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此压缩包为该文章相关资料，包含代码等，建议使用jupyter打开 https://blog.csdn.net/weixin_39858881/article/details/107235037

用于Macbook、Trackpad等的触控板优化，实现更多操作 使用方法参考：https://blog.csdn.net/weixin_39858881/article/details/107091728

使用MATLAB求解线性规划问题，并将结果和单纯形表导出到Excel，包含简单线性规划问题、无界解的线性规划问题和无穷多解的线性规划问题

本代码为MATLAB solve函数的示例，通过一个线性方程组的求解演示该函数的用法，希望对您有所帮助！！！

用于实现DBCP连接池所用的JAR依赖文件，包括数据库驱动及创建连接池所需的其他依赖： * commons-collections  * commons-dbcp2  * commons-logging  * commons-pool2  * mysql-connector

windows下实现端口转发的工具

插件描述：Validator是一个jQuery验证插件,可以验证文本,文本区,密码复选框并选择元素。验证器支持:需要依赖复选框,最小/最大长度、电子邮件、数量、数字和一些定位的错误消息

When writing a library it is very useful to log information. However there are many logging implementations out there, and a library cannot impose the use of a particular one on the overall application that the library is a part of. The Logging package is an ultra-thin bridge between different logging implementations. A library that uses the commons-logging API can be used with any logging implementation at runtime. Commons-logging comes with support for a number of popular logging implementations, and writing adapters for others is a reasonably simple task. Applications (rather than libraries) may also choose to use commons-logging. While logging-implementation independence is not as important for applications as it is for libraries, using commons-logging does allow the application to change to a different logging implementation without recompiling code. Note that commons-logging does not attempt to initialise or terminate the underlying logging implementation that is used at runtime; that is the responsibility of the application. However many popular logging implementations do automatically initialise themselves; in this case an application may be able to avoid containing any code that is specific to the logging implementation used.

Commons-Collections seek to build upon the JDK classes by providing new interfaces, implementations and utilities. There are many features, including: Bag interface for collections that have a number of copies of each object BidiMap interface for maps that can be looked up from value to key as well and key to value MapIterator interface to provide simple and quick iteration over maps Transforming decorators that alter each object as it is added to the collection Composite collections that make multiple collections look like one Ordered maps and sets that retain the order elements are added in, including an LRU based map Reference map that allows keys and/or values to be garbage collected under close control Many comparator implementations Many iterator implementations Adapter classes from array and enumerations to collections Utilities to test or create typical set-theory properties of collections such as union, intersection, and closure

The Apache Commons Pool open source software library provides an object-pooling API and a number of object pool implementations. Version 2 of Apache Commons Pool contains a completely re-written pooling implementation compared to the 1.x series. In addition to performance and scalability improvements, version 2 includes robust instance tracking and pool monitoring.

非常好看而又实用的HTML页面模板，包括按钮、表单、单选按钮、复选按钮、提示框等

基于jQuery的Validate表单输入验证插件，附带使用示例页面

CryptoTool安装包 CryptoTool安装文件 支持私钥公钥加解密，MD5等操作

压缩包包含wav,mp3,ogg,flac,m4r,mmf,mp2,amr,ape,aac,wma,wv共12种格式的音频样本和mkv,ogg,mp4,3gp,avi,flv,mov,mpg,rmvb,swf,vob,wmv共12种格式的视频样本，供浏览器、播放器等的测试使用

是用Java编写的免费开源的跨平台的 Java API，Apache POI提供API给Java程式对Microsoft Office格式档案读和写的功能。POI为“Poor Obfuscation Implementation”的首字母缩写，意为“简洁版的模糊实现”。

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 com Directories 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 working For references on each library check REFS.md Setup ----- 1. Download. 2. addpath(genpath('DeepLearnToolbox')); Example: Deep Belief Network --------------------- ```matlab function test_example_DBN load 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 weights rand('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 NN rand('state',0) %train dbn dbn.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 nn nn = dbnunfoldtonn(dbn, 10); nn.activation_function = 'sigm'; %train nn opts.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 --------------------- ```matlab function test_example_SAE load 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 FFNN nn = nnsetup([784 100 10]); nn.activation_function = 'sigm'; nn.learningRate = 1; nn.W{1} = sae.ae{1}.W{1}; % Train the FFNN opts.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 --------------------- ```matlab function test_example_CNN load 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% error rand('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 error figure; plot(cnn.rL); assert(er<0.12, 'Too big error'); ``` Example: Neural Networks --------------------- ```matlab function test_example_NN load 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 net rand('state',0) nn = nnsetup([784 100 10]); opts.numepochs = 1; % Number of full sweeps through data opts.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 decay rand('state',0) nn = nnsetup([784 100 10]); nn.weightPenaltyL2 = 1e-4; % L2 weight decay opts.numepochs = 1; % Number of full sweeps through data opts.batchsize = 100; % Take a mean gradient step over this many samples nn = 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 dropout rand('state',0) nn = nnsetup([784 100 10]); nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through data opts.batchsize = 100; % Take a mean gradient step over this many samples nn = 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 function rand('state',0) nn = nnsetup([784 100 10]); nn.activation_function = 'sigm'; % Sigmoid activation function nn.learningRate = 1; % Sigm require a lower learning rate opts.numepochs = 1; % Number of full sweeps through data opts.batchsize = 100; % Take a mean gradient step over this many samples nn = nntrain(nn, train_x, train_y, opts); [er, bad] = nntest(nn, test_x, test_y); assert(er < 0.1, 'Too big error'); %% ex5 plotting functionality rand('state',0) nn = nnsetup([784 20 10]); opts.numepochs = 5; % Number of full sweeps through data nn.output = 'softmax'; % use softmax output opts.batchsize = 1000; % Take a mean gradient step over this many samples opts.plot = 1; % enable plotting nn = 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 data vx = 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 output opts.numepochs = 5; % Number of full sweeps through data opts.batchsize = 1000; % Take a mean gradient step over this many samples opts.plot = 1; % enable plotting nn = 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")

数学建模国赛2014B 优秀论文附件 MATLAB源代码程序 折叠桌问题