利用HttpRequest登录到某个网站,然后获取网站信息的程序示例(转)

鸿蒙Harmony编程开发:使用HttpRequest模拟登录示例 鸿蒙API内置了对HTTP协议客户端的支持,遗憾的是目前(API 9,包括API 10、11)还不支持服务端(倒是可以在API 10或更高版本中通过TCP Server自行实现一个),本文将讲解HTTP客户端相关方法的使用。 阅读详情

原地址:http://www.430000.net/printpage.asp?ArticleID=69

 

问题:有的网站的相关内容必须要在登录后才可以查看,其登录信息保存在session变量之中。这样,使用asphttp等组件就难以正确得到所要的信息。

解决:使用asp.net中的httprequest和httpresponse来实现。

要点:
1。 通过附加一个cookiecontainer到httprequest对象中,可以得到登录后返回的代表SESSION ID的COOKIE。 见Login方法
2。 将此COOKIE包含在一个cookiecontainer中并附加到另一个HTTPREQUEST请求中,则可以实现SESSION的还原。见getPage方法

源程序如下:

getHttpInfo.aspx:
<%@ Page language=c# Codebehind=getHttpInfo.aspx.cs AutoEventWireup=false Inherits=PdfTest.getHttpInfo %>
















getHttpInfo.aspx.cs:
using System;
using System.Collections;
using System.ComponentModel;
using System.Data;
//using System.Data.OleDb;
using System.Drawing;
using System.Web;
using System.Web.SessionState;
using System.Web.UI;
using System.Web.UI.WebControls;
using System.Web.UI.HtmlControls;
using System.Net;
using System.IO;
using System.Text;
using System.Text.RegularExpressions;
using Microsoft.Data.Odbc;

namespace PdfTest
{
///
/// Summary description for WebForm1.
///

public class getHttpInfo : System.Web.UI.Page
{
protected static string cookieheader;


private void Page_Load(object sender, System.EventArgs e)
{
// Put user code to initialize the page here

string strResult;

if (HttpContext.Current.Application[cookieheader] != null)
{
cookieheader = (string)HttpContext.Current.Application[cookieheader];
}
else
{
//Login into the website and keep the cookie for the session in the application variable
string strLogin = Login(http://www.thesiteyouwanttovisit/theloginpage.asp, Action=&USERID=&Password=) ;
}


strResult = getPage(http://www.thesiteyouwanttovisit/theloginpage.asp, Action=&data=) ;


//Write the result to htm file
FileStream htmFile = new FileStream(c:/save.htm, FileMode.OpenOrCreate);
StreamWriter sw = new StreamWriter(htmFile);
sw.Write(strResult);
sw.Close();
htmFile.Close();

// output the result
Response.Write(strResult);
}


public static string Login(String url, String paramList)
{
HttpWebResponse res = null;
string strResult=;

try
{

HttpWebRequest req = (HttpWebRequest)WebRequest.Create(url);
req.Method = POST;
req.ContentType = application/x-www-form-urlencoded;
req.AllowAutoRedirect = false;
CookieContainer cookieCon = new CookieContainer();
req.CookieContainer = cookieCon;

StringBuilder UrlEncoded = new StringBuilder();
Char[] reserved = {'?', '=', '&'};
byte[] SomeBytes = null;

if (paramList != null)
{
int i=0, j;
while(i
{
j=paramList.IndexOfAny(reserved, i);
if (j==-1)
{
UrlEncoded.Append(HttpUtility.UrlEncode(paramList.Substring(i, paramList.Length-i)));
break;
}
UrlEncoded.Append(HttpUtility.UrlEncode(paramList.Substring(i, j-i)));
UrlEncoded.Append(paramList.Substring(j,1));
i = j+1;
}
SomeBytes = Encoding.UTF8.GetBytes(UrlEncoded.ToString());
req.ContentLength = SomeBytes.Length;
Stream newStream = req.GetRequestStream();
newStream.Write(SomeBytes, 0, SomeBytes.Length);
newStream.Close();
}
else
{
req.ContentLength = 0;
}


res = (HttpWebResponse)req.GetResponse();
cookieheader = req.CookieContainer.GetCookieHeader(new Uri(url));
HttpContext.Current.Application.Lock();
HttpContext.Current.Application[cookieheader] = cookieheader;
HttpContext.Current.Application.UnLock();

