結果
| 問題 | No.5007 Steiner Space Travel |
| ユーザー |
 prussian_coder
|
| 提出日時 | 2023-04-24 17:36:22 |
| 言語 | PyPy3 (7.3.15) |
| 結果 |
TLE
|
| 実行時間 | - |
| コード長 | 8,313 bytes |
| コンパイル時間 | 489 ms |
| コンパイル使用メモリ | 87,744 KB |
| 実行使用メモリ | 94,740 KB |
| スコア | 804,190 |
| 最終ジャッジ日時 | 2023-04-24 17:36:27 |
| 合計ジャッジ時間 | 4,955 ms |
|
ジャッジサーバーID （参考情報） |
judge15 / judge14 |
（要ログイン）
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 | 362 ms
88,804 KB |
| testcase_01 | AC | 386 ms
89,140 KB |
| testcase_02 | AC | 366 ms
88,768 KB |
| testcase_03 | TLE | - |
| testcase_04 | -- | - |
| testcase_05 | -- | - |
| testcase_06 | -- | - |
| testcase_07 | -- | - |
| testcase_08 | -- | - |
| testcase_09 | -- | - |
| testcase_10 | -- | - |
| testcase_11 | -- | - |
| testcase_12 | -- | - |
| testcase_13 | -- | - |
| testcase_14 | -- | - |
| testcase_15 | -- | - |
| testcase_16 | -- | - |
| testcase_17 | -- | - |
| testcase_18 | -- | - |
| testcase_19 | -- | - |
| testcase_20 | -- | - |
| testcase_21 | -- | - |
| testcase_22 | -- | - |
| testcase_23 | -- | - |
| testcase_24 | -- | - |
| testcase_25 | -- | - |
| testcase_26 | -- | - |
| testcase_27 | -- | - |
| testcase_28 | -- | - |
| testcase_29 | -- | - |
ソースコード
INF=10**20
import random
from pathlib import Path
import time
LOCAL = False
in_path = "./test"
img_path = "./image"
color_ls = ["red","blue","green","orange","gray","pink","cyan","black"]
def read_data(file):
if LOCAL:
with open(file,mode="r") as f:
data = f.readlines()
N,M = map(int,data[0].split())
pos = [[int(x) for x in data[i+1].split()] for i in range(N)]
else:
N,M=map(int,input().split())
pos = [[int(x) for x in input().split()] for i in range(N)]
return N,M,pos
#2点間の距離を返す
def dist(p1,p2,a=25):
return ((p1[0]-p2[0])**2 + (p1[1]-p2[1])**2)*a
#中心点と各惑星の距離を全探索し、短い順に並びかえる
def calc_dist_from_center(N,M,center_pos,pos):
dist_list = []
for m in range(M):
for n in range(N):
d = dist(pos[n],center_pos[m])
dist_list.append((d,m,n))
return sorted(dist_list)
#中心点から近い惑星順にクラスわけする
def allocate_cluster(N,M,center_pos,pos):
dist_list = calc_dist_from_center(N,M,center_pos,pos)
allocate = [-1]*N
cluster_counts = [0]*M
total_count = 0
for d,m,n in dist_list:
if allocate[n]==-1:
allocate[n]=m
cluster_counts[m]+=1
total_count+=1
if total_count==N:
break
return allocate,cluster_counts
#クラスター分けされた点をもとに、k-means法に倣って中心点を算出する。
# グループカウントが多いものは中心点を2つにして、クラスターを分ける。
def calc_center(N,M,pos,allocate,cluster_counts,max_size = 15,split_mode = True):
point_sums = [[0,0] for _ in range(M)]
for i in range(N):
m=allocate[i]
point_sums[m][0]+=pos[i][0]
point_sums[m][1]+=pos[i][1]
center_pos = []
#clsuter_countが大きい順に見ていき、中心点がM個を超えたら中断する
if split_mode:
order = sorted([(cluster_counts[i],i) for i in range(M)],reverse=True)
else:
order = [(cluster_counts[i],i) for i in range(M)]
for _,m in order:
if cluster_counts[m]==0:
center_pos.append([random.randint(0,1000),random.randint(0,1000)])
else:
center_pos.append([point_sums[m][0]//cluster_counts[m],point_sums[m][1]//cluster_counts[m]])
if cluster_counts[m]>=max_size and split_mode:
x = point_sums[m][0]//cluster_counts[m]
y = point_sums[m][1]//cluster_counts[m]
dx = random.randint(-20,20)
dy = random.randint(-20,20)
while not (0<=x+dx<=1000 and 0<y+dy<=1000):
dx = random.randint(-5,5)
dy = random.randint(-5,5)
center_pos.append([x+dx,y+dy])
if len(center_pos)==M:
break
return center_pos
#K-means法に倣って惑星の点をグループ分けする
def clustering(N,M,pos,max_size):
center_pos = [[random.randint(0,1000),random.randint(0,1000)] for _ in range(M)] #ランダム初期化
