Revenge of kNN
Time Limit: 2000/1000 MS (Java/Others) Memory Limit: 32768/32768 K (Java/Others)
Total Submission(s): 584 Accepted Submission(s): 136
Problem Description
In
pattern recognition, the k-Nearest Neighbors algorithm (or k-NN for
short) is a non-parametric method used for classification and
regression. In both cases, the input consists of the k closest training
examples in the feature space.
In k-NN regression, the output is the property value for the object. This value is the average of the values of its k nearest neighbors.
---Wikipedia
Today, kNN takes revenge on you. You have to handle a kNN case in one-dimensional coordinate system. There are N points with a position Xi and value Vi. Then there are M kNN queries for point with index i, recalculate its value by averaging the values its k-Nearest Neighbors. Note you have to replace the value of i-th point with the new calculated value. And if there is a tie while choosing k-Nearest Neighbor, choose the one with the minimal index first.
In k-NN regression, the output is the property value for the object. This value is the average of the values of its k nearest neighbors.
---Wikipedia
Today, kNN takes revenge on you. You have to handle a kNN case in one-dimensional coordinate system. There are N points with a position Xi and value Vi. Then there are M kNN queries for point with index i, recalculate its value by averaging the values its k-Nearest Neighbors. Note you have to replace the value of i-th point with the new calculated value. And if there is a tie while choosing k-Nearest Neighbor, choose the one with the minimal index first.
Input
The first line contains a single integer T, indicating the number of test cases.
Each test case begins with three integers N, M and K, in which K indicating the number of k-Nearest Neighbors. Then N lines follows, each line contains two integers Xi and Vi. Then M lines with the queried index Qi follows.
[Technical Specification]
1. 1 <= T <= 5
2. 2<=N<= 100 000, 1<=M<=100 000
3. 1 <= K <= min(N – 1, 10)
4. 1 <= Vi <= 1 000
5. 1 <= Xi <= 1 000 000 000, and no two Xi are identical.
6. 1 <= Qi <= N
Each test case begins with three integers N, M and K, in which K indicating the number of k-Nearest Neighbors. Then N lines follows, each line contains two integers Xi and Vi. Then M lines with the queried index Qi follows.
[Technical Specification]
1. 1 <= T <= 5
2. 2<=N<= 100 000, 1<=M<=100 000
3. 1 <= K <= min(N – 1, 10)
4. 1 <= Vi <= 1 000
5. 1 <= Xi <= 1 000 000 000, and no two Xi are identical.
6. 1 <= Qi <= N
Output
For each test case, output sum of all queries rounded to six fractional digits.
Sample Input
1
5 3 2
1 2
2 3
3 6
4 8
5 8
2
3
4
Sample Output
17.000000
做这题让我知道了如何解决一个很常见但是也很难想的问题:一个数组如果排序后被打乱了,知道它原来的位置,如何对应出现在的位置?
题意:X轴上有 n 个点,每个点都有个初始位置和权值(这些位置是杂乱无章的),现在给m次询问:每次询问第 i 个给出的点,找出离他最近的 k 个点,然后将 k 个点的权值相加取平均值赋值给当前询问的点,输出的最终答案就是这m 次询问每次询问的平均值之和。还有就是关于 k 个点的选取,当距离相同时,选择给出顺序小的。
题解:由于要找到邻居,排序是无疑的,但是询问却是询问的以前的位置。排完序之后如何找到原来的位置??循环?不行,10^5次询问每次找点也要10^5,肯定会超时?这时我们可以利用一个数组来记录排完序后元素原来的位置。但是,元素的下标是从 1 <= Xi <= 1 000 000 000,数组肯定存不下,怎么办?离散化呗,这样寻找的时间就变成 O(1)了。
可能有人不懂离散化后数组存的意思,这里举个例子:
原来的X轴分布假设为:
3 1 2 100 99
我们记录一下每个Xi 出现的位置 3(1) 1(2) 2(3) 100(4) 99(5)
排个序: 1(2) 2(3) 3(1) 99(5) 100(4)
弄个数组将括号里面的数表示成下标分别赋值给 1-5 a[2]=1 a[3]=2 a[1]=3 a[5]=4 a[4]=5
然后对应查询,假设我们查询初始位置是第 4 位的那个 Xi=100,对应排完序后的数组的位置是 a[4] = 5 而新的数组第5个数字等于100!!是不是很神奇,这就是离散化的好处了.不懂我说的同学可以看下acdreamer大神的博客:http://blog.csdn.net/acdreamers/article/details/8520096
#include <iostream> #include <stdio.h> #include <string.h> #include <algorithm> using namespace std; typedef long long LL; const int N = 111111; struct Node{ LL x; int id1; double v; }node[N]; int idx[N]; int cmp(Node a,Node b){ return a.x<b.x; } int main() { int tcase,n,m,k,x; scanf("%d",&tcase); while(tcase--) { scanf("%d%d%d",&n,&m,&k); for(int i=1;i<=n;i++){ scanf("%lld%lf",&node[i].x,&node[i].v); node[i].id1 = i; } sort(node+1,node+1+n,cmp); for(int i=1;i<=n;i++){ idx[node[i].id1] = i; } double sum = 0; while(m--){ int id; scanf("%d",&id); int now = idx[id]; double avg = 0; int l = now-1,r = now+1; for(int i=1;i<=k;i++){ if(l>=1&&r<=n){ LL dis1 = node[now].x - node[l].x; LL dis2 = node[r].x - node[now].x; if(dis1<dis2){ avg+=node[l--].v; }else if(dis1>dis2){ avg+=node[r++].v; }else { ///相等的话按照邻居原来的下标进行选择 int ori_l = node[l].id1; int ori_r = node[r].id1; if(ori_l<ori_r) { avg+=node[l--].v; }else{ avg+=node[r++].v; } } }else if(l>=1){ avg+=node[l--].v; }else if(r<=n){ avg+=node[r++].v; } } /* ///不知道WA的原因 int l=1,r=1; for(int i=1;i<=k;i++){ if(now-l>=1&&now+r<=n){ LL dis1 = node[now].x - node[now-l].x; LL dis2 = node[now+r].x - node[now].x; if(dis1<dis2){ avg+=node[now-l].v; l++; }else if(dis1>dis2){ avg+=node[now+r].v; r++; }else { ///相等的话按照邻居原来的下标进行选择 int ori_l = node[now+l].id1; int ori_r = node[now+r].id1; if(ori_l<ori_r) { avg+=node[now-l].v; l++; }else{ avg+=node[now+r].v; r++; } } }else if(now-l>=1){ avg+=node[now-l].v; l++; }else if(now+r<=n){ avg+=node[now+r].v; r++; } }*/ node[now].v = avg/k; sum+=avg/k; } printf("%.6lf ",sum); } return 0; }