墨衍会员
·
AI 创作全网分发
墨衍智能分发
本文由作者通过墨衍一键同步至各平台
1分发平台
2.1k累计阅读
2.1k平均单平台
已同步平台
优快云
微信公众号
微博
知乎
掘金
百家号
博客园
抖音
小红书
你的文章也可以这样分发
墨衍会员 ¥399起/年，写一次发全网
我也要
R语言：生成结果汇报的HTML文件
近年来，R语言在数据分析和可视化方面的应用越来越广泛。其中，使用R Markdown将代码、结果和文本整合到一个HTML文件中，可以方便地分享分析过程和结果。本文将介绍如何使用R语言生成结果汇报的HTML文件，并附上相应的源代码。
首先，我们需要安装并加载必要的R包。在R控制台中输入以下命令：
install.packages("rmarkdown")
library(rmarkdown)
接下来，我们创建一个新的R Markdown文档。在R控制台中输入以下命令：
rmarkdown::draft("result_report.Rmd", template = 【R语言绘图】R在气象、水文中数据处理及结果分析、绘图技术
本文详细讲解了R语言在气象水文领域的数据处理及绘图高阶，以及一些常用气象水文图形的汇制技术
使用R语言和R Markdown（Rmd）可以方便地生成漂亮的HTML输出。本文将介绍如何设置Rmd文件以生成具有吸引力的HTML输出。
使用R语言和R Markdown（Rmd）可以方便地生成漂亮的HTML输出。本文将介绍如何设置Rmd文件以生成具有吸引力的HTML输出。首先，我们需要了解R Markdown的基本结构。Rmd文件由普通文本和代码块组成。文本使用Markdown语法编写，而代码块使用三个反引号（```）括起来，并指定代码块的语言（例如R）。
R语言使用R2HTML包以HTML格式输出文本、表和图、形成结论报告
R语言使用R2HTML包以HTML格式输出文本、表和图、形成结论报告
R语言R-markdown实战示例、R-markdown、R-markdown生成结果汇报的HTML文件
R语言R-markdown实战示例、R-markdown、R-markdown生成结果汇报的HTML文件
使用R语言生成R Markdown报告
在这一部分，您可以总结您的分析结果，并得出结论。保存文件并将其命名为`report.Rmd`（或您喜欢的其他名称）。## 步骤3：编写R代码和文本内容在R Markdown文档的每个部分中，您可以编写R代码和文本内容。在第一部分中，您可以编写用于数据导入和清洗的R代码。在第二部分中，您可以编写用于数据分析和可视化的R代码。在第三部分中，您可以编写总结和结论。例如，以下是一个简单的示例：# 第一部分：数据导入和清洗。
R语言使用knitr生成机器学习模型全流程步骤示例：knitr与自动化结果报告、knitr常用参数
R语言使用knitr生成机器学习模型全流程步骤示例：knitr与自动化结果报告、knitr常用参数
autoware 激光-相机联合标定
autoware进行激光-相机联合标定 ~~~ 刚开始进行激光-相机联合标定的时候，出现了不少问题，在此记录一下整体标定流程，以防忘记。在此，特别感谢佳明师弟，在录制数据集时提供的帮助。 ~~~ 0.系统配置： Ubuntu18.04系统 ROS Melodic opencv 3.2.0 vlp-16 一.安装autoware 这部分网上教程挺多的，但我在编译的过程中仍存在不少bug,唉～_～。 二．安装calibration_camera_lidar，从a...
点云直线检测-3DLineDetection复现
点云直线分割-Fast 3D line detection
JavaSpringBoot + VUE 相关开发环境配置合集
1.Java环境-JDK安装 参照：Java基础1-环境篇：JDK安装与环境变量配置 Java基础1-环境篇：JDK安装与环境变量配置_第一段代码的博客-优快云博客_jdk 2.Web服务器-Tomcat安装 原创Java服务器Tomcat安装和配置 3.Maven(后面为了项目兼容性,使用了3.0.5版本) 原创Maven安装和配置（Maven3.0.5） 4.nginx 原创nginx轻量级Web服务器安装和配置 5 数据库mysql 原创数据库Mysql安装和配置 ...
PSINS工具箱15状态组合导航仿真程序(test_SINS_GPS_153)浅析-卡尔曼滤波设置+导航解算
文章目录test_SINS_GPS_153源码poserrsetkfinitgabiaskfupdate流程 test_SINS_GPS_153源码 poserrset function poserr = poserrset(dpos0, dlon, dhgt) % position errors dpos=[dlat;dlon;dhgt] setting. % % Prototype: poserr = poserrset(dpos0) % Input: dpos0=[dlat; dlon; dhgt],
b站up“麦辣翅翅”《YOLOv8修改损失函数》问题解决RuntimeError: The size of tensor a (4) must match the size of tensor
b站up“麦辣翅翅”《YOLOv8修改损失函数》问题解决RuntimeError: The size of tensor a (4) must match the size of tensor ；TypeError: bbox_iou() got an unexpected keyword argument 'xywh‘
华为OD机考双机位C卷 - 螺旋数字矩阵 （Java & Python& JS & C/C++ & GO ）
华为OD机考双机位C卷 - 数字螺旋矩阵（OD上机考试双机位C卷）。提供在线OJ刷题。Java、C++、Python、GO语言、C语言、JavaScript题解，并提供完整思路讲解。
FPGA原型验证（八）：SoC设计适配与实现策略
时钟门控在SoC设计中很常见，应该小心处理门控时钟，以成功地在FPGA上原型化SoC设计。现代FPGA综合工具在正确约束的情况下，可以自动处理大多数门控时钟。通过遵循本章中的指导方针，即使具有复杂时钟门控的SoC设计也可以得到处理，并成功地在FPGA上进行原型化。
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
VB摄像头监控
该VB模块可对摄像头捕获的画面进行录像等，可在创建的窗口进行控制
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
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")
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
订阅专栏 解锁全文
1169
扫一扫
