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
以上就是本文关于tf-idf理解及其java实现代码实例的全部内容，希望对大家有所帮助。如有不足之处，欢迎留言指出。
原文链接：https://www.cnblogs.com/ywl925/archive/2013/08/26/3275878.html
