_d in range(-cheat_length, cheat_length + 1)
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for col_d in range(
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-cheat_length + abs(row_d), cheat_length - abs(row_d) + 1
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)
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if 0 <= (row_n := row + row_d) < n_rows
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and 0 <= (col_n := col + col_d) < n_cols
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and grid[row_n][col_n] != "#"
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)
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return _fn
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class Solver(BaseSolver):
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class Solver(BaseSolver):
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def solve(self, input: str) -> Iterator[Any]: ...
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def find_cheats(
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self,
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path: Sequence[Node],
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cost: float,
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costs_to_target: dict[Node, float],
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neighbors_fn: Callable[[Node], Iterable[tuple[Node, float]]],
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):
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cheats: dict[tuple[tuple[int, int], tuple[int, int]], float] = {}
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for i_node, node in enumerate(self.progress.wrap(path)):
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for reach_node, reach_cost in neighbors_fn(node):
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n_cost = (
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i_node + reach_cost + costs_to_target.get(reach_node, float("inf"))
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)
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if n_cost < cost:
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cheats[node, reach_node] = cost - n_cost
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return cheats
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def solve(self, input: str) -> Iterator[Any]:
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grid = input.splitlines()
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n_rows, n_cols = len(grid), len(grid[0])
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start = next(
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(i, j) for i in range(n_rows) for j in range(n_cols) if grid[i][j] == "S"
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)
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target = next(
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(i, j) for i in range(n_rows) for j in range(n_cols) if grid[i][j] == "E"
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)
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reachable = dijkstra(
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target,
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None,
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make_neighbors_grid_fn(
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n_rows,
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n_cols,
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excluded=(
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(i, j)
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for i in range(n_rows)
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for j in range(n_cols)
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if grid[i][j] == "#"
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),
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),
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)
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# note: path is inverted here
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path, cost = reachable[start]
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costs_to_target = {k: c for k, (_, c) in reachable.items()}
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self.logger.info(f"found past from start to target with cost {cost}")
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for cheat_length in (2, 20):
