墨衍会员
·
AI 创作全网分发
墨衍智能分发
本文由作者通过墨衍一键同步至各平台
1分发平台
100+累计阅读
97平均单平台
已同步平台
优快云
微信公众号
微博
知乎
掘金
百家号
博客园
抖音
小红书
你的文章也可以这样分发
墨衍会员 ¥399起/年，写一次发全网
我也要
中介者模式是一种行为设计模式，它的目的是减少对象之间的直接通信，将复杂的交互逻辑集中在一个中介者对象中。这种模式可以降低对象之间的耦合性，使得系统更易于维护和扩展。
在中介者模式中，多个对象之间的通信通过一个中介者对象进行协调。对象之间不再直接相互引用，而是通过中介者进行通信。这样，当一个对象发生改变时，只需要通知中介者，而不需要直接影响其他对象。
下面我们将使用C#语言来实现中介者模式的示例。假设我们有一个简单的聊天室程序，其中包含多个用户对象，它们可以相互发送消息。我们将使用中介者模式来管理用户之间的消息传递。
首先，我们定义一个抽象中介者类 Mediator，其中包含一个抽象的发送消息方法 SendMessage：
public abstract class Mediator
{
public abstract C#设计模式教程（18）：中介者模式
中介者模式（Mediator Pattern）是一种行为设计模式，它允许一组对象相互通信，而不需要知道彼此的内部结构。这样做可以降低多个对象和类之间的通信复杂性，将多对多的通信转变为一对多的通信，使得对象之间的耦合松散，并且可以更容易地独立地改变和复用这些对象。Mediator：定义了一个接口，用于与各个Colleague（同事）对象通信。：实现Mediator接口并协调各个Colleague对象之间的交互。Colleague。
C#实现中介者模式
在上面的示例中，我们定义了一个中介者接口IMediator和具体实现类ConcreteMediator，其中ConcreteMediator含有两个组件 ComponentA 和 ComponentB。在具体中介者类中，我们可以根据不同的组件做出不同的处理。在C#中，我们只需要使用抽象类或接口来定义中介者，并在具体中介者类中实现业务逻辑，即可快速实现中介者模式。在C#中实现中介者模式非常简单，我们可以使用接口或者抽象类实现中介者，具体实现可以放在具体的继承类中。
C#中介者模式设计与实现代码实战
在构建可维护、可扩展的软件系统过程中，对象之间的交互复杂度往往会随着功能模块的增长呈指数级上升。尤其是在多组件协同工作的场景中，若缺乏统一协调机制，极易形成“网状依赖”结构，导致代码难以测试、重构成本高、新增功能风险大。为解决这一问题，中介者模式提供了一种将通信逻辑集中管理的设计思路，而其核心起点正是IMediator接口的合理设计。该接口作为整个模式的契约规范，不仅定义了消息传递的基本行为，更决定了系统的解耦程度和未来演进能力。在复杂系统中，多个对象之间频繁交互是常态。
新手避坑指南：用COMSOL Multiphysics仿真气体放电（汤逊vs.流注）
本文详细介绍了使用COMSOL Multiphysics仿真气体放电的实战指南，涵盖汤逊理论和流注模型的实现方法。通过对比两种放电模型的关键参数和物理场配置，帮助新手避免常见错误，准确预测气体击穿过程。文章还提供了工程应用案例，如高压绝缘子设计优化，展示了仿真技术在电力设备绝缘设计中的实际价值。
Pluto SDR新手避坑指南：从环境配置到第一个MATLAB收发程序（附完整代码）
本文为Pluto SDR新手提供全面的避坑指南，从硬件连接到MATLAB环境配置，再到第一个信号收发程序的实现。详细介绍了驱动安装、常见问题排查以及关键参数设置，帮助用户快速上手软件定义无线电(SDR)技术，并附有完整代码示例。
芯片硅前验证技术与驱动工程师实战指南
硅前验证是芯片开发流程中的关键环节，通过pre-silicon simulation等技术手段在流片前验证设计正确性。其核心技术包括软件仿真、FPGA原型验证和硬件仿真器解决方案，涉及Linux驱动开发、芯片架构理解和波形分析等核心技能。作为连接芯片设计与系统集成的桥梁，硅前验证能早期发现设计缺陷，节省数百万美元流片成本。以Cadence Palladium为代表的硬件仿真器，通过专用服务器系统实现比纯软件方案快100-1000倍的仿真速度，为驱动工程师提供完整的外设模型验证环境。掌握硅前验证技术可显著提升
主流算力卡性能对比分析
超大规模AI训练/HPC：优先选择NVIDIA H100或AMD MI300X，需关注显存带宽和多卡互联能力。企业级AI训练：NVIDIA A100性价比更高，生态成熟。国内自主可控需求：华为昇腾910B是首选，支持液冷和低功耗设计。本地AI推理/游戏：NVIDIA RTX 4090或RTX 5090性价比突出，但需注意显存容量限制。边缘计算/低功耗场景：华为昇腾310或清微智能TX536（能效比6.3TOPS/W@INT8）更合适。
模型训练篇 | 手把手教你使用ResNet训练自己数据集和推理，ResNet50通用物体分类识别，含完整代码和数据集，全网最详细教程
手把手教你使用ResNet训练自己数据集和推理，ResNet50通用物体分类识别，含完整代码和数据集，全网最详细教程。本篇文章将带你学习 ResNet50 模型构建、ResNet50训练自己数据集、数据集处理、推理和验证、模型剪枝与模型改进等。 本篇教程适配各种分类识别任务，可以说是一个通用的教程，只需修改自己的数据集即可完成自己领域的任务和系统实现。
读懂ARM汇编程序，你只差这篇！保姆级教程（附完整示例剖析）！
ARM汇编中的符号、标号、伪操作有哪些？C语言和汇编语言如何混合编程？GNU ARM汇编和ARM汇编的区别有哪些？
OpenClaw-遇到的问题- FailoverError: Model context window too small (4096 tokens). Minimum is 16000
摘要：OpenClaw配置文件中deepseek-reasoner模型的上下文窗口被默认设置为4096 tokens（实际支持128K），导致系统报错"Model context window too small"。解决方案是删除配置文件中的contextWindow和maxTokens字段，让系统自动获取模型真实能力，并重启OpenClaw服务。配置文件通常位于用户目录下的.openclaw/openclaw.json中。
二极管限幅电路和钳位电路
二极管限幅电路和钳位电路学习
文档：亲测可用解决vnc灰屏，黑屏，鼠标大黑叉情况
主要解决vnc-viewer灰屏，黑屏，鼠标大黑叉等现象，详细见文档。
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")
jdk-8u144-linux-x64.tar.gz.
该资源为搭建Linux系统Java环境用，资源的后缀为tar.gz格式的压缩包
android 蓝牙通讯demo
android 下蓝牙通讯demo，实现了发现蓝牙，使能蓝牙，通过蓝牙发送消息。
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
订阅专栏 解锁全文
374
扫一扫
