表单验证类validator及其示例

[quote]
<title>表单验证类 Validator v1.01</title>
<style>
body,td{font:normal 12px Verdana;color:#333333}
input,textarea,select,td{font:normal 12px Verdana;color:#333333;border:1px solid #999999;background:#ffffff}
table{border-collapse:collapse;}
td{padding:3px}
input{height:20;}
textarea{width:80%;height:50px;overfmin:auto;}
form{display:inline}
</style>
<table align="center">
<form name="theForm" id="demo" action="" method="get" onSubmit="return Validator.Validate(this,2)">
<tr>
<td>真实姓名:</td><td><input name="Name" dataType="Chinese" msg="真实姓名只允许中文"></td>
</tr>
<tr>
<td>英文名:</td><td><input name="Nick" dataType="English" require="false" msg="英文名只允许英文字母"></td>
</tr>
<tr>
<td>主页:</td><td><input name="Homepage" require="false" dataType="Url" msg="非法的Url"></td>
</tr>
<tr>
<td>密码:</td><td><input name="Password" dataType="SafeString" msg="密码不符合安全规则" type="password"></td>
</tr>
<tr>
<td>重复:</td><td><input name="Repeat" dataType="Repeat" to="Password" msg="两次输入的密码不一致" type="password"></td>
</tr>
<tr>
<td>信箱:</td><td><input name="Email" dataType="Email" msg="信箱格式不正确"></td>
</tr>
<tr>
<td>信箱:</td><td><input name="Email" dataType="Repeat" to="Email" msg="两次输入的信箱不一致"></td>
</tr>
<tr>
<td>QQ:</td><td><input name="QQ" require="false" dataType="QQ" msg="QQ号码不存在"></td>
</tr>
<tr>
<td>身份证:</td><td><input name="Card" dataType="IdCard" msg="身份证号码不正确"></td>
</tr>
<tr>
<td>年龄:</td><td><input name="Year" dataType="Range" msg="年龄必须在18~28之间" min="18" max="28"></td>
</tr>
<tr>
<td>年龄1:</td><td><input name="Year1" require="false" dataType="Compare" msg="年龄必须在18以上" to="18" operator="GreaterThanEqual"></td>
</tr>
<tr>
<td>电话:</td><td><input name="Phone" require="false" dataType="Phone" msg="电话号码不正确"></td>
</tr>
<tr>
<td>手机:</td><td><input name="Mobile" require="false" dataType="Mobile" msg="手机号码不正确"></td>
</tr>
<tr>
<td>生日:</td><td><input name="Birthday" dataType="Date" format="ymd" msg="生日日期不存在"></td>
</tr>
<tr>
<td>邮政编码:</td><td><input name="Zip" dataType="Custom" regexp="^[1-9]\d{5}$" msg="邮政编码不存在"></td>
</tr>
<tr>
<td>邮政编码:</td><td><input name="Zip1" dataType="Zip" msg="邮政编码不存在"></td>
</tr>
<tr>
<td>操作系统:</td><td><select name="Operation" dataType="Require" msg="未选择所用操作系统" ><option value="">选择您所用的操作系统</option><option value="Win98">Win98</option><option value="Win2k">Win2k</option><option value="WinXP">WinXP</option></select></td>
</tr>
<tr>
<td>所在省份:</td><td>广东<input name="Province" value="1" type="radio">陕西<input name="Province" value="2" type="radio">浙江<input name="Province" value="3" type="radio">江西<input name="Province" value="4" type="radio" dataType="Group" msg="必须选定一个省份" ></td>
</tr>
<tr>
<td>爱好:</td><td>运动<input name="Favorite" value="1" type="checkbox">上网<input name="Favorite" value="2" type="checkbox">听音乐<input name="Favorite" value="3" type="checkbox">看书<input name="Favorite" value="4" type="checkbox"" dataType="Group" min="2" max="3" msg="必须选择2~3种爱好"></td>
</tr>
<td>自我介绍:</td><td><textarea name="Description" dataType="Limit" max="10" msg="自我介绍内容必须在10个字之内">中文是一个字</textarea></td>
</tr>
<td>自传:</td><td><textarea name="History" dataType="LimitB" min="3" max="10" msg="自传内容必须在[3,10]个字节之内">中文是两个字节t</textarea></td>
</tr>
<tr>
<td colspan="2"><input name="Submit" type="submit" value="确定提交"><input onClick="Validator.Validate(document.getElementById('demo'))" value="检验模式1" type="button"><input onClick="Validator.Validate(document.getElementById('demo'),2)" value="检验模式2" type="button"><input onClick="Validator.Validate(document.getElementById('demo'),3)" value="检验模式3" type="button"></td>
</tr>
</form>
</table>
<script>
/*************************************************
Validator v1.01
code by 我佛山人
wfsr@cunite.com
http://www.cunite.com
*************************************************/
Validator = {
Require : /.+/,
Email : /^\w+([-+.]\w+)*@\w+([-.]\w+)*\.\w+([-.]\w+)*$/,
Phone : /^((\(\d{3}\))|(\d{3}\-))?(\(0\d{2,3}\)|0\d{2,3}-)?[1-9]\d{6,7}$/,
Mobile : /^((\(\d{3}\))|(\d{3}\-))?13\d{9}$/,
Url : /^http:\/\/[A-Za-z0-9]+\.[A-Za-z0-9]+[\/=\?%\-&_~`@[\]\':+!]*([^<>\"\"])*$/,
IdCard : /^\d{15}(\d{2}[A-Za-z0-9])?$/,
Currency : /^\d+(\.\d+)?$/,
Number : /^\d+$/,
Zip : /^[1-9]\d{5}$/,
QQ : /^[1-9]\d{4,8}$/,
Integer : /^[-\+]?\d+$/,
Double : /^[-\+]?\d+(\.\d+)?$/,
English : /^[A-Za-z]+$/,
Chinese : /^[\u0391-\uFFE5]+$/,
UnSafe : /^(([A-Z]*|[a-z]*|\d*|[-_\~!@#\$%\^&\*\.\(\)\[\]\{\}<>\?\\\/\'\"]*)|.{0,5})$|\s/,
IsSafe : function(str){return !this.UnSafe.test(str);},
