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近期，一项名为TDengine的高性能时序数据库在物联网领域取得了显著的应用成果。通过其出色的存储压缩能力，TDengine成功地降低了存储需求，使得传统的HBase等集群在物联网环境中迅速下线。本文将详细介绍TDengine的应用实例，并提供相应的源代码。
TDengine是一款专为物联网场景优化设计的高性能时序数据库。其具备高效的数据存储和查询能力，尤其在大规模数据写入和实时查询方面表现出色。以下是一个简单的TDengine示例代码，演示了如何创建数据库、创建数据表、写入数据以及进行查询：
# 导入TDengine Python驱动程序
import taos
# 连接数据库
conn = taos.connect(host='localhost', user='root'<TDengine与OpenTSDB对比测试
TDengine与OpenTSDB对比测试 为帮助用户了解TDengine的指标，特将TDengine与OpenTSDB做了读写性能对比测试。 测试环境 对比测试的测试程序和数据库服务在同一台4核8GB的Dell台式机上部署，台式机型号为OptiPlex-3050，详细配置如下 OS: Ubuntu 16.04 x64 CPU: Intel(R) Core(TM) i3-7100 CPU @ 3....
【项目实战】TDengine是一款专门为时序数据设计的数据库，它具有高效的压缩功能，可以显著减少存储空间的使用。TDengine 的压缩比例取决于多种因素，包括数据的特性、压缩算法选择以及数据库配置
TDengine 是一款专门为时序数据设计的数据库，它具有高效的压缩功能，可以显著减少存储空间的使用。TDengine 的压缩比例取决于多种因素，包括数据的特性、压缩算法的选择以及数据库的配置。TDengine 的压缩比通常在 5:1 到 10:1 之间，具体压缩效果取决于数据特性和压缩算法的选择。根据你的数据量 4.7 GB，压缩后的数据大小可能在 0.47 GB 到 0.94 GB 之间。
【项目实战】TDengine的时间戳压缩索引技术入门介绍
时间戳压缩索引技术通过将时间戳进行压缩，减少存储空间占用，同时保持索引的性能，保持时间戳的精度和可靠性。首先，使用一种粗粒度的索引来快速定位到包含目标时间范围的大致区域，然后在该区域内使用细粒度的索引来精确查找数据点。对于具有重复时间戳的情况，可以使用字典编码将频繁出现的时间戳映射为较小的整数或符号，从而减少存储空间。当需要进行数据查询时，使用时间戳压缩索引快速定位到对应的时间段，然后检索该时间段内的数据。通过这些方法，可以将多个连续的时间戳合并为一个较大的时间戳，从而减少存储空间的使用。
Replay音频转换时samplerate缺失的解决方案指南
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什么时候勾选Bake into pose，当不希望动画带动游戏对象旋转时。在旋转的动画中一般是不用勾选的，因为就需要用它来带动我们的角色对象旋转。但在比如前进、后退、待机等动画来说，我们就可以勾选这里，以避免因动画文件本身的瑕疵而给我们带来的旋转。loop match：代表了这个动画在角度上的吻合度。如果这个动画在播放中转角过大，那么就会显示红色，告诉我们如果勾选bake into pose，则会带来不好的效果。
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chroot_list_file=/etc/vsftpd/chroot_list # 对chroot_list下用户限制。[root@localhost ~]# vim /etc/vsftpd/vsftpd.conf #打开配置文件。# 指定用户的shell： -s /usr/bin/nologin。# 在配置文件中找到以下几行代码, 去掉注释(去掉 "#"号)# 指定用户主目录：/data/ftp/ftpuser -M。（2）修改配置文件，路径/etc/vsftpd/ 下的文件。
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在编写接口自动化测试用例或其他脚本的过程中，经常会遇到需要绕过用户名/密码或验证码登录，去请求接口的情况，一是因为有时验证码会比较复杂，比如有些图形验证码，难以通过接口的方式去处理；再者，每次请求接口前如果都需要先去登录一次，这样不仅效率低，还耗费资源。 有些网站是使用cookie辨别用户身份的，此时我们便可以先登录一次，拿到登录成功后的cookie，后续请求时在请求头中加入该cookie，便可保持登录状态直接请求。
Linux下开机异常(fsck exited with status code 4)
Linux下开机异常事情是这样的，本来我呢跑着程序听着歌，结果忽然就死机了，我只能强制关机，再次打开系统的时候，就提示这样fsck from util-linux 2.26.2 /dev/sda10 contains a file system with errors, check forced. /dev/sda10: Inodes that were part of a corrupted pr
飞塔CVE-2022-40684复现
admin 无密码直接回车（会提示修改密码）
NX二次开发常用函数——从一个坐标系到另一个坐标系的转换（UF_MTX4_csys_to_csys ）相同体坐标转化
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pscad IEEE标准系统搭建说明
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TMS_WebGMaps_1.9.8.0_Full_Source
TMS_WebGMaps_1.9.8.0_Full_Source 全源码 delphi cbuilder
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")
复分析 阿尔福斯(Ahlfors) 第三版答案 英文
答案分为十章，有部分习题没有答案，全英文复分析阿尔福斯(Ahlfors) 第三版答案
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