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dev/2024/1
@ -1,58 +1,7 @@
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from typing import Any, Iterator
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from typing import Any, Iterator
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from ..base import BaseSolver
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from ..base import BaseSolver
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from ..tools import graphs
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class Solver(BaseSolver):
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class Solver(BaseSolver):
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def print_grid(self, grid: list[tuple[int, int]], n_rows: int, n_cols: int):
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def solve(self, input: str) -> Iterator[Any]: ...
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values = set(grid)
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if self.files:
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self.files.create(
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"graph.txt",
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"\n".join(
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"".join(
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"#" if (row, col) in values else "." for col in range(n_cols)
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)
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for row in range(n_rows)
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).encode(),
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text=True,
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)
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else:
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for row in range(n_rows):
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self.logger.info(
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"".join(
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"#" if (row, col) in values else "." for col in range(n_cols)
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)
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)
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def dijkstra(self, corrupted: list[tuple[int, int]], n_rows: int, n_cols: int):
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return graphs.dijkstra(
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(0, 0),
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(n_rows - 1, n_cols - 1),
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graphs.make_neighbors_grid_fn(n_rows, n_cols, set(corrupted)),
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)
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def solve(self, input: str) -> Iterator[Any]:
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values = [
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(int(p[0]), int(p[1])) for r in input.splitlines() if (p := r.split(","))
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]
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_is_test = len(values) < 100
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n_rows, n_cols, n_bytes_p1 = (7, 7, 12) if _is_test else (71, 71, 1024)
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bytes_p1 = values[:n_bytes_p1]
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self.print_grid(bytes_p1, n_rows, n_cols)
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path_p1, cost_p1 = self.dijkstra(bytes_p1, n_rows, n_cols) or ((), -1)
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yield cost_p1
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path = path_p1
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for b in range(n_bytes_p1, len(values)):
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if values[b] not in path:
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continue
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path, _ = self.dijkstra(values[: b + 1], n_rows, n_cols) or (None, -1)
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if path is None:
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yield ",".join(map(str, values[b]))
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break
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@ -1,42 +1,7 @@
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from functools import cache
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from typing import Any, Iterator
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from typing import Any, Iterator
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from ..base import BaseSolver
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from ..base import BaseSolver
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@cache
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def is_valid(design: str, towels: tuple[str, ...]) -> bool:
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if not design:
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return True
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return any(
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design.startswith(towel) and is_valid(design[len(towel) :], towels)
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for towel in towels
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)
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@cache
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def count_valid(design: str, towels: tuple[str, ...]) -> int:
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if not design:
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return 1
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return sum(
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design.startswith(towel) and count_valid(design[len(towel) :], towels)
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for towel in towels
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)
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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 solve(self, input: str) -> Iterator[Any]: ...
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towels_s, designs_s = input.split("\n\n")
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towels = tuple(s.strip() for s in towels_s.split(","))
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designs = [
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design
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for design in self.progress.wrap(designs_s.splitlines())
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if is_valid(design, towels)
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]
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yield len(designs)
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yield sum(count_valid(design, towels) for design in self.progress.wrap(designs))
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@ -1,95 +1,7 @@
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import itertools
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from typing import Any, Iterator
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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 find_cheats(
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def solve(self, input: str) -> Iterator[Any]: ...
