使用boost::fusion::as_nview进行序列视图转换

C/C++ 专栏收录该内容
¥59.90 ¥99.00
152篇文章

使用boost::fusion::as_nview进行序列视图转换

boost::fusion库提供了一组操作序列的工具,可以让我们使用集合的方式来操作C++中的基本数据类型。as_nview是其中的一种实用工具,它能够将序列转换为视图,并在视图范围内执行想要的操作。

示例程序:

#include <iostream>
#include <boost/fusion/container.hpp>
#include <boost/fusion/algorithm.hpp>
#include <boost/fusion/view.hpp>

int main()
{
    // 定义一个Fusion序列
    boost::fusion::vector<int, double, char> fusion_seq(42, 3.14, 'a');

    // 将序列转换为视图
    auto view = boost::fusion::as_nview<2>(fusion_seq);

    // 对视图上的元素执行for_each操作
    boost::fusion::for_each(view, [](auto& elem) {
        std::cout << elem << " ";
    });

    return 0;
}

在这个示例程序中,我们首先定义了一个包含int、double和char三种类型的Fusion序列fusion_seq。然后,我们使用as_nview将该序列转换为

SECS/GSM 测试工具 SecsExpress 的配置与实战应用 本文详细介绍了半导体设备通讯测试工具SecsExpress的配置与实战应用。作为专业的SECS/GSM协议调试工具,它能模拟Host与设备端进行双向测试,帮助工程师快速部署、验证通讯链路,并高效排查与RMS系统或真实设备间的联调问题,是提升半导体工厂设备集成与维护效率的利器。 阅读详情

相关推荐

Vector CANape的使用记录

开门见山,今天想和大家分享下CANape使用记录,分享原因是近日在分析一个客户现场软件问题时,需要用到CANape来实时的监测ECU中的变量,因为之前对CANape的认知比较浅薄,觉得标定工程师使用他就足够了,但是这次的经历让我觉得需要写点东西来记录自己学习并使用他的过程。 相信大家能够在网上找到很多关于如何使用CANape的文章,从新建Project到后面的数据的观测、保存以及回放等等。我个人也是这么学过来的,所以我不打算从那些方面着手去描述如何使用她,而是根据我的真实使用过程以及问题的抛出到如何解决来

2万+

显著性算法 + 伪彩变换

利用opencv得到显著图和伪彩图

正在挣扎中的人 2298

MTK 人工智能生态系统 <一> 简介

Neuropilot

New_Tank的博客 3495

Exposure Fusion

MertensTMO原理详解: 文章:2007年Exposure Fusion 论文链接:http://ntp-0.cs.ucl.ac.uk/staff/j.kautz/publications/exposure_fusion.pdf 论文代码:C版本:https://github.com/JawThrow/ExposureFusion python版本:https://github.com/lzocro/Exposure-Fusion matlab版本:https://github.com/Mericam

大长腿不解释 1652

直流有刷电机控制 - 驱动电路(二)

1.如图所示为最常见的推挽驱动电路,我附加了具有正反转功能的电机驱动控制,正反转是由两个继电器组成。使用推挽电路来驱动NMOS管时充电电流和放电电流都很大,因此NMOS管导通快,关断也快,而且NMOS温度不会很高。下面来介绍一下该电路的原理。(1)PWM_MCU是由单片机给出的PWM波信号,该信号是由一些列高低电平的方波构成。当信号为高电平的时候Q3导通,经过R47再到Q5的g极,此时mos导通,如果是K1闭合是正转那么K2闭合就是反转。(2)当PWM低电平时使得Q3关断,Q6导通。

u011030644的博客 4720

Creo报许可证错误License request failed for feature的解决方案

有些朋友用了一段时间打开creo软件,出现icense request failed for feature”错误,这个是Creo许可证出现了问题,一般是许可证文件无效或是已过期,导致Creo软件通信出现故障,就会出现Creo License request failed for feature, Requsesting CreoPMA_SSQ ,Run ptcstatus.bat server status。第三步:键盘按win+R,在运行窗口里输入:cmd,然后点确定。第八步:点击系统,找到系统信息。

Q82556499的博客 3万+

基于C#的ASP.NET开发的党史学习网站课设

开展党史学习教育,就是要以史为镜、以史明志,把苦难辉煌的过去、日新月异的现在、光明宏大的未来贯通起来,在鉴往知来中砥砺前行,在乱云飞渡中把准航向,在凝心聚力中团结奋斗,共同创造新的历史伟业。登陆按钮与数据库进行连接,逻辑实现是如图3.1.3所示,自己定义一个SQL查询语句,将账号、密码放入SQL查询语句中,用sqlDataReader.HasRows语句进行逐一比较,找出结果,将用户名存入session并调用cookies,如果结果存在就关闭数据库,进行数据跳转,并弹出登陆成功的提示框,并关闭数据库。

m0_62408555的博客 974

uniapp - 最新实现APP连接使用MQTT流程及源码教程,uniapp app端接入mqtt发布订阅和接收消息队列传送消息、断开重连等各种示例代码(完美兼容安卓Android与苹果ios手机)

uniapp app端接入mqtt示例代码教程,uni-app中使用mqtt,uniapp安卓app接入mqtt,uniApp苹果ios集成MQTT教程,uniapp vue3 mqtt app端的接入流程,uniapp开发安卓和ios如何在项目里集成mqtt,Mqtt协议,uniapp开发APP,主动连接mqtt,订阅消息,实现发布,订阅和接收消息,如何在uniapp项目中使用mqtt,uniapp mqtt的使用示例,mqtt连接报错,搭建MQTT通讯做聊天项目,uniapp与设备通信 通过mqtt实现

🏆 前端领域优质创作者 6570

Devexpress16.2.6破解工具

Devexpress16.2.6破解工具Devexpress16.2.6破解工具Devexpress16.2.6破解工具Devexpress16.2.6破解工具

C# WinForm 基于DirectShow.dll编写的程序,可边预览边保存视频,音频和图片

基于微软的DirectShow.dll的二次开发1.自动识别连接的摄像头跟麦克风且可以对其进行选择(默认选中第一个)2.可录制wmv和avi两种格式的视频并且都带有音频,avi可以录制的分辨率和帧数进行选择,wmv默认进行了淸晰度优化如不喜欢可去除代码中有相关提示3.可在预览点开之后进行拍照,也可边录制视频边拍照4.添加了视频和音频的压缩功能(酌情使用)这次二次开发发现C#对DirectShow的相关信息太少,这次做完也不敢藏私共享出来,还望大家指正

RU DOS版、EFI版32bit/64bit BIOS常用工具

DOS版、EFI版的RU工具,可用来查看IO、MEM、PCI、SMBIOS等资源。亲测在EFI SHELL下可用。

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');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

上一篇: 深度优先搜索的Boost库应用示例
下一篇: QML实现示波器界面
普通网友
博客等级 码龄3年 86粉丝 269原创
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符  | 博主筛选后可见
 
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付:10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值