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cheats = self.find_cheats(
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list(reversed(path)),
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cost,
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costs_to_target,
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make_neighbors_fn(grid, cheat_length),
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)
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for saving, count in sorted(Counter(cheats.values()).items()):
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self.logger.debug(
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f"There are {count} cheats that save {saving} picoseconds."
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)
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target_saving = 100 if len(grid) > 20 else 50
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yield sum(saving >= target_saving for saving in cheats.values())
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@ -0,0 +1,141 @@
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#############################################################################################################################################
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#.......#.........#.....#.....#...#...###...###.....###...#...###.....#.....#.......#.......#.....#...#####.....#...#...#...#...............#
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#.#####.#.#######.#.###.#.###.#.#.#.#.###.#.###.###.###.#.#.#.###.###.#.###.#.#####.#.#####.#.###.#.#.#####.###.#.#.#.#.#.#.#.#############.#
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#...#...#.......#.#...#.#...#.#.#...#...#.#...#...#.....#.#.#...#...#.#...#.#.....#.#...#...#.#...#.#...#...#...#.#...#.#.#.#...#...........#
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###.#.#########.#.###.#.###.#.#.#######.#.###.###.#######.#.###.###.#.###.#.#####.#.###.#.###.#.###.###.#.###.###.#####.#.#.###.#.###########
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#...#.....#.....#.#...#.#...#.#.......#.#.#...###.#.......#.#...#...#.....#.......#...#.#...#.#...#.#...#...#.###.....#...#...#.#...........#
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#.#######.#.#####.#.###.#.###.#######.#.#.#.#####.#.#######.#.###.###################.#.###.#.###.#.#.#####.#.#######.#######.#.###########.#
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###.#.#########.#.#.#.#####.###.#.#.#.#######.#.#########.#.###.###########.#.#####.#####.###.#.###.###.#.#####.#.#.#.#.#######.#.#######.#.#
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#.#.#########.#.###.#.#.#.#.###.#.#.#.#.#.#.###.###.#####.#####.#.#####.#####.#.#.###########.#.#.#.#####.#.#.###.#.#.###########.###.#.#####
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#.#######.#########.#.#.#.#.#################.#.#.#.#.#.#.###.#.#########.#.#.#######.#.###.#.###.#.#######.#.#.#############.#.#####.#.#.#.#
|
||||||
|
#.......#.#...#.....#...#...#.............###.#...#...#.#...#.#.........#.#.#.....#...#.#...#.....#.....#...#...#.......#...#...#...#.#...#.#
|
||||||
|
#######.#.#.#.#.#############.###########.###.#########.###.#.#########.#.#.#####.#.###.#.#############.#.#######.#####.#.#.#####.#.#.#####.#
|
||||||
|
#...#...#...#...#.............#...........#...#.......#.#...#.#...#...#.#.#.#.....#...#...#.....#.....#...###.....#.....#.#...#...#.#.#.....#
|
||||||
|
#.#.#.###########.#############.###########.###.#####.#.#.###.#.#.#.#.#.#.#.#.#######.#####.###.#.###.#######.#####.#####.###.#.###.#.#.#####
|
||||||
|
#.#...#.......#...###...........#...#...###...#.....#.#.#...#.#.#.#.#...#...#...#...#...#...###...#...#.......#.....#.....#...#.#...#.#.#...#
|
||||||
|
#.#####.#####.#.#####.###########.#.#.#.#####.#####.#.#.###.#.#.#.#.###########.#.#.###.#.#########.###.#######.#####.#####.###.#.###.#.#.#.#
|
||||||
|
#...#...#.....#.#...#...........#.#.#.#.....#.#.....#...###.#.#.#.#...#...#...#.#.#...#...#.......#.....#...#...#...#.#.....#...#.....#...#.#
|
||||||
|
###.#.###.#####.#.#.###########.#.#.#.#####.#.#.###########.#.#.#.###.#.#.#.#.#.#.###.#####.#####.#######.#.#.###.#.#.#.#####.#############.#
|
||||||
|
#...#.#...#...#.#.#.#.........#...#...#.....#.#...#...###...#.#.#...#...#.#.#...#.#...#...#.....#.........#...###.#...#.......#...#...#...#.#
|
||||||
|
#.###.#.###.#.#.#.#.#.#######.#########.#####.###.#.#.###.###.#.###.#####.#.#####.#.###.#.#####.#################.#############.#.#.#.#.#.#.#