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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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@ -1,121 +1,7 @@
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import heapq
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from typing import Any, Iterator
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from dataclasses import dataclass
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from typing import Any, Iterator, Literal, Sequence, TypeAlias, cast
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from ..base import BaseSolver
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from ..base import BaseSolver
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Action: TypeAlias = Literal[">", "<", "v", "^", "A"]
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NUM_PAD = ((7, 8, 9), (4, 5, 6), (1, 2, 3), (None, 0, "A"))
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MOV_PAD: tuple[tuple[Action | None, ...], ...] = ((None, "^", "A"), ("<", "v", ">"))
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@dataclass(frozen=True, order=True)
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class Node:
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robot_1: tuple[int, int] = (0, 2)
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robot_2: tuple[int, int] = (0, 2)
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robot_3: tuple[int, int] = (3, 2)
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code: str = ""
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def apply_action(
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robot: tuple[int, int],
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action: Action,
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pad: tuple[tuple[int | str | None, ...], ...],
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):
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d_row, d_col = {"^": (-1, 0), "v": (1, 0), ">": (0, 1), "<": (0, -1)}[action]
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row, col = robot[0] + d_row, robot[1] + d_col
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if 0 <= row < len(pad) and 0 <= col < len(pad[row]) and pad[row][col] is not None:
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return (row, col)
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return None
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def create_node(node: Node, action: Action) -> Node | None:
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# main pad moves -> move first robot
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if action != "A":
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robot = apply_action(node.robot_1, action, MOV_PAD)
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if robot is not None:
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return Node(
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robot_1=robot,
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robot_2=node.robot_2,
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robot_3=node.robot_3,
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code=node.code,
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)
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return None
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# activate pad 1 -> action on robot 1
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robot_1_action = MOV_PAD[node.robot_1[0]][node.robot_1[1]]
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assert robot_1_action is not None
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if robot_1_action != "A":
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robot2 = apply_action(node.robot_2, robot_1_action, MOV_PAD)
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if robot2 is not None:
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return Node(
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robot_1=node.robot_1,
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robot_2=robot2,
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robot_3=node.robot_3,
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code=node.code,
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)
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return None
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# activate pad 2 -> action on robot 2
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robot_2_action = MOV_PAD[node.robot_2[0]][node.robot_2[1]]
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assert robot_2_action is not None
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if robot_2_action != "A":
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robot3 = apply_action(node.robot_3, robot_2_action, NUM_PAD)
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if robot3 is not None:
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return Node(
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robot_1=node.robot_1,
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robot_2=node.robot_2,
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robot_3=robot3,
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code=node.code,
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)
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return None
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value = NUM_PAD[node.robot_3[0]][node.robot_3[1]]
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assert value is not None
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return Node(
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robot_1=node.robot_1,
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robot_2=node.robot_2,
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robot_3=node.robot_3,
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code=node.code + str(value),
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)
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class Solver(BaseSolver):
|
class Solver(BaseSolver):
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def dijkstra_for_code(self, target: str):
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def solve(self, input: str) -> Iterator[Any]: ...
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queue: list[tuple[float, Node, tuple[str, ...]]] = [(0, Node(), ())]
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preds: dict[Node, tuple[str, ...]] = {}
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|
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while queue:
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dis, node, path = heapq.heappop(queue)
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|
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if not target.startswith(node.code):
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continue
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|
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if node in preds:
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continue
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preds[node] = path
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|
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if node.code == target:
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self.logger.info(f"found [{target}]: {''.join(path)} ({len(path)})")
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return path
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|
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for action in cast(Sequence[Action], "A^v<>"):
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node_2 = create_node(node, action)
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if node_2:
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heapq.heappush(queue, (dis + 1, node_2, path + (action,)))
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return None
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|
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def solve(self, input: str) -> Iterator[Any]:
|
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yield sum(
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len(self.dijkstra_for_code(code) or ()) * int(code[:-1], 10)
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for code in input.splitlines()
|
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)
|
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@ -3,57 +3,5 @@ from typing import Any, Iterator
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|||||||
from ..base import BaseSolver
|
from ..base import BaseSolver
|
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|
|
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|
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def mix(secret: int, value: int) -> int:
|
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return secret ^ value
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|
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|
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def prune(secret: int) -> int:
|
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return secret % 16777216
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|
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|
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def next_number(secret: int) -> int:
|
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# Calculate the result of multiplying the secret number by 64. Then, mix this
|
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# result into the secret number. Finally, prune the secret number.
|
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secret = prune(mix(secret, secret * 64))
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|
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# Calculate the result of dividing the secret number by 32. Round the result down
|
|
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# to the nearest integer. Then, mix this result into the secret number. Finally,
|
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# prune the secret number.
|
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secret = prune(mix(secret, secret // 32))
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||||||
|
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# Calculate the result of multiplying the secret number by 2048. Then, mix this
|
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||||||
# result into the secret number. Finally, prune the secret number.
|
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secret = prune(mix(secret, secret * 2048))
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||||||
|
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return secret
|
|
||||||
|
|
||||||
|
|
||||||
class Solver(BaseSolver):
|
class Solver(BaseSolver):
|
||||||
def solve(self, input: str) -> Iterator[Any]:
|
def solve(self, input: str) -> Iterator[Any]: ...
