wap常用转换函数

将PC站点的链接转换成对应WAP站点的链接 最近做了一个项目,需要做到这个事情,将一些网站的链接转换成其对应wap站点的链接,比如将凡客诚品的商品链接,转换成对应的wap站点的地址,这样就可以直接在手持设备上直接访问。 这里代码备份一下。 <!-- name : 配置的名称,用于标 阅读详情

汉字转换为UTF-8的一段代码
一个ASP写的中文转UTF-8,大家可以试试

汉字转换为UTF-8

function chinese2unicode(Str) 
  dim i 
  dim Str_one 
  dim Str_unicode 
  for i=1 to len(Str) 
    Str_one=Mid(Str,i,1) 
    Str_unicode=Str_unicode&chr(38) 
    Str_unicode=Str_unicode&chr(35) 
    Str_unicode=Str_unicode&chr(120) 
    Str_unicode=Str_unicode& Hex(ascw(Str_one)) 
    Str_unicode=Str_unicode&chr(59) 
  next 
  Response.Write Str_unicode 
end function  




UTF-8 To GB2312

function UTF2GB(UTFStr)
    for Dig=1 to len(UTFStr)
        if mid(UTFStr,Dig,1)="%" then
            if len(UTFStr) >= Dig+8 then
                GBStr=GBStr & ConvChinese(mid(UTFStr,Dig,9))
                Dig=Dig+8
            else
                GBStr=GBStr & mid(UTFStr,Dig,1)
            end if
        else
            GBStr=GBStr & mid(UTFStr,Dig,1)
        end if
    next
    UTF2GB=GBStr
end function 


function ConvChinese(x) 
    A=split(mid(x,2),"%")
    i=0
    j=0
    
    for i=0 to ubound(A) 
        A(i)=c16to2(A(i))
    next
        
    for i=0 to ubound(A)-1
        DigS=instr(A(i),"0")
        Unicode=""
        for j=1 to DigS-1
            if j=1 then 
                A(i)=right(A(i),len(A(i))-DigS)
                Unicode=Unicode & A(i)
            else
                i=i+1
                A(i)=right(A(i),len(A(i))-2)
                Unicode=Unicode & A(i) 
            end if 
        next
        
        if len(c2to16(Unicode))=4 then
            ConvChinese=ConvChinese & chrw(int("&H" & c2to16(Unicode)))
        else
            ConvChinese=ConvChinese & chr(int("&H" & c2to16(Unicode)))
        end if
    next
end function

function c2to16(x)
    i=1
    for i=1 to len(x)  step 4 
        c2to16=c2to16 & hex(c2to10(mid(x,i,4))) 
    next
end function 
    
function c2to10(x)
    c2to10=0
    if x="0" then exit function
    i=0
    for i= 0 to len(x) -1
        if mid(x,len(x)-i,1)="1" then c2to10=c2to10+2^(i)
    next 
end function

function c16to2(x)
    i=0
    for i=1 to len(trim(x)) 
        tempstr= c10to2(cint(int("&h" & mid(x,i,1))))
        do while len(tempstr)<4
        tempstr="0" & tempstr
        loop
        c16to2=c16to2 & tempstr
    next
end function

function c10to2(x)
    mysign=sgn(x)
    x=abs(x)
    DigS=1
    do 
        if x<2^DigS then
            exit do
        else
            DigS=DigS+1
        end if
    loop
    tempnum=x
    
    i=0
    for i=DigS to 1 step-1
        if tempnum>=2^(i-1) then
            tempnum=tempnum-2^(i-1)
            c10to2=c10to2 & "1"   
        else
            c10to2=c10to2 & "0"
        end if
    next
    if mysign=-1 then c10to2="-" & c10to2
end function
'========第二种
Function c2u(sGBStr)
Dim i,c
For i = 1 to Len(sGBStr)
c = Mid(sGBStr,i,1)
c2u = c2u & "&#x" & Hex(AscW(c)) & ";"
Next
End Function
'======这个函数将二进制流转成字符串:
Public Function TransBinaryToStr(objObject As Variant) As String
Dim i As Integer
Dim nCount As Integer
Dim bFlag As Boolean
Dim sRtn As String
Dim varChar As Variant
Dim sHeadLetter As String
bFlag = False
sRtn = ""
nCount = LenB(objObject)


