cf1062e E. Company xdtree+最近公共祖先

本文探讨在一棵树中通过线段树维护区间最值来高效求解一堆点的最低公共祖先(LCA)问题,利用DFS序与倍增技巧优化查询过程。

题意:

在一棵树中,每次选择一个区间[l,r]最多删除一个点,使得这个区间内所有点的lca的深度最大。

思路:

首先有一个点,就是一颗树中一堆点的LCA其实就是这堆点DFS序最小和最大的两个点的LCA,线段树维护区间max1,max2,min1,min2,然后倍增求LCA

#include <iostream>
#include <cstdio>
#include <algorithm>
#include <cstring>
#include <string>
#include <queue>
#include <vector>
using namespace std;

int n,q;
vector<int> e[100005];
int dep[100005];
int tid[100005];
int atid[100005];
int cnt;

int fa[100005][20];
int inf=1e9+7;

void dfs(int u,int d)
{
    cnt++;
    tid[u]=cnt;atid[cnt]=u;dep[u]=d;
    int len=e[u].size();
    for(int i=0;i<len;i++)
    {
        int v=e[u][i];
        dfs(v,d+1);
    }
}

struct node
{
    int l,r;
    int max1,max2;
    int min1,min2;
}tr[100005*5];

node up(node rt,node a,node b)
{
    node ntr=rt;
    if(a.max1>b.max1)
    {
        ntr.max1=a.max1;
        ntr.max2=max(a.max2,b.max1);
    }
    else
    {
        ntr.max1=b.max1;
        ntr.max2=max(a.max1,b.max2);
    }

    if(a.min1<b.min1)
    {
        ntr.min1=a.min1;
        ntr.min2=min(a.min2,b.min1);
    }
    else
    {
        ntr.min1=b.min1;
        ntr.min2=min(a.min1,b.min2);
    }
    return ntr;
}

void build(int i,int l,int r)
{
    tr[i].l=l;tr[i].r=r;
    if(l==r)
    {
        tr[i].max1=tid[l];tr[i].max2=0;
        tr[i].min1=tid[l];tr[i].min2=inf;
    } else
    {
        int mid=(l+r)/2;
        build(i*2,l,mid);build(i*2+1,mid+1,r);
        tr[i]=up(tr[i],tr[i*2],tr[i*2+1]);
    }
}

node get(int i,int l,int r,int ql,int qr)
{
    if(l==ql && r==qr)
        return tr[i];
    else
    {
        int mid=(l+r)/2;
        if(qr<=mid)
            return get(i*2,l,mid,ql,qr);
        else if(ql>mid)
            return get(i*2+1,mid+1,r,ql,qr);
        else
        {
            node ans;
            return up(ans,get(i*2,l,mid,ql,mid),get(i*2+1,mid+1,r,mid+1,qr));
        }
    }
}

int gf(int x,int y)
{
    if(x==y)
        return x;
    else
    {
        if(dep[x]<dep[y])
            swap(x,y);
        for(int i=19;i>=0;i--)
        {
            if(dep[fa[x][i]]>=dep[y])
            {
                x=fa[x][i];
            }
        }
        if(x==y)
            return x;
        for(int i=19;i>=0;i--)
        {
            if(fa[x][i]!=fa[y][i])
            {
                x=fa[x][i];y=fa[y][i];
            }
        }
        return fa[x][0];
    }
}

int main() {

    while(~scanf("%d%d",&n,&q))
    {
        memset(fa,0,sizeof(fa));
        for(int i=0;i<=100001;i++)
        {
            e[i].clear();
        }
        for(int i=2;i<=n;i++)
        {
            int tp;scanf("%d",&tp);
            e[tp].push_back(i);
            fa[i][0]=tp;
        }

        for(int i=1;i<20;i++)
        {
            for(int j=1;j<=n;j++)
            {
                fa[j][i]=fa[fa[j][i-1]][i-1];
            }
        }

        cnt=0;
        dfs(1,0);

        build(1,1,n);

        while(q--)
        {
            int l,r;
            scanf("%d%d",&l,&r);
            node ntp=get(1,1,n,l,r);

            int x1=atid[ntp.max1],y1=atid[ntp.min2],ans1=atid[ntp.min1];int anss1=dep[gf(x1,y1)];

            int x2=atid[ntp.max2],y2=atid[ntp.min1],ans2=atid[ntp.max1];int anss2=dep[gf(x2,y2)];

            if(anss1>anss2)
                printf("%d %d\n",ans1,anss1);
            else
                printf("%d %d\n",ans2,anss2);
        }
    }
    return 0;
}

