dom4j解析实例

import java.io.File;
import java.io.FileOutputStream;
import java.util.HashMap;
import java.util.Iterator;
import java.util.List;
import java.util.Map;

import org.dom4j.Document;
import org.dom4j.DocumentException;
import org.dom4j.DocumentHelper;
import org.dom4j.Element;
import org.dom4j.io.OutputFormat;
import org.dom4j.io.XMLWriter;

/**
 * @Title: TestDom4j.java
 * @Package
 * @Description: 解析xml字符串
 * @author 无处不在
 * @date 2012-11-20 下午05:14:05
 * @version V1.0
 */
public class TestDom4j {

 public static void readStringXml(String xml) {
  Document doc = null;
  try {

   // 读取并解析XML文档
   // SAXReader就是一个管道,用一个流的方式,把xml文件读出来
   //
   // SAXReader reader = new SAXReader(); //User.hbm.xml表示你要解析的xml文档
   // Document document = reader.read(new File("User.hbm.xml"));
   // 下面的是通过解析xml字符串的
   doc = DocumentHelper.parseText(xml); // 将字符串转为XML

   Element rootElt = doc.getRootElement(); // 获取根节点
   System.out.println("根节点:" + rootElt.getName()); // 拿到根节点的名称

   Iterator iter = rootElt.elementIterator("head"); // 获取根节点下的子节点head

   // 遍历head节点
   while (iter.hasNext()) {

    Element recordEle = (Element) iter.next();
    String title = recordEle.elementTextTrim("title"); // 拿到head节点下的子节点title值
    System.out.println("title:" + title);

    Iterator iters = recordEle.elementIterator("script"); // 获取子节点head下的子节点script

    // 遍历Header节点下的Response节点
    while (iters.hasNext()) {

     Element itemEle = (Element) iters.next();

     String username = itemEle.elementTextTrim("username"); // 拿到head下的子节点script下的字节点username的值
     String password = itemEle.elementTextTrim("password");

     System.out.println("username:" + username);
     System.out.println("password:" + password);
    }
   }
   Iterator iterss = rootElt.elementIterator("body"); // /获取根节点下的子节点body
   // 遍历body节点
   while (iterss.hasNext()) {

    Element recordEless = (Element) iterss.next();
    String result = recordEless.elementTextTrim("result"); // 拿到body节点下的子节点result值
    System.out.println("result:" + result);

    Iterator itersElIterator = recordEless.elementIterator("form"); // 获取子节点body下的子节点form
    // 遍历Header节点下的Response节点
    while (itersElIterator.hasNext()) {

     Element itemEle = (Element) itersElIterator.next();

     String banlce = itemEle.elementTextTrim("banlce"); // 拿到body下的子节点form下的字节点banlce的值
     String subID = itemEle.elementTextTrim("subID");

     System.out.println("banlce:" + banlce);
     System.out.println("subID:" + subID);
    }
   }
  } catch (DocumentException e) {
   e.printStackTrace();

  } catch (Exception e) {
   e.printStackTrace();

  }
 }

 /**
  * @description 将xml字符串转换成map
  * @param xml
  * @return Map
  */
 public static Map readStringXmlOut(String xml) {
  Map map = new HashMap();
  Document doc = null;
  try {
   // 将字符串转为XML
   doc = DocumentHelper.parseText(xml);
   // 获取根节点
   Element rootElt = doc.getRootElement();
   // 拿到根节点的名称
   System.out.println("根节点:" + rootElt.getName());

   // 获取根节点下的子节点head
   Iterator iter = rootElt.elementIterator("head");
   // 遍历head节点
   while (iter.hasNext()) {

    Element recordEle = (Element) iter.next();
    // 拿到head节点下的子节点title值
    String title = recordEle.elementTextTrim("title");
    System.out.println("title:" + title);
    map.put("title", title);
    // 获取子节点head下的子节点script
    Iterator iters = recordEle.elementIterator("script");
    // 遍历Header节点下的Response节点
    while (iters.hasNext()) {
     Element itemEle = (Element) iters.next();
     // 拿到head下的子节点script下的字节点username的值
     String username = itemEle.elementTextTrim("username");
     String password = itemEle.elementTextTrim("password");

     System.out.println("username:" + username);
     System.out.println("password:" + password);
     map.put("username", username);
     map.put("password", password);
    }
   }

