遍历窗体中的所有TextBox控件,并为其赋值

Excel 2010 VBA 入门 129 利用窗体向工作表中录入数据 题 如图所示,该表为某公司订单记录表,其中所有的列都是必填的,订单编号为自动生成的序列号。如何建立窗体,并通过窗体向该表内添加订单。 序号 姓名 科室 级别 身份 职称 受聘专业 性别 出生年月 年龄 参工时间 工作年限 入院时间 入院年限 职务 等级 身份证号 党员团员 1 张201101 后勤科 中级 干部 主治医师 医生 男 1954-01-04 阅读详情

这是我自己在编程过程中遇到的问题,在这里总结一下。遍历窗体中的所有TextBox控件,并为其赋值为Coool。

1.C#方法

定义一个函数,使用递归的方法,将页面中所有的TextBox控件都找出来

 void FindTextBox(Control ctl)
    {
        foreach (Control parent in ctl.Controls)
        {
            foreach (Control c in parent.Controls)
            {
                if (c.GetType().ToString() == "System.Web.UI.WebControls.TextBox")
                    ((TextBox)c).Text = "Coool";
                FindTextBox(parent);
            }           
        }
    } 

2.Javascript方法

<script type="text/javascript">
function ResetTextBox(){
  var controls = document.getElementsByTagName('input');
  for(var i=0; i<controls.length; i++){
    if(controls[i].type=='text'){
      controls[i].value='Coool';
    }
  }
}
</script>

欢迎拍砖!

C#递归遍历窗体所有textbox控件并设置textbox事件的方法 本文实例讲述了C#递归遍历窗体所有textbox控件并设置textbox事件的方法。分享给大家供大家参考。具体实现方法如下: 代码如下:/// <summary> /// /// </summary> /// <param name=”sender”></param> /// <param name=”e”></param> public virtual void SetTextBoxOnEnterStyle(object sender, EventArgs e) {     if (sender is TextBox)     {  TextBox tbox = sender as Te 立即下载

相关推荐

GitHub 镜像站点

国内访问 GitHub 有时会遇到速度慢或不稳定的情况,这时 GitHub 镜像站点就能帮上忙。它们通过代理或缓存机制,让你更顺畅地浏览仓库、下载资源甚至克隆代码。希望这些信息能帮你更顺畅地访问 GitHub 资源。下面表格汇总了一些常见的镜像站及其主要用途。

BillKu的博客 6万+

遍历页面textbox并赋值

编程遍历页面上所有TextBox控件并给它赋值为string.Empty? 答:foreach(System.Windows.Forms.Control control in this.Controls) {     if (control is System.Windows.Forms.TextBox) {       System.Windows.Forms.TextBox tb =(System.Windows.Forms.TextBox)control ; tb.Text = String.Emp

.Net9527的技术博客 482

随机森林模型及案例(Python)

1 集成模型简介 集成学习模型使用一系列弱学习器(也称为基础模型或基模型)进行学习,并将各个弱学习器的结果进行整合,从而获得比单个学习器更好的学习效果。 集成学习模型的常见算法有Bagging算法和Boosting算法两种。 Bagging算法的典型机器学习模型为随机森林模型,而Boosting算法的典型机器学习模型则为AdaBoost、GBDT、XGBoost和LightGBM模型。 1.1 Bagging算法简介 Bagging算法的原理类似投票,每个弱学习器都有一票,最终根据所有弱学习器的投

qq_42433311的博客 4万+

批量给控件赋值

批量给控件赋值 方法一:利用Controls.Find方法 方法二:利用反射(该方法可批量给控件任何属性赋值) 方法一:利用Controls.Find方法: for(int i = 0 , i { Label newLabel = (System.Windows.Forms.Label)this.Controls.Find("label1" +

root_x 2651

Brainstorm简单分析近红外数据(fNIRS)(一)

Brainstorm分析fNIRS数据

weixin_43604817的博客 1352

使用TBtools进行物种间共线性分析

Sequence Toolkit ---Fasta Tools ----ID Prefix (fasta文件)

sunny_dayone的博客 8751

循环为TextBox赋值!!asp.net

<div id="TextBoxs" runat="server"> <asp:TextBox ID="com1" runat="server" ></asp:TextBox> <asp:TextBox ID="com2" runat="server" ></asp:TextBox> <asp:TextBox ID="com2...

weixin_30297281的博客 513

【全网最新】使用 1panel面板 部署若依系统( springboot 和 vue)项目

打开前端项目的vue.config.js文件,检查devServer下的proxy代理部分,然后将下面这个端口修改为你要在打包后访问的端口,即使用nginx代理前的接口。这个端口默认是80端口,这里把其修改为不会冲突的8010端口,后台接口的地址配置为9080,不过感觉这里设置的8010和9080可能没有用,因为部署前端静态页面是,也配置了一次网页的外部接口和api的接口,后期有空了再验证。打开右侧的Maven,点击:生存期->package,然后点击运行,等待打包成功,包在如图所示的文件夹中。

老薛的专栏 1756

asp.net(C#)遍历页面上所有的TextBox控件并给它赋值

最近要做SQL注入的验证,所以要遍历页面所有的TextBox 过滤,发现很多博客写的方法都有问题。。。只有一个循环,懂的人都知道,代码肯定有问题,之前都是用2个循环,但是看到网上有新的方法了private void InitialControl(ControlCollection objControlCollection) { foreach (System.Web.UI.Control objControl in objControlCollection) {