|
||||||
starts = [int(r) for r in input.splitlines()]
|
|
||||||
|
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||||||
ends: list[int] = []
|
|
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prices: list[int] = [0 for _ in range(2**16)]
|
|
||||||
|
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||||||
for secret in self.progress.wrap(starts):
|
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checked: list[bool] = [False] * len(prices)
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|
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hashed: int = 0
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|
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|
|
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for i in range(2000):
|
|
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last = secret % 10
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|
||||||
secret = next_number(secret)
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next = secret % 10
|
|
||||||
|
|
||||||
hashed = ((hashed << 4) & 0xFFFF) | ((last - next) & 0xF)
|
|
||||||
|
|
||||||
if i >= 3 and not checked[hashed]:
|
|
||||||
checked[hashed] = True
|
|
||||||
prices[hashed] += next
|
|
||||||
|
|
||||||
ends.append(secret)
|
|
||||||
|
|
||||||
for start, end in zip(starts, ends, strict=True):
|
|
||||||
self.logger.info(f"{start}: {end}")
|
|
||||||
|
|
||||||
yield sum(ends)
|
|
||||||
yield max(prices)
|
|
||||||
|
|||||||
File diff suppressed because it is too large
Load Diff
@ -1,402 +0,0 @@
|
|||||||
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#...#.#...#...#.#.#.#.........#...#...#.....#.#...#...###...#.#.#...#...#.#.#...#.#...#...#.....#.........#...###.#...#.......#...#...#...#.#
|
|
||||||
#.###.#.###.#.#.#.#.#.#######.#########.#####.###.#.#.###.###.#.###.#####.#.#####.#.###.#.#####.#################.#############.#.#.#.#.#.#.#
|
|
||||||
#...#.#...#.#.#.#.#...#.....#...#.......#...#.#...#.#.#...#...#.#...#.....#...#...#...#.#.#...#...#.............#...#...........#.#.#.#.#.#.#
|
|
||||||
###.#.###.#.#.#.#.#####.###.###.#.#######.#.#.#.###.#.#.###.###.#.###.#######.#.#####.#.#.#.#.###.#.###########.###.#.###########.#.#.#.#.#.#
|
|
||||||
###...#...#.#.#.#.#...#...#...#.#.........#.#.#...#.#.#...#...#.#...#.......#.#.....#.#.#.#.#.###...###.........#...#...........#...#.#.#...#
|
|
||||||
#######.###.#.#.#.#.#.###.###.#.###########.#.###.#.#.###.###.#.###.#######.#.#####.#.#.#.#.#.#########.#########.#############.#####.#.#####
|
|
||||||
###...#.....#...#...#.....#...#.............#.#...#.#.###.#...#...#.###...#.#.#...#.#.#.#.#.#.#...#...#...........#.......#...#...###.#.....#
|
|
||||||
###.#.#####################.#################.#.###.#.###.#.#####.#.###.#.#.#.#.#.#.#.#.#.#.#.#.#.#.#.#############.#####.#.#.###.###.#####.#
|
|
||||||
#...#.......###.......#...#.........#.......#.#.#...#...#.#...#...#...#.#.#.#.#.#.#.#.#.#.#.#.#.#...#.#.....#.......#...#...#...#...#.......#
|
|
||||||
#.#########.###.#####.#.#.#########.#.#####.#.#.#.#####.#.###.#.#####.#.#.#.#.#.#.#.#.#.#.#.#.#.#####.#.###.#.#######.#.#######.###.#########
|
|
||||||
#.........#.....#...#...#.........#.#...###.#.#...#...#.#...#.#.#.....#.#...#.#.#...#.#.#...#.#.###...#.#...#.........#.......#...#.#.......#
|
|
||||||
#########.#######.#.#############.#.###.###.#.#####.#.#.###.#.#.#.#####.#####.#.#####.#.#####.#.###.###.#.###################.###.#.#.#####.#
|
|