Stream ReceiveStream = res.GetResponseStream();
Encoding encode = System.Text.Encoding.GetEncoding(utf-8);
StreamReader sr = new StreamReader( ReceiveStream, encode );
Char[] read = new Char[256];
int count = sr.Read( read, 0, 256 );
while (count > 0)
{
String str = new String(read, 0, count);
strResult += str;
count = sr.Read(read, 0, 256);
}
}
catch(Exception e)
{
strResult = e.ToString();
}
finally
{
if ( res != null )
{
res.Close();
}
}

return strResult;
}


public static string getPage(String url, String paramList)
{
HttpWebResponse res = null;
string strResult = ;

try
{

HttpWebRequest req = (HttpWebRequest)WebRequest.Create(url);
req.Method = POST;
req.KeepAlive = true;
req.ContentType = application/x-www-form-urlencoded;
CookieContainer cookieCon = new CookieContainer();
req.CookieContainer = cookieCon;
req.CookieContainer.SetCookies(new Uri(url),cookieheader);
StringBuilder UrlEncoded = new StringBuilder();
Char[] reserved = {'?', '=', '&'};
byte[] SomeBytes = null;

if (paramList != null)
{
int i=0, j;
while(i
{
j=paramList.IndexOfAny(reserved, i);
if (j==-1)
{
UrlEncoded.Append(HttpUtility.UrlEncode(paramList.Substring(i, paramList.Length-i)));
break;
}
UrlEncoded.Append(HttpUtility.UrlEncode(paramList.Substring(i, j-i)));
UrlEncoded.Append(paramList.Substring(j,1));
i = j+1;
}
SomeBytes = Encoding.UTF8.GetBytes(UrlEncoded.ToString());
req.ContentLength = SomeBytes.Length;
Stream newStream = req.GetRequestStream();
newStream.Write(SomeBytes, 0, SomeBytes.Length);
newStream.Close();
}
else
{
req.ContentLength = 0;
}


res = (HttpWebResponse)req.GetResponse();
Stream ReceiveStream = res.GetResponseStream();
Encoding encode = System.Text.Encoding.GetEncoding(utf-8);
StreamReader sr = new StreamReader( ReceiveStream, encode );
Char[] read = new Char[256];
int count = sr.Read( read, 0, 256 );
while (count > 0)
{
String str = new String(read, 0, count);
strResult += str;
count = sr.Read(read, 0, 256);
}
}
catch(Exception e)
{
strResult = e.ToString();
}
finally
{
if ( res != null )
{
res.Close();
}
}

return strResult;
}


#region Web Form Designer generated code
override protected void OnInit(EventArgs e)
{
//
// CODEGEN: This call is required by the ASP.NET Web Form Designer.
//
InitializeComponent();
base.OnInit(e);
}

///
/// Required method for Designer support - do not modify
/// the contents of this method with the code editor.
///

private void InitializeComponent()
{
this.Load += new System.EventHandler(this.Page_Load);

}
#endregion



}
}
过程挖掘(Process Mining)1——始于颜值 下周开始每周1-2篇的过程挖掘分享 过程挖掘是什么? 过程挖掘与过程管理的关系? 过程挖掘与数据挖掘的关系? 管理学还是计算机科学? 阅读详情

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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")

利用HttpRequest登录到某个网站,然后获取网站信息的程序示例 [原创]

<!--google_ad_client = "pub-2947489232296736";/* 728x15, 创建于 08-4-23MSDN */google_ad_slot = "3624277373";google_ad_width = 728;google_ad_height = 15;//--><script type="text/javascript"

zgqtxwd的专栏 569

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

HttpRequest获取网站信息的程序示例

<!--google_ad_client = "pub-2947489232296736";/* 728x15, 创建于 08-4-23MSDN */google_ad_slot = "3624277373";google_ad_width = 728;google_ad_height = 15;//--><script type="text/javascript"

zgqtxwd的专栏 558

RU DOS版、EFI版32bit/64bit BIOS常用工具

DOS版、EFI版的RU工具,可用来查看IO、MEM、PCI、SMBIOS等资源。亲测在EFI SHELL下可用。

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