hdu 3436 线段树 一顿操作

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Queue-jumpers

Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 32768/32768 K (Java/Others)
Total Submission(s): 3348    Accepted Submission(s): 904


Problem Description
Ponyo and Garfield are waiting outside the box-office for their favorite movie. Because queuing is so boring, that they want to play a game to kill the time. The game is called “Queue-jumpers”. Suppose that there are N people numbered from 1 to N stand in a line initially. Each time you should simulate one of the following operations:
1.  Top x :Take person x to the front of the queue
2.  Query x: calculate the current position of person x
3.  Rank x: calculate the current person at position x
Where x is in [1, N].
Ponyo is so clever that she plays the game very well while Garfield has no idea. Garfield is now turning to you for help.
 

Input
In the first line there is an integer T, indicates the number of test cases.(T<=50)
In each case, the first line contains two integers N(1<=N<=10^8), Q(1<=Q<=10^5). Then there are Q lines, each line contain an operation as said above. 
 

Output
For each test case, output “Case d:“ at first line where d is the case number counted from one, then for each “Query x” operation ,output the current position of person x at a line, for each “Rank x” operation, output the current person at position x at a line.

Sample Input

3
9 5
Top 1
Rank 3
Top 7
Rank 6
Rank 8
6 2
Top 4
Top 5
7 4
Top 5
Top 2
Query 1
Rank 6

Sample Output

Case 1:
3
5
8
Case 2:
Case 3:
3
6


/*
hdu 3436 线段树 一顿操作

这个题以前用splay树做过,但是最近练习线段树中(据说线段树能解决splay树中的很多操作)
Top:     将第x个数移动到队首
Query:   查询x的位置
Rank:    找出排第x的数

top想的数之间在线段树前面预留一部分,于是线段树最多需要2e5.对于Rank可以直接查找
主要是对于Query没想到什么办法.最后直接是用数组来保存每个数的位置,然后利用求和便能得到
某数在这个队列中的位置

hhh-2016-04-12 19:20:16
*/
#include <iostream>
#include <cstdio>
#include <cstring>
#include <algorithm>
#include <vector>
#include <functional>
typedef long long ll;
#define lson (i<<1)
#define rson ((i<<1)|1)
using namespace std;

const int maxn = 1e6+10;

struct node
{
    int l,r;
    int sum,val;
    int mid()
    {
        return (l+r)>>1;
    }
} tree[maxn<<2];
int T,n,m;
int a[maxn];
int ano[maxn];
int st[maxn],en[maxn];
int pos[maxn];
char op[maxn][6];
int tot,TOT;

void push_up(int i)
{
    tree[i].sum = tree[lson].sum + tree[rson].sum;
}

void build(int i ,int l,int r)
{
    tree[i].l =l ,tree[i].r = r;
    tree[i].sum = 0;
    tree[i].val = 0;
    if(l == r)
    {
        if(tree[i].l > m)
        {
            int t = tree[i].l-m;
            tree[i].val = t;
            tree[i].sum = en[t]-st[t]+1;
        }
        return ;
    }
    int mid = tree[i].mid();
    build(lson,l,mid);
    build(rson,mid+1,r);
    push_up(i);
}

void push_down(int i)
{

}

void update(int i,int k,int val)
{
    if(tree[i].l == tree[i].r )
    {
        if(!val) tree[i].sum = 0,tree[i].val = 0;
        else tree[i].sum = en[val]-st[val]+1,tree[i].val = val;
        return ;
    }
    int mid = tree[i].mid();
    if(k <= mid)
        update(lson,k,val);
    else
        update(rson,k,val);
    push_up(i);
}

int sum(int i,int l,int r)
{
    if(tree[i].l>=l && tree[i].r <= r)
        return tree[i].sum;
    int mid = tree[i].mid();
    int su = 0;
    if(l <= mid)
        su += sum(lson,l,r);
    if(r > mid)
        su += sum(rson,l,r);
    push_up(i);
    return su;
}

int get_k(int i,int k)
{
    if(tree[i].l == tree[i].r && k <= en[tree[i].val]-st[tree[i].val]+1)
        return st[tree[i].val]+k-1;
    int mid = tree[i].mid();

    if(k <= tree[lson].sum)
        return get_k(lson,k);
    else
        return get_k(rson,k-tree[lson].sum);
    push_up(i);
}


int bin(int key)
{
    int l = 1,r = tot-1;
    while(l <= r)
    {
        int mid = (l+r)>>1;
        if(st[mid]<=key && en[mid]>=key)
            return mid;
        else if(key < st[mid])
            r = mid - 1;
        else
            l = mid + 1;
    }
}

