Angularjs input输入框值,被选中

enter键控制input光标移动到下一个框 enter键控制input光标移动,通过判断是否是最后一个input输入框来决定是继续向下跳还是跳到第一个input输入框 立即下载

即之前写的回车换行《关于angularjs框架中input按回车事件光标跳转到另一个input上》,接着又有输入框值,被选中。
个人觉得angularjs 中指令很好,在html中很多验证都会用到,但是,得多写。

如下代码实现:

/** --------------指令 按下回车事件,单击事件 ”选中“输入框的值-----------*/
    module.directive('selectline', function () {
        return {
            restrict: 'A',
            link: function (scope, element, attrs) {
                //element.bind('mouseover',function(event){//鼠标滑到数据时,选中
                //        this.select();
                //});
                element.bind('click',function(event){//点击时,选中
                    this.select();
                });
                element.bind('focus',function(event){//获取焦点时,选中
                    this.select();
                });
            }
        };
    });
angularjs中回车键触发某一事件的方法 要求:在输入框中输入值以后,按回车键触发某一事件的执行 html: <input id="input" name="input" ng-model="querykdUser.page.pageSize" ng-keyup="myKeyup($event)" class="form-control" style="width:60px;"> js: $scope.myKeyup = function(e){ //IE 编码包含在window.event.keyCode中,Firefox或Safari 包含在event.which中 var keycode = window.event?e 立即下载

相关推荐

无人驾驶汽车系统入门(三十二)——FEC算法:点云快速欧几里得聚类实战解析

本文深入解析了FEC算法在无人驾驶点云聚类中的高效应用,通过逐点处理策略和体素栅格预分割技术,实现了比传统欧几里得聚类快百倍的速度。文章详细介绍了算法原理、Python实现及参数调优指南,并对比了FEC与传统算法的性能差异,为无人驾驶系统开发提供了实用解决方案。

weixin_29065015的博客 484

只言片语——angular material input输入框可以辅助下拉选择

在 angular 使用 material 开发时,如果需要一个输入框,既可以自行输入值,还可以点击下拉选择预设的选项输入值,可以使用 autocomplete 标签。 需要引入 MatAutocompleteModule 模块 import {MatAutocompleteModule} from '@angular/material/autocomplete'; 在 xxx.ts 中定义了下...

swmlee的博客 1872

STC8H DMA 串口1全双工中断方式收发通讯C语言

串口1全双工中断方式收发通讯程序。 通过PC向MCU发送数据, MCU将收到的数据自动存入DMA空间. 当DMA空间存满设置大小的内容后,通过串口1的DMA自动发送功能把存储空间的数据原样返回. 用定时器做波特率发生器,建议使用1T模式(除非低波特率用12T),并选择可被波特率整除的时钟频率,以提高精度。

czhaii的专栏 1466

angular+将输入框内容点击按钮显示在输入框上面

html> html lang="en"> head> meta charset="UTF-8"> title>Titletitle> script src="angular/angular.js">script> script> var myapp=angular.module("myapp",[]); myapp.provider

dangai0201的博客 2206

新手必看!华为S5700交换机健康检查清单:从CPU到日志的完整巡检流程

本文为新手网管人员提供华为S5700交换机的健康检查完整指南,涵盖CPU、内存、温度、日志等核心指标的日常维护流程。通过详细的display命令和实用案例,帮助用户快速掌握设备体检技巧,确保交换机稳定运行。

weixin_29251337的博客 236

FPGA开发者必看:Xilinx PCIe IP核BAR地址配置避坑指南(附实例解析)

本文为FPGA开发者提供了Xilinx PCIe IP核中BAR地址配置的详细避坑指南。文章深入解析了32位与64位地址空间的选择逻辑、Pcie to AXI Translation参数的核心作用,并通过实例说明如何正确配置以避免地址映射错乱和数据访问失败。重点探讨了BAR空间类型、大小设定以及地址转换机制,帮助开发者构建清晰可靠的PCIe系统,是调试与优化PCIe设计的关键参考。

weixin_29252171的博客 424

angularjs 下实现即可通过select选择 也可以通过input输入

题记:本篇文章介绍,一种可以select选择输入,也可以input,主要原理就是把input输入框覆盖select,并且在选择select后,把值赋值给input 1 参考demo 可输入的select下拉框 first angular-1.3 {{greeting.text}},Angular <!-- <sp

shufanhao.top Blog 1万+

大模型推理中Prefill与Decode、KV Cache三者说明

Prefill 阶段对输入 Prompt 并行编码,一次性构建KV Cache,属于计算密集型任务。Decode 阶段逐 token 迭代生成,复用KV Cache避免重复计算,属于显存带宽密集型任务。KV Cache是自回归推理的核心基石,用显存换时间,实现两阶段衔接与高效生成。PD 分离通过任务解耦、异构硬件、异步调度、独立批处理,解决传统一体化推理的资源冲突与利用率低下问题,是当前云端大模型推理服务的主流工程优化方向。

a486259的博客 1480

Angular-UI自动完成输入框AutoComplete[项目中使用]

一、效果展示二、代码参考【view视图】<input id="countryCode" class="control-input" autocomplete="off" name="countryCode" type="text" data-ng-model="anomalieSearchPayId" placeholder="Saisir au moins 2 caractères" ty

欲望以提升热忱 毅力以磨平高山 7814

资产会计 Transaction Type 与业务交易的 Post to Affiliated Company 不一致导致的 AAPO176:成因剖析与修复指引

本文解析了SAP资产会计中常见的"AAPO176"错误提示,指出当交易类型(TType)与业务交易(Business Transaction)的"关联公司过账"(Post to Affiliated Company)标志不一致时,系统会拒绝过账。作者详细说明了该标志的作用机制,并提供了四种解决方案:修正交易类型使用、匹配正确的业务交易、调整系统配置或建立映射关系。文章还通过实际案例展示了配置错误对集团合并报表的影响,并附带了用于检查配置一致性的ABAP脚本。该错误通常

