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前言
本篇文章主要对 Netflix 进行了基本的介绍，包括什么是 Zuul？Zuul 的过滤器？以及 Zuul 2.x 的工作原理？
Zuul 基本理论概述
1、关于 Zuul
Zuul 提供的三大功能：代理 + 路由 + 过滤
Zuul 包含了对请求的 “ 路由 ” 和 “ 过滤 ” 两个最主要的功能：其中路由功能负责将外部请求转发到具体的微服务实例上，是实现外部访问统一入口的基础，而过滤器功能则负责对请求的处理过程进行干预，是实现请求校验、服务聚合等功能的基础 。
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Spring Cloud
Spring Cloud Zuul微服务网关的API限流
API限流微服务开发中有时需要对API做限流保护，防止网络攻击，比如做一个短信验证码API，限制客户端的请求速率能在一定程度上抵御短信轰炸攻击，降低损失。微服务网关是每个请求的必经入口，非常适合做一些API限流、认证之类的操作，这里有一个基于zuul微服务网关的API限流库： https://github.com/marcosbarbero/spring-cloud-zuul-ratelimit使
springcloud 服务网关Zuul实战(一)基本路由配置
本文介绍了Zuul网关的配置步骤：1. 新建microservicecloud-zuul-gateway-9527微服务项目；2. 在pom文件中添加Zuul、Eureka、Hystrix等必要依赖；3. 在启动类添加@EnableZuulProxy注解启用Zuul代理功能；4. 配置application.yml文件设置端口、服务名和Eureka注册信息；5. 修改hosts文件添加域名映射myzuul.com。配置完成后，可通过Zuul网关地址http://myzuul.com:9527/microse
Spring Cloud 微服务开发：入门、进阶与源码剖析 —— 通篇概述
前因后果 关注小编的同学应该知道，小编去年和出版社约稿写过一本书，后面种种原因放弃掉最后的审校过程了，小编说过会把这本书发出来，这次选择了 优快云 的平台也是因为能赚点小钱，价格也不贵，9.9 元，买不了吃亏买不了上当，不用担心小编烂尾或者书没写完（只是没有经过审校，可能错别字会有不少），全部内容统计将近 18w 字（未计算代码内容），预计一周发两篇到三篇左右的样子（不要问为啥不一周发十几篇，因...
SpringCloud及SpringBoot整合使用, 包含zuul, eureka服务, mybatis做持久层
SpringCloud及SpringBoot整合使用, 包含zuul, eureka服务, mybatis做持久层, Maven模块划分: boot-test父模块, admin, remote-server, eureka-server, ruul-server, commons-api模块; 其中admin用来提供web服务模块; remote-server用来做服务端, 操作数据; eureka-serve作为服务的注册及发现; ruul-server则作为请求的路由和转发; commons-api则是数据表映射的实体.
SpringCloud思维导图(全)
针对尚硅谷SpringCloud视频补全了一部分思维导图，比较完整。我为什么不能把积分调低，真不是想要这么多。
三菱JET伺服限位、原点信号通过PLC刷新设置方法
RWw及RWr软元件的初始设定值，可通过 [Pr. PA01.0 控制模式选择] 及 [Pr. PN22.0 默认映射模式选择] 的值进行切换。JET伺服参数用于CC-Link IE现场网络Basic。循环通信的请求报文与响应报文的收发数据被换读为伺服放大器的对象数据 (RWwn、RWrn)。接下来看映射字2D05具体的内容： 也就是2D05操作字的第九位、第十位、第十一位。响应报文的设定值可进行变更。变更初始设定值时，应变更PDO映射。不同的模式，对应不同的刷新映射内容。GXWORKS3程序。
python 编程 安卓脚本_详解基于Android的Appium+Python自动化脚本编写
1.AppiumAppium是一个开源测试自动化框架，可用于原生，混合和移动Web应用程序测试， 它使用WebDriver协议驱动iOS，Android和Windows应用程序。通过Appium，我们可以模拟点击和屏幕的滑动，可以获取元素的id和classname，还可以根据操作生成相关的脚本代码。下面开始Appium的配置。appPackage和APPActivity的获取任意下载一个app解压...
Python实战：3分钟搞定A股K线数据抓取（附东方财富/腾讯API完整代码）
本文为量化交易初学者提供了一份快速获取A股K线数据的实战指南。通过对比东方财富与腾讯财经两大免费API的优劣，并附上可直接运行的完整Python代码，帮助读者在3分钟内搭建起自己的数据抓取管道，为构建量化交易系统奠定坚实的数据基础。
lvgl 9虚拟键盘控件 (lv_keyboard) 使用指南
是 LittlevGL 提供的一个虚拟键盘控件，适用于在图形界面中输入文本。与按钮矩阵类似，虚拟键盘预定义了多个键位布局，支持多种模式和自定义键位映射，能够满足各种文本输入需求。通过与文本区域 (Text Area) 的结合，键盘能够自动将输入的字符传送到指定区域。控件是 LittlevGL 提供的强大虚拟键盘解决方案，适用于任何需要文本输入的界面。通过预定义的键盘模式、自定义键位映射和文本区域自动填充功能，可以大大提升用户输入体验。无论是在嵌入式系统的用户界面中，还是在其他需要虚拟键盘的应用中，
103：vue+cesium 设置球体水部分颜色为黄色
本示例在 Vue + cesium 项目中设置球体水部分颜色为黄色。这里的操作是将球体内部设置为空的，在地球内部添加一个黄色的圆球，这样水的部分就被突出出来了。直接复制下面的 vue+cesium源代码，操作2分钟即可运行实现效果.
一.镜头知识之焦点，焦距，有效焦距，前焦距，后焦距
由光学系统中镜片的最后一面到像面的距离。
C++幂运算全解析：std::pow()函数详解与实战指南
本文详细介绍了C++标准库中的std::pow()函数，包括其基本用法、数学定义和多种重载形式。重点分析了该函数在科学计算、图形处理等领域的实际应用，并提供了性能优化方案如快速幂算法和预计算表。文章还探讨了精度控制、错误处理等关键问题，最后通过一个完整计算器实现展示了综合应用。std::pow()作为基础数学函数，合理使用能显著提升计算效率和代码可靠性。
RH850从0搭建Autosar开发环境【2X】- Davinci Configurator之EcuC模块配置详解(上)
RH850从0搭建Autosar开发环境【2X】- Davinci Configurator之EcuC模块配置详解(上)
ssm框架搭建
这个是一个在idea上成功跑起来的ssm框架项目，保存在这里方便以后使用直接下载
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")
精雕软件(JDpaint) 5.5 绿色版
精雕软件(JDpaint) 5.5 绿色版精雕软件(JDpaint) 5.5 绿色版精雕软件(JDpaint) 5.5 绿色版
IDEA的使用指南
IDEA基于java的使用手册,可以最快速的上手,在使用中不断的丰富自己的使用技巧.
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