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a55455
V2EX  ›  Java

Java 中给定一个字符串 与一个字符串集合,如何判定该字符串与字符串集合中每个字符串的相似度

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  •   a55455 · 352 天前 · 1400 次点击
    这是一个创建于 352 天前的主题,其中的信息可能已经有所发展或是发生改变。

    请教方法

    11 条回复    2023-12-08 16:48:46 +08:00
    wolfie
        1
    wolfie  
       352 天前
    cn.hutool.core.text.TextSimilarity
    28Sv0ngQfIE7Yloe
        2
    28Sv0ngQfIE7Yloe  
       352 天前
    相似度的定义是什么
    yyf1234
        3
    yyf1234  
       352 天前 via iPhone
    关键字:编辑距离
    xuld
        4
    xuld  
       352 天前
    public class LevenshteinDistance {

    public static int distance(String s1, String s2) {
    int l1 = s1.length();
    int l2 = s2.length();

    // base cases
    if (l1 == 0) return l2;
    if (l2 == 0) return l1;

    int[][] distance = new int[l1+1][l2+1];

    for (int i = 0; i <= l1; i++) {
    distance[i][0] = i;
    }

    for (int j = 0; j <= l2; j++) {
    distance[0][j] = j;
    }

    for (int i = 1; i <= l1; i++) {
    for (int j = 1; j <= l2; j++) {

    int cost = (s1.charAt(i-1) == s2.charAt(j-1)) ? 0 : 1;

    distance[i][j] = Math.min(
    distance[i-1][j] + 1, // deletion
    distance[i][j-1] + 1, // insertion
    distance[i-1][j-1] + cost // substitution
    );
    }
    }

    return distance[l1][l2];
    }

    }
    chippai
        5
    chippai  
       352 天前
    编辑距离,曾经给一个账号注册做优化,前人暴力比较,千万+的账号比完一个请求跑 3s 。
    ldyisbest
        6
    ldyisbest  
       352 天前
    先定义相似度,问题要明确
    crazyweeds
        8
    crazyweeds  
       352 天前
    相似度算法。。
    knightdf
        9
    knightdf  
       352 天前
    相似度的算法有好多,这个就要看你的数据类型了
    Aresxue
        10
    Aresxue  
       351 天前
    https://github.com/tdebatty/java-string-similarity
    推荐标准莱茵斯坦算法和余弦算法,字符串较大就用余弦,对准确率要求高就莱茵斯坦算法。
    附上测试一个测试类自己修改下,用自己的实际数据多跑跑

    public class SimilarityAlgorithmTest {

    @Test
    public void test() {
    String str1 = "ares";
    String str2 = "kele";

    LevenshteinDistance levenshtein = LevenshteinDistance.getDefaultInstance();
    System.out.println("Levenshtein distance: " + levenshtein.apply(str1, str2));
    System.out.println("Levenshtein distance: " + levenshtein.apply(str2, str2));

    LevenshteinDistance levenshteinWithThreshold = new LevenshteinDistance(3);
    // Returns -1 since the actual distance, 4, is higher than the threshold
    System.out.println("Levenshtein distance: " + levenshteinWithThreshold.apply(str1, str2));

    LevenshteinDetailedDistance levenshteinDetailed = LevenshteinDetailedDistance.getDefaultInstance();
    System.out.println("Levenshtein detailed distance: " + levenshteinDetailed.apply(str1, str2));
    }

    @Test
    public void testLevenshteinDistance() {
    int sLength = 13_162;
    int strLength = 4_000;
    String s = StringUtil.random(sLength);
    String s1 = StringUtil.random(strLength) + s;
    String s2 = StringUtil.random(strLength) + s;

    int threshold = 4_096;
    LevenshteinDistance levenshtein = new LevenshteinDistance(threshold);
    long startTime = System.currentTimeMillis();
    /*
    threshold 为 32766 时花费 7022ms
    threshold 为 16384 时花费 2164ms
    threshold 为 8192 时花费 463ms
    threshold 为 4096 时花费 146ms
    threshold 为 2048 时花费 62ms
    threshold 为 1024 时花费 41ms
    threshold 为 512 时花费 29ms
    */
    Integer result = levenshtein.apply(s1, s2);
    System.out.println(System.currentTimeMillis() - startTime);
    System.out.println("Levenshtein distance: " + result);

