TF-IDF的java实现(权重排序显示)
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原文地址：http://blog.csdn.net/qy20115549/article/details/54173243
#TFIDF的主要思想 TFIDF的主要思想是：如果某个词或短语在一篇文章中出现的频率TF高，并且在其他文章中很少出现，则认为此词或者短语具有很好的类别区分能力，适合用来分类。 TFIDF实际上是：TF * IDF，TF词频(Term Frequency)，IDF逆向文件频率(Inverse Document Frequency)。TF表示词条在文档d中出现的频率。 IDF的主要思想是：如果包含词条t的文档越少，也就是n越小，IDF越大，则说明词条t具有很好的类别区分能力。如果某一类文档C中包含词条t的文档数为m，而其它类包含t的文档总数为k，显然所有包含t的文档数n=m+k，当m大的时候，n也大，按照IDF公式得到的IDF的值会小，就说明该词条t类别区分能力不强。 但是实际上，如果一个词条在一个类的文档中频繁出现，则说明该词条能够很好代表这个类的文本的特征，这样的词条应该给它们赋予较高的权重，并选来作为该类文本的特征词以区别与其它类文档。这就是IDF的不足之处. 在一份给定的文件里，词频（term frequency，TF）指的是某一个给定的词语在该文件中出现的频率。这个数字是对词数(term count)的归一化，以防止它偏向长的文件。（同一个词语在长文件里可能会比短文件有更高的词数，而不管该词语重要与否。）【来自百度百科】
以下程序可实现TF-IDF。其核心程序来自于这篇博客：http://blog.csdn.net/endless_yy/article/details/12745405
#程序使用
在此基础上，本人修改了其分词部分，因为我的分词是单独写的。同时，本人自己加了权值的排序，以及输入文件，输出文件。
程序的输入是分词之后文档的目录。
注意这里是分词之后的目录
本程序可以直接使用。
package TfIdf;
import java.io.*;
import java.util.*;
import java.util.Map.Entry;
public class Test {
/**
* @param args
*/
private static ArrayList<String> FileList = new ArrayList<String>(); // the list of file
//获取文件名
public static String getFileNameWithSuffix(String pathandname) {
int start = pathandname.lastIndexOf("\\");
if (start != -1 ) {
return pathandname.substring(start + 1);
} else {
return null;
}
}
//get list of file for the directory, including sub-directory of it
public static List<String> readDirs(String filepath) throws FileNotFoundException, IOException
{
try
{
File file = new File(filepath);
if(!file.isDirectory())
{
System.out.println("输入的[]");
System.out.println("filepath:" + file.getAbsolutePath());
}
else
{
String[] flist = file.list();
for(int i = 0; i < flist.length; i++)
{
File newfile = new File(filepath + "\\" + flist[i]);
if(!newfile.isDirectory())
{
FileList.add(newfile.getAbsolutePath());
}
else if(newfile.isDirectory()) //if file is a directory, call ReadDirs
{
readDirs(filepath + "\\" + flist[i]);
}
}
}
}catch(FileNotFoundException e)
{
System.out.println(e.getMessage());
}
return FileList;
}
//read file
public static String readFile(String file) throws FileNotFoundException, IOException
{
StringBuffer strSb = new StringBuffer(); //String is constant， StringBuffer can be changed.
InputStreamReader inStrR = new InputStreamReader(new FileInputStream(file), "gbk"); //byte streams to character streams
BufferedReader br = new BufferedReader(inStrR);
String line = br.readLine();
while(line != null){
strSb.append(line).append("\r\n");
line = br.readLine();
}
return strSb.toString();
}
//word segmentation
public static ArrayList<String> cutWords(String file) throws IOException{
ArrayList<String> words = new ArrayList<String>();
BufferedReader reader = new BufferedReader( new InputStreamReader( new FileInputStream( new File(file)),"utf-8"));
String s=null;
while ((s=reader.readLine())!=null) {
String cutWordResult[] =s.split(" ");
for (int i = 0; i < cutWordResult.length; i++) {
words.add(cutWordResult[i]);
}
}
reader.close();
return words;
}
//term frequency in a file, times for each word
public static HashMap<String, Integer> normalTF(ArrayList<String> cutwords){
HashMap<String, Integer> resTF = new HashMap<String, Integer>();
for(String word : cutwords){
if(resTF.get(word) == null){
resTF.put(word, 1);
}
else{
resTF.put(word, resTF.get(word) + 1);
}
}
return resTF;
}
//term frequency in a file, frequency of each word
public static HashMap<String, Float> tf(ArrayList<String> cutwords){
HashMap<String, Float> resTF = new HashMap<String, Float>();
int wordLen = cutwords.size();
HashMap<String, Integer> intTF = Test.normalTF(cutwords);
Iterator iter = intTF.entrySet().iterator(); //iterator for that get from TF
while(iter.hasNext()){
Map.Entry entry = (Map.Entry)iter.next();
resTF.put(entry.getKey().toString(), Float.parseFloat(entry.getValue().toString()) / wordLen);
// System.out.println(entry.getKey().toString() + " = "+ Float.parseFloat(entry.getValue().toString()) / wordLen);
}
return resTF;
}
//tf times for file
public static HashMap<String, HashMap<String, Integer>> normalTFAllFiles(String dirc) throws IOException{
HashMap<String, HashMap<String, Integer>> allNormalTF = new HashMap<String, HashMap<String,Integer>>();
List<String> filelist = Test.readDirs(dirc);
for(String file : filelist){
HashMap<String, Integer> dict = new HashMap<String, Integer>();
