Created
September 4, 2019 03:00
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tf-idf 算法
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package algorihtm; | |
import java.util.Arrays; | |
import java.util.List; | |
/** | |
* @author ahianzhang | |
* @version 1.0 | |
* @date 2019-09-04 10:25 | |
**/ | |
public class TfIdf { | |
/** | |
* 词频 | |
* 此数值越大则代表这个 term 在当前文档中越重要 | |
* @param doc | |
* @param term | |
* @return | |
*/ | |
private double tf(List<String> doc, String term) { | |
double termFrequency = 0; | |
for (String str : doc) { | |
if (str.equalsIgnoreCase(term)) { | |
termFrequency++; | |
} | |
} | |
return termFrequency / doc.size(); | |
} | |
/** | |
* 文档频率 | |
* 此值会越高越说明不重要 | |
* @param docs | |
* @param term | |
* @return 存在 term 的文档数目 | |
*/ | |
private int df(List<List<String>> docs, String term) { | |
int n = 0; | |
if (term != null && !"".equals(term)) { | |
for (List<String> doc : docs) { | |
for (String word : doc) { | |
if (term.equalsIgnoreCase(word)) { | |
n++; | |
break; | |
} | |
} | |
} | |
} else { | |
System.out.println("term 不能为 null 或者空字符串"); | |
} | |
return n; | |
} | |
/** | |
* 逆文档频率 | |
* 此值越小则说明当前 term 越不重要 | |
* @param docs | |
* @param term | |
* @return | |
*/ | |
private double idf(List<List<String>> docs, String term) { | |
return Math.log(docs.size() / df(docs, term) + 1); | |
} | |
private double tfIdf(List<String> doc, List<List<String>> docs, String term) { | |
return tf(doc, term) * idf(docs, term); | |
} | |
public static void main(String[] args) { | |
List<String> doc1 = Arrays.asList("北京", "上海", "杭州"); | |
List<String> doc2 = Arrays.asList("北京", "深圳", "南京"); | |
List<String> doc3 = Arrays.asList("南京", "北京", "深圳"); | |
List<String> doc4 = Arrays.asList("上海", "广州", "云南"); | |
List<List<String>> documents = Arrays.asList(doc1, doc2, doc3, doc4); | |
TfIdf tfIdf = new TfIdf(); | |
System.out.println("【北京】在 doc1 中的词频:" + tfIdf.tf(doc1, "北京")); | |
System.out.println("【北京】在 doc4 中的词频:" + tfIdf.tf(doc4, "北京")); | |
System.out.println("【北京】在 文档集 中的词频:" + tfIdf.df(documents, "北京")); | |
System.out.println("【北京】的 if-idf 算法:" + tfIdf.tfIdf(doc2, documents, "北京")); | |
System.out.println("【深圳】的 if-idf 算法:" + tfIdf.tfIdf(doc2, documents, "深圳")); | |
} | |
} |
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