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部门组织绿岛骑行团建活动，租用公共双人自行车骑行，每辆自行车最多坐两人、最大载重 M。
给出部门每个人的体重，请问最多需要租用多少双人自行车。
第一行两个数字 m、n，自行车限重 m，代表部门总人数 n。
第二行，n 个数字，代表每个人的体重。体重都小于等于自行车限重 m。
0<m <= 200
0 < n <= 1000000
最小需要的双人自行车数量。
10 4
9 8 6 5
4
10 5
5 5 5 5 5
3
7 6
3 4 5 3 3 7
4
华为OD机试（B+C+D卷）、机考必刷 200 分的题目整理如下，冲满分评级必备
华为机试有三道题，前 2 道 100 分，第 3 道 200 分，总分是 400 分。年度奖金计算为：A =4 个月工资，B+ =3 个月工资，B=2 个月工资。，在国外有补助，在华为干的越久，年底奖金和分红越多，这和其他公司一样。哪些是最新题，哪些该优先刷，哪些题简单，哪些题难，题目的分数是多少。每个题解都由 6 大常见编码语言实现，订阅之后，可以私信我催更。如果你距离 OD 机考时间不多了，那就刷这个精简版本的题库吧。还可以一对一进行 OD 辅导，如果机考分数不理想，订阅可以询问 OD 任何知识点。
华为OD机试 新系统 双机位C卷 往年真题 机考题库（持续收录中）以及算法考点详解（Python/JS/C/C++）
刷的越多，抽中的概率越大，每一题都有详细的答题思路、详细的代码注释发现新题目，随时更新。
华为OD机试 新系统 双机位C卷 往年真题 机考题库（持续收录中）以及算法考点详解
每一题都有详细的答题思路、详细的代码注释、样例测试，发现新题目，随时更新，全天优快云在线答疑。
华为OD高频面试真题
题目保证都是考过的真题，目前是六个，后四题都是华为od祖传的题目，很经常出 考前练练很有帮助，也可以看下大概难度
告别环境配置噩梦：用Docker Desktop + WSL2在Windows上5分钟搞定vLLM运行环境
本文详细介绍了如何在Windows系统上使用Docker Desktop和WSL2快速搭建vLLM开发环境，5分钟内完成配置。通过容器化技术解决传统环境配置中的依赖冲突、环境污染等问题，提供一键复用的高效方案，特别适合AI开发者快速部署和测试vLLM框架。
UE5垃圾回收优化实战：定位GC卡顿与内存泄漏
垃圾回收（GC）是Unreal Engine 5中保障UObject内存安全的核心机制，其基于引用图的标记-清除原理决定了性能高度依赖引用结构合理性。当GC频繁触发或单次耗时突增，往往映射出蓝图强引用冗余、C++根对象管理失控或资源生命周期设计缺陷等底层问题。技术价值在于降低主线程阻塞、稳定60帧体验、抑制内存异常增长；典型应用场景包括关卡切换卡顿、UI打开后持续GC、打包后内存不释放等。本文聚焦UE5特有的Full GC模式、UObject生命周期约束及GC触发时机不可见性，提供从蓝图变量规范、TSoft
VTK实战完整教程介绍
该专栏以VTK提供的Example为基础，讲解VTK中的相关接口和使用方法。订阅该专栏，带你了解所有VTK的功能，以便VTK更好的解决工作中遇到的问题。一起学习起来吧！
Misc-RAR隐写
题目来自于：XCTF的SimpleRAR，由于涉及到许多新知识，所以写篇小文子记录一下。 一、下载完后是一个压缩包。 里面就有一个flag.txt，打开之后发现有一串字符是flag is not here，而且我们查看他的文件大小，只要十六字节，绝对不可能再藏着什么信息了。 然后把压缩包拉到010editor里面，发现一下子出来好多新的内容，很明显是有夹杂着的附加文件。 二、rar文件头信息。 rar 是由一个一个 block 组成的，每个 block 有以下几个部分 ...
2026年3月GESP真题及题解（C++七级）：物流网络
2026年3月GESP真题及题解（C++七级）：物流网络
华东理工大学计算机预推免面试
华东理工大学预推免面试经验分享
OpenMVS ：用 colmap制作自己的数据集 重建（用colmap计算位姿，将位姿转为MVS需要的格式）
数据集制作主要是在windows进行，比较方便。
揭秘大数据领域数据产品的开发流程
随着企业数字化转型加速，数据产品已成为驱动业务增长的核心载体。不同于传统软件产品，数据产品以数据为核心生产要素，通过数据采集、处理、分析与可视化，为用户提供决策支持或智能化服务。本文旨在系统性拆解数据产品开发的全生命周期，涵盖需求分析、数据工程、模型构建、产品设计、上线运维等关键环节，揭示各阶段的技术原理、实施路径与最佳实践，帮助技术团队与产品团队建立标准化开发流程。核心概念：定义数据产品本质，对比传统软件产品差异技术体系：拆解数据采集、处理、建模、可视化的技术栈与算法原理。
假人有限元模型
假人模型，车辆碰撞假人有限元模型，适合做乘员安全的研究者利用
android 蓝牙通讯demo
android 下蓝牙通讯demo，实现了发现蓝牙，使能蓝牙，通过蓝牙发送消息。
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
Statistics and Machine Learning Toolbox
Statistics and Machine Learning Toolbox（带书签），MATLAB统计学习工具
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