    /*
    这种是截取模式比较靠前的指定字符
    threshold 为 4096 时花费 67ms
    */
    levenshtein = LevenshteinDistance.getDefaultInstance();
    startTime = System.currentTimeMillis();
    result = levenshtein.apply(s1.substring(0, threshold), s2.substring(0, threshold));
    System.out.println(System.currentTimeMillis() - startTime);
    System.out.println("Levenshtein distance: " + result);
    System.out.println();

    result = levenshtein.apply(s1, s2);
    System.out.println("Levenshtein: " + (1.0 - (double) result / (strLength + sLength)));
    NormalizedLevenshtein normalizedLevenshtein = new NormalizedLevenshtein();
    double similarity = normalizedLevenshtein.similarity(s1, s2);
    System.out.println("NormalizedLevenshtein: " + similarity);
    Jaccard jaccard = new Jaccard();
    similarity = jaccard.similarity(s1, s2);
    System.out.println("Jaccard: " + similarity);
    Cosine cosine = new Cosine();
    similarity = cosine.similarity(s1, s2);
    System.out.println("Cosine: " + similarity);
    QGram qGram = new QGram();
    similarity = qGram.distance(s1, s2);
    System.out.println("QGram: " + (1.0 - similarity / (strLength + sLength)));
    }


    @Test
    public void testAlgorithmPerformance() {
    String s1 = StringUtil.random(13_162);
    String s2 = StringUtil.random(13_162);

    LevenshteinDistance levenshtein = LevenshteinDistance.getDefaultInstance();
    long startTime = System.currentTimeMillis();
    levenshtein.apply(s1, s2);
    System.out.println("LevenshteinDistance:" + (System.currentTimeMillis() - startTime));

    NormalizedLevenshtein normalizedLevenshtein = new NormalizedLevenshtein();
    startTime = System.currentTimeMillis();
    normalizedLevenshtein.distance(s1, s2);
    System.out.println("NormalizedLevenshtein: " + (System.currentTimeMillis() - startTime));

    JaroWinkler jaroWinkler = new JaroWinkler();
    startTime = System.currentTimeMillis();
    jaroWinkler.distance(s1, s2);
    System.out.println("JaroWinkler: " + (System.currentTimeMillis() - startTime));

    LongestCommonSubsequence longestCommonSubsequence = new LongestCommonSubsequence();
    startTime = System.currentTimeMillis();
    longestCommonSubsequence.apply(s1, s2);
    System.out.println("LongestCommonSubsequence: " + (System.currentTimeMillis() - startTime));

    info.debatty.java.stringsimilarity.MetricLCS metricLCS = new info.debatty.java.stringsimilarity.MetricLCS();
    startTime = System.currentTimeMillis();
    metricLCS.distance(s1, s2);
    System.out.println("MetricLCS: " + (System.currentTimeMillis() - startTime));

    QGram qGram = new QGram();
    startTime = System.currentTimeMillis();
    qGram.distance(s1, s2);
    System.out.println("QGram: " + (System.currentTimeMillis() - startTime));

    Cosine cosine = new Cosine();
    startTime = System.currentTimeMillis();
    cosine.distance(s1, s2);
    System.out.println("Cosine: " + (System.currentTimeMillis() - startTime));

    Jaccard jaccard = new Jaccard();
    startTime = System.currentTimeMillis();
    jaccard.distance(s1, s2);
    System.out.println("Jaccard: " + (System.currentTimeMillis() - startTime));
    }

    @Test
    public void testCosine() {
    String s1 = StringUtil.random(1_000_000);
    String s2 = StringUtil.random(1_000_000);

    Cosine cosine = new Cosine();
    long startTime = System.currentTimeMillis();
    cosine.distance(s1, s2);
    System.out.println("Cosine: " + (System.currentTimeMillis() - startTime));
    }

    }
    matepi
        11
    matepi  
       351 天前
    考虑集合效率的话,还可以用 SimHash
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