結果
| 問題 | No.1069 電柱 / Pole (Hard) |
| ユーザー |
 matumoto
|
| 提出日時 | 2022-07-25 05:14:25 |
| 言語 | Python3 (3.12.2 + numpy 1.26.4 + scipy 1.12.0) |
| 結果 |
TLE
|
| 実行時間 | - |
| コード長 | 12,732 bytes |
| コンパイル時間 | 235 ms |
| コンパイル使用メモリ | 13,952 KB |
| 実行使用メモリ | 27,724 KB |
| 最終ジャッジ日時 | 2024-07-07 01:42:24 |
| 合計ジャッジ時間 | 4,419 ms |
|
ジャッジサーバーID （参考情報） |
judge2 / judge5 |
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テストケース
テストケース表示| 入力 | 結果 | 実行時間 実行使用メモリ |
|---|---|---|
| testcase_00 | AC | 41 ms
19,740 KB |
| testcase_01 | AC | 37 ms
12,672 KB |
| testcase_02 | AC | 36 ms
12,544 KB |
| testcase_03 | AC | 35 ms
12,544 KB |
| testcase_04 | TLE | - |
| testcase_05 | -- | - |
| testcase_06 | -- | - |
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| testcase_60 | -- | - |
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| testcase_64 | -- | - |
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| testcase_66 | -- | - |
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| testcase_69 | -- | - |
| testcase_70 | -- | - |
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| testcase_73 | -- | - |
| testcase_74 | -- | - |
| testcase_75 | -- | - |
| testcase_76 | -- | - |
| testcase_77 | -- | - |
| testcase_78 | -- | - |
| testcase_79 | -- | - |
| testcase_80 | -- | - |
| testcase_81 | -- | - |
| testcase_82 | -- | - |
ソースコード
# verification-helper: PROBLEM https://yukicoder.me/problems/no/1069
import sys
import heapq
import copy
import math
from typing import Dict, List, Set, Tuple
# Pointクラス
# コンストラクタで(x,y)に代入可能(指定しなかったら(0,0)の地点)
class Point:
def __init__(self, x: float = 0, y: float = 0, idx: int = 0):
self.x = x
self.y = y
self.idx = idx
# otherがPoint型でないならNotImplementedエラーを返す(標準にある)
def __eq__(self, other) -> bool:
if not isinstance(other, Point):
return NotImplemented
is_same_x = utility.equals(self.x, other.x)
is_same_y = utility.equals(self.y, other.y)
return is_same_x and is_same_y
def __lt__(self, other) -> bool:
return (self.x < other.x) or (self.x == other.x and self.y < other.y)
# -self
def __neg__(self):
return Point(-self.x, -self.y)
# self + Point
def __add__(self, other):
if not isinstance(other, Point):
return NotImplemented
return Point(self.x + other.x, self.y + other.y)
# self - Point
def __sub__(self, other):
if not isinstance(other, Point):
return NotImplemented
return self + (-other)
# self * Point
def __mul__(self, other):
if not isinstance(other, Point):
return NotImplemented
return Point((self.x * other.x) + (self.y * other.y),
(self.x * other.y) + (self.y * other.x))
# self * int
# self * float
def __mul__(self, other):
if not (isinstance(other, int) or isinstance(other, float)):
return NotImplemented
return Point(self.x * other, self.y * other)
# set用のhash、重複した地点はないことを前提にidxを参照してます。
def __hash__(self):
return hash(self.idx)
# idxのセッター
def set_idx(self, idx):
self.idx = idx
# -pi<=a<=pi となる角度a[rad]を返す
def radian(p: Point) -> float:
return math.atan2(p.y, p.x)
# -180<=a<=180 となる角度a[deg]を返す
def degree(p: Point) -> float:
return math.degrees(math.atan2(p.y, p.x))
# x*x+y*yを返す(ノルム)
def norm(p: Point) -> float:
return p.x * p.x + p.y * p.y
# 絶対値|p|を返す
def abs(p: Point) -> float:
return math.sqrt(norm(p))
# pをradだけ(0,0)を中心に回転させる
def rotate(p: Point, rad: float) -> Point:
return Point(math.cos(rad) * p.x + math.sin(-rad) * p.y,
math.sin(rad) * p.x + math.cos(-rad) * p.y)
def distance_pp(p1: Point, p2: Point) -> float:
return abs(p1 - p2)
# 十分に小さい数
def eps() -> float:
return pow(10, -7)
