結果
問題 | No.5007 Steiner Space Travel |
ユーザー | titan23 |
提出日時 | 2022-10-06 14:39:46 |
言語 | PyPy3 (7.3.15) |
結果 |
TLE
|
実行時間 | - |
コード長 | 3,113 bytes |
コンパイル時間 | 231 ms |
実行使用メモリ | 85,876 KB |
スコア | 5,974,283 |
最終ジャッジ日時 | 2022-10-06 14:40:18 |
合計ジャッジ時間 | 29,362 ms |
ジャッジサーバーID (参考情報) |
judge15 / judge11 |
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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 | 623 ms
84,092 KB |
testcase_01 | AC | 702 ms
84,128 KB |
testcase_02 | AC | 676 ms
83,764 KB |
testcase_03 | AC | 649 ms
83,976 KB |
testcase_04 | AC | 909 ms
85,500 KB |
testcase_05 | AC | 620 ms
84,264 KB |
testcase_06 | AC | 914 ms
85,608 KB |
testcase_07 | AC | 931 ms
85,644 KB |
testcase_08 | AC | 921 ms
85,876 KB |
testcase_09 | AC | 627 ms
84,140 KB |
testcase_10 | AC | 609 ms
83,968 KB |
testcase_11 | TLE | - |
testcase_12 | AC | 947 ms
85,548 KB |
testcase_13 | AC | 620 ms
84,228 KB |
testcase_14 | AC | 674 ms
83,856 KB |
testcase_15 | TLE | - |
testcase_16 | AC | 690 ms
83,700 KB |
testcase_17 | TLE | - |
testcase_18 | AC | 610 ms
84,140 KB |
testcase_19 | AC | 604 ms
83,832 KB |
testcase_20 | TLE | - |
testcase_21 | AC | 690 ms
83,816 KB |
testcase_22 | AC | 949 ms
85,852 KB |
testcase_23 | AC | 605 ms
84,228 KB |
testcase_24 | TLE | - |
testcase_25 | AC | 620 ms
83,628 KB |
testcase_26 | AC | 951 ms
85,604 KB |
testcase_27 | AC | 618 ms
83,456 KB |
testcase_28 | AC | 964 ms
85,772 KB |
testcase_29 | AC | 675 ms
84,288 KB |
ソースコード
import itertools import random import time start = time.time() N, M = map(int, input().split()) AB = [list(map(int, input().split())) for _ in range(N)] class KMeans: def __init__(self, n_clusters, max_iter = 10, random_seed = 0): self.n_clusters = n_clusters self.max_iter = max_iter def fit(self, X): self.labels_ = [] now = 0 while len(self.labels_) < len(X): self.labels_.append(now) now += 1 now %= self.n_clusters random.shuffle(self.labels_) labels_prev = [0]*len(X) count = 0 self.cluster_centers_ = [(0, 0)] * self.n_clusters while (not (self.labels_ == labels_prev) and count < self.max_iter): syuukei = [[] for _ in range(self.n_clusters)] for i in range(len(X)): syuukei[self.labels_[i]].append(X[i]) for i,l in enumerate(syuukei): if l: x, y = sum(x for x,y in l)//len(l), sum(y for x,y in l)//len(l) else: x, y = random.randint(0, 1000), random.randint(0, 1000) self.cluster_centers_[i] = (x, y) labels_prev = self.labels_[:] for i in range(len(X)): dist = 10**18 for j in range(self.n_clusters): tmp = (X[i][0] - self.cluster_centers_[j][0])**2 + (X[i][1] - self.cluster_centers_[j][1])**2 if tmp < dist: dist = tmp self.labels_[i] = j count += 1 def main(): model = KMeans(8) model.fit(AB) labels = model.labels_ centers = model.cluster_centers_ def dist(a, b): return (a[0] - b[0])**2 + (a[1] - b[1])**2 def calc(ans): score = 0 for i in range(len(ans)-1): type_pre, indx_pre = ans[i] type_now, indx_now = ans[i+1] if type_pre == 1 and type_now == 1: score += 25*dist(AB[indx_pre], AB[indx_now]) if type_pre == 1 and type_now == 2: score += 5*dist(AB[indx_pre], centers[indx_now]) if type_pre == 2 and type_now == 1: score += 5*dist(centers[indx_pre], AB[indx_now]) if type_pre == 2 and type_now == 2: score += 1*dist(centers[indx_pre], centers[indx_now]) return score vest = 1<<30 ANS = [] L = [[] for _ in range(8)] for i, cluster_number in enumerate(labels): L[cluster_number].append(i) for p in itertools.permutations(range(8)): ans = [(1, 0)] for i in p: if i >= len(L): continue ans.append((2, i)) lim = len(L[i]) j = 0 while j < lim: d1 = 5 * dist(AB[L[i][j]], centers[i]) d2 = 1<<30 if j+1 < len(L[i]): d2 = 25* dist(AB[L[i][j]], AB[L[i][j+1]]) if d2 < d1: ans.append((1, L[i][j])) else: ans.append((1, L[i][j])) ans.append((2, i)) j += 1 ans.append((1, 0)) score = calc(ans) if score < vest: vest = score ANS = ans[:] return vest, ANS, centers ANS = [] CENTERS = [] vest = 1<<30 while time.time() - start < 0.5: tmp, ans, centers = main() if tmp < vest: vest = tmp ANS = ans[:] CENTERS = centers for c,d in CENTERS: print(c, d) print(len(ANS)) for a,b in ANS: print(a, b+1)