1264. 乱头发节(badhair.pas/c/cpp)
(File IO): input:badhair.in output:badhair.out
Time Limits: 1000 ms Memory Limits: 65536 KB Detailed Limits
Description
农民John的某 N 头奶牛 (1 <= N <= 80,000) 正在过乱头发节！由于每头牛都意识到自己凌乱不堪的发型，FJ 希望统计出能够看到其他牛的头发的牛的数量。 每一头牛 i有一个高度 h[i] (1 <= h[i] <= 1,000,000,000)而且面向东方排成一排(在我们的图中是向右)。因此，第i头牛可以看到她前面的那些牛的头，（即i+1, i+2,等等），只要那些牛的高度严格小于她的高度。
每一头牛 i有一个高度 h[i] (1 <= h[i] <= 1,000,000,000)而且面向东方排成一排(在我们的图中是向右)。因此，第i头牛可以看到她前面的那些牛的头，（即i+1, i+2,等等），只要那些牛的高度严格小于她的高度。
例如这个例子:
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= =
= =
= - = 牛面向右侧 -->
= = =
= - = = =
= = = = = =
1 2 3 4 5 6
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" alt="" />
每一头牛 i有一个高度 h[i] (1 <= h[i] <= 1,000,000,000)而且面向东方排成一排(在我们的图中是向右)。因此，第i头牛可以看到她前面的那些牛的头，（即i+1, i+2,等等），只要那些牛的高度严格小于她的高度。
例如这个例子:
=
= =
= =
= - = 牛面向右侧 -->
= = =
= - = = =
= = = = = =
1 2 3 4 5 6
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" alt="" />
牛#1 可以看到她们的发型 #2, 3, 4
牛#2 不能看到任何牛的发型
牛#3 可以看到她的发型 #4
牛#4 不能看到任何牛的发型
牛#5 可以看到她的发型 6
牛#6 不能看到任何牛的发型!
让 c[i] 表示第i头牛可以看到发型的牛的数量；请输出 c[1] 至 c[N]的和。如上面的这个例子，正确解是3 + 0 + 1 + 0 + 1 + 0 = 5。
Input
Line 1: 牛的数量 N。
Lines 2..N+1: 第 i+1 是一个整数，表示第i头牛的高度。
Lines 2..N+1: 第 i+1 是一个整数，表示第i头牛的高度。
Output
Line 1: 一个整数表示c[1] 至 c[N]的和。
Sample Input
6
10
3
7
4
12
2
Sample Output
5
做法：维护一个高度不上升的队列，并更新队列中每个高度对应的牛能看到的头发的数量。
代码如下：
#include <cstdio>
#include <cstring>
#include <string>
#include <iostream>
#define N 80007
#define LL long long
using namespace std;
LL h[N], n, ans;
struct arr
{
int x, hi;
}f[N]; LL read()
{
LL s = ;
char ch = getchar();
while (ch < '' || ch > '') ch = getchar();
while (ch >= '' && ch <= '') s = s * + ch - '', ch = getchar();
return s;
} int main()
{
freopen("badhair.in", "r", stdin);
freopen("badhair.out", "w", stdout);
n = read();
for (int i = ; i <= n; i++)
h[i] = read();
int head = , tail = ;
f[++tail].hi = h[n];
for (int i = n - ; i >= ; i--)
{
int l = , ac = ;
for (int j = tail; j >= head; j--)
if (h[i] > f[j].hi)
{
ac += f[j].x + ;
l++;
}
else break;
tail = tail - l + ;
f[tail].hi = h[i];
f[tail].x = ac;
ans += ac;
}
cout << ans;
fclose(stdin);
fclose(stdout);
}