虎нǔ孓 5144

大众奥迪诊断不求人:手把手解析SAE J2819(CAN TP2.0)协议报文与连接流程

本文深入解析大众奥迪车型诊断核心标准SAE J2819(CAN TP2.0)协议,详细拆解连接建立流程与报文控制字段。通过实战案例展示通道设置、参数协商等关键步骤,提供诊断设备配置建议和常见故障排查技巧,帮助工程师掌握汽车诊断通信协议的精髓。

weixin_30421809的博客 397

USB 3.0 Rx Detect之超速U盘的识别

USB 3.0 Rx Detect之超速U盘的识别

汽车以太网和SOA 2万+

WinForm开发技巧:批量为多个控件赋值

WinForm 开发技巧:批量为多个控件赋值

我曾经是个程序员 1113

实现单芯片2400bps 音频编解码方案

终于按照朋友的需求,帮他开发了一款单芯片集音频采集编码传送并接收解码播放功能的方案,交给快递公司邮寄给远在郑州的他.整个项目从开始定型到现在一个样机送出,整整花了1个月时间,感觉挺有成就感,挺有意思,工作是辛苦的,结果却是快乐的.现在将这中快乐的的心情与大家分享一下.    真个项目用一颗主控搞定,ARM9的,200KBytes 片内RAM,主频可以跑到300MHz,64pin LQFP封装

nyxsec的专栏 3684

C#循环给多个控件赋值

需要给 多个 文本框重新赋值 1 textBox1.Text="ss"; 2 3 textBox2.Text="ss"; 4 5 textBox999.Text="ss"; 6 7 textBox99999.Text="ss"; 这样太麻烦,控件过多不方便写 通过遍历 一次性赋值,再多也不怕了 将所有TextBox赋值: 1 foreach (Control...

weixin_30493401的博客 4112

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');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

锂电池充电器电路设计 里面有电路

锂电池充电器电路设计 里面有电路 3.7V升压到5V电路设计。

关于动态创建控件,第二次赋值不能显示解决办法

动态创建控件并显示在窗体上的panel上  Label[] lb; TextBox[] txtInput; lb = null; txtInput = null; lb = new Label[ds.Tables[0].Rows.Count];

emailqjc的专栏 1745

Camera2Demo

在Camera2 的基础上进行封装,使得Camera2一堆初始化逻辑与Activity分离,降低耦合度,完成预览拍照并保存的操作

获取页面上TextBox并改变它的值

<br />请编程遍历页面上所有TextBox控件并给它赋值为string.Empty?<br />我做的时候分了2中情况:一种是窗体控件,另一种是web控件。<br />窗体控件: foreach (System.Windows.Forms.Control aa in this.Controls) { if (aa is System.Windows.Forms.TextBox) {

tony312ws的专栏 564

OV9734 DATASHEET,完整规格书

OV9734完整规格书,包括电气参数,尺寸规格,寄存器配置表等

遍历控件方法

1 private void writeContronls(string Rx, int rv) 2 { 3 foreach (Control cn in this.groupBox1.Controls) 4 { 5   if (cn is TextBox && cn.Name == Rx) 6   { 7    ...

寒风1999 190

获取和赋值checkbox,radiobutton,页面所有控件只读

赋值: foreach (Control tb in form1.Controls) { if (tb is TextBox) { TextBox t = tb as TextBox; ...

weixin_30729609的博客 154

请编程遍历页面上所有TextBox控件并给它赋值为空

private void InitialControl(ControlCollection objControlCollection) { foreach (Control objControl in objControlCollection) { if (objControl.HasControls()) {...

diaokuai5030的博客 270

请编程遍历WinForm页面上所有TextBox控件并给它赋值为string.Empty?

foreach (System.Windows.Forms.Control control in this.Controls) { if (control is System.Windows.Forms.TextBox) { System.Windows.Forms.TextBox tb = (System.Windows.Forms.TextBox)control ; tb.Text = String.Empty ; } }

didadidadidadid的博客 189

编程遍历页面上所有TextBox 控件并给它赋值为空

foreach (System.Windows.Forms.Control control in this.Controls) { if (control is System.Windows.Forms.TextBox) { System.Windows.Forms.TextBox tb = (System.Windows.Forms.TextBox)control ; tb.Text = String.Empt

weixin_45763353的博客 808

C#常用代码

1.背景图片推荐代码: Bitmap bmp=(Bitmap)Image.FromFile(@"Pictures\MyImage.jpg"); this.BackgroundImage=new Bitmap(bmp,bmp.Size); 2.TextBox焦点问题 (1)在启动窗体时候,可以使用TextBox1.TabIndex = 0; (2)在程序运行过程中,可以使用Te...

weixin_30702413的博客 85

遍历窗体上的TextBox并赋String.Empty

winform窗体遍历窗体上的TextBox病赋空值、 一、方法一 foreach (Control c in this.Controls) { if (c is TextBox) { TextBox tb = (TextBox)c; ...

weixin_33935505的博客 182

编程遍历页面上所有TextBox控件并给它赋值为string.Empty?

void EmptyTextBox(ControlCollection cc) { for (int i = 0; i < cc.Count; i++) { if (cc[i].GetType() == typeof(TextBox)) {...

weixin_30730151的博客 131
上一篇: asp.net调用存储过程方法新解
下一篇: 获取存储过程的返回值
cooolchen
博客等级 码龄20年 57粉丝 13原创
评论 1
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付:10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值