If Not IsNull(objObject) Then
For i = 1 To nCount
'If bFlag = False Then
varChar = MidB(objObject, i, 1)
If AscB(varChar) > 127 Then
sHeadLetter = CStr(Hex(AscB(varChar)))
sRtn = sRtn & "%" & sHeadLetter
'sRtn = sRtn & Chr(AscW(MidB(objObject, i + 1, 1) & varChar))
'bFlag = True
Else
sRtn = sRtn & Chr(AscB(varChar))
End If
'Else
'bFlag = False
'End If
Next
End If
TransBinaryToStr = sRtn
End Function

'======这个函数可以从Post串中提取你所要的value:
Public Function getUTF8Parameter(sParameter, sURL As Variant) As String
Dim url() As Byte
Dim sIndex As Integer
Dim nHighIndex As Integer
Dim nLowIndex As Integer
Dim sHighChar As String
Dim sLowChar As String
Dim svUrlUse As Variant
Dim sUrlUse As String
Dim sLog As String
Dim i, j, k, w As Integer
Dim Current
Dim noldflag, nnewflag
Dim sHexToInteger As String
Dim sRtn As String

sHexToInteger = "123456789ABCDEF"
sIndex = InStrB(sURL, sParameter)
svUrlUse = MidB(sURL, sIndex + Len(sParameter) + 2)
sUrlUse = TransBinaryToStr(svUrlUse)

If IsEmpty(sUrlUse) Or sUrlUse = "" Then
getUTF8Parameter = ""
Exit Function
End If

k = 0
noldflag = 2
nnewflag = 2
For i = 1 To Len(sUrlUse)
Current = Mid(sUrlUse, i, 1)
If Current = "&" Then Exit For
If Current = "%" Then
i = i + 1
sHighChar = Mid(sUrlUse, i, 1)
i = i + 1
sLowChar = Mid(sUrlUse, i, 1)
nHighIndex = InStr(sHexToInteger, UCase(sHighChar))
nLowIndex = InStr(sHexToInteger, UCase(sLowChar))
ReDim Preserve url(j + 1)
url(j) = CByte(nHighIndex * 16 + nLowIndex)

j = j + 1
k = k + 1
nnewflag = 1
Else
ReDim url(j + 1)
url(j) = CByte(Asc(Current))

j = j + 1
nnewflag = 2
End If
If (nnewflag = 1 And Mid(sUrlUse, i + 1, 1) <> "%" Then
Dim tempbyte() As Byte
url(UBound(url)) = CByte(&H60)
tempbyte = url

Dim sTemp
sTemp = StrConv(tempbyte, vbUnicode)

sRtn = sRtn & sTemp

k = 0
j = 0
ElseIf nnewflag = 2 Then
sRtn = sRtn & CStr(url)
j = 0
End If
noldflag = nnewflag
Next

Dim objHz As New AFCONVERTLib.HzConvert
Dim sGB As String
objHz.UTF8toGB sRtn, sGB


getUTF8Parameter = Replace(sGB, "`", ""
getUTF8Parameter = Replace(getUTF8Parameter, "++", "`"
getUTF8Parameter = Replace(getUTF8Parameter, "+", ""
getUTF8Parameter = Replace(getUTF8Parameter, "`", "+"


Set objHz = Nothing

End Function

WAP和web app的对比 最近有一个web网站需要提供对应的移动端的版本,客户要求说弄个wap网站就行,我一听感觉wap好遥远,应该还是智能机不流行的时候说wap,现在应该都是web app了。后来仔细研究了下,发现对wap的一些理解还是很肤浅呀,对涉及其中的很多概念还是一知半解。正好乘此机会梳理了一下。   WAP目前主要使用1.2和2.0两个版本,1.2使用的是WML(Wireless Markup Languag 阅读详情

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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")

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