 

def parse_obs_file(filename): data = [G02 2024 3 30 3 59 44-4.620058462024E-04 6.366462912410E-12 0.000000000000E+00 1.300000000000E+01-8.418750000000E+01 4.313393955886E-09 1.685714130268E+00 -4.606321454048E-06 1.605853962246E-02 4.636123776436E-06 5.153710025787E+03 5.327840000000E+05 3.241002559662E-07 3.062956154750E+00-1.545995473862E-07 9.677421142292E-01 2.914062500000E+02-1.212446390436E+00-7.922830017672E-09 -2.017941198208E-10 0.000000000000E+00 2.307000000000E+03 0.000000000000E+00 0.000000000000E+00 0.000000000000E+00-1.769512891769E-08 1.300000000000E+01 5.327840000000E+05 0.000000000000E+00 0.000000000000E+00 0.000000000000E+00 G07 2024 3 30 4 0 00-9.576790034771E-05-8.640199666843E-12 0.000000000000E+00 1.180000000000E+02-5.656250000000E+00 4.689123892260E-09-1.436227086246E+00 -2.924352884293E-07 1.823520869948E-02 8.463859558105E-06 5.153649635315E+03 5.328000000000E+05 4.563480615616E-07 5.512327987730E-03 1.639127731323E-07 9.507187524293E-01 2.069687500000E+02-2.138602168999E+00-7.874613723555E-09 -4.775198906202E-10 0.000000000000E+00 2.307000000000E+03 0.000000000000E+00 0.000000000000E+00 0.000000000000E+00-1.117587089539E-08 1.180000000000E+02 5.328000000000E+05 0.000000000000E+00 0.000000000000E+00 0.000000000000E+00 ] # 接收数据 current_time = None # 获取历元信息 prn = [] with open(filename, 'r') as f: for line in f: line = line.strip() if not line: continue # 读取卫星PRN,以 + 开头的,同时避免误读取 ++(方便查看) if line.startswith('+') and not line.startswith("++"): # 从9位开始就是prn,每三位为一个卫星标识编号 for i in range(9, len(line), 3): prn_id = line[i:i + 3] # 过滤空值和填充值 if prn_id and not prn_id.startswith('0'): prn.append(prn_id) # 当前卫星历元数据,年月日时分秒 if line.startswith('*'): parts = line.split() try: current_time = datetime( int(parts[1]), int(parts[2]), int(parts[3]), int(parts[4]), int(parts[5]), int(float(parts[6])) ) except: continue # 获取卫星轨道位置数据,prn,X,Y,Z(有必要可以获得钟差数据) if line.startswith('P'): dataline = line.split() try: data.append([current_time, dataline[0][1:], float(dataline[1]), float(dataline[2]), float(dataline[3])]) except ValueError: continue # 拉格朗日内插法 def lagrange(data,time): # 一般五阶的内插也能获得较好的结果 """ :param data: 已知函数值(如X,Y,Z), :param time: 对应历元时间,包含待预测历元 :return: 下一个历元的预测值 """ sum = 0 n = len(data) for k in range(n): multi = 1 for i in range(n): if i==k : continue else: multi *= float(time[-1]-time[i])/(time[k]-time[i]) sum += multi*data[k] return sum # 测试一下代码 if __name__ == "__main__": file_path = r"./igs22471.sp3" datas,prn = parse_obs_file(file_path) # 将时间值进行简化 datas["simply_time"] = pd.Series(time_to_numeric(datas["Time"])) # 为了简化计算量,仅用一个卫星的轨道实测数据开展实验,历元间隔为15min; data_G06 = datas[datas["PRN"]=="G06"] predict_X_G06 = [] predict_Y_G06 = [] predict_Z_G06 = [] # 进行插值 for i in range(5,len(data_G06)): predict_X_G06.append(lagrange(data_G06["X"][i-5:i].tolist(), data_G06["simply_time"][i-5:i+1].tolist())) predict_Y_G06.append(lagrange(data_G06["Y"][i-5:i].tolist(), data_G06["simply_time"][i-5:i+1].tolist())) predict_Z_G06.append(lagrange(data_G06["Z"][i-5:i].tolist(), data_G06["simply_time"][i-5:i+1].tolist())) # 创建3D图形 fig = plt.figure(figsize=(10, 8)) # 3D图展示实测点和预测点整体差异 ax1 = fig.add_subplot(121, projection='3d') ax1.scatter(data_G06["X"][5:], data_G06["Y"][5:], data_G06["Z"][5:], color="#a8e6cf") ax1.scatter(predict_X_G06, predict_Y_G06, predict_Z_G06, color="#008ba3") # 设置坐标标签 ax1.set_xlabel('X', fontsize=12) ax1.set_ylabel('Y', fontsize=12) ax1.set_zlabel('Z', fontsize=12) # 显示X,Y,Z坐标值随历元变化情况 t = data_G06["simply_time"][5:]/3600 ax2 = fig.add_subplot(222) ax2.scatter(t,data_G06["X"][5:]-predict_X_G06,color="#a8e6cf",s=8,label="X_diff") ax2.scatter(t, data_G06["Y"][5:] - predict_Y_G06, color="#cef0d5", s=8,label="Y_diff") ax2.scatter(t, data_G06["Z"][5:] - predict_Z_G06, color="#4bb89f", s=8,label="Z_diff") # 在y=0处添加一条水平直线,设置为虚线以便区分 ax2.axhline(y=0, color='#4dd0e1', linestyle='--', alpha=0.7) # 设置X轴刻度与数值一一对应 ticks=data_G06["simply_time"][5:]/3600 ax2.set_xticks(ticks[::10]) plt.xticks(fontsize=6,rotation=90) # 设置轴标签 ax2.set_xlabel('Time (hour)') ax2.set_ylabel('Residual (km)') plt.legend(["X_diff","Y_diff","Z_diff"],loc="upper right",fontsize=8) # 调整布局与显示 plt.tight_layout() plt.show()
最新发布
10-09
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