int main()
{
    int cas = 1;
    scanf("%d",&T);
    while(T--)
    {
        scanf("%d%d",&n,&m);
        tot = 0;
        for(int i = 1; i <= m; i++)
        {
            scanf("%s%d",op[i],&a[i]);
            if(op[i][0] == 'T' || op[i][0] == 'Q')
                ano[tot++] = a[i];
        }
        ano[tot++] = 1,ano[tot++] = n;
        sort(ano,ano+tot);
//        for(int i = 0;i < tot;i++)
//            cout << ano[i] <<" ";
//        cout << endl;
        TOT = tot;
        tot = 1;
        st[tot] = ano[0],en[tot] = ano[0];
        tot++;
        printf("Case %d:\n",cas++);
        for(int i = 1; i < TOT; i++)
        {
            if(ano[i] != ano[i-1])
            {
                if(ano[i] - ano[i-1] > 1)
                {
                    st[tot] = ano[i-1]+1;
                    en[tot++] = ano[i]-1;
                }
                st[tot] = ano[i];
                en[tot] = ano[i];
                tot++;
            }
        }
//        for(int i = 1;i < tot;i++)
//            cout <<st[i] << " "<<en[i] <<endl;
        build(1,1,m+tot-1);
        memset(ano,0,sizeof(ano));
        int cur = m;
        for(int i = 1; i <= m; i++)
        {
            int tp = bin(a[i]);
            //cout << "val:" << a[i] << " "<<tp<<endl;
            if(op[i][0] == 'T')
            {
                if(ano[tp])
                {
                    update(1,pos[tp],0);
                    update(1,cur,tp);
                    pos[tp] = cur;
                    cur --;
                }
                else
                {
                    update(1,m+tp,0);
                    update(1,cur,tp);
                    pos[tp] = cur;
                    cur--;
                    ano[tp] = 1;
                }
            }
            else if(op[i][0] == 'Q')
            {
                if(ano[tp])
                {
                    printf("%d\n",sum(1,1,pos[tp]));
                }
                else
                {
                    printf("%d\n",sum(1,1,m+tp));
                }
            }
            else
            {
                printf("%d\n",get_k(1,a[i]));
            }
        }
    }
    return 0;
}


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Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

hdu 4288 离线线段树+间隔求和

Coder Time Limit: 20000/10000 MS (Java/Others)    Memory Limit: 32768/32768 K (Java/Others) Total Submission(s): 4838    Accepted Submission(s): 1853 Problem Description   In mathematics a

阳光下的泡沫 729

IEM模型,地表粗糙度模拟

利用微波反演土壤水分时候,利用IEM模型可以实现对随机地表的模拟

2015 多校联赛 ——HDU5316(线段树)

Fantasy magicians usually gain their ability through one of three usual methods: possessing it as an innate talent, gaining it through study and practice, or receiving it from another being, often a g

阳光下的泡沫 700

hdu 4638 树状数组 区间内连续区间的个数(尽可能长)

Group Time Limit: 4000/2000 MS (Java/Others)    Memory Limit: 32768/32768 K (Java/Others) Total Submission(s): 1959    Accepted Submission(s): 1006 Problem Description There are n men ,eve

阳光下的泡沫 674

zoj2112 树状数组+主席树 区间动第k大

Dynamic Rankings Time Limit: 10000MS   Memory Limit: 32768KB   64bit IO Format: %lld & %llu Submit Status Description The Company Dynamic Rankings has developed a new k

阳光下的泡沫 650

hdu 4578 线段树(标记处理)

Transformation Time Limit: 15000/8000 MS (Java/Others)    Memory Limit: 65535/65536 K (Java/Others) Total Submission(s): 4095    Accepted Submission(s): 1008 Problem Description Yuanfang i

阳光下的泡沫 636

hdu4605 树状数组+离散化+dfs

Magic Ball Game Time Limit: 10000/5000 MS (Java/Others)    Memory Limit: 65536/65536 K (Java/Others) Total Submission(s): 2189    Accepted Submission(s): 634 Problem Description When the m

阳光下的泡沫 610

hdu3340 线段树+多边形

Rain in ACStar Time Limit: 4000/2000 MS (Java/Others)    Memory Limit: 32768/32768 K (Java/Others) Total Submission(s): 788    Accepted Submission(s): 218 Problem Description Maybe you hav

阳光下的泡沫 605

hdu 5493 (树状数组)

题意:在一个队列中,你知道一个人在他左边或者右边比他高的人的个数,求字典序最小的答案 思路:先将人按  矮-->高 排序,然后算出在每个人前面需要预留的位置。树状数组(也可以线段树)解决时,先二分查找合适的位置,如果合理则标记,每次查找时    枚举的人数 - 前面已占的人数 = 可预留位置 判断分别在它左边 或者 右边时可能的最小值进行比较。 预留的位置①:pnode[i].num(即比他

阳光下的泡沫 578

hdu 3397 线段树双标记

Sequence operation Time Limit: 10000/5000 MS (Java/Others)    Memory Limit: 32768/32768 K (Java/Others) Total Submission(s): 7502    Accepted Submission(s): 2233 Problem Description lxhgww

阳光下的泡沫 563

hdu 2871 线段树(各种操作)

Memory Control Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 32768/32768 K (Java/Others) Total Submission(s): 5913    Accepted Submission(s): 1380 Problem Description Memory unit

阳光下的泡沫 557

hdu 1828 线段树扫描线(周长)

Picture Time Limit: 6000/2000 MS (Java/Others)    Memory Limit: 32768/32768 K (Java/Others) Total Submission(s): 3897    Accepted Submission(s): 1978 Problem Description A number of rectan

阳光下的泡沫 553
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