ArrayList<String> cutwords = Test.cutWords(file); //get cut word for one file
dict = Test.normalTF(cutwords);
allNormalTF.put(file, dict);
}
return allNormalTF;
}
//tf for all file
public static HashMap<String,HashMap<String, Float>> tfAllFiles(String dirc) throws IOException{
HashMap<String, HashMap<String, Float>> allTF = new HashMap<String, HashMap<String, Float>>();
List<String> filelist = Test.readDirs(dirc);
for(String file : filelist){
HashMap<String, Float> dict = new HashMap<String, Float>();
ArrayList<String> cutwords = Test.cutWords(file); //get cut words for one file
dict = Test.tf(cutwords);
allTF.put(file, dict);
}
return allTF;
}
public static HashMap<String, Float> idf(HashMap<String,HashMap<String, Float>> all_tf) throws IOException{
HashMap<String, Float> resIdf = new HashMap<String, Float>();
HashMap<String, Integer> dict = new HashMap<String, Integer>();
int docNum = FileList.size();
for(int i = 0; i < docNum; i++){
HashMap<String, Float> temp = all_tf.get(FileList.get(i));
Iterator iter = temp.entrySet().iterator();
while(iter.hasNext()){
Map.Entry entry = (Map.Entry)iter.next();
String word = entry.getKey().toString();
if(dict.get(word) == null){
dict.put(word, 1);
}else {
dict.put(word, dict.get(word) + 1);
}
}
}
System.out.println("IDF for every word is:");
Iterator iter_dict = dict.entrySet().iterator();
while(iter_dict.hasNext()){
Map.Entry entry = (Map.Entry)iter_dict.next();
float value = (float)Math.log(docNum / Float.parseFloat(entry.getValue().toString()));
resIdf.put(entry.getKey().toString(), value);
//这里输入的是key值和value值,每个词对应的idf
// System.out.println(entry.getKey().toString() + " == " + value);
}
return resIdf;
}
public static void tf_idf(HashMap<String,HashMap<String, Float>> all_tf,HashMap<String, Float> idfs,String putpath) throws IOException{
HashMap<String, HashMap<String, Float>> resTfIdf = new HashMap<String, HashMap<String, Float>>();
int docNum = FileList.size();
for(int i = 0; i < docNum; i++){
String filepath = FileList.get(i);
HashMap<String, Float> tfidf = new HashMap<String, Float>();
HashMap<String, Float> temp = all_tf.get(filepath);
Iterator iter = temp.entrySet().iterator();
while(iter.hasNext()){
Map.Entry entry = (Map.Entry)iter.next();
String word = entry.getKey().toString();
Float value = (float)Float.parseFloat(entry.getValue().toString()) * idfs.get(word);
tfidf.put(word, value);
}
resTfIdf.put(filepath, tfidf);
}
DisTfIdf(resTfIdf,putpath);
}
//排序算法
public static void Rank(HashMap<String, Float> wordmap,String filename) throws IOException{
BufferedWriter Writer = new BufferedWriter( new OutputStreamWriter( new FileOutputStream( new File(filename)),"utf-8"));
List<String> wordgaopindipin=new ArrayList<String>();
List<Map.Entry<String, Float>> list = new ArrayList<Map.Entry<String, Float>>(wordmap.entrySet());
Collections.sort(list, new Comparator<Map.Entry<String, Float>>() {
//降序排序
public int compare(Entry<String, Float> o1, Entry<String, Float> o2) {
//return o1.getValue().compareTo(o2.getValue());
return o2.getValue().compareTo(o1.getValue());
}
});
//排序靠前的60个词及权值
if (list.size()>60) {
for (int i = 0; i < 59; i++) {
//写入文件
wordgaopindipin.add(list.get(i).getKey());
Writer.append(list.get(i).getKey()+" "+list.get(i).getValue()+"\r\n");
}
}else{
for (int i = 0; i < list.size(); i++) {
//写入文件
System.out.println(i);
wordgaopindipin.add(list.get(i).getKey());
Writer.append(list.get(i).getKey()+" "+list.get(i).getValue()+"\r\n");
}
}
Writer.close();
}
public static void DisTfIdf(HashMap<String, HashMap<String, Float>> tfidf,String outpath) throws IOException{
Iterator iter1 = tfidf.entrySet().iterator();
while(iter1.hasNext()){
Map.Entry entrys = (Map.Entry)iter1.next();
System.out.println("FileName: " + getFileNameWithSuffix(entrys.getKey().toString()));
HashMap<String, Float> temp = (HashMap<String, Float>) entrys.getValue();
//将排序结果输入到文本
Rank(temp,outpath+getFileNameWithSuffix(entrys.getKey().toString()));
//这里使用排序输出
}
}
public static void main(String[] args) throws IOException {
// 输入目录及输出目录
String inputpath = "F:\\QianYang\\Test\\";
String outpath="F:\\QianYang\\Test1\\";
HashMap<String,HashMap<String, Float>> all_tf = tfAllFiles(inputpath);
System.out.println();
HashMap<String, Float> idfs = idf(all_tf);
// System.out.println();
tf_idf(all_tf, idfs,outpath);
}
}
#程序结果
结果还是不错滴。
搞完之后，就可以用网站显示一个云图了。