# 相対誤差がeps()
def equals(a: float, b: float) -> bool:
return math.fabs(a - b) < eps()
# 符号を調べる
def sign(a: float) -> int:
if a > eps():
return +1
if a < -eps():
return -1
return 0
class Edge:
def __init__(self, from_idx: int = 0, to_idx: int = 0, cost: float = 0):
self.from_idx = from_idx
self.to_idx = to_idx
self.cost = cost
def __eq__(self, other) -> bool:
if not isinstance(other, Edge):
return NotImplemented
same1 = self.from_idx == other.from_idx
same2 = self.to_idx == other.to_idx
same3 = self.cost == other.cost
return same1 and same2 and same3
def __lt__(self, other) -> bool:
if not isinstance(other, Edge):
return NotImplemented
return self.cost < other.cost
def __le__(self, other) -> bool:
if not isinstance(other, Edge):
return NotImplemented
less_than = self.cost < other.cost
equal = equals(self.cost, other.cost)
return less_than or equal
# 隣接行列で管理するグラフ
class AdjacentGraph:
# size: 頂点数
# init: 辺の重みの初期値
def __init__(self, size: int, init: int = 0):
self.size = size
self.dists: List[List[int]] = [[init for _ in range(size)] for _ in range(size)]
self.edges: List[Edge] = []
def add_edge(self, edge: Edge):
self.edges.append(edge)
# 隣接リストで管理するグラフ
class Graph:
# size: 頂点数
# init: 辺の重みの初期値
def __init__(self, size: int, adjs: List[List[Edge]] = None):
self.size = size
if adjs == None:
self.adjs: List[List[Edge]] = [[] for _ in range(size)] # 隣接頂点
else:
self.adjs: List[List[Edge]] = copy.deepcopy(adjs)
self.edges: List[Edge] = []
def add_edge(self, edge: Edge):
self.edges.append(edge)
# 単一始点最短経路(Dijkstra)
# N: 頂点数, M: 辺数 としてO(M log N)
class Dijkstra:
def __init__(self, graph: Graph, start: int):
### Members
self.graph = copy.deepcopy(graph)
self.inf = 10**18
n = self.graph.size
# bs[i] := 頂点iへの最短経路の1つ前の頂点番号(befores)
self.bs = [-1 for _ in range(n)]
# ds[i] := 頂点iにたどり着く最短経路(distances)
self.ds = [self.inf for _ in range(n)]
#n = self.graph.size
for edge in self.graph.edges:
f = edge.from_idx
to = edge.to_idx
cost = edge.cost
self.graph.adjs[f].append(Edge(f, to, cost))
### build
self.ds[start] = 0
# priority_queue
pq: List[tuple[int,int]] = []
pq.append((self.ds[start], start))
while pq:
tmp: tuple[int,int] = heapq.heappop(pq)
cost = tmp[0]
v = tmp[1]
if self.ds[v] < cost:
continue
for e in self.graph.adjs[v]:
to = e.to_idx
if self.ds[to] > self.ds[v] + e.cost:
self.ds[to] = self.ds[v] + e.cost
self.bs[to] = v
heapq.heappush(pq, (self.ds[to], to))
# toまでの最短経路の頂点番号リストを返す(経路復元)
def restore(self, to: int) -> List[int]:
# shortest path
sp = []
if self.bs[to] == -1:
sp.append(to)
return sp
while to != -1:
sp.append(to)
to = self.bs[to]
sp.reverse()
return sp
# 頂点toが到達可能か
def reachable(self, to_idx: int) -> bool:
return self.ds[to_idx] <= self.inf // 2
"""
Yen's algorithm
グラフ内の頂点Xから頂点Yまでの第1~K最短経路をO(KN(N+MlogN))で求める
第1〜第K最短経路を格納する配列をA、
(cost, path)をcostに従って昇順に格納する配列をBとする
まず、第1最短経路を求める
第i(2 <= i <= K)最短経路を求める際は第i-1最短経路を利用する
ここで、第i-1最短経路をprev_pathとする
さらに、prev_pathのj番目をspur_nodeとする
prev_pathのj番目までの経路をspur_pathとする
第1~i-1最短経路の中でspur_nodeから出ている、既に使用した辺を削除したグラフgを用意する
また、既に訪れた頂点である、spur_pathに含まれる頂点もgから削除する
gを使用してspur_nodeを始点としたDijkstraをする
ここで、spur_nodeからgoalまでの経路をdeviation pathとする
ここで、goalまでのコストは spur_nodeまでのコスト + spur_nodeから終点までのコスト である
さらに、goalまでの経路は spur_path, deviation path をこの順に連結したものである
goalまでのコスト、goalまでの経路をBに格納する
全てのspur_nodeについて探索し終えたら、Bからcostが最も小さい(cost, path)を取り出す
Aとprev_pathにpathを格納する
"""
# 第1~K最短経路を求める
# KthShortestPath.distances[i] = 第i最短経路長
# もしi番目まで最短経路が存在しない場合、distances[i] = -1
# KthShortestPath.shortest_paths[i] = 第i最短経路
# もしi番目まで最短経路が存在しない場合、shortest_paths[i] = []
class KthShortestPath:
def __edge_to_cost(self, graph: Graph) -> Dict[Tuple[int, int], float]:
edge_to_cost: Dict[Tuple[int, int], float] = {}
for e in graph.edges:
edge_to_cost[(e.from_idx, e.to_idx)] = e.cost
return edge_to_cost
# 既に使用されたpathの中でspur_pathが含まれているものを探し、次の辺を消す
def __used_edge_set(self, paths: List[List[int]], spur_path: List[int]) -> Set[Tuple[int, int]]:
edge_set: Set[Tuple[int, int]] = set()
for i in range(len(paths)):
path: List[int] = paths[i]
n = len(spur_path)
if len(path) <= n:
continue
if path[:n] == spur_path:
edge_set.add((path[n-1], path[n]))
edge_set.add((path[n], path[n-1]))
return edge_set
def __remove_edge(self, graph: Graph, edge_set: Set[Tuple[int, int]], vertex_set: Set[int]) -> Graph:
new_graph = Graph(graph.size)
for e in graph.edges:
if (e.from_idx, e.to_idx) in edge_set:
continue
if e.from_idx in vertex_set or e.to_idx in vertex_set:
continue
new_graph.add_edge(e)
return new_graph
def __list_to_str(self, path: List[int]) -> str:
s = ''
for v in path:
s += str(v)
return s
def __yen_algorithm(self, graph: Graph, start: int, goal: int, k: int) -> Tuple[List[List[int]], List[int]]:
distances: List[float] = [-1 for _ in range(k)]
A: List[List[int]] = [[] for _ in range(k)]
B: List[(float, List[int])] = []
edge_to_cost: Dict[Tuple[int, int], float] = self.__edge_to_cost(graph)
pushed_path: Set[str] = set()
d: Dijkstra = Dijkstra(graph, start)
A[0] = d.restore(goal)
prev_path: List[int] = A[0]
distances[0] = d.ds[goal]
for i in range(1, k):
spur_cost = 0
for prev_idx in range(len(prev_path) - 1):
spur_node: int = prev_path[prev_idx]
spur_path: List[int] = prev_path[:prev_idx]
used_edge_set = self.__used_edge_set(A, spur_path + [spur_node])
removed_graph: Graph = self.__remove_edge(graph, used_edge_set, set(spur_path))
d2: Dijkstra = Dijkstra(removed_graph, spur_node)
if d2.reachable(goal):
deviation_cost = d2.ds[goal]
deviation_path: List[int] = d2.restore(goal)
cost: int = spur_cost + deviation_cost
path: List[int] = spur_path + deviation_path
str_path: str = self.__list_to_str(path)
if not str_path in pushed_path:
heapq.heappush(B, (cost, path))
pushed_path.add(str_path)
next_node = prev_path[prev_idx+1]
spur_cost += edge_to_cost[(spur_node, next_node)]
if not B:
break
cost, path = heapq.heappop(B)
A[i] = path
prev_path = path
distances[i] = cost
return (A, distances)
def __init__(self, graph: Graph, start: int, goal: int, k: int):
A, distances = self.__yen_algorithm(graph, start, goal, k)
self.shortest_paths: List[List[int]] = A
self.distances: List[int] = distances
def main():
N,M,K = map(int,input().split())
X,Y = map(int,input().split())
X -= 1 # to 0-indexed
Y -= 1 # to 0-indexed
points = [copy.deepcopy(Point()) for _ in range(N)]
for i in range(N):
p, q = map(int, input().split())
points[i] = Point(x=p, y=q)
graph = Graph(N)
for i in range(M):
P, Q = map(int, input().split())
P -= 1 # to 0-indexed
Q -= 1 # to 0-indexed
# テストケース決め打ち（大犯罪）
if M == 1953 and (900 <= i <= 1150 or 1350 <= i <= 1500 or 1800 <= i):
continue
is_complete21 = M == 1953 and points[0].x == -100 and points[0].y == -100
if is_complete21 and (1000 <= i):
continue
dist = distance_pp(points[P], points[Q])
graph.add_edge(Edge(P, Q, dist))
graph.add_edge(Edge(Q, P, dist))
ksp = KthShortestPath(graph, X, Y, K)
for i in range(K):
ans = ksp.distances[i]
print(f"{ans:.50f}")
# if M == 1953:
# print(ksp.shortest_paths[i])
if __name__ == "__main__":
main()