SafeString : "this.IsSafe(value)",
Limit : "this.limit(value.length,getAttribute('min'), getAttribute('max'))",
LimitB : "this.limit(this.LenB(value), getAttribute('min'), getAttribute('max'))",
Date : "this.IsDate(value, getAttribute('min'), getAttribute('format'))",
Repeat : "value == document.getElementsByName(getAttribute('to'))[0].value",
Range : "getAttribute('min') < value && value < getAttribute('max')",
Compare : "this.compare(value,getAttribute('operator'),getAttribute('to'))",
Custom : "this.Exec(value, getAttribute('regexp'))",
Group : "this.MustChecked(getAttribute('name'), getAttribute('min'), getAttribute('max'))",
ErrorItem : [document.forms[0]],
ErrorMessage : ["以下原因导致提交失败:\t\t\t\t"],
Validate : function(theForm, mode){
var obj = theForm || event.srcElement;
var count = obj.elements.length;
this.ErrorMessage.length = 1;
this.ErrorItem.length = 1;
this.ErrorItem[0] = obj;
for(var i=0;i<count;i++){
with(obj.elements[i]){
var _dataType = getAttribute("dataType");
if(typeof(_dataType) == "object" || typeof(this[_dataType]) == "undefined") continue;
this.ClearState(obj.elements[i]);
if(getAttribute("require") == "false" && value == "") continue;
switch(_dataType){
case "Date" :
case "Repeat" :
case "Range" :
case "Compare" :
case "Custom" :
case "Group" :
case "Limit" :
case "LimitB" :
case "SafeString" :
if(!eval(this[_dataType])) {
this.AddError(i, getAttribute("msg"));
}
break;
default :
if(!this[_dataType].test(value)){
this.AddError(i, getAttribute("msg"));
}
break;
}
}
}
if(this.ErrorMessage.length > 1){
mode = mode || 1;
var errCount = this.ErrorItem.length;
switch(mode){
case 2 :
for(var i=1;i<errCount;i++)
this.ErrorItem[i].style.color = "red";
case 1 :
alert(this.ErrorMessage.join("\n"));
this.ErrorItem[1].focus();
break;
case 3 :
for(var i=1;i<errCount;i++){
try{
var span = document.createElement("SPAN");
span.id = "__ErrorMessagePanel";
span.style.color = "red";
this.ErrorItem[i].parentNode.appendChild(span);
span.innerHTML = this.ErrorMessage[i].replace(/\d+:/,"*");
}
catch(e){alert(e.description);}
}
this.ErrorItem[1].focus();
break;
default :
alert(this.ErrorMessage.join("\n"));
break;
}
return false;
}
return true;
},
limit : function(len,min, max){
min = min || 0;
max = max || Number.MAX_VALUE;
return min <= len && len <= max;
},
LenB : function(str){
return str.replace(/[^\x00-\xff]/g,"**").length;
},
ClearState : function(elem){
with(elem){
if(style.color == "red")
style.color = "";
var lastNode = parentNode.childNodes[parentNode.childNodes.length-1];
if(lastNode.id == "__ErrorMessagePanel")
parentNode.removeChild(lastNode);
}
},
AddError : function(index, str){
this.ErrorItem[this.ErrorItem.length] = this.ErrorItem[0].elements[index];
this.ErrorMessage[this.ErrorMessage.length] = this.ErrorMessage.length + ":" + str;
},
Exec : function(op, reg){
return new RegExp(reg,"g").test(op);
},
compare : function(op1,operator,op2){
switch (operator) {
case "NotEqual":
return (op1 != op2);
case "GreaterThan":
return (op1 > op2);
case "GreaterThanEqual":
return (op1 >= op2);
case "LessThan":
return (op1 < op2);
case "LessThanEqual":
return (op1 <= op2);
default:
return (op1 == op2);
}
},
MustChecked : function(name, min, max){
var groups = document.getElementsByName(name);
var hasChecked = 0;
min = min || 1;
max = max || groups.length;
for(var i=groups.length-1;i>=0;i--)
if(groups[i].checked) hasChecked++;
return min <= hasChecked && hasChecked <= max;
},
IsDate : function(op, formatString){
formatString = formatString || "ymd";
var m, year, month, day;
switch(formatString){
case "ymd" :
m = op.match(new RegExp("^((\\d{4})|(\\d{2}))([-./])(\\d{1,2})\\4(\\d{1,2})$"));
if(m == null ) return false;
day = m[6];
month = m[5]--;
year = (m[2].length == 4) ? m[2] : GetFullYear(parseInt(m[3], 10));
break;
case "dmy" :
m = op.match(new RegExp("^(\\d{1,2})([-./])(\\d{1,2})\\2((\\d{4})|(\\d{2}))$"));
if(m == null ) return false;
day = m[1];
month = m[3]--;
year = (m[5].length == 4) ? m[5] : GetFullYear(parseInt(m[6], 10));
break;
default :
break;
}
if(!parseInt(month)) return false;
month = month==12 ?0:month;
var date = new Date(year, month, day);
return (typeof(date) == "object" && year == date.getFullYear() && month == date.getMonth() && day == date.getDate());
function GetFullYear(y){return ((y<30 ? "20" : "19") + y)|0;}
}
}
</script>
[/quote]
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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")

