HDU 3339 In Action (dijkskra+01背包)

VLOOKUP_fixed.py 立即下载

In Action

Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 32768/32768 K (Java/Others)
Total Submission(s): 5342    Accepted Submission(s): 1794


Problem Description

Since 1945, when the first nuclear bomb was exploded by the Manhattan Project team in the US, the number of nuclear weapons have soared across the globe.
Nowadays,the crazy boy in FZU named AekdyCoin possesses some nuclear weapons and wanna destroy our world. Fortunately, our mysterious spy-net has gotten his plan. Now, we need to stop it.
But the arduous task is obviously not easy. First of all, we know that the operating system of the nuclear weapon consists of some connected electric stations, which forms a huge and complex electric network. Every electric station has its power value. To start the nuclear weapon, it must cost half of the electric network's power. So first of all, we need to make more than half of the power diasbled. Our tanks are ready for our action in the base(ID is 0), and we must drive them on the road. As for a electric station, we control them if and only if our tanks stop there. 1 unit distance costs 1 unit oil. And we have enough tanks to use.
Now our commander wants to know the minimal oil cost in this action.
 

Input
The first line of the input contains a single integer T, specifying the number of testcase in the file.
For each case, first line is the integer n(1<= n<= 100), m(1<= m<= 10000), specifying the number of the stations(the IDs are 1,2,3...n), and the number of the roads between the station(bi-direction).
Then m lines follow, each line is interger st(0<= st<= n), ed(0<= ed<= n), dis(0<= dis<= 100), specifying the start point, end point, and the distance between.
Then n lines follow, each line is a interger pow(1<= pow<= 100), specifying the electric station's power by ID order.
 

Output
The minimal oil cost in this action.
If not exist print "impossible"(without quotes).
 

Sample Input
  
2 2 3 0 2 9 2 1 3 1 0 2 1 3 2 1 2 1 3 1 3
 

Sample Output
  
5 impossible
 

Author
Lost@HDU
 

Source
HDOJ Monthly Contest – 2010.03.06

题意:这个题意比赛过程一直没看懂。。(没有看出一个坦克只能占领一个地方),看懂很简单
       就是要用很多坦克去占领电网,所有坦克都在0位置,有n个电站编号为1~n,每个电站有它自己的能量值,坦克当且仅当停在上面才能破坏电站(要是再移动了那就不算占
领),现在要破坏其中一些电站,要让电网的总能量值损失一半以上,且要使所有坦克去目的地耗费的油量最少。

解题思路:先用Dijkstra算法求出0点到其它任何位置的最短距离,接下来就是0-1背包问题了,把每条路当作花费,目的地能量值当作价值。注意的是需要破坏一半以上的能量值。

<span style="font-size:18px;">#include<iostream>
#include<cstdio>
#include<algorithm>
#include<cmath>
#include<cstring>
using namespace std;
const int INF=0x3f3f3f3f;
int dp[10010];
int n,m,v[110],map[110][110],d[110],a[110];
int main()
{
    int t;
    scanf("%d",&t);
    while(t--)
    {
        memset(map,0x3f3f,sizeof(map));
        scanf("%d %d",&n,&m);
        int s,e,load;
        for(int i=0;i<m;i++){
            scanf("%d %d %d",&s,&e,&load);
            map[s][e]=map[e][s]=min(map[s][e],load);
        }
        for(int i=1;i<=n;i++) scanf("%d",&a[i]);
        memset(v,0,sizeof(v));
        for(int i=0;i<=n;i++) d[i]=(i==0 ? 0 : INF);
        for(int i=0;i<=n;i++){ //Dijkstra模板求固定点到任意点的最短路
            int x,ans=INF;
            for(int y=0;y<=n;y++) if(!v[y] && d[y]<=ans) ans=d[x=y];
            //cout << 1 << endl;
            v[x]=1;
            for(int y=0;y<=n;y++) d[y]=min(d[y],d[x]+map[x][y]);
            //cout << 2 << endl;
        }
        int load_sum=0,sum=0;
        for(int i=1;i<=n;i++)
            sum+=a[i];
        sum=sum/2+1; // 能量要超过一半以上,所以要加一
        for(int i=1;i<=n;i++)
            if(d[i]!=INF)
                load_sum+=d[i];
        memset(dp,0,sizeof(dp));
        for(int i=1;i<=n;i++) // 进行0-1背包
        {
            if(d[i]==INF) continue; // 如果路不存在,跳过
            for(int j=load_sum;j>=d[i];j--){
                 dp[j]=max(dp[j],dp[j-d[i]]+a[i]);
            }
        }
        int res=-1;
        for(int i=0;i<=load_sum;i++)
          if(dp[i]>=sum){ // 找到第一个满足条件的,就是需要最少的花费(也就是耗油量)
             res=i;
             break;
          }
        if(res==-1) printf("impossible\n");
        else printf("%d\n",res);
    }
    return 0;
}</span>


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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. 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 1428 漫步校园(BFS+DFS)

漫步校园Time Limit:1000MS    Memory Limit:32768KB    64bit IO Format:%I64d & %I64u SubmitStatusPracticeHDU 1428 Description LL最近沉迷于AC不能自拔,每天寝室、机房两点一线。由于长时间坐在电脑边,缺乏运动。他决定充分利用每次从寝室到机房的时间,在校园里

hellohelloC的博客 645

POJ 1390 Blocks(DP)

BlocksTime Limit:5000MS    Memory Limit:65536KB    64bit IO Format:%lld & %llu SubmitStatus Description Some of you may have played a game called 'Blocks'. There are n blocks in a row, ea

hellohelloC的博客 633

POJ 2441 Arrange the Bulls(状态压缩DP入门)

- Arrange the BullsTime Limit:4000MS    Memory Limit:65536KB    64bit IO Format:%lld & %llu SubmitStatus Description Farmer Johnson's Bulls love playing basketball very much. But none of

hellohelloC的博客 621

HDU 5532 Almost Sorted Array(状态压缩DP)

Almost Sorted Array Time Limit: 4000/2000 MS (Java/Others)    Memory Limit: 262144/262144 K (Java/Others) Total Submission(s): 2526    Accepted Submission(s): 627 Problem Description We ar

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POJ 3616 D - Milking Time(DP)

Milking TimeTime Limit:1000MS    Memory Limit:65536KB    64bit IO Format:%lld & %llu SubmitStatus Description Bessie is such a hard-working cow. In fact, she is so focused on maximizi

hellohelloC的博客 523

POJ 3046 Ant Counting(dp----多重集组合数)

题目链接:http://poj.org/problem?id=3046 Ant Counting Time Limit: 1000MS Memory Limit: 65536K Total Submissions: 4763 Accepted: 1816 Description Bessie was poking aroun

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