angular.js多个控制器提示错误Error: [ng:areq]的解决方法

Angular CLI 实操指南:从 ng new 到 ng build 的完整工程链 Angular CLI 是 Angular 应用的工程化基石,它不仅封装了 TypeScript 编译、模块解析与 AOT 构建等底层逻辑,更通过标准化契约保障框架版本兼容性与构建可预测性。其核心命令如 ng serve、ng generate 和 ng test 并非孤立工具,而是深度集成开发、代码生成与测试验证的闭环流水线。ng serve 启动的不仅是开发服务器,更是支持 HMR、路径别名与代理配置的全功能调试环境;ng generate 基于 schematics 实现可编程的模块化代码重构;ng 阅读详情

今天玩angular.js,在一个module下写了多个控制器,遇到这样一个问题,如图:


刚开始我以为是angular.js版本问题,换了低版本的1.2后还是会报这样的错误,所以我就改写了angular控制器的写法,如下:

    app.controller('myCtrl',["$scope",function($scope){   //Angular.js控制器
    $scope.firstName="zhao";
    $scope.lastName="yongwei";
    console.log($scope);
    }]);
    app.controller('myCtrl1',["$scope",function($scope){   //Angular.js控制器
    $scope.firstName="lu";
    $scope.lastName="hongxia";
    }]);

问题解决!

Angular中掌控ng2-charts:原理、避坑与高性能实践 Chart.js 是前端最主流的轻量级可视化库,但直接集成到 Angular 项目常引发渲染失效、更新不响应、内存泄漏等问题。其根源在于 Chart.js 的命令式 canvas 绘图机制与 Angular 声明式变更检测存在底层冲突。ng2-charts 并非简单封装,而是承担类型桥接、生命周期同步和输入变更翻译的关键角色——尤其在 v4.x 后转向‘零实例托管’模式,要求开发者理解不可变数据绑定、插件显式注册及 resize 响应原理。掌握这些机制,才能支撑实时监控、BI 看板、微前端图表等高要求场景, 阅读详情

相关推荐

EDEM在农业机械设计中的实战应用:以谷物收割机优化为例

本文以谷物收割机优化为例,详细阐述了EDEM离散元仿真软件在农业机械设计中的实战应用。通过构建精准的“数字谷物”模型与农机部件动态界面,利用GPU加速完成大规模颗粒系统仿真,有效诊断并解决了籽粒夹带损失与清选负荷不均等田间难题,实现了从仿真分析到田间验证的设计优化闭环。

weixin_29256771的博客 108

angularJs Error: [ng:areq] Argument ‘fn‘ is not a function, got undefined

今天做项目,重构一个组件的时候,无端报了一个这样的错!Error: [ng:areq] Argument ‘fn’ is not a function, got undefined,花了我很久的时间才找到这个错误!原来是因为我注册的component组件时候出了问题!项目结构比较复杂,我直接用一个最简单的例子来说下! <body> <div ng-app="App"> <custom-tag> </custom-tag>

3423

ArcGIS+PLUS+InVEST三件套实战:从零搞定土地利用变化与碳储量预测(附完整数据包)

本文详细介绍了如何整合ArcGIS、PLUS模型和InVEST模型,构建土地利用变化与碳储量预测的完整研究流水线。通过多情景模拟和生态系统服务评估,提供从数据准备到结果验证的实战指南,助力研究者高效完成生态评估项目。

weixin_30399821的博客 543

angular中出现错误的提示指令[ng:areq]的原因

报出来的时候,出现这种错误,是因为在引入控制器的时候没有引入成功,我遇到这个错误是在因为没有将父控制器引入到子控制器中.

weixin_48622654的博客 372

Unipush2.0 App消息推送(在线+离线推送)

使用unipush2.0进行消息推送,包含在线跟离线

weixin_50251467的博客 3699

从零到一:基于GeneMark-ES/ET的新基因组注释实战指南

本文提供了一份基于GeneMark-ES/ET的新基因组注释实战指南。详细介绍了从环境搭建、数据准备到核心运行的全流程,重点解析了ES(从头预测)与ET(结合RNA-Seq证据)两种模式的选择与操作,并分享了Perl模块依赖、并行处理等常见问题的解决方案,帮助生物信息学研究者高效完成基因预测工作。

weixin_29266647的博客 209

angular10兼容IE9

Angular官方文档列出了对于IE浏览器的支持程度。所以如果要兼容IE9,angular的最高版本只能是10。

好女绪的博客 1721

HFSS学习记录——0. HFSS软件安装(ANSYS Electromagnetics Suite 2023 R1)

HFSS 2023 R1安装步骤

plmxu34的博客 15万+

AngularJs出现错误Error: [ng:areq]

错误描述:Error: [ng:areq] http://errors.angularjs.org/1.4.8/ng/areq?p0=HelloCtrl&p1=not%20a%20function%2C%20got%20undefined Angular出现这种错误的原因,是由于没有在页面中使用模块引入controller导致的,修改内容如下: 添加:ng-app='app

生存不忘生活 1万+

Adaboost入门教程——最通俗易懂的原理介绍(图文实例)

Adaboost入门教程——最通俗易懂的原理介绍(图文实例)

Max的杂物仓 11万+

升级最新IDEA版本(2022.1.1)

