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tf-idf
前言
前段时间，又具体看了自己以前整理的tf-idf，这里把它发布在博客上，知识就是需要不断的重复的，否则就感觉生疏了。
tf-idf理解
tf-idf（term frequency–inverse document frequency）是一种用于资讯检索与资讯探勘的常用加权技术, 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反文档频率(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.
tf公式：
以上式子中
是该词在文件
中的出现次数，而分母则是在文件中所有字词的出现次数之和。
idf公式：
|d|：语料库中的文件总数
：包含词语 ti 的文件数目（即 ni,j不等于0的文件数目）如果该词语不在语料库中，就会导致被除数为零，因此一般情况下使用
然后
tf-idf实现（java）
这里采用了外部插件ikanalyzer-2012.jar，用其进行分词
具体代码如下：
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package tfidf;
import java.io.*;
import java.util.*;
import org.wltea.analyzer.lucene.ikanalyzer;
public class readfiles {
/**
* @param args
*/
private static arraylist<string> filelist = new arraylist<string>();
// the list of file
//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>();
string text = readfiles.readfile(file);
ikanalyzer analyzer = new ikanalyzer();
words = analyzer.split(text);
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);
system.out.println(word);
} else{
restf.put(word, restf.get(word) + 1);
system.out.println(word.tostring());
}
}
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 = readfiles.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 = readfiles.readdirs(dirc);
for (string file : filelist){
hashmap<string, integer> dict = new hashmap<string, integer>();
arraylist<string> cutwords = readfiles.cutwords(file);
//get cut word for one file
dict = readfiles.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 = readfiles.readdirs(dirc);
for (string file : filelist){
hashmap<string, float> dict = new hashmap<string, float>();
arraylist<string> cutwords = readfiles.cutwords(file);
//get cut words for one file
dict = readfiles.tf(cutwords);
alltf.put(file, dict);
}
return alltf;
}
public static hashmap<string, float> idf(hashmap<string,hashmap<string, float>> all_tf){
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);
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){
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);
}
system.out.println("tf-idf for every file is :");
distfidf(restfidf);
}
public static void distfidf(hashmap<string, hashmap<string, float>> tfidf){
iterator iter1 = tfidf.entryset().iterator();
while(iter1.hasnext()){
map.entry entrys = (map.entry)iter1.next();
system.out.println("filename: " + entrys.getkey().tostring());
system.out.print("{");
hashmap<string, float> temp = (hashmap<string, float>) entrys.getvalue();
iterator iter2 = temp.entryset().iterator();
while(iter2.hasnext()){
map.entry entry = (map.entry)iter2.next();
system.out.print(entry.getkey().tostring() + " = " + entry.getvalue().tostring() + ", ");
}
system.out.println("}");
}
}
public static void main(string[] args) throws ioexception {
// todo auto-generated method stub
string file = "d:/testfiles";
hashmap<string,hashmap<string, float>> all_tf = tfallfiles(file);
system.out.println();
hashmap<string, float> idfs = idf(all_tf);
system.out.println();
tf_idf(all_tf, idfs);
}
}
结果如下图：
常见问题
没有加入lucene jar包
lucene包和je包版本不适合
总结
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