字符串相似度的计算 使用Levenshtein距离计算方法,我们可以将两个字符串之间的相似度定义为1减去Levenshtein距离除以两个字符串中较长字符串的长度。 下面是使用Java代码计算字符串相似度的示例: publicclassStringSimilarity{publicdoublecalculateSimilarity(Strings1,String
接下来,我们使用Java代码来实现Levenshtein算法。以下是实现代码: publicclassStringSimilarity{// 计算两个字符串之间的Levenshtein距离publicstaticintlevenshtein(Stringa,Stringb){int[][]dp=newint[a.length()+1][b.length()+1];// 初始化第一行和第一列for(inti=0;i<=a.length();i++){dp[i][0]=i...
SELECTUTL_MATCH.edit_distance_similarity ('h1e2l3l4o','ddddhello')ASsimilarity 结果 3.2、Jaro-Winkler相似度 解释:我也看不懂,自行取用:https://www.jianshu.com/p/a4af202cb702 使用、测试 Stringstr1="h1e2l3l4o"; Stringstr2="ddddhello"; //Jaro-Winkler相似度 @Test publicvoidtest03()throws...
1.首先,在Java代码中导入CosineSimilarity库的相关类: ```java import info.debatty.java.stringsimilarity.Cosine; ``` 2.然后,我们可以创建CosineSimilarity对象并调用`similarity()`方法来计算两个文本之间的余弦相似度,示例代码如下: ```java Cosine cosine = new Cosine(); String text1 = "Java is a pro...
String Similarity .NET A .NET port of java-string-similarity:https://github.com/tdebatty/java-string-similarity A library implementing different string similarity and distance measures. A dozen of algorithms (including Levenshtein edit distance and sibblings, Jaro-Winkler, Longest Common Subsequence,...
(norm1 * norm2); } /** * 计算两个句子的相似度 * @param sentence1 第一个句子 * @param sentence2 第二个句子 * @param model 词向量模型 * @return 句子相似度值 */ private static double sentenceSimilarity(String sentence1, String sentence2, Word2Vec model) { List<INDArray> vectors1 =...
publicstaticfloatgetSimilarityRatio(String str, String target){intd[][];// 矩阵intn=str.length();intm=target.length();inti;// 遍历str的intj;// 遍历target的charch1;// str的charch2;// target的inttemp;// 记录相同字符,在某个矩阵位置值的增量,不是0就是1if(n ==0|| m ==0) {return...
("A Faster Algorithm Computing String Edit Distances"). This method splits the matrix in blocks of size t x t. Each possible block is precomputed to produce a lookup table. This lookup table can then be used to compute the string similarity (or distance) in O(nm/t). Usually, t is ...
以上所有方法的完整代码如下,使用SimilarityUtil.getSimilarity(String s1,String s2)即可得到s1和s2的语句相似度: package com.yuantu.dubbo.provider.questionRepo.utils; import com.hankcs.hanlp.HanLP; import com.hankcs.hanlp.dictionary.CustomDictionary; ...
下面是一个使用编辑距离算法判断中文字符串相似度的示例代码: ```java public class ChineseSimilarity { public static int calculateSimilarity(String s1, String s2) { int[][] dp = new int[s1.length() + 1][s2.length() + 1]; for (int i = 0; i <= s1.length(); i++) { dp[i][0]...