提出 #42186701

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ソースコード 拡げる

from sklearn import cluster
import numpy as np
import typing


class DSU:
    def __init__(self, n: int = 0) -> None:
        self._n = n
        self.parent_or_size = [-1] * n

    def merge(self, a: int, b: int) -> int:
        assert 0 <= a < self._n
        assert 0 <= b < self._n

        x = self.leader(a)
        y = self.leader(b)

        if x == y:
            return x

        if -self.parent_or_size[x] < -self.parent_or_size[y]:
            x, y = y, x

        self.parent_or_size[x] += self.parent_or_size[y]
        self.parent_or_size[y] = x

        return x

    def same(self, a: int, b: int) -> bool:
        assert 0 <= a < self._n
        assert 0 <= b < self._n

        return self.leader(a) == self.leader(b)

    def leader(self, a: int) -> int:
        assert 0 <= a < self._n

        parent = self.parent_or_size[a]
        while parent >= 0:
            if self.parent_or_size[parent] < 0:
                return parent
            self.parent_or_size[a], a, parent = (
                self.parent_or_size[parent],
                self.parent_or_size[parent],
                self.parent_or_size[self.parent_or_size[parent]]
            )

        return a

    def size(self, a: int) -> int:
        assert 0 <= a < self._n

        return -self.parent_or_size[self.leader(a)]

    def groups(self) -> typing.List[typing.List[int]]:
        leader_buf = [self.leader(i) for i in range(self._n)]

        result: typing.List[typing.List[int]] = [[] for _ in range(self._n)]
        for i in range(self._n):
            result[leader_buf[i]].append(i)

        return list(filter(lambda r: r, result))


def distance(a, b):
    return ((a[0] - b[0]) ** 2 + (a[1] - b[1]) ** 2)

def main():
    # Input
    n, m, k = map(int, input().split())
    verticles = np.array([list(map(int, input().split())) for _ in range(n)])
    edges = [list(map(int, f"{_} {input()}".split())) for _ in range(m)]
    people = np.array([list(map(int, input().split())) for _ in range(k)])

    used = [0]
    power = np.zeros(n)

    # Clustering
    model = cluster.KMeans(n_clusters=50).fit(people)
    result = model.predict(people)
    for i in range(50):
        # クラスタの中心を求める
        center = model.cluster_centers_[i]
        # クラスタの中心から最も近い点を求める
        minim = (0, 10 ** 9)
        for j in range(n):
            dst = distance(center, verticles[j])
            if minim[1] > dst:
                minim = (j, dst)
        # 最も遠い人
        farthest = 0
        for j in range(k):
            if result[j] == i:
                dst = distance(people[j], verticles[minim[0]])
                if farthest < dst:
                    farthest = dst
        power[minim[0]] = farthest
        used.append(minim[0])

    # Kruskal
    dsu = DSU(n)
    used_edges = []
    edges = sorted(edges, key=lambda x: x[3])
    for i in range(m):
        if dsu.same(edges[i][1] - 1, edges[i][2] - 1):
            continue
        dsu.merge(edges[i][1] - 1, edges[i][2] - 1)
        used_edges.append(edges[i][0])

    for i in range(n):
        print(min(int(power[i]), 5000), end=" ")
    print()
    for i in range(m):
        if i in used_edges:
            print(1, end=" ")
        else:
            print(0, end=" ")
    print()


if __name__ == "__main__":
    main()

提出情報

ジャッジ結果