用Java實現特徵提取計算tf-idf

（1）計算反文件頻次公式如下：

（2）計算TF-IDF公式如下：

tf-idf=tf*idf

（2）Java程式碼實現

package com.panguoyuan.datamining.first;

import java.io.BufferedReader;
import java.io.FileReader;
import java.util.ArrayList;
import java.util.Collection;
import java.util.Collections;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Iterator;
import java.util.List;
import java.util.Map;
import java.util.Set;

public class TestTFIDF {
	public static List<Map> articleList = new ArrayList<Map>();
	
	public static List<Result> listResult = new ArrayList<Result>();
	//文章  詞  tf-idf
	public static void main(String[] args) throws Exception{
		BufferedReader fr = new BufferedReader(new FileReader("F:\\workspace1\\datamining\\data\\test_sohu_news_data"));
		initD(fr);
		calculateTF_IDF();print();
	}
	
	
	private static void print(){
		Collections.sort(listResult);
		for(Result r : listResult){
			System.out.println("第【"+r.getArticleId()+"】篇文章 ："+r.getWord()+"  "+r.getTFIDF());
		}
	}
	public static void calculateTF_IDF() throws Exception{
		int d = articleList.size();
		for(int i=0;i<articleList.size();i++ ){
			Map<String,Integer> wordMap = articleList.get(i);
			Set set = wordMap.entrySet();
			Iterator iter = set.iterator();
			while(iter.hasNext()){
				String obj = iter.next().toString();
				String[] wordcount = obj.split("=");
				int tf =Integer.parseInt(wordcount[1]);
				double idf = log(d/(getDF(wordcount[0])+1),2);
				double tf_idf = tf * idf ;
			//	if(tf_idf > 0){
				//	System.out.println("第【"+i+"】篇文章:"+wordcount[0]+"      "+tf_idf);
				Result r = new Result(i,wordcount[0],tf_idf);
				listResult.add(r);
			//	}
			}
		}

	}
	
	public static void initD(BufferedReader br) throws Exception{
		int i;
		while ((i = br.read()) != -1) {
			String line = br.readLine();
			Map<String,Integer> wordMap = getWordCountMap(line);
			articleList.add(wordMap);
		}
	}
	
	public static int getDF(String word){
		int count = 0;
		for(int i=0;i<articleList.size();i++){
			Map<String,Integer> map = articleList.get(i);
			if(map.containsKey(word)){
				count ++;
			}
		}
		return count;
	}
	
	public static Map<String,Integer> getWordCountMap(String article) throws Exception{
		 Map<String,Integer> wordMap = new HashMap<String,Integer>();
		String[] words = article.split(" ");
		for (int j = 0; j < words.length; j++) {
			String word = words[j];
			if (wordMap.get(word) != null && !"".equals(wordMap.get(word))) {
				wordMap.put(word, wordMap.get(word) + 1);
			} else {
				wordMap.put(word, 1);
			}
		}
		
		return wordMap;
	}
	
	
	public static double log(double value, double base) {
		return Math.log(value)/Math.log(base);
	 }
	

}
 

package com.panguoyuan.datamining.first;


public class Result implements Comparable {

	private int articleId;
	private String word;
	private double TFIDF;

	public Result(int articleId, String word, double tFIDF) {
		super();
		this.articleId = articleId;
		this.word = word;
		TFIDF = tFIDF;
	}

	public int getArticleId() {
		return articleId;
	}

	public void setArticleId(int articleId) {
		this.articleId = articleId;
	}

	public String getWord() {
		return word;
	}

	public void setWord(String word) {
		this.word = word;
	}

	public double getTFIDF() {
		return TFIDF;
	}

	public void setTFIDF(double tFIDF) {
		TFIDF = tFIDF;
	}

	@Override
	public int compareTo(Object o) {
		Result r = (Result)o;
		if(r.getTFIDF() > this.TFIDF){
			return 1;
		}else if(r.getTFIDF()<this.TFIDF){
			return -1;
		}else{
			return 0;
		}
	}
	
}
 

（3）排序後輸出結果