3732: Network

题目链接

题目大意:同noip货车运输

题解:生成树+倍增

我的收获:水题++

#include <iostream>
#include <cstdio>
#include <queue>
#include <cstring>
#include <algorithm>
using namespace std;
const int M=15005;
#define INF 0x3f3f3f3f

int n,m,q,t;
int f[M][21],l[M][21];
int dis[M],dep[M];
int fa[M],vis[M],head[M];

struct edge{int to,vl,nex;}e[M*2];//存生成树图
struct node{int u,v,vl;}a[M*2];//存初始图

void add(int i,int j,int w){e[t].to=j,e[t].vl=w;e[t].nex=head[i];head[i]=t++;}
bool cmp(node x,node y){return x.vl<y.vl;}

void dfs(int x)
{
    vis[x]=1;
    for(int i=head[x];i!=-1;i=e[i].nex)
    {
        int v=e[i].to;
        if(!vis[v]){
            f[v][0]=x;
            l[v][0]=e[i].vl;
            dep[v]=dep[x]+1;
            dfs(v);
        }
    }
}

int lca(int x,int y){
    int mx=-INF;
    if(dep[x]<dep[y]) swap(x,y);
    for(int i=0;i<20;i++)if((dep[x]-dep[y])&(1<<i)) mx=max(mx,l[x][i]),x=f[x][i];
    if(x==y)return mx;
    for(int i=19;i>=0;i--)if(f[x][i]!=f[y][i]) mx=max(mx,max(l[x][i],l[y][i])),x=f[x][i],y=f[y][i];
    return max(mx,max(l[x][0],l[y][0]));
}

int fid(int x){
    return fa[x]==x?x:fa[x]=fid(fa[x]);
}

void uniom(int x,int y,int v){
    int p=fid(x),q=fid(y);
    if(p!=q) fa[p]=q,add(x,y,v),add(y,x,v);
}

void kruskal()
{
    for(int i=1;i<=n;i++) fa[i]=i;
    for(int i=1;i<=m;i++)
    uniom(a[i].u,a[i].v,a[i].vl);
}

void cl()
{
    for(int j=1;j<=20;j++)
        for(int i=1;i<=n;i++)
            f[i][j]=f[f[i][j-1]][j-1],
            l[i][j]=max(l[i][j-1],l[f[i][j-1]][j-1]);
}

void work()
{
    kruskal();
    for(int i=1;i<=n;i++) 
    if(!vis[i]) dfs(i);
    cl();
    int x,y;
    for(int i=1;i<=q;i++)
        scanf("%d%d",&x,&y),printf("%d\n",lca(x,y));
}

void init()
{
    memset(head,-1,sizeof(head));
    scanf("%d%d%d",&n,&m,&q);
    for(int i=1;i<=m;i++)
        scanf("%d%d%d",&a[i].u,&a[i].v,&a[i].vl);
    sort(a+1,a+1+m,cmp);
}