   // 获取根节点下的子节点body
   Iterator iterss = rootElt.elementIterator("body");
   // 遍历body节点
   while (iterss.hasNext()) {
    Element recordEless = (Element) iterss.next();
    // 拿到body节点下的子节点result值
    String result = recordEless.elementTextTrim("result");
    System.out.println("result:" + result);
    // 获取子节点body下的子节点form
    Iterator itersElIterator = recordEless.elementIterator("form");
    // 遍历Header节点下的Response节点
    while (itersElIterator.hasNext()) {
     Element itemEle = (Element) itersElIterator.next();
     // 拿到body下的子节点form下的字节点banlce的值
     String banlce = itemEle.elementTextTrim("banlce");
     String subID = itemEle.elementTextTrim("subID");

     System.out.println("banlce:" + banlce);
     System.out.println("subID:" + subID);
     map.put("result", result);
     map.put("banlce", banlce);
     map.put("subID", subID);
    }
   }
  } catch (DocumentException e) {
   e.printStackTrace();
  } catch (Exception e) {
   e.printStackTrace();
  }
  return map;
 }

 public static void main(String[] args) {

  // 下面是需要解析的xml字符串例子
  /*
   * String xmlString = "<html>" + "<head>" + "<title>dom4j解析一个例子</title>"
   * + "<script>" + "<username>yangrong</username>" +
   * "<password>123456</password>" + "</script>" + "</head>" + "<body>" +
   * "<result>0</result>" + "<form>" + "<banlce>1000</banlce>" +
   * "<subID>36242519880716</subID>" + "</form>" + "</body>" + "</html>";
   */

  /*
   * Map map = readStringXmlOut(xmlString); Iterator iters =
   * map.keySet().iterator(); while (iters.hasNext()) { String key =
   * iters.next().toString(); // 拿到键 String val = map.get(key).toString();
   * // 拿到值 System.out.println(key + "=" + val); }
   */

  // readStringXml(xmlString);

  String xmlString = "<?xml version=\"1.0\" encoding=\"utf-8\"?><message><result><classifies><classify id=\"PS_ConstructDesignAudit\" name=\"主网项目:施工图审批\" count=\"13\"><record index=\"1\"><title></title><assigner></assigner><assignDate></assignDate><url>http://10.10.5.5:7009/web/ssoLogin.do?modelUrl=%2Flcam%2Fproject%2Fprojectplan%2Fconstructdesign%2FConstructDesignTab.jsp%3FtagFlag%3DtodoTag%26pDesktop%3Dyes%26nodeString%3Dnull%3Anull%3BPS_ConstructDesignAudit%3B000137%26pMenu%3Dyes%26projectType%3D1%26moduleId%3DCSG_ConstructDesign</url></record><record index=\"2\"><title></title><assigner></assigner><assignDate></assignDate><url>http://10.10.5.5:7009/web/ssoLogin.do?modelUrl=%2Flcam%2Fproject%2Fprojectplan%2Fconstructdesign%2FConstructDesignTab.jsp%3FtagFlag%3DtodoTag%26pDesktop%3Dyes%26nodeString%3Dnull%3Anull%3BPS_ConstructDesignAudit%3B000137%26pMenu%3Dyes%26projectType%3D1%26moduleId%3DCSG_ConstructDesign</url></record></classify></classifies><totalCount>61</totalCount></result></message>";
  try {
   Document document = DocumentHelper.parseText(xmlString);
   // 输出操作
   // OutputFormat format = OutputFormat.createPrettyPrint() ; //实例化
   // format.setEncoding("GBK") ;
   // XMLWriter writer = new XMLWriter(new FileOutputStream(new
   // File("d:" + File.separator + "dom4j_demo.xml")),format) ;
   // writer.write(docment) ; // 进行输出
   // writer.close() ;

   List listNodes = document
     .selectNodes("message/result/classifies/classify");
   for (int i = 0; i < listNodes.size(); i++) {
    // 获取classify
    Element element = (Element) listNodes.get(i);
    // 获取record
    for (Iterator it = element.elementIterator("record"); it
      .hasNext();) {
     Element temp = (Element) it.next();
     System.out.println(temp.element("url").getText());
     break;
    }
   }
  } catch (Exception e) {
   e.printStackTrace();
  }

 }

}

 

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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]); 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