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我也要
给你一列数，问你这些数中间的那个数是多少。
#include <iostream>
#include <cstdio>
#include <cstring>
#include <algorithm>
using namespace std;
int a[10000];
int main()
{
int n,i;
scanf("%d",&n);
for (i=0; i<n; i++)
scanf("%d",a+i);
sort(a,a+n);
printf("%d\n",a[n/2]);
}FPGA实战：手把手教你实现VESA DSC编码（附Verilog代码解析）
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poj 2388 Who's in the Middle
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POJ 2388.Who's in the Middle
Who's in the Middle Time Limit:1000MSMemory Limit:65536KB64bit IO Format:%I64d & %I64u SubmitStatusPracticePOJ 2388 Description FJ is surveying his herd to find the mo...
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POJ2388 Who's in the Middle
很简单的一道题目 排序，输出中位数即可 #include<cstdio> #include<algorithm> using namespace std; const int maxn = 1e4 + 5; int a[maxn]; void quicksort(int head,int tail) { int i = head, j = tail, k = ...
精益六西格玛Python数字化应用开发-Day18 应用场景：家电企业生产效率优化
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POJ2388——Who's in the Middle
POJ2388——Who's in the Middle Description FJ is surveying his herd to find the most average cow. He wants to know how much milk this 'median' cow gives: half of the cows give as much or more than t
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poj2388 Who's in the Middle
见到水题就高兴… 不过好像这样也没什么提高 题解就直接写在前面了，题意就是给你n个数 然后让你找出中间的那个数，直接用sort排起来输出中间的就好了 或者我记得以前接触stl的时候有个可以只排一部分的函数，可以节省点时间 不过对这个题来说全排就足够了 Who's in the Middle Time Limit: 1000MS Memory Limit: 65
扫码登录原理
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POJ-2388-Who's in the Middle
数据比较小，用快排或堆结构都行。 mysol: STL 堆 reference:http://www.cplusplus.com/reference/algorithm/make_heap/?kw=make_heap #include #include #include #include #include #include #include #include
深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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');```[](https://bitdeli.com/free "Bitdeli Badge")
NTscan扫描工具
ntscan是一个扫描器，可以扫描没有密码和弱密码的共享，先到配置文件中填写猜测的用户名和密码，扫描过程中右侧出现的是扫描日志。
【POJ】【P2388】【Who's in the Middle】【题解】【水题】
传送门：http://poj.org/problem?id=2388 求n个数的中位数 #include #include using namespace std; int a[1000001]; int main(){ ios::sync_with_stdio(false); int n; cin>>n; for(int i=0;i<n;i++) cin>>a[i]; nth_e
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Who's in the Middle poj 2388
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POJ2388-Who's in the Middle
全解题报告索引目录 -> 【北大ACM – POJ试题分类】 转载请注明出处：http://exp-blog.com ------------------------------------------------------------------------- 水题一道 给定n个数，输出中间值（注意不是求平均） 可以用sort，干脆快捷，但是注意排序起止位置 也可...
poj2388——Who's in the Middle
题目大意：给出奇数个奶牛的产奶量，找出他们的中位数 输入：奶牛个数N(1 <= N < 10,000) 第i个奶牛的产奶量(1..1,000,000) 输出：中位数 分析：可以直接用sort函数，也可以用快排、归并、堆排序，但是注意不要用冒泡、选择排序、直接排序等时间复杂度为O(N²)的排序方法 代码： 1.快排：选数据第一...
POJ 一 2388 Who's in the Middle
Description FJ is surveying his herd to find the most average cow. He wants to know how much milk this 'median' cow gives: half of the cows give as much or more than the median; half give as much o
poj2388Who's in the Middle【堆排序】
Language: Default Who's in the Middle Time Limit: 1000MS Memory Limit: 65536K Total Submissions: 35209 Accepted: 20554 Description FJ is surveying his herd to f
POJ 2388 Who's in the Middle
/* 中文题意：给你一个奇数n（odd number），让你求出这n个数的中位数 题目大意：如上 关键点：求出中位数的位置，sort排序 解题人：lingnichong 解题时间：2014/08/06 11:10 解题感受：求中位数的位置，水题一道 */ Who's in the Middle Time Limit: 1000MS
[POJ] 2388 -> Who's in the Middle
Who's in the Middle Time Limit: 1000MS Memory Limit: 65536K Total Submissions: 33719 Accepted: 19632 Description FJ is surveying his herd to find the most average cow.
POJ--2388 Who's in the Middle
忘了是POJ的哪到了……问题如下： DescriptionFJ is surveying his herd to find the most average cow. He wants to know how much milk this median cow gives: half of the cows give as much or more than the median; h
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