2007 年 ~ 2025 年,深耕 SAP 技术 18 年 392

浅谈innovus Update io latency相关作用

同样我们使用update io latency,工具取launch clock latency负值反标到clock端口上,同时认为capture clock latency与launch clock latency相等,这样可以相互抵消,这样setup time slack = T – Tsetup -Tcomb – Toutput_delay,因此采用update io latency后,reg2out timing path的setup check变得真实些。图3 in2reg示意图。

芯冰乐 473

angularjs监听readonly属性input框值变化

本身ng-change是监听input 值变化的函数,但是在赋予了readonly之后,,这个方法就失效了,无法实时监听变化,于是我们就使用scope.scope.scope.watch 来监听,看代码例子: $scope.$watch('data.clrkBO.PRO_NAME_',function(newValue,oldValue){ if (newValue === oldVal...

weixin_39433171的博客 1239

Lammps实现单轴压缩-应力应变曲线以及弹性模量计算

Lammps实现单轴压缩-应力应变曲线以及弹性模量计算

Rockyoung 1万+

angularJS中input相关指令详解

input相关指令 ng-disabled(按钮、输入框禁用的状态) ng-readonly ng-checked ng-value

outsiderlcy的博客 2336

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")

C# WinForm 基于DirectShow.dll编写的程序,可边预览边保存视频,音频和图片

基于微软的DirectShow.dll的二次开发1.自动识别连接的摄像头跟麦克风且可以对其进行选择(默认选中第一个)2.可录制wmv和avi两种格式的视频并且都带有音频,avi可以录制的分辨率和帧数进行选择,wmv默认进行了淸晰度优化如不喜欢可去除代码中有相关提示3.可在预览点开之后进行拍照,也可边录制视频边拍照4.添加了视频和音频的压缩功能(酌情使用)这次二次开发发现C#对DirectShow的相关信息太少,这次做完也不敢藏私共享出来,还望大家指正

angularjs教程——input相关指令详解

一、angularjs的指令 *ng-disabled(按钮禁用)—服务$interval <!DOCTYPE HTML> <html ng-app='myApp'> <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> &l...

209

SAP B1中文培訓教材_SDK

公司内部培训资料,对开发工具 ,DIAPI, UIAPI,Add-ON有详细解说

AngularJS学习-(左侧导航栏筛选、input输入框筛选)

1、HTML代码             首页         {{nav.classify}}                                                         {{product.name}}                 {{product.describe}}

web前端开发 5873

Probability and Statistics - 4th edition

Probability and Statistics - 4th edition

angularjs—指令input

input[text] input一般和ngModel结合使用来实现双向绑定,同时angular提供了很多表单校验的指令 required 必填 ngRequired 必填(ngRequired可以控制是否是必填校验) ngMinlength 最小长度 ngMaxlength 最大长度 pattern 正则匹配 ngPattern 正则匹配 ngChange 内容改变时触发 ngTrim 是否trim数据,默认true #html <div ng-controller="LearnCtrl">

xiaozhen0610的博客 833

angularJS控制input输入框值的的大小

ng-app = "MetronicApp" 用法:控制输入在-999到999 <input type="number" name="wineScore" number-max="999" number-min="-999" ng-model="vo.number"> /** * 控制输入最大值 */ MetronicApp.directive('numbe...

吃瓜群众的博客 1830

angularjs 在指令中通过dom修改input 的Value值同时更新model

//angularjs 在指令中通过dom修改input 的Value值同时更新model obj.directive('test', ['$parse', function ($parse) { var dateFilter = $filter('date'); return { require: 'ngModel', link: function (scope, ele

wall 技术日志 8672

angular监听输入框值的变化_angular 实时监听input框value值的变化触发函数方法

用angulajs + ionic 做了一个登陆页面。效果要通过监听输入框的变化来判断登陆按钮是否可点击。当至少一个输入框为空时登录按钮不可点击。一开始是用的jquery的方法做的,后来发现刷新当前页可以实现效果,但是当经过路由跳转后再回到登陆页后,方法就被执行了。后来经过查找资料,利用angular.js的$watch方法解决了。代码大概如下:$scope.input = {//初始化,避免ng...

weixin_39782782的博客 847

angularjs input

input[checkbox]http://docs.ngnice.com/api/ng/input/input%5Bcheckbox%5D复选框:ng-model为true,即为选中状态(选中后,ng-model置为true)ng-true-value:设置选中的值 ng-false-value:设置未选中的值<script> function Ctrl($scope) { $sco

superjunjin的专栏 1848

angular监听输入框值的变化_angular 监听input单选,多选值的变化

{{item.name}}({{item.cnt}})页面效果是这样:ts的代码是这样的:getCheckList(): void {let statusList: any = [{ name: '机审中', value: 0, cnt: 0 },{ name: '机审失败', value: 2, cnt: 0 }];statusList.map((item) => {return item...

weixin_39580042的博客 360

angularJs如何获取input焦点

angularjs文档只会告诉你用自定义指令获取焦点,如代码: var app = angular.module('myApp',[]); app.directive('setFocus', function(){ return function(scope, ele

相约9.28,幸福那一刻 2425
上一篇: 关于angularjs框架中input按回车事件光标跳转到另一个input上
下一篇: 解决PowerDesigner 错误:Invalid repository user or password!
加州小熊
博客等级 码龄16年 46粉丝 30原创
评论
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