||||||
###.....#.......#.#.............#...#...#...#.......#.#...#.#...#...#...#.....#.#.....#.#.....#...#...#.#...#...#...........#.#...#...#.....#
|
|
||||||
###.###.#######.#.#############.#####.###.###########.###.#.#######.#.###.#####.#.#####.#.#######.###.#.###.#.#.#.#########.#.#.#######.#####
|
|
||||||
#...#...#.....#...###...........#.....#...#.....#.....###.#.....###.#.###...#...#.#...#.#...#.....#...#...#.#.#.#.....#...#...#.......#.....#
|
|
||||||
#.###.###.###.#######.###########.#####.###.###.#.#######.#####.###.#.#####.#.###.#.#.#.###.#.#####.#####.#.#.#.#####.#.#.###########.#####.#
|
|
||||||
#...#...#...#.#.......#.....#...#.#.....#...###.#.......#...#...#...#.....#.#...#.#.#...###.#...###...#...#...#.#...#...#...........#...#...#
|
|
||||||
###.###.###.#.#.#######.###.#.#.#.#.#####.#####.#######.###.#.###.#######.#.###.#.#.#######.###.#####.#.#######.#.#.###############.###.#.###
|
|
||||||
#...#...#...#...#...#...#...#.#.#.#.....#.....#...#.....###...###.#.....#.#...#.#.#.......#...#...#...#.#...###...#...#.....#.......#...#...#
|
|
||||||
#.###.###.#######.#.#.###.###.#.#.#####.#####.###.#.#############.#.###.#.###.#.#.#######.###.###.#.###.#.#.#########.#.###.#.#######.#####.#
|
|
||||||
#...#.....#...#...#.#.###...#.#...#...#.#...#.#...#.......#.......#...#.#.#...#.#.#.....#...#.#...#...#...#.....#.....#...#.#.......#...#...#
|
|
||||||
###.#######.#.#.###.#.#####.#.#####.#.#.#.#.#.#.#########.#.#########.#.#.#.###.#.#.###.###.#.#.#####.#########.#.#######.#.#######.###.#.###
|
|
||||||
###...#...#.#.#.###...#.....#...#...#...#.#...#...#...#...#...#...#...#.#.#...#.#...###...#.#...#...#...#.......#.#.......#.#.......#...#...#
|
|
||||||
#####.#.#.#.#.#.#######.#######.#.#######.#######.#.#.#.#####.#.#.#.###.#.###.#.#########.#.#####.#.###.#.#######.#.#######.#.#######.#####.#
|
|
||||||
#.....#.#.#.#.#.#.....#.....#...#...#...#.....###...#.#.....#.#.#.#.###...#...#.....#.....#...#...#.....#.......#.#.....#...#.....###.......#
|
|
||||||
#.#####.#.#.#.#.#.###.#####.#.#####.#.#.#####.#######.#####.#.#.#.#.#######.#######.#.#######.#.###############.#.#####.#.#######.###########
|
|
||||||
#...#...#.#.#...#...#.#.....#.#####...#.#...#.....###.#.....#...#.#...#.....#.......#.......#.#.#...#...#.......#...#...#.........#.........#
|
|
||||||
###.#.###.#.#######.#.#.#####.#########.#.#.#####.###.#.#########.###.#.#####.#############.#.#.#.#.#.#.#.#########.#.#############.#######.#
|
|
||||||
###.#...#.#.#.......#...#...#...#.......#.#.#.....#...#...#.......#...#.....#.....#.........#.#.#.#.#.#.#.......#...#.........###...#...#...#
|
|
||||||
###.###.#.#.#.###########.#.###.#.#######.#.#.#####.#####.#.#######.#######.#####.#.#########.#.#.#.#.#.#######.#.###########.###.###.#.#.###
|
|
||||||
#...#...#...#.....#...#...#...#.#...#...#.#.#.....#.#...#.#.........#...###.#...#.#.#.......#.#...#.#.#.#.......#.....#.....#.....#...#.#...#
|
|
||||||
#.###.###########.#.#.#.#####.#.###.#.#.#.#.#####.#.#.#.#.###########.#.###.#.#.#.#.#.#####.#.#####.#.#.#.###########.#.###.#######.###.###.#
|
|
||||||
#...#.....#####...#.#.#.....#.#.#...#.#.#.#...#...#...#.#.....#.......#.....#.#.#.#...#.....#...#...#.#.#...........#.#...#.#...#...#...#...#
|
|
||||||
###.#####.#####.###.#.#####.#.#.#.###.#.#.###.#.#######.#####.#.#############.#.#.#####.#######.#.###.#.###########.#.###.#.#.#.#.###.###.###
|
|
||||||