int main()
{
    init();
    work();
    return 0;
}
Epoch 1/100, Loss: 2.2603 Epoch 2/100, Loss: 2.4991 Epoch 3/100, Loss: 2.4372 Epoch 4/100, Loss: 2.2205 Epoch 5/100, Loss: 2.3881 Epoch 6/100, Loss: 2.6599 Epoch 7/100, Loss: 2.5602 Epoch 8/100, Loss: 2.2650 Epoch 9/100, Loss: 2.4058 Epoch 10/100, Loss: 2.3264 Epoch 11/100, Loss: 2.4267 Epoch 12/100, Loss: 2.2541 Epoch 13/100, Loss: 2.2609 Epoch 14/100, Loss: 2.3024 Epoch 15/100, Loss: 2.4205 Epoch 16/100, Loss: 2.3909 Epoch 17/100, Loss: 2.2053 Epoch 18/100, Loss: 2.2614 Epoch 19/100, Loss: 2.2924 Epoch 20/100, Loss: 2.2455 Epoch 21/100, Loss: 2.3485 Epoch 22/100, Loss: 2.1968 Epoch 23/100, Loss: 2.3013 Epoch 24/100, Loss: 2.3486 Epoch 25/100, Loss: 2.2744 Epoch 26/100, Loss: 2.1556 Epoch 27/100, Loss: 2.2270 Epoch 28/100, Loss: 2.2016 Epoch 29/100, Loss: 2.3039 Epoch 30/100, Loss: 2.3380 Epoch 31/100, Loss: 2.2371 Epoch 32/100, Loss: 2.3144 Epoch 33/100, Loss: 2.2527 Epoch 34/100, Loss: 2.2947 Epoch 35/100, Loss: 2.2233 Epoch 36/100, Loss: 2.2868 Epoch 37/100, Loss: 2.4342 Epoch 38/100, Loss: 2.2607 Epoch 39/100, Loss: 2.2878 Epoch 40/100, Loss: 2.3667 Epoch 41/100, Loss: 2.2737 Epoch 42/100, Loss: 2.3418 Epoch 43/100, Loss: 2.3055 Epoch 44/100, Loss: 2.2552 Epoch 45/100, Loss: 2.3095 Epoch 46/100, Loss: 2.3394 Epoch 47/100, Loss: 2.2664 Epoch 48/100, Loss: 2.3548 Epoch 49/100, Loss: 2.2735 Epoch 50/100, Loss: 2.1793 Epoch 51/100, Loss: 2.3917 Epoch 52/100, Loss: 2.1601 Epoch 53/100, Loss: 2.2042 Epoch 54/100, Loss: 2.2368 Epoch 55/100, Loss: 2.3281 Epoch 56/100, Loss: 2.3050 Epoch 57/100, Loss: 2.2709 Epoch 58/100, Loss: 2.2671 Epoch 59/100, Loss: 2.3046 Epoch 60/100, Loss: 2.3077 Epoch 61/100, Loss: 2.3124 Epoch 62/100, Loss: 2.2244 Epoch 63/100, Loss: 2.2525 Epoch 64/100, Loss: 2.3315 Epoch 65/100, Loss: 2.2640 Epoch 66/100, Loss: 2.2615 Epoch 67/100, Loss: 2.3875 Epoch 68/100, Loss: 2.2832 Epoch 69/100, Loss: 2.2064 Epoch 70/100, Loss: 2.2256 Epoch 71/100, Loss: 2.2989 Epoch 72/100, Loss: 2.3339 Epoch 73/100, Loss: 2.2240 Epoch 74/100, Loss: 2.2574 Epoch 75/100, Loss: 2.2880 Epoch 76/100, Loss: 2.2826 Epoch 77/100, Loss: 2.2343 Epoch 78/100, Loss: 2.3457 Epoch 79/100, Loss: 2.3322 Epoch 80/100, Loss: 2.2281 Epoch 81/100, Loss: 2.1721 Epoch 82/100, Loss: 2.2848 Epoch 83/100, Loss: 2.3732 Epoch 84/100, Loss: 2.2222 Epoch 85/100, Loss: 2.2548 Epoch 86/100, Loss: 2.2337 Epoch 87/100, Loss: 2.2511 Epoch 88/100, Loss: 2.2360 Epoch 89/100, Loss: 2.3256 Epoch 90/100, Loss: 2.3210 Epoch 91/100, Loss: 2.2526 Epoch 92/100, Loss: 2.2735 Epoch 93/100, Loss: 2.1523 Epoch 94/100, Loss: 2.3176 Epoch 95/100, Loss: 2.2644 Epoch 96/100, Loss: 2.2442 Epoch 97/100, Loss: 2.3447 Epoch 98/100, Loss: 2.2706 Epoch 99/100, Loss: 2.3454 Epoch 100/100, Loss: 2.3162 Test Accuracy: 20.28%
最新发布
06-06
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