結果

問題 No.1479 Matrix Eraser
ユーザー convexineq
提出日時 2021-04-16 21:05:44
言語 PyPy3
(7.3.15)
結果
AC  
実行時間 738 ms / 3,000 ms
コード長 5,374 bytes
コンパイル時間 549 ms
コンパイル使用メモリ 82,176 KB
実行使用メモリ 161,152 KB
最終ジャッジ日時 2024-07-03 00:03:26
合計ジャッジ時間 19,631 ms
ジャッジサーバーID
(参考情報)
judge5 / judge2
このコードへのチャレンジ
(要ログイン)
ファイルパターン 結果
sample AC * 2
other AC * 39
権限があれば一括ダウンロードができます

ソースコード

diff #
プレゼンテーションモードにする

"""
from
https://github.com/not522/ac-library-python/blob/master/atcoder/mincostflow.py
"""
from typing import NamedTuple, Optional, List, cast
class MFGraph:
class Edge(NamedTuple):
src: int
dst: int
cap: int
flow: int
class _Edge:
def __init__(self, dst: int, cap: int) -> None:
self.dst = dst
self.cap = cap
self.rev: Optional[MFGraph._Edge] = None
def __init__(self, n: int) -> None:
self._n = n
self._g: List[List[MFGraph._Edge]] = [[] for _ in range(n)]
self._edges: List[MFGraph._Edge] = []
def add_edge(self, src: int, dst: int, cap: int) -> int:
assert 0 <= src < self._n
assert 0 <= dst < self._n
assert 0 <= cap
m = len(self._edges)
e = MFGraph._Edge(dst, cap)
re = MFGraph._Edge(src, 0)
e.rev = re
re.rev = e
self._g[src].append(e)
self._g[dst].append(re)
self._edges.append(e)
return m
def get_edge(self, i: int) -> Edge:
assert 0 <= i < len(self._edges)
e = self._edges[i]
re = cast(MFGraph._Edge, e.rev)
return MFGraph.Edge(
re.dst,
e.dst,
e.cap + re.cap,
re.cap
)
def edges(self) -> List[Edge]:
return [self.get_edge(i) for i in range(len(self._edges))]
def change_edge(self, i: int, new_cap: int, new_flow: int) -> None:
assert 0 <= i < len(self._edges)
assert 0 <= new_flow <= new_cap
e = self._edges[i]
e.cap = new_cap - new_flow
assert e.rev is not None
e.rev.cap = new_flow
def flow(self, s: int, t: int, flow_limit: Optional[int] = None) -> int:
assert 0 <= s < self._n
assert 0 <= t < self._n
assert s != t
if flow_limit is None:
flow_limit = cast(int, sum(e.cap for e in self._g[s]))
current_edge = [0] * self._n
level = [0] * self._n
def fill(arr: List[int], value: int) -> None:
for i in range(len(arr)):
arr[i] = value
def bfs() -> bool:
fill(level, self._n)
queue = []
q_front = 0
queue.append(s)
level[s] = 0
while q_front < len(queue):
v = queue[q_front]
q_front += 1
next_level = level[v] + 1
for e in self._g[v]:
if e.cap == 0 or level[e.dst] <= next_level:
continue
level[e.dst] = next_level
if e.dst == t:
return True
queue.append(e.dst)
return False
def dfs(lim: int) -> int:
stack = []
edge_stack: List[MFGraph._Edge] = []
stack.append(t)
while stack:
v = stack[-1]
if v == s:
flow = min(lim, min(e.cap for e in edge_stack))
for e in edge_stack:
e.cap -= flow
assert e.rev is not None
e.rev.cap += flow
return flow
next_level = level[v] - 1
while current_edge[v] < len(self._g[v]):
e = self._g[v][current_edge[v]]
re = cast(MFGraph._Edge, e.rev)
if level[e.dst] != next_level or re.cap == 0:
current_edge[v] += 1
continue
stack.append(e.dst)
edge_stack.append(re)
break
else:
stack.pop()
if edge_stack:
edge_stack.pop()
level[v] = self._n
return 0
flow = 0
while flow < flow_limit:
if not bfs():
break
fill(current_edge, 0)
while flow < flow_limit:
f = dfs(flow_limit - flow)
flow += f
if f == 0:
break
return flow
def min_cut(self, s: int) -> List[bool]:
visited = [False] * self._n
stack = [s]
visited[s] = True
while stack:
v = stack.pop()
for e in self._g[v]:
if e.cap > 0 and not visited[e.dst]:
visited[e.dst] = True
stack.append(e.dst)
return visited
from itertools import chain
def f(lst):
hh = set()
ww = set()
for i,j in lst:
hh.add(i)
ww.add(j)
zh = {hi:i for i,hi in enumerate(hh)}
zw = {wi:i for i,wi in enumerate(ww)}
h = len(hh)
w = len(ww)
g = MFGraph(h+w+2)
S = h+w
T = S+1
for i in range(h):
g.add_edge(src=S, dst=i, cap=1)
for i in range(h,h+w):
g.add_edge(src=i, dst=T, cap=1)
for i,j in lst:
g.add_edge(src=zh[i], dst=zw[j]+h, cap=1)
r = g.flow(S,T)
return r
h,w = map(int,input().split())
res = [[] for _ in range(5*10**5+1)]
for i in range(h):
*b, = map(int,input().split())
for j in range(w):
if b[j]: res[b[j]].append((i,j))
ans = 0
for ri in res:
if ri:
if len(ri)==1: ans += 1
else: ans += f(ri)
print(ans)
הההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההה
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0