allocate,cluster_counts = allocate_cluster(N,M,center_pos,pos)
loop = 0
while True:
center_pos = calc_center(N,M,pos,allocate,cluster_counts,max_size=max_size)
allocate,cluster_counts = allocate_cluster(N,M,center_pos,pos)
#print(loop,cluster_counts)
if loop>=20 and max(cluster_counts)<=max_size:
break
loop+=1
center_pos = calc_center(N,M,pos,allocate,cluster_counts,split_mode=False)
#visualize(N,M,pos,center_pos,allocate)
return center_pos,allocate,cluster_counts
def f(S,x,n):
return S*(n+1)+x
#クラスター内にてBitDPでTSPを解く O(n^2*2^n)
def tsp(id_list,pos,center_pos):
#point 0~n-1が惑星、nが中心点
n=len(id_list)-1
dp = [INF]*(n+1)*(1<<n)
dp[f(0,n,n)]=0
for S in range(1<<n):
for s in range(n+1):
if dp[f(S,s,n)]==INF:
continue
for t in range(n):
if (S>>t)&1:
continue
S2 = S|(1<<t)
if s!=n:
dp[f(S2,t,n)]=min(dp[f(S,s,n)] + dist(pos[id_list[s]],pos[id_list[t]]),dp[f(S2,t,n)])
else:
dp[f(S2,t,n)]=min(dp[f(S,s,n)] + dist(center_pos,pos[id_list[t]],a=5),dp[f(S2,t,n)])
dp[f(S2,n,n)]=min(dp[f(S2,t,n)] + dist(center_pos,pos[id_list[t]],a=5),dp[f(S2,n,n)])
#BitDPから復元
path_list = [n]
state = (1<<n)-1
now = n
v = dp[-1]
e = 10**(-5)
while state != 0 or now !=n:
found = False
if now == n:
for t in range(n):
d = dist(center_pos,pos[id_list[t]],a=5)
if dp[f(state,t,n)]==INF:
continue
if v - dp[f(state,t,n)] >= d - e:
path_list.append(t)
now = t
v -= d
found = True
break
else:
state = state ^ (1<<now)
for t in range(n+1):
if dp[f(state,t,n)]==INF:
continue
if t!=n:
if not (state>>t)&1:
continue
d = dist(pos[id_list[now]],pos[id_list[t]])
else:
d = dist(pos[id_list[now]],center_pos,a=5)
if v - dp[f(state,t,n)] >= d - e:
path_list.append(t)
now = t
v -= d
found=True
break
return [id_list[i] for i in path_list]
def tsp_between_space(center_pos,pos):
n = 9
dp = [[INF for _ in range(n)] for _ in range(1<<n)]
dp[0][-1]=0
for S in range(1<<n):
for s in range(n):
if dp[S][s]==INF:
continue
for t in range(n):
if (S>>t)&1:
continue
S2 = S|(1<<t)
if s!=n-1 and t!=n-1:
d = dist(center_pos[s],center_pos[t],a=1)
elif t!=n-1:
d = dist(pos[0],center_pos[t],a=5)
elif s!=n-1:
d = dist(pos[0],center_pos[s],a=5)
dp[S2][t]=min(dp[S][s] + d, dp[S2][t])
#BitDPから復元
path_list = []
state = (1<<n)-1
s = n-1
v = dp[-1][-1]
e = 10**(-5)
while state != 0:
state ^=(1<<s)
for t in range(n):
if not (state>>t)&1:
continue
if dp[state][t]==INF:
continue
if s!=n-1 and t!=n-1:
d = dist(center_pos[s],center_pos[t],a=1)
elif t!=n-1:
d = dist(pos[0],center_pos[t],a=5)
elif s!=n-1:
d = dist(pos[0],center_pos[s],a=5)
if v - dp[state][t] >= d - e:
path_list.append(t)
s = t
v -= d
break
return path_list
def main(N,M,pos):
center_pos,allocate,cluster_counts = clustering(N,M,pos,24)
ans = [0]
space_order = tsp_between_space(center_pos,pos)
for m in space_order:
id_list = [i for i in range(N) if allocate[i]==m]
n = len(id_list)
if n<=12:
ans += tsp(id_list+[-m-1],pos,center_pos[m])
else:
pos2 = [pos[i] for i in id_list]
_,allocate2,_ = clustering(n,2,pos2,10)
for i2 in range(2):
id_list2 = [id_list[i] for i in range(n) if allocate2[i]==i2]
ans += tsp(id_list2+[-m-1],pos,center_pos[m])
ans.append(0)
return center_pos,ans
def output(center_pos,ans):
for x,y in center_pos:
print(x,y)
print(len(ans))
for a in ans:
if a<0:
print(2,-a)
else:
print(1,a+1)
if LOCAL:
file_ls = Path(in_path).glob("*.txt")
for file in file_ls:
print(file)
N,M,pos = read_data(file)
center_pos,ans = main(N,M,pos)
output(center_pos,ans)
else:
N,M,pos = read_data("")
center_pos,ans = main(N,M,pos)
output(center_pos,ans)
# %%