#...#...#...#...#...#.....#.#...#...#.#.#.###.#.......#...#...#.....#...#...#.#.#.....#.#...#...#...#.#.#.....#...#.#.#...#...#...###.#...###
|
|
||||||
#.###.#.###.#.###.#######.#.#######.#.#.#.###.#######.###.#.#######.#.#.#.#.#.#.#####.#.#.#.#.#####.#.#.#.###.#.#.#.#.#.#############.#.#####
|
|
||||||
#.....#.....#...#.......#.#.#.......#.#...#...#...#...###.#...#.....#.#...#.#.#.#.....#...#.#.#...#...#.#...#.#.#.#.#.#.............#...#...#
|
|
||||||
###############.#######.#.#.#.#######.#####.###.#.#.#####.###.#.#####.#####.#.#.#.#########.#.#.#.#####.###.#.#.#.#.#.#############.#####.#.#
|
|
||||||
###...#.........#...#...#...#.....#...###...#...#...###...#...#.#...#...#...#.#.#...#.......#...#.###...#...#.#.#.#.#.#...#...#...#.....#.#.#
|
|
||||||
###.#.#.#########.#.#.###########.#.#####.###.#########.###.###.#.#.###.#.###.#.###.#.###########.###.###.###.#.#.#.#.#.#.#.#.#.#.#####.#.#.#
|
|
||||||
#...#...#.....#...#.#...#.........#.....#...#.........#...#...#.#.#...#.#...#.#...#.#.#.....#...#.#...#...#...#.#.#.#.#.#.#.#.#.#.#...#...#.#
|
|
||||||
#.#######.###.#.###.###.#.#############.###.#########.###.###.#.#.###.#.###.#.###.#.#.#.###.#.#.#.#.###.###.###.#.#.#.#.#.#.#.#.#.#.#.#####.#
|
|
||||||
#.........###...###.....#...............###...........###.....#...###...###...###...#...###...#...#.....###.....#...#...#...#...#...#.......#
|
|
||||||
#############################################################################################################################################
|
|
||||||
@ -1,5 +0,0 @@
|
|||||||
129A
|
|
||||||
540A
|
|
||||||
789A
|
|
||||||
596A
|
|
||||||
582A
|
|
||||||
File diff suppressed because it is too large
Load Diff
@ -1,25 +0,0 @@
|
|||||||
5,4
|
|
||||||
4,2
|
|
||||||
4,5
|
|
||||||
3,0
|
|
||||||
2,1
|
|
||||||
6,3
|
|
||||||
2,4
|
|
||||||
1,5
|
|
||||||
0,6
|
|
||||||
3,3
|
|
||||||
2,6
|
|
||||||
5,1
|
|
||||||
1,2
|
|
||||||
5,5
|
|
||||||
2,5
|
|
||||||
6,5
|
|
||||||
1,4
|
|
||||||
0,4
|
|
||||||
6,4
|
|
||||||
1,1
|
|
||||||
6,1
|
|
||||||
1,0
|
|
||||||
0,5
|
|
||||||
1,6
|
|
||||||
2,0
|
|
||||||
@ -1,10 +0,0 @@
|
|||||||
r, wr, b, g, bwu, rb, gb, br
|
|
||||||
|
|
||||||
brwrr
|
|
||||||
bggr
|
|
||||||
gbbr
|
|
||||||
rrbgbr
|
|
||||||
ubwu
|
|
||||||
bwurrg
|
|
||||||
brgr
|
|
||||||
bbrgwb
|
|
||||||
@ -1,15 +0,0 @@
|
|||||||
###############
|
|
||||||
#...#...#.....#
|
|
||||||
#.#.#.#.#.###.#
|
|
||||||
#S#...#.#.#...#
|
|
||||||
#######.#.#.###
|
|
||||||
#######.#.#...#
|
|
||||||
#######.#.###.#
|
|
||||||
###..E#...#...#
|
|
||||||
###.#######.###
|
|
||||||
#...###...#...#
|
|
||||||
#.#####.#.###.#
|
|
||||||
#.#...#.#.#...#
|
|
||||||
#.#.#.#.#.#.###
|
|
||||||
#...#...#...###
|
|
||||||
###############
|
|
||||||
@ -1,5 +0,0 @@
|
|||||||
029A
|
|
||||||
980A
|
|
||||||
179A
|
|
||||||
456A
|
|
||||||
379A
|
|
||||||
@ -1,4 +0,0 @@
|
|||||||
1
|
|
||||||
10
|
|
||||||
100
|
|
||||||
2024
|
|
||||||
@ -1,4 +0,0 @@
|
|||||||
1
|
|
||||||
2
|
|
||||||
3
|
|
||||||
2024
|
|
||||||
@ -1,118 +0,0 @@
|
|||||||
import heapq
|
|
||||||
from typing import Callable, Iterable, TypeVar, overload
|
|
||||||
|
|
||||||
_Node = TypeVar("_Node")
|
|
||||||
|
|
||||||
|
|
||||||
def make_neighbors_grid_fn(
|
|
||||||
rows: int | Iterable[int],
|
|
||||||
cols: int | Iterable[int],
|
|
||||||
excluded: Iterable[tuple[int, int]] = set(),
|
|
||||||
diagonals: bool = False,
|
|
||||||
):
|
|
||||||
"""
|
|
||||||
Create a neighbors function suitable for graph function for a simple grid.
