L2-005. 集合相似度
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" alt="" />
题目地址 https://www.patest.cn/contests/gplt/L2-005
>>>**********************************************************************>>>
其实就是一个如何去重的问题。 又是实数, 直接桶排序的思想做,回超内存(char ans[1000000001] )。去重那就排序,排序不如跳舞,不对,是不如优先队列。 还有一件事, 处理完一个集合, 去重的结果要记起来。
Hint: 优先队列不会用的: http://www.cnblogs.com/heqinghui/p/3225407.html
>>>**********************************************************************>>>
#include <stdio.h>
#include <string.h>
#include <iostream>
#include <queue>
using namespace std; priority_queue <int> q;
int n, m, a, b, k, chong, unique, temp, point;
int maps[][], wing[]; int main(){
// freopen("in.txt", "r", stdin);
scanf("%d", &n);
for(int i=; i<n; ++i){
scanf("%d", &maps[i][]);
m = maps[i][];
for(int j=; j<=m; ++j){
scanf("%d", &temp);
q.push(temp);
}
point = ;
chong = ;
maps[i][point++] = q.top();
q.pop();
while(!q.empty()){
if(maps[i][point-] != q.top()){
maps[i][point++] = q.top();
}else{
chong++;
}
q.pop();
}
maps[i][] -= chong;
}
//cout << "test 1111\n";
scanf("%d", &k);
for(int i=; i<k; ++i){
scanf("%d%d", &a, &b);
a--;
b--;
chong = ;
unique = ;
for(int i=; i<=maps[a][]; ++i){
q.push(maps[a][i]);
}
for(int i=; i<=maps[b][]; ++i){
q.push(maps[b][i]);
}
//cout << "test 2222\n";
point = ;
chong = ;
wing[point++] = q.top();
q.pop();
while(!q.empty()){
if(wing[point-] != q.top()){
wing[point ++] = q.top();
}else{
chong ++;
}
q.pop();
}
double res = (double) chong / (maps[a][] + maps[b][] - chong) * 100.00;
printf("%.2lf%%\n", res);
}
return ;
}
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