|
||||||
|
#...#.#...#.#.#.#.#...#.....#...#.......#...#.#...#.#.#...#...#.#...#.....#...#...#...#.#.#...#...#.............#...#...........#.#.#.#.#.#.#
|
||||||
|
###.#.###.#.#.#.#.#####.###.###.#.#######.#.#.#.###.#.#.###.###.#.###.#######.#.#####.#.#.#.#.###.#.###########.###.#.###########.#.#.#.#.#.#
|
||||||
|
###...#...#.#.#.#.#...#...#...#.#.........#.#.#...#.#.#...#...#.#...#.......#.#.....#.#.#.#.#.###...###.........#...#...........#...#.#.#...#
|
||||||
|
#######.###.#.#.#.#.#.###.###.#.###########.#.###.#.#.###.###.#.###.#######.#.#####.#.#.#.#.#.#########.#########.#############.#####.#.#####
|
||||||
|
###...#.....#...#...#.....#...#.............#.#...#.#.###.#...#...#.###...#.#.#...#.#.#.#.#.#.#...#...#...........#.......#...#...###.#.....#
|
||||||
|
###.#.#####################.#################.#.###.#.###.#.#####.#.###.#.#.#.#.#.#.#.#.#.#.#.#.#.#.#.#############.#####.#.#.###.###.#####.#
|
||||||
|
#...#.......###.......#...#.........#.......#.#.#...#...#.#...#...#...#.#.#.#.#.#.#.#.#.#.#.#.#.#...#.#.....#.......#...#...#...#...#.......#
|
||||||
|
#.#########.###.#####.#.#.#########.#.#####.#.#.#.#####.#.###.#.#####.#.#.#.#.#.#.#.#.#.#.#.#.#.#####.#.###.#.#######.#.#######.###.#########
|
||||||
|
#.........#.....#...#...#.........#.#...###.#.#...#...#.#...#.#.#.....#.#...#.#.#...#.#.#...#.#.###...#.#...#.........#.......#...#.#.......#
|
||||||
|
#########.#######.#.#############.#.###.###.#.#####.#.#.###.#.#.#.#####.#####.#.#####.#.#####.#.###.###.#.###################.###.#.#.#####.#
|
||||||
|
###.....#.......#.#.............#...#...#...#.......#.#...#.#...#...#...#.....#.#.....#.#.....#...#...#.#...#...#...........#.#...#...#.....#
|
||||||
|
###.###.#######.#.#############.#####.###.###########.###.#.#######.#.###.#####.#.#####.#.#######.###.#.###.#.#.#.#########.#.#.#######.#####
|
||||||
|
#...#...#.....#...###...........#.....#...#.....#.....###.#.....###.#.###...#...#.#...#.#...#.....#...#...#.#.#.#.....#...#...#.......#.....#
|
||||||
|
#.###.###.###.#######.###########.#####.###.###.#.#######.#####.###.#.#####.#.###.#.#.#.###.#.#####.#####.#.#.#.#####.#.#.###########.#####.#
|
||||||
|
#...#...#...#.#.......#.....#...#.#.....#...###.#.......#...#...#...#.....#.#...#.#.#...###.#...###...#...#...#.#...#...#...........#...#...#
|
||||||
|
###.###.###.#.#.#######.###.#.#.#.#.#####.#####.#######.###.#.###.#######.#.###.#.#.#######.###.#####.#.#######.#.#.###############.###.#.###
|
||||||
|
#...#...#...#...#...#...#...#.#.#.#.....#.....#...#.....###...###.#.....#.#...#.#.#.......#...#...#...#.#...###...#...#.....#.......#...#...#
|
||||||
|
#.###.###.#######.#.#.###.###.#.#.#####.#####.###.#.#############.#.###.#.###.#.#.#######.###.###.#.###.#.#.#########.#.###.#.#######.#####.#
|
||||||
|
#...#.....#...#...#.#.###...#.#...#...#.#...#.#...#.......#.......#...#.#.#...#.#.#.....#...#.#...#...#...#.....#.....#...#.#.......#...#...#
|
||||||
|
###.#######.#.#.###.#.#####.#.#####.#.#.#.#.#.#.#########.#.#########.#.#.#.###.#.#.###.###.#.#.#####.#########.#.#######.#.#######.###.#.###
|
||||||
|
###...#...#.#.#.###...#.....#...#...#...#.#...#...#...#...#...#...#...#.#.#...#.#...###...#.#...#...#...#.......#.#.......#.#.......#...#...#
|
||||||
|
#####.#.#.#.#.#.#######.#######.#.#######.#######.#.#.#.#####.#.#.#.###.#.###.#.#########.#.#####.#.###.#.#######.#.#######.#.#######.#####.#
|
||||||
|
#.....#.#.#.#.#.#.....#.....#...#...#...#.....###...#.#.....#.#.#.#.###...#...#.....#.....#...#...#.....#.......#.#.....#...#.....###.......#
|
||||||
|
#.#####.#.#.#.#.#.###.#####.#.#####.#.#.#####.#######.#####.#.#.#.#.#######.#######.#.#######.#.###############.#.#####.#.#######.###########
|
||||||
|
#...#...#.#.#...#...#.#.....#.#####...#.#...#.....###.#.....#...#.#...#.....#.......#.......#.#.#...#...#.......#...#...#.........#.........#
|
||||||
|
###.#.###.#.#######.#.#.#####.#########.#.#.#####.###.#.#########.###.#.#####.#############.#.#.#.#.#.#.#.#########.#.#############.#######.#
|
||||||
|
###.#...#.#.#.......#...#...#...#.......#.#.#.....#...#...#.......#...#.....#.....#.........#.#.#.#.#.#.#.......#...#.........###...#...#...#
|
||||||
|
###.###.#.#.#.###########.#.###.#.#######.#.#.#####.#####.#.#######.#######.#####.#.#########.#.#.#.#.#.#######.#.###########.###.###.#.#.###
|
||||||
|
#...#...#...#.....#...#...#...#.#...#...#.#.#.....#.#...#.#.........#...###.#...#.#.#.......#.#...#.#.#.#.......#.....#.....#.....#...#.#...#