升级最新IDEA版本(2022.1.1)

软件老王 3万+

angularJS1 报错 Error: [ng:areq] Error: [$controller:ctrlreg]

angularJS1 报错 Error: [ng:areq] Error: [$controller:ctrlreg] 2016年12月18日 15:29:08 xiejunna 阅读数:14936 标签: angularjs 更多 个人分类: angularJS1 版权声明:本文为博主原创文章,未经博主允许不得转载。 https://blog.csdn.net/xiejunna/artic...

weixin_42858906的博客 2325

SPSS 多重共线性问题:编程解决方案

多重共线性会导致回归模型的不稳定性,使得估计的回归系数失去解释力,降低模型的准确性和可靠性。通过执行这些代码,你将能够计算和查看VIF和CI的结果,并确定是否存在多重共线性问题。上述代码执行了一个回归分析,其中A是我们感兴趣的因变量,B、C、D和E是我们要检查的自变量。接下来,我们将执行多重共线性的检测。通过执行上述代码,你将能够看到每个自变量的VIF和CI值,以及预先设置的阈值。上述代码将计算VIF和CI的值,并将结果写入指定路径的结果文件中。接下来,我们可以使用以下代码来计算和显示VIF和CI的值。

CyberGenius的博客 1426

angular.min.js:118 Error: [ng:areq] http://errors.angularjs.org/1.5.8/ng/areq?

1、错误描述angular.min.js:118 Error: [ng:areq] http://errors.angularjs.org/1.5.8/ng/areq?p0=username&p1=not%20a%20function%2C%20got%20undefined at Error (native) at http://127.0.0.1:8020/AngularJS/

游海东的技术专栏 1万+

怀旧版电脑GBC模拟器

再现原版GBC掌机的界面,电脑运行连游戏界面都一样大小,很怀旧,很真实,(英文版)强烈推荐,很小的文件,很难才找到的。

Probability and Statistics - 4th edition

Probability and Statistics - 4th edition

Error: [ng:areq] Argument 'xxxx' is not a function, got undefined

"Error: [ng:areq] Argument 'keywords' is not a function, got undefined" 代码类似这样的: <div ng-app="xxxx" ng-controller="xxxx"> //... </div> var app = angular.module("xxxx", []); ...

weixin_30398227的博客 190

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")

Error: [ng:areq]

html> html lang="en"> head> meta charset="UTF-8"> title>Titletitle> script src="js/angular.min.js">script> head> body ng-app> div ng-controller="HelloCtr"> say Hello to :input type="te

Masha 755

华为的C语言编程规范.pdf

华为技术有限公司内部技术规范。本规范制定了编写C语言程序的基本原则、规则和建议。从代码的清晰、简洁、可测试、安全、程序效率、可移植各个方面对C语言编程作出了具体指导。

angular min js 118 Error ng areq http //errors angularjs

angular min js 118 Error ng areq http //errors angularjs

jrrfgj的博客 436

ng-token-auth事件系统全解析:轻松处理登录、注册与密码重置

ng-token-auth是一个基于Angular.js的令牌认证模块,它提供了完整的事件系统来处理用户认证流程中的各种状态变化。本文将详细介绍如何利用这些事件轻松实现登录、注册和密码重置功能,让你的应用认证体验更加流畅和可靠。 ## 核心事件概览:掌握认证流程的关键节点 ng-token-auth的事件系统是构建用户认证流程的核心,它通过`$rootScope.$broadcast`方法触发

gitblog_00808的博客 759

报错#angular.min.js:12330 Error: [ng:areq] Argument ‘config_in_portlistening_ctrl‘ is not a function,

报错描述 我在封装一个组件引入我的另外一个界面的时候,运行项目,产生的报错。 文字描述 angular.min.js:12330 Error: [ng:areq] Argument 'config_in_portlistening_ctrl' is not a function, got undefined 翻译结果: 参数“config_in_portlistening_ctrl”不是函数,未定义 AngularJS 经常使用辅助函数断言某些值将存在且为真。如果断言失败,则抛出此错误。要解决此问题,请确

老衲的少女心i~ 269

用angularjs 模块时Error: [ng:areq] Argument 'controller' is not a function,

今天学习angular时,发生了这个错误,网上大多数是说,不能直接用function写controller了,要用module,但我就是用的module啊代码如下:js : var mainApp = angular.module("mainApp",[]); mainApp.controller("controller",function($scope){ $scope.student={

荒野之鹰 1万+

Angular路由中常见错误描述

1、错误描述:Error: [ng:areq] http://errors.AngularJS.org/1.4.8/ng/areq?p0=HelloCtrl&p1=not%20a%20function%2C%20got%20undefined at Error (native) at http://localhost:8080/web/rs/angular.min.js:1:5...

weixin_34393428的博客 184

angular报错:angular.min.js:118Error: [ng:areq] http://errors.angularjs.org/1.5.8/ng/areq

报错代码如下: <div ng-controller="HelloAngular"> <p>{{greeting.text}},angular</p> </div> function HelloAngular($scope){ $scope.greeting = { t...

weixin_33895657的博客 447
上一篇: 判断图片是否全部加载
下一篇: jquery实现瀑布流布局+无限下拉加载
水中残月
博客等级 码龄11年 8粉丝 21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