|
|
||||||
|
|
||||||
Args:
|
|
||||||
rows: Rows of the grid. If an int is specified, the rows are assumed to be
|
|
||||||
numbered from 0 to rows - 1, otherwise the iterable should contain the list
|
|
||||||
of valid rows.
|
|
||||||
cols: Columns of the grid. If an int is specified, the columns are assumed to be
|
|
||||||
numbered from 0 to cols - 1, otherwise the iterable should contain the list
|
|
||||||
of valid columns.
|
|
||||||
excluded: Cells of the grid that cannot be used as valid nodes for the graph.
|
|
||||||
diagonals: If True, neighbors will include diagonal cells, otherwise, only
|
|
||||||
horizontal and vertical neighbors will be included.
|
|
||||||
|
|
||||||
"""
|
|
||||||
ds = ((-1, 0), (0, 1), (1, 0), (0, -1))
|
|
||||||
if diagonals:
|
|
||||||
ds = ds + ((-1, -1), (-1, 1), (1, -1), (1, 1))
|
|
||||||
|
|
||||||
if isinstance(rows, int):
|
|
||||||
rows = range(rows)
|
|
||||||
elif not isinstance(rows, range):
|
|
||||||
rows = set(rows)
|
|
||||||
|
|
||||||
if isinstance(cols, int):
|
|
||||||
cols = range(cols)
|
|
||||||
elif not isinstance(cols, range):
|
|
||||||
cols = set(cols)
|
|
||||||
|
|
||||||
excluded = set(excluded)
|
|
||||||
|
|
||||||
def _fn(node: tuple[int, int]):
|
|
||||||
return (
|
|
||||||
((row_n, col_n), 1)
|
|
||||||
for dr, dc in ds
|
|
||||||
if (row_n := node[0] + dr) in rows
|
|
||||||
and (col_n := node[1] + dc) in cols
|
|
||||||
and (row_n, col_n) not in excluded
|
|
||||||
)
|
|
||||||
|
|
||||||
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(
|
|
||||||
start: _Node,
|
|
||||||
target: _Node,
|
|
||||||
neighbors: Callable[[_Node], Iterable[tuple[_Node, float]]],
|
|
||||||
) -> 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,
|
|
||||||
using the given neighbors function.
|
|
||||||
|
|
||||||
Args:
|
|
||||||
start: Starting node of the path.
|
|
||||||
target: Target node for the path.
|
|
||||||
neighbors: Function that should return, for a given node, the list of
|
|
||||||
its neighbors with the cost to go from the node to the neighbor.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
One of the shortest-path from start to target with its associated cost, if one
|
|
||||||
is found, otherwise None.
|
|
||||||
"""
|
|
||||||
queue: list[tuple[float, _Node, tuple[_Node, ...]]] = [(0, start, (start,))]
|
|
||||||
preds: dict[_Node, tuple[tuple[_Node, ...], float]] = {}
|
|
||||||
|
|
||||||
while queue:
|
|
||||||
dis, node, path = heapq.heappop(queue)
|
|
||||||
|
|
||||||
if node in preds:
|
|
||||||
continue
|
|
||||||
|
|
||||||
preds[node] = (path, dis)
|
|
||||||
|
|
||||||
if node == target:
|
|
||||||
break
|
|
||||||
|
|
||||||
for neighbor, cost in neighbors(node):
|
|
||||||
if neighbor in preds:
|
|
||||||
continue
|
|
||||||
|
|
||||||
heapq.heappush(queue, (dis + cost, neighbor, path + (neighbor,)))
|
|
||||||
|
|
||||||
if target is None:
|
|
||||||
return preds
|
|
||||||
|
|
||||||
return preds.get(target, None)
|
|
||||||
Loading…
Reference in New Issue
Block a user