使用WVTool进行文本分类

最新推荐文章于 2024-07-19 15:53:24 发布

最新推荐文章于 2024-07-19 15:53:24 发布 · 117 阅读

文章标签：

#Java #算法 #F# #.net

数据挖掘专栏收录该内容

30 篇文章

订阅专栏

本文介绍如何使用Java实现文本向量化及基于KNN算法进行文本分类，包括构建词频矩阵、生成词组文件、创建文本向量空间，并通过KNN算法进行分类演示。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >


import java.io.BufferedReader;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileWriter;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.Calendar;
import java.util.List;

import edu.udo.cs.wvtool.config.WVTConfiguration;
import edu.udo.cs.wvtool.config.WVTConfigurationFact;
import edu.udo.cs.wvtool.generic.output.WordVectorWriter;
import edu.udo.cs.wvtool.generic.stemmer.DummyStemmer;
import edu.udo.cs.wvtool.generic.stemmer.WVTStemmer;
import edu.udo.cs.wvtool.generic.tokenizer.WVTTokenizer;
import edu.udo.cs.wvtool.generic.vectorcreation.TFIDF;
import edu.udo.cs.wvtool.generic.wordfilter.DummyWordFilter;
import edu.udo.cs.wvtool.generic.wordfilter.WVTWordFilter;
import edu.udo.cs.wvtool.main.WVTDocumentInfo;
import edu.udo.cs.wvtool.main.WVTFileInputList;
import edu.udo.cs.wvtool.main.WVTWordVector;
import edu.udo.cs.wvtool.main.WVTool;
import edu.udo.cs.wvtool.wordlist.WVTWordList;

public class MyTest{

     public static void main(String[] args) throws Exception {
    	 //初始化一个WVTool对象
        WVTool wvt = new WVTool(false);

        //初始化一个configuration对象
        WVTConfiguration config = new WVTConfiguration();

       WVTStemmer stemmer = new DummyStemmer();
        WVTTokenizer tk = new ChineseTokenizer();

        //DummyStopWordFilter filter = new DummyStopWordFilter();
        WVTWordFilter filter = new DummyWordFilter();


        config.setConfigurationRule(WVTConfiguration.STEP_TOKENIZER, new WVTConfigurationFact(tk));
        config.setConfigurationRule(WVTConfiguration.STEP_STEMMER, new WVTConfigurationFact(stemmer));
        config.setConfigurationRule(WVTConfiguration.STEP_WORDFILTER, new WVTConfigurationFact(filter));

         WVTFileInputList list = new WVTFileInputList(2);

        // Add entries
        //为输入添加一个文档信息对象 （WVTDocumentInfo）,其中sourceName对象可以是一个文件夹的名称，也可以是一个文件名称, 最后一个0这个文档信息对象的类别 
        //样本数据
        //list.addEntry(new WVTDocumentInfo("a.txt", "txt", "", "", 0));
        //list.addEntry(new WVTDocumentInfo("b.txt", "txt", "", "", 1));
        list.addEntry(new WVTDocumentInfo("D:/temp/1", "txt", "", "chinese", 0));
        list.addEntry(new WVTDocumentInfo("D:/temp/2", "txt", "", "chinese", 1));

        //生成wordList
        WVTWordList wordList = wvt.createWordList(list, config);
        //对wordList中词频做出一个限制，即词频在1<n<5之间
        wordList.pruneByFrequency(1, 5);

        //生成词组文件
        wordList.storePlain(new FileWriter("wordlist.txt"));

        // 生成词频文件  
        wordList.store(new FileWriter("wordVector.txt"));

        //将生成的文本向量空间写入一个特定的文件
        FileWriter outFile = new FileWriter("wv.txt");

        //DummyWordVectorWriter wvw = new DummyWordVectorWriter(outFile, true);
        WordVectorWriter wvw = new WordVectorWriter(outFile,true); 

        config.setConfigurationRule(WVTConfiguration.STEP_OUTPUT, new WVTConfigurationFact(wvw));
        config.setConfigurationRule(WVTConfiguration.STEP_VECTOR_CREATION, new WVTConfigurationFact(new TFIDF()));

        //Create the vectors
        WVTWordVector[] vectors = wvt.createVectors(list, config, wordList,null);

        //Close the output file
        wvw.close();
        outFile.close();

        // 一个使用wordList构建文本空间向量的实例
        //WVTWordVector q = wvt.createVector("cmu harvard net", wordList);

        //测试的文本
        WVTDocumentInfo d = new WVTDocumentInfo("", "txt", "", "chinese");  
        //测试文本的内容
        String txt = getContent("test.txt");
        //根据wordlist和config 生成向量
    	WVTWordVector q = wvt.createVector(txt, d, config, wordList);

        FileWriter outFile1 = new FileWriter("test_wv1.txt");
        WordVectorWriter wvw1 = new WordVectorWriter(outFile1, true);       
        wvw1.write(q);        
        wvw1.close();
        outFile1.close();

        //knn算法分类
        KNN knn = new KNN();	  
        //分类结果
        List result = knn.LazyLearning(q, vectors, list.getNumClasses());	     	        
        for(int i=0;i<result.size();i++){
        	CategoryResult cr = (CategoryResult) result.get(i);
        	System.out.println("rs:"+cr.getCategoryName()+" "+cr.getSimilarity());
        }

    }

 	public static String getContent(String file) throws IOException{
		File myfile = new File(file);
        if (!myfile.exists()) {
          return "";
        }
        File f=new File(file);
        InputStreamReader  read = new InputStreamReader (new FileInputStream(f));
        BufferedReader reader = new BufferedReader(read);
        String line;
        String strContent = "";
        while((line=reader.readLine())!=null){
        	strContent+=line;
        }
        return strContent;

	}

}