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http://noi.openjudge.cn/ch0205/1805/
算法 暴力搜索，Dfs；
注意 有些情况下可以直接判断：
1.把要“剪碎”的数全都拆成一位数，此时和最小，如果这种情况下和都比目标数大，说明没有成立的情况，输出“error”；
2.要“剪碎”的数本身比目标数还小（或相等），那么最优的情况就是不进行任何处理，直接输出两遍原数。
代码实现
</pre><div><span style="font-size:14px"></span><pre name="code" class="cpp">#include<iostream>
#include<algorithm>
#include<cmath>
#include<cstring>
#define inf 210000000;Axure原型模板与元件库APP交互设计素材（附资料）
在Axure中，手机模型是展示APP或小程序界面效果的重要工具。通过导入手机模型元件，设计师可以更加直观地模拟真实设备的显示效果，从而更准确地评估设计的可行性和用户体验。Axure官方及第三方资源库中提供了多种品牌和型号的手机模型，如iPhone、Android等，设计师可以根据项目需求选择合适的模型进行使用。Axure作为一款强大的原型设计工具，为APP和小程序的设计与开发提供了丰富的界面组件元件库和交互设计素材。
IC芯片验证 - 手把手教你搭建UVM验证环境
这是一个UVM的demo项目
LabVIEW 调用MATLAB 进行图像处理的实现
程机械及设备的变形、振动、冲击、粗糙度、刚度和强度等特性，还可用在土木结构、水利设施的变形测量[4][5]。因此，该技术在机械、土木、水利、电器、航空航天、兵器工业及生物医学等领域的检测中具有非常重要的地位。将物面前方空间的散斑用摄像设备记录下来，当物体运动或由于受力而变形时，这些随机分布的散斑图也随之在空间按一定的规律运动，因此能用记录的散斑图在计算机上分析物体的运动和变形，这就是电子散斑技术。当物体的漫反射表面被激光照射时，漫反射的光波在物体表面的前方相互干涉，由于漫反射光的。
《华为项目管理之道》第1章笔记
performance indicator，KPI)纳入个人绩效承诺书，贯彻结果导向的理念，以向员工。客户需求是华为发展的原动力，为客户服务是华为存在的唯一理由。目经理‘用兵’，依靠市场机制‘调兵’”，打破了人员在功能部门的板结，实现了听得。为管理者选拔的必要条件，项目经理成为人人向往的岗位。《华为项目管理之道》，是新出的华为官方的项目管理书，整个书不错。伴随着项目型组织的建立，华为逐步形成了完备的项目管理流程和制度，从而将业务运。为此，通过改革，重新明确了项目经理承担的经营责任，并开始以项。
msf使用木马控制android手机
使用msfvenom生成木马控制手机
从零搭建Obsidian智能知识库：Ollama本地模型与Copilot插件实战指南
本文详细介绍了如何从零搭建Obsidian智能知识库，结合Ollama本地模型与Copilot插件实现高效知识管理。通过本地部署AI助手，用户可以在保证隐私安全的同时，快速检索和整理笔记，提升工作效率。文章涵盖硬件准备、Ollama部署、插件配置及实战技巧，适合需要高效知识管理的专业人士。
“反激电源”是AC/DC隔离电源还是DC/DC隔离电源？
所以在一些场景，我们的输入电压是一个DC，但是我们仍然需要隔离电源的需求，我们就会只采用图9.2的右半部分。在这个场景下“反激电源”是DC/DC。所以此AC/DC的本质是将AC变成高压的DC之后，用带隔离变压器的反激式开关电源产生一个隔离的、稳定的直流电压。一般在讲述各种电源的拓扑的文章和书籍都是在讲述AC/DC的电源拓扑分类的时候，讲述Flyback。反激式开关电源是指使用反激高频变压器隔离输入输出回路的开关电源，反激式隔离变压器开关电源重点表述的是开关电源包含变压器的拓扑结构。
Vue - 简约大气 404 页面
效果图 实现代码 复制即可。 <template> <div> <div class="error"> <!-- 图片可保存到本地 --> <img src="https://img-blog.csdnimg.cn/20210715111039922.png"> <p>抱歉！你访问的页面不存在或已删除 :( </p> <span class="back" @click="go(
VC中sqlite的使用
在VC中对sqlite的使用，实现数据库大批数据的快速插入，读取数据库中的数据在ListCtrl中显示
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
ET2012新版免狗免安装绿色软件
ET2012新版免狗免安祖昂绿色软件。，新界面，新视觉，
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