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2025华为OD机试题库（按算法分类）：2025华为OD统一考试题库清单（持续收录中）以及考点说明（Python/JS/C/C++）。
本专栏收录于《华为OD机试真题（Python/JS/C/C++）》。
刷的越多，抽中的概率越大，私信哪吒，备注华为OD，加入华为OD刷题交流群，每一题都有详细的答题思路、详细的代码注释、3个测试用例、为什么这道题采用XX算法、XX算法的适用场景，发现新题目，随时更新。
输入一个字符串，请按长度为8拆分每个输入字符串并进行输出，长度不是8整数倍的字符串请在后面补数字0，空字符串不处理。
连续输入字符串(每个字符串长度小于等于100)。
华为od机考 Python 目录【E卷+A卷+B卷+C卷+D卷】
1：进入机考网页之后如果链接上写着【B卷】，就表示是B卷题库（**对应着目录中的时间这一列 2023Q2**）。在之后，大多数同学收到的应该是B卷题库。2：2022年的题库，如果时间紧迫，建议直接刷2023年新题。3：如果机试挂了，6个月之后可以再考。4：订阅专栏后，后面更新的答案都可以查看。以后更新的题目也会放到这个专栏里。5：如果对题解有问题，私信我解答。
【100%通过率】2022华为机试真题 C++ 实现【磁盘容量排序】
磁盘的容量单位常用的有M，G，T这三个等级，它们之间的换算关系为1T = 1024G，1G = 1024M，现在给定n块磁盘的容量，请对它们按从小到大的顺序进行稳定排序，例如给定5块盘的容量，1T，20M，3G，10G6T，3M12G9M排序后的结果为20M，3G，3M12G9M，1T，10G6T。注意单位可以重复出现，上述3M12G9M表示的容量即为3M+12G+9M，和12M12G相等。
【持续更新】2025华为OD机试2025A卷机考真题库清单含考点说明 （已更新180+题）
【持续更新】2025华为OD机试2025A卷 机考真题库清单（全真题库）含考点说明（已更新40+题）
华为OD机试 - 字符串分割转换（Python/JS/C/C++ 2025 B卷 100分）
本专栏收录于《华为OD机试真题（Python/JS/C/C++）》。刷的越多，抽中的概率越大，私信哪吒，备注华为OD，加入华为OD刷题交流群，每一题都有详细的答题思路、详细的代码注释、3个测试用例、为什么这道题采用XX算法、XX算法的适用场景，发现新题目，随时更新。给定一个非空字符串 Q S，其被 N 个‘;’分隔成 N+1 个子串，给定正整数数组 K，要求除第一个子串外，其余的子串每 K 个字符组成新的子串，并‘-’分隔。对于新组成的每一个子串，如果它含有的小写字母比大写字母多，则将这个子串的所有大写字母
【实战指南】EC-Engineer中ESI配置与PDO Mapping全流程解析
本文详细解析了EC-Engineer中ESI配置与PDO Mapping的全流程操作，包括环境准备、ESI文件导入技巧、在线配置细节及故障处理。特别强调了WinPcap驱动的兼容性问题和PDO Mapping的最佳实践，帮助工程师高效完成EtherCAT总线配置，适用于工业自动化领域的伺服控制与运动协同场景。
5090D显卡安装LLaMA-Factory避坑指南：PyTorch与CUDA版本兼容性问题解决
本文是针对5090D显卡安装LLaMA-Factory时遇到的PyTorch与CUDA版本兼容性问题的详细解决指南。文章深入剖析了因GPU架构代际差异导致的“no kernel image”报错根源，并提供了从环境诊断、安装匹配的PyTorch Nightly版本，到最终集成验证的完整路径，帮助用户确保LLaMA-Factory在5090D上稳定运行。
使用Python进行核磁数据预处理
随着医学与人工智能技术的快速崛起，伴随算法算力、数据等关键要素的积累和突破，人工智能技术在精准医学领域的科研应用也随之飞快增长，相关科研成果和学术论文数量逐年激增，医学影像人工智能涉及的技术如图像分割、病灶识别、病灶自动勾画、影像组学、深度学习模型构建和临床信息解析越来越趋于分析流程化，模块化，帮助越来越多的医生在临床科研实践中获得突破性的进展。根据具体的应用和数据类型，你可能需要调整或添加额外的步骤。对于更高级的处理，如图像分割或NMR数据的复杂处理，你可能需要查阅更专业的文献或使用更专业的软件工具。
【实战指南】Green Hills MULTI-IDE 从零安装到工程创建全流程
本文详细介绍了Green Hills MULTI-IDE从零安装到工程创建的全流程，包括系统要求检查、安装步骤、工程配置及调试技巧。特别适合嵌入式开发新手快速上手这款专业IDE工具，提升开发效率。
告别色差！用Python+TCS34725颜色传感器实现精准色彩还原（附完整校准代码）
本文详细介绍了如何利用Python和TCS34725颜色传感器实现专业级色彩还原方案。通过双阶校准法（白光校准和黑电平校准）和进阶的光照自适应技术，解决传感器原始数据不准的问题，并提供完整的校准代码和实战案例，帮助开发者实现ΔE<3的高精度色彩识别。
RH850_U2A系列HSM安全核开启但是固件未烧录导致无法启动也无法再烧入固件废板问题分析解决
hsm核导致的安全锁死补救方案
PCB布局规范及其注意事项（纯干货）
这样做的好处是信号1，3层不会有串扰，4和5作为电源有紧密的耦合，3个信号层都有GND平面可以参考，且不会互相产生串扰，唯一的缺点是布线的层面只有3个信号层（信号03的是最适合来传输高速信号，因为该层的二面都有屏蔽，防止干扰传出，也防止干扰传入）33、影响阻抗匹配的参数（ER介电常数（1GHZ以下默认为4.2，以上的话问板厂），H介质厚度（就是内层压合的PP片厚度），W走线的线宽，T走线的厚度（铜厚/减小线的厚度可以增大阻抗））我这里使用的是CITS25来计算阻抗。
2023推免（夏令营+套实验室）面试总结（博文附问题汇总和参考答案）
2023推免（夏令营+套实验室）面试总结（博文附问题汇总和参考答案）
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
win10 跳过U8环境检测的IE WebControls.rar
win10 安装u8的时候 提示IE WebControls 不通过，这个工具可以欺骗u8的检查。 跳过U8环境检测的IE WebControls。提示导入失败后，重新检测通过。
最新的sdio 4.0 spec 协议标准 带书签 高清完整PDF版
Part-E1-SDIO-Specification-Ver4.00-Final-120220.pdf：最新的sdio 4.0 spec 协议标准 带书签 高清完整PDF版，包括SDIO 接口的外设，SDIO 接口官方协议等。
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")
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