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
1.3k累计阅读
1.3k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
图是一种非线性表数据结构。
图中的元素我们就叫作顶点（vertex）。
一个顶点可以与任意其他顶点建立连接关系，这种建立的关系叫作边（edge）。
跟顶点相连接的边的条数，叫作顶点的度（degree）。
边没有方向的图就叫作“无向图”。
边有方向的图叫作“有向图”。
有向图中，把度分为入度（In-degree）和出度（Out-degree）。
顶点的入度，表示有多少条边指向这个顶点；
顶点的出度，表示有多少条边是以这个顶点为起点指向其他顶点。
带权图中，每条边都有一个权重 （weight），可以通过这个权重来表示 QQ 好友间的亲密度。
邻接矩阵的底层依赖一个二维数组，
深入理解图结构：邻接矩阵与邻接表的C++实现
邻接矩阵是一种用于表示图结构的二维数组。对于无向图和有向图，邻接矩阵的表示有所不同。对于无向图来说，邻接矩阵是对称的，意味着如果节点i和节点j之间有一条边，则矩阵中的(matrix[i][j])和(matrix[j][i])都为1（或边的权重，如果图是加权的）。如果两者之间没有边，则为0。例如，考虑以下无向图：1---2| |3---4其对应的邻接矩阵是：1 2 3 41 0 1 1 02 1 0 0 13 1 0 0 14 0 1 1 0对于有向图，邻接矩阵可能非对称。
STM32+Air724UG基于阿里云物联网平台的OTA远程升级实战指南
本文提供了一套基于STM32微控制器与Air724UG 4G Cat.1通信模组，通过阿里云物联网平台实现OTA远程升级的完整实战指南。文章详细阐述了BootLoader与用户程序的分工、云端配置、固件下载与烧写流程，并分享了关键代码实现与常见问题排查方法，帮助开发者快速为物联网设备部署稳定可靠的无线升级功能。
Godot 城市模拟 – 005 OpenStreetMap 介绍：数据结构与详细示例
**摘要：OpenStreetMap（OSM）是一个开源协作奔驰地图的全球地图项目，采用社区编辑模式Tube数据自由共享。其核心数据结构包括节点（Node）、路径（Way）和关系-关系（Relation），通过标签（Tag）描述属性。节点代表坐标点，路径由节点序列组成线或面，关系则关联多个元素表达复杂地理实体。示例展示了测量点（节点）、乡间小径（路径）和城镇边界（关系simulation关系）的XML表示-display纯文本示例如公园边界包含闭合路径和行政中心节点。OSM的轻量级数据模型支持ising开放
FPGA学习之路—应用程序—原码二位乘法器及Verilog代码分析
FPGA学习之路——原码二位乘法器及Verilog代码分析 原理 原码乘法可以分为原码一位乘和原码二位乘，两者在实现规则上大同小异。原码一位乘每次判断乘数的最低位，对被乘数和部分积进行相应操作。而原码二位乘则是对乘数的低二位进行判断，并执行相关操作。 两位乘数的取值可以有四种可能组合,每种组合对应于以下操作: 00相当于0x，部分积Dresult右移2位，不进行其他运算； 01相当于1x，部分积D...
华大HC32F460 TCP Client 实验
华大HC32F460 TCP Client 实验
tuts4you-lena151-Crackme-tutorial06
一、前言 本次研究的是lena151-Crackme-tutorial06，也是一个应用程序，但因为平台迭代的关系，程序不能正常运行，只能完成部分破解。 （一）·本次我们要破解的目标是个应用程序，如下图 (二)、使用工具：x32dbg 二、过程 这个程序的破解的方法很多，首先最简单的一种不外乎就是搜索字符串，我就按照这个思路来完成。 （一）查找字符串<Unregis...
基于stm32的数控恒压恒流电源设计方案
基于stm32的数控恒压恒流电源设计方案 基于stm32的数控恒压恒流电源设计方案 不包实物，可自己动手制作，可代做 本设计采用220V市电输入工频变压器,将220V交流电压降为24V交流电压,经过全桥整流加电容滤波,输出约32V直流电压.可调输出电压方案采用线性稳压调整器方案,即运放处于比较调整状态,运放输出驱动P型MOS管,电压输出端由电阻分压反馈至运放同相输入端,运放反相输入端由STM32单片机控制TLC5615数模转换器输入模拟电压,根据运放构成比较器原理,运放的同相端和反相端始终趋向于电压相等的特
配置 SAP Web Dispatcher 的实战指南：打通 SAP Fiori、SAP Gateway 与前端系统的关键一跳
SAP Web Dispatcher 在 Fiori 架构中扮演关键角色，作为统一入口处理 HTTPS 请求、实现安全代理和流量分发。它将分散在不同系统的访问路径统一收口，解决浏览器同源策略限制，集中管理 HTTPS 终止和客户端信息传递。典型配置中，Web Dispatcher 监听 443 端口，根据路径将 OData 请求转发至 Gateway 系统，静态资源请求送至前端服务器。SSL 相关配置（如 PSE 文件）确保安全通信，而路由规则（wdisp/system_xx）决定请求转发目标。实施时需注意
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。
LinePhone3.2.7(android端最新版本) sip客户端
SIP客户端For android：LinePhone3.2.7(最新版本)，官网地址：http://www.linphone.org/
jre9下载 jdk9
(Java SE Runtime Environment 9.0) 9.0u175 官方最新版 【x86|x64】
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")
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
669
扫一扫
