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我们在使用libaio库的时候，在编译时可能会报如下错：
undefined reference to ‘io_getevents’
undefined reference to ‘io_setup’
undefined reference to ‘io_submit’
这时，只需要在编译中要加 '-laio’参数即可
同样的还有thread，如报如下错：
undefined reference to ‘pthread_create’
undefined reference to ‘pthread_join’
加’-lpthread’参数即可
g++ -O2 test.c -o test -laio -lpthread
模板匹配加速之金字塔分层搜索
图像金字塔是一种多分辨率图像表示方法，它通过一系列逐渐缩小或放大的图像来表示原始图像的不同尺度。高斯金字塔（Gaussian Pyramid）：通过不断下采样（降采样）生成更小的图像。拉普拉斯金字塔（Laplacian Pyramid）：通过高斯金字塔和差分操作生成一组残差图像。在模板匹配中，我们主要使用高斯金字塔来实现多尺度匹配。金字塔灰度模板匹配通过多尺度搜索策略，在保持较高准确率的同时显著提升匹配速度，尤其适用于：高分辨率图像处理实时检测系统移动端设备等资源受限场景。
驱动函数分类
ExXxx(): ExAllocatePoolWithTag() ExAcquireFastMutex ExGetPreviousMode IoXxx():io管理器相关的KeXxx IoCreateDevice IoCreateSymbolicLink IoGetCurrentIrpStackLocation IoAttachDeviceToDeviceStack I...
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一个基于Fluent的超声防堵算例
The method xxx() of type xx must override or implement a supertype method
出错情况：The method xxx() of type xx must override or implement a supertype method 在一个UserDao中定义了几个方法，然后在UserDaoImpl中实现方法（几个方法先后写的），原本前面几个写着标注@Override都没问题，但到后面实现的两个方法都出现了该错误：The method xxx() of type xx ...
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今天用IDEA打开之前的springboot项目，运行时，出现错误：找不到或无法加载主类 io.admin.AdminApplication 百度后发现，出现这个错误，很大可能是idea本身的缓存造成的，所以可以选择清理缓存并重启IDEA，选择file–>Invalidate catchs/Restart; 还可以尝试mvn clean/install 一下 ...
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Invalid HTTP_HOST header: 'xxx.xx.xxx.xxx:8000'.
用python3 manage.py runserver 0.0.0.0:8000命令运行django程序后，通过浏览器访问服务器网址的8000端口，出现访问错误，报错为 Invalid HTTP_HOST header: ‘xxx.xx.xxx.xxx:8000’. You may need to add ‘xxx.xx’ to ALLOWED_HOSTS 解决办法： 修改创建项目时生成的set...
logisim中，如何设计一个位于十字路口的交通信号灯控制系统？
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组合预测模型 | LSTM-XGBoost长短期记忆神经网络结合极限梯度提升树时间序列预测（Matlab程序）
组合预测模型 | LSTM-XGBoost长短期记忆神经网络结合极限梯度提升树时间序列预测（Matlab完整程序）
Undefined Reference to Typeinfo
转载自:转载自:http://richardustc.github.io/blog/2013/07/undefined-reference-to-typeinfo/ 在项目中遇到了这样一个问题：C++文件编译都OK，但链接的时候报错：undefined reference to `typeinfo for xxx’。typeinfo是C++中的RTTI(RunTime Type Identif...
visio安装包
Microsoft Office Visio 是一款专业的制图软件。利用经过更新的形状、协作工具和数据链接图表，创建专业级图表以简化复杂的信息。
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
code blocks 新建静态库工程 在其他项目中链接导入静态库 编译，解决: undefined reference to xxx
静态库 动态库 区别 静态库 (扩展名为 .a 或 .lib) 是包含函数的文件，用于在link阶段整合执行程序，动态链接库(扩展名 .dll)是不在link阶段整合进执行程序中的。DLL文件在执行阶段动态调用。 嵌入式开发 一般用 静态库 lib ...
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【编译错误】xxxx.cpp:(.text+0x190): undefined reference to `std::ios_base::Init::Init()‘
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Linux系统编程手册 错误 undefined reference to `usageErr‘ errExit errExit getLong cmdLineErr collect2: erro
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undefined reference to 与 /usr/lib/ld: cannot find -l
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C/C++编译和链接原理,undefined reference
因此，我们需要注意，在链接命令中给出所依赖的库时，需要注意库之间的依赖顺序，这样才能真正避免undefined reference的错误，完成编译链接。依赖其他库的库一定要放到被依赖库的前面，5. 在c++代码中链。
2021-07-09
/usr/bin/ld: /tmp/ccBYHb6O.o: in function __static_initialization_and_destruction_0(int, int)': test1.cpp:(.text+0x44): undefined reference tostd::ios_base::Init::Init()’ /usr/bin/ld: test1.cpp:(.text+0x59): undefined reference to `std::ios_base::Init::~In
Apache配置压缩优化时报错——undefined symbol: inflateEnd
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WW3 Quick Start 踩坑记录
WW3 Quick Start碰到的2个BUG，以及解决方案
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