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使用 Boost 库中的 intrusive::smart_ptr 进行智能指针操作
intrusive::smart_ptr 是 Boost 库中的一个智能指针，可以用于管理 C++ 对象的生命周期。它使用引用计数实现自动内存管理，而且支持环形、双向链表等容器的元素。下面是一个使用 intrusive::smart_ptr 的示例程序。
首先，需要包含 Boost 库和头文件：
#include <boost/intrusive_ptr.hpp>
#include <boost/intrusive/list.hpp>
#include boost::intrusive::smart_ptr用法的测试程序
boost::intrusive::smart_ptr用法的测试程序实现功能C++实现代码 实现功能 boost::intrusive::smart_ptr用法的测试程序 C++实现代码 #ifndef BOOST_INTRUSIVE_SMART_PTR_HPP #define BOOST_INTRUSIVE_SMART_PTR_HPP #include <boost/intrusive/pointer_plus_bits.hpp> #include <boost/intrusive/po
使用boost::intrusive::smart_ptr的示例程序
这是一个简单的示例，演示了boost::intrusive::smart_ptr的用法。本文将介绍boost::intrusive::smart_ptr的用法，并提供一个简单的示例程序。注意：为了使上述示例程序能够编译通过，您需要确保已正确安装和配置了Boost库，并将相关的头文件路径添加到编译器的包含路径中。希望这个示例程序能够帮助您理解boost::intrusive::smart_ptr的用法。使用boost::intrusive::smart_ptr的示例程序。指针所管理的对象的成员函数。
面试中常考的数学题——截木棍、圆上取点、赛马、红蓝墨水，测试毒药、坐到正确座位问题
文章目录1.将一根木棒截成三段，问能够组成三角形的概率；2.在圆上任取三点，问能够组成锐角、直角、钝角三角形的概率；3.共有25匹马，找出最快的3匹，只有5个赛道，每次比赛只能得到5匹马的速度排序，最少需要多少次比赛;4 有两瓶红蓝墨水，体积一样，现在从红墨水中舀一勺加入蓝墨水中，搅拌均匀后，在从蓝墨水中舀一勺加到红墨水中，试问这两瓶墨水哪瓶纯度更高。 1.将一根木棒截成三段，问能够组成三角形的概率； 来源：https://blog.youkuaiyun.com/weixin_41245919/article/deta
用Python玩转Milvus：从安装SDK到完成第一个向量搜索Demo
本文为Python开发者提供了一份详细的Milvus向量数据库实战指南。文章重点讲解了如何规避版本兼容性陷阱，正确部署Milvus服务端并安装匹配的Python SDK。通过一个电影推荐系统的完整案例，演示了从数据建模、集合创建、向量插入、索引构建到执行混合搜索的全流程，帮助开发者快速上手并完成第一个向量搜索Demo。
Ubuntu虚拟机IP卡在127.0.0.1？别慌，试试这条万能命令 `sudo dhclient`
本文详细解析了Ubuntu虚拟机IP卡在127.0.0.1的常见原因及解决方案，重点介绍了`sudo dhclient`命令的使用方法及其变体，帮助用户快速恢复网络连接。同时提供了网络诊断工具和预防措施，适用于开发者和系统管理员应对网络故障。
Python+Seaborn可视化：双因素方差分析结果这样呈现更专业
本文详细介绍了如何利用Python中的Seaborn和statsmodels库，专业地执行并可视化双因素方差分析结果。通过模拟实验数据，演示了从模型构建、统计检验（包括交互作用分析）到诊断验证的全流程。重点展示了如何使用Seaborn绘制交互效应图、组合分布图及热图，将复杂的统计结果转化为直观、富有洞察力的可视化图表，助力科研论文与商业报告的专业呈现。
# ubuntu 系统使用 yum命令报错 There are no enabled repos.Run “yum repolist all“ to see the repos you have...
# ubuntu 系统使用 yum命令报错 There are no enabled repos.Run "yum repolist all" to see the repos you have.You can enable repos with yum-config-manager --enable
算法真的是个很有用的东西，通过python或者golang实现对N张图片的自动排版，每张图片的长和宽都不一样，有的是横图，有的是竖图，最终需要生成一张完整的矩形图片
算法真的是个很有用的东西，通过python或者golang实现对N张图片的自动排版，每张图片的长和宽都不一样，有的是横图，有的是竖图，最终需要生成一张完整的矩形图片。
ISP 芯片作为独立设备节点的驱动实现要点：多平台结构差异与工程部署策略解析
随着 ISP（Image Signal Processor）从 SoC 集成形态向独立芯片模块演进，在多摄系统、高动态场景、AI 图像前处理等需求下，ISP 作为单独设备节点进行驱动设计已成为主流趋势。尤其在多 SoC 平台（如高通、联发科、展锐、Rockchip）中，不同厂商对 ISP 的抽象方式与驱动接口处理存在显著差异。本文以实际工程为基础，从 Linux 内核下的设备注册流程、media\_entity 配置、VideoDevice 分配、数据 DMA 管理、与 sensor 的流同步控制等关键点展
Vue2 - 最新详细实现接入天地图详细配置流程及示例代码教程，提供获取用户当前定位的城市名称及省市区数据、精准经纬度信息、IP属地定位、地图搜索标点等示例，解决天地图跨域报错/页面白屏/定位不准问题
vue2，nuxt2，天地图，使用教程，vue接入国家天地图，定位api，ip属地定位，获取用户当前位置数据，省市区，地图不显示，白屏，报错，vue2如何使用天地图，地图选点，cors跨域，白名单设置了还是不行，vue项目调用天地图api实现定位，Vue2中使用天地图，详细教程，天地图申请密钥key教程，经纬度，经纬度不准确，一直定位北京，定位在北京不准怎么办，nuxt项目天地图，网站，网页，h5页面，地图显示不全，获取当前定位，如何使用天地图，拿到客户ip地址定位城市名称，距离计算，实时搜索地址逆解析，天
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")
怀旧版电脑GBC模拟器
再现原版GBC掌机的界面，电脑运行连游戏界面都一样大小，很怀旧，很真实，（英文版）强烈推荐，很小的文件，很难才找到的。
jdk-8u144-linux-x64.tar.gz.
该资源为搭建Linux系统Java环境用，资源的后缀为tar.gz格式的压缩包
SIMATIC S7-1200, S7-1500 PID 控制[手册]
SIMATIC S7-1200, S7-1500 PID 控制[手册]
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