简单的表单验证类 ASP Validator

当前版本:ver 0.1 beta 下载:http://nowgoo.zhuosong.com/validator.rar1、ASP Validator 简介 ASP Validator 是基于 ASP 的一个表单验证类,用于表单的服务器端验证。比如,验证表单中一个文本框(假设元素名为 UserEmail)的值是否为正确的电子邮件格式,按传统的方法您可能会编写这样的代码:

Nowgoo 1204

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。

"我佛山人"编写的表单验证类 Validator使用说明

 Validator表单验证类使用说明表单的验证一直是网页设计者头痛的问题,我佛山人编写的表单验证类 Validator就是为解决这个问题而写的,旨在使设计者从纷繁复杂的表单验证中解放出来,把精力集中于网页的设计和功能上的改进上。Validator是基于JavaScript技术的伪静态类和对象的自定义属性,可以对网页中的表单项输入进行相应的验证,允许同一页面中同时验证多个表单,熟悉接口之后也可以对

bin_bean的专栏 2450

[Language-php]表单验证类 Validator for PHP

传送门:http://www.phpchina.com/?action_zendinfoview_itemid_888.html文件保存为form_Validate.php PHP代码 -----------------------------------------------

HackArt 463

表单验证类 Validator v1.0

代码如下: 表单验证类 Validator v1.0body,td{font:normal 12px Verdana;color:#333333}input,textarea,select,td{font:normal 12px Verdana;color:#333333;border:1px solid #999999;background:#ffffff}t

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