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在软件开发过程中，测试是一个至关重要的环节，它可以帮助开发团队确保软件的质量和稳定性。冒烟测试是软件测试中的一种常用方法，用于验证软件版本的基本功能是否正常工作。本文将详细介绍冒烟测试的概念、目的和步骤，并提供相应的源代码示例。
冒烟测试是一种快速而基本的测试方法，旨在验证软件版本的核心功能是否可用。它通常在软件开发的早期阶段或版本发布前进行。冒烟测试的主要目标是快速发现严重的缺陷或错误，以确保软件在更详细的测试阶段之前具备基本的可用性。
以下是一个使用Python编写的简单冒烟测试的示例代码：
import unittest
class SmokeTest(unittest.TestCase):
def test_login(self)<金仓数据库KCM认证考试介绍【2025年4月更新】
金仓数据库认证资深专家KCM认证考试介绍
[c语言]用辗转相除法求最大公约数，递归和不递归两种
在c语言的学习中必然会遇到求两个数之间的最大公约数的题目，那么对于其他经典方法，最简便快捷的方式就是利用辗转相除法 辗转相除法又叫做欧几里得算法，目的是为了求两个正整数的最大公约数 定理：gcd(a,b) = gcd(b ,a%b) //在这里用(a,b)表示a和b的最大公约数证明：a可以表示成a = kb + r，则r = a %b 假设d是a,b的一个公约数，则有 d|a, d|b，而r = a - kb，因此d|r //这里的"|"的意思是 ：d是b的因数 ,d是a的因数 因此d...
ICLR 2022）ODConv：即插即用的动态卷积 （附代码）
一定程度上讲，ODConv可以视作CondConv的延续，将CondConv中一个维度上的动态特性进行了扩展，同时了考虑了空域、输入通道、输出通道等维度上的动态性，故称之为全维度动态卷积。ODConv是一种动态卷积算法，它的原理是在卷积过程中，根据输入数据的特征动态地调整卷积核的形状和大小，以适应不同的输入数据。具体来说，ODConv通过引入一个可学习的形变模块，根据输入数据的特征动态地调整卷积核的形状和大小，从而提高了卷积神经网络的性能。此外，相比常规卷积，动态卷积的卷积核参数往往是其n倍。
Simulink中通过MEX与MinGW实现C/C++结构体变量的高效导入
本文详细介绍了在Simulink中通过MEX工具链与MinGW编译器，高效导入C/C++结构体变量的完整流程。文章从需求背景出发，逐步讲解了MinGW环境配置、.h文件编写规范、核心导入命令Simulink.importExternalCTypes的使用，以及如何在MATLAB Function模块和总线创建器中调用自定义结构体，旨在实现模型与代码的无缝对齐，为后续仿真和代码生成奠定坚实基础。
【自由谈】城域网IPv6过渡技术——MAP技术（1）
在IPv4向IPv6网络演进的浪潮中，存在着两对主要矛盾的较量，一对是IPv4地址短缺和IPv4业务蓬勃发展之间的矛盾，另一对是IPv6海量的地址空间和IPv6应用的匮乏之间的矛盾。同时两对矛盾又互相制约，IPv4地址共享机制似乎又减缓了IPv6产业链的发展，IPv6产业链的不断发展似乎又在考验着IPv4地址共享机制的部署规模。MAP成为目前兼顾IPv4和IPv6的4voer6场景中最为关注的技术。
Ethercat学习-从站源码生成
移植平台GD32F450，从站芯片AX58100
手把手教你学Simulink——基于Simulink的储能PCS（功率转换系统）离网V/f控制仿真示例
本文带你从零开始，在Simulink中硬核还原了一套完整的100kW 储能PCS离网V/f控制系统。通过引入：✅无PLL的自主角度生成机制（实现真正的“构网”能力）✅电压外环与电流内环的双闭环解耦架构（兼顾稳态精度与动态响应）✅黑启动软充电与有源阻尼等工程化手段（确保系统平滑建压与抗谐振）核心收获透彻理解了储能系统在无大电网支撑下的自主运行机理。掌握了从“跟网（Grid-Following）”向“构网（Grid-Forming）”跨越的控制算法核心差异。
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
jre9下载 jdk9
(Java SE Runtime Environment 9.0) 9.0u175 官方最新版 【x86|x64】
大中型超市收银系统(可直接运行)
光步前台收银系统系统功能：商品销售、商品退货、挂单、收银员权限、会员销售、单品取消、整单取消、商品查询、脱机销售、脱机数据上传，基本资料下载，开钱箱、小票打印、收款结算，支持多币种付款方式（现金、储值卡、礼券、微信等）,支持多种促销。运行环境：.net 4.0登陆信息用户名:1001 密码:123456内置商品信息，商品编码可输入00001000 - 00010000,测试效果相关功能键F5 结算 F9挂单 F11商品查询 F8退货 Insert单品取消 Del整单取消 F12开钱箱F2 会员 Ctrl+Z退出系统 Home 锁屏 相关源码待公开
Camera2Demo
在Camera2 的基础上进行封装，使得Camera2一堆初始化逻辑与Activity分离，降低耦合度，完成预览拍照并保存的操作
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