|
||||||
|
#.###.###########.#.#.#.#####.#.###.#.#.#.#.#####.#.#.#.#.###########.#.###.#.#.#.#.#.#####.#.#####.#.#.#.###########.#.###.#######.###.###.#
|
||||||
|
#...#.....#####...#.#.#.....#.#.#...#.#.#.#...#...#...#.#.....#.......#.....#.#.#.#...#.....#...#...#.#.#...........#.#...#.#...#...#...#...#
|
||||||
|
###.#####.#####.###.#.#####.#.#.#.###.#.#.###.#.#######.#####.#.#############.#.#.#####.#######.#.###.#.###########.#.###.#.#.#.#.###.###.###
|
||||||
|
#...#...#...#...#...#.....#.#...#...#.#.#.###.#.......#...#...#.....#...#...#.#.#.....#.#...#...#...#.#.#.....#...#.#.#...#...#...###.#...###
|
||||||
|
#.###.#.###.#.###.#######.#.#######.#.#.#.###.#######.###.#.#######.#.#.#.#.#.#.#####.#.#.#.#.#####.#.#.#.###.#.#.#.#.#.#############.#.#####
|
||||||
|
#.....#.....#...#.......#.#.#.......#.#...#...#...#...###.#...#.....#.#...#.#.#.#.....#...#.#.#...#...#.#...#.#.#.#.#.#.............#...#...#
|
||||||
|
###############.#######.#.#.#.#######.#####.###.#.#.#####.###.#.#####.#####.#.#.#.#########.#.#.#.#####.###.#.#.#.#.#.#############.#####.#.#
|
||||||
|
###...#.........#...#...#...#.....#...###...#...#...###...#...#.#...#...#...#.#.#...#.......#...#.###...#...#.#.#.#.#.#...#...#...#.....#.#.#
|
||||||
|
###.#.#.#########.#.#.###########.#.#####.###.#########.###.###.#.#.###.#.###.#.###.#.###########.###.###.###.#.#.#.#.#.#.#.#.#.#.#####.#.#.#
|
||||||
|
#...#...#.....#...#.#...#.........#.....#...#.........#...#...#.#.#...#.#...#.#...#.#.#.....#...#.#...#...#...#.#.#.#.#.#.#.#.#.#.#...#...#.#
|
||||||
|
#.#######.###.#.###.###.#.#############.###.#########.###.###.#.#.###.#.###.#.###.#.#.#.###.#.#.#.#.###.###.###.#.#.#.#.#.#.#.#.#.#.#.#####.#
|
||||||
|
#.........###...###.....#...............###...........###.....#...###...###...###...#...###...#...#.....###.....#...#...#...#...#...#.......#
|
||||||
|
#############################################################################################################################################
|
@ -0,0 +1,15 @@
|
|||||||
|
###############
|
||||||
|
#...#...#.....#
|
||||||
|
#.#.#.#.#.###.#
|
||||||
|
#S#...#.#.#...#
|
||||||
|
#######.#.#.###
|
||||||
|
#######.#.#...#
|
||||||
|
#######.#.###.#
|
||||||
|
###..E#...#...#
|
||||||
|
###.#######.###
|
||||||
|
#...###...#...#
|
||||||
|
#.#####.#.###.#
|
||||||
|
#.#...#.#.#...#
|
||||||
|
#.#.#.#.#.#.###
|
||||||
|
#...#...#...###
|
||||||
|
###############
|
@ -1,5 +1,5 @@
|
|||||||
import heapq
|
import heapq
|
||||||
from typing import Callable, Iterable, TypeVar
|
from typing import Callable, Iterable, TypeVar, overload
|
||||||
|
|
||||||
_Node = TypeVar("_Node")
|
_Node = TypeVar("_Node")
|
||||||
|
|
||||||
@ -53,11 +53,31 @@ def make_neighbors_grid_fn(
|
|||||||
return _fn
|
return _fn
|
||||||
|
|
||||||
|
|
||||||
|
@overload
|
||||||
|
def dijkstra(
|
||||||
|
start: _Node,
|
||||||
|
target: None,
|
||||||
|
neighbors: Callable[[_Node], Iterable[tuple[_Node, float]]],
|
||||||
|
) -> dict[_Node, tuple[tuple[_Node, ...], float]]: ...
|
||||||
|
|
||||||
|
|
||||||
|
@overload
|
||||||
def dijkstra(
|
def dijkstra(
|
||||||
start: _Node,
|
start: _Node,
|
||||||
target: _Node,
|
target: _Node,
|
||||||
neighbors: Callable[[_Node], Iterable[tuple[_Node, float]]],
|
neighbors: Callable[[_Node], Iterable[tuple[_Node, float]]],
|
||||||
) -> tuple[tuple[_Node, ...], float] | None:
|
) -> tuple[tuple[_Node, ...], float] | None: ...
|
||||||
|
|
||||||
|
|
||||||
|
def dijkstra(
|
||||||
|
start: _Node,
|
||||||
|
target: _Node | None,
|
||||||
|
neighbors: Callable[[_Node], Iterable[tuple[_Node, float]]],
|
||||||
|
) -> (
|
||||||
|
dict[_Node, tuple[tuple[_Node, ...], float]]
|
||||||
|
| tuple[tuple[_Node, ...], float]
|
||||||
|
| None
|
||||||
|
):
|
||||||
"""
|
"""
|
||||||
Solve shortest-path problem using simple Dijkstra algorithm from start to target,
|
Solve shortest-path problem using simple Dijkstra algorithm from start to target,
|
||||||
using the given neighbors function.
|
using the given neighbors function.
|
||||||
@ -92,4 +112,7 @@ def dijkstra(
|
|||||||
|
|
||||||
heapq.heappush(queue, (dis + cost, neighbor, path + (neighbor,)))
|
heapq.heappush(queue, (dis + cost, neighbor, path + (neighbor,)))
|
||||||
|
|
||||||
|
if target is None:
|
||||||
|
return preds
|
||||||
|
|
||||||
return preds.get(target, None)
|
return preds.get(target, None)
|
||||||
|
Loading…
Reference in New Issue
Block a user