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
6.2k累计阅读
6.3k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
var options = $("#sourceDG" ).datagrid("getPager").data("pagination").options;
curr = options.pageNumber;
console.log(curr);
size = options.pageSize;PWM控制技术+Simulink仿真详解
想要理解SVPWM控制技术，首先要了解PWM控制技术。 PWM控制技术就是对脉冲宽度进行调制的技术，通过对一系列脉冲宽度进行调制来等效获得所需要的波形（包括形状和幅值）。 PWM控制的基本原理：冲量（窄脉冲的面积）相等而形状不同的窄脉冲加在具有惯性的环节上时，其效果（输出响应波形）基本相同，即面积等效原理；用一系列幅值相等，宽度按一定规律变化的高频脉冲序列代替期望的输出波形，多用于逆变电路的控制； PWM信号生成的方法有：计算法、调制法、跟踪法，主要对调制法进行详解 ...
easyUI 获取当前页数
在使用easyUI控件添加或修改完成后，需要对页面进行刷新，这就涉及到获取当前页数的问题了，方法如下： 如果用datagrid则：$('#tt').datagrid('options').pageNumber 如果用easyui-pagination则：$('#pp').pagination('options').pageNumber 就可以获取当前页数
深入解析CRC-16校验算法及其Verilog硬件实现
本文深入解析了CRC-16校验算法的原理及其在Verilog中的硬件实现方法。从基础的模2除法到串行和并行实现方案，详细介绍了LFSR结构、状态转移分析以及工业应用中的优化技巧。通过实际案例和性能对比，帮助开发者理解如何选择适合的CRC实现方案，确保数据传输的可靠性。
JQuery EasyuidataGrid控件获取pageSize,pageNumber等方法
1. 获取datagrid当前页码跟页面记录数的方法为： 第一种写法 : currPageNumber:代表第几页 currPageSize：代表currPageNumber那一页的数据条数 获取页码：var currPageNumber = $("#dg" ).datagrid("getPager" ).data("pagination" ).optio...
大疆机场及无人机上云（航线规划、指令飞行...）
最近基于大疆上云api进行二次开发，后面将按照开发步骤对其进行说明！
Win32逆向必备：X32dbg中窗口句柄查看与消息断点的高级用法
本文深入探讨了X32dbg在Win32逆向工程中的高级应用，重点解析窗口句柄查看与消息断点的实战技巧。通过MFC程序逆向案例，详细介绍了如何利用窗口句柄层次结构、条件断点设置及消息映射表定位等技术，快速捕获关键业务逻辑，提升逆向分析效率。
easyui的datagrid获取当前页码和每页显示条数
easyui的datagrid获取当前页码和每页显示条数 var options = $('#' + j + '').datagrid('getPager').data("pagination").options; var pagej = options.pageNumber; //当前页数 var rowsj = options.pageSize; //每页的记录数（行数）...
基于 STM32 的乒乓球发球机控制系统设计与实现
乒乓球发球机是一种能够按照预设参数自动、连续发出乒乓球的训练设备。本系统以 STM32F103C8T6 为主控核心，通过控制实现球速调节、实现落点与旋转方向控制、实现出球节奏控制，并配合 OLED 显示与按键人机交互，实现速度、频率、落点、旋转四大参数的可调发球训练。
获取datagrid当前页码跟页面记录数的方法为
获取datagrid当前页码跟页面记录数的方法为： var options = $("#dg" ).datagrid("getPager" ).data("pagination" ).options; var curr = options.pageNumber; // alert(curr); var size = options.pageSi
KEPServerEX作为MQTT客户端
打开软件： 测试没有连接上MQTT代理。
YOLOv8n轻量化实战：10分钟打通实时摄像头目标检测，附参数调优与避坑指南
本文介绍了如何快速入门YOLOv8目标检测框架，通过摄像头实战实现实时检测。YOLOv8凭借轻量化架构和跨平台优势，是新手入门的理想选择。文章详细讲解了环境搭建（1分钟完成）、核心代码逻辑（3分钟掌握）以及性能调优技巧（3分钟进阶），提供可直接运行的代码示例。重点包括：模型选择（优先yolov8n.pt）、摄像头参数优化、置信度阈值调整、检测类别指定等实用技巧，帮助开发者快速实现10-15FPS的实时检测效果，并针对不同场景提供优化建议。
easyui 获取记录数 页数 当前页
需求： easyUI获取当前页，总记录数，页数 解决： var grid = $('#table'); //这个是这个表格的tableid var options = grid.datagrid('getPager').data("pagination").options; //当前页 var curr = options...
