如何实现通过回车键提交表单

OpenClaw 3.13 + Mac mini M4 集成 qmd 记忆存储:qmd embed 卡死 / 报错 sqlite-vec 不可用 终极解决教程 模型加速让 qmd 从国内镜像下载模型,避免网络超时;SQLite 扩展支持:修改 qmd 源码后,Bun 不再使用内置 SQLite,而是调用 Homebrew 安装的,该版本支持接口,可正常加载vec0.dylib向量扩展;OpenClaw 3.13 适配:核心逻辑与 OpenClaw 3.13 的 qmd 记忆调用逻辑完全兼容,无需额外调整;sqlite-vec 加载:qmd 自动通过sqlite-vec包获取vec0.dylib路径,调用完成向量扩展加载,实现高效向量搜索。 阅读详情

对于B/S的系统,在页面控制上不如C/S系统那么控制灵活,就比如现在一个页面或用户控件里有两个按钮button1和button2,两个文本框Textbox1和Textbox2,当您想控制当焦点在Textbox1时回车提交button1,在Textbox2时提交button2,这个时候就需要通过客户端脚本来实现和服务器端控件的交互,这是我写的封装好的类,
控件继承 TextBox,SubmitControl属性用来指定此文本框上按回车时由哪个按钮来响应。
 SubmitableControlConvertor增加设计时支持,就是在设计时可以用下拉框选择提交按钮。
目前只考虑了Button、LinkButton和ImageButton作为提交按钮,当然继承它们的控件也可以。

 1  using  System;
 2  using  System.Web.UI;
 3  using  System.Web.UI.WebControls;
 4  using  System.ComponentModel;
 5 
 6  namespace  MyCompany.Controls
 7  {
 8       public   class  SubmitTextBox : TextBox
 9      {
10           ///   <summary>
11           ///  用来指定按回车时由哪个按钮来响应
12           ///   </summary>
13          [TypeConverter( typeof (SubmitableControlConvertor)), DefaultValue( "" ),Category( " Behavior " )]
14           public   string  SubmitControl
15          {
16               get
17              {
18                   object  ret  =   this .ViewState[ " SubmitControl " ];
19                   if  (ret  !=   null )
20                  {
21                       return  ( string ) ret;
22                  }
23                   return   string .Empty; 
24              } 
25               set  
26              {
27                   this .ViewState[ " SubmitControl " ]  =  value;
28              }
29          }
30 
31           protected   override   void  AddAttributesToRender(HtmlTextWriter writer) 
32          {
33               base .AddAttributesToRender(writer);
34               if ( this .SubmitControl.Length  >   0 )
35              {
36                  Control con  =  FindControl(SubmitControl);
37                   if (con  !=   null )
38                  {
39                       string  script  =   " if(event.keyCode == 13){document.getElementById(' "   +  con.ClientID  +   " ').click();event.returnValue=false;} " ;
40                      writer.AddAttribute( " onkeydown " ,script);
41                  }
42              }
43          } 
44      } 
45  }

 1  using  System;
 2  using  System.Web.UI;
 3  using  System.Web.UI.WebControls;
 4  using  System.ComponentModel;
 5  using  System.Collections;
 6 
 7  namespace  MyCompany.Controls
 8  {
 9       public   class  SubmitableControlConvertor : StringConverter
10      {
11           private   object [] GetControls(IContainer container)
12          {
13              ComponentCollection components  =  container.Components;
14              ArrayList ret  =   new  ArrayList();
15               foreach  (IComponent control  in  components)
16              {
17                   if  ( ! (control  is  Button  ||  control  is  LinkButton  ||  control  is  ImageButton))
18                  {
19                       continue ;
20                  }
21                  Control button  =  (Control) control;
22                   if  ((button.ID  !=   null )  &&  (button.ID.Length  !=   0 ))
23                  {
24                      ret.Add( string .Copy(button.ID));
25                  }
26              }
27              ret.Sort(Comparer.Default);
28               return  ret.ToArray();
29          }
30   
31           public   override  System.ComponentModel.TypeConverter.StandardValuesCollection GetStandardValues(ITypeDescriptorContext context)
32          {
33               if  ((context  !=   null )  &&  (context.Container  !=   null ))
34              {
35                   object [] controls  =   this .GetControls(context.Container);
36                   if  (controls  !=   null )
37                  {
38                       return   new  TypeConverter.StandardValuesCollection(controls);
39                  }
40              }
41               return   null ;
42          }
43 
44           public   override   bool  GetStandardValuesExclusive(ITypeDescriptorContext context)
45          {
46               return   false ;
47          }
48 
49           public   override   bool  GetStandardValuesSupported(ITypeDescriptorContext context)
50          {
51               return   true ;
52          }
53      } 
54  }

在页面上只需要象下面这样调用就可以:
1  <% @ Control Language = " c# " %>
2  <% @ Register TagPreFix = " MyCompany "  Namespace = " MyCompany.Controls "  Assembly = " MyCompany.Controls " %>
3  < MyCompany:SUBMITTEXTBOX  id ="txtKeys"  SubmitControl ="btnSearch"   runat ="server"   maxlength ="20"  Value ="请输入搜索关键字"  onfocus ="if(this.value=='请输入搜索关键字')this.value='';"  onblur ="if(this.value=='')this.value='请输入搜索关键字'" ></ MyCompany:SUBMITTEXTBOX > &nbsp;&nbsp;&nbsp; < asp:imagebutton  id ="btnSearch"  runat ="server" ></ asp:imagebutton >

 

http://yangxiangwu.cnblogs.com/archive/2006/05/09/394991.html

 
差分放大电路的计算 反馈,对于运算放大电路来说,运放工作在线性区,所以这里一定是负反馈,没有反馈(开环)或者是正反馈,那是比较器电路而不是放大电路,这时候运放工作在饱和区或称为非线性工作区,正因为饱和,输出才是电源电压的幅值。图2是一种带正反馈的运放电路,这里就不能叫运算放大电路了,因为运放的开环放大倍数理想是无限大,当然实际中不可能无限大,所以如下结构是迟滞电压比较器,运放工作在非线性区或饱和区。实际应用电路中,我们为了简化计算,也是用最简方法计算,经常使用的电路也是上述电路,令电阻相等关系,简化计算。 阅读详情

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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")

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