R语言中的维度函数和矩阵计算

R语言 专栏收录该内容
¥59.90 ¥99.00
32篇文章

R语言中的维度函数和矩阵计算

在R语言中,维度函数(dim)和矩阵计算是非常有用的工具,它们可以帮助我们理解和操作数据的结构和内容。本文将详细介绍R语言中的dim函数和矩阵计算,并提供相关的源代码示例。

维度函数(dim)

在R中,维度函数(dim)用于获取或设置数据对象的维度。对于矩阵和数组等多维数据结构,维度函数可以提供有关该数据对象的行数和列数等信息。

获取维度

要获取一个数据对象的维度,可以使用dim函数,并将数据对象作为参数传递给它。下面是一个示例:

# 创建一个3行4列的矩阵
matrix_data <- matrix(1:12, nrow = 3, ncol = 4)

# 获取矩阵的维度
matrix_dim <- dim(matrix_data)
print(matrix_dim)

输出:

[1] 3 4

在这个示例中,我们首先使用matrix函数创建了一个3行4列的矩阵(matrix_data)。然后,我们使用dim函数获取了矩阵的维度,并将结果存储在matrix_dim变量中。最后,我们打印了matrix_dim的值,即矩阵的行数和列数。

设置维度

除了获取维度,我们还可以使用dim函数来设置数据对象的维度。这对于重新构造数据对象的结构非常有用。下面是一个示例:


        
R语言使用array函数生成二维度数组、使用dim函数指定数组的维度、生成固定数值的二维数组 阅读详情

相关推荐

R语言中PCA方法实现

Principal component analysis (PCA)sh R语言的基础库stats

Stay Hungry.Stay Foolish. 8502

R语言中的dim函数

R语言中的dim函数在R语言中,dim函数是一个非常常见且实用的函数,用于获取或设置对象的维度。它可以用于矩阵、数组和数据框等数据结构。在本文中,我们将深入探讨dim函数的使用方法,并通过一些示例代码来加深理解。 输出结果为: 在上面的示例中,我们首先创建了一个3行4列的矩阵。然后,通过调用dim函数并将作为参数传递给它,我们获取了该矩阵的维度。最后,我们打印了维度向量,其中第一个元素表示行数,第二个元素表示列数。 输出结果为: 在上面的示例中,我们首先创建了一个长度为10的向量。然后,通过将维度向量赋值给

2301_79331387的博客 4554

R语言中dim函数──向量化计算

例如,对于一个3行2列的矩阵,dim函数返回的结果将是一个长度为2的整数向量,其中第一个元素为3,表示行数;在R语言中,dim函数是一个非常常用的函数,用于获取或设置数组的维度。总结起来,本文介绍了R语言中dim函数的用法,包括获取和设置数组的维度信息。我们通过示例代码演示了如何使用dim函数获取和设置矩阵的维度,展示了它在向量化计算中的应用。通过上述示例,我们可以看到,在R语言中,使用dim函数可以方便地获取和设置数组的维度信息。需要注意的是,改变维度后,数组中的元素顺序可能会发生改变。

DevProPlus的博客 1728

【Creo】关于Creo一换网络就报错许可证丢失问题解决方案

依照我的个人理解,Creo只要一更换网络,则其许可证里面的网卡参数和现在的网络就对不上了,所以就会出现报错,并且有可能是报错代码(-9),提示许可证激活有问题。

weixin_52002180的博客 6036

求一个点到任意两个点所在直线距离 C#代码公式

求点C(X0,Y0) 到点A(x1,y1)和点B(x2,y2)所在直线的距离 1.已知任意两个点,求这两个点所在直线 已知点A(x1,y1),点B(x2,y2) 设直线上另外任意一个点为(x,y) 那么 (x-x1)/(y-y1) = (x2-x1)/(y2-y1); 整理后得方程: (y2-y1)x + (x1-x2)y + (y1-...

qq_29434987的博客 4492

探索热阻测试仪在半导体器件热管理中的应用与前景

T3Ster是MicReD研发的热测试仪,运用JEDEC稳态实时测试方法,专业测试分离或集成的双极型晶体管、MOS晶体管、常见的三极管、LED封装和半导体闸流管等器件的热特性。利用瞬态温升技术,可测得器件稳态热阻和温升,不但可以测得半导体器件稳态热阻和温升,而且可以直接测量各部分对于温升的贡献,计算芯片热流路径上的纵向热阻构成,对器件热可靠性设计、散热问题解决、产品性能提升和长期可靠性至关重要。关键词:T3ster,Micred,功率循环,结温测试,热阻测试,结温热阻测试,半导体热特性测试;

anscos_Olivia的博客 895

别再乱点U盘里的.exe了!手把手教你彻底清除那个伪装成Usb Disk的蠕虫病毒

本文详细介绍了如何识别、清除和预防伪装成Usb Disk的蠕虫病毒。从识别感染迹象到安全备份文件,再到深度清理病毒残留和构建防护策略,提供了一套完整的U盘病毒解决方案。特别提醒用户注意U盘病毒的风险,并养成良好的使用习惯。

weixin_30496999的博客 668

FPGA高速通信双核解析:CMAC与Interlaken硬核的实战对比

本文深入对比了FPGA中两大高速通信硬核CMAC与Interlaken的核心差异与应用场景。CMAC作为标准以太网接入方案,适用于网卡、交换机等需对外互联的场景;而Interlaken则以极低延迟和高效通道化能力,专为芯片间、板间裸数据高速传输设计。文章通过实战案例与性能指标分析,为FPGA高速通信方案选型提供清晰指南。

f9g0h的博客 843

从T型结到Wilkinson:5分钟搞懂功分器选型指南(附最新射频测试数据对比)

本文通过对比T型结、电阻型和Wilkinson三种功分器的核心原理与实测数据,提供了清晰的射频功分器选型指南。文章结合最新的2.4GHz ISM频段测试数据,分析了插入损耗、回波损耗和端口隔离度等关键指标,并针对窄带物联网、射频测试及合成系统等不同应用场景,给出了具体的选型建议和实战提醒,帮助工程师根据系统需求做出最优决策。

bush的博客 305

低速信号设计之 LPC 篇

摘要:LPC总线作为服务器低速信号设计的关键技术,在BIOS芯片连接、Super I/O芯片控制和嵌入式管理等方面发挥着重要作用。其采用33MHz时钟频率和4位并行传输机制,理论速率达16.5MB/s。设计时需重点关注布线长度(建议<10cm)、信号完整性(45°走线、合理层间隔离)和过孔优化等要点。典型应用案例表明,通过规范设计可确保在工业控制等复杂环境中稳定工作。LPC总线凭借低成本、低引脚数和良好兼容性,仍是服务器低速外设连接的重要解决方案。

qq_16237389的博客 1164

Autosar Configuration(十四)SomeIP之配置SoAd

本文主要介绍SomeIp协议栈中的SoAd在Configurator中的详细配置

king110108的专栏 2600

efm32 入门.pdf

本PDF共计22页,介绍的是操作驱动API,下载与否自己斟酌。

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")

Probability and Statistics - 4th edition

Probability and Statistics - 4th edition

TMS_WebGMaps_1.9.8.0_Full_Source

TMS_WebGMaps_1.9.8.0_Full_Source 全源码 delphi cbuilder

上一篇: 使用lmp函数生成置换检验回归分析模型
下一篇: 使用ggplot2包绘制基础分组箱图
普通网友
博客等级 码龄3年 143粉丝 666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