芒果YOLOv7改进48：主干篇：独家首发改进：目标检测新范式骨干PPHGNetv2改进版，充分融合信息特征，提升YOLOv7检测能力
YOLOv7代码改进实践 | 独家首发改进 PPHGNetv2 改进版结构
easyui datagrid获取当前页码（分页情况下）
功能：在分页情况下，获取datagrid的页码 方法
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。
霍尼韦尔SoftMaster使用说明
霍尼韦尔SoftMaster使用说明
easyui- datagrid 获取全部数据
$("#gridData").datagrid("getRows"); 原博 :https://blog.youkuaiyun.com/world_the_begin/article/details/48710835 var index = $("#gridData").datagrid("getRows"); for(var i=0;i<rows.length;i++){ index...
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
jquery easyui datagrid js获取记录数 页数 当前页
获取记录数 页数 当前页, var row = $('#t_student').datagrid('getSelected'); alert(row.sid);//获取对应的记录id var grid = $('#t_student'); var options = grid.datagrid('getPager').data("pagination").options; var p
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")
获取easyui的datagrid某行数据的某列值
工作中写代码遇到需要获取datagrid某一行（row）各个字段值并传值的要求，，由于对jQuery easyUI 不熟悉，，百度了好久都没有找到解决办法，，后来几经尝试后终于找到解决方法，，特意在此分享一下经验，，希望可以帮到遇到同样问题的人。 方法/步骤 1 如图所示，，可以看到datagrid列表，点击超链接时要将收款日期传到下一个页面并显示 2 首先，要给...
datagrid获取当前页码
var options = $("#dg" ).datagrid("getPager" ).data("pagination" ).options; //页码 var pageNo = options.pageNumber; //页展现数据条数 var size = options.pageSize; //手动设置页码 function RefreshPageNu...
easyui-datagrid获取行和列数据
1、获取当前行 var row = $('#dg').datagrid('getSelected');` 2、返回加载的数据 var row = $('#dg').datagrid('getData'); 3、返回当前页的行 var row = $('#dg').datagrid('getRows'); 4、返回底部的行 var row = $('#dg').datagrid('getFooterRows'); 5、获取所有选中行 var rows = $('#dg').datagrid('get
easyui手动设置datagrid页码
场景： 在使用datagrid的时候，当在查询页翻页到其他页码如：4，选择某条记录跳转到其他页面进行编辑，编辑完成后需要返回到编辑前的查询页4。 默认情况下是无法直接回到页码4的。需要在编辑跳转的时候把当前页面的页码4传递给编辑页，编辑完成后再把4这个值传递给查询页。 在查询页可通过以下方法得到pageNumber var pageNumber = $("#datagrid").dat
easyui 页面跳转进入新页面再返回当前页面
因为我的后台项目easyui数据采用真分页传值,传入page和rows,后台只返回当前页数值. 在当前页获取当前页面的page和rows var options = $('#dg').datagrid('getPager').data("pagination").options; var page = options.pageNumber;//当前页数 var ro
JS中获取datagrid的页码和页数
“百川东到海,何时复西归” 使用easyUI的datagrid时,如果在导出当前页的数据时,需要传入页码和每页显示的数量.如下获取: var pageopt = $('#datagridID').datagrid('getPager').data("pagination").options; //其中,datagridID是datagrid的ID var pageNumber...
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
5万+
扫一扫
